Browse Items (16481 total)
Sort by:
-
Sustainable Leadership and Organizational Responsibility
The changing environment of the workplace, which is determined by globalization, digital transformation, climate issues, and hybrid work practices, requires leadership to be more human- centered, responsible towards ethics, and sustainable performance. This chapter examines the notion of sustainable leadership and organizational responsibility and how positive psychology offers an effective system to create successful, robust, and meaningful working environments. The chapter explores how leaders can apply ideas of human flourishing, including strengths, optimism, resilience, and meaning, to the organizational culture, employee engagement, and leadership development. It shows how organizations can get beyond transactional management as it is a solution to creating an inclusive, ethical and high- performing organization and therefore can be used to encourage individual development and organizational success. Empirical studies, new models, and concrete case studies are integrated in order to close the gap between theory and practice and give viable solutions to developing sustainable leadership and responsible organizational practices in a fast- changing world. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Leadership and Performance Evaluation Strategies for Remote Healthcare Teams in the Digital Era: Effective Leadership in Remote Healthcare
Electroencephalography (EEG)- based Brain- Computer Interface (BCI) technology has appeared as a promising path for healthcare, contributing to novel solutions for detecting, treating, and dealing with neurological conditions. By capturing and construing the electrical activities of brain signals, EEG- based BCIs empow er direct brain communication with external devices, circumventing traditional neural conduits. This chapter delves into the progress of EEG- based BCI devices, emphasizing their implication in healthcare, the technical challenges tangled, and their potential to transform patient care. The key objective is to deliver a detailed exploration of the development procedure of EEG- based BCI devices, emphasizing their applications in healthcare. The chapter includes the principles of EEG signal acquisition, the design and engineering of BCI systems, the employment of machine learning algorithms for signal decoding, and the clinical validation required for medical use. Moreover, it will discuss the prospective effect of these devices on healthcare and future research directions. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Emerging Trends and Innovations in Cybersecurity Insurance Policy Customization Using Generative AI Fraud Detection
Generative AI is transforming policy customization and fraud detection. Cybersecurity insurance can be both a boon and a bane as the existing models for underwriting and assessment are relatively static in a world which is witnessing a constant escalation between attack and defense mechanisms. By assessing huge datasets, spotting emerging pattern and predicting potential vulnerabilities in real time, generative AI introduces dynamic risk modeling. Help insurers design hyperpersonalized cyber insurance products that are tailored to specific business profiles, operational risks and exposure levels to enhance coverage and premium accuracy. Generative AI helps in anomaly detection and identifying suspicious behaviour in insurance claims in fraud detection domain. Using legitimate claims scenarios to train AI model, fraud detection models can detect anomalies and fraud as never before. The intersection of expertise in cybersecurity, AI development and insurance will be key to creating strong,flexible and transparent insurance structures that can respond to the intricacies of today's cyber threats. 2026, IGI Global Scientific Publishing. All rights reserved. -
Superfoods or Hype: Decoding the Wellness Trend and Disease Cure
Superfoods have become a buzzword in the wellness world foods that can be marketed as particularly nutrient- dense and possessing extraordinary health and even disease- cure properties. Foods like these are revered for their concentrated doses of vitamins, antioxidants and essential fatty acids. Some of these claims are backed by scientific studies, supporting the notion that these foods can help reduce inflammation, lower the risk of chronic diseases and support overall well- being. For example, curcumin found in turmeric is associated with anti- inflammatory and antioxidant effects, and omega- 3 fatty acids in chia seeds contribute to heart health. Consumers should remain critical of marketing claims and be guided by the science when adding superfoods to their wellness regimens. Biological plausibility and population- based studies are not sufficient, alone, to establish the validity of disease prevention and health promotion functions for foods towards disease prevention and health promotion. 2026, IGI Global Scientific Publishing. All rights reserved. -
Centering Ground Water Scarcity: Ensuring Futuristic Sustainable Farming With Blockchain and IoT
