bluuebunny/crossref_metadata_2025 huggingface dataset:
Decoding Investor’s behavior in tax saving mutual fund: A multi-item scale for evaluating investors’ category
Authors: Priya Tiwari  , Bharat Kasar  , Vibhu Tripathi
Published: January 2025

This study aims to design and validate a multi-item scale for assessing the investment behavior of investors in tax-saving mutual funds, with a particular focus on Equity Linked Savings Schemes (ELSS). Additionally, the research seeks to identify clusters of investors with similar personality traits, offering deeper insights into how these traits influence investment decisions. By examining the interplay between capital, personality characteristics, and investment behavior, the study underscores their critical role in fostering economic growth. Despite the growth in mutual fund assets under management (AUM) in India, there is a discrepancy in the inflow to Equity Linked Savings Schemes (ELSS), indicating that investor behavior is influenced by a combination of financial and behavioral factors. Through an extensive review of the literature, the study identifies key gaps in existing research on investor psychology, particularly in relation to ELSS investments, emphasizing the need for a more nuanced understanding of the psychological factors shaping investor decision-making in this area. Using a structured questionnaire, the study gathered data from 489 mutual fund investors to explore the financial and behavioral factors influencing their investment decisions. Factor analysis was employed to identify key factors affecting these decisions, and reliability tests like KMO and Bartlett’s were applied to ensure the adequacy of the sample. The findings reveal two main factors related to financial considerations—Informative cum Magnetize Factors and Inadmissible and Undesirable Benefits—as well as behavioral factors such as overconfidence, anchoring, and herding that significantly impact investor behavior. The study concludes that a deeper understanding of these factors can help categorize and address investor needs, ultimately enhancing participation in tax-saving mutual funds. It also helps regulators in policy-making as per the clusters of Investors.
The role of technology in implementing effective education for children with learning difficulties
Authors: Pritee Rajaram Ray  , Bijal Zaveri
Published: January 2025

With the advancement of technology, inclusive education for kids with learning difficulties is now greatly enhanced. This essay investigates how technology promotes inclusivity by offering customised solutions. It draws attention to the ways in which digital technologies might improve academic performance, engagement, and cognitive capacities in kids with learning difficulties even those with high IQs. The purpose of this research is to investigate how technology may be used to develop educational interventions that help students who have learning difficulties, including dyslexia, ADHD, and autism spectrum disorders (ASD). The potential for various digital tools to enhance engagement, cognitive abilities, and academic performance is the subject of ongoing research. These technologies include adaptive learning platforms, assistive software, and interactive applications. In order to promote inclusion and equitable learning experiences for students, the study also highlights the importance of incorporating personalised technology into educational environments. This research advocates for a more technologically sophisticated inclusive education system that identifies critical concerns and effective practices. Students with learning difficulties and disabilities would have a better chance of succeeding in this technologically enhanced set up and approach.
Impact of emerging global educational trends on overseas education programs for aspiring students in South East Asia and South Asia: A decadal analysis
Authors: Uchukanokkul Santima , Bijal Zaveri
Published: January 2025

The present paper investigates the influence that current trends in international education have on students from Southeast Asia and South Asia interested in participating in educational programs in other countries throughout the next ten years. The research examines historical data on Thai students studying overseas, compares education systems, and investigates scholarship chances. It uses a qualitative methodology, including literature reviews, case studies, and interviews with important players. The study proposes methods for boosting outward and inbound foreign education in Thailand, presents a strategy for execution over ten years, and estimates prospective consequences. The findings reveal that while international education brings substantial benefits for students from these locations, it also poses problems relating to the acquisition of visas, the transition to a new culture, and the restrictions on financial resources. In its conclusion, the research offers suggestions for students, educational institutions, and politicians on how to make the most of the advantages of international education while also tackling the issues it presents.
Determinants of Gen Z’s adoption of chatbots in online shopping: An empirical investigation
Authors: Brijesh Singh  , Massand Ajay
Published: January 2025

Online shopping is in a growth phase in India. Most of the online shopping companies are leaving no stone unturned to attract customers to their websites or apps. The companies are introducing innovative website/app features to engage their prospects and customers. This study is an exploratory study followed by descriptive analysis to find out the preference of Gen Z customers towards the usage of Chatbots. The sample includes 171 Gen Z customers from Bengaluru, India comprising of different backgrounds. Their preferences have been ascertained with the help of a well-structured questionnaire. The study uses judgment sampling. A conceptual framework has been proposed comprising four factors, namely Brand Perception, Service Quality, Personalization and Demographics. Different statistical techniques like frequency tables, Dimension Reduction methods (Principal Component Analysis) and Multiple Linear Regression are used for data analysis. Authors found the factors viz Brand Percpetion, Service Quality and Personalization to be significant in the varying levels of their importance. The output of the analysis is interpreted and its implications on online shopping companies have been highlighted.
Does shopping values influence users behavioral intentions? Empirical evidence from Chennai malls
Authors: A Appu
Published: January 2025

