VOLUME 13, ISSUE 2, FEBRUARY 2026

Abstract: This study investigates the "Impact of Omni-channel on Loyalty Intentions of Customers in the Presence of Shopping Experience in Hyderabad." The research aims to understand the interplay between collaborative marketing practices, customer satisfaction, and customer loyalty in an omni-channel retail environment. Data was collected from 200 respondents through questionnaires to explore the demographic factors, factors of collaborative marketing practices, and their influence on customer satisfaction and loyalty intentions. The findings reveal positive correlations among collaborative marketing variables and underscore the significance of Sales Promotion and Price Collaboration. The study establishes a significant impact of collaborative marketing practices on customer satisfaction and identifies their indirect effect in the presence of an omni-channel experience. Moreover, customer satisfaction significantly influences customer loyalty towards omni-channel retail. The study offers suggestions for businesses to implement effective collaborative strategies and prioritize customer satisfaction to foster stronger customer loyalty. Understanding these dynamics is vital for businesses seeking sustainable success in the competitive omni-channel retail landscape in Hyderabad.

Keywords: Omni-channel, collaborative marketing practices, customer satisfaction, customer loyalty, retail environment, Hyderabad.

Abstract: Construction consistently ranks among the most hazardous occupational sectors globally, yet behavior-based safety (BBS) programs have rarely been examined in multilingual workforce settings where language barriers and cultural diversity complicate implementation. This study investigates the design features, reported safety outcomes, and perceived effectiveness of BBS programs deployed in multilingual construction environments through a cross-sectional survey administered to 167 construction safety professionals and site supervisors across multiple organizations. Respondents were recruited via professional safety networks using SurveyMonkey between January and March 2025. The analytical sample comprised 134 participants who reported direct experience with BBS implementation in settings with three or more workforce nationalities. Results indicate that respondents reported a 34.7% reduction in the mean total recordable incident rate (TRIR) following BBS implementation, with observer participation rates averaging 78.4%. Programs incorporating pictographic observation checklists achieved significantly higher participation rates than those relying solely on text-based tools (M = 84.2% vs. M = 67.1%, p

Keywords: behavior-based safety, multilingual workforce, construction safety, safety climate, survey research, cultural adaptation, incident reduction, pictographic tools

Abstract: LLM among AI has been increased powerful tool that has the potential to revolutionize research as well as general purpose. LLM (large language models) such as ChatGPT based on LLM can assist not only communication, art translation between art and text but also research such as sciences, engineering, economy trend, and forecasting of GDP growth. It enables economists to forecast the growth of GDP by describing all domain data and graph. LLM can also provide implications of LLM-powered cognitive automation for economic research, in some areas. This paper can explain how to get started and provide preparation on the latest capabilities of LLM (AI) in economics. To research on purpose, the paper of LLM should mention about what kind of language models they use, what is the core approaches, what is the tuned parameter (PEFT), and how they obtain practical data for application. These methodologies of LLM application should be provided through research and simulation of real-world LLM applications across the scientific area, engineering field, healthcare issues, and creative topic. The research of Korea econometrics based on LLM (K-EcoLLM) should be performed through a review of current economic situation and data on purpose because we cannot fully and effectively use by the LLM of general purpose. That is why this paper provide the motivation and research strategy on Korean econometric by LLM.

Keywords: GAI, LLM, ChatGPT, Econometrics, Korean Econometrics.

Abstract: The scalability of Edge Artificial Intelligence (Edge AI) is fundamentally constrained by hardware heterogeneity and uneven compute capabilities across deployment environments. This "Intelligence Gap" represents a critical architectural barrier that prevents the democratized adoption of vision intelligence. This paper proposes a theoretical framework to address the Edge AI scalability problem through three interconnected innovations: (1) the AI Readiness Index (AIRI) for standardizing intelligence measurement; (2) a modular Appization architecture for decoupling intelligence from hardware; and (3) a Vertical Solution Stack implementing the 3Ps framework (Personalization, Platforms, Performance Analytics). Central to this framework, we introduce the EdgeBox as a "Legacy Redemption" artifact-a neural interface designed to retroactively apply the Solution Stack to non-intelligent infrastructure. This allows for the "Neural Scrubbing" of legacy systems, transforming "dumb" sensors into AIRI-certified intelligence nodes. This paper employs a design science methodology, drawing on established theories from platform economics and service-dominant logic to develop a structured approach to artifact creation. We present a comprehensive model that transforms hardware heterogeneity from a scaling constraint into a managed resource. We demonstrate how the EdgeBox mediates the transition from hardware-centric to intelligence-centric edge computing. The proposed framework provides a novel theoretical foundation for Edge AI scalability. The introduction of the EdgeBox and the 3Ps framework offers a pathway to bridge the intelligence gap, enabling modular, measurable, and trustworthy AI across heterogeneous environments and present a case study.

Keywords: Edge AI, Hardware Heterogeneity, Conceptual Framework, AI Standardization, Solution Stack, Platform Economics, Theoretical Model, Indoai, AI Cameras

Abstract: The Organic Food Traceability System addresses key challenges in the organic food supply chain, such as labelling inaccuracies, certification fraud, and lack of transparency, by leveraging JSON files and AWS S3. The system securely records and stores transaction and certification data, ensuring data authenticity, privacy, and tamper resistance while overcoming issues of centralized control and information silos found in traditional traceability systems. By providing transparent access to food origin and certification details, the solution enhances trust among consumers and stakeholders. Experimental results demonstrate improved efficiency, reliable data integrity, secure handling of sensitive information, and practical applicability, establishing a robust and trustworthy framework for organic food supply chain management.

Keywords: Organic Product, certification fraud, labelling inaccuracies, JSON File, AWS S3.

Abstract: Myristica andamanica Hook.f., an endemic and vulnerable nutmeg species of the Andaman and Nicobar Islands, was investigated for its phytochemical composition, antioxidant potential, antimicrobial activity, and conservation status. Methanolic leaf extracts were subjected to qualitative and quantitative phytochemical analyses, in-vitro antioxidant assays (DPPH and ABTS), antimicrobial screening, and HPLC profiling. Preliminary screening revealed the presence of alkaloids, saponins, flavonoids, steroids, terpenoids, phenols, and carbohydrates. The extract exhibited moderate total phenolic and high flavonoid contents, with IC₅₀ values of 52.36 µg/ml and 74.99 µg/ml in DPPH and ABTS assays, respectively. HPLC analysis identified protocatechuic acid and kaempferol as major constituents. Moderate antimicrobial activity was observed against selected bacterial and fungal strains. Ex-situ and in-situ conservation strategies were successfully implemented. The present study revealed such phytochemical profiling and antioxidant property of M. andamanica for the first time and highlights the medicinal potential and conservation importance of.

Keywords: Myristica andamanica, Phytochemicals, Antioxidant activity, Antimicrobial activity, Endemic species, Conservation

Abstract: The nutritional compositions of piliostigma reticulatum seed was determined. The seeds of the species selected were analyzed for their nutrient and mineral elements. Proximate analysis, mineral analysis and antinutritional analysisof piliostigmma reticulatum was carried out using standard analytical techniques, photo spectrometric method and titrimetric method of analysis. Results obtained showed the percentage (%) Moisture contents as7.6±0.03, Ash as 9.80±0.14, Crude fiber as 6.50±0.702, Crude protein as 2.63±0.04, Crude lipid as 8.40±1.72 and total carbohydrate as 58.27.The relatively low moisture content reveals good storage stability while the high carbohydrates level indicates that the seed are potential energy source, however the low protein content suggest limited usefulness as a primary source of protein. The findings also revealed appreciable concentration of calcium contents as 1632.33±2.19mg/g, magnesium contents as 61.21±0.09mg/g, potasium contents as 1803±8.88mg/kg and that of sodium content as 86.3±7.50 highlighting the seeds as a rich source of essential macro minerals. Iron content as 7.2±0.79, zinc content as 4.56±0.19mg/g and manganese as 197mg/g.The antinutritional analysis of the seeds shows phyates as 0.28mg/100g,saponin as 0.21mg/100g,oxalate as 0.04mg//100g and tannins as 0.01mg/100g.

Keywords: Piliostigma reticulatum, Proximate analysis, Mineral composition, Antinutritional factors, Nutritional assessment.

Abstract: The swift advancement of urbanization and industrialization has led to heavy metal pollution emerging as a significant environmental concern. Drinking water contaminated with heavy metals such as Cd, Cr, Pb, Zn, and Hg presents a serious health threat to humans. Heavy metals are non-biodegradable, remaining in the environment, entering the food chain through crops, and accumulating in the human body via biomagnification. The toxicity caused by heavy metals involves mechanisms like the production of reactive oxygen species (ROS), disruption of antioxidant defenses, enzyme inactivation, and oxidative stress. Additionally, certain metals have the ability to bind with specific macromolecules. Traditional methods for addressing heavy metal pollution are not always fully effective in removing water contaminants. Phytoremediation, a relatively new technology, is increasingly acknowledged as a cost-effective, efficient, and environmentally sustainable method for extracting heavy metals from contaminated water. Aquatic plants play a crucial role in phytoremediation as they take up pollutants through their roots and, in some cases, through their leaves. Notable examples of these plants include water hyacinth, duckweed, and various submerged species such as milkweed and waterwort. These plants absorb pollutants including heavy metals, nutrients, and organic compounds, thus improving water quality. This review explores the processes through which plants absorb, transport, and detoxify heavy metals. Aquatic phytoremediation focuses on employing plants to purify pollutants in water bodies, with strategies designed to enhance plant stabilization and removal.

