Journal of Science Engineering Technology and Management Science

Wireless Sensor Networks (WSNs) have become a fundamental component of modern monitoring and data acquisition systems, supporting applications such as environmental surveillance, healthcare monitoring, industrial automation, military operations, and smart city infrastructures. Despite their widespread adoption, WSNs remain highly vulnerable to various security threats due to their distributed architecture, limited computational resources, and deployment in unattended environments. Among these threats, clone attacks represent one of the most severe security challenges, where an adversary captures a legitimate sensor node, extracts its credentials, and creates multiple replicas that are subsequently deployed within the network. These cloned nodes can disrupt network operations, compromise data integrity, launch routing attacks, and facilitate unauthorized access to sensitive information. Consequently, the development of efficient and reliable clone detection mechanisms is essential for maintaining the security and functionality of wireless sensor networks. This paper proposes an intelligent clone attack detection framework designed to identify replicated sensor nodes while minimizing energy consumption and memory overhead. The proposed approach combines node identity verification, location-based monitoring, neighbor information analysis, and anomaly detection techniques to accurately identify suspicious node behavior. Relevant network parameters, including node mobility patterns, communication frequency, packet transmission characteristics, and neighborhood consistency, are analyzed to distinguish legitimate sensor nodes from cloned replicas. The framework incorporates lightweight processing mechanisms and optimized data structures to ensure efficient operation within the resource-constrained environment of wireless sensor networks. Experimental evaluation demonstrates that the proposed clone detection framework achieves high detection accuracy, low false alarm rates, and reduced communication overhead compared with conventional detection schemes. The system effectively identifies clone attacks while preserving network lifetime through energy-aware operations and memory-efficient storage management. Furthermore, the framework exhibits strong scalability and adaptability across varying network sizes and deployment conditions. The results indicate that the proposed method provides a secure, lightweight, and practical solution for protecting wireless sensor networks against clone attacks while maintaining efficient resource utilization. The framework contributes to enhancing network reliability, security, and operational longevity in modern wireless sensing applications.

Keywords : Wireless Sensor Networks, Clone Attack Detection, Energy Efficiency, Memory Optimization, Network Security, Sensor Node Replication, Anomaly Detection, Resource-Constrained Networks, Secure Routing, Internet of Things.

Author : 1K. R.V. Durga Bhavani, 2Usha Reddygari, 3S.D. Swamy Puvvala, 4Ande Himaja

Title : INTELLIGENT CLONE ATTACK DETECTION IN WIRELESS SENSOR NETWORKS WITH REDUCED ENERGY CONSUMPTION

Volume/Issue : 2025;02(12)

Page No : 249-257

The growing demand for sustainable and decentralized energy generation has intensified research on Vertical Axis Wind Turbines (VAWTs) due to their ability to operate efficiently under low wind speed and multidirectional wind conditions. Blade geometry plays a crucial role in determining the aerodynamic performance and power extraction capability of VAWTs. This study presents an experimental investigation of twisted vertical axis wind turbine blades based on NACA 4-series airfoil profiles to evaluate their influence on turbine performance. Several twisted blade configurations were designed and fabricated using selected NACA 4-series airfoils, while maintaining identical rotor dimensions and operating conditions. Experimental testing was conducted in a controlled wind tunnel environment over a range of wind velocities to measure rotational speed, torque, power output, and coefficient of performance. The results demonstrate that the incorporation of blade twist significantly enhances aerodynamic efficiency by improving the angle of attack distribution along the blade span and reducing dynamic stall effects. Among the tested configurations, the optimized twisted blade exhibited superior performance, achieving higher rotational speed, increased power coefficient, and improved self-starting characteristics compared to conventional straight-bladed designs. The experimental findings indicate that twisted NACA 4-series blades can effectively increase energy capture and operational stability, particularly under variable wind conditions. The study confirms the potential of blade twist optimization as a practical approach for improving the performance of small-scale vertical axis wind turbines. The outcomes provide valuable insights for the design and development of efficient VAWT systems for renewable energy applications, contributing to enhanced energy harvesting and sustainable power generation.

Keywords : Vertical Axis Wind Turbine (VAWT), Twisted Blades, NACA 4-Series Airfoils, Aerodynamic Performance, Wind Energy Conversion, Experimental Investigation, Coefficient of Performance, Renewable Energy.

