VOLUME 12, ISSUE 4, APRIL 2025

Abstract: This paper examines key factors for optimizing go-kart performance through practical adjustments. Maintaining a low center of gravity improves stability, while forward positioning increases front grip (but may cause understeer) and rearward placement enhances cornering (with possible oversteer). Balanced weight distribution across axles maximizes tire contact and traction. Testing shows softer tires provide better grip but wear faster, while harder tires last longer with reduced traction. Driver position adjustments and suspension tuning further refine performance. These modifications, combined with real-time track data analysis, enable racers to achieve optimal balance between speed, handling, and consistency.

Keywords: Go-kart performance, center of gravity, weight distribution, tire selection, suspension tuning, vehicle dynamics

Abstract: This paper presents a comprehensive MATLAB/Simulink model for simulating a regenerative braking system in an electric vehicle (EV). Regenerative braking offers a significant advantage in EVs by converting the kinetic energy of the vehicle during deceleration into electrical energy, which is then fed back to the battery, thereby improving energy efficiency and extending the driving range. The developed simulation model incorporates key components of the regenerative braking system, including the vehicle dynamics, electric motor/generator, battery model, and the control logic that governs the transition between regenerative and mechanical braking. Different control strategies for distributing the braking torque between the regenerative and friction brakes are implemented and analyzed. The simulation results demonstrate the effectiveness of the proposed model in capturing the energy regeneration process under various driving conditions, including different deceleration rates and vehicle speeds. Furthermore, the impact of different regenerative braking control strategies on the energy recovery and overall braking performance is evaluated and compared. This study provides valuable insights into the design and optimization of regenerative braking systems in EVs, contributing to the development of more energy-efficient and sustainable transportation solutions.

Keywords: Electric Vehicle (EV), Regenerative Braking, MATLAB/Simulink, Simulation, Energy Efficiency, Braking Control Strategy.

Abstract: This study investigated the financial feasibility of using Augmented Reality in the construction of geometrically complex brick wall designs. Implementation Cost Analysis was conducted to determine the Cost Benefit Ratio (CBR) of AR use. The study involved the installation of three different brick walls with different curvature using AR and the traditional method. The results showed that the use of AR is financially feasible for geometrically complex brick walls. The CBR for Wall Type 1 (which was a straight wall) was 0.04, indicating the unfeasibility of using AR in the construction process. For the more complex walls (Wall Type 2 and Wall Type 3), the CBR increased almost ten times from Wall Type 2 (2.81) to Wall Type 3 (13.28), indicating that AR method is more feasible for more geometrically complex brick walls. The study found that using the AR construction method for the most geometrically complex brick wall (Wall Type 3) resulted in potential savings of $490.09 ($27.22/square feet) in the construction cost. Based on these findings, it is recommended to use AR in the construction of geometrically complex brick walls for cost savings. However, AR was found unfeasible for straight walls.

Keywords: Augmented Reality; Implementation Cost Analysis; Cost-Benefit-Analysis; Complex Design; HoloLens; Brick Wall.

Abstract: Heart disease is one of the most common causes of death around the world nowadays. Often, the enormous amount of information is gathered to detect diseases in medical science. All of the information is not useful but vital in taking the correct decision. Thus, it is not always easy to detect the heart disease because it requires skilled knowledge or experiences about heart failure symptoms for an early prediction. Most of the medical dataset are dispersed, widespread and assorted. However, data mining is a robust technique for extracting invisible, predictive and actionable information from the extensive databases. In this paper, by using info gain feature selection technique and removing unnecessary features, different classification techniques such that KNN, Decision Tree (ID3), Gaussian Naïve Bayes, Logistic Regression and Random Forest are used on heart disease dataset for better prediction. Different performance measurement factors such as accuracy, ROC curve, precision, recall, sensitivity, specificity, and F1-score are considered to determine the performance of the classification techniques. Among them, Logistic Regression performed better, and the classification accuracy is 92.76%.

Keywords: Heart, Machine learning algorithms, Supervised learning, Prediction algorithms, Classification algorithms, Decision trees.

Abstract: This paper constructs a plant disease detection system using Convolutional Neural Networks with the aid of transfer learning on the Xception model. Plant diseases remain one of the critical challenges to agricultural productivity and the detection techniques are often manual inspections by experts which is highly subjective. Our system using the transfer learning and depthwise separable convolutions of the Xception model was able to attain accuracy of more than 99% in identifying 38 different classes of plant disease image lesions from the PlantVillage dataset. The model developed is highly accurate in determining the range of diseases affecting the plants and also distinguishing healthy plants from those which are infected. Based on the experimental results, it can be concluded that the system outperforms traditional approaches that use convolutional neural networks, thus providing a reliable diagnosis tool for farmers and agronomy stakeholders. With the accessible means to swiftly identify a disease, this work serves in showcasing the technology's role in agriculture while aiming to strengthen the loss in crop yields.

Keywords: Plant disease detection, Convolutional Neural Networks, Xception architecture, Transfer learning, Agricultural technology, Image classification

Abstract: The rapid pace of urbanization in India has amplified the challenges posed by climate change, including increased vulnerability to heatwaves, flooding, and resource scarcity. To address these challenges, the Government of India launched the Smart Cities Mission (SCM) in 2015 with the aim of fostering sustainable, inclusive, and technologically advanced urban environments. This research investigates how the SCM contributes to enhancing climate resilience in Indian cities by examining strategies related to infrastructure, governance, technology, and community engagement. Through a comparative analysis of four cities-Pune, Surat, Singapore, and Copenhagen-this paper identifies key climate resilience strategies adopted at the local level and evaluates their effectiveness within the smart city framework. Pune and Surat showcase India's growing emphasis on sustainable water management, green mobility, and public participation. International case studies from Singapore and Copenhagen provide insights into advanced models of urban resilience, such as integrated water reuse systems, smart grids, nature-based solutions, and citizen-led climate action. The findings highlight that while Indian cities are progressing toward climate-responsive urban development, implementation gaps persist due to fragmented governance, funding limitations, and inconsistent stakeholder engagement. The study underscores the importance of embedding climate resilience indicators within the Smart Cities Mission framework to ensure long-term adaptation and sustainability.

Keywords: Smart Cities Mission, Climate Resilience, Urban Sustainability, Green Infrastructure, Urban Planning, Technological Innovation

Abstract: "VisionSense" is an AI-powered facial recognition system that performs real-time face identity, age, gender, and emotion prediction. It uses a Convolutional Neural Network (CNN) with pre-trained weights for feature extraction. It uses a pre-trained dlib model for face alignment. It uses a pre-trained Wide Residual Network model for age and gender predictions. It uses another Convolutional Neural Network (CNN) for emotion detection. For identity classification, the system offers a choice between K-Nearest Neighbours (KNN) and Support Vector Classification (SVC). VisionSense is designed to process images, videos, and live camera feeds, automatically saving unknown faces and updating its model through retraining whenever a new customer is detected. The system provides a web-based API for seamless image and video input, allowing users to interact with it effortlessly.

Keywords: Convolutional Neural Network, Wide Residual Network, K-Nearest Neighbours, Support Vector Classification, Machine learning, Face classification, Emotion detection

Abstract: With the exponential growth of cloud-based document storage, efficient and secure data management has become a crucial requirement. This paper presents a novel secure data deduplication framework that operates seamlessly over end-to-end encrypted documents. The proposed framework integrates Ciphertext-Policy Attribute-Based Encryption (CP-ABE) with an Attribute-Based Keyword Search (ABKS) mechanism to enable fine-grained access control and efficient encrypted search. We introduce a secure token-based deduplication method that detects redundant files without revealing file content or search keywords to the cloud server. Extensive security and performance analyses demonstrate that our solution preserves data confidentiality, resists leakage during keyword queries, and significantly reduces storage and computation costs.

Keywords: ABKS, Proxy server, deduplication, cloud storage.

Abstract: This research explores the dynamic transformations in land use patterns within the peri-urban areas of Indian cities, shaped by rapid urbanization, economic transitions, and infrastructural growth. By examining peri-urban zones of cities like Delhi and Bangalore, the study uncovers the consequences of unchecked urban expansion, including loss of agricultural land, ecosystem degradation, and social disparities. It highlights governance and policy gaps that exacerbate these challenges. The findings underscore the urgency of sustainable land use planning and propose integrative strategies focused on ecosystem conservation, zoning reforms, and inclusive urban governance.

Keywords: Peri-urban zones, Land-use change, Urban sprawl, Sustainable development, Infrastructure, Ecosystem degradation

Abstract: Calotropis commonly called milk weed is a large shrub belongs to Apocynaceae family, species Calotropis gigantea which can yield a durable fibre commercially known as "bowstrings of India". It is grown in water scary areas which do not require fertilizer andcultivation.The object of creating a knee bandit out of bamboo knit fabric and Calotropis gaigantea leaf extract is to investigate the possibilities of employing natural materials to create useful and environmentally responsible medical devices. The plant Calotropis Gaigantea is well-known for its therapeutic qualities. It has been utilized historically for its analgesic and anti-inflammatory benefits. Knitted bamboo cloth is a great starting point for making a knee bandit because of its antibacterial, breathable, and comfortable qualities. In this study, bioactive components from CALOTROPIS GIGATEA leaves are extracted and then integrated using environmentally friendly methods into bamboo knitted fabric. This plant, particularly its latex, is known for its medicinal properties, including wound healing and potential anti-inflammatory effects. The study investigates the use of leaf extract from this plant Bamboo is a sustainable and renewable resource, making it an environmentally friendly base for the bandage. Its properties, like good moisture absorption and breathability, are also beneficial for a knee bandage.The research aims to create a knee bandage that combines the wound-healing capabilities of Calotropis gigantea with the sustainability and functional properties of bamboo fabric. By using Calotropis gigantea leaf extract's unique bioactive qualities to bamboo knit fabric, the current work aims to create a functional knee bandit. Calotropis gigantea was chosen as a sustainable substitute for synthetic treatments frequently found in therapeutic fabrics because of its anti-inflammatory, analgesic, and antibacterial qualities. Because bamboo cloth is naturally soft, breathable, and antibacterial, it was selected as the perfect substrate for wearable health applications.

Keywords: Calotropis Leaves, Calotropis Extract, Bamboo Knitted Fabric , Essential oil

Abstract: The Indian Himalayan Region (IHR), encompassing states like Uttarakhand, Himachal Pradesh, and others, is renowned for its breathtaking landscapes, cultural heritage, and potential for diverse forms of tourism including pilgrimage, adventure, and ecotourism. Despite this immense potential, the region continues to grapple with infrastructural deficiencies that hinder tourism growth and threaten ecological balance. This dissertation explores the current state, challenges, and opportunities related to tourism infrastructure in the IHR, focusing on key components such as transportation, accommodation, waste management, water supply, electricity, and parking. The study employs a mixed-methods approach, combining extensive literature review, policy analysis, and case studies of three major tourist destinations in Uttarakhand-Nainital, Kedarnath, and Rudraprayag. The findings reveal significant disparities in infrastructure provision across these locations, with issues such as inadequate road maintenance, seasonal water shortages, poorly managed solid waste, and limited parking and energy facilities emerging as critical constraints. The study further assesses how existing policies and guidelines, including those under the URDPFI framework, have addressed-or failed to adequately address-these challenges. A comparative analysis highlights the varying degrees of tourism infrastructure development and offers insight into how strategic, region-specific interventions can enhance both tourist experiences and local livelihoods. The dissertation proposes an integrated planning framework rooted in sustainability, community participation, and policy coherence to ensure resilient and inclusive tourism infrastructure in the Indian Himalayan Region. Ultimately, this research contributes to the broader discourse on sustainable regional planning and development in ecologically fragile mountain areas.

Keywords: Indian Himalayan Region (IHR), Tourism Infrastructure, Hill Towns, Infrastructure

Abstract: The textile sector ranks among the largest contributors to global pollution, with synthetic dyes playing a major role in environmental harm. This research centres on the design and creation of sustainable laptop bags utilizing annatto natural dye, a plant-derived colorant extracted from the seeds of Bixa Orellana. The objective of this study is to investigate the viability of annatto natural dye as a sustainable substitute for synthetic dyes, thereby minimizing the ecological footprint of textile manufacturing. Cotton fabric was chosen as the primary material, and the annatto dye was extracted and applied with various mordanting agents. The findings indicated that annatto natural dye can yield a spectrum of vibrant colours, ranging from yellow to orange-red, while exhibiting excellent colourfastness and durability. The laptop bags designed in this study fulfilled both functional and aesthetic criteria, showcasing the practicality of incorporating annatto natural dye in textile production. This research advances sustainable practices within the textile industry by advocating for the use of natural dyes and decreasing dependence on synthetic chemicals. The results have significant implications for the textile sector, promoting the transition to environmentally friendly production techniques and sustainable materials.

Keywords: Sustainable textiles, Annatto natural dye, Bixa Orellana, Eco-friendly production, Textile design.

Abstract: This study investigates customer satisfaction and service quality among public sector banks in Tambaram, a growing commercial hub in Chennai. With public banks playing a pivotal role in financial inclusion, understanding service delivery from a customer perspective becomes critical, especially in the digital age. Using a descriptive research design and a structured survey grounded in the SERVQUAL model, primary data were collected from 385 respondents across various demographic segments. The data were analysed using descriptive statistics, ANOVA, and factor analysis to identify service quality dimensions impacting satisfaction. The findings indicate significant gaps between customer expectations and actual service delivery, particularly in responsiveness, digital banking services, grievance redressal, and personalized assistance. While tangibility and reliability dimensions showed moderate performance, low scores in responsiveness and empathy reflected concerns over long wait times, inconsistent staff behaviour, and limited digital adaptability. Qualitative feedback highlighted dissatisfaction with ATM downtimes, mobile app usability, and a lack of support for senior citizens and differently-abled customers. This study contributes to the literature by contextualizing service quality in a semi-urban Indian setting and identifying key latent factors such as digital accessibility, service efficiency, and human interaction that strongly influence customer satisfaction. Strategic recommendations are provided for public sector banks to adopt customer-centric innovations, enhance employee training, modernize branch infrastructure, and integrate feedback mechanisms to foster continuous improvement. Practical implications are offered for bank managers aiming to boost customer loyalty, reduce service delivery gaps, and build a sustainable competitive advantage in a rapidly evolving banking environment.

Keywords: Customer Satisfaction, Service Quality, Public Sector Banks, SERVQUAL, Tambaram, Digital Banking

Abstract: This study oversees the effect of Human Resource (HR) welfare policies on employee well-being and performance at Engine assembly unit unit. The goal is to gain a better understanding of how policies such as health and safety measures, work-life balance initiatives, financial benefits, training programs, and employee engagement activities influence employee motivation, satisfaction, and productivity, particularly among women. The study investigates how these welfare practices influence important behavioural outcomes such as job satisfaction, morale, and workplace commitment. It also investigates how organizational culture, managerial support, and communication influence the effectiveness of these policies. The study's goal is to gain a better understanding of how employee-centric welfare initiatives affect individual performance and overall organizational success by combining insights from existing literature and organizational context. The findings highlight the significance of inclusive and well-communicated HR policies in creating a supportive work environment, particularly in traditionally male-dominated industries such as manufacturing. The paper concludes by making recommendations for improving HR policy implementation and identifying areas for future research.

Keywords: HR Welfare Policies, Employee Wellbeing, Job Satisfaction, Performance, Manufacturing Sector, Engine assembly unit.

Abstract: Migrant workers in Dubai are finding it difficult to manage their finances. Most of them lack sufficient knowledge when it comes to managing their finances, particularly in aspects such as saving and investing. This study seeks to establish the relationship between the amount of financial knowledge these workers possess and their saving and investment choices. The research examines to what extent they know about simple financial ideas such as budgeting and investment. It also investigates how they decide to save and invest their earnings. By examining this subject, the aim is to enable migrant workers to have greater access to financial education. The survey indicated that the majority of migrant workers lack sufficient knowledge regarding banking and investment. They require assistance in knowing how to save and what investments are possible for them. The majority of them have a practice of remitting money back home, but that is not saving. On top of that, there is a clear gap in their knowledge about how to select the appropriate investment opportunities. There is a genuine need for trustworthy information regarding banking services and appropriate means of investing. To remedy this, businesses hiring migrant workers should be the ones to provide them with financial education initiatives. Such programs must utilize straightforward tools and resources, ideally in the employee's home languages which will help them understand the fundamentals of saving, budgeting, and investing. By doing so, it can actually assist them in having better finances and a general improved quality of life.