India’s economy is strongly reliant on the retail business, and the development of shopping malls over the last decade has boosted the industry. However, little empirical study has been conducted on shoppers’ experience value, user delight, and behavioral intentions in Chennai city retail malls. The study examined how atmospherics, entertainment, accessibility, culture, and shopping values affect mall shoppers’ intents using descriptive research. A structured questionnaire was distributed to 119 respondents using convenience sampling, and MRA Analysis was used to examine the hypotheses. The correlation coefficient between shopping values and customer response is 0.693, indicating a 48.02% positive association. In addition, The findings showed that shopping value influences user satisfaction and behavioral intentions. It is recommended that retail mall managers consider visitor value as a multidimensional construct to create a satisfying experience for users. This study enhances the literature regarding the varied impacts of shopping mall attributes on customer shopping happiness and behavioral intentions. This study indicates that retail malls must enhance their qualities to elevate and maintain customer satisfaction. This paper empirically demonstrates that the service quality and mall attributes can be improved by adequately implementing Shopping values in Malls.
Health status of women in slums: A comprehensive study in Raichur District Karnataka, India
Authors: Sunder Raj Rajashree , Sayar Ahmad Sheikh
Published: January 2025

Health is the major aspect of all living organisms on this earth, good health gives a healthy life span to prolong in the present disturbed environment conditions. In the case, of women's health status in slums, from the Raichur district, the study focuses on all the age group persons from young to old age group. In the seven slum areas selected for the study, from each slum 490 respondents’ data were collected. The result was analyzed with the help of SPSS software and computational data from Excel. The study revealed the infectious and non-infectious diseases spread and severity, in this came to know that younger women are highly suffering from blood pressure issues which they almost neglect. Also, the highest degree of malaria disease was seen in the slum dwellers due to water logging in the areas, and the women are prone to joint pains which is one of the most vulnerable problems arising due to stressful physical work. However, fungal diseases are intense leading to hair loss in women and also occupational health problems making them sick due to overcoming the poverty level of the family. In conclusion, women’s health especially in slums has to be aware, and in medical camps, the disease severity may be treated also due to poor sanitation parasitic diseases are more prevalent in the slums which have to be prevented by taking appropriate measures.
Analyzing the impact of crime on quality of life in Old Delhi: A quantitative approach
Authors: Shivani Goel  , Rashmi Ashtt  , Monali Wankar
Published: January 2025

This research examines the impact of crime on the quality of life in Old Delhi, historically known as Shahjahanabad. Using a quantitative approach, the study analyzes crime data and socio-economic indicators to explore the relationship between rising crime rates and various dimensions of quality of life, including perceived safety, economic activity, social cohesion, housing quality, and physical and mental health. The analysis reveals significant negative correlations between crime rates and these indicators, indicating that as crime increases, residents experience a decline in overall well-being and community cohesion. Furthermore, the study highlights how specific socio-economic factors, such as income levels and housing conditions, exacerbate the effects of crime, creating a complex and challenging urban environment. The research also compares Old Delhi’s situation with other historic urban areas globally, such as Barcelona, Istanbul, and New Orleans, to provide a broader context for the findings. These comparisons underscore the need for tailored crime prevention strategies that consider the unique cultural, social, and economic characteristics of historic districts. The research offers valuable insights for urban planners, policymakers, and scholars interested in enhancing the quality of life in historic urban settings, emphasizing the importance of integrated and community-focused approaches to crime management. The conclusions drawn aim to inform future research and policy development, particularly in similar contexts where cultural heritage preservation must be balanced with the need for modernization and safety.
India’s transformative journey: A decade and a half of growth, innovation, and inclusive progress
Authors: Vibhu Tripathi
Published: January 2025

This article delves into the substantial developmental changes across diverse sectors in India over the past 15 years, charting the nation’s remarkable journey of growth and transformation. It offers a comprehensive examination of key reforms and their far-reaching impacts in sectors such as finance, economy, technology, infrastructure, defense, internal security, international relations, and social welfare. Drawing upon data from government sources and independent reports, the analysis reflects on both the milestones achieved and the challenges encountered during this period. The article underscores India’s ascension as a burgeoning global power, with particular emphasis on inclusive development, technological innovation, and modernization across all domains.
Contribution of policy and regulations to enhance Transparency and Traceability in the Garment Industry
Authors: Aakanksha Laiker  , Promil Pande
Published: January 2025