Author : 1Dr. Meenuga Santhiraj, 2Syed Junaid Ali, 3Konda Madhu, 4Chinthakindi Mamatha

Title : EXPERIMENTAL INVESTIGATION OF TWISTED VERTICAL AXIS WIND TURBINE BLADES USING NACA 4-SERIES AIRFOIL PROFILES

Volume/Issue : 2025;02(12)

Page No : 258-264

Black cotton soil is one of the most problematic expansive soils encountered in geotechnical engineering due to its high swelling and shrinkage characteristics, low bearing capacity, and significant volume changes under varying moisture conditions. These properties often lead to structural distress in pavements, foundations, embankments, and other civil engineering structures. Soil stabilization has therefore become an essential technique for improving the engineering performance of black cotton soil. In recent years, the utilization of agricultural and industrial by-products for soil stabilization has gained considerable attention owing to their economic and environmental benefits. Among these materials, bagasse ash, a waste product generated from sugar industries, has emerged as a promising stabilizing agent due to its pozzolanic properties and silica-rich composition. This study investigates the influence of bagasse ash and selected additives on the strength characteristics of black cotton soil. Various soil mixtures were prepared by incorporating different percentages of bagasse ash along with suitable additives to enhance the stabilization process. Laboratory tests were conducted to evaluate important engineering properties, including compaction characteristics, unconfined compressive strength, California Bearing Ratio (CBR), and shear strength parameters. The results indicated that the inclusion of bagasse ash significantly improved the strength and stability of black cotton soil by reducing plasticity and enhancing soil particle bonding. Furthermore, the addition of stabilizing additives accelerated pozzolanic reactions, resulting in improved load-bearing capacity and increased resistance to deformation. The experimental findings demonstrated that an optimum combination of bagasse ash and additives produced substantial improvements in soil strength compared to untreated black cotton soil. The stabilized soil exhibited higher compressive strength, enhanced bearing capacity, and improved durability characteristics, making it suitable for geotechnical and pavement construction applications. The study confirms that bagasse ash, when combined with appropriate additives, can serve as an effective, economical, and environmentally sustainable material for black cotton soil stabilization while promoting the beneficial utilization of agricultural waste products.

Keywords : Black Cotton Soil, Bagasse Ash, Soil Stabilization, Additives, Unconfined Compressive Strength, California Bearing Ratio (CBR), Expansive Soil, Pozzolanic Reaction, Geotechnical Engineering, Sustainable Soil Improvement.

Author : 1Dr. Thirunavukkarasu N, 2Pranay Bhaskar, 3 Suresh Nenavath, 4 Jarpati Bhavani

Title : INFLUENCE OF BAGASSE ASH AND ADDITIVES ON THE STRENGTH CHARACTERISTICS OF BLACK COTTON SOIL

Volume/Issue : 2025;02(12)

Page No : 265-273

Rapid urbanization and the exponential growth of vehicle ownership have led to severe traffic congestion in metropolitan areas worldwide. Conventional road infrastructure and fixed lane management systems often fail to adapt to dynamic traffic conditions, resulting in inefficient road utilization, increased travel times, fuel wastage, and environmental pollution. Intelligent Transportation Systems (ITS) have emerged as effective solutions for addressing these challenges through the integration of sensing, communication, automation, and real-time traffic management technologies. Among these solutions, movable road divider systems offer a promising approach for dynamically reallocating road space based on prevailing traffic demands. However, traditional movable divider systems generally lack real-time monitoring, autonomous decision-making, and remote control capabilities. This study presents an IoT-Enabled Intelligent Movable Road Divider for Dynamic Vehicular Traffic Management and Congestion Reduction. The proposed framework integrates Internet of Things (IoT) technology, wireless communication networks, traffic sensors, cloud-based analytics, and automated actuator mechanisms to dynamically adjust road lane configurations according to real-time traffic conditions. Traffic density data are continuously collected using smart sensors and transmitted to a centralized control platform where intelligent algorithms analyze traffic patterns and determine optimal divider positions. Based on these decisions, motorized movable dividers automatically reconfigure lane allocations to accommodate varying traffic volumes in different directions. The proposed system enhances traffic flow efficiency by reducing congestion, minimizing vehicle waiting times, optimizing lane utilization, and improving emergency vehicle movement. Real-time monitoring and remote management capabilities enable transportation authorities to respond effectively to traffic fluctuations and incident conditions. Experimental evaluation demonstrates significant improvements in traffic throughput, reduced average travel time, lower vehicle queue lengths, and enhanced road utilization efficiency compared with conventional fixed-divider systems. The integration of IoT-based sensing and intelligent control mechanisms provides a scalable, cost-effective, and adaptive solution for smart city transportation infrastructure. The proposed framework contributes toward the development of intelligent traffic management systems capable of improving urban mobility, reducing fuel consumption, and supporting sustainable transportation objectives.

