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Particle swarm optimization- based support vector regression for predictions: Approach and applications
For centuries, people have drawn inspiration from nature, and there is always more to learn and discover. The Particle Swarm Optimization (PSO) algorithm, a stochastic optimization algorithm based on population and inspired by the intelligent collective behavior of certain animals like fish schools or flocks of birds, is one of the most well-known nature-inspired algorithms presented in this work. As more was known about the fundamentals of this methodology, researchers produced new iterations to satisfy varying needs, new applications in diverse domains, theoretical research on the effects of different parameters, and a multitude of algorithm variations. PSO-support vector regression (SVR) is one such variant of this algorithm. SVR is a kind of Support Vector Machine (SVM) that solves regression problems. It seeks to identify a function that diverges from the actual values observed by no more than a given margin. The main idea is to retain the error under a certain threshold. PSO optimizes SVR parameters, including regularization, epsilon, and kernel parameters. This combination takes advantage of the strengths of both approaches. In this chapter, we will discuss the importance of the PSO-SVR algorithm in predicting the outcomes of real-world applications classified as healthcare, environmental, industrial, commercial, smart city, and other broad applications. 2025 selection and editorial matter, Sulabh Bansal, Aprna Tripathi, Shilpa Srivastava and Prem Prakash Vuppuluri; individual chapters, the contributors. -
Real- coded genetic algorithm for optimal ordering and pricing in segmented market with freshness and price- dependent demand, advance payment, and trade credit
We study the inventory model of a product having demand affected by its freshness and selling price in the context of supply chains, freshness, and price-dependent demand, where the supplier is dominated, as is usually the case with producers of agri-based products. The product when received exhibits heterogeneous quality. The retailer subdivides the product into quality-dependent segments, which he sells simultaneously during the selling season at prices commensurate with the quality. The sizes of the segments are random variables. The supplier can get a partial advance payment from the dominant retailer by providing a discount on the partial advance with the proportion of partial payment as well as the epoch of partial payment chosen by the supplier. The retailer can, at times, choose the advance proportion to be paid, and the discounted price which we call the endogenous case but takes a loan for the advance payment from a financer, whom he repays with interest when a delayed payment period permitted by the supplier gets over. The retailer in turn gets some time before he can pay his remaining dues and pays the supplier a fraction of the cost price commensurate with the quality of the product. Lost sales shortages are considered for fresh items. The model is aimed at obtaining optimal values of ordering amount, selling price, and discounted selling prices for the various segments. It is also aimed to obtain advance proportion and the discount on advance payment for the endogenous case. Real-coded genetic algorithm (RCGA) and Hybrid RCGA have been used to obtain the optimal solutions for numerical examples and the results are compared. Finally, sensitivity analysis to evaluate the effects of changes in some parameter values has also been presented. 2025 selection and editorial matter, Sulabh Bansal, Aprna Tripathi, Shilpa Srivastava and Prem Prakash Vuppuluri; individual chapters, the contributors. -
Introduction to optimization: Techniques and applications in engineering
A key idea in computer science, engineering, economics, and mathematics is optimization, which seeks to identify the optimal option from a range of workable options. An overview of optimization, its importance, and its many uses are given in this chapter. It highlights various forms of optimization, such as linear, nonlinear, convex, and combinatorial optimization, and examines the fundamental concepts of optimization, such as objective functions, constraints, and viable regions. In addition to contemporary strategies such as evolutionary algorithms, machine learning-based optimization, and metaheuristic techniques like genetic algorithms and simulated annealing, the chapter explores few traditional optimization techniques. Real-world applications in banking, logistics, AI, and industrial process optimization are also covered. This chapter offers insights into issue formulation, solution approaches, and efficiency concerns, with a focus on both theoretical underpinnings and real-world applications. It also presents important optimization tools and software that are frequently used in both industry and academics. By the end of this chapter, readers will have a basic understanding of optimization concepts that will allow them to use these ideas to effectively tackle challenging issues. 2025 selection and editorial matter, Sulabh Bansal, Aprna Tripathi, Shilpa Srivastava and Prem Prakash Vuppuluri; individual chapters, the contributors. -
Time-Frequency Analysis of ECG Signal
