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Lightweight Spectral-Spatial Squeeze-and- Excitation Residual Bag-of-Features Learning for Hyperspectral Classification
Of late, convolutional neural networks (CNNs) find great attention in hyperspectral image (HSI) classification since deep CNNs exhibit commendable performance for computer vision-related areas. CNNs have already proved to be very effective feature extractors, especially for the classification of large data sets composed of 2-D images. However, due to the existence of noisy or correlated spectral bands in the spectral domain and nonuniform pixels in the spatial neighborhood, HSI classification results are often degraded and unacceptable. However, the elementary CNN models often find intrinsic representation of pattern directly when employed to explore the HSI in the spectral-spatial domain. In this article, we design an end-to-end spectral-spatial squeeze-and-excitation (SE) residual bag-of-feature (S3EResBoF) learning framework for HSI classification that takes as input raw 3-D image cubes without engineering and builds a codebook representation of transform feature by motivating the feature maps facilitating classification by suppressing useless feature maps based on patterns present in the feature maps. To boost the classification performance and learn the joint spatial-spectral features, every residual block is connected to every other 3-D convolutional layer through an identity mapping followed by an SE block, thereby facilitating the rich gradients through backpropagation. Additionally, we introduce batch normalization on every convolutional layer (ConvBN) to regularize the convergence of the network and scale invariant BoF quantization for the measure of classification. The experiments conducted using three well-known HSI data sets and compared with the state-of-the-art classification methods reveal that S3EResBoF provides competitive performance in terms of both classification and computation time. 1980-2012 IEEE. -
Enhancing English Learning Through Digital Storytelling in Indian Schools
This study examines the effectiveness of the Digital Storytelling (DST) teaching approach in improving English learning among ninth graders in four schools in Bengaluru, India. Using a sequential mixed-methods design, the quantitative phase included a non-randomized, post-test-only quasi-experimental design with 200 students divided into a DST-based experimental group and a traditional control group of 100 students each. Quantitative data were collected using a 12-item survey questionnaire, while qualitative data included self-reflection logs from 100 and interviews with 20 students from the experimental group. The results show that DST significantly improves language development and student satisfaction. This is evidenced by higher and more consistent post-test scores in the experimental group, with statistical significance confirmed by the Wilcoxon test. Increased engagement, understanding, and motivation reported by students are consistent with the quantitative improvements. 2025 IGI Global. All rights reserved. -
Mitigating Subjectivity and Annotation Inconsistencies in Sentiment Analysis via an SVM-RoBERTa Ensemble
This research addresses a main limitation in the Natural Language Processing that is the impact of subjectivity and annotation inconsistencies on the accuracy of the sentiment classification. We did a systematic comparison of two fundamentally different architectures. A traditional feature based Support Vector Machine and a deep contextual fine tuned RoBERTa transformer using a challenging, noisy, real-world Twitter dataset. This corpus retains ambiguity and sarcasm on purpose and serve the crucible for testing model robustness. We developed a soft voting ensemble method that combines the probability scores from both models to obtain the best predictive capabilities. The results showed a clear technological hierarchy. The RoBERTa model with its deep semantic grasp outperformed the SVM by a substantial margin achieving 90% accuracy against 83.5% accuracy. But the hybrid ensemble model attained the highest overall accuracy of 91.35% and showed better reliability across all the sentiment classes. These findings shows that a hybrid approach fusing a transformer's nuanced understanding with the stabilization provided by ensemble learning is the most effective and robust method for mitigating data imperfections in modern sentiment analysis. 2025 IEEE. -
Designing optimization frameworks for ICT-enabled e-leadership strategies
The rapid development of information and communication technology (ICT), especially, makes it easier for individuals to create, organize, as well as access information, which has significant effects on the skills required of leaders. The allocation of power and the emergence of connections in organizations might be impacted by new technologies. As a result, leadership is being placed in a new context in an information technology-enabled economy. It is crucial to consider how technological advancement and leadership interact to affect both the structure and outcomes of leadership, as well as how leadership itself may affect the adoption of cutting-edge information technology and its effects on organizations. In the internet era, leadership is undoubtedly different. As the world continues to change as a result of the apparent and astonishing advancements in computer and communications technology, it is imperative that we consider what has changed and what has stayed the same. The impact of the e-factor on leadership is one very significant setting for leadership. 2026 selection and editorial matter, Mukesh Kumar Awasthi, Ashwani Kumar, Manoj Gupta; individual chapters, the contributors. -
Prioritizing Risks in AI-Enabled EdTech Platforms: An Analytic Hierarchy Process Approach
