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Emotion-Aware Sign Language Recognition Using CNN and UNET Architectures
This paper proposes an AI-based system for the recognition of sign language with the detection of emotions for more expressive communication among speech-impaired and hearing individuals and others. Traditional sign language systems focus mainly on the aspect of hand gestures and neglect the signs for emotions that add meaning and context. In order to overcome the limitation, the project proposes a system that utilizes Convolutional Neural Nets (CNNs) for the recognition of hand signs and UNET for the segmentation of the picture so that the area of the hands can be discriminated from the background. Facial Emotion Recognition (FER) is also incorporated in order to detect signs such as happiness, sadness, or anger. Overall, the parts together constitute a multimodal recognition system that can read signs and emotions and produce more natural and expressive outputs. The paper delves into architecture, dataset challenges, and implementation concepts with publicly available databases such as RWTH-PHOENIX-Weather 2014T. The combined approach can enhance inclusivity and access in learning, communication, and assistive technology. 2025 IEEE. -
Decision Flow Tracing and Word Impact Analysis in Hybrid Transformer-Conditioned Diffusion Models for Text-to-Image Generation
Text-to-image diffusion models have become a cornerstone of modern generative AI, offering high-quality synthesis yet remaining constrained by their black-box nature, which limits controllability and interpretability. In this work, we propose a hybrid transformer-conditioned diffusion model that integrates UNet-based denoising with multi-head cross-attention transformer blocks at critical latent stages of the diffusion process. The architecture is trained on a curated set of 50,000 samples from DiffusionDB with a 200-step latent diffusion schedule. Text prompts are encoded using a 16-token BERT encoder and mapped into a 256-dimensional latent feature space. Cross-attention layers with eight heads are interlaced within the UNet bottleneck and decoder, enabling token-to-region correspondence and fine-grained semantic propagation. To ensure interpretability, we design an explainability framework that combines hierarchical token-level attention heat maps, temporal attention rollouts, and perceptual ablation studies based on learned image patch similarity. Analysis reveals that object tokens remain spatially and temporally consistent, while attribute tokens demonstrate sharper temporal volatility. JensenShannon divergence quantifies this redistribution of attention across diffusion steps. Experimental evaluation against a standard UNet diffusion baseline demonstrates clear improvements: Frhet Inception Distance decreases by 19.6, CLIP alignment score increases by 5.4, and Inception Score improves by 18.6. Moreover, attention coherence improves by 22%, underscoring the gains in explainability. The proposed framework establishes a pathway toward accountable, high-fidelity, and interpretable text-to-image synthesis. Beyond performance, it supports critical tasks such as bias evaluation, fairness auditing, and quality assurance, offering a robust foundation for the next generation of explainable generative AI systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Modeling of non-Gaussian time series in the presence of measurement error
Measurement error in time series refers to inaccuracies or deviations in recorded data that can distort the true underlying patterns, potentially leading to biased estimates and misleading conclusions in statistical analysis. AR models with non-Gaussian errors are crucial for accurately capturing real-world time series data with skewness, heavy tails, or outliers, leading to better forecasts and more reliable statistical inferences. Our focus in this study is on an optimal estimating function (EF) method that accounts for measurement error while estimating the parameters of AR(1) with logistic innovation. The asymptotic properties of the obtained optimal EF are also proved. The simulation study shows that incorporating measurement error into the model yields better estimates compared to ignoring its presence when measurement error exists. This shows that ignoring the measurement error leads to biased estimates. 2026 Taylor & Francis Group, LLC. -
REGRESSION WITH VOLATILE ERRORS IN THE PRESENCE OF MEASUREMENT ERRORS
