VOLUME 12, ISSUE 1, JANUARY 2025

Abstract: A study was conducted to investigate the effect of varying moisture content levels on the physical properties of castor seeds namely, these physical properties include; seed dimensions, surface area, sphericity, geometric diameter, bulk and true density, repose angle, static coefficient of friction at varying moisture contents on glass, aluminium and plywood surfaces. The physical properties of Castor seeds were evaluated at moisture contents of 10 %, 12 % and 14 % d.b. The average major, minor and intermediate diameters were 15.509 mm, 12.4992 mm and 7.093 mm respectively at moisture content of 10 % d.b. The geometric mean diameter, sphericity and surface area increased from 11.116 to 11.8497 mm, 0.717 to 0.719 and 388.635 to 441.935 mm2 as moisture content increased from 10 to 14 % d.b. At the same moisture range, bulk density decreased from 0.4944 to 0.4632 g/cm3, true density decreased from 2.6632 to 2.5626 g/cm3, and the corresponding porosity increased from 0.0081 to 0.0082 %. The repose angle was found to increase from 29.67o to 33.76o and as the moisture content increased from 10% to 14% d.b., the static coefficient of friction increased on all three structural surfaces, from 0.364 - 0.445 for glass, 0.42868 - 0.52282 for aluminium and from 0.518 - 0.615. Knowledge of these properties constitute essential engineering data in the design of equipment, machineries, processes and controls for castor seeds.

Keywords: castor seeds, surface area, sphericity, geometric diameter, bulk and true density

Abstract: Stingless bee's pollination service is provisioned at various terrestrial ecosystems. Many flowering plant species are rely on stingless bees and in turn stingless bees are rewarded with nectar, pollen, resin, honeydew and nest-building materials with their minimum flight range amidst natural and man-made ecosystems. However, published reports on floral source and floral calendar for stingless bees is poor. Hence, systematic investigation was undertaken at nine districts which represented maidan, malnad, hilly terrain, coastal region, arid zone and agro-ecosystems at the vicinity of Western Ghats by following standard methods. Beekeepers doing Meliponiculture were randomly selected and visited their apiary/meliponary to record the floral source in one square kilometer area during different seasons from 2023 to 2024. Flowering plants for their nectar, pollen and both nectar and pollen source were recorded and photographed by spending 10 minutes per plant/flower after confirming the visit of stingless bees during 0700 to 1800 hours of observation. Total 84 flowering plant species which belong to 45 plant families provided foraging source to stingless bees at different agro-ecosystems of Karnataka. Asteraceae and Rutaceae family members were more predominant (7.1% each) and it was followed by Cucurbitaceae, Lamiaceae and Myrtaceae family members (5.8% each) contributed good foraging source to stingless bees. Mimosaceae, Euphorbiaceae, Fabaceae and Solanaceae family members also contributed considerable amount of foraging source to stingless bees during different seasons. The floral source consists of avenue trees, commercial plants, fruit yielding plants, horticultural plants, medicinal plants, ornamental plants, vegetable crops and weeds which were grouped into climbers, herbs, shrubs and trees have provided foraging source at various agro-ecosystems. Floral calendar revealed the occurrence of pollen (P1, P2 and P3) and nectar (N1, N2 and N3) plants based on their potential source of both pollen and nectar (P1N1, P1N2, P1N3, P2N1, P2N2, P2N3, P3N1, P3N2 and P3N3) plants to stingless bees by blooming during different months around the year i.e., January to December. Thus, stingless bees avail good floral source during different seasons amidst apiary/meliponary at various agro-ecosystems that could help encourage Meliponiculture on large scale basis along with apiculture in Karnataka.

Keywords: Flora, stingless bees, Meliponiculture, Karnataka

Abstract: The present study evaluates the physico-chemical properties of water from Shivnandanpur, Bishrampur, located in the Surajpur district of Chhattisgarh, India. This region relies heavily on its water resources for domestic, agricultural, and industrial purposes, necessitating a comprehensive assessment of water quality to ensure environmental and public health. Samples were collected from various water sources, including groundwater and surface water, during different seasons. Key parameters analyzed include pH, electrical conductivity, total dissolved solids (TDS), turbidity, dissolved oxygen (DO), and concentrations of major cations (calcium, magnesium, sodium, potassium) and anions (chloride, sulfate, nitrate, bicarbonate). The results reveal significant variations in water quality, with some parameters exceeding permissible limits set by national and international water quality standards. Seasonal fluctuations were observed, indicating the influence of natural and anthropogenic factors such as agricultural runoff, industrial discharge, and local geological conditions. The findings highlight areas of concern regarding water usability for drinking and irrigation purposes and suggest the need for targeted remediation strategies. This study provides a baseline for future research and underscores the importance of sustainable water management practices in Shivnandanpur to safeguard environmental and public health.

Keywords: Water quality, Physical properties, chemical properties, conductivity.

Abstract: "Control by one power over a dependent area or people" is the definition of colonialism. In actuality, colonialism is the violent invasion and occupation of another nation by one nation, which then claims the territory and relocates its citizens, dubbed "settlers." Despite their frequent interchangeability, colonialism and imperialism are not the same concepts. Imperialism is characterized as a system of laws and customs that increases a country's authority over the social, political, and cultural spheres of other countries. Imperialism can be defined as the rationale or philosophy that justifies colonial endeavors. Queen Victoria founded the British Raj in India in 1858, and until Indian independence in 1947, Britain remained the country's dominating power structure. Despite the fact that a great deal of fiction and critical research has attempted to depict aspects of the British Empire's existence in India and its psychological consequences on Indian residents.

Keywords: Colonialism, Civilize, Red-Nosed Boy, Missionaries, Anglo-Indian.

Abstract: The AI-based Logistics Management System is a pioneering solution that leverages the force of artificial intelligence and machine learning to transform logistics. It is a comprehensive system integrating various modules driven by artificial intelligence to optimize logistics processes, including demand forecasting, route optimization, real-time tracking, and supply chain visibility. The system allows logistics stakeholders to make data-driven decisions based on predictive analytics and machine learning algorithms, enabling them to predict possible disruptions and thus mitigate risks proactively. The advanced route optimization module by the system uses AIdriven insights to derive the most efficient routes, thereby minimizing fuel consumption, lowering emissions, and reducing delivery times. Realtime tracking provides end-to-end visibility for the logistics manager, thus tracking shipments and inventory, while he is always ready to respond to exceptions and disruptions. Additionally, the demand forecasting module of the system utilizes machine learning algorithms for the analysis of historical data, seasonal trends, and external factors to predict demand, and logistics stakeholders can optimize their levels of inventory, reduce the possibility of stockouts, and minimize waste. The AI-based Logistics Management System also has a supply chain visibility module that offers real-time insights into inventory levels, shipment status, and performance in the supply chain. This allows logistics stakeholders to identify bottlenecks, optimize inventory allocation, and improve overall supply chain efficiency. Moreover, the advanced analytics module of the system provides actionable insights, allowing logistics stakeholders to measure key performance indicators, identify areas for improvement, and optimize logistics operations to meet evolving business needs. Market Basket Analysis, Hybrid Algorithm, FP-Growth, ECLAT, Neural.

