Browse Items (14421 total)
Sort by:
-
Indian folk art form grooms and improves education with personalized learning methods
The chapter investigates the intricacies and complexities involved in the making of an art form kathakali from India known for its unique formation process and exposition procedure. It unravels its mysteries involved in the exceptional personalized learning methods born out of them. The chapter discusses the various nuances in merging personalized learning methods with education, each in a selected singular appropriate method. It refers to a much refined and reformed process. It focuses on whetting of learner's comprehension skills to evolve befitting solutions to education problems producing effective redress for every serious global issue. The chapter proposes convincing concepts exemplified to apply in individual instances. The researcher suggests that these personalized methods are exemplary and worth emulating for the future generations. 2025, IGI Global Scientific Publishing. All rights reserved. -
The impact of the COVID-19 pandemic on e-learning adoption in an emerging market: a longitudinal study using the UTAUT model
Purpose: The COVID-19 pandemic provided unprecedented impetus to the evolution of the e-learning learning ecosystem by compelling students to adopt e-learning systems. This paper aims to use the UTAUT model to provide insight into the differences in factors influencing the adoption of e-learning systems before and after the pandemic. Design/methodology/approach: This longitudinal study uses two surveys conducted among graduate students in the city of Bengaluru in India. One prior to the start of the COVID-19 pandemic and a second in its aftermath. PLS-SEM is used to analyze both data sets to draw insights into the constructs that influence Behavioral intention to adopt e-learning systems. The moderating effect of gender is also analyzed. Findings: Pre COVID-19, Facilitating Conditions, Performance Expectancy and Effort Expectancy (quadratic behavior) were dominant factors influencing the adoption of e-learning technologies. Post pandemic, Performance Expectancy and Social Influence are drivers of e-learning adoption. Effort Expectancy and Facilitating Conditions grouped as Ease of Use is a significant driver of e-learning adoption post pandemic. Gender is found to not have a moderating influence. Originality/value: The unique longitudinal study of the differences in factors influencing students intention to adopt e-learning pre- and post-COVID-19 can prove useful to policy makers in the higher education sector. Academics can use the post-pandemic e-learning models findings in multiple contexts such as generational cohorts, educational contexts and social contexts. 2024, Emerald Publishing Limited. -
Localised actor roles in post-disaster housing recovery: A case study from Kerala
The effectiveness of post-disaster housing reconstruction (PDHR) is increasingly being challenged by the frequency and complexity of climate-induced disasters. In the Indian state of Kerala-particularly the highland regions of Kottayam and Idukki-landslides and floods have caused significant housing losses in recent years. While the government initiated housing recovery interventions after the 2021 landslide event, multiple civil society actors, including faith-based organisations, political parties, and professional groups, also participated in reconstruction efforts. This study examines the actor-specific approaches to community consultation in PDHR and their impact on beneficiary satisfaction. Using a qualitative case study design, the analysis identifies variations in participation across planning, design, and construction stages, and maps these to outcomes such as reconstruction speed, satisfaction levels, and community cohesion. While some actors offered comprehensive engagement strategies, others limited their consultation, resulting in mismatches between needs and outcomes. Findings suggest that community consultation remains uneven and often symbolic, with beneficiaries perceiving external aid as benevolence rather than entitlement. The study underscores the importance of meaningful participation in PDHR, especially in the context of localized climate events. These insights offer practical implications for designing inclusive recovery frameworks and enhancing community resilience in hazard-prone regions. The Authors, published by EDP Sciences, 2025. -
Housing sector recovery trajectories after the 2021 landslides in Kerala, India
