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Navigating the self-service revolution with smart machines
The rise of smart machines transforms service interactions in various sectors, ushering in the self-service revolution. From AI-powered kiosks in airports to automated checkouts in retail and intelligent virtual assistants in banking, these technologies redefine convenience, efficiency, and customer expectations. As businesses adopt self-service solutions, the challenge lies in implementing the right technologies and ensuring they enhance the user experience. Navigating this revolution requires a careful balance between automation and human-centered design, where smart machines serve as tools to empower human interaction. Navigating the Self-Service Revolution With Smart Machines explores self-service technology as a transformative force in the retail landscape, scrutinizing its complexities, dualities, and far-reaching implications across diverse environments. It delves into the multifaceted nature of self-service technology, examining how its rise reshapes customer experiences, operational efficiencies, and business models in urban centers while contrasting these developments with the challenges in rural areas. This book covers topics such as machine learning, automation, consumer behavior, and is a useful resource for business owners, computer engineers, academicians, researchers, and data scientists. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Practical applications of self-service technologies across industries
Self-service technologies (SSTs) have practical applications across various industries, improving operational efficiency and customer satisfaction. In retail, self-checkout kiosks and mobile payment apps streamline the purchasing process, reducing waiting times and enhancing convenience. The hospitality industry utilizes SSTs through self-service check-in kiosks and digital concierge services. In healthcare, patients can use self-service portals to schedule appointments, access medical records, and complete pre-visit forms. In banking and finance, ATMs, mobile apps, and AI-powered chatbots offer access to essential services without the need for in-person assistance. These practical implementations demonstrate the versatility and importance of SSTs in modernizing service delivery across sectors. Practical Applications of Self-Service Technologies Across Industries explores self-service technology (SST) as a transformative force across industries. It examines practical applications of SST for improved customer service and business operations. This book covers topics such as smart technology, consumer behavior, and blockchain, and is a useful resource for business owners, computer engineers, academicians, researchers, and data scientists. 2025 by IGI Global Scientific Publishing. All rights reserved. -
A hybrid ensemble framework with particle swarm optimization for network anomaly detection
The increasing complexity of cyber threats necessitates the development of a robust and adaptive Intrusion Detection System (IDS) capable of safeguarding network infrastructures. Traditional IDS approaches often struggle to detect sophisticated attacks due to their reliance on predefined patterns. We propose an adaptive particle swarm optimization (PSO)-optimized ensemble learning framework tailored to address these challenges in modern IDS applications. Our approach leverages the NSL-KDD and CICIDS datasets to ensure the IDS is trained and evaluated on data reflecting current network behaviours and threat landscapes. We evaluate multiple machine learning models, including Decision Trees (DT), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Random Forests (RF), and an ensemble of these models for both binary and multi-class classification tasks. By incorporating adaptive mechanisms within the PSO algorithm, our framework dynamically adjusts hyperparameters during optimization, enhancing model robustness and convergence speed. The proposed framework is also benchmarked against state-of-the-art IDS approaches, including ASRL and PSOGSA. Empirical evaluations demonstrate that the ensemble model achieves superior detection accuracy and reduced false positive rates, thereby advancing the efficacy of intrusion detection methodologies. The Author(s) 2025. -
A novel univariate feature selection with ANOVA F-test-based machine learning model for Intrusion Detection Framework of Robotics system
Robotic systems have become popular across various industries, ranging from manufacturing and healthcare to logistics and space exploration. However, increasing the integration of robotic systems into critical infrastructures exposes devices to cybersecurity threats. The intrusion detection system (IDS) plays a vital role in safeguarding the systems from malicious activities and unauthorized access. This paper presents a novel, robotics-aware IDS framework incorporating hybrid feature selection and tailored classification strategies for robotic system. To evaluate the efficacy of the presented framework, an algorithm is also designed and tested using multiple machine-learning techniques. The NSL-KDD dataset is utilized for training and evaluating machine learning models due to the inclusion of a wide range of attack scenarios and normal instances. The results demonstrate that the proposed IDS effectively classifies cyberattacks relevant to robotic systems. The presented framework is also evaluated against existing IDS approaches in robotic systems. The results demonstrate that the proposed approach exhibits better results in terms of accuracy, robustness, and adaptability to emerging cyber threats. 2025 The Author(s). Published with license by Taylor & Francis Group, LLC. -
Investigation of Spectroscopic Parameters and Trap Parameters of Eu3+-Activated Y2SiO5 Phosphors for Display and Dosimetry Applications