The Indian garment industry comprises of complex global supply chain that frequently masks the origins and processes involved in production. Improving transparency and traceability is becoming crucial for promoting sustainable and ethical practices. The paper examines the importance of policy and regulations in accomplishing these objectives. The study highlights the necessity of increasing transparency and traceability in the apparel industry, along with the impact of policies on customer behavior, by reviewing existing literature, case studies, and policy analysis. However, significant gaps remain in achieving transparency and traceability within the industry. Many a time, a gap exists between awareness of the policies and their actual implementation by industry stakeholders. Therefore, the research aims to provide an understanding of the efficiency of current policies and their impact on consumers by examining current policies in India, international agreements, and the industry's best practices. Furthermore, the report analyzes the complications of adopting the regulations and offers insights into how one can overcome the barriers to achieve more transparency in the supply chain.
A descriptive research methodology was implemented. Secondary data and primary data were collected using a survey approach. An online survey was conducted using a stratified sampling technique, with a Google form questionnaire distributed through email. The research is also assessing the impact of current policies & regulations on consumer behavior. Through this examination, the research will highlight the importance of robust policy and regulatory frameworks in promoting a fashion industry that is both sustainable and ethical, as these measures not only safeguard the rights of workers and the environment but also build customer trust and preserve the industry's long-term viability by ensuring that all the stakeholders involved in the supply chain are accountable and transparent.
Tracing the origins and evolution of yoga darshana: A critical historical analysis
Authors: Dave Bansariben Chhellashankar  , Anil Kashyap
Published: January 2025

One of India's six classical philosophical traditions, Yoga Darshana, has its origins traced through this article's analytical and empirical research. From its inception in ancient times to its modern interpretations, this inquiry delves into the origins, evolution, and fundamental concepts of Yoga Darshana. Also included in the study is the origins of Yoga Darshana. The study's overarching goal is to fill gaps in our understanding of Yoga Darshana's place in Indian philosophy. To do this, we will look at important texts, have philosophical conversations about them, and consider how religious and cultural factors play a role. The study also delves into the ways in which yoga's fundamental principles have evolved and been reinterpreted throughout the years and how these changes have impacted traditional spiritual practices as well as contemporary health movements. The study aspires to contribute to the continuing discussion on the relevance and usefulness of yoga in both contemporary and traditional settings through its critical historical research.
Applying the risk-need-responsivity model in juvenile offender treatment: A conceptual framework
Authors: Bhavika Bhagyesh Lad  , Sonam Mansukhani
Published: January 2025

Juvenile crime in India is a pressing issue that requires tailored rehabilitation approaches. This paper explores the application of the "Risk-Need-Responsivity (RNR) model" as a conceptual model for treating juvenile offenders within the Indian legal context. The study reviews correctional practices and highlights the need for structured offender treatment based on the 'RNR model's' core principles of 'risk,' 'need,' and 'responsivity.' Drawing on criminological theories and empirical evidence, the paper emphasizes the significance of addressing criminogenic factors to reduce recidivism. By analyzing existing literature on juvenile justice, the paper demonstrates how the RNR model, typically employed in Western contexts, can be adapted for India's socio-cultural environment to enhance the effectiveness of juvenile rehabilitation. The findings suggest that integrating RNR-informed interventions into the juvenile justice system can improve long-term rehabilitation outcomes and reduce re-offense rates among young offenders.
Bioremediation of Textile Dyes Using Native Microorganisms: Sustainable Microbiological Approaches
Authors: Dadhaniya Deepa Karshanbhai  , Nilofar Bhatti
Published: January 2025

Significant environmental difficulties are posed by the textile industry's heavy reliance on synthetic dyes. Dye pollutants in wastewater are detrimental and long-lasting, which is why they create these issues. Traditional approaches to treating textile effluents are ineffective in decomposing complex color compounds, and they can be prohibitively costly. To further the area of bioremediation as an ecologically and financially responsible option, this research investigates the possibility of naturally occurring microbes degrading and cleaning textile dyes. The ability of native fungi, bacteria, and algae to degrade various color chemicals through enzymes has demonstrated promise in their isolation from polluted settings. This study delves into the ways these microbes manage to repair hues. Oxidative pathways, biosorption, and enzymatic degradation are all thoroughly described. In addition, we look at the scalability and practicability of microbiological approaches in bioreactors, specifically looking at how these techniques may be used to treat industrial wastewater. Green technology, which seeks to lessen industrial waste and safeguard the environment, is a rapidly expanding field, and the results contribute to it.
A tapestry of tradition: Revitalization of Indian Heritage and Folk Art
Authors: Joji John Panicker  , Ancy Elezabath John  , Nair Anup Chandrasekharan
Published: January 2025