Keywords: Financial Literacy, Migrant Laborers, Savings Behaviour, Investment Decisions, Dubai, Financial Education

Abstract: Entrepreneurship plays a crucial role in economic development, and government initiatives like the Prime Minister's Startup Schemes (PMSS) are designed to support young innovators. However, the success of these initiatives largely depends on the awareness and accessibility among students in higher education. This study explores the level of awareness of PMSS among students in higher education institutions in Chennai, focusing on factors influencing their understanding and utilization of these schemes. Using a descriptive research approach, data were collected through a structured questionnaire distributed via convenience sampling. The study examines the impact of institutional support, entrepreneurship education, media outreach, and demographic factors on students' awareness of PMSS. Findings reveal significant gaps in awareness, with notable differences in institutional support and government outreach efforts. The study recommends improving promotional strategies, increasing institutional involvement, and expanding government-led outreach programs. These steps are essential for creating a more inclusive entrepreneurial ecosystem, which can foster innovation, job creation, and sustainable economic growth.

Keywords: Entrepreneurship, Prime Minister's Startup Schemes (PMSS), Student Awareness, Higher Education, Institutional Support, Government Outreach, Innovation and Economic Development.

Abstract: Traditional paper certificates and electronic certificates have difficulties in preservation and management, not to mention other problems concerning inconvenient verification, poor reliability, anti-counterfeiting and anti-tampering. This paper proposes a scheme designed to build a decentralized certificate system that is based on blockchain technology and smart contract, in which a set of blockchain certificate system aiming at providing blockchain certificate services for college students' innovation and entrepreneur ship competition is developed. In this system, certain functions of the certificate about management, issuing, verification and revocation are realized via smart contract. Signer information, certificate template and certificate information are stored in a smart contract that adopts structured data, thereby realizing more convenient callings in querying and validating certificate.

Keywords: Blockchain, Decentralization, Data Integrity, cryptography, Transparency, Smart Contracts

Abstract: This study oversees the business model of Fresh & Honest, a premium coffee solution provider specializing in coffee machine rentals and coffee bean sales to high-end clients such as 5-star and 7-star hotels, luxury resorts, and corporate offices. The study focuses on understanding how bundling coffee machines with coffee bean supply impacts customer loyalty and retention compared to offering them separately. While existing research in the coffee industry discusses customer loyalty, there is limited insight into the effectiveness of bundling products in a B2B context. This research fills that gap by analyzing how such bundling strategies influence purchasing decisions and long-term partnerships. The project uses a census-based primary data collection method, targeting existing customers of Fresh & Honest. Data was collected using structured questionnaires comprising multiple-choice, Likert scale, ranking, and open-ended questions. These questions were specifically designed to assess customer satisfaction, machine usage frequency, perceived value, and likelihood of contract renewal when coffee beans and machines are offered as a bundle. The research design is descriptive in nature, aiming to observe and analyze current client behavior and attitudes. Data analysis tools such as ANOVA, correlation, and regression were used to examine relationships between product bundling and customer retention metrics. A key case study addresses the issue of underutilized coffee machines at client locations. After identifying challenges like poor machine placement, lack of staff training, and minimal promotional support, a comprehensive implementation plan was developed. The strategic repositioning of machines, combined with targeted engagement initiatives, led to a significant increase in usage and client satisfaction. This study offers actionable insights for B2B product bundling strategies, underlining the importance of integrated service models to drive loyalty and improve business outcomes.

Keywords: Customer Loyalty, Customer Retention, B2B Marketing, Coffee Machine Rentals, Coffee Bean Sales, Descriptive Research Design, Census-Based Data Collection, Client Satisfaction.

Abstract: This study investigates the behavioural aspects of personal finance among employees at Skyjet Technologies, focusing on spending habits, saving behaviour, financial stress, and the influence of job-related factors. Using a structured questionnaire, data were collected from 50 employees through a census sampling method. The study adopted a quantitative approach and employed SPSS software to analyse the data. Statistical tests such as ANOVA, Chi-square, correlation, and regression were conducted to explore relationships between variables such as monthly income, impulse spending, financial satisfaction, and job performance under stress. The findings revealed no statistically significant association between income levels and frequency of spending beyond income, suggesting that overspending is influenced more by behavioural patterns than income range. A moderate positive correlation was found between financial literacy program attendance and monthly savings, indicating the beneficial role of financial education. Regression analysis showed that job insecurity had a modest negative impact on financial decision-making and job performance, although not statistically significant. The study also observed that many employees lack formal financial education, and a notable percentage engage in impulse purchases. These insights highlight the importance of introducing structured workplace financial wellness programs, with a focus on building saving habits, reducing financial stress, and enhancing overall well-being. The study recommends further research on a larger and more diverse sample and encourages organizations to promote financial literacy as part of employee development.

Keywords: Personal finance, Employee behaviour, Spending habits, financial stress, Impulse purchases, financial literacy

Abstract: Financial security is a critical long-term objective that requires a structured and disciplined approach. Goal-based financial planning plays a vital role in achieving financial stability by aligning financial decisions with specific life goals. This study examines the significance of goal-based financial planning in ensuring long-term financial security for individuals and organizations. It explores various financial goals in long-term aspirations such as retirement planning, homeownership, and wealth creation. The research highlights the importance of strategic financial planning in managing income, expenses, savings, investments, and risk to optimize financial outcomes. Despite the growing awareness of financial planning, a significant gap remains in understanding how personalized financial strategies impact different income groups. Existing studies often adopt a generalized approach, failing to address the varying financial priorities, risk tolerance, and investment opportunities across income levels. This study aims to bridge this gap by analyzing the effectiveness of customized financial planning strategies and their influence on financial stability. The research also investigates the role of financial advisors, employer benefits, and government policies in promoting financial well-being. By assessing structured financial planning frameworks, this study provides insights into optimizing wealth accumulation, mitigating financial risks, and enhancing financial literacy. The findings aim to assist individuals, financial professionals, and policymakers in developing tailored financial planning strategies to improve financial security across diverse income segments. This research contributes to the broader understanding of goal-based financial planning as a key driver of long-term financial stability and improved quality of life.

Keywords: Financial Security - Financial stability - Emergency saving - Debt Repayment - Retirement planning -Income management - Risk management -Personalized financial strategies.

Abstract: This study aims to examine the competitive positioning of a leading non-banking financial company (NBFC) in India within the vehicle loan section from 2021 to 2024, compared to crucial challengers. The purpose is to estimate the impact of digitalization, interest rate competitiveness, profitability, and monetary stability on the establishment's market standing. A secondary data approach was adopted, deconstructing fiscal criteria similar as loan disbursement rates, interest rates, digital loan processing performance, return on equity (ROE), and Altman Z- scores across five major NBFCs. Quantitative tools including trend analysis and ratio analysis were used to assess performance. The findings show that the institution maintained stable and competitive interest rates, while it lagged behind peer NBFCs in digital loan disbursement and processing speed. Still, notable enhancement in ROE from 2021 to 2024 demonstrated effective capital application and enhanced functional performance. The Altman Z- score revealed fairly moderate fiscal risk, outperforming some peers in recent times. These perceptivities suggest that while the company faces challenges in digital structure, its harmonious pricing strategy and rising profitability mark a positive line. The study highlights the significance of digital investment, risk operation, and capital effectiveness for sustaining competitive advantage in the NBFC vehicle loan sector.

Keywords: Vehicle Loans, Non-Banking Financial Companies(NBFCs), Digital Loan Disbursement, Interest Rates, Return on Equity(ROE)

Abstract: This study presents an innovative autonomous robot designed to tackle labor shortages in agriculture by automating the harvesting of leafy vegetables. The robot employs advanced machine vision to identify plants, determine their location, and evaluate maturity for targeted harvesting. Equipped with a robotic arm, it positions a specialized end-effector for precise, damage-free cutting. The system autonomously navigates crop rows and includes an integrated collection unit for harvested produce. By improving efficiency, reducing reliance on manual labor, and maintaining high-quality output, this solution advances sustainable, precision-driven leafy green production.

Keywords: Agricultural Robotics, Automated Harvesting, Leafy Vegetables, Machine Vision.

Abstract: This study investigates the maximization of production efficiency in manufacturing environments by reducing cycle time and enhancing the productivity of workers. As manufacturing firms are always under pressure to remain competitive, there is a need to maximize operational efficiency at minimal capital outlays. This paper examines the effectiveness of various methods, including work split, workplace optimization, and inventory control, to reduce bottlenecks and maximize production throughput. A detailed inspection of a standard production plant revealed that an extra labour work-splitting method, where a high-bottleneck machine station was given an extra labour, raised output by 21%. Simulation further indicates that with other methods, such as improved inventory control and workstation redesign, the production output could be boosted by up to 30%. The study provides industrial advice to production businesses to reduce activities, cut down on cycle time, and maximize productivity by optimal deployment of man-force and improvement of the process.

Keywords: Cycle Time, Worker Productivity, Manufacturing Efficiency, Bottleneck, Work Split, Process Optimization, Lean Manufacturing.

Abstract: This research examines the central role of employee engagement in improving product quality and operational effectiveness in our company Employee engagement is a complex construct referring to the emotional involvement and motivation of the employees towards the organizational objectives. This research mainly examines the relationship between employee engagement and quality improvement in the company, focusing especially on the role of HR policies, managerial support, training programs, reward systems, and general communication. The study used both qualitative and quantitative methods to gather perceptions from employees working in the Production and Quality Control departments. A questionnaire was employed to obtain their perceptions across the different dimensions of engagement, and statistical tests were used to compare the results. The research showed that the engagement of employees at was largely determined by a number of critical factors such as transparent and equitable HR policies, managerial sponsorship of professional development, well-organized training initiatives, and robust recognition systems. The outcomes further showed that those employees who perceived HR policies to be supportive, were suitably backed by their managers, and were given adequate training were likely to exhibit increased commitment towards quality improvement and business excellence. Most notable was the high correlation between recognition and cooperative efforts and the improvement in product quality. Those employees who were recognized for their work felt encouraged to continue high levels of performance, which had a direct effect in reducing defects and operational mistakes within products. In addition, statistical testing through ANOVA and Chi-square tests did not yield significant differences in perceptions of engagement by job levels and gender, but differences were observed in satisfaction with HR communication across departments. This indicates that alignment of HR initiatives with departmental requirements may further enhance engagement results. Lastly, the study concludes by providing actionable recommendations to Our company on how to further enhance employee engagement. Some of these recommendations include fostering a more diverse environment by tackling gender inequality, improving cross-departmental communication, expanding schemes for recognition, and establishing leadership programs. The research also identifies ongoing learning and giving employees decision-making power as key factors that significantly impact engagement. By adopting these practices, Our company can look to a more effective workforce, resulting in better product quality, operational effectiveness, and ultimately, sustainable business success. This study is a stepping stone to companies that would like to learn more about the dynamic interplay between employee engagement and quality results, and is a guide to organizations that would like to develop a culture of continual improvement based on an engaged and motivated workforce.

Keywords: Product Quality, Operational Efficiency, Employee Satisfaction, Workforce Motivation, Managerial Support, HR Policies, Quality Enhancement, Employee Engagement, Training Programs, and Recognition Systems.

Abstract: Concarpus plants, as highly adaptable and resilient in cities, have received interest for their ability to make urban ecosystems stronger, increase biodiversity, and provide sustainable resource utilization. This research thoroughly analyses the environmental advantages and limitations of Concarpus plants, with particular emphasis on how they can help in urban ecosystems. We discuss their effects on air and water quality, soil condition, and carbon sequestration, highlighting their potential to mitigate the urban heat island effect and enhance overall ecosystem function. Recent research emphasizes both the beneficial role of Concarpus plants in maintaining biodiversity and the dangers of their invasive properties in some areas. Based on a critical analysis of case studies and methods employed in evaluating these plants' ecological impact, we introduce an integrated view of their contribution to urban greening. The paper further explains how Concarpus plants can be useful in resource management, especially water conservation and preventing soil erosion. Challenges such as competition with native species and their ability to disturb indigenous ecosystems are also taken into account. The research concludes by suggesting recommendations for the sustainable incorporation of Concarpus plants into urban planning, emphasizing the necessity of adaptive management practices and additional research to strike a balance between their benefits and risks in rapidly changing urban environments..

Keywords: Concarpus plants, Urban ecosystems, Biodiversity, Resource management, Environmental sustainability, Ecological impact.

Abstract: This project aims to design and develop a real-time, AI-powered trading platform that integrates multiple external financial data APIs, a custom backend with AI inference and trading logic, and brokerage APIs for order execution. The platform leverages both REST and WebSocket protocols to handle live market data, financial news, and predictive AI models to make informed trading decisions. The system is designed for reliability, scalability, and automation, suitable for both retail and professional traders.

Keywords: React JS, Machine Learning (ML), Long ShortTerm Memory (LSTM).

Abstract: Adverse drug side effects pose significant challenges in pharmaceutical research, ranking as a leading cause of treatment failure and mortality. Traditional laboratory-based evaluations of drug side effects are resource-intensive and time-consuming, necessitating the adoption of machine learning techniques for efficient and accurate predictions. This study explores the use of supervised learning approaches for drug side effect prediction, leveraging biomedical data and computational models. We employ various feature extraction techniques, including Bag of Words (BOW) and Term Frequency-Inverse Document Frequency (TF-IDF), combined with classification models such as Logistic Regression, Random Forest, and Support Vector Machines (SVM). Experimental results demonstrate that the TF-IDF-based models achieve superior performance, with Logistic Regression attaining a test accuracy of 80.88% and SVM achieving 80.90%. These findings highlight the potential of machine learning in predicting drug side effects, optimizing drug safety assessments, and reducing the risks associated with adverse reactions. Our study provides a comprehensive analysis of model effectiveness and discusses key challenges, research gaps, and future directions for improving predictive performance in this critical domain.

Keywords: Machine Learning, Supervised Learning, Feature Extraction, TF-IDF, Bag of Words, Logistic Regression, Random Forest and Support Vector Machines.

Abstract: Remote working necessitated the use of virtual desktops and remote access securely to continue working and upholding a cybersecurity posture. This study examines the extent to which the use of these technologies has been successful within organizations in Nigeria and Ghana using mixed-method research covering qualitative analysis of themes and quantitative regression analysis. Qualitative findings indicate workers welcome security enhancements and collaboration areas but are faced with extreme technical issues impacting productivity. Quantitative regression analysis indicates that perceived security change and collaboration both positively influence productivity but are not significantly different from zero, while technical issues are extremely negatively correlated with productivity (β = -0.7194, p = 0.009). Some key recommendations are overcoming technical challenges by improving network infrastructure, finding a balance between security and usability, and fine-tuning collaboration tools. The recommendations provide practical advice to organizations that wish to optimize remote work models and continue both productivity and security despite the more digital workplace.

Keywords: Remote Work, Human Resources, Virtual Desktops, Productivity, Security

Abstract: Theatre, hitherto live and physical as an art form, is fundamentally changing in the digital era. As new media technologies, from projection mapping to virtual reality, are being developed, what theatre is and how it gets practiced are evolving. This paper examines how digital media is not simply supplementing traditional theatre but reconfiguring its narrative structure, aesthetics, and audience relationships. Special focus is given to the contemporary play E=mc², which synthesizes multimedia design, scientific discourse, and theatrical storytelling. Through this case study, the research draws attention to the power of new media in enriching the theatrical experience, facilitating inter-disciplinary interaction, and reconfiguring the stage's spatial-temporal dynamics.

Keywords: Theatre, New Media, Technology, Play Production

Abstract: Human gait is a behavioural biometric that allows for person recognition from the patterns of walking. In contrast to face or fingerprint recognition, gait recognition can be done at a distance without subject participation, making it particularly valuable for surveillance and access control applications. This paper describes a real-time human recognition system that utilizes gait-based features extracted from Media Pipe pose landmarks, combined with a light K-Nearest Neighbours (KNN) classifier. The system is able to run on generic-purpose hardware utilizing a web-based interface constructed with Flask. Both offline acknowledgment through video upload as well as in real-time through webcam input is supported. Deploy ability is one of the essential strengths of this initiative: it uses no GPUs, big data, or complex training pipelines and yet provides consistency in accuracy as well as response. This places it in line to be considered a top prospect for edge-based intelligent systems across public safety, smart cities, and IoT systems.

Keywords: Gait Recognition, Human Identification, Real-Time Recognition, Pose Estimation, Media Pipe, OpenCV, Flask Web Application, KNN Classifier, Biometric Authentication, Skeleton Tracking

Abstract: This paper presents a comprehensive approach to building a real-time e-commerce price comparison tool using Python, web scraping, and modern web frameworks. With the increasing prevalence of online shopping and growing cost awareness among consumers, a centralized platform that compares prices across popular websites significantly enhances the online shopping experience. Our system integrates scrapers for major e-commerce platforms including Amazon, Flipkart, Myntra, Croma, Google Shopping, and others. The tool displays optimized price comparisons, enables wishlist tracking, and offers statistical insights to users. The implementation leverages a Flask backend with Streamlit for the user interface, with SQLite database for persistence. Results show that users can save significant amounts on purchases through effective price comparison.