This study explores the estimation and testing of regression models with volatile errors when measurement errors are present. The presence of measurement error in models with heteroscedastic disturbances, such as those following an autoregressive conditional heteroscedasticity (ARCH) or Generalized ARCH (GARCH) structure, can lead to biased estimates and misleading inferences. To address this, we develop an estimation framework that accounts for both heteroscedasticity and mismeasured observations, ensuring consistent and asymptotically normal parameter estimates. We estimate the parameters using corrected score estimation and weighted linear regression, which effectively mitigate the impact of measurement error and hetroscedasticity. Additionally, we perform a Likelihood Ratio (LR) test to assess the significance of measurement errors in regression models with volatile errors. Through Monte Carlo simulations, we analyze the bias and efficiency of traditional estimators and demonstrate the robustness of our proposed approach. Finally, the methodology is applied to real-life economic and financial data, illustrating its practical relevance and effectiveness in empirical research. The findings contribute to improving statistical inference in models where measurement error and volatility coexist, ensuring more reliable and accurate parameter estimation. 2025, Gnedenko Forum. All rights reserved. -
Estimating Function Approach for Modelling Non-Gaussian Time Series
In this study, an effective technique for estimating parameters of autoregressive models with non-Gaussian errors has been presented. For non-Gaussian error distributions, classical techniques like maximum likelihood estimation (MLE) are found to be computationally intractable. To address this issue, an estimating function (EF) technique has been utilized, which effectively estimates the model parameters by taking advantage of the error structure. Specifically, AR(1) with logistic errors has been used, and the optimal estimating functions have been derived by constructing martingale-based estimating equations tailored to logistic errors. A hybrid AR(1)-ANN model has also been developed to integrate the strengths of both linear AR(1) and nonlinear ANN models. The robustness and efficiency of the pro-posed approach in parameter estimation have been investigated using simulations, comparing its mean squared error (MSE) and bias to those of MLE. The applicability of this approach has been further demonstrated using both simulated and real datasets. The results show that, in the presence of non-Gaussian errors, the EF method provides a computationally efficient and reliable alternative to classical estimation methodologies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Corrected Score Estimation of Regression with Autocorrelation Under Measurement Errors
In regression models with autocorrelated errors, measurement errors in the covariates can lead to biased and inconsistent estimation of the regression coefficients. Measurement error refers to the discrepancy between the observed values of a variable and its true values. It occurs during the data collection process due to various factors such as inaccuracies in measurement instruments, limitations in the measuring process, human errors, or environmental influences. These errors can introduce bias into collected data, impacting the reliability and validity of statistical analyses and model outcomes. Recognizing the importance of time dependencies, our study extends to time series regression models in the presence of measurement error. A score function is the derivative of the log-likelihood function with respect to the parameters. In this paper, a correction for score function in regression with autocorrelated errors is considered to account for the impact of measurement errors on parameter estimation, and it attempts to provide a two-step estimation procedure to resolve the bias caused by both these challenges. Further, the efficiency of these estimates is compared with least square estimates by carrying out a simulation study for finite samples, and we conclude the proposed methodology provides more accurate results. The applicability of the proposed model has been illustrated using the Phillips Curve dataset and it was found that in the presence of measurement error, corrected score estimation gives more accurate estimates than OLS estimates. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Revolutionizing Education: Evaluating the Impact of AI and Ed Tech Tools on Learning Outcomes
The rapid evolution of artificial intelligence (AI) and educational technology (EdTech) tools is significantly transforming teaching and learning processes across the globe. This chapter seeks to critically examine the impact of Al-driven platforms and digi-tal learning tools on educational outcomes, learner engagement, and pedagogicalpractices. By exploring empirical evidence, theoretical frameworks, and real-world implementations, this chapter aims to provide a comprehensive understanding of how Al and EdTech tools are reshaping personalized learning, assessment, student support, and teacher facilitation. Particular emphasis will be given to the effectiveness of these tools in diverse educational contexts, including challenges related to accessibility, ethical considerations, and the digital divide. The chapter will also present recommendations for integrating Al and EdTech in ways that are pedagog-ically sound, inclusive, and outcome-focused. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Evaluation of personal development components in counselor education programs in India /