Artificial intelligence (AI) has revolutionized educational technology (EdTech) platforms, offering innovative tools and personalized learning experiences. However, the integration of AI into EdTech also introduces significant risks that must be addressed. This study aims to systematically prioritize and evaluate these risks using the Analytic Hierarchy Process (AHP), a structured multi-criteria decision-making tool. Key risks identified include data privacy and security concerns, algorithmic bias and fairness issues, technical reliability and compatibility challenges, personalization and overreliance on AI, accessibility and equity risks, ethical and privacy concerns, operational and financial risks, governance and regulatory compliance risks, and the black box problem of lack of transparency in AI decision-making processes. Through an extensive literature review and expert inputs, the study employs the AHP methodology to capture the diverse dimensions of risk and determine their relative importance. The findings highlight technical reliability and compatibility (27.43%), the black box problem (17.31%), ethical concerns (17.29%), and personalization and overreliance on AI (17.25%) as the top-ranked risks. By identifying critical risk areas, this research informs the establishment of effective risk management strategies, ensuring the responsible and sustainable adoption of AI in EdTech platforms. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Prioritizing Risks in AI-Enabled EdTech Platforms: An Analytic Hierarchy Process Approach
Artificial intelligence (AI) has revolutionized educational technology (EdTech) platforms, offering innovative tools and personalized learning experiences. However, the integration of AI into EdTech also introduces significant risks that must be addressed. This study aims to systematically prioritize and evaluate these risks using the Analytic Hierarchy Process (AHP), a structured multi-criteria decision-making tool. Key risks identified include data privacy and security concerns, algorithmic bias and fairness issues, technical reliability and compatibility challenges, personalization and overreliance on AI, accessibility and equity risks, ethical and privacy concerns, operational and financial risks, governance and regulatory compliance risks, and the black box problem of lack of transparency in AI decision-making processes. Through an extensive literature review and expert inputs, the study employs the AHP methodology to capture the diverse dimensions of risk and determine their relative importance. The findings highlight technical reliability and compatibility (27.43%), the black box problem (17.31%), ethical concerns (17.29%), and personalization and overreliance on AI (17.25%) as the top-ranked risks. By identifying critical risk areas, this research informs the establishment of effective risk management strategies, ensuring the responsible and sustainable adoption of AI in EdTech platforms. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Prioritization of Challenges in EdTech Platform to Enhance User Continuance Intention: A Multi-criteria Decision Making Approach
In the rapidly evolving digital education landscape, EdTech platforms face significant challenges that impact user continuance intention. This study employs a fuzzy logic approach within the Multi-criteria Decision Making (MCDM) framework to identify and prioritize these challenges, ensuring the long-term sustainability of EdTech solutions. Key challenges were identified through an extensive literature review and unstructured interviews with eight industry experts. The fuzzy AHP technique was used to rank these challenges, providing a structured approach for EdTech companies to enhance user continuance intention and platform effectiveness. Results reveal Personalization (32.90%) as the most critical factor, followed by Data Privacy and security (20.86%) and User Interface (12.02%). Addressing these prioritized challenges can significantly improve user engagement and contribute to the development of inclusive and accessible educational technologies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing student engagement in blended learning through personalization strategies on EdTech platforms: An MCDM-based approach
The rapid growth of educational technology highlights the essential need for personalized learning in blended environments. This study uses Multi-Criteria Decision Making (MCDM) methodologies, including the Analytic Hierarchy Process (AHP), Fuzzy AHP, and Analytic Network Process (ANP), to evaluate and prioritize personalization strategies in EdTech platforms. The research identifies data-driven adaptive learning as the most critical strategy (36.46%), followed by AI-powered content recommendations (15.46%) and personalized learning paths (15.11%). It reveals that personalization strategies are interconnected, creating dynamic feedback loops that reinforce one another, enabling continuous learning optimization. The study provides a holistic framework for educational technologists, policymakers, and designers. This approach bridges technological innovation with pedagogy, emphasizing adaptive, datainformed systems that respond dynamically to learner needs, ensuring the balance between innovation and educational quality. 2025, IGI Global Scientific Publishing. -
Prioritizing Risks in IoT-Enabled EdTech Platforms: A Fuzzy AHP Approach to Maximize User Satisfaction
