3.Book Chapter (18)

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Start date: 2024End date: 2024

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    A COMPREHENSIVE REVIEW OF LEARNING RULES AND ARCHITECTURE OF PERCEPTRON IN ARTIFICIAL NEURAL NETWORKS (ANNS) (Book Chapter)
    (CRC Press, 2024-1) Shanthini S; Devi, M. Sindhana; Grace, R. Suriya
    The complicated neural networks of the human mind have acted as a significant model for creating artificial neural networks (ANNs) of computational intelligence. ANNs can recognize patterns in data, make decisions, and perform other functions. The study provides a comprehensive review that explores ANNs by analysing the crucial elements of learning rules and perceptron architectures. This chapter clarifies the foundational learning rules underlying ANNs’ ability to adapt and generalize from data. The investigation comprehensively inspects the vital elements of learning rules and perceptron architectures in Artificial Neural Networks (ANNs) inspired by the detailed neuronal networks of the human brain. This chapter subsequently explores the dynamic realm of perceptron architectures within ANNs. Single-layer perceptrons are examined for their inability to handle intricate relationships. In contrast, multilayer perceptrons (MLPs) emerge as formidable solutions. The complex composition of MLPs, characterized by input, hidden, and output layers, is deconstructed, highlighting their potential to capture intricate non-linear patterns through the strategic deployment of activation functions. This analysis showcases a merging of academic notions and actionable effects. The combined effect between learning rules and perceptron architectures forms the foundation of ANNs’ expertise in pattern recognition, prediction, and decision-making tasks. By comprehensively understanding these underpinnings, researchers and practitioners can connect the potential of ANNs across diverse domains.
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    HARNESSING INTERNET OF THINGS (IOT) FOR OPERATIONAL EFFICIENCY IN RETAIL STORES: INNOVATIONS, BENEFITS, AND CHALLENGES (Book Chapter)
    (Springer Science and Business Media Deutschland GmbH, 2024) Karthikeyan K; Brindha T.C; Sujaritha D.; Dhanasekar E; Mathuthra O; Gowrishankar R
    This study explores how the Internet of Things (IoT) positively impacts retail store operations, integrating retail marketing, working capital, analytics, and cloud systems to achieve greater control over data and enhance technological sophistication. IoT supports decision-making in product category management, pricing strategies, promotional mix, communication channels, supply chain management, customer care services, cash management, payment systems, inventory management, data privacy and security, feature customization, and employee management through the automation of big data analysis. By addressing challenges in retail store operations, the study emphasizes the need for specific and comprehensive conceptualizations and measures of analysis within organized retail outlets. The article defines and operationalizes control activities to capture retail store effectiveness using IoT, based on data collected from 230 functional heads in organized retail stores whose well adopted with IoT. Focusing on quantitative measure validation, the study confirms that retail heads can make strategic decisions for retail expansion by leveraging IoT in their businesses. The findings are validated through simple percentage analysis, ANOVA, correlation, regression, and the SEM model, presenting both theoretical and practical implications. The Internet of Things has impact on the retail store operation and performance. Further the key variables in the segment should given focus analysis for rapid growth and development of retail sectors.
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    INTRODUCTION TO SMART AND INTELLIGENT NANOMATERIALS FOR BIOSENSORS (Book Chapter)
    (Elsevier, 2024-1) Natarajan, Arunadevi; Singh, Kshitij RB; Rai, Harshita; Singh, Jay; Pandey, Shyam S; Singh, Ravindra Pratap; Malhotra, Bansi D
    The upgraded version of nanotechnology is the development of novel material with multiple functionalities by integrating the fields of physics, chemistry, material science, engineering, and biotechnology. The small-sized nanoparticles have outstanding characteristics like improved absorption, large surface area, quantum effects, and high reactivity. Its high targeting nature, unpredictable side effects, and drug delivery to the healthy tissues when exposed largely are the major drawbacks in the biomedical field compared with other areas. Smart and intelligent materials are developed to overcome all these challenges. It offers important prospects like flexibility, sensitivity, selectivity, easily portable, and user friendly, which provides fair and accurate detection of diseases. The integration of nanotechnology-based diagnostics devices with IoT will deliver positive impacts in the food, environment, medical, and agriculture sectors. This chapter provides an overview of classification, tuneable properties of the smart material by varying external stimuli, and recent applications in the biomedical area.