Keywords: AI Logistics, Logistics Management System, Supply Chain Optimization, Route Optimization, Real-time Tracking, Demand Forecasting, Predictive Analytics.

Abstract: India being an agriculture country, its economy predominantly depends on agriculture yield growth and agro industry products. Maintaining a high yield is a very important issue in agriculture. Any farmer is interested in knowing how much yield he is about to expect. By analyzing the various related attributes like location, pH value from which alkalinity of the soil can be determined. Along with this, percentage of nutrients like Nitrogen (N), Phosphorous (P), and Potassium (K) Location can be used alongside the use of third-party apps like APIs, weather and temperature, type of soil, nutrient value of the soil in that region, quantity of rainfall, soil composition can also be determined. All these attributes of data will be analyzed, train the data with various suitable machine learning algorithms for creating a model. The system comes with a model to be precise and accurate in guiding the end user with proper recommendations about required fertilizer ratio based on atmospheric and soil parameters of the land which enhance to increase the crop yield and increase farmer revenue. It also suggests a gel, oil, or any other chemical agent that would help crops grow better. Furthermore, the system will use crop yield history data to fine-tune the predictions and recommendations. Integrating satellite imagery will enable the model to analyze vegetation health and detect issues before they occur. The inclusion of real-time sensor data from IoT devices on the farm will enhance the accuracy of the predictions. The system will also consider pest and disease predictions so that appropriate pre-emergent preventative actions and readiness notification are received in time. Moreover, it will provide advice to clients for the best times for plantings under seasonal weather regimes.

Keywords: K- Nearest Neighbor, Support Vector Machine, Machine learning, Efficient Farming, System Architecture.

Abstract: Accurate interpretation of doctor's handwritten prescription is a critical task in healthcare for the patient safety and minimization of medication errors. Moreover, illegible handwriting poses a great risk, which leads to misinterpretation resulting in adverse health outcomes in many cases. Recent breakthroughs in AI, more so the deep learning segment, introduce robust methods to automate handwritten text interpretations, hence providing a way to streamline processes and improve the accuracy in the healthcare setup. In previous studies, models like CNN, RNN were explored, but the standalone CNN and RNN were giving suboptimal responses. In this study, a Hybrid CNN-BiLSTM model is proposed to predict and analyze words in handwritten prescriptions. This research utilizes CNN for capturing the spatial features and BiLSTM networks for analyzing sequential dependencies, which makes this approach suitable for complex handwriting pattern recognition in medical documents. To evaluate the performance of the proposed model, its performance was compared with Google's Vision API. It is a machine learning-powered service for image content analysis. The results are indicative of the great potential that lies in the application of the CNN-BiLSTM architecture for advancements in automated prescription analysis to improve patient safety and operational efficiency within healthcare settings.

Keywords: Convolutional Neural Network (CNN), Bidirectional Long-Short Term Memory (Bi-LSTM), Deep Learning, Machine Learning

Abstract: A proposed algorithm based on Homomorphic Encryption built based on Homomorphic Encryption and Elliptic curve. In cloud computing, big amount of users 'data are allowed to de collected on cloud server storage for next use, and any computations on stored data will be implemented in the cloud. To keep the stored data that is on the cloud we necessities have to use an encryption system that can do computations on the encrypted data called homomorphic encryption. In this paper, Comparison between a Proposed algorithm Based on Homomorphic Encryption and Elliptic curve(PAHEEC) with traditional Algorithms for security of data in cloud computing ,", Elliptic curve cryptography used to generate algorithm's private key , A new algorithm reduces the time of processing, space of storage and make available high security because of its key generated depends on ECC. The use of 64-bit provides enough security to be used in a comparison.

Keywords: Security of cloud computing, Homomorphic Encryption, Elliptic curve.

Abstract: - The integration of 3D rendering with augmented reality (AR) and virtual reality (VR) technologies has revolutionized interior house design by offering immersive and interactive experiences. This project presents a novel system that combines AR/VR with machine learning (ML) to create personalized, real-time visualizations of interior spaces. By leveraging advanced 3D modeling tools, photorealistic rendering techniques, and user-centric design principles, the system enables users to explore and modify virtual interior environments interactively. The incorporation of ML algorithms ensures adaptive recommendations tailored to user preferences and spatial constraints, while AR/VR enhances engagement by allowing users to visualize and refine designs within real-world or immersive settings. This approach democratizes high-quality interior design, reducing iteration cycles, improving user satisfaction, and fostering innovative design solutions. The proposed system addresses challenges like computational demands and data scalability while setting a new standard for efficiency and accessibility in interior design..

Keywords: Augmented Reality, Virtual Reality, 3D Rendering, Machine Learning, Interior Design, Real-Time Visualization, User Interaction.

Abstract: Artificial Intelligence (AI) is transforming decision-making in critical fields like healthcare, finance, and governance. However, its "black box" nature undermines trust and comprehension. Explainable AI (XAI) addresses this by enhancing transparency and interpretability, yet aligning explainability with human cognitive and emotional needs remains challenging. This paper explores principles and methodologies for designing human-centered XAI, emphasizing user profiling, dynamic explanations, and ethical considerations like fairness and accountability. Key contributions include adaptive explanations tailored to diverse user needs and strategies to mitigate biases, advancing AI systems that are transparent, accessible, and trustworthy.

Keywords: Artificial Intelligence (AI), Explainable AI (XAI), Human-centered design, Dimensions of trust in AI.

Abstract: Generative AI is revolutionizing the way industries operate with its positive impact on patient care, claims processing, and customer service being the most recognizable. However, alongside this advancement, these is a darker side to AI that many overlook. And as technology becomes more integral to our daily lives, cybercriminals are increasingly leveraging artificial intelligence (AI) to carry out highly sophisticated attacks, posing serious risks to both individuals and organizations. Recent high-profile data breaches, such as the one involving Star Health which led to personal data of 31 million customers being compromised underscore the growing danger of this new threat landscape. In this article, we'll explore the various types of attacks being carried out by cybercriminals using AI, shedding light on the growing threats they pose.