Depletion of groundwater is a major threat to agricultural sustainability, especially in and around irrigated areas. This chapter discusses monitoring water usage, soil moisture and environmental using sensors powered by IoT and how such technologies offers real-time insights into water usage for efficient management of water. It also examines decentralized smart contracts that can promote fair water use rights and incentive farmers' conservation behaviours. Combining the adaptation of technological innovation and sustainable water management, the paper offers policy prescriptions to climate proof agriculture. With means of interdisciplinary views, practical applications and policy suggestions, this book snapshots the agro-environmental management policy and the smart irrigation systems towards the protection of water resources, and future generations of smart farming applications worldwide. 2026, IGI Global Scientific Publishing. All rights reserved. -
Assessing Security and Privacy Concerns in LLM Applications: Legal and Social Concerns
Large language models (LLMs) like Open AI's GPT- 4 have transformed many industries, including natural language processing. But, still the applications of these need a properly secure mechanism as it is getting attached to everyday technology at on an extensive rate and large security & privacy risk are encapsulated in its use which must be controlled for safe and ethical use. LLM models are trained with an avalanche of data, often from web- scraped sources that could be handling personal and sensitive content. This may inadvertently have the LAZ model hold on to, and possibly leak, some private data of users who use the API. Having measures to address them through comprehensive strategies and conforming with regulations are important in the responsible usage of LLM applications. 2026 by IGI Global Scientific Publishing. All rights reserved. -
An Intelligent Approach for Breast Cancer Diagnosis Using Fuzzy Logic and Extreme Learning Machine
The long-term prognosis and mortality rates can be improved with early identification of breast cancer. The time-consuming and expensive procedures of mammography, MRI, ultrasound, CT, PT, and biopsy have been the subject of much research; nevertheless, these approaches are not suitable for younger women and can be rather expensive. This study employed cutting-edge image processing to improve early breast cancer detection. The researchers utilised anisotropic filtering to reduce background noise in medical images after picking mammograms at random from the Digital Database for Screening Mammography. The use of morphology-based feature extraction allowed for autonomous and accurate categorisation after mass segmentation using a genetic algorithm with recurrent thresholding. By merging a KF with an ELM enhanced with an AV, a new model named KF-av-elm improves diagnostic accuracy. Medical imaging noise and estimating errors are both significantly reduced by the combination method. Their accuracy rating of 98.28% allowed them to outperform other approaches. The KF-av-elm model appears to be a reliable, efficient, and effective diagnostic tool; its adoption may lead to better identification and outcomes for breast cancer patients. 2025 IEEE. -
Predicting sustainable equity indices using deep long short-term memory neural network: Evidence from developed and emerging markets
The present study aims to propose a predictive model to forecast the sustainable stock indices. For this, the Long Short-Term Memory (LSTM) neural network model is applied through Keras and TensorFlow to closing values of six developed and emergingmarkets: the US, the UK, Japan, Brazil, South Africa, and China. Further, the Adam optimiser and mean squared error loss function are used to train the model. To gauge the superiority of the LSTM model, a rolling window Autoregressive Integrated Moving Average (ARIMA) model is also employed. The performance accuracy of both models is evaluated using the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R2). The LSTM model, with two LSTM and two dense layers, yields the best results, achieving the highest precision in predicting the values of sustainable indices. The values of RMSE and MAPE confirmed this, and the accuracy is also verified by the R2 values. LSTM shows superior predictive accuracy and is indicated to be fit for non-linear market patterns than rolling window ARIMA. The study enables policymakers and practitioners to forecast these indices and design policies to motivate related investments. 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Customer churn behaviour prediction in telecommunication using classification algorithms and modelling
The cost of obtaining a high-quality client is usually five times more than the cost of keeping an existing customer. This is why it is very important that businesses keep their customers at home. To retain and improve their customers' satisfaction, researchers in various fields such as marketing, information technology, and business intelligence studied various ways to deliver the best possible services. Despite the good performance of the work done before, there is still a considerable gap in their prediction of the churners. In most cases, the training dataset is too large, and the high dimensionality of it causes the classification algorithms to fail. In the present paper, an attempt was made to estimate customer churn with greater accuracy in the membership of cellular wireless services using a call details records dataset consisting of 3333 clients having 21 attributes each. With the advancement of Machine Learning (ML) and artificial intelligence, most popular approaches such as logistic regression, CART, and C5 algorithms have been used with and without using the data balancing technique SMOTE. The performance evaluation of these predictive models is done using the model accuracy, confusion matrix, AUC value, ROC curve, and Cohen's Kappa statistics. The study results indicate that the C5 algorithm could estimate customer churn with an accuracy of more than 92% for both balanced and imbalanced datasets. 2025 Author(s). -