Keywords: heavy metals, contaminants, pollutants, phytoremediation

Abstract: The Western Lowland Gorilla, G. gorilla gorilla (Primate: Hominidae) is one of the largest living primates, maintained at Sri Chamarajendra Zoological Gardens, Mysuru (Latitude: 12.3028° N, Longitude: 76.6552° E). It is an exotic species, kept in a well maintained enclosure for public display for creating awareness among the public and for education, conservation and scientific studies. During the present study, after obtaining the permission from the higher authority, Sri Chamarajendra Zoological Gardens, Mysuru, proper planning was made by consulting with Zoological Garden Authority, Range Forest Officer, Animal Caretaker, Education Officer and Biological Scientist to conduct observation so as to record the behaviour of Gorilla. Observations were made two days in a week from morning (10.00 AM), afternoon (01.00PM) and evening (04.00 PM) hours for a period of 56 days i.e., from 4th April to 31st May, 2025. Observations were made using the focal animal sampling method with each session lasting for a period of 20 minutes by distance outside the enclosure, without creating any disturbance for its activity. Total 17 behaviours were recorded namely: locomotion, drinking, eating, sleeping, resting, vigilance, vocalisation, positive and negative interaction, approach, depart, jumping, hanging, grooming, jumping, chest beating, chasing and sun bathing etc., by following standard methods. All these observations were non-invasive and no interaction or interference with the normal activity of Gorilla in its enclosure. The study was conducted under the awareness of Zoological Gardens Authorities and aligned with the ethical standards for observational studies in Zoological Gardens. Collected data was systematically compiled and analysed using standard methods. Sixteen different behaviors were recorded during morning hours, however, during afternoon and evening hours, all the 17 behaviors shown by G. g. gorilla. Analysis of variance of different behaviors of G. g. gorilla did indicate significant variation (F=6.797; P

Abstract: Background: Refractive error is a leading cause of visual impairment among school-aged children globally, yet population-specific determinants remain inadequately characterized in resource-limited settings. Objective: This study sought to quantify the prevalence of refractive error and develop predictive models identifying sociodemographic, environmental, dietary, and behavioral determinants among school-going children in Mathura, Uttar Pradesh. Methods: A cross-sectional study was conducted among 2,000 school children aged 6-16 years. Comprehensive vision screening was performed, and data on sociodemographic characteristics, housing conditions, dietary patterns, lifestyle factors, and academic variables were collected through structured questionnaires. Multivariable logistic regression and elastic net regularized regression were used to identify independent predictors of refractive error. Results: The prevalence of refractive error was 24.1% (482/2,000). In multivariable analysis, urban residence (adjusted OR 1.71, 95% CI: 1.31-2.22, p

Abstract: The stock market is an interlinked and fast-changing financial ecosystem shaped by many economic, political and psychological forces. This thus creates considerable difficulty for both investor and analyst in accurately forecasting stock price movements. With the advent of AI in recent past a lot of research has focused on improving forecast precision for assisting in better trading decision making. The paper critically evaluates the previous research on AI-propelled stock market prediction in three main fields: Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) methodologies. While machine learning techniques such as Support Vector Machines (SVM) and Random Forests stand out, the discussion also brings together recent developments in deep learning architectures like Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks. It also covers reinforcement learning approaches for optimizing automated trading schemes. The review demonstrates how sentiment analysis and hybrid architectures have an impact on predictive efficacy. It describes the main results, comparative evaluations and gaps that are found in the present research to provide a structured information on the recent evolution in this area. It's hoped that through this work, researchers and practitioners will find a treasure trove of knowledge when creating intelligent and effective stock market advisory systems.

Keywords: Artificial Intelligence, Deep Learning, Machine Learning, Reinforcement Learning, Stock Market Prediction, Trading Strategy.

Abstract: The human body consists of several organs and skin is the largest organ that covers human body. Any disorder that affects skin is known as skin disease. The main causes of skin lesion are bacteria, virus, fungal infections and some genetic factors. The accurate diagnosis of skin conditions is crucial for effective treatment and management. Dermatologist rely heavily on visual inspection and physical examination to identify and differentiate between various skin disease. This paper is highlighting frequently used datasets and figuring out the gaps, listing out existing machine learning algorithms and their accuracy. This paper gives the clear view of many algorithms' efficiency and limitations in existing approaches.

Keywords: Dermatology, Skin Lesion, Fungal Infection, Diagnosis, Accuracy, Machine learning algorithms.

Abstract: There are significant impacts of climate change on user code, mainly in terms of food affordability, availability, and consumption practices. This study attempted to understand the challenges faced by urban consumers in terms of sustainable food consumption practices in response to changes resulting from climate change. For primary data collection, a structured questionnaire method was used among 130 user codes, and the results were analyzed using percentage analysis, ANOVA, and rank analysis. The results indicate that food affordability is a significant challenge, and satisfaction regarding available food does not vary significantly between genders. Consumers ranked limiting packaged and processed foods as of highest priority, therefore focussing on raising awarenes nothing on sustainable consumption practices in Coimbatore City.

Keywords: Climate Change, Urban Users, Food Consumption, Food Affordability, Sustainable Practices.

Abstract: Choosing a right project is always a difficult for students as well as for professionals due to lack of right guidance, a mismatch between one's skills, experience, interests and project required. Hence it causes low performance and ineffective results. To overcome this problem, we have designed an innovative concept for project recommendation system which recommends projects to users based on their skills, interests and experience. The objective of this paper is to highlight the current trend in project recommendation systems which addresses the problems faced by users and recommends best possible results by applying Machine Learning algorithms like content, based filtering, Cosine Similarity and TF- IDF Vectorisation in order to match interests of users with project requirements. The System resulted in achieving an overall average accuracy of 72.4 % match.

Keywords: Machine Learning, Project recommendation, Content-based filtering, TF-IDF, Cosine similarity.

Abstract: The rapid growth of digital technology and internet penetration has significantly transformed the retail sector, especially in online grocery delivery services. Quick-commerce platforms like Blinkit and Zepto have emerged as major players by offering ultra-fast delivery and convenient shopping experiences. This study aims to analyse customer preference, satisfaction, and behavioural patterns towards these two platforms through a comparative approach. The research was conducted in Coimbatore city with a sample size of 100 respondents. Primary data were collected using a structured questionnaire, along with supporting secondary data from journals and websites. Statistical tools such as percentage analysis, Chi-square test, ANOVA, and correlation analysis were used for interpretation. The study examines factors like delivery speed, pricing, product availability, app usability, customer support, and promotional offers. The findings indicate that speed and convenience are the key drivers of customer preference. Competitive pricing and discounts also strongly influence purchase decisions. Both platforms show high levels of customer satisfaction, though slight differences exist in perceived service quality. The research highlights changing consumer behaviour towards quick-commerce services. Overall, the study provides valuable insights and recommendations to improve customer experience and strengthen competitive positioning in the online grocery market.

Keywords: Online Grocery Delivery, Blinkit, Zepto, Customer Preference, Satisfaction, Behaviour.

Abstract: Virtual laboratory has become a trend to students in this generation in which they can easily access, manipulate understands the theory by simple click. There is other institution who are already using this kind of technology in teaching science subject. This quasi-experimental study was conducted to find out if the physics education technology can help improve the Science Performance of Grade 12 Senior High School Students, for the school year 2022-2023. Participants of this study were twenty-six (26) randomly selected Grade 12 students for the experimental group and also twenty-six (26) randomly selected Grade 12 students for the control group. The independent variables were the physics education technology and conventional laboratory. On the other hand, the dependent variable was the Science performance. Data in this study were gathered using a researcher-made Science performance test which had undergone validation and item analysis by the members of the panel. The descriptive statistics used were mean and standard deviation. The inferential statistics used were t-test for independent samples, paired sample t-test, and Cohen's d calculator, to compare the Science performance of grade 12 students using the physics education technology and conventional laboratory. The alpha level of significance was set at 0.05. The major findings of the study revealed that the Science performance of the students during the pretest of the control and experimental group were both interpreted as "low". In the posttest the data revealed that Science performance of control and experimental group "high". There is no significant difference in the pretest scores on the Science performance both for experimental and control groups. However, a significant difference existed on the Science performance of grade 12 students in the pretest and posttest of the control group and experimental group in favor of the used of experimental variable. There is a significant difference in the posttest scores on the Science performance in favor of the experimental group using the physics education technology. Finally, it was found out that physics education technology has a large effect in the Science performance of students.

Keywords: Physics Education Technology

Abstract: This study examines the relationship between digital payment usage and spending behaviour among Generation Z consumers in Coimbatore. Focusing on payment methods such as UPI, mobile wallets, debit and credit cards, and Buy Now Pay Later (BNPL) services, the research investigates how digital transactions influence frequency of spending, impulsive buying tendencies, and financial awareness. Data were collected from 100 respondents using a structured questionnaire and analyzed through percentage analysis, Chi-square test, ANOVA, and regression analysis. The findings reveal that transaction speed, discount influence, and security concerns significantly shape spending behaviour, while income level is closely associated with changes in expenditure patterns post-adoption. The study concludes with actionable recommendations for digital payment service providers, financial institutions, and policymakers to promote responsible and informed usage among young consumers.

Keywords: Digital Payments, Gen Z, Spending Behaviour, UPI, Financial Awareness, Impulsive Buying, Cashless Economy.

Abstract: In this world of digital era, mobile phones have become an essential part of communication. SMS for communication is primarily used by banks, governments, and other organizations. SMS have also led to many scams. It has both merits and demerits. SMS have led to an increasing number of spam and fake SMS there is a severe threat to the safety and security of users. To reduce these spam messages, Airtel has launched an AI based SMS filtering system that automatically identifies and filters spam messages. This study examines the awareness level of Airtel users about the AI filtering system. It also helps in understanding the effectiveness of the system and satisfaction of the customers. This study considers Airtel users only to understand clearly. This study will analyse effective the AI system is in identifying the real and fake messages. It also helps in understanding the customer perception and trust on the system.

Keywords: SMS, AI filtering system, awareness level.