Keywords: Artificial Intelligence (AI), cybercriminals, Generative AI, Multi-Factor Authentication (MFA).

Abstract: In the digital age, the proliferation of counterfeit goods has led to an increasing need for reliable methods to detect fake logos, which often signify counterfeit products. To address this challenge, this project attempts to develop a robust Fake Logo Detection System which exploits advanced machine learning. A convolutional neural network (CNN) is used to analyze and categorize logo pictures and differentiate genuine logos versus fraudulent ones with high accuracy. The approach involves collecting a diverse dataset of authentic and fake logos, preprocessing the images to enhance quality and consistency, and training the CNN model on these datasets. Key steps include data augmentation to improve model generalization, feature extraction to identify distinguishing characteristics of logos, and fine-tuning the network to optimize performance. The system's effectiveness is evaluated through rigorous testing and validation, ensuring it can handle various logo designs and counterfeiting techniques. The ultimate goal is to provide a scalable and efficient solution for businesses and consumers to verify logo authenticity, thereby reducing the impact of counterfeiting and protecting brand integrity.

Keywords: Fake logo detection, CNN algorithm, model generalization, feature extraction.

Abstract: Global warming and its associated climate impacts, particularly drought, present significant challenges worldwide. In Kenya, the National Drought Management Authority (NDMA) highlights that 80% of the country's land is classified as Arid or Semi-Arid Land (ASAL). Collaborative drought mitigation initiatives involving diverse stakeholders, including communities, government bodies, donor organizations, and political figures, are pivotal in mitigating the adverse effects of drought. Effective stakeholder management is crucial for the success of community-based projects. However, existing literature indicates a lack of full stakeholder engagement, leading to project failures. This study investigated project stakeholder management and the performance of drought mitigation projects in Makueni County, Kenya. The study's specific objectives were; to assess how stakeholder identification, training, engagement, and monitoring impact the performance of drought mitigation projects in Makueni County. The research was guided by normative stakeholder, transtheoretical, system, and control theories, which informed the study variables. The study employed a descriptive design in conjunction with purposive sampling to choose participants. The study included 16 projects from a target population of 43, whereby primary data was collected with the aid of a questionnaires and secondary data obtained from the NDMA. Data analysis involved both descriptive and inferential statistics. The study established that stakeholder identification, stakeholder training, stakeholder engagement and stakeholder monitoring were significant predictors of performance of drought mitigation projects in Makueni county. An adjusted R2=.726, taken as set predictors of stakeholder identification, stakeholder training, stakeholder engagement and stakeholder monitoring accounted for 72.6% of the variance in performance of drought mitigation projects in Makueni county. It was thus established that stakeholder management was an effective way of enhancing performance of drought mitigation projects in Makueni county.

Keywords: Project Stakeholder Management, Drought Mitigation Projects, Project Performance, stakeholder identification, stakeholder training, stakeholder engagement, stakeholder monitoring.

Abstract: An Image Analyzer is an advanced system designed to process, analyze, and interpret visual data from digital images. By employing techniques from image processing and artificial intelligence, it automates the extraction of meaningful information, making it a powerful tool across diverse domains. The system typically consists of key modules are input handling, preprocessing, feature extraction, analysis, and output generation. The analysis module integrates traditional computer vision algorithms with modern AI techniques, including convolutional neural networks (CNNs), for tasks like object detection, classification, and segmentation. The results are presented in user-readable formats, enabling actionable insights.

Keywords: Image Analyzer, Artificial Intelligence, Computer Vision, Convolutional Neural Network.

Abstract: Natural dyes are gaining significance in industry due to their lower toxicity compared to synthetic dyes. This study explores the potential of selected plant-based sources, including pomegranate peel, betel nut seed, and prickly pear fruit, as natural dye sources. Cotton fabrics were dyed using extracts from these plants, employing various dye combinations and mordants. The effects of dyeing methods, extraction techniques, and mordants on color strength and fastness properties were investigated. This research contributes to the growing demand for eco-friendly products and highlights the potential of natural dyes in sustainable textile production.

Keywords: Natural Dyes, Plant-Based Colorants, Eco-Friendly Textiles, Sustainable Production, Cotton Fabrics, Dye Extraction, Mordanting Techniques.

Abstract: Handwriting identification is a critical technology that bridges handwritten content with digital systems, enabling automation in tasks such as document digitization, form processing, and signature verification. However, handwriting poses challenges such as variability in styles, distortions, and noise, which traditional approaches strug-gle to handle effectively. This study presents a handwriting recognition system using Convolutional Neural Networks (CNNs), a deep learning architecture that excels at extracting spatial features from images. The proposed system is de-signed to recognize handwritten digits, characters, or words with high accuracy. Preprocessing techniques, such as normalization and data augmentation, are applied to ensure the model generalizes well to various handwriting styles and environments. Experiments conducted on benchmark datasets like MNIST and EMNIST demonstrate the effec-tiveness of the model, achieving competitive accuracy and performance.

Keywords: Hand Written, Automation, MNIST, Convolutional Neural Network, EMNIST

Abstract: The Chennai Automotive Common Facility Centre (CFC) in Tirumudivakkam, Chennai, is a groundbreaking initiative aimed at empowering automotive component manufacturers, particularly Micro, Small, and Medium Enterprises (MSMEs). The objective is to study the Common Facility Centre using 5 point scale and find value description on AI and Robotics implementation before and after cluster development approach at Chennai Automotive Components Industrial Cluster at Tirumudivakkam. The methodology adopted is study on 40 Automotive Components Manufacturers at Tirumudivakkam, Chennai using statistical techniques such as the T-test, Discriminant Analysis and Structural Equation Modelling, The research measures the improvements in production performance driven by AI and robotics integration. The T-test is applied to assess changes in key performance metrics, before and after the cluster development approach. The Discriminant Analysis method identifies the key factors influencing the success of AI and robotics in smart production. Results indicate that the integration of AI and robotics leads to substantial improvements in production effectiveness. Businesses in the cluster experience stronger competitiveness, higher customer satisfaction, and reduced operational costs after adopting these technologies. By leveraging Artificial Intelligence (AI) and robotics, the CFC seeks to drive innovation, enhance operational efficiency, and improve global competitiveness. The facility will provide access to advanced technologies, support end-to-end project development, facilitate the production of higher value-added products, and meet stringent testing standards. It is anticipated to boost cluster turnover by 10%-15%, reduce operational costs, generate employment opportunities, and enhance workforce skills through specialized training programs. Furthermore, the CFC will promote collaboration through shared infrastructure, joint marketing, and collective raw material procurement. With a focus on sustainable growth and economic development, the CFC is set to become a benchmark for industrial excellence and global market integration. To conclude, the adoption of AI and robotics through a cluster development approach offers significant benefits to the automotive components industry in Chennai. This study provides practical insights and recommendations for companies seeking to leverage these technologies to optimize production strategies, enhance operational efficiency, and secure a competitive advantage in the global market.