Keywords : Internet of Things (IoT), Intelligent Transportation System, Movable Road Divider, Traffic Congestion Reduction, Dynamic Lane Management, Smart Traffic Control, Wireless Sensor Networks, Traffic Density Monitoring, Smart Cities, Automated Traffic Management.

Author : 1Madhu Talari, 2Hymavathi Korra, 3Yekkaluri Venkatesh, 4Bandari Usha

Title : IOT-ENABLED INTELLIGENT MOVABLE ROAD DIVIDER FOR DYNAMIC VEHICULAR TRAFFIC MANAGEMENT AND CONGESTION REDUCTION

Volume/Issue : 2025;02(12)

Page No : 274-284

Heart disease remains one of the leading causes of mortality worldwide, necessitating the development of accurate and efficient predictive systems for early diagnosis and risk assessment. Traditional diagnostic approaches often rely on clinical expertise and manual evaluation of multiple patient parameters, which may be time-consuming and prone to variability. Recent advances in artificial intelligence, particularly deep learning, have demonstrated significant potential in healthcare analytics by enabling automated extraction of complex patterns from medical data. This study presents a deep learning-based framework for the early detection and prediction of heart disease risk using patient health records and clinical attributes. The proposed model utilizes multiple cardiovascular risk indicators, including age, blood pressure, cholesterol levels, blood glucose, heart rate, electrocardiographic measurements, and other relevant clinical factors to predict the likelihood of heart disease occurrence. The developed framework incorporates data preprocessing, feature normalization, and deep neural network architecture to improve prediction accuracy and model generalization. The model is trained and evaluated on a benchmark cardiovascular dataset, where its performance is assessed using standard evaluation metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Experimental results demonstrate that the proposed deep learning model outperforms several conventional machine learning techniques in identifying high-risk individuals. The system effectively captures complex nonlinear relationships among clinical variables, leading to enhanced diagnostic reliability and reduced false predictions. The findings indicate that deep learning can serve as a valuable decision-support tool for healthcare professionals, facilitating timely intervention and personalized treatment planning. The proposed approach contributes to the advancement of intelligent healthcare systems by enabling early risk identification, improving patient outcomes, and supporting preventive cardiovascular care.

Keywords : Heart Disease Prediction, Deep Learning, Artificial Intelligence, Cardiovascular Risk Assessment, Neural Networks, Early Diagnosis, Healthcare Analytics, Clinical Decision Support.

Author : 1Dr. T.N. Srinivas Rao, 2Bunga Rajesh, 3Sree Phani Kumar, 4Bandari Sathish

Title : EARLY DETECTION AND PREDICTION OF HEART DISEASE RISK USING DEEP LEARNING

Volume/Issue : 2025;02(12)

Page No : 285- 292

MATLAB has become one of the most widely used computational platforms for signal processing and realtime data analysis due to its powerful mathematical capabilities, extensive toolbox support, and userfriendly programming environment. The growing demand for efficient processing of large volumes of data generated from sensors, communication systems, biomedical devices, and industrial applications has necessitated the development of robust frameworks capable of performing accurate and real-time analysis. This study presents a MATLAB-based framework for signal processing and real-time data analysis designed to acquire, process, visualize, and interpret dynamic data streams effectively. The proposed framework integrates signal acquisition modules, preprocessing techniques, filtering algorithms, feature extraction methods, and real-time visualization tools within a unified environment. Advanced digital signal processing techniques, including Fast Fourier Transform (FFT), wavelet analysis, adaptive filtering, and statistical analysis, are employed to enhance signal quality and extract meaningful information from noisy datasets. The framework supports real-time monitoring and decision-making through automated analysis and graphical user interfaces. Experimental evaluation demonstrates improved processing accuracy, reduced computational latency, and enhanced analytical performance across various signal types. The results indicate that the proposed MATLAB framework provides a flexible, scalable, and efficient solution for signal processing and real-time data analytics in engineering, scientific, and industrial applications.