Abstract: Copper nickel oxide (CuNiO2) thin films were deposited onto unheated glass substrates by DC reactive magnetron sputtering the composite target of Cu50Ni50 at a fixed oxygen partial pressure of 3x10-4 mbar and sputter pressure of 3x10-2 mbar. The as-deposited copper nickel oxide films were annealed in air at temperature of 200oC in one hour. The as-deposited and annealed film was characterized for their chemical composition, electrical, optical and Hydrogen gas sensing properties. The as-deposited films were amorphous in nature. The electrical resistivity of the film increased with annealing temperature due to the filling of oxygen ion vacancies. The films annealed at 200oC exhibited the crystallite size of 25 nm, electrical resistivity of 12 Ωcm and optical band gap of 1.97 eV.

Keywords: CuNiO2 thin films, DC magnetron sputtering, Electrical, Optical and Hydrogen gas sensing properties.

Abstract: The current study aims to explore the effect of social status and lifestyle factors in brand selection, with special reference to Apple iPhone users in Coimbatore City. In the modern market, brand selection represents a sense of identity and status instead of just only benefiting from using that brand. Apple is one of the best-selling iPhones despite other similar brands that operate at similar price points. The current study utilized a quantitative descriptive design and collected data through a questionnaire from 126 iPhone users in Coimbatore City. The study showed that age, income, and residing place influence iPhone users significantly due to social status and lifestyle, whereas occupation did not influence service-related attributes.Key words Social Status, Lifestyle, Brand Choice, Consumer Behavior, Apple iPhone, Urban Consumers, Demographic Variables, Purchase Decision, Brand Preference

Keywords: Social Status, Lifestyle, Brand Choice, Consumer Behaviour, Apple iPhone, Purchase Decision, Brand Preference

Abstract: Informal education plays a significant role in the success of many individuals by providing practical knowledge, skills, and real-life experiences beyond formal academic systems. Many successful people have achieved their goals through self-learning, work experience, training, mentor-ship, and continuous learning rather than relying only on degrees or certificates. Informal education helps individuals develop problem-solving abilities, creativity, adaptability, and confidence, which are essential for success in today's dynamic world. This overview highlights how learning through everyday experiences and personal effort has contributed to the achievements of successful people, proving that education is not limited to classrooms but continues throughout life. This overview focuses on how informal education supports personal and professional development, highlighting that success is not determined only by degrees but also by skills, experience, and lifelong learning.

Keywords: Informal education, success, self learning, work experience, practical knowledge, skill development, adaptability, personal and professional development.

Abstract: Independent navigation remains a critical challenge for the visually impaired, with existing assistive technologies often limited by computational constraints and poor real-time performance. This paper proposes an IoT-enabled smart assistive system that integrates lightweight YOLO-based deep learning models for autonomous obstacle detection, path planning, and environmental interaction. Deployed on edge devices (Raspberry Pi 5, NVIDIA Jetson Nano), the system employs optimized YOLOv8n/YOLOv5s variants with INT8 quantization, achieving 92% mAP@0.5 on custom visually impaired datasets while maintaining >35 FPS inference at

Abstract: This study examines customer awareness, perception, and service satisfaction toward Star Health Life Insurance. The purpose of the research is to understand how well customers recognize the company's insurance products, how they perceive policy features and benefits, and how satisfied they are with the services provided. The study is based on primary data collected through a structured questionnaire from policyholders, along with supporting information from secondary sources such as reports and articles. The analysis focuses on factors such as awareness level, reasons for policy purchase, claim settlement experience, cashless hospital network, premium affordability, and overall service quality. The findings indicate that higher awareness and clear communication of policy details significantly influence customer perception and satisfaction. Service efficiency, claim processing speed, and support from representatives are also key drivers of positive customer experience. The study suggests that improving customer education and strengthening service responsiveness can further enhance satisfaction and trust in Star Health Life Insurance.

Keywords: Customer awareness, service satisfaction, claim settlement, service quality.

Abstract: LLM (large language model) families have a big powerful as one of many AI models that has the potential to revolutionize research and scientific, including general purposes. That is, current LLMs assist not only general purposes but also domains such as scientific research and engineering, the prediction of economic trend, health diagnosis, and policy decision. It also enables graphic works, data analysis, coding workface. However, unfortunately, the characteristics and application possibilities of the several structure of LLMs did not work through study on application areas. Current LLMs as well as GAI (General AI) models respond to question of user by re-organization these data after learn vast data (Text, Number, Image). That is, inference algorithms of current LLMs do not have the cause on why this task happen. To recover this problem, causal inference is quite important as core technology. The causal inference by structures of LLM such as standard LLM, offline causal RL with backdoor adjustment (Confounder control), offline causal model with explicit confounder, and backdoor adjustment intervention must fully study through step by step of case because these responses is quite different from structures. This paper offers how to apply and what structures of LLMs is useful for user case, depending structure of LLM causal inference. The learning capabilities of LLM is quite different from causal inference model of LLM on application. To research on purpose, what is the core approaches by causal inference model, what is the tuned parameter and structure, and how they obtain practical data for application. These methodologies of LLM causal inference on application should be provided through research and simulation of real-world LLM applications across the target. The prediction of Korea economic growth based on the causal inference of LLM should be performed through a review of current S. Korean situation and data on purpose because we do not fully study and effectively use by the causal inference of LLM for research target. This paper shows the prediction about GDP growth of S. Korea, China, and world, and stock market trend as example, and compares these graphs to see the results by the causal inference of LLM.

Keywords: LLM, ChatGPT, Causal Inference, Korean GDP growth prediction, Korean Stock market prediction.

Abstract: Medication non-adherence among elderly patients is a common healthcare problem that can lead to serious health risks and increased medical costs. Traditional pillboxes and reminder systems do not verify whether medicines are actually taken and fail to monitor storage conditions that may affect drug quality. This paper presents an IoT-based smart pillbox designed to improve medication adherence and safety. The system uses RFID to identify scheduled doses, a load cell to confirm pill removal, and a temperature sensor to monitor storage conditions. A WiFi-enabled microcontroller sends real-time notifications to caregivers and logs intake data to the cloud for basic adherence analysis. By combining intake verification with environmental monitoring and adherence tracking, the proposed system provides reliable support for medication management in elderly care.

Keywords: Smart Pillbox, Medication Adherence, Internet of Things (IoT), RFID, Elderly Healthcare Monitoring

Abstract: In today's society, pet adoption is often hindered by fragmented communication between shelters, foster caretakers, and adopters, as well as the lack of a centralized platform to manage the process. Traditional methods involve manual searches, paperwork, and limited outreach, which can delay adoption and reduce transparency. To overcome these challenges, this project presents a Web-Based Pet Adoption System, designed to simplify and streamline the adoption process through a unified online platform. The system enables shelters and individuals to upload detailed pet profiles, including age, breed, health status, and availability, ensuring that adopters have access to accurate and comprehensive information. Users can search and filter pets based on location and personal preferences, making the adoption process more efficient and user-friendly. The system also supports foster care management, allowing caretakers to track availability and schedule fostering periods. Additionally, a lost and found pet reporting feature helps reunite missing pets with their owners, while an integrated donation module enables users to support shelters financially. To ensure responsible adoption, the platform incorporates basic verification mechanisms, fostering trust and authenticity between adopters and shelters. Developed using HTML, CSS, and JavaScript for the frontend and PHP/Python with MySQL for the backend, the system ensures scalability, transparency, and reduced manual workload. By combining modern web technologies with socially impactful features, the Web-Based Pet Adoption System promotes responsible pet ownership, enhances shelter visibility, and encourages compassionate choices. This project demonstrates how digital innovation can effectively contribute to animal welfare and community engagement

Keywords: Web-Based System, Pet Adoption, Animal Shelters, Foster Care Management, Lost and Found Pets.

Abstract: The rapid growth of user-generated content on video-sharing platforms such as YouTube has significantly enhanced global communication while simultaneously increasing the prevalence of cyberbullying within comment sections. Offensive and harmful comments negatively impact users' psychological well-being and degrade online interactions. Manual moderation of such large-scale textual data is inefficient, necessitating automated intelligent detection systems. This study proposes a machine learning-based framework for multi-class classification of YouTube comments into bullying, non-bullying, and supportive categories. The system integrates Natural Language Processing (NLP) techniques including text cleaning, tokenization, stop-word removal, and lemmatization. Feature extraction is performed using TF-IDF vectorization with n-gram representations, along with sentiment-based features. To address class imbalance, SMOTE is applied during training. The classification framework employs XGBoost and Logistic Regression models, which are combined using a stacking ensemble approach to improve generalization and predictive performance. Experimental results demonstrate that the ensemble model effectively captures linguistic patterns in cyberbullying-related content and achieves reliable performance across accuracy, precision, recall, and F1-score. The proposed system provides a scalable and computationally efficient solution for automated cyberbullying detection on YouTube. Keywords Cyberbullying Detection, YouTube Comment Analysis, Social Media Monitoring, Machine Learning, Natural Language Processing, Text Classification, Sentiment Analysis.

Abstract: Agriculture is pivotal to global food security and economic stability, particularly in developing nations like India, where 2.5 billion smallholder farmers confront climate variability, labor shortages, inefficient weed management, and fertilizer overuse-resulting in 20-30% yield losses and environmental harm. Accurate yield forecasting, precise weed control, and optimized fertilizer application enable data-informed decisions on crop selection, irrigation, input management, and risk mitigation, thereby alleviating uncertainty, elevating productivity, and advancing sustainable practices while informing policy. This study presents an integrated Smart Farming Assistant powered by artificial intelligence, encompassing: a convolutional neural network (CNN) for real-time weed detection from drone imagery (93-99% precision); a random forest classifier for NPK fertilizer recommendations derived from soil, meteorological, and crop data (97% accuracy); and an XGBoost regressor-chosen for its robustness in modeling intricate feature interactions across extensive historical and environmental datasets-delivering yield predictions with \( R^2 > 0.92 \). Deployed on edge IoT platforms, this system curtails herbicide application by 70%, enhances resource efficiency, augments yield by 15-20%, and offers scalable utility for resource-limited agricultural contexts.