Keywords: Chennai Automotive Common Facility Centre (CFC), Artificial Intelligence (AI), robotics, Micro, Small, and Medium Enterprises (MSMEs), innovation, global competitiveness, advanced technologies, operational efficiency, value-added products, testing standards, workforce skill development, collaboration, shared infrastructure, joint marketing, raw material procurement, sustainability, economic growth, industrial excellence.

Abstract: This research paper undertakes an exhaustive disquisition of the multifarious issue of banking fraud in India's public sector banks since the Economic Reforms of the early 1990s to the present era. It elucidates high-profile cases such as the Vijay Mallya, Nirav Modi, Yes Bank Scandal, and DHFL Fraud, illuminating systemic deficiencies including regulatory lacunae, inadequate corporate governance, technological vulnerabilities, and the complicity of bank employees with malefactors. The study meticulously investigates the economic repercussions, notably substantial pecuniary losses, a diminution of public trust, augmented regulatory vigilance, and escalated compliance expenditures. Additionally, it examines the socio-economic ramifications on stakeholders, encompassing depositors, employees, and policymakers. The analysis propounds a series of well-founded recommendations for the establishment of rigorous regulatory architectures, heightened accountability, cutting edge fraud detection technologies, and comprehensive training regimens for banking personnel. The paper endeavours to discern the various types and frequencies of banking fraud, evaluate their impact on the banking sector and broader economy, unearth root causes, and propose effective stratagems to ameliorate fraud risks and fortify the resilience of public sector banks. Utilizing data from the RBI, the study accentuates the indispensability of enhanced regulatory oversight, superior internal controls, and pioneering technological solutions to safeguard the Indian banking ecosystem. The findings underscore the necessity for continuous surveillance, proactive risk management, and the assimilation of best practices across the banking sector to foster a secure and trustworthy financial environment.

Keywords: Banking Frauds, Financial Malfeasance, Public Trust Erosion, Reserve Bank of India

Abstract: The integration of autonomous systems in aviation presents significant challenges and opportunities for enhancing aircraft defense mechanisms. This project focuses on developing deep learning Convolutional Neural Networks (DCNN) specifically designed for real-time threat detection and classification in aircraft defense systems. By utilizing advanced computer vision techniques, the proposed system aims to identify potential threats, such as unauthorized drones and missile launches, while also addressing cyber threats in an increasingly digital landscape. The architecture will be trained on diverse datasets that encompass various operational scenarios, thereby ensuring robustness and adaptability. This research seeks to establish a framework that not only leverages artificial intelligence to improve situational awareness but also enables rapid response capabilities for autonomous aircraft systems.

Keywords: Autonomous Systems, Aircraft Defence, Deep Learning, Threat Detection, Convolutional Neural Networks (DCNN).

Abstract: The research deals with deep learning-based, voice-operated traffic sign recognition to improve road safety. A deep learning-based model is presented for the recognition of traffic signs with CNN, trained on GTSRB Dataset with an identification and categorization precision of 95%. The developed system detects signs and, through audio warnings by speakers, assists drivers to make quick decisions. The system aims to mitigate accidents caused by missed or misinterpreted signage by alerting drivers to nearby traffic signs and rules. This approach has potential applications in both driver assistance systems and autonomous vehicles.

Keywords: CNN, Keras, Tensor flow, GTSRB Dataset, Traffic Signs.

Abstract: Objective The purpose of the study was to compare Speed in Power lifting, Judo and Weight Lifting. Sample size The 40 players were selected for sample size of each group of the study and their age ranged between 20 -25 years. Exclusion criteria were the presence of chronic medical conditions such as asthma, heart disease or any other condi-tion that would put the subjects at risk when performing the experimental tests. Assessment of Speed Speed measured by using the Standing Broad Jump etc . The mean, S.Ds and ANOVA and LSD Post hoc Test was uti-lized the level of significant was set up at 0.05 level. Results The result shows significant difference of skill related abilities among three groups of Combat Sports. the findings of the study shows that Judo players was found to have good speed abilities as compare to their counterparts powerlifting Players the findings of the study shows that Judo players was found to have good speed abilities as compare to their counter parts weight lifting Players.

Keywords: Speed, Standing Broad Jump, Combat

Abstract: Efficient thermal management is critical for ensuring the performance, safety, and longevity of batteries in electric vehicles (EVs). This study explores the design and optimization of battery thermal management systems using advanced simulation tools. Various fin configurations no fin, rectangular fins, elliptical fins, and irregular fins were designed using CATIA software, and their thermal and flow performance was analyzed through ANSYS and CFD simulations. The results demonstrate that while irregular fins exhibited the highest total heat flux (0.82196 W/m²), elliptical fins provided superior directional heat flux (0.095758 W/m²) and reduced eddy viscosity (7.185), leading to enhanced cooling efficiency and improved flow characteristics. These findings underline the significance of fin configuration in achieving optimal heat dissipation and efficient cooling for EV batteries. The elliptical fin design emerged as the most balanced and effective solution, making it a promising candidate for next-generation EV battery systems.

Keywords: Electric Vehicles (EVs), Battery Thermal Management, Fin Configurations, Heat Dissipation, Cooling Efficiency.

Abstract: The development of tall buildings have been rapidly increasing worldwide because of rapid growth of the urban population, high cost of land and the need to preserve important agricultural production. It is inevitable to create high-rise structure and on high-rise structure lateral forces due to wind or seismic loading is governing criteria. It is found that the design of tall buildings is governed by lateral loads. In the tubular system, closely spaced periphery columns create very high moment of inertia compared to the simple frame system. The tube is formed by closely spaced peripheral columns. This effect provides stiff moment of resistance against lateral loads. For profound effect, the created group of tubes is known as a bundled tube system. For the study purpose in this article 64, 80, 96 stories building structure have been studied for peak displacement, story drift, base moment, story shear and structural system weight. The comparative study shows that the overall weight of bundled tube system is very low, hence it is economical. The peak displacement response of building is within the permissible limit of IS: 800-2007. Moreover, shear-leg effect seems to be disappeared.