Using the solid-state reaction technique, varied Y2SiO5 phosphors activated by europium (Eu3+) ions at varied concentrations were made at calcination temperatures of 1000 C and 1250 C during sintering in an air environment. The XRD technique identified the monoclinic structure, and the FTIR technique was used to analyze the generated phosphors. Photoluminescence emission and excitation patterns were measured using varying concentrations of Eu3+ ions. The optimal strength was observed at a 2.0 mol% concentration. Emission peaks were detected at 582 nm and 589 nm for the 5D0?7F1 transition and at 601 nm, 613 nm, and 632 nm for the 5D0?7F2 transition under 263 nm excitation. Because Eu3+ is naturally bright, these emission peaks show how ions change from one excited state to another. This makes them useful for making phosphors that emit red light for use in optoelectronics and flexible displays. Based on the computed (1931 CIE) chromaticity coordinates for the photoluminescence emission spectra, it was determined that the produced phosphor may be used in light-emitting diodes. The TL glow curve was examined for various doping ion concentrations and durations of UV exposure levels, revealing a broad peak at 183 C. Using computerized glow curve deconvolution (CGCD), we calculated the kinetic parameters. 2024 by the authors. -
Artificial Intelligence and Human Rights: Safeguarding Data Privacy and Ethical Values in the Digital Age
In the 21st century, the rapid advancement of Artificial Intelligence (AI) presents governments with the dual challenge of fostering technological innovation while safeguarding the rights and freedoms of their citizens. This comparative analysis examines how India addresses data privacy, human rights and ethical concerns arising from AI deployment. The study begins by tracing the evolution of Indias legal and policy framework, from the Information Technology Act, 2000 to the Digital Personal Data Protection Act, 2023, assessing how these measures respond to emerging AI-related threats and their implications for individual privacy and civil liberties. Further, the paper evaluates Indias engagement with international standards and best practices. Lastly, the study examines ethical and human rights challenges posedby AIapplications, including algorithmic bias, automateddecisionmaking, surveillance and deepfakes with a focus on high-stakes sectors like law enforcement, and welfare delivery. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
Safeguarding children's rights in the digital age: A critical analysis of India's cyber laws
This chapter critically examines the legal framework governing child safety in India's digital landscape. It analyzes the IT Act, 2000, POCSO Act, 2012 and the DPDP Act, 2023, highlighting their relevance in combating cyber threats faced by children. It also discusses implementation gaps, lack of digital literacy and enforcement issues. Further, drawing insights from global practices. Thereafter, chapter emphasizes the importance of multi-stakeholder collaboration and proactive regulatory reforms to ensure a safe digital environment for minors. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Smart Villages and Cities: A Sustainable Imperative for Emerging IndiaA Journey from Painful to Thoughtful
Smart cities and villages refer to making cities and villages more beautiful and fuller of all the requirements that can make the lives of the people living in the cities and villages more comfortable and satisfactory. On 25th June 2015, this initiative was launched by the Central government, and the time limit for completion was 5 years, but due to the pandemic situation, it was further extended by the central government, and this year, all the states are required to submit their respective report about the progress in this regard. In India, 70% of the total population lives in Villages and only 30% are part of urban areas. In India, villages should be a combination of smart and digital villages. If the work is done at the grassroots level, it will 436solve many problems, such as migration, employment, poverty, education, and medical facilities. Sustainability of the environment is one of the basic requirements for humankind globally. In 2015, the United Nations introduced Sustainable Development Goals (SDGs) to address environmental and economic issues and challenges and to provide and promote better and more sustainable pathways for the future generation globally. It is not a matter of concern for a group of countries, but every single species is affected by environmental issues. Owing to the dense population, infrastructure, buildings, and commercialization, cities are more prone and susceptible to the effects of climate change and natural disasters. while increasing the sustainability of urbanization processes is necessary to protect the environment, reduce the risk of disasters, and address climate change, building urban resilience is essential to preventing losses in terms of people, society, and the economy. Goal 11 of the SDGs deals with Sustainable Cities and Communities. Focusing more on urbanization in such a way that comprehensively controls and plans the development of cities and villages using different technologies and sciences to make them smart cities and villages. By ensuring that smart cities and villages will help us in many ways, they can control the migration of the human population from one city to another in search of employment and basic needs. In India, the government should focus on creating smart villages rather than smart cities. Around 69% of the population lives in the villages, and they do not have the basic amenities like electricity, clean drinking water, roads, transportation, and a source of employment. 2026 Jenny Stanford Publishing Pte. Ltd. -
Enhancing IoT Security Through Multilayer Unsupervised Learning and Hybrid Models
