Paper Title

HUMAN POWER INDEX

Abstract

Ancient man invented wheel, which has been the genesis for development of innovative products for the betterment, ease and comfort of human kind. Ancient man invented External combustion engine, which is called steam engine, furthermore Internal combustion engine and electrical motor was invented. There are metrics and measures to assess the power of afore mentioned prime mover. Human being is considered to be extremely competent in terms of governing the invented machineries as well as further development of advance machineries. There are metrics to measure the power of engines and machines like Break Horse Power or Horse Power, where as there are no metrics to measure the power of human being. In current era of internet, most of the citizens and students are not able to recognize their own power and idling their precious time. If the citizen or the student realizes their own potential, they would concentrate of enriching towards doing productive things. The authors who authored Human Excellence Index, are now sensibly making their effort to discover metrics to assess the Human Power entitled Human Power Index. Human Excellence Index deals with the Humanity Component of an individual, whereas, Human Power Index is the ability of an individual to do work. It is believed that, the metrics will help the students, families, state, nation and other nations to implement in-turn lead peaceful life.
KEYWORDS : Power, Horse Power, Human Power, Self development

Paper Title

OPTIMIZING RELIABILITY AND EFFICIENCY IN DATA AGGREGATION FOR LOW-POWER WSNS

Abstract

In Wireless Sensor Networks (WSNs), data redundancy is a challenging issue that not only introduces network congestion but also consumes a considerable amount of sensor node resources. Data redundancy occurs due to the spatial and temporal correlation among the data gathered by the neighbouring nodes. Data aggregation is a prominent technique that performs in-network filtering of the redundant data and accelerates the knowledge extraction by eliminating the correlated data. However, most of the data aggregation techniques have lower accuracy as they do not cater for erroneous data from faulty nodes and pose an open research challenge. To address this challenge, we have proposed a novel, lightweight, and energy-efficient function-based data aggregation approach for a cluster-based hierarchical WSN. Our proposed approach works at two levels, i.e., at the node level and at the cluster head level. At the node level, the data aggregation is performed using Exponential Moving Average (EMA) and a threshold-based mechanism is adopted to detect any outliers for improving the accuracy of aggregated data. At the cluster head level, we have employed a modified version of Euclidean distance function to provide highly-refined aggregated data to the base station.
KEYWORDS : Wireless Sensor Networks (WSNs), Data redundancy, Exponential Moving Average (EMA), Data aggregation, cluster head level.

Paper Title

EFFICIENT ROUTING AND DATA FUSION IN IOT-WSNS FOR RESILIENT FOREST FIRE MONITORING

Abstract

The routing protocol is a crucial technology for data transmission in UAV-WSNs. In this study, we propose an efficient routing protocol for UAV-WSNs data collection to address the high energy consumption and premature death of some sensor nodes (SNs) caused by existing routing protocols. Forest fires have become a major threat around the world, causing many negative impacts on human habitats and forest ecosystems. Climatic changes and the greenhouse effect are some of the consequences of such destruction. Interestingly, a higher percentage of forest fires occur due to human activities. Therefore, to minimize the destruction caused by forest fires, there is a need to detect forest fires at their initial stage. This research proposes a system and methodology that can be used to detect forest fires at the initial stage using a wireless sensor network. Furthermore, to acquire more accurate fire detection, a machine learning regression model is proposed. Because of the primary power supply provided by rechargeable batteries with a secondary solar power supply, a solution is readily implementable as a standalone system for prolonged periods. Numerous trials conducted in real tropical forest sites found that the proposed system is effective in alerting forest fires with lower latency than the existing systems.
KEYWORDS : Forest fires, wireless sensor network, Numerous trials, UAV-WSNs data collection, high energy consumption, human habitats.