Keywords: Smart Farming, AI Agriculture, Weed Detection, Fertilizer Recommendation, Yield Prediction, Convolutional Neural Network, XGBoost Regressor, NPK optimization, Edge Computing.

Abstract: The application of Artificial Intelligence (AI) in human resource activities has dramatically altered the hiring process in industries. This research examines the use of AI in contemporary recruitment strategies, noting how AI-based tools and technologies are utilized to increase the efficiency, accuracy, and effectiveness of employee hire decisions. The investigation examines multiple uses of AI, including resume screening, chatbots for candidate interaction, predictive analytics in employee hiring decisions, and reducing bias in hiring. By way of a synthesis of secondary data analysis and review of case studies, the research assesses the advantages and disadvantages of AI implementation in talent recruitment. The research finds that AI can optimize the hiring processes, save time-to-hire, enhance candidate matching, and improve the general candidate experience. AI is, however, also said to be accompanied by issues of data privacy, algorithmic bias, ethical openness, and the risk of dehumanizing the recruitment process. Methodologically, the study relies on qualitative evidence from recent case studies, reports, and academic literature in order to provide insights into real-world applications and challenges. The research indicates that although AI significantly enhances operational efficiency and accuracy in hiring, it also introduces ethical and technical challenges-like data security, algorithmic fairness, lack of transparency, and the risk of job displacement for HR professionals.

Keywords: HR Technology, Data-Driven Hiring, Digital Recruitment Tools, Intelligent Hiring Systems

Abstract: Every day we are experiencing changes in price of shares in the stock markets. the changes are attributed to several factors. one of the core factors that contribute to the change in share price is the corporate actions of companies. the corporate activities can affect up to the shareholders. Investors may suppose that the corporate behaves either in positive way or negative way based on corporate acts, investors form an opinion about the future performance of the companies. based on this opinion, investors purchase or sell securities. The companies undertake distributions of dividend interest, issue of rights/ bonus shares, issue of fresh securities by issuers, splits etc. These are referred to as corporate actions. corporate actions are essential to all companies. The study considers the rights issue and the dividend announcement of 8 companies and its effect and the significance on the stock prices and the dividend amount announced by the companies.

Keywords: Corporate Action, Share Price Movements Dividend Announcements Rights Issues, Stock Market Reactions, Investor Behavior Abnormal Returns, Trading Volume, Financial Performance, Bonus Shares.

Abstract: The high rate of digital technology and internet penetration has greatly impacted the buying habits of consumers, especially in the area of online purchasing. Through this research, the study intends to examine the most important factors that drive consumers' attitudes toward online shopping, with a special emphasis on demographic profiles, security and privacy issues, and the influence of social influence like social media and word-of-mouth. A systematic questionnaire was utilized to gather primary data from 100 respondents through online questionnaires. Descriptive statistics, correlation, chi-square tests, and linear regression analysis were utilized using SPSS and Excel to examine the data. The study findings indicated that demographic factors like age and education play a significant role in the perception of online shopping. In addition, privacy and data security concerns have a negative influence on consumers' intentions to shop online, whereas social word-of-mouth and peer recommendation highly influence the decision to purchase online. The research offers insights of value to e-commerce sites to formulate strategies that respond to consumer fears and exploit social influence in order to enhance online interaction. The limitations are the relatively limited sample size and geographical concentration, which can influence the generalizability of the findings.

Keywords: Online Shopping, Consumer Behaviour, Demographic Factors, Privacy and Security, Social Influence, Purchase Intention.

Abstract: Digital payment platforms, which provide speed, convenience, and increased transparency, have completely changed how financial transactions are carried out in recent years. This change has spread beyond India's cities and is progressively affecting the country's rural areas. The perceptions, adoption trends, and influencing factors surrounding rural consumers' use of digital payment systems are examined in this study, which was carried out in Kodad Mandal. This study's main goal is to evaluate how rural consumers perceive the usability, convenience, and security of digital payment platforms. Primary data was gathered from 120 respondents using structured questionnaires as part of a descriptive research design, and regression analysis was used for analysis. Journal articles, government publications, and digital payment reports were the sources of secondary data. a sizable percentage of users (70.83%) use digital payment systems on a daily basis, and awareness of these systems is remarkably high in rural Kodad (96.67%). Well-known platforms like PhonePe, Google Pay, and Paytm are used for a variety of things, including online shopping, utility bill payments, grocery shopping, and mobile recharges. The use of digital payments is strongly positively correlated with perceived security, convenience, and general satisfaction, according to regression analysis. Digital literacy, mobile internet accessibility, platform trust, and support from banks and local businesses are important factors that impact adoption.Some obstacles still exist despite widespread adoption, most notably network problems (53.33%), fraud anxiety, and sporadic transaction failures. The overwhelming majority of respondents (86.67%) indicate a readiness for a deeper integration of digital finance in rural life by expressing interest in learning more about digital payment systems.

Keywords: Digital Payments, Consumer Perception, Financial Inclusion, UPI, Mobile Wallets, Payment Security, Convenience, Demonetization, Transaction Challenges.

Abstract: The research examines the effect of data-driven hiring through Artificial Intelligence (AI) on candidate experience and employee retention in medium- to large-sized organizations. While organizations become more digital in their drive, the use of AI in hiring has become one of the primary ways to enhance recruitment success and workforce stability. The study seeks to evaluate the impact of AI-driven hiring practices on how job applicants participate throughout the recruitment process and how these practices correlate with long-term job retention in the company. The study findings indicate widespread AI adoption in companies under investigation, as respondents confirmed that AI-driven recruitment assisted in finding appropriate candidates and eliminating human prejudice. The majority of respondents concurred that recruitment practices were in line with real job duties and organizational culture, contributing to improved job satisfaction and employee retention. Statistical testing proved the relationship between AI-based hiring, engagement of candidates, and retention of employees to be very positive. Engagement was also highly connected to seeing that evaluated skills could be applied in everyday jobs. The research indicates that the candidates who undergo transparent, fair, and meaningful recruitment are more likely to stay with the organization. These findings confirm the rejection of the null hypotheses and establish that AI-based recruitment has a strong positive effect on engagement and retention. The results imply that organizations need to embrace ethically sound and strategically aligned AI-based recruitment strategies to attract, engage, and retain best talent in an increasingly competitive labour market.

Keywords: Data-Driven Recruitment, Candidate Engagement, Employee Retention Rate, Predictive analytics, Compliance and ethical Consideration.

Abstract: Polycystic Ovary Disorder (PCOD) is one of the most common hormonal disorders affecting women of reproductive age. Early detection and continuous monitoring are essential to prevent severe long-term health complications such as infertility, diabetes, and cardiovascular diseases. However, traditional diagnosis methods rely heavily on manual medical analysis and delayed clinical evaluation. This paper proposes an AI-based PCOD SmartCare System that uses Machine Learning algorithms to predict the risk level of PCOD and provide personalized health recommendations. The system collects patient health data including symptoms, hormone levels, and medical reports. Logistic Regression and Random Forest algorithms are used to analyse the data and classify risk levels as Normal, Moderate, or High. Based on the prediction results, the system generates customized diet plans, lifestyle suggestions, and exercise recommendations. The proposed solution aims to assist early diagnosis, reduce medical costs, and improve women's healthcare using an intelligent and user-friendly platform.

Keywords: PCOD, Machine Learning, Healthcare AI, Logistic Regression, Random Forest, Disease Prediction, Women Health, Smart Healthcare System

Abstract: The rapid growth of digital healthcare services has introduced significant challenges in data security, privacy, interoperability, and real-time access to medical resources. Traditional centralized healthcare systems are vulnerable to data breaches, unauthorized access, single points of failure, and inefficient emergency response mechanisms. To address these issues, this paper proposes MEDILOCATOR - a Blockchain-Based Secure Healthcare Locator, an intelligent and decentralized platform designed to provide secure healthcare discovery, appointment management, and emergency assistance services. The proposed system integrates blockchain technology, real-time data synchronization, geolocation services, and AI-based symptom triage to deliver a secure, transparent, and patient-centric healthcare ecosystem. Blockchain ensures tamper-proof storage, data integrity, decentralized authentication, and transparent medical transaction tracking, thereby eliminating unauthorized data manipulation and enhancing patient trust. The system enables users to locate nearby hospitals, clinics, and specialists based on real-time availability, geographic proximity, and healthcare requirements. In emergency scenarios, MEDILOCATOR supports automated emergency triggering, live ambulance tracking, and dynamic ETA estimation, ensuring rapid response and life-saving interventions. An AI-driven symptom triage module assists patients in assessing the severity of symptoms and recommends appropriate medical departments, reducing unnecessary hospital visits and improving clinical workflow efficiency. Additionally, smart appointment scheduling, real-time doctor availability updates, and secure digital health records contribute to improved service accessibility and operational efficiency. Experimental analysis and system evaluation demonstrate that MEDILOCATOR significantly enhances data security, response time, scalability, transparency, and user experience compared to traditional healthcare platforms. The proposed architecture provides a robust foundation for next-generation decentralized healthcare systems and contributes toward the development of secure, intelligent, and resilient digital health infrastructures.

Keywords: Blockchain, Healthcare Locator, Secure Data Transmission, Smart Contracts, Emergency Response System, AI-Based Symptom Analysis, Real-Time Monitoring, Distributed Ledger Technology, Medical Data Privacy, Appointment Scheduling.

Abstract: Workplace bullying and harassment have emerged as significant organizational issues affecting employees' psychological and emotional well-being, particularly in the fast-paced and highly demanding IT sector. The present study examines the impact of workplace bullying and harassment on employees' well-being from a psychological perspective. The IT industry, characterized by long working hours, high performance expectations, strict deadlines, and competitive work culture, often creates an environment where negative behaviours such as verbal abuse, exclusion, intimidation, and excessive criticism may occur. This study aims to analyse how workplace bullying and harassment influence employees' stress levels, anxiety, job satisfaction, self-esteem, and overall mental health. Primary data were collected through structured questionnaires from IT employees, and appropriate statistical tools were used to interpret the findings.