Tackling homelessness is a critical priority after disasters especially in the Western Ghats of India, where landslides are becoming increasingly frequent and severe. This situation has rendered recovery in the housing sector more essential than ever. The present study specifically aims at investigating the recovery in this sector among households impacted by the 2021 landslides in Kerala, India, using Recovery Trajectory Index (RTI). It shows how the dynamic interplay between initial severity of impacts and subsequent delays in reconstruction together can shape the changing recovery status of households. Focusing on four critically impacted wards under the jurisdiction of Koottickal, a village level local government body, the research aims at understanding the current trajectory of recovery efforts. The highest RTI value refers to a slower recovery pace, and the lowest value signifies a faster pace. The index calculation is intended to contribute to prevailing quantitative recovery assessment frameworks for the identification of spatial and household-level disparities in recovery performance in similar hazard-prone contexts. The results highlight the need to prioritize equity in resource allocation, promote inter-agency collaboration to ease financial strain, and maintain a detailed household recovery database. Incorporating these measures would enhance preparedness and support more efficient, inclusive recovery planning. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Navigating Brand Hate Research: Deciphering Enablers and Emanating Potential Implications
In todays world, consumers possess a unique power, i.e., the power to praise, to critique, or even to reject a brand. At the zenith of challenges stands an issue known as Brand Hate which is a negative set of perceptions consumers develop towards a brand or product etc. Unlike simple dissatisfaction, brand hate stems from frustration, ethical objections and betrayal, with reactions from boycotting to even running anti-brand campaigns online. Social media amplified these feelings of frustration and can spread to more people, greatly affecting the reputation of the brand. The roots of brand hate vary, for some, it begins with a disappointing product experience; while for others, it is the result of unethical steps takenby the company. Whether it's an issue with the quality or a failure of the brand to meet the expectations of the consumers, regardless of the case the consequences are severe. Our study aims to explore the complexity in these variables leading to brand addiction and provide dependence and driving power of these factors and provide recommendations for the same. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Mind and Nature: Study on Mental Health, Nature Connectedness, Pro-Nature Conservation Behaviors and Geographical Green Cover among Indian Adults
For centuries the relation between mind and nature has been represented through literature, songs and cultural traditions. However with increasing urgency of the climate crisis and the corresponding growing distance between humans and nature, we find very limited scientific work exploring their relationship, which could perhaps help re-bridge the connection between the two. A significant, yet not directly observable, and often overlooked impact of the climate crisis is its impact on mental health. This study looks at this relationship in the Indian context, through a relatively unexplored perspective, by investigating the effects of nature connectedness (NC), pro-nature conservation behaviours (ProCoB) and geographical green cover (GGC) on mental health (MH) among middle-aged adults residing in India, and the existing inter-relationships. 180 middle-aged Indian adults, selected through purposive and snowball sampling, from across 21 states and 2 Union Territories (UTs), were administered questionnaires through a Google form. Their data was collected and scored, and the GGC was calculated for each state/ UT from the India State of Forest Report 2021. Correlation and Regression analysis were conducted on the scores using SPSS. A positive and statistically significant correlation exists between the variables NC, ProCoB and MH; NC, MH and GGC; and NC and ProCoB. NC and ProCoB predict MH. Gardening also predicts MH. The findings are new and contribute to the field of Environmental Psychology. It provides a scientific basis for the often romanticized relationship between man and nature as found in literature. It has great implications for the future, such as increasing awareness and understanding, and planning interventions to improve both environment and wellbeing. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
ANALYZING THE BEHAVIORAL CORRELATES OF GUILT-FREE FOOD CONSUMPTION: STUDY OF GEN Z PERSPECTIVE
This study investigated the factors influencing Gen Z's purchase intentions for guilt-free food products in India. The aim of the study is to examine the relationship of food habits among the current generation using the Theory of Planned behaviour. Using purposive sampling, the data was collected from 318 Gen Z students in major Indian cities. SPSS and AMOS were utilised to conduct the analysis of the sample. The analysis revealed that attitude, perceived behaviour control and subjective norms significantly influenced the purchase behaviour. The study provides valuable insights for marketers, policymakers, and food producers seeking to promote guilt-free food products among this influential demographic. 2025 Amity University. All rights reserved. -
Employee experience, well-being and turnover intentions in the workplace