This research addresses the challenge of limited unsupervised learning in current IoT security research, which heavily relies on labelled datasets, hindering the detection of unknown threats. To overcome this constraint, the study proposes a sophisticated methodology integrating K-means clustering, autoencoders, and a hybrid model (combining both). The aim is to enhance detection capabilities without being reliant on prior labelled data. Emphasizing the need to go beyond traditional models, the research underscores the significance of incorporating a diverse range of smart home IoT devices to gain comprehensive insights. Tests conducted on the N-BaIoT dataset, which incorporates authentic traffic data from nine commercial IoT devices afflicted with Mirai and BASH-LITE infections, demonstrate the effectiveness of the suggested models. K-means clustering demonstrates excellence in precision, recall, and F1-scores, particularly in Doorbell and Thermostat categories. The Hybrid model consistently achieves high precision and recall metrics across various device categories by leveraging the strengths of both Kmeans and autoencoder techniques. Notably, the Autoencoder model stands out for its exceptional ability to achieve a perfect 100% detection rate for anomalies across all devices. This study highlights the robust performance of the proposed unsupervised learning models, emphasizing their strengths and potential areas for refinement in enhancing IoT network security. 2024 IEEE. -
Edge Computing in Aerial Imaging A Research Perspective
Internet of Drones (IoD) is a field that has a vast scope for improvement due to its high adaptability and complex problem statements. Aerial vehicles have been employed in various applications such as rescue operations, agriculture, crop productivity analysis, disaster management, etc. As computing and storage power have increased, satellite imaging and drone imaging have become possible, with vast datasets available for study and experiments. The recent work lies in the edge computing sector, where the captured aerial images are processed at the edge. Our paper focuses on the algorithms and technologies that easily facilitate aerial image processing. The applications and their architectures are focused on which can efficiently function using aerial processing. The various research perspectives in aerial imaging are concentrated on paving the way for further research. 2024 Scrivener Publishing LLC. All rights reserved. -
Optimized Multi-Scale Attention Convolutional Neural Network for Micro-Grid Energy Management System Employing in Internet of Things
The combination of micro-grid energy management systems (EMSs) with the Internet of Things (IoT) offers a promising way to improve energy use and distribution. However, challenges such as device compatibility and the difficulty of managing energy efficiently make it hard to implement these systems effectively. This study offers a significant advancement in energy management by using IoT for microgrid systems. An Optimized Multi-scale Attention Convolutional Neural Network for microgrid EMS employing IoT (OMACNN-MGEMS-IoT) is proposed in this study, which enables efficient monitoring and control of energy resources. The proposed model's input data are gathered from the MQTT dataset. This research employs a Regularized Bias-aware Ensemble Kalman Filter (RBAEKF) for pre-processing input data, ensuring the removal of outliers and updating missing values. The MACNN is then used for effective fault detection within the microgrid. To enhance its performance, the Sheep Flock Optimization Algorithm (SFOA) is introduced to optimize the MACNN parameters, ensuring accurate fault detection. Implemented on the MATLAB platform, the performance of the OMACNN-MGEMS-IoT method is assessed through various performance metrics, demonstrating significant improvements. Notably, the proposed method achieves higher cost reductions of 25%, 22%, and 26% compared to existing approaches such as the IoT platform for energy management in multi-micro grid systems (IoT-PEM-MMS), a micro-grid system infrastructure implementing IoT for efficient energy management in buildings (MSII-IoT-EEM) and a hybrid deep learning-based online energy management scheme for industrial microgrids (HDL-OEM-IM). The findings highlight the impact of the proposed OMACNN-MGEMS-IoT method in enhancing energy efficiency and cost-effectiveness in microgrid systems. 2025 John Wiley & Sons Ltd. -
Optimized Multi-Scale Attention Convolutional Neural Network for Micro-Grid Energy Management System Employing in Internet of Things
The combination of micro-grid energy management systems (EMSs) with the Internet of Things (IoT) offers a promising way to improve energy use and distribution. However, challenges such as device compatibility and the difficulty of managing energy efficiently make it hard to implement these systems effectively. This study offers a significant advancement in energy management by using IoT for microgrid systems. An Optimized Multi-scale Attention Convolutional Neural Network for microgrid EMS employing IoT (OMACNN-MGEMS-IoT) is proposed in this study, which enables efficient monitoring and control of energy resources. The proposed model's input data are gathered from the MQTT dataset. This research employs a Regularized Bias-aware Ensemble Kalman Filter (RBAEKF) for pre-processing input data, ensuring the removal of outliers and updating missing values. The MACNN is then used for effective fault detection within the microgrid. To enhance its performance, the Sheep Flock Optimization Algorithm (SFOA) is introduced to optimize the MACNN parameters, ensuring accurate fault detection. Implemented on the MATLAB platform, the performance of the OMACNN-MGEMS-IoT method is assessed through various performance metrics, demonstrating significant improvements. Notably, the proposed method achieves higher cost reductions of 25%, 22%, and 26% compared to existing approaches such as the IoT platform for energy management in multi-micro grid systems (IoT-PEM-MMS), a micro-grid system infrastructure implementing IoT for efficient energy management in buildings (MSII-IoT-EEM) and a hybrid deep learning-based online energy management scheme for industrial microgrids (HDL-OEM-IM). The findings highlight the impact of the proposed OMACNN-MGEMS-IoT method in enhancing energy efficiency and cost-effectiveness in microgrid systems. 2025 John Wiley & Sons Ltd. -