Keywords: Workplace Bullying, Workplace Harassment, Psychological Well-Being, IT Sector, Employee Performance, Work Environment, Psychological Impact etc.,

Abstract: Energy efficiency remains a major challenge in Internet of Things (IoT) sensor networks due to the limited battery capacity of sensor nodes. Inefficient routing strategies often lead to unbalanced energy consumption and early node failure. This paper proposes an energy-efficient routing framework based on the Squirrel Optimization Algorithm (SOA). In the proposed approach, SOA is employed to simultaneously optimize cluster head selection and routing path formation using a multi-objective fitness function considering residual energy, transmission distance, and network coverage. The algorithm balances exploration and exploitation to achieve optimal routing decisions while preventing premature energy depletion of critical nodes. Simulation results demonstrate that the proposed SOA-based routing method significantly improves network lifetime, residual energy distribution, and convergence speed compared with the Genetic Algorithm (GA). The results confirm that SOA provides an effective and scalable solution for energy-aware routing in IoT sensor networks.

Keywords: IoT Sensor Networks, Energy-Efficient Routing, Squirrel Optimization Algorithm, Metaheuristic Algorithms, Network Lifetime

Abstract: Voltas Limited is one of India's leading air conditioning and engineering solutions companies and a part of the renowned Tata Group. It's established on 1954. Voltas operates through various business segments, including Unitary Cooling Products, Electro-Mechanical Projects and Services, and Engineering Products and Services. The company is widely recognized for its energy-efficient air conditioners and cooling appliances, catering to residential, commercial, and industrial sectors. Voltas has expanded its market presence both domestically and internationally, leveraging innovation, strategic partnerships, and sustainable practices. The company has also entered the home appliances segment through a joint venture with Arçelik, strengthening its product portfolio.

Keywords: Revenue growth, profitability, liquidity, Return on Equity (ROE), Return on Assets (ROA), retail sector, operational efficiency, Financial Analysis and Ratio analysis, etc.

Abstract: Technological development has brought major changes in the banking industry, especially through the introduction of electronic banking services. This study explores customer preference towards e-banking facilities in Coimbatore city. The research focuses on understanding the level of awareness, frequency of usage, customer satisfaction, and the challenges faced while using digital banking services such as mobile banking, internet banking, ATM services, and UPI payments. Primary data were collected from 150 bank customers using a structured questionnaire. Younger customers show higher usage compared to older age groups. Although the majority of respondents express satisfaction with e-banking services, concerns related to security and internet connectivity still influence customer confidence. The study suggests that banks should improve security measures and provide awareness programs to enhance customer trust and promote wider adoption of e-banking services.

Keywords: E-Banking, Customer Preference, Online Banking, Mobile Banking, Customer Satisfaction, Digital Transactions etc.,

Abstract: The internet and social media have really changed the way we make and share information. We can talk to people faster. Reach people all around the world. There is a bad side to this. It is easy for fake news to spread. Fake news is when someone makes up a story or lies, on purpose. Says it is real news. They do this to trick people. When fake news spreads quickly it can cause a lot of problems. These problems include people not agreeing on things not trusting each other being upset and angry and fake news can even affect how our countries are run. Fake news is a deal because it can really hurt people and communities. The spread of news can lead to people not trusting the news at all and it can also lead to people fighting with each other. Fake news is a problem that we need to worry about because it can affect our societies and our democratic systems. The old way of checking facts by hand is good.Fake news detection systems that use intelligence look at news to see if it is real or not. They use machine learning and other things like learning and natural language processing to do this. These systems look at a lot of news stories that are labeled as real or fake to learn what makes them different.They learned from ways of doing things like Logistic Regression and other methods to figure out what makes news fake. They looked at things like how the newss written and what words are used to decide if it is real or not. We have made a lot of progress.This is a problem because we need to be able to trust intelligence systems and understand how they work.Nevertheless, AI-based fake news detection systems offer substantial societal benefits. They support content moderation, assist journalists in verification, and help protect public discourse. Future research aims to improve model robustness, enhance explainability, and strengthen collaboration between human experts and AI systems.

Keywords: Natural Language Processing -Fake News Detection-Logistic Regression-Content Moderation-Fact-Checking- Explainable AI

Abstract: Attendance management is an essential requirement in educational institutions and organizations, as it plays a significant role in tracking discipline, participation, and performance. The traditional attendance system, such as manual registers, signature sheets, and roll calls, is time-consuming, prone to errors, and susceptible to proxy attendance. To overcome these drawbacks, this research work proposes a Face Attendance System based on Machine Learning and Computer Vision concepts. The proposed system uses live image acquisition through a camera, face detection through the Haar Cascade algorithm, and face recognition to identify registered users for automatic attendance marking with precise date and time stamping. The proposed system is developed using Python, OpenCV, Flask, and SQLite with a modular approach to distinguish application logic and database management. The experimental results demonstrate that the proposed system greatly minimizes manual labor, increases accuracy, and improves reliability compared to the traditional attendance system. The proposed system can be efficiently employed in educational institutions and organizations as a cost-effective, user-friendly, and scalable solution for automated attendance management.

Keywords: Face Recognition, Attendance System, Machine Learning, Computer Vision, OpenCV, Haar Cascade, Flask, SQLite.

Abstract: Bharatanatyam, among India's most ancient classical dance traditions, employs intricate hand mudras and facial expressions as fundamental elements of narrative expression, whose accurate interpretation conventionally demands extensive training under expert supervision. This research proposes an intelligent web-based Bharatanatyam Mudra and Emotion Detection System that automates recognition through advanced deep learning and artificial intelligence techniques applied to input images. The architecture features a custom-trained ConvNeXtV2 convolutional neural network for precise mudra classification, complemented by the Google Gemini AI API for comprehensive facial emotion analysis, delivering instantaneous predictions with confidence metrics and interpretive descriptions via a robust Flask backend integrated with MySQL for secure data persistence. Facilitating user authentication, image upload, real-time inference, and historical result retrieval, the system empowers dance practitioners, educators, and scholars with an accessible platform for systematic analysis. By synergistically fusing classical artistic heritage with contemporary computational intelligence, this framework advances cultural preservation while enabling interactive, technology-enhanced pedagogical and analytical methodologies.

Keywords: Bharatanatyam, Hand Gesture Recognition, Facial Emotion Analysis, ConvNeXtV2, Machine Learning, Artificial Intelligence, Deep Learning, Flask Web Application

Abstract: In the digital era, passwords serve as the primary authentication mechanism for securing online accounts, financial systems, enterprise applications, and personal data. However, weak passwords and password reuse practices significantly increase the risk of cyberattacks such as brute force attacks, dictionary attacks, credential stuffing, and phishing. Data breaches have exposed millions of user credentials worldwide, making password security a critical concern. This research presents a Password Strength Analyzer and Breach Detection Tool that evaluates password robustness using machine learning techniques and checks whether a password has been exposed in known data breaches. The system analyzes password complexity based on multiple parameters including length, entropy, character diversity, and pattern predictability. Additionally, the tool integrates breach database verification using secure hashing techniques to detect compromised credentials without exposing sensitive information. Experimental results demonstrate improved detection accuracy and real-time performance, making the system suitable for cybersecurity awareness, enterprise authentication systems, and secure web applications.

Keywords: Cybersecurity, Password Strength, Breach Detection, Machine Learning, Data Security, Hashing, Authentication.

Abstract: In today's fast-paced lifestyle, many individuals consume food without having proper knowledge of its nutritional content, which often results in unhealthy eating habits, obesity, and lifestyle-related health issues. Manual calculation of calories and nutrients is time-consuming and requires expert knowledge, making it impractical for everyday users. To overcome these challenges, this project presents an AI-Powered Food Nutrition Analyzer using Image Recognition, which automatically identifies food items from images and provides accurate nutritional information. The proposed system enables users to upload food images through a simple and intuitive web interface developed using React.js. Once the image is uploaded, it undergoes preprocessing techniques such as resizing, normalization, and noise reduction to enhance image quality. The processed image is then analyzed using a Convolutional Neural Network (CNN) model, which is trained to recognize various food items with high accuracy. CNN is chosen due to its effectiveness in image classification and feature extraction. After successful food recognition, the system retrieves corresponding nutritional details including calories, proteins, fats, carbohydrates, and vitamins from a structured MongoDB database. The backend of the application is developed using Python and Flask, which handles image processing, model integration, and communication between the frontend and the database. This architecture ensures fast response time and scalability. The proposed solution eliminates the need for manual calorie estimation and provides instant nutritional feedback to users. It is especially useful for health-conscious individuals, diet planners, fitness enthusiasts, and people managing specific dietary requirements. By combining artificial intelligence, deep learning, and web technologies, this project demonstrates an efficient and user-friendly approach to dietary analysis. Overall, the AI-Powered Food Nutrition Analyzer promotes nutritional awareness and encourages healthier food choices, showcasing the practical application of deep learning in real-world healthcare and wellness domains.

Keywords: AI, Image Recognition, Food Nutrition Analysis, Convolutional Neural Network (CNN), Deep Learning, Calorie Estimation, Flask, MongoDB.

Abstract: Small-scale garment industries often depend on manual methods for managing employees, work orders, shift schedules, and salary records. These practices result in inefficiencies, data loss, and calculation errors. This paper presents Micro Manage for Jayam Tex, a web-based micro-enterprise management system designed to digitalize daily industrial operations.The system integrates employee management, work tracking, shift reporting, and salary processing into a centralized platform. Developed using Node.js and MongoDB, the system ensures scalability, flexibility, and secure data handling. Experimental evaluation demonstrates improved efficiency, reduced paperwork, and enhanced transparency, making the system suitable for small-scale garment industries.