Purpose This study aims to examine the role of employee experience in influencing employee well-being and turnover intentions within organizations. The mediating role of well-being will also be investigated, along with an exploration of whether these relationships differ across genders, specifically in the Indian corporate context. Design/methodology/approach A descriptive, quantitative study was conducted using structured questionnaires to gather data from 111 employees in the Indian corporate sector. The study used a non-probability judgment sampling method. Data was analyzed through SPSS for descriptive and inferential statistics, and partial least squares was used to explore mediation and model fit. Findings The study found a significant impact of employee experience on well-being, as well as a negative correlation between both employee experience and turnover intention and well-being and turnover intention. Well-being was found to partially mediate the relationship between employee experience and turnover intention. Gender-based analysis revealed no significant differences in the relationships between these variables for men and women. Originality/value This research highlights the universal applicability of employee experience as a predictor of well-being and turnover intention, irrespective of gender. By establishing that gender does not moderate these relationships, this study provides new insights challenging traditional assumptions about gender disparities in workplace outcomes. 2024 Emerald Publishing Limited -
Workplace aesthetics and employee behaviour: introducing the Office Peacocking Scale (OPS)
Purpose This study introduces the concept of Office Peacocking, defined as the deliberate enhancement of office aesthetics and amenities to attract attention and influence employee behaviour. The purpose of this paper is to develop and validate the Office Peacocking Scale (OPS), a psychometric tool that measures how workplace aesthetics impact employee engagement, time spent in the office and social dynamics. Design/methodology/approach Using the DeVellis scale development method, the study followed a multi-stage process involving item generation, expert validation, pilot testing and exploratory factor analysis. A survey was administered to 375 employees across corporate sectors such as IT, finance, marketing and operations. Reliability was assessed using Cronbachs alpha, and factor analysis was conducted to determine underlying dimensions. Findings The OPS scale demonstrated acceptable reliability and revealed two dimensions: Aesthetic-Experiential Display and Symbolic-Social Signalling. The results suggest that enhanced office aesthetics significantly influence employee motivation, visibility-seeking behaviours and emotional connection to the workplace. Research limitations/implications The findings are based on a cross-sectional survey within a limited geographic and sectoral scope, which may affect generalizability. Future studies could explore longitudinal validation and cross-cultural applicability of the scale. Practical implications The OPS scale offers HR professionals and workspace designers a practical tool to evaluate how employees perceive and respond to office enhancements. It supports strategic decisions in workplace design aimed at boosting engagement, retention, and organizational identity. By understanding the psychological and social effects of office aesthetics, organizations can foster inclusive and meaningful work environments that go beyond superficial design trends. Originality/value This study pioneers the empirical measurement of Office Peacocking, contributing a validated scale and offering fresh insights into the symbolic and behavioural implications of workplace aesthetics. 2026 Emerald Publishing Limited -
Relationship Between Job Stress, Employee Engagement and Job Satisfaction: A Study Based on Women Managers in 4 and 5 star Hotels in India
Women account for a very small percentage of the employee population in Indian luxury hotels. While they have proved themselves as valuable assets, the average tenure of a woman in a managerial role in the sector is still around 2 to 4 years. The Government of India in its India Skills report has identified the sector as a focus area, in the drive to achieve better gender ratios. This study takes a small step towards understanding the factors that could influence the tenure of women in the hotel sector. The study examines the role of job stress in determining the levels of job satisfaction of women in the Indian hotel industry. The study also examines the mediating effect that employee engagement may have on the relation. The researchers have studied women in managerial roles in 4 and 5-star hotels, across India. The findings suggest that there is a strong negative correlation between job stress and job satisfaction and that this relationship is partially mediated by the presence of employee engagement. The findings are particularly important for the hospitality sector in India, as it struggles to retain its talented female employees. 2022 K. J. Somaiya Institute of Management -
NEUROPLASTICITY UNLEASHED: Receiving the Brain for Recovery