Keywords : MATLAB; Signal Processing; Real-Time Data Analysis; Digital Signal Processing (DSP); Fast Fourier Transform (FFT); Adaptive Filtering; Wavelet Transform; Data Acquisition; Feature Extraction; Real-Time Monitoring; Data Visualization; Computational Framework.

Author : 1Guguloth Ramesh, 2Dr M. Srikanth, 3Dr. Ashok Pagolu, 4Akula Rajasekhar

Title : MATLAB FRAMEWORK FOR SIGNAL PROCESSING AND REAL-TIME DATA ANALYSIS

Volume/Issue : 2025;02(12)

Page No : 293-302

Human Resource (HR) policies play a pivotal role in aligning organizational objectives with employee performance, satisfaction, and overall workplace effectiveness. In the contemporary pharmaceutical industry, strategic HR practices are essential for attracting, developing, and retaining talented employees while ensuring compliance with organizational and regulatory standards. This study examines the strategic human resource policies adopted by Hetero Drugs and evaluates their implementation practices within the organization. The research focuses on key HR functions, including recruitment and selection, training and development, performance management, compensation and benefits, employee welfare, and employee engagement initiatives. The study employs a descriptive research design and utilizes both primary and secondary data sources. Primary data were collected through structured questionnaires administered to employees across different functional departments, while secondary data were gathered from company reports, HR manuals, journals, and relevant literature. Statistical tools such as percentage analysis and graphical representation were used to interpret the collected data and assess employee perceptions regarding the effectiveness of HR policy implementation. The findings indicate that Hetero Drugs has established comprehensive HR policies that contribute significantly to employee satisfaction, skill enhancement, and organizational productivity. Employees generally perceive the implementation of HR policies positively, particularly in areas related to training opportunities, workplace safety, and performance evaluation. However, the study also identifies opportunities for improvement in communication, policy awareness, and employee participation in decision-making processes. The research concludes that strategic HR policies, when effectively implemented, foster a positive work environment and support sustainable organizational growth. The study provides valuable insights and recommendations for strengthening HR policy implementation and enhancing overall human resource management practices within the pharmaceutical sector

Keywords : Human Resource Management, HR Policies, Policy Implementation, Employee Satisfaction, Training and Development, Performance Management, Hetero Drugs, Pharmaceutical Industry.

Author : 1Dr. Sanga Pratap, 2Dr. Siva Shankar Rao, 3N. Sravani, 4Ch Reshmitha

Title : STRATEGIC HUMAN RESOURCE POLICIES AND THEIR IMPLEMENTATION PRACTICES AT HETERO DRUGS

Volume/Issue : 2025;02(12)

Page No : 303-311

Women’s entrepreneurship is increasingly recognized as an important pathway for economic empowerment, employment generation, household resilience, and sustainable development. Home Science, traditionally associated with household and family management, has developed into a multidisciplinary field encompassing food and nutrition, textiles and apparel, human development, resource management, consumer studies, health, communication, and community development. These areas provide women with practical and scientific skills that can be transformed into innovative products and services. This paper examines the potential of Home Science as a foundation for women entrepreneurship, with particular emphasis on innovation, self-reliance, skill development, digital technology, food processing, textiles, sustainability, and institutional support. A narrative review of literature published through 2025 indicates that women entrepreneurs continue to face barriers involving access to finance, markets, technology, digital skills, networks, and social expectations. At the same time, evidence suggests that skill development, self-help groups, digital platforms, financial inclusion, and entrepreneurship education can strengthen women's economic participation. Home Science has particular potential because it combines technical knowledge with practical applications that can often be initiated at household or community level. Food processing, nutrition-related services, tailoring, sustainable textiles, handicrafts, childcare, wellness products, and digital businesses are examples of possible enterprises. The paper argues that Home Science education should increasingly integrate entrepreneurship, financial literacy, branding, digital marketing, business planning, food safety, quality control, and innovation. Such integration can transform practical knowledge into sustainable enterprises and contribute to women's self-reliance. However, entrepreneurship should not be viewed as an individual responsibility alone; supportive financial, institutional, technological, and social environments are necessary for sustainable enterprise development.

Keywords : women entrepreneurship, Home Science, innovation, self-reliance, women empowerment, skill development, food processing, textiles, digital entrepreneurship, rural entrepreneurship

Author : Dr. Chhaya Bhimdeo Bhakare

Title : WOMEN ENTREPRENEURSHIP THROUGH HOME SCIENCE: INNOVATION AND SELF-RELIANCE

Volume/Issue : 2025;02(12)

Page No : 312-321