Neural Dynamics of Heartfulness Meditation: EEG Alpha Modulation Across Experience Levels
Background: Electroencephalography (EEG) studies consistently associate alpha-band oscillations with relaxation, internalized attention, and sensory disengagement during meditation. However, limited evidence exists on how Heartfulness Meditation (HM), particularly its unique transmission phases, modulates alpha activity across different experience levels. Purpose: This study investigated experience-dependent modulation of EEG alpha-band power during multiple phases of HM, with a specific focus on transmission and post-meditation periods. Method: Thirty-three healthy adults were categorized as long-term meditators (LTMs; n = 12), short-term meditators (STMs; n = 11), and non-meditating controls (CGs; n = 10). High-density EEG (129 channels) was recorded across seven consecutive five-minute phases: baseline, meditation (M1, M2), transmission (T1, T2), and post-rest (P1, P2). EEG data were preprocessed using RANSAC-based bad-channel detection and independent component analysis. Alpha power (812 Hz) was computed using Welchs method and analyzed using linear mixed-effects models with false discovery rate correction. Results: A significant Group Phase Region interaction (pFDR < 0.05) indicated experience- and phase-dependent alpha modulation. Both LTMs and STMs exhibited higher alpha power than controls, particularly in frontal, parietal, and occipital regions during meditation and post-meditation phases. Effect sizes ranged from small to moderate (Cohens d = 0.340.70). Notably, STMs showed alpha enhancements comparable to LTMs during early meditation. Conclusion: HM induces region- and phase-specific increases in alpha-band EEG activity, reflecting enhanced internal attention and sensory disengagement. Even short-term practice produces measurable neural changes, underscoring the potential neuroplastic effects of HM. The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). -
Socio-political Challenges and Aspirations of the Tarao Tribe in Manipur: A Qualitative Study on Marginalisation and Empowerment
This qualitative study investigates the Tarao group, one of Manipur's smallest Indigenous tribes. Using interviews, observation, and archival research, this research examined the Tarao community's socio-political status in the Chandel and Tengnoupal districts. It discusses how larger ethnic groups dominate while smaller communities like the Tarao remain marginalised. The findings show that the Tarao community is under-represented in governance, marginalised at the state level, and dominated by larger ethnic groups, resulting in a lack of political voice, social exclusion, economic restrictions, and fragmentation. This study examines how socio-political challenges affect the Tarao community's livelihood and negotiation with Manipur's socio-political framework. 2025 The Editor of Ethnopolitics. -
Manipurs crisis of inclusion: why ignoring smaller tribes undermines peace
[No abstract available] -
Voluntary cybersecurity risk disclosures and firms characteristics: the moderating role of the knowledge-intensive industry
Purpose: This study examines voluntary cybersecurity risk disclosures (VCRD) by listed Indian companies. It also investigates how it relates to firm-specific characteristics such as size, leverage, profitability, liquidity, beta, market growth and industry. Design/methodology/approach: The extent of VCRD was measured by assessing the cumulative occurrence of cybersecurity risk keywords in the annual report of 100 listed Indian non-financial companies. Keyword extraction and occurrence counts were performed using Python software. A multiple regression analysis was applied to predict the characteristics of VCRD. Findings: The results showed that the theoretical frameworks underpinned by agency and signalling theories continued to provide a valid explanation of VCRD by Indian companies. Specifically, the findings emphasized the importance of firm size, leverage, and beta as significant VCRD determinants. Additionally, the study found that knowledge-intensive industries had a favourable impact on the extent of VCRD. Research limitations/implications: This study is relevant because it informs company management, regulators and investors about the nature and characteristics of companies that satisfy stakeholder demands to prevent cyber breaches. Originality/value: Understanding disclosure characteristics is crucial from policy and regulatory perspectives. Studies on cybersecurity disclosures are related to developed economies such as the United States of America and Canada. This is the first study to explore this issue in a developing nation, in general, and in India, in particular, where cybersecurity risk disclosure has yet to be recognized. 2025, Harmandeep Singh. -
Is corporate reputation associated with voluntary cybersecurity risk reporting?