Keywords: Bundled Tube System, Bracing System, Dynamic analysis, Story Displacement, Story Drift, ETABS, Tube-in tube Structure.

Abstract: The exact solution of a nonlinear fractional Volterra-Fredholm integro-differential equation is found in this paper through the successful application of the Abaoub Shkheam decomposition method. These techniques have a wider range of applications due to their dependability and decreased computational effort. Additionally, analytical approximations can be used to formally determine the solution's behaviour. Lastly, an exam-ple is provided in this study to show the reliability and suitability of the suggested methodologies.

Keywords: Abaoub-Shkheam transform, Adomian Decomposition Method, A nonlinear Fractional Volterra Fredholm integro-differential equations.

Abstract: With detrimental effects on human health and marine ecosystems, microplastic pollution of water sources has emerged as a major environmental concern. Using Support Vector Machine (SVM) techniques, this project offers a novel method for identifying and categorizing microplastics. To enable precise and effective identification of microplastic particles based on their physical and spectral characteristics, the methodology combines cutting-edge imaging technologies with machine learning. Preprocessing methods are used to enhance image clarity and separate microplastic particles after high-resolution imaging is used to evaluate water samples. To accurately categorize the different forms of microplastics, an SVM classifier is trained using key properties such as size, shape, and texture. Because of its great precision and dependability, the suggested system is a useful instrument for monitoring and analysis in real time. This initiative facilitates the creation of efficient mitigation plans and improves the capacity to monitor the sources of pollution by automating the classification process. The findings support sustainable water management techniques and advance our knowledge of the behaviour of microplastics in aquatic ecosystems.

Keywords: Microplastic classification, Support Vector Machine (SVM), high-resolution imaging, particle separation, spectral analysis, automated monitoring, sustainable water management.

Abstract: This research paper delves into the use of bibliometric analysis within the context of the "Journal of Academic Librarianship" from 1988 to 2022. It aims to investigate various aspects such as trends in annual scientific output, author productivity, global contributions, and research networks. The study reveals significant shifts in yearly scientific production and underscores the importance of considering both citation rates and the duration of an author's active contributions when evaluating their impact. Prominent contributors to scientific output include the United States, Canada, China, Spain, and the United Kingdom. Additionally, the research brings attention to highly cited countries and uncovers co-citation networks, which provide insights into shared research themes and collaborations among authors and institutions. In summary, this study highlights the ever-changing nature of academic knowledge and research interest in shaping research patterns. It offers a valuable resource for professionals and educators navigating the intricate realm of academic research, offering a comprehensive understanding of scholarly communication and its quantitative facets.

Keywords: Bibliometric analysis; Academic librarianship; Scholarly communication; Scientific production trends; Author productivity; Research impact; Citation analysis; Co-citation networks.

Abstract: This scholarly treatise delves into the transformative role of financial technology (Fintech) in promoting financial inclusion in India, emphasizing its capacity to address the inherent deficiencies of traditional banking systems. India's substantial unbanked demographic and heterogeneous socio-economic landscape provides an unparalleled context for assessing the impact of digital financial services on accessibility. This study meticulously scrutinizes the contributions of pivotal Fintech innovations-including mobile payments, digital lending, Insurtech, and investment platforms-in ameliorating financial exclusion. Emphasis is placed on the extensive utilization of mobile wallets to enhance access to financial services, particularly in rural and underserved regions, alongside the expansion of digital lending platforms that bridge the credit gap for Micro, Small, and Medium Enterprises (MSMEs). Furthermore, the paper explores the role of Insurtech in broadening insurance accessibility for previously marginalized populations, as well as the democratization of investment opportunities through online investment platforms. The investigation also delves into governmental initiatives such as Jan Dhan Yojana, Aadhaar, UPI, and India Stack, which have fostered a conducive environment for the proliferation of Fintech and financial inclusion. Through an exhaustive literature review, secondary data from authoritative sources such as the Reserve Bank of India (RBI) and the World Bank, and case studies of successful Fintech ventures, this research delineates both the opportunities and challenges associated with Fintech adoption. These challenges encompass infrastructural constraints, digital literacy deficits, regulatory complexities, cybersecurity threats, data privacy concerns, and the imperative for robust consumer protection mechanisms. The paper culminates in strategic policy recommendations to foster sustainable and inclusive Fintech development while ensuring financial stability and consumer welfare.

Keywords: Financial Technology Financial Inclusion Sustainable Development Digital Banking Online Banking

Abstract: The primary objective of the research is to know the role of Pradhan Mantri Ujjwala Yojana (PMUY) to improve the health aspect of women living in villages . tribal women have to face many problems related to health, marriage and menstruation .Due to unhealthy lifestyle and poor lifestyle of women living in tribal area, Pradhan Mantri Ujjwala Yojana (PMUY) was launched in May 2016 by the Ministry of Petroleum and Natural Gas with the objective of ensuring availability of clean cooking fuels like LPG to rural and underprivileged households, who are otherwise using traditional cooking fuels and improve health by reducing the non-communicable diseases to the rural women. Pradhan Mantri Ujjwala Yojana aims to provide free cooking gas connections to women living below the poverty line. The population included in the research included tribal women of selected taluka of Nanded district of Maharashtra, beneficiaries of Pradhan Mantri Ujjwala Yojana (PMUY). A total of 142 samples were the target population of the study. Self-administered Interview Schedule (Marathi version ) was used to measure health status of Pradhan Mantri Ujjwala Yojana beneficiaries. Data was collected through demographic information and interview schedule from women living in tribal area in selected taluka in Nanded district of Maharashtra. The result shows that Pradhan Mantri Ujjwala Yojana (PMUY) is playing an important role in improving the health of tribal women.

Keywords: Health, Tribal, women, PMUY

Abstract: 'Asthma' is derived from the Greek word aazein (ασθμαινω). aazein is 'short for breath'. The term originally did not define a disease, but was employed to describe respiratory symptoms of a variety of pulmonary conditions. This study includes analyzing the factors affecting on asthmatic patients and treatments of asthma. Asthma was shown to be associated with transient increases in airway resistance, reductions in forced expiratory volumes and flows, hyperinflation of the lungs and increased work of breathing, as well as abnormalities in the distribution of ventilation, perfusion and arterial blood gases. Today, asthma is seen as a chronic inflammatory disease which is not yet fully understood in its pathophysiology; therefore, therapy is still on the path to becoming optimal. Asthma is a common chronic disease in children. Uncontrolled asthma is a significant contributor to school absenteeism, emergency room visits, and hospitalization, all of which can lead to low school performance, financial burdens, and emotional problems for children and their parents.