Keywords: E-commerce, Price Comparison, Web Scraping, Python, Flask, Streamlit, Data Analysis.

Abstract: Brain tumors represent one of the most complex and life-threatening conditions affecting individuals worldwide. Timely and accurate detection, along with precise classification, is critical for effective treatment planning and improved patient outcomes. With the evolution of medical imaging technologies and the rapid development of machine learning techniques, there is growing interest in leveraging these advancements to enhance diagnostic capabilities. The paper begins by presenting an overview of brain tumor types and their key characteristics, emphasizing the critical importance of early and accurate diagnosis. It then explores the medical imaging modalities commonly employed for brain tumor diagnosis, including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). The discussion highlights the unique advantages and limitations of each modality in capturing tumor-specific features and guiding clinical decisions. Subsequently, the survey delves into the application of machine learning across the diagnostic pipeline-covering stages such as image preprocessing, feature extraction, feature selection, and the application of various classification algorithms. Machine learning models such as support vector machines (SVM), artificial neural networks (ANN), random forests, and convolutional neural networks (CNN) are examined in detail, with attention to their performance and suitability for different diagnostic tasks.Additionally, the paper reviews publicly available brain tumor datasets used to train and evaluate machine learning models. It outlines the challenges inherent in these datasets, including class imbalance, limited sample sizes, and heterogeneity in imaging protocols.The survey also discusses standard evaluation metrics-such as sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC-ROC)-used to assess the performance of detection and classification systems.

Keywords: Brain tumor segmentation, MRI, Convolutional Neural Network, U-Net, Deep learning, Dice coefficient.

Abstract: Customer support onboarding is a critical phase that directly impacts client satisfaction and operational efficiency. Traditional onboarding methods often involve repetitive tasks and manual data handling, leading to delays, errors, and inconsistent service quality. This paper presents an intelligent automation framework designed to streamline the customer support onboarding process using Robotic Process Automation (RPA) techniques, developed with Python and deployed through a Streamlit based web interface. The architecture, system components, and automation logic are thoroughly discussed, emphasizing modularity, scalability, and user-centric design. Experimental evaluation confirms the effectiveness of the solution in reducing onboarding time and operational workload, demonstrating its suitability for broader enterprise integration and automation of customer service workflows.

Keywords: Robotic Process Automation, Customer Support, Onboarding, Streamlit, Python, Process Automation, Web Application.

Abstract: The impact of Quality of Work Life (QWL) on organizational commitment and employee productivity at pharmaceuticals industry, a major participant in the Indian pharmaceutical sector, is examined in this study. It focuses on things like work-life balance, job stability, career advancement, pay, and workplace conditions. Structured questionnaires and literature reviews were used to gather data for a descriptive study design. Employees from different departments were included in the sample, providing a comprehensive picture of QWL throughout the company. The associations between QWL components, productivity, and loyalty were investigated using statistical techniques such as regression analysis and correlation. According to the results, higher QWL considerably boosts worker productivity and fortifies organizational loyalty. Fair compensation, opportunity for growth, and supportive work environments all increase employee retention and happiness. The study emphasizes that to promote long-term organizational performance, HR strategies must be focused on employee well-being. Pharmaceutical industry will use the knowledge gathered to create practical strategies that would promote industry competitiveness, lower attrition, and improve working conditions.

Keywords: Quality of Work Life (QWL), Organizational Commitment, Employee Productivity, Work-Life Balance

Abstract: Highways are instrumental in shaping patterns of land use, particularly in regions undergoing rapid urbanization and economic transformation. This dissertation investigates the long-term impact of operational highways on land use changes, with a focus on urban expansion, the transformation of peri-urban areas, and the development of residential, commercial, and industrial infrastructure. Unlike studies that emphasize highway construction or short-term effects, this research centers on highways that have been functional for at least two decades, providing a comprehensive view of their lasting influence. Through a detailed literature review, policy analysis, and two case studies-National Highway-48 in the National Capital Region of India and the Antalya-Alanya Highway in Turkey-the study reveals that highways significantly contribute to land conversion, particularly the shift from agricultural to urban and industrial uses. These transformations are accompanied by environmental challenges, such as biodiversity loss, soil degradation, and increased pollution, along with socio-economic implications including population relocation, infrastructure pressure, and livelihood disruptions. The findings underscore the necessity for integrated land use planning and environmental assessments that consider the full spectrum of impacts induced by highways. By adopting sustainable development frameworks, incorporating stakeholder participation, and enhancing regulatory mechanisms, highway-induced land use changes can be better managed to support both economic growth and ecological balance.

Keywords: Highways, land use, land use planning, urbanization, National Highway

Abstract: SILENT SPEAK is an intelligent real-time communication system designed to empower individuals with speech and hearing impairments by translating non-verbal cues into spoken language. The system captures hand gestures using the MediaPipe framework and classifies them through a TensorFlow-based deep learning model trained for precision and efficiency. Simultaneously, facial emotions such as happiness, anger, sadness, and surprise are detected using the ResidualMaskingNetwork model integrated via the DeepFace library. These combined inputs are then converted into audible speech through a text-to-speech (TTS) engine, enabling fluid and expressive communication. A user-friendly graphical interface, developed with Tkinter, displays real-time predictions and allows users to interact with the system seamlessly. With its ability to interpret both gestures and facial expressions, SILENT SPEAK offers a comprehensive solution for augmenting communication, supporting inclusive interactions, and bridging the gap between verbal and non-verbal communication in real-world scenarios.

Keywords: Non-Verbal Communication, Hand Gesture Detection, Emotion Recognition, Real-Time Speech Output, Assistive Communication Technology, MediaPipe, DeepFace, TensorFlow, Human-Centered AI, Multimodal Interaction.

Abstract: Walking is a fundamental and sustainable mode of transportation, particularly crucial in Central Business District (CBD) where economic, social, and commercial activities are concentrated. Despite nearly half of urban commuters in India depending on walking, rapid urbanization has led to inadequate pedestrian infrastructure, increasing risks and diminishing accessibility. This study emphasizes the urgent need to evaluate and enhance walkability within CBD, addressing challenges such as pedestrian-vehicular conflicts, unsafe crossings, congestion, and lack of inclusive infrastructure. The research aims to analyze key factors affecting pedestrian movement, assess existing conditions, and propose strategic urban interventions to improve pedestrian safety and mobility. Anchored in literature on walkability parameters and indices, the study also explores the relationship between pedestrian-friendly environments and public transport usage, environmental comfort, and urban vitality. Through a focused scope limited to a specific CBD, the study incorporates walkability audits, international best practices, and planning strategies-such as tactical urbanism, urban cooling, and mixed-use development-to frame a replicable model for improving pedestrian environments in dense urban cores. The findings contribute toward sustainable urban mobility and the creation of safer, accessible, and vibrant public spaces.

Keywords: Walkability, Pedestrian Safety, Central Business District (CBD), Pedestrian Infrastructure, Traffic Congestion, Walkability Index.

Abstract: This project presents an innovative offline speech translator that utilizes Natural Language Processing (NLP) and Natural Language Toolkit (NLTK) to enable real-time language translation. Our system integrates automatic speech recognition (ASR) and machine translation (MT) to facilitate accurate and efficient language translation. We employ a cascaded architecture, incorporating NLTK's tokenization, stemming, and lemmatization techniques to enhance text preprocessing. Experimental results demonstrate the effectiveness of our approach, achieving competitive translation accuracy on benchmark datasets. Our offline speech translator has far-reaching implications for global communication, enabling individuals to transcend language barriers and connect with others in real-time, regardless of internet connectivity.

Keywords: Offline speech translation, NLP, NLTK, ASR, MT, real-time translation, gTTs, pyttsx3, Argos Translate, Vosk

Abstract: Systems for electronic voting (E- Voting) have drawn a lot of interest as a way to improve the effectiveness, openness, and accessibility of the voting process. In order to verify voters and guarantee the fairness of the voting process, this study suggests a unique e- voting system that makes use of face recognition techniques based on machine learning and deep learning algorithms. The proposed system leverages advancements in computer vision and artificial intelligence to address the challenges of traditional voting systems, such as identity fraud, impersonation, and multiple voting instances

Keywords: E-voting, Face Recognition, Image Processing, Machine learning, KNN Algorithm, Open CV

Abstract: Retirement planning is a critical aspect of financial security, particularly in a rapidly changing economic landscape. With increasing life expectancy, rising healthcare costs, inflation, and limited pension coverage in the private sector, individuals must take proactive steps to secure their financial future. This study, titled "Retirement Planning and Financial Security: Assessing Awareness and Preparedness among Individuals in Chennai", aims to assess the level of financial awareness, preparedness, and investment behavior related to retirement among the general population in Chennai. Using a structured survey methodology, the study evaluates how demographic factors such as age, income, education, and occupation influence retirement planning. The research highlights key financial challenges faced by individuals, including insufficient savings, lack of awareness, inflationary pressures, and dependence on family support. Findings reveal that a significant portion of younger individuals in their 20s exhibit low levels of preparedness, while financial readiness tends to improve with age, particularly in the 40s and 50s. The study also explores the importance of financial literacy in shaping investment decisions and long-term financial behaviour. The insights derived from this study offer valuable inputs for policymakers, financial institutions, and individuals to encourage early planning, improve financial decision-making, and ensure a secure and independent retirement.

Keywords: Retirement Planning, Financial Security, Financial Literacy, Investment Behaviour, Demographics, Awareness, Chennai, Economic Preparedness.

Abstract: Identifying and classifying counterfeit and genuine currency has become essential to protecting economies around the world.Advanced systems to efficient Counterfeit cash has been a major issue in India; according to a 2016 estimate, there was approximately ₹1,000 crore worth of counterfeit currency in circulation. Traditional methods for counterfeit detection have relied heavily on physical characteristics such as watermarks, security threads, and ultraviolet detection, but these techniques have proven insufficient against the sophistication of contemporary counterfeiters.In order to classify currencies and detect counterfeits, this research makes use of deep learning, more especially DenseNet.Using a dataset of 3753 images of Indian Rupee currency notes sourced from Kaggle, the model was trained to classify currency into eight distinct classes, distinguishing between real and fake notes. The model's performance was enhanced by using the DenseNet architecture, which is renowned for its effective feature reuse and increased accuracy. A web-based user interface was developed to allow users to upload currency images and receive instant feedback on the authenticity of the notes.In order to tackle the problem of counterfeit currency, this approach offers a scalable and easily accessible solution.

Keywords: Fake currency detection, Real currency classification, Deep learning, DenseNet, Image classification, currency security, Indian Rupee, Neural networks.

Abstract: In the contemporary world, ensuring women's safety remains a significant challenge due to the rising number of crimes. Despite technological advancements, women often feel insecure about stepping out alone, particularly during odd hours. Another critical issue is the tampering of evidence during criminal investigations, which delays justice. Existing safety solutions mostly provide reactive measures, sending alerts only after an incident occurs. This paper proposes an AI-powered, proactive software system that utilizes machine learning, computer vision, and IoT technologies. This system includes real-time video surveillance, weapon detection, and GPS tracking. Emergency alerts and live evidence were stored in the cloud for quick action and investigation. The proposed system aimed to offer a holistic, preventive safety mechanism for women. This system aims to enhance situational awareness and reduce response time, thereby creating a robust, proactive safety framework for women.

Keywords: Women's safety, AI, Predictive crime alerting, Real-time monitoring.

Abstract: This study presents the successful design and simulation of a waste heat recovery system (WHRS) tailored for reactivating the heating medium in an offshore gas turbine at the Nigerian National Petroleum Company Exploration and Production Limited (NNPC E&P Ltd) Production Facility. The research is driven by the ongoing pursuit of enhanced energy efficiency and sustainability in offshore operations. A standard modeling approach was employed, utilizing process flow diagrams (PFDs) for system integration and Computational Fluid Dynamics (CFD) analysis, coupled with Aspen HYSYS simulations to assess system performance under operating conditions. Flue gas composition analysis was conducted to determine input parameters for the WHRS design. Optimization techniques were implemented to establish the optimal heat exchanger design configuration. The heat exchanger was designed as a shell-and-tube system with 130 tubes of 20 m length and 0.025 m outer diameter. Simulations were conducted to evaluate the heat exchanger performance, determining a heat duty of 648,985 W, an overall heat transfer coefficient of 112.66 W/m²K, and a corrected Log Mean Temperature Difference (LMTD) of 281°C. Aspen HYSYS simulations validated system performance, yielding a tube-side outlet temperature of 130°C and a shell-side outlet temperature of 335.5°C. The results indicate improved energy recovery throughout the simulated process, confirming the feasibility of implementing waste heat recovery in crude oil preheating operations, contributing to improved energy management in offshore production facilities.

Keywords: Waste Heat Recovery (WHR), Simulation, Aspen HYSYS, Heat Exchanger Design, Offshore Gas Turbine, Shell-and-Tube Heat Exchanger, Computational Fluid Dynamics (CFD), Energy Recovery .

Abstract: This study investigates how the employee engagement process affects performance, satisfaction, and retention outcomes in the context of a consulting firm. Employee engagement refers to the intellectual and emotional commitment of the employees towards the job and the organizational goals. For consulting firms, in which human capital drives the delivery of services, engagement is critical in determining both organizational and individual performance. The research explores the relationship between engagement practices such as leadership support, communication, recognition, career development, and work-life balance and their consequential outcomes. A mixed-method approach was used to measure employee attitudes, employing structured questionnaires and statistical methods to analyse the findings. The research found that high levels of engagement were strongly related to greater collaboration, innovation, and commitment to client success. Workers who had encountered open communication, appreciation for their efforts, and career development showed a greater commitment to remain in the organization and work at their best. Further, statistical analysis based on ANOVA and Chi-square tests revealed that although the levels of engagement were uniform across age and gender groups, departmental variations in communication effectiveness exerted a significant impact on employee motivation. These findings suggest that department-level specific engagement strategies that are aligned with department-level requirements can yield more effective organizational results. The research ends by recommending actionable strategies for the consulting firm, such as enhancing leadership capabilities, implementing tailored recognition programs, encouraging inter-departmental knowledge exchange, and establishing consistent feedback practices. These initiatives are expected to contribute to better organizational performance, higher levels of employee satisfaction, and long-term competitive edge. This study serves as a valuable reference for companies seeking to improve employee engagement and achieve excellence by cultivating a committed and enthusiastic workforce. Important suggestions to improve employee engagement in the consulting company are included in the research's conclusion. It suggests developing cross-functional information sharing, adopting continuous feedback systems, embracing customized appreciation programs, and honing leadership abilities. Improved teamwork, motivation, and communication are guaranteed by the practices. Consequently, the company can achieve improved performance, improved workers' well-being, and a decrease in worker turnover. Such enhancements lead to long-term competitive advantage. This research therefore provides valuable information for consulting organizations that seek to develop a committed and high-performance workforce through proper engagement practices.

Keywords: Employee Engagement, Consulting Firm, Organizational Outcomes, Leadership Support, Work-Life Balance, Recognition Systems, Communication, Career Development, Employee Retention, Performance Enhancement

Abstract: The work demonstrates a touchless Human- Computer Interaction (HCI) solution that allows computer users to interface with their PC via natural, intuitive hand movements. Utilizing machine learning and computer vision technology, the system employs a webcam and the mediaPipe framework for real-time identification and tracking of hand landmarks. By smart gesture recognition and mapping, users can carry out system actions like mouse movement, clicking, scrolling, volume and brightness adjustment, screenshot capture, double click and all without ever having to physically touch an input device. It was implemented in Python and incorporates a number of libraries such as OpenCV, mediapipe and other.

Abstract: The system allows users to upload a dataset of fingerprint images, preprocess them, and train a CNN model for live vs. fake fingerprint detection. An alternative model using a simplified VGG16-like structure is also implemented for comparison purposes. Once trained, the models can predict the authenticity of a given fingerprint image with associated confidence scores. During prediction, the system applies multiple image processing techniques such as grayscale conversion, HSV transformation, and Canny edge detection to visualize intermediate steps and aid understanding. The trained models and their performance metrics, including accuracy and loss, are stored and can be visualized using built-in plotting functions. Additionally, a comparative analysis of CNN and VGG16 performance is provided through a bar chart. Overall, this system serves as a practical tool for demonstrating how deep learning models can be used in biometric security applications to combat spoofing attacks and enhance fingerprint authentication systems.

Keywords: Convolutional Neural Network (CNN), VGG16 Model, Spoof Detection, Image processing.