The integration of the Internet of Things (IoT) in educational technology (EdTech) platforms offers personalized, adaptive learning but introduces significant risks. This study identifies and prioritizes these risks using the Analytic Hierarchy Process (AHP) and fuzzy AHP techniques. Seven industry experts provided input, complemented by a comprehensive literature review. The analysis reveals ethical considerations (30.17%) and data privacy/security (29.22%) as top concerns, followed by regulatory compliance (12.91%), high implementation costs (10.99%), and technical expertise requirements (8.37%). Surprisingly, scalability concerns (1.70%) and data accuracy/reliability (2.63%) rank lower. These findings emphasize the need for a human-centric approach in IoT-enhanced EdTech deployment, focusing on responsible implementation and regulatory adherence. The study provides valuable insights for EdTech companies and educational institutions, guiding strategic decision-making to enhance user satisfaction and ensure sustainable development of IoT-enhanced educational platforms. Future research could explore more advanced mathematical models and context-specific challenges to refine risk prioritization strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Data Analytics in Diabetes Treatment: Approaches and Applications
These days, managing diabetes has become particularly more beneficial with advancements in data analytics, using machine learning, predictive analytics, and patient-generated health data to optimize the outcome for patients. This paper explores the latest techniques and innovations in this field, including predictive modelling, wearable technology integration, and artificial intelligence for better personalized care. The study covers various analytical frameworks, compares the performance of multiple machine learning models, and discusses future directions in the integration of data analytics with telemedicine for diabetes care. The following words refer to diabetes management, data analytics, predictive simulation, AI, and algorithms for learning, wearable technologies, patient-generated health data, predictive analytics, continuous glucose monitoring, health informatics, personalized care, health data privacy, predictive algorithms, electronic health records, diabetes complications, telemedicine, feature selection, and model evaluation along with having patient-centric systems and chronic disease management. 2026 American Institute of Physics Inc.. All rights reserved. -
Leveraging Usage of AI in education: Knowledge, Attitude and Behavioral Analysis on Students
The paper explores the possible advantages and drawbacks of artificial intelligence (AI) on sustainability, with an emphasis on using AI to positively achieve SDGs. The study finds a significant vacuum in the literature on the association between knowledge, attitudes, and behaviors towards the use of AI tools and techniques in education and demographic characteristics (sex, age, education level, area of study, and city of origin). The purpose of this research is to close this knowledge gap and advance our understanding of how these demographic factors affect the integration of AI in educational environments. The study specifically aims to comprehend how students awareness, beliefs, and actions towards AI in educational situations are influenced by demographic characteristics. This research attempts to offer insights into practical methods for utilizing AI in education while addressing potential obstacles and minimizing negative effects through a thorough analysis of data gathered from students across a range of demographic backgrounds. 2025, Binghamton University Libraries. All rights reserved. -
Environmental justice and rural poverty: Socioeconomic drivers of environmental impact in the Indian Sundarbans
This study explores the complex relationship between environmental degradation and rural poverty through the perspective of environmental justice in the Indian Sundarbans. This region is recognized for its ecological richness but faces significant socio-economic vulnerabilities. Despite the areas abundant natural resources and biodiversity, rural poverty persists, shaping resource use patterns and environmental outcomes. The main objective is to examine how rural poverty affects the environment and to identify key socio-economic factors influencing ecosystem services in the region. A stratified sampling technique was used to select households and data were collected through structured questionnaires and focus group discussions. Household-level CO2 emissions were estimated from domestic fuel and energy consumption. Results show nearly half of the household emissions come from burning firewood for cooking and heating. These practices not only release stored carbon but also reduce the regions carbon removal capacity, directly contributing to environmental degradation. The people of the Sundarbans pollute far less than the national average, but they face the harshest impacts of climate change: rising sea levels, salinity intrusion and ecosystem decline that highlighting a profound environmental injustice. Among rural households, the extremely poor emit more CO2 than the less poor because they depend on cutting trees to survive, further weakening the regions natural carbon sink. A log-linear regression model identifies education, dependency ratio, livelihood diversification and access to transport as major factors influencing natural resource-based income. The findings suggest that poverty, isolation and low education reinforce ecological stress, forming a povertyenvironment trap. This study argues that protecting the Sundarbans environmental integrity and enhancing its carbon sequestration potential are inseparable from improving human well-being. Achieving Indias net-zero target by 2070 requires policies that expand clean energy access, build human capabilities and ensure justice for communities who contribute least to emissions but suffer the most from climate disruption. Implications: This study emphasizes the urgent need to integrate poverty alleviation with environmental sustainability in climate-vulnerable regions like the Indian Sundarbans. Rural poverty contributes to environmental degradation by increasing dependence on forest resources, leading to unsustainable practices and higher CO? emissions. Low education levels, limited livelihood opportunities and inadequate infrastructure intensify both poverty and ecological stress. To tackle environmental degradation, it is essential to implement conservation efforts alongside interventions that improve education, enhance connectivity, diversify livelihoods and strengthen social protection. Aligning environmental justice with rural development can break the cycle of poverty and ecological damage, promoting long-term resilience and sustainability. 2025 Air & Waste Management Association. -