Journal Of Asia Pacific Counseling, Vol: 6, Issue 1, pp.1-20, ISSN: 2233-6710(Print), 2384-2121(Online). -
Improved piezoelectric energy harvester design using aluminum nitride for improved voltage and power output
This research focuses on improving the performance of piezoelectric energy harvesters (PEHs), which convert ambient kinetic energy into electricity. One of the primary challenges with piezoelectric harvesters is their high resonant frequencies, which often do not align with the lower natural frequencies of ambient vibrations, limiting their efficiency. The goal of this research is to propose a new technique to optimize the design of PEHs, enhancing voltage output and power conversion efficiency. The proposed method combines an Arithmetic Optimization Algorithm to optimize the harvesters dimensions with a Dual Temporal Gated Multi-Graph Convolution Network (DTGMGCN) to forecast resonant frequency and harvested voltage. The principal objective is to reduce resonant frequency errors and enhance energy conversion efficiency. The results, implemented on a MATLAB platform, demonstrate that the proposed method outperforms the existing techniques, such as robust chaotic Harris Hawk optimization, K-Nearest Neighbor Algorithm, and Heaviside Penalization of Discrete Material Optimization. The existing techniques show errors of 0.04%, 0.06%, and 0.08%, while the proposed method achieves an error of only 0.02%. Additionally, in terms of efficiency, the proposed method reaches 98%, significantly higher than the 65%, 78%, and 85% achieved by the existing techniques. These findings indicate the efficiency of the proposed approach in improving the design and performance of piezoelectric energy harvesters, offering a promising solution for more efficient energy harvesting systems. King Abdulaziz City for Science and Technology 2025. -
A Study on Impact of Dopants on Piezoelectric Parameters of Aluminium Nitride
Doping is a critical in piezoelectric base materials used as sensors or energy harvesters. It has a significant effect in determining the output parameters of an energy harvester. This paper studies the effect and impact on piezoelectric properties when materials are co doped and singly doped by considering some common dopants found in the literature. From the study, the elastic constant and c/a ratio are found to be decreasing and all other lattice parameters such as piezoelectric stress coefficient, electromechanical coupling constant, Born effective charge etc. are increasing, although some discrepancies are there. Scandium, magnesium, Lithium and its co-dopants are mainly considered for the study. Another factor that should be considered is doping concentration. Excessive doping with scandium (Sc) has been demonstrated to reduce the coercive field (Ec), enabling ferroelectric switching in AlScN thin films. The critical factor for achieving ferroelectric switching lies in sufficiently lowering the energy barrier between the two polarization states made of the wurtzite structure. This can be accomplished either by increasing the proportion of the non-Group-III metal or through strain engineering. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Ageing in Home: Place Attachment in Malayalam Films Android Kunjappan, #Home and Pookkaalam
[No abstract available] -
Surveilling bodies, governing morality: Biopower and the contagious diseases acts in colonial India
This article explores the Contagious Diseases Acts (CD Acts) in colonial India using Michel Foucault's theory of biopower, focusing on their impact on devadasis. The Acts subjected women's bodies to medical and legal scrutiny, pathologizing female sexuality while reinforcing patriarchal and caste hierarchies. Devadasis, historically associated with temple practices, were recast as vectors of disease and moral disorder, aligning with the colonial administration's broader project of governance through regulation and surveillance. This study examines how the CD Acts functioned as a technology of power, reshaping devadasis identities and controlling their bodies to sustain social and political order. It also investigates how these mechanisms were challenged by resistance, demonstrating the adaptive and contested nature of colonial power. By situating the devadasis within the framework of biopower, this analysis illuminates the intersection of health policies, sexuality, and governance in shaping colonial hierarchies and marginalizing vulnerable communities. 2025 Elsevier Masson SAS -
Bridging the Digital Chasm: A Comprehensive Analysis of the Digital Divide in Shaping Healthcare Professionals