Abstract: Individual voices play an important role in group dynamics, especially in high-stakes, consensus-based environments, as demonstrated by 12 Angry Men. The play mostly showcases the courage and influence of Juror 8, who brings in the idea of reasonable doubt in an effort to reverse the initial majority verdict in a murder trial. By his composed determination and logical argument, the story illustrates how an individual's reluctance to follow may provoke thinking, challenge assumptions, and eventually change the attitudes of a whole group. The play provides a compelling examination of social pressure, moral duty, and the processes of large-scale change and opposition. Therefore, 12 Angry Men is a powerful metaphor for the importance of individual initiative in group judgements. emphasizing that even in the face of overwhelming opposition, a single voice can catalyse.

Abstract: To enhance the security of the ATM. To avoid ATM robberies and the wrong person misusing the ATM so that they can lead a secure and reliable life. The system is to support the intelligence system. Without hesitation, use ATMs to make the world a conversion community. Once the client inserts the cardboard into the ATM, a session is processed, so the system can start a face detection victimization camera placed close to the ATM and builds short-lived identity information of the clients, and the user face verification to perform on the ATM. A good user would use the traditional method because the invalid user cannot access the ATM card, they offer the secondary identification to the system mechanically, and the unauthorized person would continue the transactions. The objective of the proposed study in this paper is to prevent ATM fraud as well as secure transactions from the user side.

Keywords: ATM, face recognition, LRR, OTP.

Abstract: In today's digital era, emotional expression has found new mediums, with text-based communication via social media and messaging apps becoming increasingly dominant. As individuals frequently share their thoughts, experiences, and opinions online, there is a growing need to analyze and interpret the emotional context embedded in textual content. However, the massive influx of unstructured data poses challenges in distinguishing meaningful emotional cues from irrelevant information. This calls for efficient systems that can process such data in real time while identifying linguistic biases. Emotional and sentiment analysis plays a pivotal role in understanding the writer's stance-whether positive, negative, or neutral-towards a topic, service, or individual. Despite advancements, accurately assessing the psychological or emotional state of users remains complex and necessitates emotionally intelligent systems.

Keywords: Unstructured data, real-time processing, linguistic bias, emotional evaluation, text analysis.

Abstract: This study investigates the role of Training Needs Analysis (TNA) in identifying and addressing employee skill gaps at Montra Electric, a key player in India's electric vehicle sector. By examining workforce demographics, training patterns, and skill development outcomes, the research assesses how targeted training interventions influence individual performance and organizational efficiency. The findings reveal a strong preference for leadership and problem-solving training, with most employees acknowledging the effectiveness of recent development programs. However, the study also identifies barriers such as lack of management support and relevance misalignment. Recommendations include role-specific skill mapping, training customization, and continuous feedback integration. The study contributes to understanding how TNA serves as a strategic tool to enhance workforce capabilities in a rapidly evolving industry.

Keywords: Skill gaps, Training Needs Assessment, employee performance, organizational efficiency, electric mobility, Montra Electric.

Abstract: Augmented analytics leverages artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to automate and simplify the data analytics process, making actionable insights accessible to users of all skill levels. By integrating these technologies, augmented analytics streamlines data preparation, discovery, and visualization, reducing reliance on specialized technical expertise and enabling broader participation in data-driven decision-making. Key components include automated data wrangling, smart recommendation engines, and natural language generation, which collectively accelerate time-to-insight and enhance data accuracy while minimizing human bias and error. This democratization of analytics empowers organizations to improve data literacy and agility, as business users can interact with data conversationally, uncover hidden patterns, and derive insights more efficiently. Sectors such as finance, healthcare, retail, and HR benefit from faster, more accurate decisions and operational efficiencies. However, challenges remain, including data quality concerns, potential over-reliance on automation, and ethical considerations regarding AI-driven recommendations. As organizations increasingly adopt data-driven cultures, augmented analytics is transforming business intelligence by fostering more inclusive, agile, and knowledge-driven decision-making across all levels of the enterprise.

Keywords: Augmented Analytics, Data Democratization, Artificial Intelligence, Machine Learning, Natural Language Processing, Business Intelligence, Data Visualization, Insight Generation, Data Wrangling, Natural Language Generation, Data-Driven Decision-Making

Abstract: This study investigated the influence of organizational factors on employee engagement and the perceived impact of training and service personalization on customer-related outcomes within a service-oriented organization. The purpose of this study is to examine the relationship between employee engagement confines such as authorization, administrative support, engagement observation, and personalization and their impact on client experience quality. By breaking down worker understandings, the study aims to understand how these internal factors contribute to the delivery of personalized services and overall client satisfaction within the association. Regression analysis examined the effects of workplace culture, leadership, recognition, and leadership style on employee engagement, revealing a non-significant overall model and weak individual predictor effects. One-Way ANOVA indicated that perceived training effectiveness significantly enhanced post-training employee engagement and perceived service quality but did not significantly impact perceived customer satisfaction. Correlation analysis demonstrated a strong positive relationship between perceived service personalization and customer experience quality. Additionally, a weak and non-significant positive trend was observed between perceptions of superior employee engagement initiatives and a more supportive work environment compared to competitors. These findings suggest that while internal factors and training play a role in engagement and service delivery, the direct link to perceived customer satisfaction is complex and influenced by personalization. The further research with larger samples and diverse methodologies is recommended to explore these relationships more comprehensively.

Keywords: Employee Engagement, Customer Satisfaction, Service Quality, Training Effectiveness, Workplace Culture, Service Personalization.

Abstract: This paper presents a design aimed at achieving optimal performance within the designated frequency range of 12GHz to 18GHz.The proposed antenna exhibits low side lobes, high bandwidth, high gain, and good impedance matching. This antenna was created using EM simulation software (CST Microwave Studio) and features a rectangular patch with two HSRR (Hexagonal Split Ring Resonator) slots that can be utilized to increase bandwidth and gain. The design features an 8mm x 5mm microstrip patch with FR4 substrate for the Ku band applications. The antenna achieves gains of 4.11dBi and 3.51dBi, a VSWR of 1.01 and a wide bandwidth of 4.8GHz. This antenna resonates with dual frequencies at 13.33GHz and 15.854GHz.

Keywords: Ku-band, Satellite Communication, Microstrip Patch Antenna, Antenna Design, Gain, Side Lobes, Impedance Matching, Return Loss, Radiation Pattern, CST Microwave Studio.

Abstract: Cloud Data Warehouses (CDWs) are widely adopted for their scalable storage and on-demand access capabilities. To protect sensitive analytical data, encryption is commonly applied before outsourcing it to the cloud. However, executing complex Boolean keyword searches over encrypted data remains a significant challenge due to the limitations of existing Searchable Encryption (SE) schemes. This paper presents a verifiable and efficient Boolean keyword search system tailored for encrypted cloud warehouses. The proposed system leverages Partial Homomorphic Encryption (PHE), B+ Trees, Inverted Indexing, and bitmapping to enable secure and expressive query support. To ensure result integrity without relying on third-party verification, blockchain and smart contracts are utilized for automated authentication, index management, and trapdoor generation. Performance evaluations demonstrate that the system achieves high efficiency and scalability while maintaining strong security guarantees, outperforming existing approaches in both search speed and verifiability.

Keywords: Cloud Security, Encrypted Search, Boolean Keyword Search, Verifiable Search, Secure Indexing, Cryptographic Proofs

Abstract: The rapid expansion of internet usage has led to an increase in cyber threats, especially through malicious URLs that host phishing pages, malware, or exploit kits. Traditional blacklisting methods are often inadequate due to the dynamic nature of these threats. This paper proposes Secure Linker, a system that utilizes ensemble learning techniques to detect malicious URLs with higher accuracy and resilience. The system combines multiple Machine Learning classifiers, leveraging their individual strengths to make more reliable predictions. Experimental results show that ensemble methods outperform individual models in terms of accuracy, precision, recall, and overall robustness.

Keywords: Malicious URL, Machine learning, Phishing, Spamming, Malware, Spoofing.

Abstract: Chronic diseases like diabetes, cardiovascular, and respiratory conditions require continuous monitoring, which traditional healthcare systems often fail to provide. To address this, the proposed system introduces an AI-powered, IoT-integrated health monitoring solution for real-time, remote tracking. A Raspberry Pi 4 acts as the processing unit, receiving data from a Near-Infrared (NIR) sensor managed by an ESP8266 microcontroller. This setup allows for non-invasive monitoring of key health metrics such as Haemoglobin A1c, insulin levels, caloric intake, and lung function indicators. The system uses a Long Short-Term Memory (LSTM) neural network to analyse time-series data and predict health risks. It classifies a patient's condition as normal or at risk, enabling early detection and timely intervention. Continuous monitoring reduces hospital visits and empowers patients to manage their health independently. Healthcare providers receive real-time, actionable insights for better decision-making. The integration of AI and IoT ensures accurate data collection and intelligent analysis. Overall, the system supports a proactive, patient-focused approach to chronic disease management.

Keywords: AI-based algorithms, diabetes management, esp8266 module, non-invasive monitoring, NIR sensor, raspberry pi 4b, LSTM neural network, remote health monitoring, wireless data transmission, sensor-based systems.

Abstract: Text Craft AI - The Smartest Document Editor Ever is a robust, fully offline document editing solution designed to simplify and optimize the content creation and editing process. It operates independently of cloud services or third-party APIs, offering seamless support for a variety of file formats, including PDF, DOCX,TXT, XLSX, CSV, HTML, and XML. Notable features include real-time voice typing, transcription of audio and video content, sentence rephrasing, and translation across over 30 languages. The editor also incorporates intelligent features such as predictive text suggestions, grammar and spell checking, auto-save, and version history tracking. With all operations handled locally, Text Craft AI - The Smartest Document Editor Ever ensures user privacy at all times. This project offers a comprehensive, offline solution for effective document editing, aimed at enhancing productivity while safeguarding user information.

Abstract: In an era of rapid digital transformation, small and medium enterprises (SMEs) in India face an urgent need to enhance operational efficiency and customer satisfaction through technology. Task and order management software such as Jugl presents a promising solution to address these demands. This study investigates the effectiveness of Jugl in streamlining workflows, improving employee productivity, enhancing internal communication, and fostering better customer interaction. Utilizing a structured survey with 27 Likert-scale questions and robust statistical analysis through SPSS, the study derives insights from 52 active Jugl users. Key statistical methods employed include descriptive statistics, t-tests, correlation, regression analysis, and reliability testing (Cronbach's Alpha). The results reveal a strong positive user perception of Jugl, with particular appreciation for its intuitive interface, productivity-enhancing features, and support systems. The findings suggest that Jugl not only simplifies operations but also significantly contributes to business growth and customer satisfaction, particularly in SME contexts. Strategic recommendations are proposed to enhance Jugl's usability, adaptability, and long-term value.

Keywords: Task Management Software, Jugl, Workflow Optimization, SME Productivity, Digital Transformation, Operational Efficiency, Customer Communication, SPSS Analysis

Abstract: The project titled "Financial Analysis through Financial Management Techniques at VKS Enterprises" provides a focused evaluation of the company's financial position using key financial tools such as ratio analysis, leverage analysis and comparative financial statements. These techniques are essential for understanding the financial health, risk profile and operational effectiveness of a business over time. The primary objective of this analysis is to offer a detailed assessment of the company's financial strengths and weakness based on its performance over the past three financial years. Ratio analysis is used to interpret figures related to profitability, liquidity, and efficiency-including key ratios such as the current ratio, quick ratio, net profit margin, return on capital employed (ROCE) and inventory turnover ratio. In addition, leverage analysis explores the company's debt-equity ratio and interest coverage ratio, which help determine the degree of financial risk and the company's ability to meet long-term obligations. The analysis highlights areas where VKS Enterprises is performing well and points out sectors that require improvements, particularly in capital structure and short-term financial planning. The study concludes by emphasizing the value of these financial tools in driving strategic decisions and improving overall performance.

Keywords: Ratio Analysis, Leverage, Profitability, Liquidity, Financial Performance.

Abstract: A cloud-native grading environment that leverages advanced language models to assess and comment on open‐ended student submissions. Implemented with a modern JavaScript framework and Firebase's real-time backend, the platform offers dedicated upload portals for instructors' exemplar responses and learners' work. An AI-driven analysis engine transforms text into semantic representations, compares student answers against reference solutions, and generates bespoke feedback statements. By automating scoring and commentary, the system not only lightens educators' workloads but also ensures uniformity in evaluation and supplies students with clear, actionable insights. The platform also supports continuous learning by refining its feedback strategies based on historical assessment data. Additionally, it incorporates adaptive analytics dashboards for instructors to monitor class performance trends and intervene proactively.

Keywords: Automated assessment, Semantic embeddings, real-time synchronization, Personalized feedback, educational AI, adaptive learning, performance analytics

Abstract: This study examines the comparative effectiveness of traditional and online job boards in the context of talent acquisition, with a focus on the IT and technology industry. It aims to evaluate key aspects such as reach, cost-efficiency, hiring speed, and candidate quality associated with both methods. Through a combination of primary data collection and secondary research, supported by statistical analysis using ANOVA and Chi-square tests, the research identifies trends and preferences among employers and job seekers. The findings indicate that online job boards are generally preferred for their broader reach, faster processing, and cost-effectiveness, despite challenges like an excess of unqualified applications. Traditional job boards, while still relevant in certain scenarios, are often limited by higher costs and longer processing times. The study offers insights into the strengths and limitations of each platform and provides strategic recommendations for enhancing recruitment efficiency through improved filtering algorithms and integration of digital tools. The results highlight the importance of aligning recruitment strategies with demographic and regional factors to attract and retain qualified talent effectively.

Keywords: Effectiveness in talent sourcing, Reach and accessibility, Cost comparison, Speed and efficiency, Candidate quality Traditional job boards, Candidate quality Online job boards

Abstract: Recruitment and selection are critical processes within human resource management that significantly influence organizational performance and success. The evolution of recruitment methods, driven by technological advancements and changing workforce expectations, necessitates an in-depth examination of contemporary practices. This study critically evaluates various recruitment and selection techniques employed by Computer Age Management Services, focusing on traditional methods and modern innovations like AI-driven hiring and psychometric testing. Using quantitative data collected from a sample of fifty participants and analyzed through statistical tools, the research identifies the effectiveness, challenges, and impacts of different methods on organizational success. Findings highlight significant correlations between recruitment practices and organizational outcomes, suggesting the need for strategic, ethical, and technologically adaptive hiring approaches. The study concludes by emphasizing best practices for recruitment and suggesting areas for future research to optimize talent acquisition.

Keywords: Recruitment Methods, Selection Techniques, Human Resource Management, AI in Hiring, Organizational Performance.

Abstract: This study evaluates the financial efficiency and stability of selected modern enterprises using the CAMEL framework an analytical model comprising five core indicators: Capital Adequacy, Asset Quality, Management Efficiency, Earnings, and Liquidity. Financial data were gathered and analyzed over a defined period to compute relevant ratios and assess overall institutional performance. Findings indicate varying degrees of strength across the CAMEL components, with particular weaknesses observed in liquidity positions in some enterprises. The research highlights areas for improvement and strategic intervention and demonstrates the utility of the CAMEL model as a diagnostic tool in financial analysis and corporate performance benchmarking.

Keywords: CAMEL Model, Financial Efficiency, Capital Adequacy, Liquidity, Enterprise Stability, Financial Analysis

Abstract: This research investigates the efficiency of technology-based Human Resource (HR) solutions in boosting employee retention in different organizational contexts. As quick digital transformation unfolds, organizations increasingly turn to HR solutions like SAP Fieldglass, HRMS, VMS Workday, and employee self-service portals to optimize HR efficiency, decrease administrative burdens, and improve employee engagement. The study examines how these tools lead to enhanced hiring, on-boarding, compliance, communication, and career development programs. Data was gathered through systematic surveys of professionals across various experience levels and organizational functions. The results point out that although digital HR solutions greatly enhance operational processes and decision-making, issues like technical problems, absence of training, and resistance to change continue to exist. The research underlines the significance of easy-to-use design, automation, and customized HR strategies in promoting employee satisfaction and long-term commitment.