The study investigates the importance of Indian heritage and folk art, outlining their origins, development, and present-day significance. Indian folk art, profoundly entrenched in tradition, reflects the varied socio-cultural structure of the nation, from Madhubani paintings to Warli art. These art structures, influenced and molded by religious and philosophical behavior, face challenges owing to globalization and urbanization, intimidating their conservation. The study highlights the significance of restoring these art forms to reinforce cultural individuality, endorse economic progress, promote social inclusion, and persuade ecological sustainability. Approaches for revitalization consist of education, documentation, community involvement, cultural tourism, policy support, economic empowerment, and digital preservation. The part of highly developed technologies, such as AI, in digital conservation and immersive experiences is emphasized as critical for preserving these cultural resources. This comprehensive advance guarantees that India's rich tradition and folk art persist in instigating and supplementing the global cultural setting.
Children’s literature as a tool for social change: Teaching values and social awareness
Authors: Roop Kanwal
Published: January 2025

This paper explores the role of children’s literature as a driving force for social change. By examining how children’s books portray different cultures, confront stereotypes, and weave social values into their stories, the study looks at the ways in which literature shapes young minds. Children’s books can both mirror a child’s own experiences and provide a window into the lives of others, helping them understand complex issues like identity, diversity, and justice. The research delves into how stories can influence children’s cognitive and emotional growth, encouraging them to think critically and develop a sense of empathy. It also discusses the challenges authors face in creating stories that are engaging while also carrying meaningful messages. Balancing education with entertainment is a key consideration for those writing socially aware children’s literature. In addition, the paper touches on the role of adults—such as parents, teachers, and publishers—in choosing and promoting books that foster inclusivity and respect. By providing examples of children’s books that successfully address social issues, the study demonstrates how these stories can inspire young readers to question societal norms and consider their own role in making the world a better place. Ultimately, this paper argues that children’s literature has the potential to be more than just a source of entertainment. It can be a powerful tool for instilling values, building empathy, and sparking a desire for social justice in the next generation. Through the stories they read, children can gain the awareness and compassion needed to tackle the challenges of their world.
Views of undergraduates on Vikshit Bharat@2047
Authors: Darji Chirag , Rajesh Chauhan
Published: January 2025

With a view to studying the views of undergraduate students studying in various disciplines, i.e., Science, Commerce and Arts, the researchers have collected the opinions from the undergraduate students on various aspects of Vikshit Bharat mission in 2047. The variables of the study were male-female & rural and urban areas. The survey research method was used for the study, and the nature of the study was qualitative. The population of the study comprised all the students of Gujarat state, India and the sample of the study was simply a random sampling technique. The self constructed research tool was prepared and administered among the students. The research tool was descriptive. The experts validated the tool, and then the data were collected from the students. The students answered the questions in detail, and it was analyzed through content analysis. It was found from the study that there were certain factors affecting Vikshit Bhart and the Government and the people should initiate to resolve them at the earliest in order to complete the dream of Vikshit Bharat. Unemployment, poverty, quality of education etc, are to be addressed. The students of the undergraduate colleges were found dedicated and committed to contributing to the mission of Vikshit Bharat. There were also suggestions from the students on how India would be the Vikshit Bharat on or before 2047. It was suggested that the Government should bring more transparency in each work, one nation one rule, one tax, common act and civil code, no corruption and more dedication among the government system. Regional differences and gender differences were observed in the opinions.
A comparative analysis of virtual machines and containers using queuing models
Authors: A. Anand  , A. Nisha Jebaseeli
Published: January 2025

Virtual machines (VMs) and containers are two prevalent technologies in cloud computing, each offering distinct advantages depending on the use case. VMs emulate entire operating systems, including kernels, while containers share the host OS kernel, making them lightweight and resource-efficient. This paper presents a novel method for comparing the performance of VMs and containers using queuing models. The proposed method not only provides a more accurate and flexible comparison but also significantly reduces the time required to calculate and perform performance metrics compared to traditional empirical benchmarking and simulation-based approaches. Through this comparison, the paper highlights the conditions under which containers outperform VMs, particularly in modern, cloud-native environments.
AI-driven real-time performance optimization and comparison of virtual machines and containers in cloud environments
Authors: A. Anand  , A. Nisha Jebaseeli
Published: January 2025