Industry 5.0 has ushered in a transformative era for the healthcare industry, propelling it towards a digitally empowered ecosystem. This paradigm shift underscores the indispensability of integrating advanced digital technologies into healthcare, not merely as an option but as a crucial necessity. This research delves into the profound impact of Industry 5.0 on healthcare, emphasizing the intersection of digital technologies, patient-centric care, and sustainable practices. Central to the investigation is the digital divide, which manifests as a catalytic intrusion into the healthcare sector's digital landscape. Regardless of demographic considerations among healthcare providers, the existing digital divide manifests in varying levels of access, skill, and offline outcomes and benefits. Our study meticulously explores the intricate dynamics of the digital divide within healthcare professionals, highlighting its influence on micro-level patient experience. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
WATCHING AND BEING WATCHED: POWER, SURVEILLANCE AND AGENCY WITHIN THE DEVADASI SYSTEM
This article explores the devadasi system in India through the dual thoughts of Michael Foucaults Panopticism and Agency Theory, examining how power dynamics and social control mechanisms were both enforced and resisted. The devadasi system, originally a revered religious practice, evolved into a complex structure of exploitation and marginalization, where women dedicated to temple service were subjected to pervasive surveillance and disciplinary practices. By applying Panopticism, this study reveals how the British administrators and the nationalists maintained strict control over devadasis, ensuring their subjugation within the social hierarchy. Simultaneously, Agency Theory illuminates how devadasis challenged these oppressive structures, asserting their autonomy in subtle but significant ways. This analysis not only deepens our understanding of the devadasi system but also contributes to broader discussions on the intersections of power, control and agency in marginalized communities. The study also highlights the importance of examining historical systems of oppression through critical and theoretical frameworks to uncover both the mechanisms of control and the resilience of those subjected to them. 2025 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore), ISSN: 0253-7222. -
Utilizing GIS for Crime Mapping to Identify Crime Hotspots in the Urban Context of Kerala
This study employs Geographic Information Systems (GIS) for crime mapping to pinpoint hotspots within the Museum police stations jurisdiction in Trivandrum, Kerala, which saw the highest number of crimes reported in 2022, as per the National Crime Records Bureau (NCRB), making it an important issue to be tackled. Trivandrum was chosen for its high population density, aligning with criminological theories linking dense urban areas to increased criminal activity. The jurisdiction of the Museum police station area was explicitly selected due to its significant incidence of various crimes as per the data available from the Kerala police website while comparing it with the overall 21 jurisdiction boundaries. Data collection encompassing seven crime categories as per the analysis of previous literature studies, which includesrape, theft, molestation, kidnapping, murder, hurt, auto theft, and robberywas meticulously gathered from the Museum police station and organized using Excel, then analysed through GIS techniques. These methods included Average Nearest Neighbours Analysis to identify crime pattern types, Kernel Density Estimation to visualize crime density maps, Choropleth mapping to highlight wards with heightened crime rates, and Standard Deviation Ellipse Analysis to explore trends in crime distribution. These analytical approaches and their comparison with buffered maps facilitated a comprehensive spatial examination, uncovering distinct crime hotspots and illuminating factors contributing to their concentration. The study concludes by pointing out the main vulnerable areas of the study with the help of the previously mentioned mapping analysis, helping in providing suitable areas to be focused on to provide design strategies to curb crime. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Greening the click-environmental initiatives in global e-commerce marketing
Convenience and accessibility are the features that drive today's consumers flocking to the retail market. This has given rise to widespread environmental impacts implying that the green concept includes sustainable strategic protocols committed to ethical consumerism and corporate social responsibility. The key operative word is sustainability in all aspects of business from design to delivery. An effective component of strategy is innovation, coupled with transparency. Education of the customers on environmental issues ensures their loyalty and enhances brand image. A Nielson report testifies that consumers are attracted more to products from companies behaving like good corporate citizens providing them with competitive advantages and leading to cost savings. Moreover, positive engagement in legal requirements keeps it ahead of risks associated with environmental laws. Two considerations on strategies are projected upfront. One is Green Branding, meaning thereby a strong and positive association with environmental responsibility in the minds of the buyers, boosting brand loyalty. The other, being Eco-labeling, is a visible proof of environmental benefits. Eco-labels are certifications awarded from recognized organizations. 2025 Thomas K.V., Punam Rattan and Raji Ramakrishnan Nair. Published under exclusive licence by Emerald Publishing Limited. All rights reserved. -