Keywords: Digital HR Tools, Employee Retention Automation, SAP Fieldglass, Employee Engagement, Workforce Analytics, Human Resource Management System, On-boarding Efficiency

Abstract: The Role of Financial Literacy in Influencing Perceptions and Behaviors of Urban and Rural Investors Financial literacy significantly affects how choices are made and contributes to economic stability in both metropolitan and countryside regions. This research examines the impact of financial awareness on perception, decision-making, and risk-taking behaviors of investors from various socio-economic backgrounds. The study emphasizes that urban investors, with increased access to financial education and resources, often embrace varied and riskier include stocks, mutual funds, and digital assets. Conversely, rural investors, frequently limited by restricted access to financial literacy and official banking services, tend to favor safer alternatives like fixed deposits, gold, and real estate. Moreover, the research highlights obstacles like misinformation, distrust in financial organizations, and socio-cultural factors that affect financial literacy patterns. By encouraging financial education via specialized programs and digital literacy efforts, both rural and urban investors can improve their financial choices and aid in economic development. The results highlight the necessity for customized financial education initiatives to close the knowledge gap and promote informed financial literacy choices among various demographic groups

Keywords: financial literacy, Urban Investors, Rural Investors, Investment Behavior, Risk Perception, Socio-economic Factors, Financial Education, Economic Development, Digital Financial Inclusion, Behavioral Finance

Abstract: The evolution of financial technology has led to the increased adoption of digital investment platforms and AI-driven financial advisories, reshaping how individuals manage their investments. This study explores the factors influencing the adoption and perceived effectiveness of these technologies among employees of public companies in Chennai. By combining both digital investment platforms and AI-based financial advisories into a unified framework, the research identifies and analyzes five key independent variables - Accessibility & Ease of Use, AI Personalization & Recommendation Accuracy, Cost of Services, Financial Literacy Level, and Risk Appetite and their impact on the single dependent variable: Adoption and Perceived Effectiveness of Digital Investment Platforms and AI-Driven Financial Advisories. Data was collected through a structured questionnaire and analyzed using SPSS, employing correlation and regression techniques. The findings offer insights into user behavior, preferences, and the role of AI and digital tools in investment decisions, ultimately aiding stakeholders in enhancing the design and delivery of tech-enabled financial services.

Keywords: Digital Investment Platforms, AI-Driven Financial Advisories, Financial Literacy, Risk Appetite, AI Personalization

Abstract: With the growing need for intelligent human-computer interaction systems, recognizing human emotions and interpreting sign language have become essential components in bridging communication gaps. This research presents a unified deep learning-based system that integrates both facial emotion recognition and sign language translation. The proposed model utilizes pre- trained VGG16 and VGG19 architectures to extract high-level spatial features from facial images and sign language gestures. For facial emotion recognition, the FER2013 dataset is used, and real- time emotion prediction is achieved using live webcam input. In parallel, sign language gestures are interpreted using the American Sign Language (ASL) dataset, where the temporal dynamics are captured and processed. The extracted features are used to train classifiers to enhance recognition accuracy. Experimental evaluations demonstrate the effectiveness of the combined approach, showing promising performance in accurately detecting emotions and translating sign gestures. This integrated system offers a valuable tool for enhancing communication, especially for individuals with speech impairments and in emotionally aware interactive systems.

Keywords: Facial Emotion Recognition, Sign Language Translation, VGG16 and VGG19, Deep Learning, Human-Computer Interaction and Real-Time Gesture Recognition

Abstract: The information technology industry with its fast-track innovation and challenging deadlines tends to exert tremendous amounts of stress over the employees working there. Here is a study on the interface between work stress and work-life balance among workers in Femtosoft Technology. Descriptive and correlational research designs have been used with structured questionnaires as primary data and statistical packages such as correlation, regression, ANOVA, and thematic analysis. It shows that workload, management pressure, and deadline all play crucial roles in producing job stress leading to work-life imbalance. It proposes organizational intervention through flexible work arrangements, well-being initiatives, and supportive managerial policies.

Keywords: Job Stress, Work-Life Balance, IT Industry, Employee Well-Being, Organizational Performance, Stress Management.

Abstract: The Healthcare industry is a broad and dynamic sector that plays a critical role in safeguarding public health through the prevention, diagnosis, treatment, and management of diseases. It encompasses a wide range of interconnected services and professions, including hospitals, clinics, pharmaceutical and biotechnology firms, medical device manufacturers, health insurance providers, and research institutions. This industry not only delivers essential medical care to individuals but also drives innovation in medical technologies and treatments. Central to its operation are healthcare professionals-such as primary care physicians, nurses, and specialists-who are responsible for providing direct patient care and clinical services. Complementing this care delivery system are pharmaceutical and biotechnology companies, which invest heavily in research and development to produce new drugs, therapies, and diagnostic tools. Additionally, health insurance organizations play a vital role in financing care and ensuring access to medical services for diverse populations. The complex structure of the healthcare industry demands extensive interdisciplinary collaboration and is continually evolving in response to technological advancements, demographic shifts, regulatory changes, and emerging public health challenges. This paper aims to examine the fundamental components of the healthcare industry, evaluate its key drivers, and explore current trends shaping its future. By analyzing both the operational and innovative aspects of this sector, the research seeks to provide a comprehensive understanding of its significance in promoting global health outcomes and its potential for continued transformation in the face of new challenges.

Abstract: In the food industry, customer feedback plays a vital role in improving dish quality and service. This project introduces a multilingual feedback system where users can select dishes like idly, dosa, pongal, or vada and provide feedback through text or voice. The system supports multiple languages, including Telugu, Hindi, Tamil, and English, making it accessible to a wide range of users. User feedback is stored in a database and processed using natural language processing (NLP) techniques to classify sentiments as positive or negative. A user-friendly interface ensures easy dish selection and review submission. For administrators, a separate login portal is provided to view, filter, and sort feedback based on dish name and date, helping them monitor customer opinions effectively. By enabling both voice and text input and offering real-time sentiment analysis, the system enhances user engagement and assists food providers in making data-driven improvements. Future extensions may include trend analysis and personalized recommendations based on user feedback patterns.

Keywords: Multilingual Feedback, Sentiment Analysis, Natural Language Processing, Voice and Text Input, Customer Review System

Abstract: Stroke remains a leading cause of death and disability globally, demanding prompt diagnosis and intervention to enhance recovery outcomes. Leveraging recent advancements in machine learning, this study presents an early stroke detection framework utilizing neuroimage analysis, particularly brain CT scans. A Residual Network (ResNet) model is employed to improve classification performance by extracting critical features from CT images. Cross-validation techniques evaluate the model's accuracy using precision, recall, F1 score, and ROC-AUC metrics. The proposed system empowers healthcare professionals with a reliable, automated tool for earlier and more accurate stroke detection, potentially reducing patient morbidity and mortality rates.

Keywords: Stroke Detection, Neuroimaging, Machine Learning, Residual Networks (ResNet), Early Diagnosis.

Abstract: This study examines the effectiveness of Moving Averages (MAs) and Bollinger Bands (BBs) in trading strategies, focusing on Apple Inc. (AAPL) and Reliance stocks. By testing different MA periods (10-day, 50-day, 100-day) and BB deviations (1.5, 2, 2.5), the research evaluates impacts on profitability, win rates, and risk-adjusted returns. Backtesting over 5-10 years of daily data reveals that shorter MAs outperform on volatile stocks like AAPL, while longer MAs suit more stable stocks like Reliance. Additionally, customizing BB deviations according to stock volatility significantly enhances trade signal accuracy. Integrating MAs and BBs together reduces false signals and improves trading outcomes across both trending and range-bound markets. These findings offer actionable insights for traders seeking to optimize technical strategies and contribute to academic literature by systematically testing non-standard indicator settings.

Keywords: Moving Averages, Bollinger Bands, trading strategies, technical analysis, stock market, volatility

Abstract: To protect one's wealth against inflation and economic volatility, gold is a vital financial asset. Nevertheless, a number of economic variables and market volatility make accurate gold price predictions difficult. Gold price forecasts made using more antiquated methodologies are notoriously inaccurate and unable to keep up with the ever-changing market. This research proposes a machine learning-based strategy for forecasting gold prices using past data and economic factors to solve this problem. To determine which machine learning algorithms are most successful in accurately predicting future gold prices, the suggested approach uses a battery of them. The research finds the best algorithm for predicting future prices by comparing the results of several models. In order to improve the accuracy of the forecasts, the implementation employs data preprocessing methods, feature selection, and predictive modeling. To help policymakers, financial analysts, and investors make educated judgments about gold investments, this study presents a data-driven method.

Keywords: Machine Learning, Gold Price Prediction, Economic Variables, Forecasting, Data Analysis

Abstract: The current study investigates the awareness, availability, and effectiveness of employee welfare measures and their impact on job satisfaction and organizational behaviour in the Indian IT sector. Based on a sample of 133 respondents, mostly young female interns, data were analysed via descriptive statistics, chi-square tests, t-tests, and ANOVA. Findings indicate that perceptions of welfare differ highly by education and work categories, and stress-based issues continue to be present regardless of welfare arrangements. The findings highlight the necessity of adaptive, tailored welfare measures to facilitate longer-term well-being and organizational allegiance.

Keywords: Employee Welfare, Organizational Behaviour, Job Satisfaction, Stress, IT Industry, India, ANOVA, Human Resources.

Abstract: Population projections are pivotal to shaping urban planning strategies, as they provide essential data that informs decisions on infrastructure, resource allocation, and long-term city development. As urbanization accelerates, cities face challenges such as overcrowding, resource shortages, and environmental stress. Accurate population forecasting allows planners to anticipate these challenges and implement sustainable solutions. This dissertation explores the dimensions of population forecasting, emphasizing the methodologies used and their applicability in preparing city master plans. Case studies, particularly from Indian cities, for instance, the Master Plan for Chennai Metropolitan Area illustrates the effective use of the Urban-Rural Growth Difference (URGD) method, while the Bengaluru Master Plan adopts cubic modelling to account for urban growth patterns. These examples showcase how tailored approaches to population projections can address unique urban challenges. This research highlights the importance of a multi-method approach, blending traditional and modern techniques to achieve precision in population projections. It advocates for continuous investment in data quality and accessibility, fostering informed and adaptable urban planning strategies for the future. Through a combination of theoretical exploration and practical application, the study contributes to the evolving field of urban demography and planning.

Keywords: Dimensions, Population Projection, Urban Planning, Methods of Population Projection.

Abstract: Urban growth is a major concern in rapidly developing cities like Vijayawada, Andhra Pradesh. This study focuses on land use and land cover (LULC) changes using satellite imagery and GIS tools on the Budameru Rivulet. The increasing of urban growth around the rivulet has resulted on the loss of natural flood buffers and an increase in impervious surfaces, thereby exacerbating flood risks. Satellite data from 2016 & 2025 were utilized to assess the spatial and temporal evolution of land cover in the region, revealing an enormous change of 3.5% variation of agricultural area, water bodies like mainly flood plain areas being encroached by 0.5%, vegetation has reduced by 0.09%, the total catchment area of the region is 1400 sq km and in that catchment area nearly 32 sq. km area occupied by urban activities in the period of 2016 to 2025. These LULC changes were mapped using visual interpretation revealing a significant rise in the built-up areas and a decline in agriculture land over past one decade. This study underscores the importance of LULC mapping providing critical insights for urban planning.

Keywords: Land Use and Land Cover (LULC), urban growth, GIS, visual interpretation.

Abstract: As environmental issues become a global concem and take prominence in our world, organizations across the globe are going out of their way adopt sustainable to practices within their business models. This is best achieved through the implementation of Green Human Resource Management (GHRM) practices. This large-scale study attempts to examine the adoption and effects of several GHRM practices specifically within IT companies based in Chennai, which is known to be a fast-emerging technological hub in India GHRM, by definition, is a chain of environmentally friendly human resource practices that include, but are not limited to, green recruitment practices, comprehensive environmental training programs, sustainable performance assessment processes, and well-planned reward systems meant to generate a strong sense of environmental responsibility among employees The study focuses mainly on generating a better understanding of how these specific practices influence not just workforce behaviour but also the overall organizational culture, and the long-term sustainability objectives that these organizations seek to achieve. The research carried out here confirms that Information Technology companies set up in Chennai are embracing initiatives of Green Human Resource Management (GHRM) increasingly enthusiastically as a strategic move to effectively minimize their carbon footprint, significantly improve the level of employee engagement, and effectively integrate their operations with wider corporate social responsibility (CSR) goals.

Keywords: Green HRM, sustainability, IT industry, Chennai, employee engagement, ecofriendly HR practices, workforce management, corporate social responsibility, green recruitment, environmental training.

Abstract: This study explores the performance evaluation of distribution channels within a manufacturing context, focusing on both operational efficiency and customer satisfaction. As manufacturing processes evolve, integrating digital and traditional distribution strategies has become vital for maintaining competitiveness. The research examines how distribution mechanisms influence product delivery, operational costs, and service quality, thereby impacting overall organizational effectiveness. Emphasis is placed on understanding the integration of omnichannel models and their role in enhancing market reach. The research methodology includes the analysis of internal distribution processes to identify inefficiencies, delays, and geographic challenges. Data collection involves both primary and secondary sources to assess how distribution performance correlates with customer experiences. The study also investigates the influence of consumer behavior on channel preferences, highlighting the growing need for businesses to adapt distribution methods in response to shifting market demands. Additionally, it addresses internal organizational conflicts that arise during channel management and how these affect strategic decision-making. Key findings suggest that optimizing distribution networks through digital transformation and internal alignment significantly improves efficiency, customer satisfaction, and market adaptability. The study recommends that organizations invest in integrated distribution models, enhance their decision-making processes, and prioritize consumer-driven strategies to ensure long-term sustainability and profitability. These insights contribute to a broader understanding of how businesses can refine their distribution frameworks to achieve a competitive advantage in an increasingly dynamic global market.

Keywords: Distribution Channel Performance, Customer Satisfaction, Service Quality, Manufacturing Sector, Process Improvement

Abstract: This study analyzes techniques for managing employee relations and resolving workplace conflicts within the context of MyInceptiontech, a growing technology company. Effective employee relations and conflict resolution are vital components of organizational health, directly influencing employee satisfaction, productivity, and retention. The research explores key techniques such as transparent communication, grievance redressal mechanisms, mediation strategies, leadership involvement, and the role of HR policies in managing interpersonal and organizational conflict. Using a mixed-method research design, the study combines qualitative interviews with quantitative survey data to assess the effectiveness of conflict management strategies across departments. Statistical tools, including Chi-square and correlation analysis, were employed to identify patterns and associations between employee relation practices and conflict outcomes. The findings reveal that proactive communication, timely conflict intervention, and supportive leadership significantly contribute to reducing workplace tensions and improving employee morale.The analysis also uncovered that while demographic variables such as age and job level did not significantly affect perceptions of conflict, team dynamics and managerial behavior had a notable influence on conflict frequency and resolution effectiveness. The study concludes with practical recommendations for MyInceptiontech, including leadership training in conflict resolution, standardized communication frameworks, employee assistance programs, and structured feedback systems. These measures are expected to enhance workplace harmony, employee engagement, and overall organizational performance. This research serves as a valuable reference for companies seeking to build stronger employee relations and sustainable conflict management frameworks.

Keywords: Employee Relations, Conflict Resolution, Workplace Communication, Dispute Management, Organizational Behavior, Leadership Involvement, Mediation Strategies, HR Practices, Employee Satisfaction, Workplace Harmony

Abstract: This study investigates the various pricing strategies utilized within the manufacturing sector, which significantly contribute to economic development, innovation, and market competitiveness. In an environment shaped by industrial growth, technological change, and global integration, manufacturing organizations face constant pressure to uphold quality, manage costs effectively, and swiftly adapt to market shifts. Approaches such as cost-oriented, value-driven, and competition-based pricing have become essential in shaping a firm's market identity, influencing customer perception, and encouraging brand loyalty. The research analyzes the impact of evolving external factors such as volatile input costs, digital advancements, and the rising emphasis on sustainability on pricing decisions. It further assesses how different pricing models influence business outcomes like profit margins, customer satisfaction, and sales performance. Additionally, the study explores how manufacturers maintain a balance between cost efficiency and market demands to ensure financial viability and strategic growth. By reviewing current industry practices, the study highlights existing research gaps, especially in terms of how pricing approaches affect financial success in conventional manufacturing environments. It seeks to offer practical insights into refining pricing frameworks to support stronger market positioning, improved customer relationships, and long-term business resilience. These insights are intended to guide manufacturers in developing adaptive and data-informed pricing strategies suitable for changing market landscapes.

Keywords: Strategic Pricing, Manufacturing Industry, Market Adaptability, Consumer Behavior, Sustainable Business Growth.

Abstract: In today's unpredictable and competitive business environment, managing risks effectively is vital for the long-term sustainability and growth of small and medium-sized enterprises (SMEs). This study focuses on understanding how SMEs, particularly in Chennai, identify, assess, and mitigate various forms of business risk, including financial, operational, regulatory, and environmental threats. With limited resources and increasing exposure to market volatility, SMEs must adopt structured risk management practices that align with their strategic goals. This research explores key components of business risk management, such as risk identification methods, prioritization using risk assessment tools, and implementation of mitigation strategies like avoidance, transfer, reduction, and acceptance. The study further examines how SMEs develop adaptive strategies to cope with challenges in supply chains, labour markets, and changing regulatory landscapes. Using primary data collected through surveys from 100 SMEs in Chennai and supported by secondary research, the analysis employs percentage methods and chi-square tests to identify patterns and significant associations among risk management practices and business outcomes. The findings highlight the importance of regular risk evaluation, proactive planning, and organizational culture in building resilience. Moreover, the research identifies gaps in risk diversification beyond financial aspects and emphasizes the value of integrating sustainability and ethical practices into risk frameworks. This study contributes to a better understanding of risk management in the SME sector and provides practical insights for business owners and policymakers aiming to strengthen risk readiness and promote stable, responsible growth in a rapidly evolving business environment.