Time-frequency analysis (TFA), especially well suited for biomedical applications, is a potent method for deciphering non-stationary signals, where frequency characteristics vary with time. These dynamic signals are too complex for conventional frequency analysis approaches, which calls for sophisticated techniques like the discrete wavelet transform, continuous wavelet transform, and short-time Fourier transform. This research focuses on the uses of TFA techniques in biomedical signal processing and how well they expose transitory phenomena and temporal patterns that are missed by conventional methods. In particular, we look at how TFA is applied to the analysis of electrocardiogram (ECG) signals. The chapter discusses baseline wander, notch filtering, and low-pass filtering as crucial pre-processing techniques for clean ECG readings. Furthermore, we present the symbolic aggregate approximation paradigm for effective data retrieval and storage. 2026 selection and editorial matter, Ganesh R. Naik. -
Skilful Leadership and Management: The Importance of Emotional Intelligence
Emotional intelligence (EI) has become more important in the study of organisational behaviour, particularly in relation to management and effective leadership. EI is the ability to identify, understand, and control ones own emotions as well as those of others. Those with high EI find it easier to navigate complex social interactions, build strong relationships, and resolve conflicts. EI is the ability to recognise, manage, and evaluate emotions. The ability to express ones emotions in a healthy way and to empathise with others is a sign of great emotional intelligence in a leader, and it will enhance both performance and workplace relationships. The study employed a range of machine learning (ML) methods, such as ANN, BRDT, Naive Bayes, and Random Forest, to predict EI based on behaviour credits. ML approaches have become more and more common. The results showed that the BRDT has the accuracy of 98.3 which is higher in all other machine learning models and gives better results. Seven behavioural attributes and seven additional individual attributes made up the prediction dataset. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
The Tracking of (Machine) Intelligences Evolution Using an Intelligence Catalogue
The purpose is to investigate the usage of capabilities that identify intelligence in the scientific discourse on AI, to track the evolution of the mythology around intelligence and how it appears in both people and computers across time. The form of a catalog, and covering various domains, including AI, intelligence capabilities, and related traits that are used to define intelligence were extracted from prior research in this area. Even if intelligence is still a nebulous, ill-defined term, examining and comprehending the language surrounding it could influence how we utilize it as well as how intelligent artifacts are made now and in the future. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
State-of-the-Art and Upcoming Trends in IoT-Enabled Smart Cities
Modern cities tremendous development of urbanization necessitates smart responses to pressing problems like mobility, medical care, power, and civil construction. The Internet of Things (IoT), which can use sustainable data and communication innovations, is evolving into the foundation for the upcoming trends of smart cities. To meet the demands of the expanding populace, several demands of the smart city must be taken into account. The IoT expansion has greatly generated a variety of study avenues for the smart city on the flip side of developing innovation. The suggested research proposal offers the analytic network procedure (ANP) for analyzing smart cities while maintaining in mind application instances of the smart city. In complicated circumstances when there are ambiguous options, the ANP technique performs effectively. The projected methods experimental findings demonstrate its viability for use case-based evaluation of IoT-enabled smart cities. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Think Big with Big Data: Finding Appropriate Big Data Strategies for Corporate Cultures
The aim of this research is to learn how big data strategies (BDS) as a corporate culture might improve confidence and cooperative performance across civil and defence sectors involved in disaster relief activities. The research conceptualized a unique conceptual framework to demonstrate, employing the competitive value model (CVM), how BDS influences swift confidence (SC) and cooperative performance (CP) beneath the moderating impact of the corporate culture. The findings have four significant consequences. Initially, the BDS has a strong beneficial influence on SC and CP. Secondly, neither adaptable orientation (AO) nor regulated orientation (RO) had any effect on constructing SC. Thirdly, AO has a strong and beneficial moderating influence on the path connecting BDS and CP. As a result, RO shows a considerable negative moderating impact on the path linking BDS and CP. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Future Battlefield System Using Graph Database and Internet of Things (IoT)
The Internet of Things (IoT) concept is rapidly evolving and is expected to influence each field of the computational realm. These advances have an impact on any nations defence force. The defense industrys solution mostly depends on detectors and their installations. The major goal of sensory statistics is to provide information that may be used for strategic choices and evaluation in future battling fields. Each piece of statistics, from documenting a soldiers essential health metrics to its ammunition, weapons, and position circumstance, has a function and is especially important to the strategic commander stationed in the control unit. This research proposes an innovative approach that combines the IoTs with the growing graph database to produce a contextual consciousness regarding each characteristic of the personnel on the battlefield. We show a projected future battlefield application condition in which we explore the graph database for contextual consciousness patterns to gain a strategic benefit over our competitors. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Concerns in IoT Environments: Adoption, Architecture, and Innovation of Enterprise IoT Systems