Keywords: Asthma, symptoms, causes, impact, treatment

Abstract: While isometric drawing and orthographic projection are crucial for engineers and designers, traditional methods can be a hurdle. This study investigated whether the ISOTOP mobile app could improve students' technical drawing skills. The app aimed to bridge the gap between traditional and modern methods in making Isometric and Orthographic Projections, irrespective of their background or experience. The research suggests that engaging mobile applications can make technical drawing both more enjoyable and effective. The study employed a quasi-experimental design, specifically a single-group pretest-posttest approach to achieve this. Grade 10 students' initial technical competence in manual isometric and orthographic drawing was assessed. A sample object (photo) was presented to the group without using ISOTOP. Experts then evaluated the output using scoring rubrics. The intervention with the ISOTOP application was then introduced. The results, based on the scoring rubrics, indicated that students' technical competence improved significantly using ISOTOP compared to the traditional manual method. It showed a significant improvement, with average scores jumping from "Satisfactory" in the pre-test to "Excellent" in the post-test. This suggests that the ISOTOP application can effectively address skill gaps and enhance students' technical abilities in these areas. Additionally, the low standard deviation in the post-test scores indicates a consistent level of improvement across the participants. Therefore, these findings suggest that technology-aided learning with ISOTOP can be a highly effective method for enhancing students' skills in isometric and orthographic drawing. This approach can not only foster a deeper understanding of spatial relationships but also create a more engaging learning experience for students.

Keywords: ISOTOP, Technology, Isometric, Orthographic, Technical Competence, Drawing.

Abstract: The creation of the alphabet can be understood from a prehistoric era. The early man would write on caves as wall painting and then would take to clay tablets, later to parchment, paper and so on. In the modern world, there was printed text on a typewriter and in the current era there is written word on the computing devices which can then be printed out via a printer on paper. Artificial Intelligence, Machine Learning and Data Science have led to the development of Natural Language Processing which can help people in the output of text. Natural Language Processing involves constructs that require careful assistance by people. Automated Generation of sentences has been achieved utilizing Data Science and Artificial Intelligence. In this research article, we would like to present a survey of the present state of the art in sentence output. Also an algorithm called TAVERN which stands for Text Applying Vernacular is presented which helps in the output of sentences.

Keywords: Text, Artificial Intelligence, Machine Learning, Natural Language Processing, TAVERN.

Abstract: Leadership is a critical aspect of modern business and warrants significant attention. Ethical leadership, in particular, has been applied to enhance employee motivation, commitment, and financial performance in the technol-ogy industry. This study examines leadership as a concept, exploring key contemporary theories and modern business examples. The research was motivated by the limited literature assessing the impact of ethical leadership on both em-ployee satisfaction and financial performance, especially in the context of tech industry. To address this gap, research questions were developed and explored. The study employed qualitative research of a secondary nature to examine theoretical perspectives and organize contextual findings systematically. A thematic analysis was used to ensure that the findings were presented coherently and aligned with the research questions. Key findings revealed that ethical leadership is the most influential style in achieving both employee satisfaction and financial performance. Transactional leadership was found to positively influence employee satisfaction, although its impact on financial performance yielded mixed opinions. Transformational leadership also contributed to employee satisfaction but demonstrated only a weak overall influence. Lastly, charismatic leadership was noted for its substan-tial impact on employee satisfaction and its positive relationship with financial performance. These insights highlight the nuanced effects of various leadership styles on organizational success.

Keywords: Ethical Leadership, financial performance, Employee Satisfaction, Tech Industry.

Abstract: This study presents an advanced finite element analysis (FEA) of a polylactic acid (PLA)-based dental implant reinforced with bio mineral additives, aiming to optimize its structural integrity, vibrational response, and biomechanical performance. The implant undergoes static structural, modal, and harmonic response analyses to evaluate stress distribution, deformation characteristics, and resonance susceptibility under physiological loading conditions. The results indicate minimal deformation, well-distributed stress concentrations, and a stable vibrational profile, highlighting the implant's suitability for long-term dental applications. By integrating sustainable biomaterials with computational modeling, this research contributes to the development of next-generation dental implants that ensure mechanical reliability and environmental responsibility.

Keywords: Dental Implant, Finite Element Analysis, PLA, Biomechanics, Harmonic Response, Modal Analysis

Abstract: Bird voice recognition can be used for a variety of purposes, such as bird conservation, research, and monitoring. For example, conservation organizations can use bird voice recognition to monitor populations of endangered or threatened bird species, researchers can use it to study bird behaviour and ecology, and birdwatchers can use it to identify birds in the field. The primary objective of bird voice recognition is to establish a dependable and effective approach to identifying bird species through their vocalizations. This functionality allows users to conveniently recognize birds in their surroundings without relying on visual cues.

Keywords: voice recognition, ecology, machine learning, convolutional neural networks.

Abstract: In a major step to develop the multi sports infrastructure in our institute, we have proposed to design a INDOOR STADIUM at S.G.Balekundri Institute of Technology Belagavi. The principle objective of this project is to provide a design of indoor stadium for our institute. The design methods used in STAAD.Pro analysis are Limit State Design conforming to Indian Standard Code of Practice. From model generation, analysis and design to visualization and result verification, STAAD.Pro is the professional's choice. This indoor stadium has a playing court that meets the standards for district level sports events. The total area of stadium is 1577m2. In the total area we have innovated 32m x 46.78 m area as column free, so it gives an unobstructed view of the play court from anywhere in the stadium. All the designs are based as per IS 456-2000 and IS 875 Part I, II & III. The wind load values were generated by STAAD.Pro considering the given wind intensities at different heights and strictly abiding by the specifications of IS 875 part III. Limit state method is adopted for designing various components of the stadium. The stadium consists of free court were indoor games namely basketball, volley ball, badminton and table tennis can be played. All the facilities like refreshment rooms, storage room etc. is also provided in the stadium.