Keywords: Business Risk Management, Small and Medium Enterprises (SMEs), Risk Mitigation Strategies, Operational Resilience, Regulatory Challenges, Sustainable Business Practices.

Abstract: This study examines the changing landscape of content creation and its crucial influence on shaping digital marketing strategies. As digital platforms increasingly serve as the main points of contact between brands and consumers, the need for authentic, engaging, and strategically crafted content is more critical than ever. The research delves into how various types of content-such as video storytelling, social media initiatives, blog posts, and infographics-impact consumer engagement, brand image, and purchasing decisions. In today's highly competitive digital environment, content has evolved beyond a mere communication tool to become a powerful force for building influence, trust, and value. The research adopts a multi-faceted approach to assess the effects of content strategies on marketing performance indicators like customer acquisition, retention, and conversion rates. It also explores regional and global patterns, emphasizing the role of mobile accessibility, expanding internet reach, and evolving digital consumer behaviors in driving the need for innovative, data-informed content strategies. Additionally, the study highlights the growing role of emerging technologies, particularly artificial intelligence, in transforming content creation through automation and personalization. It identifies key challenges in current digital marketing practices, especially in effectively incorporating new technologies into content development workflows. The aim is to provide actionable insights for businesses seeking to leverage content as a strategic resource to strengthen their digital footprint and adapt to future marketing trends.

Keywords: Consumer behaviors, Audience engagement, Customer satisfaction, Brand awareness, Loyalty.

Abstract: This study focuses on the underlying function of competency mapping in optimizing recruitment efficiency in a consulting firm. Competency mapping is an HR strategic instrument that enables organizations to recognize the essential skills, behaviour, and values needed for different job functions to make quality hiring decisions. The underlying motive behind competency mapping in achieving highest hiring effectiveness in a consultancy firm is the focus area of this research. A strategic HR approach referred to as competency mapping assists companies in finding out the core skills, behavior, and values required by a given job so that they can make recruitment decisions on sound judgment. The theme focus of this study is the link between competence mapping and higher recruiting success in areas like role clarity, skills fit, time-to-hire, and retention. The study collected perspectives of department managers and HR professionals both qualitatively and quantitatively. A standardized questionnaire was used for capturing opinions on the impact of competency mapping on the recruitment process. The study attested that competency mapping has significant roles in the efficiency of the recruitment process, the shortening of recruitment cycle time, and enhancing new hire performance. ANOVA and Chi-square statistical methods were applied to determine the differences between departments and job groups. Interestingly, the research found that high correlation existed between competency maps and successful hiring. Mapped competencies-based recruitment was reported by respondents to yield better cultural fit, faster recruitment, and improved retention. Discrepancy in the implementation of competency maps across departments indicated potential for formalized HR procedures. The study concludes by giving actionable suggestions to the consultancy firm for institutionalizing competency mapping. These are creating competency dictionaries for each job, competency-based interviewing training for the recruiters, and integrating mapping into workforce planning. By adopting these practices, the firm can improve recruitment efficiency, reduce turnover, and create a high-performing workforce.

Keywords: Competency Mapping, Recruitment Efficiency, Talent Acquisition, HR Strategy, Hiring Process, Organizational Fit, Skill Matching, Retention.

Abstract: This study investigates the effectiveness of the performance appraisal system at El Revgen Healthcare Solutions Pvt. Ltd., a Chennai-based healthcare BPO specializing in revenue cycle management and medical billing. The research aims to evaluate how the existing appraisal practices influence employee motivation, performance, and organizational commitment. Using a descriptive research design and a simple random sampling technique, data were collected from 100 employees across various departments through structured questionnaires and Likert scale-based assessments. Quantitative tools such as descriptive statistics, correlation, and regression analysis were employed to interpret the findings. Results indicate that while a majority of employees understand the appraisal objectives and feel their roles are fairly evaluated, a notable proportion remain neutral or uncertain, suggesting gaps in communication and clarity. The appraisal system is generally viewed as structured and transparent, yet some employees expressed concerns about fairness and relevance. A strong positive correlation was found between effective feedback and increased motivation, though regression analysis showed that motivation alone does not significantly predict perceptions of system transparency. The findings emphasize the need for improved feedback mechanisms, clearer evaluation criteria, and stronger links between appraisals and professional development. The study concludes that while El Revgen's performance appraisal system supports employee growth to some extent, enhancements in transparency, communication, and recognition practices are essential for optimizing workforce potential and aligning individual goals with organizational objectives. These insights offer practical implications for strengthening appraisal systems in healthcare outsourcing firms operating in competitive and dynamic environments.

Keywords: Performance Appraisal, Employee Motivation, Organizational Commitment, Healthcare BPO, El Revgen Healthcare Solutions.

Abstract: This study investigates the application of on-page and off-page Search Engine Optimization (SEO) thats leading digital marketing and web development company based in Chennai. As businesses increasingly shift to online platforms, SEO has become a pivotal strategy to enhance website visibility, user engagement, and brand credibility. The research highlights how on-page SEO practices-such as keyword placement, content structuring, metadata optimization, and mobile responsiveness-improve user experience and search engine rankings. In addition to on-page methods, the study explores off-page SEO strategies including social media promotion, influencer collaborations, and backlink generation. These techniques contribute significantly to building domain authority and increasing online traffic. The paper also considers the growing influence of AI-driven SEO tools and the challenges of maintaining a balance between local and global SEO strategies in a rapidly evolving digital landscape. A comprehensive literature review supports these observations and provides context to current industry trends and innovations. This study investigates the application of on-page and off-page search engine optimization (SEO) techniques within the broader framework of digital marketing. On-page SEO practices-such as keyword integration, content quality, metadata optimization, and site structure-are analyzed for their role in enhancing user experience and boosting search engine visibility. Off-page SEO elements, including social media engagement, influencer outreach, and backlink generation, are explored for their contribution to brand credibility and domain authority. The study also highlights the growing influence of AI and automation tools in SEO practices, emphasizing the need to assess how modern technologies like machine learning and large language models impact search rankings and user behavior. Furthermore, the research identifies a gap in understanding how SEO strategies can be customized for both local and global markets. By addressing these dimensions, the study provides a comprehensive view of SEO's evolving role in digital marketing and its impact on online engagement and business growth.

Keywords: On-page SEO, Off-page SEO, Search Engine Optimization, Keyword integration, Metadata optimization, Content quality, Site structure, Mobile responsiveness.

Abstract: This study examines the financial performance of an oil factory, focusing on key profitability and liquidity indicators such as Net Profit Margin, Gross Profit Margin, Return on Assets (ROA), Return on Equity (ROE), and liquidity ratios including Current Ratio, Quick Ratio, and Cash Ratio. The research utilizes a quantitative methodology based on secondary data comprising 25 financial observations. Descriptive statistics were used to summarize the data, followed by correlation analysis to explore interrelationships between profitability indicators, and multiple linear regression to identify significant predictors of net profitability. The results reveal a strong positive correlation between Gross Profit Margin and Net Profit Margin, suggesting that operational efficiency plays a central role in overall profitability. Conversely, ROA and ROE displayed weaker or insignificant associations, indicating their limited predictive power in this context. Liquidity ratios, while important for short-term financial health, showed minimal influence on profitability in this analysis. The regression model accounted for nearly 80% of the variance in Net Profit Margin, with Gross Profit Margin emerging as the most influential factor. These findings highlight the importance of effective cost control, pricing strategy, and production efficiency in sustaining profitability. This study contributes to the growing body of empirical research in India's edible oil manufacturing sector and offers practical insights for financial managers, investors, and policymakers aiming to improve performance evaluation and strategic decision-making.

Keywords: Financial Analysis, Profitability Ratios, Liquidity Ratios, Regression Analysis, Edible Oil Industry, Return on Assets, Return on Equity

Abstract: The shift toward remote work has significantly transformed Human Resource (HR) practices, particularly within Chennai's burgeoning Information Technology (IT) sector. As companies continue to adopt virtual work environments, ensuring high levels of employee engagement and performance has become a strategic priority. This study explores the HR strategies that can effectively address the unique challenges faced by remote IT workforces in Chennai. These include communication gaps, lack of interpersonal connection, motivation decline, and difficulties in monitoring performance. The research emphasizes the importance of fostering a strong organizational culture, transparent communication, leadership development, and the use of technology-driven solutions. Drawing from contemporary literature, industry insights, and regional analysis, the study examines HR practices such as virtual onboarding, continuous feedback mechanisms, digital collaboration tools, career development programs, recognition and rewards systems, and employee wellness initiatives. It also investigates the impact of leadership styles and inclusive practices on employee engagement and performance. The research highlights a gap in region-specific studies and proposes a framework of HR strategies tailored to Chennai's socio-economic and cultural context

Keywords: Human Resource Strategies, Remote Workforce, Employee Engagement, Employee Performance, Information Technology, Chennai, Virtual Work, Leadership, Digital HR Tools, Work Life Balance.

Abstract: In today's digitally-driven business context, digital marketing tools such as Search Engine Optimization (SEO), website redesigning, and artificial intelligence (AI)-based technologies are essential to boost online presence, customer engagement, and business growth, especially for small and medium enterprises (SMEs). This study quantifies the strategic relevance and effectiveness of such online marketing practices, wherein it analyzes the effect of SEO methods on website traffic and lead generation, how website redesigning can aid in enhancing user experience, and employing AI tools for content and communication improvement. This study's findings provide insights and actionable recommendations to SMEs as a way of elevating their online marketing practices towards concrete business success.

Keywords: Digital Marketing Tools, SEO Strategies, Website Redesign, AI Integration, Online Visibility, User Engagement, AI-powered Tools, Chatbots

Abstract: SUSTAINABLE SUCCESS Sustainable success is a situation where there is growth and stability in a business over a long time with good performance, customer satisfaction, and good operations. It goes beyond short-term profit maximization. Few challenges faced during achieving sustainable success are Inconsistent or unreliable ingredient supply, Frequent staff changes or Failure to update the menu, embrace technology, or improve customer experience which can cause stagnation in achieving sustainable success by the firm. This study explores the challenges and strategic opportunities associated with operating coffee franchises in India's fast-growing café sector. As the franchise model continues to expand rapidly across the country, many franchisees encounter critical hurdles related to supply chain management, customer service, business operations, and local market adaptation. These issues often hinder sustainable growth, affect profitability, and challenge the consistency of customer experience across outlets.

Keywords: Franchisee challenges, Sustainable business strategies, Franchisor support, Customer engagement, Business development, Supply chain management, Operational efficiency.

Abstract: The rapid pace of urbanization in India has amplified the need for efficient public transportation systems. Metro rail networks play a vital role in addressing mobility challenges; however, their effectiveness is often hindered by inadequate last-mile connectivity (LMC). This study explores strategic interventions to enhance LMC in Indian cities through an evaluation of current challenges and a review of best practices from both domestic and international contexts. Using case studies from Delhi, Bangalore, Singapore, and Norway, this research identifies core barriers including infrastructure limitations, affordability issues, and institutional fragmentation. The findings underscore the significance of integrated, multimodal, and user-centered approaches. Recommendations include the adoption of unified ticketing, electric feeder services, pedestrian and cycling infrastructure upgrades, and stakeholder collaboration. Improving LMC not only supports increased metro ridership but also contributes to sustainable and inclusive urban mobility.

Keywords: Last-Mile Connectivity, Metro Rail Transit, Urban Mobility, Multimodal Integration, Sustainable Transport, Public Transit Access, Non-Motorized Transport, Transport Policy

Abstract: This research explores the recruitment sources at Infologia Technology Pvt Ltd, a software firm based in Chennai, India, to explain the entire IT solutions market. Particularly relevant is the development and provision of a vast array of services, including software development, cloud computing, cybersecurity, data analytics, and so forth, in the field of information technology, where the rate of growth is usually quite high. Founded in 2014, Infologia Technology Pvt Ltd has made its way from being a startup to a firm serving the telecommunication and automotive industries, thus speaking for the vigor of the industry. It is very important for IT organizations to develop effective recruiting methods so as to hire the right people to manage the hurdles of continuous innovation and technology. The staff of Infologia is demographically and professionally studied, showing a mainly young, educated staff composed of full-time, part-time, and self-employed workers. Thus, the results determine to what extent personal management aimed at training, mentoring, and diversity has an effect on employee development and retention, which is at the same time very obvious. The report also says that for the IT business, now so competitive, companies can pull in and keep competent workers by optimizing recruiting channels and doing better employer branding. This study finally gives us an insight concerning how a certain IT business cultivated successful recruiting tactics, and it bears some importance for future development in human resource management and organization growth in the area of technology.

Keywords: Recruitment, Market, Work Life Balance, Information Technology, Business, Organization.

Abstract: This research explores the role of digital marketing in supporting business growth and organizational success, with a detailed study on company that provides IT solutions. In today's fast-paced digital world, businesses need to use online platforms effectively to stay competitive and connect with their target audience. This research focuses to adopted digital marketing strategies to improve brand visibility, attract new customers, and increase revenue. The study examines several important areas of digital marketing, including search engine optimization (SEO), social media marketing, email marketing, paid advertising (such as Google Ads), and content marketing. A combination of research methods was used to gather insights, including employee interviews, customer surveys, and analysis of digital marketing performance data. The research findings show that company has been able to achieve strong results by focusing on consistent branding, data-driven campaigns, and engaging content. Social media platforms like LinkedIn and Instagram helped the company reach a wider audience, while SEO and targeted ads improved website traffic and lead generation. The use of tools such as Google Analytics and social media insights also helped the company measure the success of its campaigns and make better decisions. Overall, the research highlights that a well-planned and executed digital marketing strategy can have a significant positive impact on an organization's growth. It not only helps increase sales in the short term but also builds long-term brand value and customer relationships. Based on the findings, several suggestions are made to help further improve its digital marketing strategies in the future.

Keywords: Search Engine Optimization (SEO) - Search Engine Marketing (SEM) - Organizational Success - Digital Marketing Integration -Lead Generation Strategies.

Abstract: The Rapid Evolution of digital banking has completely changed the financial industry by increasing the speed, accessibility, and use of banking services. This study investigates how elements like ease of use, trustworthiness, convenience, customer service, and ease of use affect people's intentions to utilize digital financial services. Structured questionnaires were used to gather data from a sample of online bank customers, and ANOVA, regression, and correlation analysis were used for analysis. The findings show a significant positive, and statistically significant relationship between the intention to adopt digital banking and perceived usefulness and ease of use. Convenience and trust were also found to be important indicators of usage behavior, highlighting the need of a secure, simple, and accessible online banking experience. Customer service, on the other hand, had a lesser relationship with trust and intention to use, suggesting that banks should enhance their responsiveness in providing services on digital platforms. Additionally, it was discovered that demographic variables including age, gender, and marital status had no discernible effect on digital adoption, indicating that considerations relating to technology are the primary drivers behind behavior. In order to boost adoption rates, it is advised that Bank should concentrate on improving the user experience through app redesign, fostering consumer trust through enhanced security measures, and ensuring real-time, individualized support. This study provides valuable insights into the critical antecedents of digital banking adoption and offers practical recommendations for enhancing customer engagement and satisfaction in the digital era.

Keywords: Digital Banking, Ease of Use, Usefulness, Trust, Convenience, Customer Support, Intention to Use.

Abstract: This study explores the optimization of talent acquisition within organizations, focusing on enhancing recruitment efficiency and job satisfaction. In a competitive business landscape, effective talent acquisition is crucial for organizational success, necessitating innovative, data-driven approaches to streamline recruitment processes. The research examines key strategies and tools that organizations can leverage, including AI, recruitment automation, and data analytics, to reduce hiring time and costs while ensuring alignment with organizational culture. The study highlights the importance of job satisfaction, emphasizing that recruitment should not only fill positions but also ensure new employees are satisfied with their roles. Through a comprehensive analysis of the software development industry, particularly Organisation, the research identifies best practices and technological innovations that can improve recruitment outcomes. The findings reveal a predominantly young and educated workforce, with significant correlations between employer branding and recruitment challenges. Recommendations include strengthening employer branding, enhancing technology integration, and focusing on career development to retain talent. Overall, the study underscores the need for organizations to adopt strategic talent acquisition practices to remain competitive and foster a satisfied, engaged workforce.