The accurate calculation and comparison of performance in cloud environments are critical for optimizing resource utilization, particularly with the increasing use of virtual machines (VMs) and containers. This research proposes an AI-driven resource management framework that surpasses traditional machine learning algorithms by enabling real-time, autonomous performance optimization. While machine learning models provide predictive capabilities, they often require manual tuning and retraining for changing workloads. In contrast, the proposed AI-driven system, utilizing techniques such as reinforcement learning and adaptive optimization, continuously adjusts resource allocation based on real-time performance metrics like response time, throughput, and server utilization. This dynamic, self-improving system can respond to fluctuating workloads and network conditions without the need for constant retraining, offering superior flexibility and faster response times. The framework will be validated through extensive experiments across multi-cloud and edge computing environments, demonstrating its ability to significantly reduce calculation time while improving scalability and efficiency. Additionally, this approach incorporates enhanced security mechanisms, combining the isolation benefits of VMs with the lightweight efficiency of containers, providing a comprehensive, real-time solution for cloud-native applications.
Distribution of virtual machines with SVM-FFDM approach in cloud computing
Authors: D. Jayadurga  , A. Chandrabose
Published: January 2025

Virtual machine (VM) distribution in cloud computing plays a pivotal role in optimizing resource allocation and improving overall system performance. This study proposes a novel approach for efficient VM distribution using a combination of support vector machine (SVM) and the finest fit decreasing modifier (FFDM) algorithm. SVM is employed to classify and predict resource utilization patterns, ensuring that VMs are allocated based on predicted workloads. The FFDM algorithm, a modified version of the traditional first fit decreasing (FFD) algorithm, is then applied to optimize the packing of VMs onto physical servers by minimizing resource wastage and enhancing load balancing. By integrating machine learning techniques with optimization algorithms, the proposed approach achieves a more effective VM allocation strategy, leading to improved system efficiency, reduced energy consumption, and enhanced scalability in cloud environments. Simulation results demonstrate the superior performance of the SVM-FFDM method compared to traditional VM allocation techniques in terms of resource utilization and operational cost.
Feature selection in HR analytics: A hybrid optimization approach with PSO and GSO
Authors: Jayalakshmi K.  , M. Prabakaran
Published: January 2025

In the field of Human Resources (HR) analytics, effective feature selection is critical for improving the accuracy and efficiency of predictive models used for workforce management, talent retention, and performance evaluation. This paper proposes an improved feature selection approach that integrates optimization techniques such as particle swarm optimization (PSO) and gravitational search optimization (GSO) to enhance the performance of HR analytics. By leveraging the exploration-exploitation balance of PSO and the mass-based search capability of GSO, the proposed method efficiently identifies the most relevant features from large and complex HR datasets. The hybrid approach reduces dimensionality, minimizes computational costs, and boosts the accuracy of machine learning models used in HR analytics. Comparative analysis with traditional feature selection methods demonstrates that the proposed technique achieves superior results in terms of prediction accuracy, computational efficiency, and overall model performance. This study highlights the potential of advanced optimization techniques in driving data-driven decision-making processes in HR, offering a robust and scalable solution for managing and analyzing HR data more effectively.
Improving image quality assessment with enhanced denoising autoencoders and optimization methods
Authors: V. Karthikeyan  , C. Jayanthi
Published: January 2025

In the field of image quality assessment, effective noise reduction is critical for enhancing the perceptual quality of images and improving the accuracy of subsequent analyses. This study proposes an enhancement to denoising autoencoders (DAEs) through optimization techniques aimed at significantly improving image quality assessment outcomes. Traditional DAEs, while effective in reconstructing clean images from noisy inputs, can sometimes fail to adequately preserve intricate image details and structures, which are essential for quality evaluation. Our approach incorporates optimization strategies, including adaptive learning rates, regularization techniques, and advanced loss functions, to refine the DAE architecture and improve its denoising capabilities. By training the enhanced model on diverse datasets containing various noise types and image content, we achieve superior performance in noise reduction. The effectiveness of the optimized denoising autoencoder is rigorously evaluated using standard image quality metrics, including Peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and other perceptual quality measures. Results demonstrate a marked improvement in image quality, leading to more reliable assessments in various applications, including medical imaging, remote sensing, and multimedia content. This work highlights the potential of leveraging optimization techniques to enhance denoising autoencoders, thereby providing a robust solution for improving image quality assessment methodologies.
Some properties of maximal product of two picture fuzzy soft graph
Authors: M. Vijaya  , D. Hema
Published: January 2025