Neuroplasticity, the brains dynamic ability to reorganize and adapt across the lifespan, underpins contemporary approaches to neurorehabilitation. This chapter critically examines the clinical, neuroimaging, and neurophysiological evidence for plasticity-driven recovery. Drawing on longitudinal studies and case-based analyses, we illuminate how recovery can occur even in late stages, challenging the traditional notion of static chronic phases. The chapter highlights the role of task-specific practice, intensity, and timing in shaping neural reorganization, emphasizing that plasticity is not merely a spontaneous biological process but one that can be modulated through structured intervention. We further explore how electroencephalography (EEG)-based markers offer temporally precise insights into reorganization across cognitive, sensory, and affective domains. Neuroimaging findings reveal compensatory activation, network shifts, and bilateral engagement as hallmarks of adaptive plasticity. Affect, motivation, and goal-directed behavior are positioned as central to driving experience-dependent changes, especially when integrated into patient-centered therapy. In addition, we examine the intersection of individual difference factorsincluding personality and cognitive reservewith neuroplastic potential and propose frameworks for personalized rehabilitation. Finally, the chapter outlines emerging directions in tech-enabled plasticity interventions and translational models of care. Together, the evidence underscores neuroplasticity not only as a recovery mechanism but also as a target for strategic, evidence-based rehabilitation. The interdisciplinary approach adopted here aims to bridge neuroscience, clinical practice, and lived patient experiences to inform future research and therapeutic innovation. 2026 selection and editorial matter, K. Jayasankara Reddy; individual chapters, the contributors. All rights reserved. -
Tri-projection gated cross-modal fusion for robust multilingual emotion recognition
Existing multimodal approaches in emotion recognition (ER) rely on static or pairwise fusion strategies. These systems do not adequately address the challenges in real-world conversational systems, which require resilience to both multilingual code-switching and variable reliability of multiple modalities. We propose a transformer-based tri-modal emotion identification framework with a novel Tri-Projection Gated Cross-Modal Fusion (T-GCMF) module the first multimodal emotion recognition architecture explicitly designed for code-switched conversational input. T-GCMF simulates tri-modal interactions by explicitly calculating modality-specific confidence and cross-modal consistency, allowing for dynamic suppression of unreliable modalities during inference. Acoustic and visual cues are retrieved using CNNLSTM and deep CNN encoders, respectively. Textual representations are generated using XLM-RoBERTa to handle code-switched language reliably. We introduce Hinglish-MELD, the first multimodal emotion recognition dataset with aligned text, audio, and visual streams containing code-switched conversational content, filling a critical gap in the literature. With an accuracy of 88.3% and an F1-score of 87.0, the suggested confidence-aware fusion technique greatly surpasses unimodal, monolingual, and non-gated multimodal baselines. These findings demonstrate T-GCMF as a successful approach for emotion recognition in linguistically heterogeneous, real-world interactive systems and emphasize the significance of confidence-driven tri-modal integration. 2026 -
Impact of Multi-domain Features for EEG Based Epileptic Seizures Classification
Accurate detection and classification of epileptic seizures play a pivotal role in clinical diagnosis and treatment. This study introduces an innovative approach that leverages multi-domain features extracted from Electroencephalogram (EEG) data in conjunction with Supervised learning classification techniques. Initially, EEG data undergoes preprocessing through data standardization, followed by the extraction of essential features per instance, encompassing combination of Time domain, Frequency domain, and Time-Frequency domain features. These extracted feature combinations are subsequently fed into the machine learning-based boosting classifier Adaptive Boosting (ADABOOST) for an accurate and precise classification of epileptic signals. Validation of the proposed method is conducted using EEG data from the BEED (Bangalore EEG Epilepsy Dataset) and BONN (University of BONN, Germany) database to detect epileptic seizures. The experimental results show remarkably high levels of classification accuracy for various conditions: 99% accuracy for BEED data, 98% accuracy for BONN data for classifying seizures from healthy states, and 91% accuracy for classifying seizure onset from seizure events. Furthermore, the study applies the Gaussian Nae Bayes (GNB) classifier to differentiate various types of epileptic seizures, employing evaluation metrics such as the confusion matrix, ROC curve, and diverse performance measures. This method demonstrates significant potential in supporting experienced neurophysiologists decision in the clinical classification of epileptic seizure types. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Automated epileptic seizure classification using adaptive fast Fourier transform with non-uniform sampling and improved deep belief network