Keywords: Talent Acquisition, Recruitment Optimization, Recruitment Efficiency, Job Satisfaction Hiring Process, Candidate Experience.

Abstract: Employee performance is a critical determinant of organizational success, and skill development plays a vital role in enhancing that performance. This study explores the interrelationship between employee skill enhancement initiatives and improvements in performance metrics across various industries. It examines the impact of training programs, workshops, mentorship, and self-learning efforts on individual and team productivity. The research uses both qualitative and quantitative data collected through surveys, interviews, and performance reports from employees across different sectors. The findings suggest that continuous skill development not only boosts morale but also results in measurable performance outcomes such as increased efficiency, better quality of work, and higher employee retention. Moreover, this study investigates the role of management in fostering a learning culture within organizations. Leadership involvement, encouragement of innovation, and investment in professional development resources emerge as critical factors in bridging skill gaps and enhancing workforce capabilities. In conclusion, the research underlines the necessity of integrating skill development into HR strategies for sustainable employee performance improvement. The paper recommends best practices for organizations to optimize training investments and create a culture that supports lifelong learning. The methodology employed in this research includes a mixed-methods approach to ensure both statistical robustness and contextual understanding. Surveys were distributed among 500 employees and HR professionals, and semi-structured interviews were conducted to extract qualitative insights. Quantitative results were analyzed using correlation and regression models to assess the relationship between development initiatives and performance metrics such as KPIs, efficiency rates, and goal achievement. Qualitative data were thematically coded to uncover recurring patterns related to training effectiveness and workplace application. One significant finding of the study was that companies that provided a variety of learning opportunities-ranging from e-learning platforms to cross-departmental collaboration-saw the most notable performance improvements. This suggests that flexibility and accessibility in training delivery are key drivers of success. In sum, the abstract encapsulates a growing corporate imperative: skill development is no longer optional. It is a strategic necessity that directly feeds into business sustainability, competitiveness, and employee satisfaction.

Keywords: employee performance, skill development, training programs, productivity, human resource development, organizational growth

Abstract: HR professionals concerns and practices in terms of recruiting sources are analysed in this research, with emphasis on their perception of job portals as recruiting sources. The study employs quantitative methods like ANOVA, t-tests, and factor analysis on the data from a sample of HR professionals with diverse demographics. The find that experience does not impact applicants from job sites at all on the perceived quality, but many otherwise underlying characteristics do affect recruitment source rating. It includes the areas of cost-effectiveness, ROI, the ability to attract particular personnel, and the growing fit of the digital platforms in recruiting. The research also noted that having no effect on calculating how HR professionals appraise applicant quality means that they evaluate them based on a degree of neutrality. Additionally, the study emphasises the fact that younger professionals have been increasing in prominence in the HR field and that this increases the usefulness of digital recruitment techniques. The results have a profound impact on corporations and human resource professionals. Therefore, organizations are being encouraged to select their data-driven recruitment sources, improve training for HR professionals to master capabilities related to evaluating personnel, and implement technology to automate and streamline the process of recruiting

Keywords: job portals, recruitment, sources, most effectivessness, least effectivessness.

Abstract: Finite Impulse Response (FIR) filters play a crucial role in digital signal processing applications, demanding high-speed and power-efficient arithmetic operations. The design and implementation of a 7-tap Finite Impulse Response (FIR) filter using Verilog is a crucial task in digital signal processing (DSP) applications. In this project, a high-performance, area-efficient, and fast 7-tap FIR filter is designed utilizing Kogge-Stone Adder (KSA) for the addition operations within the filter structure. The Kogge-Stone Adder, known for its parallel prefix structure and logarithmic delay, significantly improves the speed of the filter compared to conventional ripple-carry adders. The development process is carried out using Xilinx ISE 14.7 software, installed within a VirtualBox environment for compatibility and ease of access. The project covers the complete flow from RTL design, functional simulation, synthesis, and timing analysis.

Keywords: Digital Signal Processing, Finite Impulse Response Filter, Kogge-Stone Adder, Verilog, Xilinx ISE 14.7.

Abstract: This study aims to examine the relationship between work-life balance (WLB) and employee retention, focusing on how various work-life balance practices impact the likelihood of employees staying with an organization. In today's competitive work environment, employees are increasingly seeking flexibility and support in managing both professional and personal responsibilities. The research investigates the extent to which organizations' WLB initiatives such as flexible work schedules, remote work options, and wellness programs contribute to employee satisfaction and retention rates. The study explores the perception of employees regarding their work-life balance and how it influences their decision to remain with the company. By gathering data through surveys and interviews with employees, the study aims to identify key factors that impact WLB and retention, including the challenges employees face in achieving a balanced life, and how organizations can address these concerns. Findings from the study reveal that employees who report better work-life balance are more likely to express a higher level of job satisfaction and are less inclined to leave the company. Conversely, employees who struggle with long hours, high stress, or lack of flexibility show a higher intention to seek employment elsewhere, highlighting the significant role WLB plays in employee retention. The research concludes with several recommendations for organizations to improve their work-life balance offerings, such as implementing more flexible working hours, promoting mental health initiatives, and fostering a culture of open communication regarding workload expectations. By addressing these factors, companies can improve employee retention, leading to higher engagement, productivity, and a more positive work environment.

Keywords: Work-life balance (WLB), Employee retention, Organizational commitment, Flexible work policies, Employee engagement

Abstract: The main goal of this research is to assist investors and financial decision-makers in making well-informed decisions by employing time series analysis to estimate future gold prices. In India, gold has great cultural and economic significance and is seen as a safe haven investment, particularly in times of market volatility. Planning investments and controlling financial risks require accurate gold price forecasting because of its sensitivity to variables like inflation, exchange rate swings, and global economic trends. The study uses EViews software and the well-known forecasting tool ARIMA (Autoregressive Integrated Moving Average) model to accomplish this. In order to understand trends, volatility, and patterns in the time series, historical gold price data is analyzed. The ARIMA model is used to accurately forecast future movements in the price of gold by leveraging an established, successful forecasting technology compared to developing a brand-new one. The results generated through this approach provide actionable insights for investors, policymakers, and financial analysts. The initiative helps with strategic financial planning and encourages wise investment choices by showing the direction of gold prices in the future. This study shows how useful time series forecasting is in assessing the behavior of commodity prices, especially for a widely held asset like gold.

Keywords: Forecasting, Time series analysis, ARIMA model, EViews, Historical data.

Abstract: The integration of blockchain technology with artificial intelligence (AI) presents a transformative approach to enhancing cybersecurity systems. This paper proposes a comprehensive framework combining decentralized AI models with blockchain's immutable ledger capabilities to create robust security solutions. Our methodology employs federated learning for privacy-preserving threat detection while utilizing smart contracts for automated response mechanisms. Through extensive experiments on a dataset of 150,000 cyber threat samples across 25 attack categories, we demonstrate a 98.7% detection accuracy with 45% reduction in false positives compared to centralized systems. The implemented system shows particular effectiveness against advanced persistent threats (APTs) and zero-day attacks, achieving 97.1% recall for previously unseen threats. We develop a practical deployment architecture suitable for enterprise environments with throughput of 2,500 transactions per second, and conduct real-world validation with five industry partners. This work contributes significant advances to the field of decentralized cybersecurity by providing a scalable, tamper-proof solution that maintains data privacy while improving threat intelligence sharing among organizations, along with detailed performance benchmarks across multiple deployment scenarios.

Keywords: Blockchain, Artificial Intelligence, Cybersecurity, Federated Learning, Smart Contracts, Threat Detection, Decentralized Systems, Privacy Preservation

Abstract: This study focuses on the modeling and 3D printing of an industrial gearbox to explore the feasibility of using additive manufacturing for prototyping and educational purposes. A detailed 3D CAD model of a standard industrial gearbox was developed using SolidWorks, incorporating key mechanical components such as gears, shafts, bearings, and housing. The model was analyzed for dimensional accuracy and assembly compatibility. Fused Deposition Modeling (FDM) 3D printing technology was used to fabricate the physical prototype using PLA material. The printed model was evaluated for geometric precision, ease of assembly, and visualization of internal gear mechanisms. This project demonstrates the effectiveness of 3D printing for producing cost-effective and functional scale models for design verification and demonstration applications.

Keywords: Industrial Gearbox, 3D Printing, CAD Modeling, Additive Manufacturing, FDM, PLA Material, Gear Assembly, Prototyping

Abstract: This paper presents a real-time traffic prediction and navigation system that integrates GPS-based vehicle tracking with Google Maps API, TomTom Traffic API, and Weather APIs to enhance route optimization and safety. The proposed system dynamically updates routes based on live traffic and weather conditions, while providing users with real-time notifications about potential hazards. Algorithms such as Dijkstra, A*, Bellman-Ford, Kalman Filter, and K-Means Clustering are employed to ensure efficient routing and accurate vehicle tracking. The solution is tested using realistic scenarios and validated for reliability, responsiveness, and user experience.

Keywords: Real-time GPS, traffic prediction, route optimization, weather API, traffic API, Kalman Filter, A* algorithm, vehicle tracking

Abstract: Deep learning technology is an essential tool for real-time recognition and translation of sign language, allowing for fluid conversations between sign language users. This approach uses deep neural networks to decode camera-captured hand gestures, analysing each movement and translating it into written or spoken words. Deep learning algorithms can recognize sophisticated patterns and discern minute changes in hand forms, gestures, and facial expressions when given a large amount of data. This system, which requires no additional equipment and can identify both static and dynamic signals, promotes inclusive interactions across language boundaries. Labelling ensures uniformity and clarity in gesture detection. TensorFlow's SSD (Single Shot Multibox Detector) technique enhances the speed and utility of real-world interactions by recognizing gestures as full sentences rather than Letters are written individually. This flexible system can detect signals using both American and Indian standards and adjusts differences in background, skin tone, and illumination. The results show exceptional accuracy, with 85% for static motions and 97% for dynamic sequences utilizing LSTM-GRU layers.

Keywords: Deep Learning, Real-time Recognition, Neural Networks, TensorFlow SSD, Gesture Detection, LSTM-GRU Layers

Abstract: Propeller-wing aerodynamic interaction is critical in aircraft performance, especially in configurations involving closely integrated components. This study presents a CFD-based analysis of a scaled three-bladed propeller operating with and without a downstream wing based on the Dornier 217 geometry. Using the Sliding Mesh approach in ANSYS Fluent, unsteady simulations were conducted to capture the transient effects of rotating flow fields. The domain was divided into a rotating region for the propeller and a stationary zone for the wing to replicate realistic conditions. Results show that the inclusion of the wing within the propeller slipstream alters flow behavior-leading to increased local velocity and static pressure near the wing, along with a slight reduction in thrust output compared to the standalone propeller case. These trends align with prior literature and underline the importance of careful aerodynamic integration in propeller-driven aircraft systems.

Keywords: Propeller, Dornier 217, ANSYS Fluent, CFD

Abstract: Strategic financial analysis is an in-depth methodology that allows companies to evaluate their financial performance, maximize resource utilization, and make sound long-term decisions. By combining financial information with corporate strategy, organizations can analyze profitability, liquidity, solvency, and operational effectiveness while determining risks and opportunities for growth. This analysis utilizes key methodologies like ratio analysis, trend analysis, cash flow forecasting, and scenario planning to deliver actionable insights to stakeholders. A key element of strategic financial analysis is ratio analysis, which analyzes liquidity (current ratio, quick ratio), profitability (net margin, return on equity), and leverage (debt-to-equity ratio). Trend analysis enables monitoring of financial performance over time, and cash flow forecasting guarantees sustainable liquidity management. Scenario planning enables firms to plan for economic uncertainty, regulatory change, and market shocks. Benchmarking against industry norms also assists in determining competitive positioning. Sophisticated financial techniques like discounted cash flow (DCF) analysis, SWOT analysis, and sensitivity testing also support better decisions in capital budgeting, mergers and acquisitions (M&A), and risk management. Organizations that use these methods can maximize investment strategy, enhance cost efficiency, and align business goals with corporate vision. Strategic financial analysis is crucial in today's uncertain economic environment to sustain resilience and competitive edge. Organizations that implement evidence-based financial decisions build stakeholder trust, manage risks, and achieve sustainable growth. Additionally, combining financial analysis with cutting-edge technologies such as AI and big data analytics enhances forecasting accuracy and operational efficiency. Ultimately, strategic financial analysis gives organizations the authority to make proactive, well-educated decisions, guaranteeing long-term stability and value creation. Through constant fine-tuning of financial strategies, businesses can adapt to changing market conditions, profit from opportunities, and find sustainable success.

Keywords: Strategic financial analysis, financial performance, ratio analysis

Abstract: Employer branding has emerged as a crucial component in attracting and retaining talent in today's competitive job market. This study explores the impact of employer branding on talent acquisition practices among organizations. The research focuses on how factors such as organizational culture, reputation, work environment, compensation, and career development opportunities influence job seekers' decisions. By analysing survey responses through correlation and regression analysis, the study provides empirical insights into the significance of employer branding in shaping candidates' perceptions and preferences. This technology is seen as a pathway to improving external and internal interactions and communication between jobseekers, firms, employees, and other stakeholders, as well as to build data bases to store and recall data. These are the main strategic advantages of a recruitment system. The findings of this study also show that recruitment in Indian public sector is still in-progress have not fully implemented technology.

Keywords: Employer Branding, Talent Acquisition, Organizational Culture, Recruitment Job Seekers, Employer Value Proposition, Retention, Human Resources.

Abstract: This study aims to explore the current marketing approaches adopted by healthcare supply firms in the Middle East, assessing their effectiveness in improving market share, customer engagement, and financial performance. The healthcare sector in the Middle East has witnessed substantial growth due to rising population levels, increasing chronic disease prevalence, and greater government investment in healthcare infrastructure. Within this context, healthcare supply firms are striving to optimize their profitability by deploying effective marketing strategies tailored to the region's unique socio-economic and cultural dynamics. The research focuses on key marketing elements such as market segmentation, branding, digital transformation, B2B relationship management, and pricing strategies. By examining case studies and market data from selected firms across GCC countries, the study identifies which strategic approaches yield the highest returns and operational efficiency. Special attention is given to the role of digital marketing and e-commerce platforms in enhancing the distribution and visibility of medical products, especially post-COVID-19, where digital adaptation has accelerated across the region. Findings from this study provide valuable insights for healthcare supply firms aiming to expand or refine their marketing operations in the Middle East. The research concludes with strategic recommendations on how firms can leverage localized marketing efforts, partnerships with healthcare institutions, and regulatory alignment to improve profitability. These insights not only contribute to academic literature but also offer practical implications for business leaders and marketers in the healthcare supply domain.

Keywords: Marketing strategies, Profitability, Digital transformation, B2B marketing, Market segmentation, Healthcare industry, Strategic analysis.

Abstract: A comprehensive financial performance analysis not only aids in assessing the current position of a company but also provides insights into its future viability These four dimensions offer a multifaceted view of how well a business is managing its resources, meeting its obligations, and preparing for future growth. The study relied on secondary data collected from TOFLER website for this study. This study evaluates the company's profitability, liquidity, efficiency, and solvency using tools such as, Time series analysis, Ratio analysis, and Percentage analysis. The objective is to identify financial strengths and weaknesses and offer recommendations for improved financial health and strategic growth of a company. Analysing a five-year period of (2020-2024) and understand their impact on the organization's goals. The objective is to identify financial strengths and weaknesses and offer recommendations for improved financial health and strategic growth. EBT, EAT (Gross Profit, Net profit) - 2020,2021 these 2 years company gets loss but 2023-2023-2024 improving their financial health and operational efficiency. Liquidity indicators suggest the need for improved working capital management to enhance short-term financial flexibility. Solvency ratios show a well-managed capital structure with moderate reliance on debt, ensuring long-term financial stability. This research also addresses the gap in industry-specific financial analysis by integrating practical data insights with theoretical frameworks with this study.

Keywords: Financial Performance, Ratio Analysis, Percentage analysis and Time series analysis.

Abstract: This paper examines the growing significance of online platforms specifically Flipkart to the sale of motor vehicle lubricants in India. With increasing digital commerce, conventional motor vehicle sales models are increasingly being complemented or substituted with digital strategies consistent with changing consumer behavior. The study investigates the impact of price, flash sales, product exposure, customer ratings, and online trust on lubricant buying behavior in online shopping. A quantitative method involving stratified sampling and descriptive statistics was utilized to test consumer behavior. The findings indicate that an overwhelming majority of respondents have purchased lubricants from Flipkart, motivated primarily by offer prices, competitive pricing, and product ratings. Strong correlations were also established between purchasing frequency and factors of trust such as authenticated reviews, reputation, and visibility during search. Regression and Anova statistical measures show that strategies such as price personalization, improved product descriptions, and engagement of customers yield significant improvements in repeat buys as well as in total sales. The research advises sellers to adopt dynamic pricing, enhance customer satisfaction, and deploy digital marketing so as to engage and enhance profitability. These findings contribute to the comparatively understudied area of e-commerce optimization within the automotive lubricants industry.