Tracing Fe K X-Ray Reverberation Lag in the Energy-resolved Spectra of Narrow-line Seyfert 1 Galaxy Ton S180
We report the Fe K relativistic reverberation feature for the first time in the narrow-line Seyfert 1 galaxy Ton S180. Using a long observation from XMM-Newton we find that the Fe K emission lag peaks at 117 49 s in the lag energy spectrum computed for frequencies (0.3-8.5) 10?4 Hz. The lag amplitude drops to 22.85 14.20 s as the frequency increases to (8.5-30) 10?4 Hz. The time-averaged spectrum of the source shows a relatively narrow Fe K line at ?6.4 keV, indicating a low black hole spin ( a = 0 . 4 3 ? 0.14 + 0.10 ) based on the reflection modeling. We perform general relativistic transfer function modeling of the lag energy spectra individually. This provides an independent timing-based measure of the spin at a = 0.3 0 ? 0.17 + 0.34 , a black hole mass M BH = 0.2 9 ? 0.16 + 0.01 1 0 8 M ? , comparable to the previous measurement, and a coronal height h = 2.5 9 ? 0.33 + 5.17 r g . Further, we observe that the Fe K lag and the black hole mass fit well in the linear lag-mass relation shown by other Seyfert 1 galaxies. 2026. The Author(s). Published by the American Astronomical Society. -
She Shores A Study on the Lives, Challenges and Resilience of Women of the Koli Fishing Community in Mumbai
This study delves into the lives of women from the Koli fishing community in Mumbai, aiming to illuminate their unique life experiences and the daily struggles that often remain hidden beneath their prosperous facade. It endeavours to examine their agency and adaptive strategies employed to navigate these challenges. The research was conducted in Pachubandar, Vasai, located in the western suburbs of Mumbai, which stands as one of the prominent Koli settlements in the city. Employing a qualitative research approach coupled with an exploratory research design, the study engaged ten participants, comprising seven Koli women and three key informants from the community. Additionally, an observational analysis of four retail and wholesale fish markets in Mumbai was conducted to gain insight into the working conditions of Koli fisherwomen. This study adopts a gender-focused perspective to scrutinise the contextual vulnerabilities that shape the lives of Koli women. It underscores the paradox wherein, despite playing a pivotal role in sustaining both their families and the traditional fishing occupation, their contributions often go unnoticed. The Koli women face severe deprivation due to their limited access to property and decision-making authority. They find themselves entangled within traditional norms and patriarchal structures, which impede their access to essential assets and diverse livelihood resources. Although they significantly contribute to the fishery sector, their struggles, needs, and aspirations are frequently disregarded due to their lack of representation and involvement in decision-making bodies. The majority of these women work under precarious conditions, devoid of proper infrastructure, resources, and security. Furthermore, the evolving dynamics within the fishery sector, driven by rapid urbanisation and modernisation, have a profound impact on the lives and traditional livelihoods of Koli women. They now confront issues such as dwindling fish catches due to environmental degradation, heightened market competition, reduced livelihood spaces brought about by shifting urban and coastal landscapes, altered labour relations, and technological advancements. Consequently, they find themselves caught between the conflicting forces of tradition and modernity. The research also sheds light on the strategies devised by Koli women to resist and adapt to the uncertainties and challenges they encounter, ultimately safeguarding their livelihoods through self-organisation. The study emphasises the imperative to acknowledge their contributions as visible work and advocates for the incorporation of gender considerations when formulating policies and development strategies within the fisheries sector. MEGHNA ROY AND JYOTI SINGH, 2024. -
Analysing the Effectiveness of Solana Blockchain Platform and PoH Consensus Algorithm in Providing a Solution for Blockchain Scalability Problem
Solana started its journey in April 2018 and is now a public blockchain - based platform which aspires better scalability than other existing blockchains while providing security and decentralization. It backs the development of decentralized applications and smart contracts (DApps). The goal of the study is to confirm several of its characteristics, like its transaction throughput, or the pace in which legitimate transactions are committed to a Solana network block over the course of a one-second period (TPS). A secondary dataset that was gathered over the course of 60 days and made available on GitHub was utilized. Our data analysis findings demonstrate that the transaction throughput on an average is about 3006 TPS at a much lower transaction fees than the fees users pay for many other blockchains that facilitate the same operations, such as use of smart contracts and the development of DApps. The document explains the workings of the Solana blockchain, which, in the words of its creators, claims to address the scalability issue without compromising security and decentralization. Grenze Scientific Society, 2025. -
Attitude of public towards higher education: Conceptual analysis /