In automated brain-computer interaction (BCI), EEG signals are essential. This research uses AI to detect epileptic seizures, employing data from the BONN dataset (UCI), CHB-MIT dataset (physionet server), and Bangalore EEG Epilepsy Dataset (BEED). The goal is to develop an automated system for accurate seizure detection using adaptive fast Fourier transform with non-uniform sampling (AIFFT-NS) and an improved deep belief network (IDBN) model to enhance classification accuracy. The AIFFT-NS model serves as a channel for transforming spectro-temporal data. Using various EEG datasets, a number of extensive experiments are carried out, resulting in the validation of the efficacy of the proposed approach. High accuracy metrics, with 96.16% for the BEED dataset, 99.41% for the BONN dataset, and 96.31% for the CHB-MIT dataset, represent the evidentiary outcomes. This study emphasises the critical function of AI-facilitated spectro-temporal EEG analysis within the domain of medical diagnostics, going beyond the realm of automated seizure onset classification. Copyright 2024 Inderscience Enterprises Ltd. -
Feature Engineering for Epileptic Seizure Classification Using SeqBoostNet
Epileptic seizure, a severe neurological condition, profoundly impacts patients social lives, necessitating precise diagnosis for classification and prediction. This study addresses the need for reliable automated seizure detection in epilepsy by employing Artificial Intelligence (AI) driven analysis of Electroencephalography (EEG) signals. Key innovations include combining spectral and temporal features using Uniform Manifold Approximation and Projection (UMAP) with Fast Fourier Transformation (FFT), and the introduction of the Sequential Boosting Network (SeqBoostNet), a robust stacking model integrating machine learning and deep learning for effective seizure classification. Validated on benchmark datasets such as the BONN dataset from the UCI repository and the BEED from the Bangalore EEG Epilepsy Dataset, this approach achieved high accuracy, distinguishing Focal and Generalized seizure onsets with 95.91% accuracy and overall average accuracies of 96.71% on BEED and 97.11% on BONN. Existing models frequently struggle with the variability of seizure events. However, these findings underscore the models strength in distinguishing between seizure onset types, even with the inherent fluctuations in seizure patterns. This research not only advances automated seizure detection but also underscores the value of integrating AI with EEG analysis to improve neurological diagnostics, offering the potential for significant enhancements in diagnostic accuracy and patient outcomes. 2025 University of Bahrain. All rights reserved. -
Exploration and Analysis of Seizure Spikes Through Spectral Domain Transformation
Seizure detection is the most crucial area of investigation when it comes to understanding brain disorders. This proposed research study embarked on an automated model for epileptic seizure diagnosis by means of different kinds of Spectral transformation using EEG inputs from seizure sufferers and healthy subjects. This automated model accommodates non-invasive brain electrical activity monitoring. This method aims to facilitate the analysis and identification of epileptic seizure states since, monitoring and diagnosing such brain electrical activity is a complex task due to its numerous divisions and underlying features. The primary objective of this research study is to distinguish between EEG-based seizures and healthy individuals. To achieve this goal, a combination of spectral transformation and EEG analysis techniques is utilized. These techniques include examining the frequency spectrum, magnitude spectrum, correlation, and T-Distributed Stochastic Neighboring Embedding (T-SNE) analysis. This analysis yields valuable insights from EEG data, refining the input data and making it more suitable for prediction and identification. The models performance is evaluated using two distinct datasets: real-time EEG data from individuals experiencing epileptic seizures and EEG data from healthy subjects. These datasets are sourced from the Bangalore EEG Epilepsy Dataset (BEED), India and the BONN epilepsy dataset from the UCI repository. In a comparative study of spectral transformation methods, including Complex Fast Fourier Transform (CFFT) and Real-Valued Fast Fourier Transform (RFFT), it is discovered that reducing the data dimension by using feature extraction is not the optimal approach. This simplification leads to the loss of valuable information. Therefore, preserving the full spectrum of EEG characteristics is crucial for gaining valuable insights into brain neuronal functions, ultimately enabling more accurate seizure prediction. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Explainable artificial intelligence in epilepsy management: Unveiling the model interpretability