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    HUMAN DIGITAL TWIN PROCESSES AND THEIR FUTURE
    (Springer Nature, 2024-01-01) Gnanamalar R, Hepziba
    In recent years, the concept of the human digital twin has emerged as a promising approach to revolutionize healthcare and unlock the full potential of precision medicine. By creating a virtual replica of an individual’s biological, physiological, and behavioral characteristics, a human digital twin can be used to develop personalized treatment plans, improve disease prevention, and discover genetics that contribute to health and disease. A human digital twin enables a deeper understanding of the complex interplay between environmental and lifestyle factors. It is used in various industries, such as aerospace and automotive, to optimize product design, predict performance, and improve maintenance. However, the application of this concept to human health is still in its early stages. Significant challenges remain to be overcome, and significant gains could be realized. One of the key benefits of the human digital twin is the ability to integrate and analyze large numbers of data from various sources, such as electronic medical records, genomic information, wearable devices, and even social media. A comprehensive view of human health can identify early warning signs of disease, enabling timely intervention and prevention strategies. For example, digital twins can be used to predict an individual’s risk of developing diabetes on the basis of their genetic makeup, lifestyle, and environmental factors, allowing healthcare providers to tailor diet and exercise regimens to reduce that risk. Plans can be recommended. The human digital twin could unlock the full potential of precision medicine and transform medicine as we know it. Digital twins provide comprehensive and dynamic representations of an individual’s health status to improve patient outcomes, provide personalized prevention and treatment, and broaden our understanding of the complex factors that influence health and disease. Strategies can be formulated. However, realizing these benefits will require overcoming significant technical, ethical, and regulatory challenges and fostering collaboration among researchers, healthcare providers, patients, and policymakers. The successful integration of the human digital twin into healthcare will undoubtedly pave the way for a new era of personalized, data-driven medicine that improves individual and collective health. This chapter consists of seven sections. Section “Introduction” discusses the history, evolution, process, advantages, and disadvantages of human digital twin technologies. Section “HDT Process with Various Recent Technologies” deliberates about the integration of various recent technologies with human digital twin (HDTs). A conceptual paradigm for HDTs is described in section “Conceptual Paradigm for DT and HDT”. An HDT and a human being are compared in section “Human Digital Twin Technology and Human Beings”. The different types of wearable technologies featuring HDTs are categorized in section “Wearable Devices and HDTs”. Section “Development of Human Digital Twins in Healthcare” provides information on the development of HDTs in healthcare. Finally, section “Conclusion” concludes all other sections.
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    REFINING TALENT MANAGEMENT: NAVIGATING EMPLOYEE PERFORMANCE THROUGH ORGANIZATIONAL COMMITMENT
    (Springer Science and Business Media Deutschland GmbH, 2024) Mythili, D; Saranya, V; Akalya, V; Ranganayaki, C; Mathiyarasan, M
    In today's global landscape, marked by intense competition, innovation, and creativity emerge as pivotal corporate strategies to excel and elevate oneself in this knowledge-driven economy. Within this context, human resources stand out as the most valuable and indispensable asset, playing a crucial role in the smooth and effective functioning of organizations. The success of any enterprise hinges on the commitment, hard work, and loyalty of its employees, making talent the primary driving force behind organizational triumphs. Consequently, effective workforce management becomes paramount for organizations seeking to thrive in the modern economic climate. For HR managers, the task of identifying, recruiting, selecting, and retaining competent individuals tailored to their specific industrial needs poses a significant challenge. This paper is dedicated to providing insights into how talent management strategies, particularly within the Information Technology sector, contribute to an organization's competitive advantage.
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    PERSEVERANCE MATTERS: ADULT RISK ATTITUDE TOWARD HEALTH AND PSYCHOLOGICAL WELL-BEING
    (Elsevier, 2024-01-01) Maya, R; Anjana, V.J; Nanthini, B
    Health holds immense importance in our lives, affecting multiple dimensions of our well-being and life satisfaction. The significance of well-being in our lives cannot be overstated, influencing diverse facets of our overall health and life satisfaction. A cautious and health-conscious approach often leads to better psychological well-being because it impacts our ability to pursue our dreams, enjoy daily activities, and contribute to meaningful society. The paper is focused on to examine the relationship between health-risk attitude and psychological well-being. A total of 132 samples (age between 18 and 45) were collected by using convenience sampling method. Sociodemographic details, 13 items Health-Risk Attitude Scale (HRAS-13), and 18 items Ryff Psychological well-being were used to collect data. Results show that based on standardized coefficient, autonomy comes in second place to positive relation with others (0.336) as the most significant variables to extract health-risk mindset (0.296). Individual perception toward health can impact their overall psychological well-being. It empowers individuals to take charge of their health, enables effective health interventions, and contributes to building healthier and happier communities. Prioritizing health through a balanced lifestyle, regular exercise, proper nutrition, and mental well-being strategies is essential for a fulfilling and purposeful life.