Scholedge International Journal Of Multidisciplinary And Allied Studies, Vol.2, Issue 12, pp.19-28, ISSN No: 2394-336X. -
End-to-End Imitation Learning for Autonomous Driving: Design and Implementation on a Custom Robotic Platform
This paper presents an end-to-end imitation learning system for autonomous driving on a custom robotic vehicle. A PilotNet-inspired CNN, trained on 20 laps of Udacity Beta simulator data from three cameras, was deployed on an NVIDIA Jetson Xavier for real-time steering. The robot features a 48V, 1.5kW BLDC motor and a precision steering system using dual linear actuators. Manual override is enabled via an RC controller. The integration of deep learning with custom hardware highlights challenges in transferring simulation-trained models to real-world systems. 2025 IEEE. -
Effective on-site waste minimisation approaches in Indian construction projects
Indias rapidly expanding construction sector generates substantial material waste, creating environmental and economic challenges that are often intensified by weak on-site waste management (WM) practices. This study investigates effective approaches for minimising material waste in building construction projects using a mixed-methods research design. The research integrates a systematic literature review, expert interviews, and a large-scale questionnaire survey to identify and prioritise waste minimisation strategies. From an initial set of 38 practices, 15 key strategies were shortlisted through descriptive and inferential statistical analysis. Their practical effectiveness was then evaluated through longitudinal monitoring of material waste across seven construction stages at four active residential sites. The results indicate a consistent reduction in material wastage following the implementation of targeted interventions. By triangulating expert insights, industry perceptions, and empirical site-based evidence, this study moves beyond perception-based assessments and provides empirical validation of waste minimisation strategies under real construction conditions in a developing-country context. The findings demonstrate that sustained waste reduction depends on an integrated approach combining human behaviour, managerial control, and proactive planning from early project stages. The study offers practical guidance for improving material efficiency, reducing costs, and advancing sustainable construction practices in India. The Author(s), under exclusive licence to Springer Nature Japan KK, part of Springer Nature 2026. -
Effectiveness of integrated waste minimisation strategies in high-rise residential construction projects
Construction waste has become a significant sustainability concern in fast-growing Indian cities, especially in high-rise residential projects characterised by intensive material flows. This study conducted a comparative analysis of material waste across the various stages of eight high-rise residential projects in Bengaluru, India. Four of the projects followed the conventional method, while the remaining four used an efficient method to reduce material waste. The material usage and generation were recorded for seven phases, each lasting two months, both quantitatively and qualitatively, using data and observations. Additionally, Relative Reduction (RR) values were calculated to assess the effectiveness of the implemented interventions by comparing the projected values for the baseline scenarios of uncontrolled and controlled projects. Uncontrolled projects exhibited an average wastage growth of 23% and negative RR values (? 4.48% to ? 9.15%), indicating a deterioration in waste management performance. At the same time, the sites implementing waste control measures demonstrated waste stability or reduction, with RR values of 713%, due to improvements in site supervision, material storage, batch extraction accuracy, and control of material issues. Material-wise analysis further supported the reduction in waste under controlled conditions. The benchmarking system developed in this research will provide practical support for waste tracking and remedial actions. The study demonstrates, using data, that low-cost, straightforward process interventions can substantially increase the effectiveness of resource use in achieving SDG 11.6 and SDG 12.5. The Author(s) 2026. -
Construction Waste: Key Causes and Reduction Approaches
The construction industry is a significant contributor to global waste, with Construction and Demolition (C&D) waste comprising a substantial portion. This paper investigates the key causes and reduction approaches. Key sources of waste include material offcuts, packaging, and unused materials due to excess procurement or design changes. Factors that exacerbate waste include inadequate project planning, poor site management, and insufficient worker training. Economic factors often favor new materials over recycled ones due to cost and time concerns. Rapid urbanization and redevelopment further escalate C&D waste as older buildings are demolished for new construction. Technological advancements and innovative methods, such as prefabrication and modular construction, have the potential to reduce waste generation. However, traditional construction practices and resistance to change impede widespread adoption. This paper highlights the urgent need for integrated waste management strategies, emphasizing the roles of policy, education, and technology in reducing C&D waste. Sustainable practices, such as using recycled materials, improving on-site waste segregation, and adopting circular economy principles, are crucial for minimizing the environmental impact of construction activities. Addressing these factors is essential for achieving sustainability in the construction industry and supporting global environmental conservation efforts. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.