Resource Provisioning in Fog Computing - A Survey
The internet world has created an era where any device can interconnect with each other. Gathering intelligence from streaming data is challenging and can create wonders and valuable innovations for humanity. The shortcomings of connectivity due to the remote location of the cloud induce latency and performance issues in real-time. Thus, a traditional cloud may not be suitable for all applications. A secure, low latent bandwidth infrastructure under research led to Fog Computing. The fog nodes have limited resources, and effective utilization can boost the applications performance. Ensuring effective routing of the tasks and load balancing among the nodes is essential and tedious in any network. Resource management becomes challenging due to heterogeneity, dynamic workload, unpredictability of the computing environment, and so on. In such cases, using Artificial Intelligence (AI) can be promising, provided the complexity and the computing are handled. Proactive load handling based on the changes in network traffic has a huge scope for research. This article gives a detailed survey of the various fog network architectures and the intelligent methodologies in resource allocation in a fog network using machine learning algorithms. Furthermore, the article shows the directions of research in intelligent resource allocation and handling. 2025 Copyright held by the owner/author(s) -
Innovative Approaches to Employee Compensation and Motivation: Designing Strategic Reward Systems
This explores the evolution and strategic relevance of reward systems in enhancing employee motivation and organizational performance. Moving beyond traditional pay structures, it emphasizes the integration of innovative compensation models aligned with business objectives and employee expectations. Drawing upon key motivational and behavioral theories, the discussion covers both monetary and non-monetary components of total rewards, including performance-based pay, recognition programs, flexible benefits, and career development opportunities. This examines the role of technology, sectoral differences, and cultural contexts in shaping reward strategies across global organizations and contemporary challenges such as pay equity, remote work compensation, and ethical considerations. Case examples and global practices are analyzed to provide actionable insights. The chapter concludes with future-oriented recommendations for designing inclusive, adaptive, and sustainable reward systems that foster engagement, retention, and strategic alignment in a rapidly changing workforce environment. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Impact of the Pandemic on Entrepreneurial Ecosystems
Entrepreneurship is crucial for the global economy, as it helps ideas develop from the drawing board to an executable stage. An excellent economic state of a country is the outcome of a well-designed system where the stakeholders interact with each other towards innovation and social development. This study is an empirical investigation into the COVID pandemic and its effects on the Indian entrepreneurial eco-system. Primary data was collected from 155 entrepreneurs of India, who were independent and first movers of entrepreneurs in their family during COVID times. Poor planning, exhausting resources, a slowdown in productivity, lower employment, and employee retention were the after-effects of the pandemic. It was found that the pandemic negatively affected the entrepreneurial ecosystem and its stakeholders. However, constant support by the government and well-designed policy measures would help assist existing businesses affected due to COVID-19 and encourage the entrepreneurial future in India. Copyright 2022, IGI Global. -
Cryptography: Advances in Secure Communication and Data Protection
In the innovative work secure communication and data protection are being main field, which are emerged by cryptography as a fundamental pillar. Strong cryptographic methods are now essential given the rising reliance on digital technologies and the threats posed by bad actors. This abstract examines the evolution of secure communication protocols and data protection techniques as it relates to the advancements in cryptography. The development of post-quantum cryptography is the most notable development in cryptography discussed in this study. As quantum computers become more powerful, they pose a serious threat to traditional cryptographic algorithms, such as RSA and ECC. Designing algorithms that are immune to attacks from quantum computers is the goal of post-quantum cryptography. Lattice-based, code-based, and multivariate-based cryptography are only a few of the methods that have been investigated in this context. 2023 EDP Sciences. All rights reserved. -
Security analysis in multi-tenant cloud computing healthcare system
Cloud Computing is an innovation in the field of Information Technology and in healthcare system because of the deployment models which services as profitability for the tenants. Cloud Computing is cost-effective, flexible and a delivery platform which provides business and services over internet. Multi-tenancy with its hardware sharing and high degree of configurability is utilized in cloud computing health care system even though many health care organizations are unwilling to adapt due to infrastructure and security shortcoming. In order to store the sensitive health care data cloud service providers should include promising security features where both the trusted and untrusted parties should be addressed in it. This paper addresses the security requirements and security issues in multi-tenant healthcare system, a frame is proposed for analyzing the security issues based on the available requirements and possible counter measures been suggested. The security concerns are analyzed by trust, confidentiality, integrity, audit and compliances and furthermore insight for the security is provided in multi cloud with possible security recommended for healthcare system. IAEME Publication.