Keywords: Indoor Stadium, Analysis, design, STAAD Pro, RCDC

Abstract: Electric vehicle is defined as a vehicle that can be powered by an electric motor that draws electricity from a battery and is capable of being charged from an external source. Electric vehicles (EVs) use electricity as their primary fuel or to improve the efficiency of conventional vehicle designs. EVs include all-electric vehicles, also referred to as battery electric vehicles (BEVS), and plug-in hybrid electric vehicles (PHEVs). The introduction of electric vehicles (EVs) marks a pivotal shift in the automotive industry towards sustainable mobility. With increasing concerns over environmental degradation and the depletion of fossil fuel reserves, electric vehicles have emerged as a promising solution to mitigate these challenges. However, the successful adoption and widespread acceptance of EVs is not only on their technological advancements but also on user satisfaction. Understanding and addressing the factors influencing user satisfaction is crucial for the continued growth and market penetration of electric vehicles. Therefore, this project aims to investigate the various dimensions of EV user satisfaction comprehensively. By delving into aspects such as cost-effectiveness, convenience, charging infrastructure, performance, and overall user experience in an effort to pinpoint the critical factors that influence consumer happiness. we seek to identify the key determinants that contribute to user satisfaction or dissatisfaction. Ultimately, the findings of this study can serve as a valuable resource for policymakers, industry stakeholders, and researchers seeking to promote the widespread adoption and acceptance of electric vehicles. By aligning technological advancements with user expectations and preferences, we can accelerate the transition towards a sustainable and environmentally friendly transportation ecosystem while ensuring a fulfilling experience for EV users.

Abstract: The Sulba Sutras, ancient Indian texts dating back to 800-500 BCE, primarily used as guidance for building Vedic altars with precise geometrical configurations. Their contributions to mathematics, particularly geometry, are significant and underestimated. The Sulba Sutras contain fundamental geometrical concepts while also demonstrating ancient Indian mathematicians' inventive problem-solving approaches. This research paper examines the mathematical contributions of the Sulba Sutra tradition and how they relate to modern mathematical theory. It seeks to understand the innovative concepts that underpin these old techniques, as well as their impact on problem-solving methodologies.

Keywords: Ancient Mathematics, Vedic mathematics, Ancient Geometry, Computer Science

Abstract: The construction industry is facing increasing challenges related to sustainability, resource depletion, and environmental pollution caused by traditional building materials. Cement, a key component in concrete, significantly contributes to CO₂ emissions during its production. Similarly, the accumulation of waste glass and plastic poses environmental hazards, necessitating innovative strategies for waste management. This study addresses these issues by developing paver blocks that utilize glass powder as a partial replacement for cement and incorporate waste HDPE plastic fibers as reinforcement. The primary objective is to reduce the environmental impact of traditional cement-based paver blocks by incorporating recycled materials while enhancing their performance characteristics. This experimental work involves replacing cement with glass powder at varying percentages from 0%, 10%, 20% & 30% and reinforcing the mix with HDPE fibers at 0.5% by volume derived from plastic waste. Key tests, including water absorption and compressive strength, are conducted to assess the durability and structural performance of the paver blocks. The results reveal that the addition of glass powder significantly reduces cement consumption and improves sustainability without compromising compressive strength. Meanwhile, the inclusion of HDPE fibers enhances crack resistance and contributes to better mechanical properties. This study demonstrates the feasibility of using waste materials in producing eco-friendly paver blocks, aligning with the principles of sustainable construction and circular economy.

Keywords: Glass powder, waste plastic fibers, sustainability.

Abstract: The present study was carried out to develop a ragi-based nutrient-enriched rusk. The micronutrient-fortified rusk was developed by using ragi flour, soy flour, dried beetroot, and Moringa oleifera leaves powder with milk, yeast, oil, salt, and sugar. Using a five-point rating hedonic scale, the formulated ragi-based Nutrient-enriched rusk was subjected to organoleptic evaluation for its quality attributes like flavour, appearance, taste, texture, and overall acceptability. From the sensory evaluation, the variation found to be with high scores was subjected to physiochemical, nutrient, and microbial analysis.

Keywords: Ragi, rusk, nutrient, moringa oleifera

Abstract: Natural fibers such as coir, jute, and bamboo are renewable resources that offer high tensile strength and low environmental impact compared to traditional synthetic fibers. The study aims to determine the effects of different proportions of natural coconut fiber on the compressive strength of concrete. This research contributes to developing eco-friendly concrete materials that can reduce the construction industry's carbon footprint while maintaining structural integrity. Moreover, using natural fibers offers ecological advantages by reducing the reliance on non-renewable resources and lowering the carbon footprint of concrete production. The results indicate that adding natural fibers improves the toughness and crack resistance of concrete, making it a viable alternative for sustainable construction practices. The compressive strength test of concrete cubes incorporating artificial fiber named recron in the same proportion as natural fiber was also performed to compare the results.

Keywords: Natural coconut fibers, Compressive strength, Crack resistance, Sustainable Construction practices, Artificial reconfiber.

Abstract: A Python-based online application called the Women's Safety Protocol was created to enhance individual safety by providing rapid emergency response capabilities. The website, which was created using Flask, offers a user-friendly way for women in particular to initiate emergency notifications in dangerous circumstances. As a security precaution, users establish a passcode when they enter emergency mode. The website sends notifications to specified contacts via WhatsApp if the passcode is entered incorrectly or not within a predetermined window of time. The website helps responders with precise placement by using the Geopy package to display the user's current location within a 200-meter radius. To guarantee a prompt reaction, the system notifies several contacts at once. Periodic passcode prompts also aid in confirming the user's safety; if the passcode is not entered, alarms are sent again.The user-friendly interface, which is made for desktop and mobile access, offers a quick, simple experience that is essential in an emergency. The Women's Safety Protocol, which combines automated alerts, real-time location tracking, and a discrete interface, gives users a dependable tool for effectively and discreetly signaling for help, enhancing personal security by facilitating timely assistance from loved ones or authorities.

Keywords: Women's safety, Emergencies, Real Time, Protocol, Alert, Internet of Things (IOT), Location, Quick-response, Security.