The Internet of Things (IoT) has received a lot of interest in recent times. IoT depicts the upcoming internet and is defined as an environment of linked gadgets, computational processes, and other items that collaborate to transmit information or data with greater ease and economic advantages. Nevertheless, because of the presence of numerous concerns, IoT adoption, architecture, and innovation continue concerns. As a result, the purpose of this study was to identify and analyze the concerns in the adoption, architecture, and innovation of IoT systems in construction enterprises in the Indian environment. The research analysis and professional comments have been employed to identify the barriers to IoT adoption, architecture, and innovation. This research may assist professionals and policymakers in addressing barriers to successful IoT adoption and spread. At last, findings and potential research possibilities are provided. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Monitoring the Development of the IoT Concept in Various Application Domains
For several decades, the concept and technology of combining actuators and sensors into a system to monitor and operate tangible structures distantly was understood and developed. Nevertheless, slightly over a decade back, the notion of the Internet of Things (IoT) emerged and was utilized to merge such techniques into a prevalent architecture. The study outlines and addresses IoT conceptual structures suggested as part of continuing standardization attempts, layout problems regarding IoT hardware and software parts, and delegates of IoT application domains like healthcare, smart cities, the farming industry, and nano-scale uses. The research verifies the argument that an agreement on the precise scope of the IoTs will likely be formed, as enabling innovation evolves and novel application domains have been presented. Current modifications, nevertheless, are a bit muted, and their variants on application domains have been distinct, with statistics and information technologies serving a significant part in the IoT environment. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Research on Big Data for Industry 4.0 Cyber-Physical Systems
The objective of the revolution known as Industry 4.0 seeks to optimize goods creation based on consumer requirements, specifications for quality, and financial viability. Big data collected by the Internet of Things (IoT)-based commercial Cyber-Physical Systems (CPS) plays an essential part in boosting platform operation efficiency to promote throughput with improved consumer encounters in Industry 4.0. This study shows big databases derived from IoT-based Optical-Wireless CPS (OWCPSs) for optimizing the functioning of maintenance networks in the electronics-manufacturing Industry 4.0. This research collected and analyzed big databases including five parameters: data delivery, delay, overload, throughput, and package error percentage in OWCPSs. The information gathered is important for optimizing the functioning of service systems in the production of electronic goods Industry 4.0. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
An Empirical Research of AI Approaches in Electronic Engineering
The function of artificial intelligence (AI) in electronic engineering has been suggested to overcome the issue of an elevated structure error rate in electronic engineering. Using LPWAN innovation in AI as an instance, an innovative structure is presented to increase the safety of the Internet of Things wireless communication infrastructure. The business, processing of information, terminal accessibility, and communication innovation layers make up the majority of the framework. The empirical findings indicate that the structures setup transmits four types of data transfer directions every 30 seconds, and the receiver port constantly gathers the aforementioned command information for 4 hours, contrasted to the conventional framework (15.6 percent), and the package loss rate is 4 percent, significantly enhancing the systems throughput and processing performance. The developed framework is more stable, has a lower bit error rate, and may assist wireless communication effectively. It has been demonstrated that the novel design, using LPWAN innovation as an instance, has a big total capacity as well as a good performance of its electronic engineering connection. The structure number error rate is considerably lowered, and the signal structure number is highly accurate as an outcome. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Electronic Voting Systems Using a Blockchain-Based Encrypted Identity Management
The use of electronic voting technologies has grown in popularity as a way to make elections more secure and accessible. The implementation of blockchain-based encrypted identity management in electronic voting is explored in this study, which also offers a solid option to improve the reliability and credibility of voting systems. This study explores the possibilities for anonymous and transparent electronic voting while preserving voter privacy and anonymity by incorporating blockchain technology. It has always been challenging to create an electronic voting system that properly satisfies the requirements of administrators. This problem is now being resolved by blockchain technologies, which provide a distributed database with irreversible, encrypted identity management and secure transactions. A fascinating advancement in the realms of data innovation, dependability, and transparency is distributed ledger technology. Distributed ledger technology is commonly used in public blockchain. Virtually limitless potential for earning from sharing economies are provided by blockchain technology. This project aims to determine whether blockchain technology can be used to create electronic voting devices are used as a service. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Role of Additive Manufacturing and Thermal Spray Processed Materials in Electric Vehicle (EV) and Hybrid Electric Vehicle (HEV) Applications