Paper Title

EFFECT OF GEOMETRIC DISCONTINUITIES ON THERMO-MECHANICAL FRACTURE RESPONSE OF FIBER REINFORCED POLYMER COMPOSITES

Abstract

Fiber reinforced polymer (FRP) composites are increasingly employed in aerospace, automotive, marine, and civil engineering sectors due to their high specific strength, stiffness, and corrosion resistance. This study presents a comprehensive thermo-mechanical fracture analysis of FRP composites with geometric discontinuities, integrating experimental characterization with numerical modeling. However, their structural integrity is significantly challenged by the combined influence of thermal and mechanical loading, particularly in the presence of geometric discontinuities such as holes, notches, and cut-outs that are often unavoidable in practical design. These discontinuities act as stress concentrators, reducing fracture toughness and accelerating crack initiation and propagation. The influence of temperature variations, fiber orientation, and stacking sequence on crack initiation and growth was systematically investigated. Results demonstrate that elevated temperatures lead to matrix softening, fiber–matrix debonding, and reduced interfacial strength, whereas sub-ambient temperatures promote brittle fracture of the polymer phase.
KEYWORDS : Fiber reinforced polymer (FRP), influence of temperature variations, mechanical loading, geometric discontinuities, fiber–matrix debonding.

Paper Title

OPTIMIZING BIG DATA PROCESSING THROUGH MACHINE LEARNING-BASED REDUNDANCY REDUCTION TECHNIQUES

Abstract

As the volume and complexity of data continue to grow exponentially, optimizing big data processing has become a critical challenge for organizations seeking to derive actionable insights from vast datasets. Machine learning (ML) techniques are playing a pivotal role in enhancing the scalability, efficiency, and accuracy of big data analytics. This paper explores various ML approaches for optimizing big data processing, focusing on methods such as distributed machine learning, deep learning, and parallel processing. We discuss how these techniques can be applied to reduce the computational cost, improve data storage management, and accelerate decision- making in big data environments. Additionally, we examine the integration of advanced ML models with cloud-based and edge computing frameworks, which facilitate real-time data analysis and reduce latency. Through case studies and empirical results, we highlight the effectiveness of these approaches in a variety of industries, including finance, healthcare, and e-commerce. This paper also addresses key challenges, such as data privacy, model interpretability, and system scalability, and proposes future directions for research in optimizing big data processing through ML.
KEYWORDS : Big Data, Machine Learning, Data Processing, Scalability, Efficiency, Distributed Learning, Deep Learning, Parallel Processing, Cloud Computing, Real-Time Analytics

Paper Title

INTELLIGENT TRUST MANAGEMENT AND INTRUSION MITIGATION IN IOT WIRELESS SENSOR NETWORKS USING DEEP LEARNING

Abstract

The Internet of Things (IoT) enables transformative applications; however, it faces significant security and privacy challenges. Due to the impracticality of securing individual devices, network-level security is preferred. However, attack diversity, device heterogeneity, and traditional security limitations necessitate advanced data analysis. Researchers increasingly use deep learning, which excels in handling large-scale data, to develop robust intrusion detection systems. Performance was evaluated using standard classification metrics including accuracy, precision, recall, and F1-score. Intrusion detection in Wireless Sensor Networks (WSNs) is an emerging area of research, given their extensive use in sensitive fields like military surveillance, healthcare, environmental monitoring, and smart cities. Their deployment in open, unattended environments makes them especially vulnerable to threats like eavesdropping, interference, and jamming. To address this problem, Random Forest (RF) is a popular machine learning model. The RF model can be tweaked because of its multiple hyperparameters.
KEYWORDS : Internet of Things (IoT), Wireless Sensor Networks (WSNs), Random Forest (RF), machine learning model, network-level security.

Paper Title

DESIGN AND ANALYSIS OF CMOS MEMS FINGERPRINT SENSORS USING ELECTROMECHANICAL SIMULATION FOR ENHANCED IMAGING

Abstract

In this study a new design of MEMS capacitive fingerprint sensors is presented. The capacitive sensor is made of two parallel plates with air gap. In these sensors, the capacitance changes is very important factor. It is caused by deformation of the upper electrode of sensor. In this study with making slots in upper electrode, using T-shaped protrusion on diaphragm in order to concentrate the force from finger ridges, making holes in lower electrode to reduce the air damping and using low stress material for diaphragm, we have been succeeded to design a novel MEMS fingerprint sensor with high sensitivity compared with the previous one. Identification technology based on biometrics is a branch of research that employs the unique individual traits of humans to authenticate identity, which is the most secure method of identification based on its exceptional high dependability and stability of human biometrics. Common biometric identifiers include fingerprints, irises, and facial sounds, among others. In the realm of biometric recognition, fingerprint recognition has gained success with its convenient operation and fast identification speed. Different fingerprint collecting techniques, which supply fingerprint information for fingerprint identification systems, have attracted a significant deal of interest in authentication technology regarding fingerprint identification systems. mainly describes the technology related to the CMOS MEMS process platform provided by the Taiwan Semiconductor Research Institute (TSRI), including the process flow, design verification, back-end dicing, and packaging of the CMOS MEMS integrated sensing single chip
KEYWORDS : MEMS capacitive, fingerprint sensor, CMOS MEMS integrated, human biometrics