In this manuscript, the concept of a picture fuzzy soft graph is formally established. Subsequently, we explore the notion of maximal product between two picture fuzzy soft graphs. Additionally, we investigate the complement of the maximal product of picture fuzzy soft graphs, along with the maximal product involving strong and complete picture fuzzy soft graphs. Furthermore, a discussion of various outcomes stemming from this exploration is presented.
Improving the resource allocation with enhanced learning in wireless sensor networks
Authors: M. Prabhu  , A. Chandrabose
Published: January 2025

Efficient resource allocation is crucial for optimizing performance and extending the lifespan of wireless sensor networks (WSNs), which are often constrained by limited energy and bandwidth. This paper proposes an enhanced learning approach (ELA) for dynamic resource allocation in WSNs, leveraging augmented reinforcement learning to adaptively manage energy consumption, optimize routing, and schedule node activity. ELA integrates predictive feedback and real-time data from network states to refine policy decisions, enabling the network to maintain optimal performance under varying traffic loads and environmental conditions. Comparative analyses with existing methods, including deep neural networks (DNN), artificial neural networks (ANN), and support vector machines (SVM), demonstrate that ELA achieves superior results across multiple key metrics: energy consumption, network lifetime, packet delivery ratio, end-to-end delay, and throughput. Our findings indicate that ELA can sustain higher data reliability and throughput while minimizing latency and energy depletion, addressing fundamental challenges in WSNs. The proposed approach presents a scalable and adaptive solution that is well-suited for real-time and large-scale IoT applications, making it a valuable contribution to the advancement of intelligent resource management in WSNs.
Trust aware clustering approach for the detection of malicious nodes in the WSN
Authors: Rekha R.  , P. Meenakshi Sundaram
Published: January 2025

Wireless sensor networks (WSNs) are pivotal in a range of applications such as environmental monitoring, healthcare, and defense. However, their decentralized and resource-constrained nature makes them vulnerable to various security threats, particularly from malicious nodes that can disrupt the network’s functionality. To address this issue, this paper proposes a novel trust aware clustering (LEACH) approach integrated with an optimization-based technique for the detection of malicious nodes in WSNs. The proposed model leverages the low-energy adaptive clustering hierarchy (LEACH) protocol for efficient clustering and energy management while incorporating a trust-based mechanism to evaluate the behavior of nodes. Additionally, an optimization algorithm is employed to enhance the accuracy of malicious node detection and improve the overall network performance. The trust model dynamically updates based on node interactions, ensuring that compromised nodes are detected and isolated promptly. Simulation results demonstrate the efficacy of the proposed approach in terms of increased detection accuracy, reduced energy consumption, and prolonged network lifetime, making it a robust solution for securing WSNs against malicious attacks.
Enhanced malicious node identification in WSNs with directed acyclic graphs and RC4-based encryption
Authors: Rekha R.  , P. Meenakshi Sundaram
Published: January 2025

In wireless sensor networks (WSNs), ensuring secure data transmission while preventing malicious activity is a critical challenge. This paper presents a novel approach for the identification of malicious nodes in WSNs by integrating directed acyclic graphs (DAGs) with the RC4 encryption algorithm. DAGs are employed to establish a hierarchical structure that enables efficient data flow and tracking of communication patterns across the network. By utilizing DAGs, the system can monitor the consistency and integrity of data transmission, making it easier to detect anomalies caused by malicious nodes. The RC4 encryption algorithm further strengthens the approach by securing the communication between nodes, preventing unauthorized access and tampering. In combination, DAGs and RC4 provide a robust framework for both detecting malicious nodes and securing data exchanges. Experimental simulations demonstrate that the proposed approach enhances network security by identifying compromised nodes with high accuracy while maintaining efficient communication and low overhead. This method offers a scalable and secure solution for protecting WSNs from malicious threats.
Effective gorilla troops optimization-based hierarchical clustering with HOP field neural network for intrusion detection
Authors: S. Hemalatha  , N. Vanjulavalli  , K. Sujith  , R. Surendiran
Published: January 2025

An intrusion detection system (IDS) armed with signature and attack pattern databases as reference tools are used to protect computer networks from intrusion. This article provides a hybrid machine learning algorithm for gorilla troops optimization (GTO) integrating hierarchical clustering and Hopfield neural network. In this paper, the authors present a model to improve intrusion detection accuracy and contain high operational flexibility of these techniques. It is inspired by social behavior in gorillas and optimizes the clustering process HNN. Experimental results show that the proposed approach enhances the traditional methods in intrusion detection for a variety of intrusions and it presents an effective solution that can help cybersecurity application development better.
Optimizing predictive accuracy: A comparative study of feature selection strategies in the healthcare domain
Authors: M. A. Shanthi
Published: January 2025