Purpose: This study investigated the effect of voluntary cybersecurity risk reporting (VCRR) on corporate reputation. By examining the association between VCRR and corporate reputation, this study aims to provide exploratory evidence of how cybersecurity risk is sensitive to a companys image and reputation. Design/methodology/approach: An automated content analysis of VCRR by 95 Bombay Stock Exchange-listed companies was undertaken using Python code. Signaling and legitimacy theories were adopted to interpret the findings, establishing whether VCRR was related to corporate reputation. Findings: The results confirm that VCRR improves the corporate reputation in the financial market. The results also confirm the signalling and legitimacy theory that a company can manage reputational risks through higher voluntary risk disclosure. Practical implications: The corporations managers can gain insights from the studys findings and proactively address cybersecurity risks through strategic disclosure and management practices. In addition, organizations can recognize that investors value transparency and establish a positive reputation for those who communicate openly. Social implications: A significant association between VCRR and corporate reputation implies that such disclosures enhance trust and transparency in the business sector and induce security and accountability among investors engaging with the company. Originality/value: To the best of the authors knowledge, this study is the first that empirically investigates this issue and adds to the international literature a new explanatory variable, corporate reputation, to explain VCRR practices. 2024, Emerald Publishing Limited. -
Drivers of Hybrid Teaching in Higher Education: A Post-Adoption Usage Analysis
Post-adoption behaviors in technology acceptance research, particularly in hybrid teaching, are not yet understood. We aimed to predict faculty perceptions and experiences with hybrid teaching at the post-adoption stage. Data were collected from 529 faculty members via an internet-based survey, and structural equation modeling was used to assess the accuracy of the research model. Our findings suggest that at the post-adoption stage, organizational and individual factors influence perceived usefulness and post-adoption usage (exploratory and extended). Perceived usefulness strongly predicts exploratory and extended usage. Perceived usefulness mediates the association between organizational and individual factors and post-adoption usage. Organizations should recognize that adopting hybrid teaching requires collaboration between faculty and administrators, a comprehensive technology infrastructure, intellectual property rights, and development agreements with faculty. Further, by understanding faculty perceptions of exploratory and extended usage, institutions transform their technical infrastructure to help students with innovative pedagogies, multimodal learning, and digital pathways essential for deep engagement and reading comprehension in hybrid environments. 2026 College Reading and Learning Association. -
Enhancing Road Safety and Efficiency Through IoT-Enabled Car-to-Car Communication
Today's vehicles have been revolutionized by integrating Internet of Things (IoT) technology, which facilitates communication between cars, known as car-to-car (C2C) communication. This paper explores the potential of IoT-enabled C2C communication systems to improve security and efficiency by creating dynamic, real-time data exchange between vehicles. Through a comprehensive review of existing literature and technological advances, this study leads to an understanding of how IoT-based C2C communication can reduce incidents, reduce traffic accidents, and create a more peaceful driving environment. It also highlights the potential impact of C2C communications on transportation and policy development. This paper highlights the potential of IoT-enabled C2C communications as a revolutionary technology for the automotive industry, promoting road safety and better vehicle management. The findings highlight the importance of regulatory frameworks, data processing, and stakeholder collaboration for the successful deployment of communications systems of IoT-based C2C networks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
IoT-Powered Health Monitoring System for Protecting Vital Organs Through Cloud-Based Diagnosis