Keywords: Micro-Enterprise Management, Garment Industry, Web Application, Node.js, MongoDB

Abstract: Efficient job scheduling plays a crucial role in improving productivity, reducing operational costs, and optimizing resource utilization in modern companies. Organizations often handle multiple tasks simultaneously across limited resources such as processors, employees, or machines. Improper scheduling may lead to increased waiting time, resource conflicts, reduced throughput, and missed deadlines. This research presents a Job Scheduling Simulator for Companies based on classical and advanced scheduling algorithms. The system simulates real-world task allocation scenarios using algorithms such as First Come First Serve, Shortest Job First, Priority Scheduling, and Round Robin. The simulator evaluates performance metrics including waiting time, turnaround time, throughput, and CPU utilization. The developed application provides a graphical interface for dynamic job input and real-time performance analysis. Experimental results demonstrate that algorithm selection significantly influences system efficiency and resource optimization. The proposed simulator serves as an educational and practical tool for analyzing scheduling strategies in corporate environments.

Keywords: Job Scheduling, CPU Scheduling, Simulation, Resource Allocation, Performance Metrics, Optimization, Operating Systems.

Abstract: Traditional banking transaction settlement systems rely on centralized infrastructures that often involve intermediaries, delayed settlement processes, high transaction fees, and vulnerability to fraud. The increasing demand for secure, transparent, and real-time financial settlements has led to the adoption of Blockchain technology in banking systems. This paper proposes a Blockchain-Based Transaction Settlement System designed to enable secure, transparent, and tamper-proof financial transactions between banking entities. The system uses distributed ledger technology and cryptographic hashing to validate and record transactions across a decentralized network. Smart contracts are used to automate settlement processes and eliminate intermediaries. The proposed system enhances security, reduces transaction time, minimizes operational costs, and ensures data integrity in financial transactions.

Keywords: Blockchain, Transaction Settlement, Banking System, Distributed Ledger, Smart Contracts, Financial Security, Decentralized System

Abstract: The rapid digitalization of healthcare systems has improved medical service accessibility and operational efficiency; however, prescription fraud remains a significant challenge affecting patient safety, regulatory compliance, and pharmaceutical accountability. Traditional paper-based prescriptions are highly vulnerable to duplication, tampering, unauthorized reuse, and identity misuse, resulting in medication abuse and financial loss. To address these challenges, this paper proposes a Smart QR-Based Prescription Fraud Prevention System, an intelligent and secure digital platform designed to authenticate prescriptions, prevent duplication, and ensure transparent medication dispensing. The proposed system integrates QR code technology, secure backend validation, role-based access control, and real-time verification mechanisms to create a trustworthy prescription management ecosystem. QR codes generated by authorized doctors encode unique prescription identifiers linked to centralized records, enabling pharmacists to validate authenticity through scanning. The system also incorporates duplicate detection logic, audit logging, and automated alert mechanisms to identify suspicious prescription reuse attempts. Additionally, the platform supports secure patient record management, real-time prescription tracking, role-based dashboards for doctors and pharmacists, and centralized monitoring for administrators. Experimental implementation demonstrates that the proposed system significantly enhances prescription authenticity, reduces fraudulent dispensing attempts, improves operational transparency, and strengthens healthcare trust relationships. The Smart QR-Based Prescription Fraud Prevention System provides a scalable and cost-effective solution for modern healthcare environments aiming to combat prescription fraud and enhance digital healthcare security.

Keywords: QR Code, Prescription Fraud Prevention, Digital Prescription, Healthcare Security, Authentication System, Duplicate Detection, Role-Based Access Control, Pharmacy Verification, Medical Data Integrity, Healthcare Digitization.

Abstract: The gaming industry has seen remarkable growth over the past decade, driven by advances in game engines, programming languages, and interactive design principles. Unity Engine has emerged as one of the most popular and accessible platforms for developing both 2D and 3D interactive games. This paper explores the development process of interactive games using Unity Engine, with a focus on game object management, C# scripting, physics simulation, user interface design, and testing methodologies. The study presents a structured approach to game development, covering the complete lifecycle from conceptualization to deployment. A prototype game was developed to demonstrate the practical application of Unity's core features including scene management, animation systems, collision detection, and input handling. The results indicate that Unity provides an efficient and beginner-friendly environment for developing high-quality interactive games. The paper concludes with observations on the strengths and limitations of Unity for student-level game development projects and offers suggestions for future enhancements.

Keywords: Unity Engine, Game Development, C# Programming, Interactive Games, 2D 3D Games, Game Objects, Scripting, Game Testing

Abstract: Traditional dairy delivery operations rely heavily on manual record-keeping methods such as paper ledgers and handwritten billing registers. These practices often result in inefficiencies, human errors, delayed billing cycles, and limited transparency for customers. This paper presents Tamils Dairy Farm, a web-based digital milk delivery management system designed to modernize dairy distribution through automation, real-time tracking, and customer engagement. The system integrates an administrator dashboard for managing deliveries, subscriptions, and billing with a customer portal that enables delivery scheduling, consumption monitoring, and automated invoice access. Developed using modern technologies such as React, TypeScript, Vite, and Tailwind CSS, the system initially supports Local Storage for offline data persistence and is designed for future migration to a cloud backend using Supa base. A custom billing engine ensures accurate monthly calculations based on recorded delivery logs. The proposed solution enhances operational efficiency, improves financial accuracy, and provides transparency in dairy service management.

Keywords: Dairy management system, digital ledger, milk delivery automation, subscription billing, React application, agricultural digitization.

Abstract: Expansive clayey soils, predominantly composed of montmorillonite minerals, pose significant geotechnical challenges owing to their high swelling potential and poor load-bearing characteristics. The utilization of chemical stabilizers such as lime and cement, while effective, raises environmental and sustainability concerns. This study investigates the physicochemical interaction mechanisms between two naturally derived polysaccharide biopolymers-xanthan gum and guar gum-and expansive clay at dosage levels of 0.5%, 1%, 1.5%, and 2% by dry weight of soil. The untreated soil exhibited a Liquid Limit (LL) of 68%, Plasticity Index (PI) of 36%, Free Swell Index (FSI) of 92%, Unconfined Compressive Strength (UCS) of 165 kPa, and soaked California Bearing Ratio (CBR) of 2.8%. Treatment with 1.5% xanthan gum yielded the most favorable outcomes, reducing LL to 58%, PI to 22%, and FSI to 40%, while elevating UCS to 480 kPa and CBR to 8.5%. Guar gum demonstrated comparatively moderate enhancement, achieving peak UCS of 420 kPa and CBR of 7.2%. Physicochemical characterization via Fourier Transform Infrared Spectroscopy (FTIR) confirmed hydrogen bonding between the hydroxyl (-OH) groups of the polysaccharide chains and the clay mineral surface, evidenced by a shift in the O-H stretching vibration from 3420 cm⁻¹ to 3385 cm⁻¹. Scanning Electron Microscopy (SEM) revealed a transition from dispersed flaky clay particles to a cohesive, aggregated matrix following biopolymer treatment. The interdisciplinary findings demonstrate that xanthan gum, at an optimal dosage of 1.5%, represents a viable, biodegradable, and low-carbon substitute for conventional chemical stabilizers in expansive soil treatment.

Keywords: Expansive clay, xanthan gum, guar gum, biopolymer stabilization, montmorillonite, hydrogen bonding, sustainable geotechnics, FTIR, SEM, UCS.

Abstract: Kidney stones (nephrolithiasis) represent a widespread urological disorder affecting millions of individuals globally, frequently causing severe pain, obstruction of urine flow, urinary tract infections, and potentially irreversible renal damage when early detection is missed. Traditional diagnostic approaches rely on imaging modalities-principally ultrasound and computed tomography (CT)-which require expert radiological interpretation and may introduce delays or inter-observer variability. This study presents a Convolutional Neural Network (CNN)-based deep learning framework for the automated detection and classification of kidney stones from medical images. The proposed model integrates preprocessing pipelines, data augmentation strategies, hierarchical feature extraction, and rigorous performance evaluation using accuracy, sensitivity, and specificity metrics. Experimental results demonstrate that transfer learning architectures (VGG16, ResNet50, EfficientNet) significantly outperform classical machine learning classifiers and custom CNN designs, particularly when trained on CT imaging datasets. The system offers a cost-effective, scalable, and clinically integrable solution for diagnostic assistance, with the potential to reduce diagnosis time, minimize human error, and enhance patient outcomes.

Keywords: Kidney Stone Detection; Deep Learning; Convolutional Neural Networks; Medical Image Analysis; Transfer Learning; CT Scan; Automated Diagnosis; Nephrolithiasis

Abstract: Financial markets can be influenced by quantitative price movements or by qualitative behavioral factors such as news sentiment and investor psychology. Historical stock prediction models are traditionally based on data, but they are usually not good for finding any trading decisions and tend to fail us. This paper presents an AI stock market trading strategy advisor system that combines Long Short-Term Memory (LSTM) networks (for price prediction) with FinBERT-based Natural Language Processing (NLP) using financial sentiment and Deep Q-Network (DQN) reinforcement learning as deep neural networks for intelligent decision making. It simulates actual trading conditions using historical market data and financial news. The LSTM module predicts future price trends, FinBERT estimates sentiment based on financial headlines, and the DQN agent learns with regard to the return and sentiment signal the best trading decisions including Buy / Sell / Hold in the market based on the expected values and direction of the returns. Experimental study shows that the integrated strategy of this approach in their experimental implementation enables better decision-making support than the classic strategies based on combining technical forecasting with behavior-based decision support, because their combination is more effective than the application-level forecasting approach.