Introduction
Beginning The Rashtriya Uchchatar Shiksha Abhiyan (RUSA) in 2013, a significant development in higher education, in addition to several other initiatives such as the introduction of the New Methodology by the National Assessment and Accreditation Council (NAAC), the New Education Policy (NEP2020) and technological challenges of the COVID pandemic in the current times, Indian higher education has been undergoing a profound transformation, especially intending to revamp, through policy changes, upgrading and enhancing quality, ranking, research, bridging the skill gap, technological innovations and global perspectives. There is an urgent need to understand the evolving dynamics and emerging perspectives that appear to challenge the higher education system in India. The conference intends to draw attention to those key stakeholders in the present context to realise the anticipated drive towards overhauling the higher education system in India. 2025 selection and editorial matter, Kennedy Andrew Thomas, Joseph Chacko Chennattuserry and Joseph Varghese Kureethara; individual chapters, the contributors. -
Introduction
This book Education and Pedagogical Experiences: Coping with Human Emergencies and Exploring Resilience Strategies is an array of chapters contributed by a panel of experts that explains different human emergencies and the role of education and pedagogy in addressing these crises. The contributions illustrate how educational practices during emergencies have evolved, persisted, and impacted communities globally. 2025 selection and editorial matter, Kennedy Andrew Thomas and Joseph Varghese Kureethara; individuals, the contributors. -
Education and Pedagogical Experiences: Coping with Human Emergencies and Exploring Resilience Strategies
This book analyses how the educational ecosystem undergoes a paradigm shift during human emergencies - be it natural, manmade, environmental, ethnic or a global pandemic like COVID-19. It discusses varied approaches, experiences, and the steadfast desire to share information, demonstrating the value of teaching and learning in difficult conditions. This volume aims to build resilience and inspire young minds to persevere through challenging times. It explores the continuity of education during emergencies, various teaching and learning approaches, and the importance of maintaining a resilient attitude. Each addresses the cultural and locational specifics of emergencies, illustrating how education and pedagogy have made a global impact. They also examine a specific aim, enriched by cultural, geographical, and human variables, and how education and pedagogy aim to resolve these concerns. This book would be useful to students, researchers and teachers working in Education, Environmental Science and Disaster Management, Political Science, Public Administration, International Relations, Peace Education, Psychology and Cognitive Science, Neuroscience, Sociology and Social work. It would also be an invaluable companion to practicing pre-service and in-service teachers and their trainers, policy makers, professionals from government and non-government organizations working in education and social development. 2025 selection and editorial matter, Kennedy Andrew Thomas and Joseph Varghese Kureethara; individuals, the contributors. -
Education and Human Emergencies Conundrum
This chapter aims to make sublime the phenomenological research design to bring out the essence of human teaching during emergencies. The approach identifies distinguishing attributes through subjective comprehension, experiences, and beliefs of teaching when the routine is disrupted through emergencies, disasters, and conflicts. It is intended to increase the comprehension of subjective attitudes, beliefs, and experiences while working in emergencies. It also looks at participant engagement in teaching and learning and identifying distinctive patterns or factors emerging from these convergent or divergent thought processes or experiences during emergencies. The chapter summarises the findings of this study and hopes to throw light on how to stabilise, structure, and teach values, skills, tolerance, and disaster risk reduction during disrupting emergencies. This chapter delves into the realm of phenomenological research to explore the essence of human teaching amidst emergencies. By summarising the studys findings, the chapter aims to offer insights into how to effectively navigate and adapt teaching practices to stabilise, structure, and impart values, skills, tolerance, and disaster risk reduction during disruptive emergencies. It contributes to a deeper understanding of teaching in crisis situations, ultimately informing strategies for educational resilience and response. 2025 selection and editorial matter, Kennedy Andrew Thomas and Joseph Varghese Kureethara; individuals, the contributors.