The main objective of this research was the development and evaluation of an IoT- and machine learning-based health monitoring system capable of protecting patients vital organs through cloud diagnosis. This could be achieved by connecting a set of sensors, including temperature, pressure, heart rate, and oxygen sensors, to the patient and allowing them to communicate with the cloud to transmit real-time data via IoT technologies. The data could be further analyzed and predicted using cloud-based machine learning algorithms. This study investigated the performance of different machine learning models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Naive Bayes (NB), and Negative Decision Trees (DT), for the purpose of patients health prediction based on the sensor data. After experimentation and evaluation, we found that the ANN model demonstrated the best predictive ability, with an accuracy level of 99.45%. The SVM, NB, and DT models also demonstrated good performance, with the accuracies of 96.5%, 94.34%, and 91.2%, respectively. Therefore, this research demonstrated that IoT and machine learning technologies could be successfully employed in healthcare for remote patient monitoring and timely prediction. The created system allows for real-time monitoring, which enables early prediction, potentially leading to improved patient outcomes, cost savings, and higher efficiency of provided care. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Forecasting Breast Cancer with Integrated Pre-trained CNN and Machine Learning Framework from CT Images
This article investigates machine learning techniques effectiveness at using computed tomography (CT) images to forecast breast cancer, hoping to expedite early identification and plan treatment. Drawing on many different machine learning models, such as CNN, SVM, VGG16, RNN and RF, we did extensive work to measure their performance distinguishing between malignant and benign breast tissue regions. The dataset includes 2,430 CT pictures, with 70% for training and 30% for testing. It has been carefully selected and prepared in order to guarantee robustness and consistency. The precision, and in-sensitivity measure the accuracy, sensitivity, specificity is used as analytic indicators to measure the models ability to predict regions of breast cancer accurately. Our findings show that the proposed CNN model achieved an accuracy of 98.75%, superior performance. Other machine learning models are also highlighted in this study, demonstrating how breast cancer can be predicted using various methods. This research will determine the forms and technologies suitable for breast cancer forecasting. Medical imaging and clinical decision-making can move forward because of this research, offering a glimpse into how integrated machine-learning systems can bring greater precision to diagnosis and prognosis. By careful experimentation and analysis, we hope to prepare people for early intervention and personalized treatment methods. This will make for improved patient outcomes in fighting breast cancer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing Weed Recognition in Cotton Fields Through Advanced Imaging and Learning Techniques
This research investigates the efficacy of weed recognition models in cotton fields through advanced imaging and machine learning techniques. Utilizing 10 trials, the models, namely K-NN and GBM, were evaluated across multiple performance metrics. Results reveal that GBM consistently outperformed K-NN in accuracy, precision, recall, and F1 score, with average values of 0.88, 0.89, 0.86, and 0.88, respectively, compared to K-NN's averages of 0.85, 0.87, 0.82, and 0.85. Moreover, GBM exhibited higher AUC values (0.94) than K-NN (0.92) in ROC curve analysis, indicating superior discrimination ability. Additionally, k-fold cross-validation demonstrated GBM's higher mean accuracy (0.89) and F1 score (0.88) compared to K-NN (mean accuracy: 0.86, mean F1 score: 0.85). Additionally, integrating temporal data analysis could improve the models ability to detect weed growth patterns over time. Real-time monitoring capabilities and automated decision-making systems could streamline weed management practices in agricultural settings. Furthermore, expanding the study to encompass diverse geographical regions and crop types would provide valuable insights into the generalizability and robustness of the developed models. Overall, continued research in this domain holds the potential to revolutionize weed management strategies and contribute to sustainable agriculture practices. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
The Influence of Emotional Branding on Consumer Loyalty and Purchasing Decisions
Emotional branding has emerged as a powerful tool for businesses to create deep connections with consumers, influencing both loyalty and purchasing decisions. This paper explores how brands leverage emotional branding strategies to build strong emotional bonds with their customers, resulting in increased brand loyalty and a higher likelihood of repeat purchases. The study examines the key components of emotional branding, including brand attachment, trust, and engagement, and how these factors contribute to consumer decision-making processes. Empirical data gathered from various industries support the conclusion that emotional branding is a crucial factor in driving long-term consumer loyalty and sustainable business success. 2026, IGI Global Scientific Publishing. All rights reserved.