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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. -
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) -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
Finance future patterns in the market using artificial intelligence /
Patent Number: 202221052876, Applicant: Dr.Shiva Johri.
A methodology that makes use of natural language processing (N.L.P.) methods to extract information from online news feeds and then makes use of the information that was thus extracted to anticipate changes in stock prices or volatilities. This approach is known as news feed mining. These forecasts may be used to develop trading strategies that result in a profit. Parsing or pattern matching may be done on terms in or close to the phrase that contains the business name in order to identify the company name and automatically fill up basic templates that describe the acts that the organization does. -
Practices for measuring business in construction engineering organizations /
Patent Number: 202221034683, Applicant: Dr. Anil Zende.
The fundamental drives of every organization are profitability and achievement. The sustainability among these organizations relies on numerous elements that seem to have a substantial influence on performance. Estimating the implementation of sustainability organizations helps to discover weaknesses in terms of enhancing its productivity and profitability. Because of the enormous diversity of construction companies, it is harder for development organizations to develop or sustain a scientific approach for measuring their present effectiveness. Previous research utilized questionnaires and scientific and management consultations. -
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. -
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. -
Artificial intelligence attitudes and resistance to use robo-advisors: exploring investor reluctance toward cognitive financial systems
Introduction: The study investigates resistance towards Financial Robo-Advisors (FRAs) among retail investors in India, grounded in innovation resistance theory. The study examines the impact of functional barriers and psychological barriers on resistance to FRAs, while considering users attitudes towards Artificial Intelligence (AI) as a moderator. It further evaluate the influence of such resistance on users intentions to use and recommend FRAs. Methods: Utilizing purposive sampling data was collected from 409 investors and further analyzed using structural equation modelling. Results: The findings revealed that all barriers under study, expect value barrier, substantially derive resistance towards robo-advisors, with inertia being the strongest determinant. Further, this resistance impedes both the intention to use FRAs and to recommend them. Moderation analysis results finds that users attitude towards AI significantly weakens the influence of inertia, overconfidence bias and data privacy risk on resistance, with no such impact on other relationships. Discussion: Overall, the study enriches IRT in Fintech context and provides theoretical and practical insights to enhance FRAs adoption in emerging markets. Copyright 2025 Verma, Schulze, Goswami and Upreti.