Feature selection is a critical preprocessing step in the development of machine learning models, particularly in the healthcare domain, where datasets often contain numerous features that may not contribute significantly to predictive performance. This study presents a comparative analysis of various feature selection techniques applied to healthcare datasets, evaluating their effectiveness in improving model accuracy and reducing computational costs. We investigate traditional filter-based methods, such as information gain and chi-square, alongside wrapper-based approaches and hybrid techniques that combine the strengths of both. Using multiple healthcare datasets encompassing diverse medical conditions, we assess the impact of these techniques on classification performance using metrics such as accuracy, precision, recall, and F1-score. Additionally, we analyze the robustness and scalability of each method in handling high-dimensional data. The findings reveal significant differences in performance, highlighting the strengths and weaknesses of each feature selection approach within the healthcare context. This comparative study provides valuable insights for researchers and practitioners, guiding them in selecting appropriate feature selection techniques to enhance predictive modeling in healthcare applications.
An efficient key establishment for pervasive healthcare monitoring
Authors: Sruthy M.S  , R. Suganya
Published: January 2025

Health is one of the issues that present more challenges in the world. These challenges not only come from the requirements of the region itself yet in addition result from outside conditions that impact individuals’ health conditions and access to therapeutic administrations. To augment the security strength of real-time healthcare applications in the IoT environment, a novel framework, namely, an Enhanced and IoT-based medical healthcare security scheme (EIMSS), has been proposed in this chapter. The proposed EIMSS adapts the AUP authentication technique proposed in the previous chapter for authentication while transferring the patient’s data. The proposed EIMSS approach offers flexible services to aged people like confidentiality, integrity, and authentication for protecting their vital biological and medical data. The simulation results, analysis and comparison confirm that the proposed EIMSS outperforms existing protocols with improved security strength.
A robust finger detection based sign language recognition using pattern recognition techniques
Authors: Subin M. Varghese  , K. Aravinthan
Published: January 2025

Sign language recognition based on finger detection is arguably the main sign language used by most dumb people. It has its own phonetics, grammar and syntax that set it apart from other sign languages. Research related to sign language (SL) is only now becoming standardized. Considering the challenge of recognizing SL, in this work a new method for recognizing SL dynamic gestures is proposed. Sign language (SL) translation systems can be used to help dumb people interact with normal people with the help of a computer. Most studies on continuous recognition of sign language are done by processing frames obtained from videos at regular/equal intervals. If a developed system is powerful enough to handle both static and dynamic motions, then it will be the best system for processing frames obtained from processing consecutive gestures. The algorithm developed for the gesture recognition system in SL formulates a vision-based approach using two-dimensional discrete sinusoidal transforms (DSTs) for image compression and self-organizing maps (SOMs), or self-organizing feature maps. Kohonen’s (SOFM) Neural Networks for Pattern Recognition, simulated in MATLAB. The system showed an accuracy rate of 91 percent.
An enhanced support vector machine bbased multiclass classification method for crop prediction
Authors: A. Tamilmani  , K. Muthuramalingam
Published: January 2025

Crop type classification is a fundamental task in precision agriculture, enabling informed decision-making for crop management and resource allocation. Support vector machines (SVMs) have emerged as robust and effective tools for multiclass classification tasks. This study explores the application of SVM-based multiclass classification techniques to accurately categorize various crop types based on remote sensing data. The SVM algorithm is employed to create decision boundaries that maximize the margin between different crop classes while minimizing classification errors. To enhance classification performance, various kernel functions such as linear, polynomial, and radial basis functions are evaluated to capture complex relationships within the data. The proposed SVM-based approach is compared with other commonly used classification methods to assess its superiority in terms of accuracy, precision, recall, and F1 score.
Fuzzy optimization trust aware clustering approach for the detection of malicious node in the wireless sensor networks
Authors: A. Rukmani  , C. Jayanthi
Published: January 2025

Wireless sensor networks (WSNs) are pivotal in various applications, ranging from environmental monitoring to military operations. However, their susceptibility to security threats, particularly from malicious nodes, poses significant challenges to network integrity and data reliability. This paper proposes an innovative methodology that integrates clustering with an optimization approach to effectively identify and mitigate malicious nodes in WSNs. In the proposed methodology, the network is divided into clusters, each managed by a cluster head responsible for monitoring the behavior of nodes within its cluster. Trust values are assigned to nodes based on parameters such as data forwarding accuracy, communication consistency, and energy consumption. These trust metrics are optimized using a sophisticated optimization algorithm, which fine-tunes the decision-making process for identifying malicious nodes. By leveraging clustering, the method efficiently distributes computational tasks, while the optimization algorithm enhances the accuracy of malicious node detection by dynamically adjusting trust thresholds. The approach not only reduces the incidence of false positives but also extends the network lifetime by preventing compromised nodes from disrupting network operations. This trust-aware, optimized clustering strategy offers a robust solution for securing WSNs in critical applications, ensuring reliable and secure data transmission across the network.
Exploring learning-assisted optimization for mobile crowd sensing
Authors: P. Ananthi  , A. Chandrabose
Published: January 2025