Keywords: E-commerce, Vehicle Lubricants, Flipkart, Online Sales, Strategic Framework

Abstract: Effective payroll processing and compliance management are essential for ensuring operational excellence and client confidence, particularly for companies with a diversified client base. This research, entitled "A Study on Streamlining Payroll Processes and Ensuring Compliance Division," targets consultancy companies handling payroll services for different organizations where accuracy, punctuality, and compliance with the law are paramount to maintaining client satisfaction and ensuring future growth. The main aim of this study is to assess the existing payroll procedures in the Compliance Division, pinpoint inefficiencies and risks, and recommend viable improvement measures. The data was gathered through surveys, interviews, and reviews of internal documents to develop a comprehensive picture of the problems encountered. The research also compares the firm's practices to industry standards to determine gaps and opportunities for technological and procedural improvements. Major takeaways point towards the necessity of automation, enhanced inter-departmental collaboration, frequent audits, and more robust compliance tracking mechanisms. According to the analysis, the research provides recommendations that will increase payroll efficiency, reduce manual errors, decrease administrative burden, and enhance regulatory compliance. Adopting these steps will not only improve internal processes but also solidify the firm's reputation for reliability and excellence. Finally, the research advocates for creating a scalable, efficient, and compliant payroll system that will lead to further growth and success.

Keywords: payroll Processes, compliance division, consultancy services, Legal Compliance.

Abstract: This study explores the impact of training and development programs on employee engagement and retention within a hospital setting. As healthcare institutions face increasing pressure to retain skilled staff and maintain high levels of service quality, understanding the role of training becomes crucial. The research investigates how structured training initiatives influence employee motivation, job satisfaction, commitment, and overall engagement. Additionally, the study examines whether effective training correlates with lower turnover rates and increased organizational loyalty. Data was collected through surveys and interviews with hospital employees across various departments. The findings suggest that comprehensive training programs significantly enhance employee engagement and contribute to improved retention rates. This study underscores the strategic importance of investing in continuous learning and professional development as a means to foster a more committed and stable healthcare workforce.

Keywords: Employee Engagement, Employee Retention, Training and Development, Hospital Workforce, Healthcare HRM, Staff Motivation, Professional Development, Job Satisfaction, Turnover Reduction, Organizational Commitment, Human Resource Strategies, Healthcare Management.

Abstract: Innovation and risk-taking are crucial for the growth and success of new businesses. This study explores how supporting new ideas within a company is connected to entrepreneurs taking calculated risks. Data were collected from 87 startup founders through structured questionnaires. The results showed a weak positive link between encouraging innovation and risk-taking, suggesting that promoting new ideas might slightly encourage entrepreneurs to take risks. However, further analysis found that this encouragement only accounted for 3.6% of the change in risk-taking, and this was not statistically significant. This means that while fostering innovation is important, it may not strongly influence entrepreneurs to take risks. Factors like personal traits, market conditions, access to resources, and leadership support could play a larger role. The study adds to our understanding by highlighting the complexity of the link between innovation and risk-taking, especially in startups. Practically, this implies that startup environments should combine support for innovation with other types of entrepreneurial help. The study faced limitations such as a small participant group and self-reported data. Future research should examine more factors that could affect this relationship, using long-term studies to track changes over time. This study improves our understanding of how a business's environment influences crucial entrepreneurial behaviours essential for startup success.

Keywords: Innovation, Risk-Taking, Entrepreneurs, Startups, Organizational Support, Entrepreneurial Orientation.

Abstract: This study examines the effectiveness of Economic Value Added (EVA) as a performance measure, comparing it with traditional profitability metrics through simulation. EVA, calculated as Net Operating Profit After Tax (NOPAT) minus Weighted Average Cost of Capital (WACC), is analyzed for 10 NIFTY companies over five fiscal years. The research reveals EVA's high sensitivity to the cost of equity and unexpected insensitivity to the cost of debt under normal conditions. Firm growth policies and leverage significantly impact EVA and its variability. Furthermore, EVA is found to be more volatile than return on investment (ROI) and closely related to return on equity (ROE). The analysis indicates no strong pattern of wealth creation among the studied companies, with EVA varying yearly based on the cost of capital, particularly the cost of equity. Ultimately, the study finds no strong correlation between EVA and market price for the selected companies.

Keywords: EVA, NOPAT, WACC, EBIT

Abstract: In today's fast-paced world, maintaining a balanced diet is increasingly challenging, especially with the limitations of manual food tracking systems. This paper presents an AI-driven food tracking and diet recommendation system that utilizes YOLOv8 for real-time food recognition, combined with a Decision Tree algorithm for personalized meal suggestions. Users upload an image of their meal, and the system detects food items, estimates portion sizes, and calculates nutritional values using a pixel-to-gram ratio. Personalized recommendations are generated based on dietary preferences, user goals, and daily intake, while a tracking module monitors consumption patterns over time. The proposed system enhances user convenience, improves accuracy in calorie estimation, and promotes healthier eating habits through data-driven insights. Experimental results demonstrate the system's ability to accurately identify diverse food items, provide meaningful dietary suggestions, and enable continuous health monitoring.

Keywords: Food recognition, calorie estimation, YOLOv8, decision tree, diet recommendation, Indian cuisine, food tracking, nutrition analysis.

Abstract: This research study investigates the performance management and career development systems implemented at Fangs Technology Pvt. Ltd., a key player in the retail sector. The project primarily focuses on the practices followed within the HR & Admin, Finance, Marketing, and Sales departments. The organization employs a structured performance management system that includes 360-degree feedback, KPI-based assessments, and sales target achievement tracking. These mechanisms are designed to evaluate employee performance comprehensively and encourage continuous improvement. The study also explores the company's career development strategies, particularly through training programs such as NHIT (New Hire Induction Training) and OJT (On-the-Job Training). These programs aim to enhance employee skills, prepare them for higher responsibilities, and align individual growth with organizational objectives. The research methodology includes a detailed employee survey, the responses to which provide insights into how staff members perceive the fairness, effectiveness, and impact of these performance and development systems. Responses indicate that while employees generally acknowledge the presence and benefits of structured performance evaluation and development programs, there are also concerns about the consistency and transparency of some processes, particularly related to internal promotions and feedback mechanisms. The study also analyses how these systems influence employee motivation, job satisfaction, and engagement across different experience levels. The uniformity in perception among respondents suggests a standardized approach to implementation, yet variations in satisfaction levels point to potential gaps in personalization and communication. This research contributes to understanding how integrated performance and development frameworks operate in practice within the retail industry and offers a foundation for further exploration of HR effectiveness in dynamic organizational settings.

Abstract: This work presents an innovative approach to enhancing GHz-range MIMO antenna systems through the implementation of an advanced Frequency Selective Surface (FSS) framework. Leveraging modern computational methodologies and the principles of metamaterials, the design targets substantial improvements in both gain and isolation performance metrics. The antenna prototype is realized on a silicon dioxide substrate, measuring 70 × 76 mm2 with a thickness of 1.52 mm. Central to the design is a novel FSS unit cell architecture, inspired by metamaterial behaviour, which enables precise frequency filtration and refined electromagnetic wave manipulation. To elevate the antenna's overall efficiency, a dual-layer FSS strategy is adopted. One FSS layer is placed at the rear of the antenna to reflect reverse-propagating waves, thereby amplifying directional gain. A second FSS layer is strategically embedded between the MIMO elements, effectively mitigating mutual coupling and enhancing radiation quality. Simulation data confirms the efficacy of this configuration, showcasing an increase in antenna gain from 7.6 dBi to 10.6 dBi and an isolation performance exceeding 85 dB. These improvements underscore the potential of the proposed structure as a forward-looking solution for next-generation wireless systems that operate in the high-frequency GHz domain.

Keywords: Frequency Selective Surface (FSS), Gain Enhancement, Isolation Improvement, FSS reflector.

Abstract: Hospital staff experience a variety of psychological stressors that have far-reaching effects on their mental wellbeing and overall welfare. This research examines the central factors behind these issues, and how workplace stressors, emotional exhaustion, organizational support. With a descriptive research design, the data was gathered using questioner and hospital institutional reports from staff across various professional levels, such as doctors, nurses, and administrative personnel. The results show that long working hours, heavy job demands, and inadequate periods of rest are primary causes of burnout and emotional exhaustion. In addition, direct patient care staff reported severe emotional exhaustion as a result of the pressure of dealing with critically ill patients and managing the emotional toll of their work. Institutional support, such as access to mental health services and employee wellness programs, was directly related to employees' mental health, with those who received sufficient support having higher satisfaction and lower stress levels. The COVID-19 pandemic worsened pre-existing mental health conditions, adding new challenges, including fear of infection and a huge surge in workload. Drawing on these findings, the research recommends a number of recommendations for enhancing the mental health of hospital staff, including the roll-out of wellness programs, continual mental health checks, and the promotion of a positive work culture. These initiatives are key to building a healthier, more resilient healthcare workforce.

Keywords: Mental health, Hospital employees, burnout, work-related stress, wellbeing, healthcare workers

Abstract: Emotional intelligence (EI) and quality of work life (QWL) are two crucial dimensions influencing employee engagement, productivity, and organizational success in the contemporary corporate world. This study explores the interplay between emotional intelligence and the quality of work life among employees at Hexaware Technologies, a prominent IT and BPS company based in Chennai. The aim is to assess how EI impacts employee satisfaction, work-life balance, interpersonal relationships, and career development within the organization. Adopting a descriptive research design, the study employed structured questionnaires to gather primary data from a sample of 161 employees, selected using convenience sampling. The responses were evaluated using statistical tools like percentage analysis and correlation techniques. The study reveals that emotional intelligence significantly contributes to better workplace relationships, effective stress management, and higher levels of job satisfaction. It also highlights that organizations promoting emotional awareness and empathy tend to see improved employee retention and organizational performance. This research adds value to existing literature by linking emotional intelligence with quality of work life in the IT and BPS sector, particularly focusing on Hexaware's workforce. It provides actionable insights for HR practitioners aiming to design emotionally resilient, people-centered workplace strategies.

Keywords: Emotional Intelligence, Quality of Work Life, Employee Engagement, Stress Management, Work-Life Balance, BPS Sector, Hexaware Technologies.

Abstract: This study investigates how well informed consumers are about home loan interest rates and how that knowledge affects their borrowing decisions. Understanding consumer awareness and decision-making is essential since the home loan industry plays a significant role in both the financial wellbeing of people and the overall economy. Customers' sensitivity to rate changes, their understanding of fixed versus floating interest rates, and the influence of financial literacy on loan selection are all examined in this study. The study uses survey data and behavioral analysis to show that borrowers have a substantial awareness gap that frequently results in less-than-ideal financial decisions. The results highlight the necessity of more transparent lending policies and improved financial education in order to enable consumers to make well informed borrowing decisions.

Keywords: Home Loans , Interest Rates , Borrowing Behavior, Financial Literacy, Consumer Awareness

Abstract: This study investigates the impact of Customer Relationship Management (CRM) adoption on customer satisfaction and retention within Jugl Technology Solution Pvt. Ltd. The research explores how CRM implementation enhances customer interactions, streamlines business operations, and fosters loyalty. Using a mixed-methods approach, data were collected through structured questionnaires and analyzed with SPSS using chi-square, ANOVA, and correlation techniques. The study found that CRM adoption positively influences customer satisfaction, which in turn significantly improves customer retention. These insights provide valuable guidance for optimizing CRM strategies in SMEs.

Keywords: CRM adoption, customer satisfaction, customer retention, Jugl Technology, SPSS analysis, employee perception

Abstract: An analysis of the relationship between employee engagement, recognition programs, and organizational outcomes within Tech Mahindra, a prominent IT company, highlights key insights. Through a comprehensive analysis of survey data collected from employees, the study examines various aspects of employee perceptions, satisfaction levels, and the effectiveness of recognition initiatives. Key findings indicate a strong positive correlation between employee participation in recognition programs and enhanced job performance and engagement. Employees who actively engage with these programs demonstrate higher levels of motivation and satisfaction, contributing positively to organizational productivity and morale. Furthermore, the study underscores the importance of providing clear career development pathways and regular performance feedback to foster employee growth and retention. Employees value opportunities for advancement and skill development, which are crucial for maintaining a talented and committed workforce. Additionally, work-life balance and employee well-being emerge as critical factors influencing overall job satisfaction and organizational loyalty. Respondents express a desire for greater flexibility and support to manage personal and professional commitments effectively. study emphasizes the significance of effective employee engagement strategies and recognition programs in driving organizational success. The insights gained from this research offer practical implications for HR practitioners and organizational leaders seeking to enhance employee satisfaction, retention, and overall workplace effectiveness within the context of a dynamic and competitive industry.

Keywords: Employee recognition, Employee motivation, Employee performance, Employee engagement, HR practices.

Abstract: This study examines how negative behaviours at work, like bullying and theft, impact employee performance. By reviewing previous research and conducting surveys, we found that such behaviours significantly lower job satisfaction, productivity, and increase the likelihood of employees wants to leave. The company's culture and leadership can either mitigate or worsen these effects. To improve performance and create a healthier workplace, we recommend clear policies, regular training, and a supportive environment that discourages bad behaviour and promotes ethics. This study offers practical insights for managers and HR professionals to enhance employee well-being and organizational success.

Keywords: Workplace deviant behaviour, Personality, Stress level, Work culture, Employee performance.

Abstract: This study presents a fuzzy logic-based diagnostic system for the early detection of Diabetes Mellitus, aimed at improving diagnostic accuracy and interpretability in the presence of uncertain clinical data. Traditional diagnostic techniques, such as threshold-based glucose and HbA1c evaluations, often fail to capture the gradual transition between normal and diabetic conditions. To address this limitation, a Mamdani-type Fuzzy Inference System (FIS) was developed using input parameters including fasting blood glucose, HbA1c, BMI, age, and family history. The model converts crisp clinical data into linguistic variables (Low, Normal, High) and applies a structured rule base to evaluate diabetes risk levels. Implementation was carried out using MATLAB's Fuzzy Logic Toolbox and Python's scikit-fuzzy library, with validation performed using the Pima Indians Diabetes Dataset. The system achieved high diagnostic performance with an accuracy of 90.5%, sensitivity of 92.4%, and specificity of 88.7%, demonstrating its efficiency and reliability. The results indicate that fuzzy logic provides a robust and human-like reasoning framework for medical decision-making, making it an effective tool for early diabetes diagnosis and clinical decision support.

Keywords: Fuzzy Logic, Diabetes Mellitus, Medical Diagnosis, Fuzzy Inference System, Early Detection, Mamdani Model, Decision Support System

Abstract: Construction workers in the United States are exposed to a uniquely intense combination of occupational stressors; however, the mental health burden of this workforce remains significantly understudied relative to its physical safety record. This study presents a secondary analysis of four nationally representative federal surveillance datasets: the Bureau of Labor Statistics (BLS) Survey of Occupational Injuries and Illnesses (SOII) 2018 to 2022, the National Institute for Occupational Safety and Health (NIOSH) National Health Interview Survey (NHIS) Occupational Health Supplement, the Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS) 2020 to 2022, and OSHA Injury Tracking Application (ITA) establishment-level data 2017 to 2022, to characterize the prevalence, trends, and organizational predictors of occupational stress and mental health distress in the U.S. construction workforce. BRFSS analysis identified a frequent mental distress (FMD) prevalence of 18.3% among construction and extraction workers, representing an adjusted odds ratio of 1.61 (95% CI [1.39, 1.87]) relative to all employed workers. SOII trend data document a 20.4% increase in construction-sector illness-related days-away-from-work cases between 2018 and 2022 against a backdrop of declining total recordable case rates, indicating a growing share of occupational illness relative to injury. NHIS data show that 48.7% of construction workers reported often or always finding their work stressful, compared to 34.2% of all employed workers, and that 67.8% lacked access to a workplace health program. Multivariate logistic regression identified work-attributed sleep disturbance (odds ratio [OR] = 2.47), high job demands (OR = 2.14), and low supervisor support (OR = 1.83) as the strongest independent predictors of FMD in the construction workforce. These findings, drawn entirely from government-administered surveillance systems, provide a nationally representative and methodologically rigorous evidence base for integrating occupational mental health management into construction safety programs in the United States.

Keywords: occupational stress, mental health, construction workers, secondary data analysis, BRFSS, NIOSH NHIS, BLS SOII, frequent mental distress, sleep disturbance, occupational surveillance