Keywords: AI Trading Advisor, Deep Q-Network (DQN), FinBERT, LSTM, Reinforcement Learning, Sentiment Analysis, Stock Market Prediction

Abstract: We study stability and convergence properties of standard finite‐difference time-stepping schemes for the one-dimensional heat equation u_t=αu_xx on [0,1] with homogeneous Dirichlet boundaries. Using von Neumann analysis, we recover the classical results: FTCS is conditionally stable with a CFL restriction on r=α∆t/(∆x)^2 , while BTCS and Crank-Nicolson (CN) are unconditionally stable. Local truncation error analysis shows FTCS is first-order in time and second-order in space, and CN achieves second-order accuracy in both time and space. Numerical experiments with the analytical benchmark e^(-απ^2 t) sin⁡(πx) confirm the theory: log-log error plots exhibit slopes consistent with the predicted orders, and 3D surface/contour comparisons show close agreement between CN and the exact solution. A performance summary highlights the trade-offs between explicit simplicity versus time-step restrictions, implicit robustness versus linear-solve cost, and CN's balanced accuracy-stability-cost profile.

Keywords: Heat equation; parabolic PDE; finite differences; von Neumann stability; Crank-Nicolson; FTCS; BTCS; convergence; truncation error; error norms.

Abstract: A personal expense tracker designed for everyday use can significantly simplify managing your daily finances. Modern tools allow users to scan their bills or receipts, automatically extract spending details, store records, and visually display spending habits through charts. How It Works Scan Your Bills/Receipts Use your smartphone to upload bills or receipts. Optical Character Recognition (OCR) reads key information like amount, date, merchant, and category. Automatic Storage and Categorization The tool saves scanned receipts digitally and logs expenses. Expenses are automatically categorized (groceries, utilities, dining, etc.), making it easy to organize your spending. Visual Reports & Charting Your expenses are shown in pie charts, bar graphs, or line charts. Charts help you quickly see: Where most of your money goes Monthly or weekly spending trends Areas for potential savings Key Features Easy Bill Scanning: Capture receipts on the spot-no manual data entry required. Expense Categorization: Autoassigns categories, or you can set custom ones. Chart-Based Dashboards: Instantly access graphical reports of your spending. Data Sync & Backup: Store your expense data in the cloud for access on multiple devices. Privacy: Many apps offer data encryption and privacy controls. Export Options: Download your data or reports for personal analysis or sharing. Example Apps Supporting These Features Expensify: Known for effortless receipt scanning, automatic expense entries, and customizable reports with visual charts .Money Manager: Offers receipt attachment, category breakdowns, and daily/monthly spending graphs. Walnut/Axio: SMS/Bill scanning and real-time insights with chart visualizations. Google Lens with Google Sheets: Scan receipts and log details into a connected spreadsheet to generate custom charts. Advantages for Daily Life Saves Time: Reduces manual effort by scanning instead of typing entries. Tracks Every Rupee: Ensures every purchase is accounted for, preventing missed expenses .Promotes Smarter Spending: Graphs make it clear where you might be overspending, helping you stay on budget. Prepares for Taxes or Claims: Having all receipts digitized makes year-end calculations and claims far easier. This approach is ideal for anyone wanting a seamless, automated, and visually intuitive way to manage daily finances and develop healthy spending habits.

Keywords: Expense Tracking, Optical Character Recognition (OCR), Budget Monitoring, Financial Analysis, Data Visualization, Personal Finance Management System

Abstract: Gas leakage incidents pose significant risks to human safety, property, and industrial environments due to the possibility of explosions, toxic exposure, and fire hazards. Traditional gas detection methods rely on manual monitoring and standalone alarms, which lack remote alerting capabilities and real-time environmental monitoring. With the advancement of Internet of Things (IoT) technologies, intelligent gas detection systems can be developed to provide continuous monitoring, automated alerts, and remote supervision. This paper proposes a Gas Leakage Detection System using IoT, an intelligent embedded monitoring solution designed to detect hazardous gas concentrations and environmental variations while providing instant user notifications. The system integrates ESP32 as the core IoT controller, MQ-2 and MQ-135 gas sensors for detecting combustible and harmful gases, a DHT11 sensor for temperature and humidity monitoring, and SIM800L GSM module for SMS-based alert communication. Power management is handled using dual 3.7 V batteries and LM2596 voltage regulator to ensure stable system operation. The proposed system continuously monitors gas concentration levels and environmental parameters. When abnormal conditions are detected, the ESP32 triggers local alerts and transmits SMS notifications to predefined users, enabling rapid response. Experimental implementation demonstrates improved safety monitoring, real-time alerting capability, energy-efficient operation, and cost-effective deployment. The proposed IoT-based Gas Leakage Detection System provides a reliable and scalable solution suitable for residential, industrial, and commercial safety applications.

Keywords: Gas Leakage Detection, IoT Safety System, ESP32, MQ-2 Sensor, MQ-135 Sensor, DHT11, SIM800L GSM, Embedded Monitoring, Hazard Detection, Smart Safety System.

Abstract: Road traffic violations are one of the major causes of road accidents worldwide. Traditional traffic monitoring systems rely heavily on manual enforcement and reactive measures, which are inefficient in reducing violations proactively. This research proposes a Machine Learning-based Traffic Violation Prediction System that predicts the probability of traffic violations based on various input parameter such as driver behaviours, vehicle characteristics, location type, and time conditions. The proposed system utilizes classification algorithms to determine the likelihood of violation occurrence and provides multi-dimensional output including risk level, probability score, and safety recommendations. Experimental results show improved prediction accuracy and decision-support capability. The system can assist traffic authorities in proactive enforcement and smart city development.

Keywords: Machine Learning, Traffic Violation Prediction, Road Safety, Classification, Smart Traffic System, Risk Analysis

Abstract: The rapid expansion of global supply chains and the increasing demand for efficient transportation services have significantly transformed the logistics industry. Traditional freight management systems rely heavily on manual coordination, telephonic negotiations, and fragmented data handling mechanisms, which often lead to pricing inefficiencies, underutilization of fleet resources, delayed shipments, and limited operational transparency. This paper presents a web-based truck bidding platform named OptiFreight Logistics, designed to digitally connect shippers and truck owners within a unified ecosystem. The system is developed using modern web technologies including React, TypeScript, and Vite, and integrates AI-assisted components to enhance operational intelligence. The platform enables real-time freight posting, competitive bidding by truck owners, automated bid evaluation, fleet monitoring, and performance analytics visualization. Through role-based access control and structured workflow management, the system improves transparency, reduces manual intervention, optimizes truck allocation, and enhances decision-making efficiency. The proposed solution demonstrates how intelligent web-based platforms can modernize freight coordination and provide scalable solutions for digital logistics transformation.

Keywords: Smart Logistics, Freight Management System, Fleet Optimization, Real-Time Bidding, AI in Logistics, Web-Based Transportation Platform.

Abstract: Cardiovascular diseases continue to be one of the leading causes of mortality worldwide, placing a significant burden on global healthcare systems. Early detection of heart disease plays a crucial role in reducing complications, improving survival rates, and enabling timely medical intervention. With the advancement of computational intelligence, machine learning techniques have emerged as effective tools for analyzing medical datasets. These techniques are capable of identifying hidden patterns and relationships that may not be immediately visible through manual assessment. In this study, a predictive framework is developed using the Random Forest algorithm to assess heart disease risk. The model processes structured patient health records containing both demographic and clinical attributes relevant to cardiovascular evaluation [6]. A systematic methodology was implemented, including data preprocessing, feature optimization, supervised model training, and validation. These steps were performed to ensure data consistency, improve model efficiency, and enhance predictive accuracy. The ensemble learning mechanism underlying the Random Forest classifier combines multiple decision trees to produce stable and reliable predictions. This approach reduces overfitting, improves generalization performance, and enhances classification robustness. Experimental evaluation using standard performance metrics demonstrates that the proposed system achieves consistent and dependable results. The developed framework has the potential to function as an effective clinical decision-support tool, assisting healthcare professionals in identifying high-risk patients and supporting early preventive care strategies [9].

Keywords: Cardiovascular Risk Prediction, Ensemble Learning, Random Forest Model, Clinical Data Analysis, Supervised Classification, Predictive Healthcare, Data-Driven Diagnosis, Intelligent Medical Systems

Abstract: The rapid growth of the tourism industry has led to an overwhelming amount of travel information, making itinerary planning a complex and time-consuming task for travellers. This paper presents a Personalised Travel Planning System with an AI Assistant, a web-based application that leverages artificial intelligence to provide customized travel recommendations, intelligent itinerary generation, destination insights, flight suggestions, and real-time travel tips. The system is built using the Flask framework, integrates with the OpenRouter API to access large language models (GPT-3.5-turbo), and incorporates user authentication with email notifications. The proposed solution simplifies travel planning by offering an interactive conversational agent and automated planning tools, thereby enhancing user experience and decision-making. Experimental results demonstrate the system's effectiveness in generating relevant, context-aware travel plans and advice. Future enhancements include integration with live booking APIs, multi-language support, and a mobile application.

Keywords: Travel planning, AI assistant, personalized itinerary, chatbot, destination insights, OpenRouter, Flask, recommendation system.

Abstract: The Reserve Bank of India (RBI) and banks' treatment of loans secured by pledged gold jewellery, in particular, has a substantial impact on the financial stability and credit availability of Indian farmers. Because formal credit channels like Kisan Credit Cards might not adequately satisfy their needs, many small and marginal farmers have historically relied on pledging gold as collateral to obtain short-term credit for agricultural inputs, emergencies, and seasonal working capital. Up to the specified limits recent regulatory clarifications permit the voluntary pledge of gold or silver as collateral for agricultural loans without going against the collateral-free loan norms.

Abstract: This study investigates the effect of World Bank financing (IBRD and IDA) on economic performance in selected developing countries between 2005 and 2024. Using panel data from twelve developing economies, the research analyses the relationship between World Bank loan disbursements and major macroeconomic indicators such as GDP growth, debt-to-GDP ratio, fiscal balance, and gross capital formation. The analysis applies descriptive statistics, Pearson correlation, and fixed-effects UNIANOVA models to evaluate how World Bank funding influences economic outcomes. The results show that World Bank disbursements do not exhibit a statistically significant impact on GDP growth or investment trends. However, the findings indicate a meaningful relationship with increasing public debt levels and deteriorating fiscal balances in several borrowing nations. In addition, event study observations suggest that periods of higher disbursements are often followed by sustained increases in debt burdens without noticeable improvements in economic growth. Overall, the study adds to the broader discussion on the effectiveness of multilateral development financing and emphasizes the importance of stronger debt monitoring mechanisms and sustainable fiscal management in countries that rely on external financing.