Additive manufacturing (AM) significantly contributes to the development of electric vehicles (EVs) and hybrid electric vehicles (HEVs), providing lightweight, complex, and customized components. This study explores AMs role in advancing EV and HEV technology, with a special focus on integrating thermal spray coatings (TSCs) to enhance component performance. By employing TSCs in AM-fabricated components, manufacturers can improve surface characteristics, wear resistance, and corrosion protection critical factors for long-lasting EV/HEV systems. The synergy between AM and TSC enhances key parts such as battery enclosures, thermal management systems, and structural frameworks by optimizing their thermal insulation, durability, and energy efficiency. Additionally, AM enables efficient material use and lightweighting, which reduces vehicle weight and enhances energy conservation, addressing industry needs for sustainable solutions. This chapter reviews the current applications and future potential of TSC in AM components, highlighting its role in meeting the rigorous demands of the automotive sector. Findings suggest that combining AM and TSC opens pathways for advanced, sustainable EV and HEV designs, aligning with the global shift toward cleaner energy and resource-efficient manufacturing. 2026 selection and editorial matter, R. Suresh, C. Durga Prasad, Satish Kumar, K.N. Bharath, and Ajith G. Joshi; individual chapters, the contributors. -
DIGITIZATION AS A TOOL FOR CONSERVATION AND SUSTAINABILITY OF THE BUILT HERITAGES: A DISTANT DREAM OR AN EXISTING REALITY?
The idea of built heritage is as old as human civilization, and its objective stretches far beyond our understanding of the past. These built heritages as artifacts express humanitys history, diverse cultures, skills, and experiences exchanged and shared across generations. They provide specific characteristics for places, making them the material manifestation of social, economic, political, territorial, and environmental values. Unfortunately, natural and man-made disasters have been posing repetitive threats to the sustainability of these built heritages. Therefore, there has been a growing interest in and calls for the conservation and protection of these built heritages. As we have ushered in the era of technical advancements, the world is abuzz with the word digitization. The continual adaptation of digital technologies in various spheres of life and the growing popularity of digital devices have revolutionized consumption patterns in recent times. Conse quently, the use of digital technologies to conserve and promote monuments has gained prevalence in various parts of the West. Further, in recent times, this trend seems to be catching on in developing countries like India as well. However, given the novelty of the concept, the adoption and accep tance of the concept of digitized monuments are still surrounded by many unanswered questions. The study aims to reveal the opinions of the tourists visiting the various built heritage sites and understand their experiences with digital heritage and its influence on the tourist experience while visiting a built heritage monument. The chapter also tries to dive deep into their thoughts and understanding of the influence of digitization in promoting sustainable consumption. In an attempt to achieve the aforementioned objective of the study, the data would be collected from the urban populace of India. A qualitative approach has been adopted to conduct the study, and semi-structured inter view forms consisting of open-ended questions have been used to collect the data. MAXQDA software would be employed to analyze the responses collected and draw conclusions from the data collected. The study intends to bring out the opinion of tourists in regard to the digitization of built heritage in India. As the trend of digitization of built heritage is still in its nascent stage in India, only a few heritage monuments have been digitized in recent times. 2025 by Apple Academic Press, Inc. -
Advancing Interpretable Machine Learning: Principles, Challenges, and Practical Insights
[No abstract available] -
Role of Augmented Reality (AR) in Promoting Media Literacy and Sustainability Awareness: A Mixed Method Approach
Augmented reality (AR) has emerged as a transformative tool in education, offering immersive experiences that enhance engagement and understanding across various domains. This study explores the potential of AR in promoting media literacy and sustainability awareness, two critical competencies in the modern information landscape. Through a mixed-methods approach, the research investigates how AR interventions can improve individuals ability to critically assess media content while simultaneously raising awareness about environmental sustainability. The study employs pre- and post-test evaluations, focus groups, and user interaction data to measure changes in media literacy and sustainability awareness among participants exposed to AR-based educational content. Findings indicate that AR significantly enhances media literacy by enabling users to better identify fake news, understand media bias, and critically evaluate information sources. The implications of these findings suggest that AR is not only a powerful tool for enhancing media literacy and sustainability awareness but also a catalyst for promoting informed, responsible, and proactive citizenship in the digital age. 2026 selection and editorial matter, Sonal Trivedi, Vishal Jain, Balamurugan Balusamy, Subhendu Pani, and Danish Ather.