Abstract: In the contemporary financial environment, proficient expense management is crucial for achieving success at both personal and organizational levels. Conventional approaches to expense tracking, which often involve manual data entry or the maintenance of physical records, tend to be labour-intensive, susceptible to inaccuracies, and inadequate in scalability to accommodate the increasing volume of both digital and paper transactions. To overcome these obstacles, automated expense tracking solutions have been developed, utilizing advancements in Artificial Intelligence (AI) and Optical Character Recognition (OCR). These solutions are designed to streamline financial management by facilitating efficient, precise, and real-time tracking of expenses. The implementation of OCR technology allows for the automatic extraction of essential financial information, such as dates, amounts, and vendor identities, from various documents, including both printed and handwritten receipts and invoices. Recent technological advancements, including the integration of Tesseract OCR with Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), have markedly improved the accuracy of text recognition, even in the case of low-quality or intricate receipts. Additionally, machine learning algorithms enhance these systems by categorizing expenses, identifying spending trends, and providing predictive analytics, thereby equipping users with the tools necessary for making informed financial choices. Automated expense tracking systems address prevalent challenges in financial management, such as errors in data entry, misclassification of transactions, and delays in the reconciliation process. By offering functionalities like real-time categorization and tailored financial insights, these systems meet the diverse needs of both individuals and organizations. However, challenges persist, including the recognition of poorly printed receipts and the computational requirements associated with training sophisticated AI models. Nonetheless, as AI and OCR technologies continue to advance, these systems are set to revolutionize financial management by minimizing manual labour and enhancing accuracy.

Keywords: Automated Expense Tracking, Optical Character Recognition (OCR), Machine Learning Algorithms, Financial Management, Expense Categorisation, Tesseract OCR, Spending Insights, Neural Networks (CNN, LSTM), Real- Time Expense Monitoring, Data Extraction.

Abstract: This compilation showcases developments in skin cancer detection using artificial intelligence (AI). In order to increase lesion classification accuracy while adjusting to various datasets and clinical applications, it places a strong emphasis on CNNs, transfer learning, and hybrid techniques. Important developments include multi-modal systems that combine imaging and information to improve diagnostic accuracy, mobile AI for underserved areas, and ensemble learning for increased sensitivity. Additionally covered are ethical issues like biases and patient privacy. Lightweight models and real-time diagnostic tools increase accessibility and make use possible in settings with limited resources. AI integration in healthcare was pioneered by early research on dermoscopic segmentation and the use of CNNs in dermatology. When taken as a whole, these methods demonstrate how AI is revolutionizing skin cancer management through early diagnosis, risk assessment, and individualized treatment.

Keywords: Bias Mitigation, Patient Data Privacy, Resource-Constrained Environments Clinical Applications of AI, Ensemble Learning, Lesion Classification, Dermo scopic Image Segmentation

Abstract: The Indian automobile industry has witnessed significant growth over the past few decades, with family cars forming a major segment of the market. Among the various factors influencing car sales, children have emerged as a surprising yet crucial element in the decision-making process. Their influence extends beyond mere preferences, shaping marketing strategies and purchase decisions within families.

Keywords: Child influencer, family car sales, family purchase decisions

Abstract: This study evaluates the effectiveness of three stretching techniques, which includes Stretch and Hold training, Ballistic Training, and Proprioceptive Neuromuscular Facilitation (PNF) training in Improving hamstring and lower back flexibility among cricket players. A total of 100 participants (25 per group) were assessed, with the Shapiro-Wilk test confirming data normality. Paired t-tests and ANCOVA were conducted to determine training effectiveness. The results indicated significant flexibility improvements across all methods, with PNF training demonstrating the greatest increase (40.75%), followed by Ballistic Training (31.50%) and Stretch and Hold (28.37%). Post-hoc analysis further confirmed PNF's superiority over the other techniques. These findings highlight the effectiveness of dynamic and neuromuscular stretching in improving flexibility, with important implications for athletic training and injury prevention.

Keywords: Cricket Training, Hamstring Flexibility, Lower back Flexibility, PNF Training, Ballistic Training, Stretch and Hold Training, Flexibility Enhancement, Sports Performance.

Abstract: In today's fast-paced, unpredictable environment, leaders face constant pressure, uncertainty, and quick change. Being resilient is no longer enough for leaders; they have to grow antifragile, or people who can not only survive adversity but get greater as the outcome of it. This study aims to explore the concept of antifragile administration and ascertain how it might be fostered in practical situations. Antifragile leaders possess the following qualities: mental ability, learning agility, adaptability, and the ability to make bold decisions under ambiguous conditions. They foster an environment that values continuous improvement, accepts mistakes as an essential part of learning, and stimulates creativity. Instead of viewing disruption as something to be dreaded, these leaders embrace it as a chance for strategic advantage and transformation. This study focusses on how leaders become stronger by conquering obstacles and how antifragile leadership influences their ability to make decisions under duress. To better understand their unique experiences, leadership challenges, and growth trends, data was gathered from both seasoned and up-and-coming leaders. It highlights the increasing importance of fostering antifragility in leadership development programs and provides useful information for businesses trying to create leaders who can prosper in a constantly shifting world.

Keywords: Fragile, Resilience, Volatile, Robustness

Abstract: In today's evolving work environment, Human Resource (HR) policies play a crucial role in shaping employee behaviour, satisfaction, and retention. This study explores the impact of HR policies-especially those related to moonlighting-on employee job satisfaction and retention rates. With the growing gig economy and flexible work models, moonlighting has become more prevalent, raising questions about how organizations manage this trend. The research investigates employee perceptions of moonlighting regulations and their influence on job-related attitudes. A quantitative approach was adopted using structured questionnaires distributed across various sectors, with responses analysed through statistical tools like frequency analysis and Likert scales. Findings reveal that transparent and flexible HR policies enhance employee satisfaction and retention, while rigid or poorly communicated policies contribute to dissatisfaction and turnover. Gender-based differences in perception were also observed. The study emphasizes the importance of employee-centric HR practices that align with the changing expectations of the modern workforce.

Keywords: Human Resource Policies, Moonlighting, Job Satisfaction, Employee Retention, Gig Economy, Work-Life Balance, HR Policy Perception, Organizational Commitment.

Abstract: This paper presents the design and implementation of an intelligent fuzzy control system for optimizing drug dosage in intensive care unit (ICU) patients. The controller continuously monitors key physiological parameters such as heart rate (HR), mean arterial pressure (MAP), and drug concentration to determine the optimal infusion rate. By integrating fuzzy logic with adaptive tuning mechanisms, the proposed system effectively manages nonlinearities, uncertainties, and interpatient variability inherent in physiological systems. Simulation results show that the intelligent fuzzy controller significantly reduces overshoot, settling time, and steady-state error compared to conventional PID and basic fuzzy controllers. The approach ensures smoother control actions, enhances patient safety, and maintains hemodynamic stability during critical care drug administration.

Keywords: Fuzzy logic control, ICU automation, drug infusion system, adaptive control, pharmacokinetic modeling, intelligent control, neuro-fuzzy system.