Keywords: World Bank Financing, Economic Growth, Developing Countries, GDP Growth, Public Debt, Development Finance.

Abstract: This study examines the impact of global trade integration on India's economic, social, and environmental indicators during the period 2011-2025. The research analyses how trade openness influences economic growth, inflation, unemployment, human development, renewable energy usage, and CO₂ emissions. The study is based on secondary data collected from reliable international sources and applies statistical tools such as normality test, Pearson correlation analysis, and t-test. The findings indicate that global trade integration significantly contributes to economic stability and social development, particularly through reduced inflation and improved human development indicators, while its direct impact on GDP growth and unemployment is statistically insignificant. The study also reveals a strong positive relationship between trade openness and CO₂ emissions, highlighting environmental challenges associated with globalization. The study concludes that balanced and sustainable policy measures are necessary to achieve inclusive economic growth while minimizing environmental impacts.

Keywords: Global Trade Integration, Trade Openness, Economic Growth, Human Development, Inflation, CO₂ Emissions, Sustainable Development.

Abstract: In recent years, rapid urbanization, industrial growth, and changing lifestyles have significantly altered food consumption patterns, leading to increased demand for instant food products. Busy schedules, rising workforce participation, and the growth of nuclear families have reduced the time available for traditional cooking, encouraging consumers to opt for convenient, ready-to-cook, and ready-to-eat food options. This study focuses on customer purchase decisions towards instant food products in Coimbatore, a city characterized by industrial development, educational institutions, and a diverse population comprising students, working professionals, and business families. The research aims to analyze the key factors influencing purchase decisions, including price, quality, brand image, taste, availability, packaging, promotional activities, and health considerations. It also examines the role of demographic variables, lifestyle changes, technological advancements in food processing, and marketing strategies such as advertisements, discounts, and digital promotions

Keywords: Customer Purchase Decisions, Instant Food Products, Consumer Buying Behaviour, Convenience and Lifestyle, Health and Nutrition Awareness, Brand Image and Pricing, Promotional Strategies, Urban Consumers, Retail and Online Availability.etc.,

Abstract: Despite the importance of athletic power in sports performance, limited studies have compared power abilities between football players and non-football players using standardized field tests. Athletic power is a critical component of performance in football, as it underpins explosive actions such as jumping, sprint initiation, tackling, and rapid directional changes. Hence, the present study was undertaken to compare the athletic power of football players and non-football players. The purpose of the present study was to analyze differences in athletic power between football players and non-football players. A total of 40 football and non-football players were selected as subjects and randomly divided into two equal groups. The results revealed a statistically significant difference in athletic power, with football players demonstrating superior power compared to non-football players. This finding suggests that participation in football, which involves repeated high-intensity actions such as sprinting, jumping, tackling, and rapid changes of direction, contributes positively to the development of athletic power.

Keywords: Athletic Power, Football.

Abstract: Football is one of the most popular and physically demanding sports in the world, involving high-intensity activities such as running, kicking, jumping, twisting, and turning. These movements place players at considerable risk of injury. The present study aimed to examine the prevalence and types of injuries among Indian elite football players and to analyze injury patterns across different age groups. A descriptive retrospective research design was adopted for this study. The sample consisted of 1000 elite football players aged between 14 and 30 years from different clubs, universities, and state teams affiliated with the All India Football Federation. Data were collected through a self-developed football injury questionnaire modified from Singh (2012). The collected data were analyzed using descriptive statistics and percentage analysis through SPSS software. The results indicated that muscle injuries were the most common (40.84%), followed by upper and lower back pain (24.92%), ligament injuries (15.33%), fractures (7.33%), and other injuries (10.67%). Similar trends were observed across different age groups, particularly among players aged 14-17 and 18-21 years, where muscle injuries and back pain were predominant. The findings suggest that muscle strain and back-related injuries are the most prevalent injuries among football players, highlighting the need for improved conditioning programs, injury prevention strategies, and proper training supervision.

Keywords: Football injuries, muscle injury, ligament injury, fracture, sports injury epidemiology, elite football players

Abstract: The increasing health concerns and environmental impacts associated with conventional meat consumption have intensified the demand for sustainable and nutritious alternatives. This study, "Formulating Meat analogue: Health-Promising Sustainable Meat Substitute", aimed to develop and evaluate a plant-based meat analogue using kidney bean, soya bean, chickpea, mushroom, and wheat gluten. Three formulations (V1, V2, and V3) were prepared with varying proportions of ingredients and assessed for sensory, physicochemical, nutritional, and microbial characteristics. Sensory evaluation by a semi-trained panel using a 5-point hedonic scale identified Variation 1 (40% kidney bean with balanced inclusion of other ingredients) as the most acceptable, with high scores for appearance, texture, and taste. Nutrient analysis revealed the product contained 23.5% protein, 18.7% carbohydrates, 3.16% fat, and provided 197.24 kcal per 100 g, alongside appreciable amounts of fiber, calcium, sodium, and iron. Physicochemical assessments confirmed favorable water and oil absorption capacities, while microbial analysis demonstrated a safe shelf life of 21 days. The findings indicate that the formulated meat analogue is a protein-rich, low-fat, and nutrient-dense product with desirable sensory properties, offering a sustainable alternative to meat. This study highlights the potential of plant-based meat substitutes to improve dietary protein intake, especially among vegetarians, while contributing to environmental sustainability

Keywords: Meat analogue, Kidney bean, Plant-based meat, Protein alternative.

Abstract: The present study investigates the level of professional competence of teachers and its influence on classroom practices in inclusive education for children with special needs in Malda District, West Bengal. The present study adopts a quantitative research approach, aiming to objectively measure teachers' professional competence and classroom practices using statistical techniques. To systematically collect data from secondary school teachers, a descriptive survey method was employed. The population of the study comprised all secondary school teachers in Malda District, West Bengal, involved in inclusive education for children with special needs. A total of 80 teachers were selected as the sample to ensure adequate representation and manageable data collection. Simple random sampling was employed to provide each teacher an equal chance of selection, minimizing bias and enhancing the representativeness of the sample. For the present study, a structured Likert scale questionnaire, named the "Teachers' Competence and Classroom Practices Scale (TCCPS)," was used as the primary tool to assess teachers' professional competence and classroom practices in inclusive education of children with special needs. Statistical techniques such as mean, standard deviation, t-test, and Pearson correlation were applied. The findings reveal that teachers possess a moderate to high level of professional competence; however, classroom practices remain moderately effective. A significant positive correlation (r = 0.68) was found between competence and classroom practices, indicating that higher competence leads to better implementation.

Keywords: Professional Competence, Classroom Practices, Inclusive Education, Special Needs.

Abstract: Football is one of the most popular sports worldwide, but it is also associated with a high risk of injuries due to the dynamic and physically demanding nature of the game. The present study aimed to examine the prevalence of injuries and their impact on training participation and match performance among elite football players. A descriptive retrospective research design was adopted for the study. A total of 1000 elite football players aged between 14 and 30 years were selected from various clubs, universities, and state-level teams affiliated with the All India Football Federation using purposive sampling. Data were collected through a self-developed football injury questionnaire modified from Singh (2012). The questionnaire included demographic information and injury-related details. The collected data were analyzed using descriptive statistics and percentages with the help of SPSS version 16. The findings revealed that 18.67% of players reported absence from training sessions due to injuries, while 22.50% reported absence from matches or tournaments. Age-wise analysis indicated that younger players aged 14-17 years showed slightly higher absence rates compared to other age groups. The study highlights that injuries significantly influence player participation in both training and competitions. The findings emphasize the importance of injury prevention strategies, proper training methods, and sports medicine support systems in football to reduce injury risks and enhance athlete performance and longevity.

Keywords: Football injuries, sports participation, training absence, match absence, injury prevention, elite football players

Abstract: This study investigates the hemodynamic behavior of Casson blood flow through a stenosed arterial segment under the combined effects of magnetic field (MHD), wall slip, and shear-dependent viscosity. The mathematical model incorporates a steady, axisymmetric, and incompressible flow with a transverse magnetic field applied to an electrically conducting Casson fluid. Analytical solutions are obtained under mild stenosis and low magnetic Reynolds number approximations. The influence of Hartmann number, slip parameter, and yield stress on velocity distribution, volumetric flow rate, and wall shear stress is analyzed. Results reveal that increasing the magnetic field strength significantly suppresses the volumetric flow rate while enhancing wall shear stress due to intensified Lorentz forces. Conversely, higher wall slip reduces the flow resistance and shear stress, promoting smoother motion. The findings provide important insights into the magnetohydrodynamic regulation of blood flow in diseased arteries and the potential therapeutic relevance of wall slip effects in microvascular transport.

Keywords: Casson fluid, Magnetohydrodynamics (MHD), Wall shear stress, Slip parameter, Stenosed artery,Hartmann number, Hemodynamics, Non-Newtonian blood flow.

Abstract: Despite the importance of athletic power in sports performance, limited studies have compared power abilities between football players and non-football players using standardized field tests. Athletic power is a critical component of performance in football, as it underpins explosive actions such as jumping, sprint initiation, tackling, and rapid directional changes. Hence, the present study was undertaken to compare the athletic power of football players and non-football players. The purpose of the present study was to analyze differences in athletic power between football players and non-football players. A total of 40 football and non-football players were selected as subjects and randomly divided into two equal groups. The results revealed a statistically significant difference in athletic power, with football players demonstrating superior power compared to non-football players. This finding suggests that participation in football, which involves repeated high-intensity actions such as sprinting, jumping, tackling, and rapid changes of direction, contributes positively to the development of athletic power.

Keywords: Athletic Power, Football.