The field of epileptic seizure classification has witnessed significant advancements in the use of electroencephalogram (EEG) data for accurate and timely diagnoses. This study introduces a comprehensive framework for EEG-based seizure classification, encompassing data preprocessing and the application of machine learning techniques, specifically the supervised learning classifier known as Extreme Gradient Boosting (Xgboost). Machine learning methods have shown promising accuracy in binary classification tasks, particularly in distinguishing between seizure and healthy EEG signals. However, the need for a robust explanation of these results and decision-making processes is imperative for technical verification and clinical validation, especially for potential clinical applications. Explainable Artificial Intelligence (XAI) emerges as a critical component in addressing this need. In this chapter, we propose and discuss a binary classification model that leverages Xgboost to classify EEG signals as either Seizure or normal, a crucial aspect in epilepsy diagnosis. XAI techniques such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (Shapley Additive Explanations) are incorporated to elucidate the model's predictions. LIME offers localized interpretability by creating surrogate models for individual predictions, revealing the essential EEG features influencing each classification decision. Conversely, SHAP provides a global perspective on feature importance, shedding light on the collective impact of EEG features on classification outcomes. The synergy between LIME and SHAP enhances our understanding of the model's predictions and the intricate nuances within EEG data. This research highlights the transformative potential of LIME and SHAP in EEG-based seizure classification. The integration of XAI techniques not only enhances the transparency and interpretability of the model but also empowers clinicians and researchers to make more informed decisions, ultimately improving patient care and outcomes in epilepsy management. By bridging the gap between complex EEG data and actionable insights, this study marks a significant paradigm shift in the application of XAI techniques in medical diagnostics. It paves the way for a new era in epilepsy diagnosis and management, where advanced machine learning models guided by LIME and SHAP play a crucial role in revolutionizing healthcare practices. 2025 Elsevier Inc. All rights reserved. -
Impact ofFeature Selection Techniques forEEG-Based Seizure Classification
A neurological condition called epilepsy can result in a variety of seizures. Seizures differ from person to person. It is frequently diagnosed with fMRI, magnetic resonance imaging and electroencephalography (EEG). Visually evaluating the EEG activity requires a lot of time and effort, which is the usual way of analysis. As a result, an automated diagnosis approach based on machine learning was created. To effectively categorize epileptic seizure episodes using binary classification from brain-based EEG recordings, this study develops feature selection techniques using a machine learning (ML)-based random forest classification model. Ten (10) feature selection algorithms were utilized in this proposed work. The suggested method reduces the number of features by selecting only the relevant features needed to classify seizures. So to evaluate the effectiveness of the proposed model, random forest classifier is utilized. The Bonn Epilepsy dataset derived from UCI repository of Bonn University, Germany, the CHB-MIT dataset collected from the Childrens Hospital Boston and a real-time EEG dataset collected from EEG clinic Bangalore is accustomed to the proposed approach in order to determine the best feature selection method. In this case, the relief feature selection approach outperforms others, achieving the most remarkable accuracy of 90% for UCI data and 100% for both the CHB-MIT and real-time EEG datasets with a fast computing rate. According to the results, the reduction in the number of feature characteristics significantly impacts the classifiers performance metrics, which helps to effectively categorize epileptic seizures from the brain-based EEG signals into binary classification. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Synthesis, properties, and state-of-the-art advances in surface tuning of borophene for emerging applications
Being composed of boron atoms that can be maneuvered to orchestrate low planar hexagonal structures, this two-dimensional material carefully exhibits versatility and has conventional covalent bonds between each atom. Borophene has recently proliferated the scientific research community by storm, trailblazing industries from fine chemicals, electrical equipment manufacturing, and biomedical innovation up to sustainable energy. Here, we provide streamlined information and particulars about the recent advances in the evolution of borophene since its inception and the essence of its electrocatalytic applications. We first introduce the sophisticatedly cultivated progress in borophene's structural, mechanical, optical, and electrical properties and further discuss its variegated polymorphism. Subsequently, we also delve into several capable synthesis techniques and recently concocted surface tuning and doping methods. Finally, we analyze the advancing state-of-the-art applications of this two-dimensional nanomaterial under investigation, ranging from bioimaging, energy storage, electrode reduction, and electrochemical sensing. Further, we have broadly discussed the future insights and challenges that borophene brings. 2024