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    POTASSIUM-ION BATTERIES RECENT TRENDS AND CHALLENGES
    (CRC Press, 2024-01-01) Nithya, C; Kiruthiga, R
    Potassium-ion batteries (KIBs) have attracted much attention for their multifarious applications in next-generation energy storage devices because of their high abundance and low cost. KIBs utilize K+ ions for charge storage, which gained considerable attention due to their unique properties. The low redox potential of K+/K couple, along with a great mobility of K+ ion in organic electrolytes due to the weak Lewis acidic nature of non-aqueous electrolytes, results in high energy density with fast reaction kinetics. However, the development of suitable electrode and electrolyte materials is essential for large-scale grid storage applications. Recent research on KIBs is still under progress; however, the overall understanding of the shortcomings of each component and possible approaches for further development is essential for the exploration of new electrode materials/electrolytes and designing strategies for high-performing potassium-ion batteries. In this chapter, we systematically summarize and discuss the recent progress of electrode materials on the nanoscale and address the choice of electrolytes as they directly influence the electrochemical performance of KIBs. Here we further discuss the challenging issues associated with intercalation, conversion, and alloying-based electrode materials. In the end, rational designing of appropriate and efficient electrode materials is also proposed for further developing KIBs for practical applications.
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    IMPACT OF SELECT COMPANIES FROM INFORMATION TECHNOLOGY SECTORAL INDICES
    (Springer Science and Business Media Deutschland GmbH, 2024) Lekha Shree, S; Amudha, A; Baby, M K; Bharathi K, Dhivya
    Performance on the stock market as a whole is thought to reflect a country’s financial and economic condition. The market for shares is a crucial component of the financial system and a catalyst for industrial growth. Since share prices are extremely sensitive to changes in the economic and political arenas, they serve as a window into how a nation’s economy is doing. Additionally, company investment as well as economic expansion may be directly impacted by stock market volatility. With the rise in F.I.I investment, the two main issues of return and volatility have taken on greater significance for Indian Investors, regulators, brokers, policymakers, dealers, and researchers. Analyzing how Sectoral listings influence the pricing of certain companies is one of the study’s main goals. The data utilized throughout this probe are secondary. The current research is centered on industrialization and Sectoral analysis. The researcher examined the stock returns and the effects of certain firms from the NSE Sectoral Indices using a variety of statistical methodologies. From the standpoint of an investor, these research results might prove highly beneficial for assisting them in deciding what type of derivative to buy in particular those with a wider range and yield greater financial success.
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    ENHANCING HEALTHCARE DECISION SUPPORT SYSTEMS WITH ADVANCED ANALYTICS AND MACHINE LEARNING TECHNIQUES
    (IGI Global, 2024-07-23) Renugadevi R, R; Ranjith Kumar, P; Kalaiarasi, G; Raj A, Arul Edwin; Settu, Sivabalan; Ruthravarshini, R
    This chapter outlines the need for intelligent decision support, growing complexity of healthcare systems, key concepts of advanced analytics, and elucidating techniques such as data preprocessing and feature engineering. The survey extends to address challenges and opportunities within the realm of healthcare analytics, offering insights into ethical considerations, privacy concerns, and regulatory implications. Real-world case studies serve to illuminate successful implementations and extract valuable lessons, fostering a deeper understanding of practical applications. This chapter explores the integration of analytics with electronic health records (EHR), examining strategies to enhance decision support through the utilization of comprehensive healthcare data. The chapter, by distilling pertinent information from myriad sources, aims to provide a valuable resource for researchers, practitioners, and policymakers navigating the dynamic intersection of advanced analytics, machine learning, and healthcare decision support systems.
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    DROWSINESS DETECTION IN DRIVERS: A MACHINE LEARNING APPROACH USING HOUGH CIRCLE CLASSIFICATION ALGORITHM FOR EYE RETINA IMAGES
    (CRC Press, 2024-01) Viji Gripsy, J; Sheela Selvakumari N., N A; Sahul Hameed, S; Jamila Begam, M
    Driving has become one of the most important routine works in our everyday life. For many people it is difficult to imagine a life without driving. Accidents are a persistent and inevitable part of driving. Hence automatic drowsiness detection has become a major challenge in research perspective. In this research work, drowsiness detection technique has been implemented using machine learning (ML) techniques. In this methodology, a preprocessing, segmentation, feature extraction and classification steps to perform. This work proposed hough circle (HC) classification algorithm for detecting drowsiness of the eye retina images. The primary objective of this study is to evaluate the performance of the suggested hierarchical clustering method through the utilization of diverse metrics. According to the results of the performance evaluation, the suggested HC algorithm demonstrated a 90.8% accuracy rate, along with a minimal execution time and a lower error rate compared to existing algorithms. © 2025 selection and editorial matter, Jaiteg Singh, S B Goyal, Rajesh Kumar Kaushal, Naveen Kumar and Sukhjit Singh Sehra; individual chapters, the contributors.