Introducing sensing mobile crowds (SMC), a novel paradigm for real-time location-dependent urban sensing data collection. It is critically important to optimize the SMC process such that it provides the highest sensing quality at the lowest feasible cost due to its practical use. As an alternative to the combinatorial optimization algorithms utilized in previous research, a new approach to SMC optimization is to apply learning approaches to extract knowledge, such as patterns in participants’ behavior or correlations in sensing data. In this work, we thoroughly research learning-assisted optimization approaches for SMC. Using the existing literature as a starting point, we will describe various learning and optimization methods and evaluate them from the perspectives of the task and the participant. How to combine different approaches to get a complete solution is also discussed. Lastly, we point out the limitations that exist at the moment, which might lead to research directions in the future.
The socio-technical opportunities and threats of crowdsensing
Authors: P. Ananthi  , A. Chandrabose
Published: January 2025

The exponential growth of mobile crowd sensing (MCS) has provided unparalleled opportunities to collect large-scale data through a network of mobile devices, empowering diverse applications in smart cities, healthcare, and environmental monitoring. However, the inherently participatory nature of MCS raises critical privacy concerns, as sensitive user information is often at risk of exposure. This literature review examines recent advancements in employing machine learning techniques to enhance privacy preservation in MCS frameworks. It explores methods such as federated learning, differential privacy, and encryption-enhanced neural networks that aim to minimize data leakage while maintaining model accuracy. Additionally, this review analyzes the efficacy and limitations of various privacy-preserving algorithms, particularly regarding their adaptability to different MCS contexts and their impact on computational overhead and communication efficiency. Through a comprehensive synthesis of current studies, this review highlights emerging trends, identifies research gaps, and suggests future directions for developing robust privacy-preserving machine learning models tailored to the unique demands of MCS systems.
Energy efficient routing with cluster approach in wireless networks – A literature review
Authors: Chinnadurai U  , A. Vinayagam
Published: January 2025

This literature review examines the cluster-based approaches in wireless networks, focusing on their effectiveness in conserving energy, enhancing routing efficiency, and improving network credibility. Wireless networks are increasingly employed in various applications, from internet of things (IoT) systems to mobile ad hoc networks, where energy efficiency is critical due to the limited battery life of devices. Cluster-based techniques group nodes into clusters to optimize resource utilization, facilitating energy conservation by enabling localized communication and reducing redundant transmissions. The review explores various clustering algorithms, such as low-energy adaptive clustering hierarchy (LEACH) and hybrid energy-efficient distributed clustering (HEED), highlighting their impact on network longevity and scalability. Additionally, the study addresses the challenges of maintaining robust routing protocols within clustered networks, emphasizing the importance of reliable data transmission and node credibility to mitigate risks from malicious attacks. By synthesizing current research findings, this review provides insights into the future directions of cluster-based strategies in wireless networks, suggesting potential enhancements to ensure efficient energy management and reliable network performance.
The role of big data in transforming human resource analytics: A literature review
Authors: Jayalakshmi K.  , M. Prabakaran
Published: January 2025

The growing integration of human resources (HR) analytics with big data analytics has transformed the way organizations manage their workforce, offering deeper insights into employee performance, recruitment strategies, talent retention, and overall organizational efficiency. This literature review explores the convergence of HR analytics and Big Data, highlighting key trends, methodologies, and challenges faced in this rapidly evolving field. It examines various HR analytics frameworks that leverage data-driven approaches to optimize human capital management, including predictive modeling, machine learning, and sentiment analysis. By reviewing a wide range of studies, this paper identifies the critical role of Big Data in enhancing decision-making processes, enabling more accurate predictions about employee behavior, and supporting strategic HR initiatives. Additionally, the review addresses ethical concerns related to data privacy, biases in algorithms, and the complexities of integrating Big Data tools into existing HR systems. The findings suggest that, while HR analytics has immense potential, the successful implementation of Big Data in HR requires a robust technological infrastructure, skilled professionals, and adherence to ethical standards. This review provides a comprehensive understanding of the current state of HR analytics and its future trajectory in the era of Big Data.