Paper Title

THE ROLE OF DATA SELECTION AND CLEANING IN IMPROVING AI-BASED CYBERSECURITY SYSTEMS

Abstract

In the era of rapidly evolving cyber threats, Artificial Intelligence (AI) has emerged as a vital tool for enhancing cybersecurity through intelligent threat detection, anomaly analysis, and automated response mechanisms. However, the performance and reliability of AI-based cybersecurity systems are critically dependent on the quality of data used for model training and evaluation. This study investigates the role of data selection and cleaning techniques in improving the accuracy, efficiency, and robustness of AI-driven cybersecurity solutions. It explores various preprocessing methods such as noise reduction, deduplication, outlier handling, and feature relevance analysis to ensure the integrity and representativeness of cybersecurity datasets. Experimental results demonstrate that proper data cleaning and informed selection significantly reduce false positives, enhance detection precision, and improve generalization across diverse attack scenarios. The findings emphasize that data preprocessing is not merely a preparatory step but a decisive factor in optimizing AI models for real-time threat mitigation and adaptive security frameworks.
KEYWORDS : Data cleansing, Data Integration, Data Mining, machine learning (ML) model, Data collected, ML community.

Paper Title

A COMPARATIVE STUDY OF FEATURE SELECTION TECHNIQUES TO IMPROVE MACHINE LEARNING-BASED SOFTWARE FAULT PREDICTION

Abstract

This study presents a systematic literature review (SLR) that investigates recent advancements in Software Fault Prediction (SFP) methodologies. The quality of a fault prediction model depends on the software metrics that are used to build the prediction model. Feature selection represents a process of selecting a subset of relevant features that may lead to build improved prediction models. Feature selection methods, particularly wrapper techniques, are often employed to improve predictive accuracy. Evaluation of models predominantly relies on confusion matrix-based metrics such as Accuracy, Precision, Recall, and F1-Score. The review focuses on key dimensions including techniques, datasets, feature selection methods, software metrics, and evaluation criteria. Findings reveal that machine learning approaches— particularly neural networks, deep learning, and ensemble methods— are increasingly employed due to their capability to manage the complexity of software fault data. By analyzing significant studies from renowned digital libraries such as ACM, IEEE, Springer Link, and Science Direct, five research questions were defined to guide the assessment of current trends in SFP research. Public datasets, notably those from the PROMISE and NASA MDP repositories, are widely utilized, underlining the importance of dataset diversity for enhancing model performance.
KEYWORDS : systematic literature review (SLR), Software Fault Prediction (SFP), prediction models, datasets, feature selection methods.

Paper Title

MACHINE LEARNING–BASED DIABETIC CLASSIFICATION USING DATA MINING AND ENSEMBLE STRATEGIES

Abstract

Diabetes mellitus is a chronic health condition that poses serious health risks if not detected and managed early. Accurate and timely classification of diabetic status can significantly aid in preventive healthcare and treatment strategies. This paper proposes a robust framework integrating data mining techniques and ensemble learning methods to enhance the accuracy and reliability of diabetic classification using machine learning models. The proposed framework begins with comprehensive data pre-processing, including handling missing values, feature selection, and normalization. Key features influencing diabetic outcomes are extracted through correlation analysis and feature importance measures. Multiple machine learning models such as Decision Trees, Random Forests, Support Vector Machines (SVM), and Gradient Boosting are employed for initial classification. To further improve predictive performance, ensemble methods like Bagging, Boosting, and Voting Classifiers are implemented. The framework is evaluated using standard performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC on benchmark datasets like the Pima Indians Diabetes Dataset. Experimental results demonstrate that ensemble learning significantly outperforms individual models, offering a reliable approach for diabetic classification. This study underscores the potential of combining data mining and ensemble techniques to build intelligent and accurate diagnostic systems in healthcare.
KEYWORDS : Multiple machine learning models, diabetic status, diabetic classification, combining data mining and ensemble techniques