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CSR as an agent of financial stability: A use case of banking industry
The study was undertaken to examine the importance of corporate social responsibility (CSR) as an agent to improve the firm's performance and financial stability by enhancing goodwill and competitive advantage in the Indian banking industry. In the study, it has been hypothesized that CSR expenses have a positive relationship with financial stability. A correlation study has also been undertaken to determine any relationship among these variables, followed by a dependency regression test to show the levels of dependency of financial stability on CSR expenses and the Granger Causality test to find their causal relationship. The study has revealed a significant relationship between the financial stability variables and CSR expenses, and the Granger Causality level supports the findings. 2021 Ecological Society of India. All rights reserved. -
Method of enhancing quality of services in cloud computing environment using load balancer /
Patent Number: 202211006218, Applicant: Dr. Pratibha Giri. -
A smart attendance system and method for permission inventory during the class /
Patent Number: 202111060922, Applicant: Shivani Chaudhry.A smart attendance system (1). The system (1) comprises a smart lecture stand (2), which having an electronic unit (2A) which is connected to the other smart door, smart bench, and smart chair of the system; a smart bench (3), which having an electronic unit (3A), which is connected to the other smart door, smart stand, and smart chair of the system; a smart chair (4) comprises which having an electronic unit (4A); which is connected to the other smart door, smart bench, and smart stand of the system; a smart door (5) comprises a electronic unit (5A), which is connected to the other smart door, smart bench, and smart chair of the system. -
Financing for SDGs in India in Post Pandemic era - Challenges & Way forward
In 2015, a resolution known as Agenda 2030 was passed by United Nations General Assembly in which seventeen goals for Sustainable Development were laid down for global dignity, peace and prosperity. The post- pandemic era became full of uncertainties in pursuing those Sustainable Development Goals (SDGs) and its implementation became a challenge especially for the developing economies like India. The country is facing a tremendous gap in arranging for resources to meet the climatic changes and attaining the SDGs. India requires 170 billion dollars per year from 2015-2030 to fulfill the Sustainable Development Goals as per the estimation done by National Determined Contribution, a body setup after Paris agreement 2015 to monitor the efforts of the country towards reducing national emissions and adapting to climate change. There is a huge concern amongst the various agencies on exploring the ways to fill this financing gap especially after the economic slowdown seen in the post pandemic era. This research paper analyses the challenges imposed by the COVID 19 pandemic on financing for SDGs and also explores the options to mitigate them. The articles and research papers related to SDG financing are reviewed by the researchers to arrive at the above mentioned statements. This paper is an attempt to draw the attention of worldwide authorities towards this grim situation as sustainable finance is far from reality in India and requires immediate up scaling. The Electrochemical Society -
SMOTE-Based Sampling for Addressing Class Imbalance
Various real-world applications, including as text categorization, categorization of gender in facial recognition for medical evaluation, fraud detection, and satellites analysis of images for oil-spill monitoring, are frequently plagued by imbalanced data. The majority class is commonly the primary focus of machine learning algorithms, with the minority samples being ignored or classified in a secondary manner. Nevertheless, despite their rarity, these minority samples are very important. When it comes to classification tasks, the issue of class imbalancewhere one class is underrepresented relative to anotherpresents a significant barrier. Specialized approaches including SMOTE, ADASYN, and cost-sensitive voting classifiers have been developed to address this problem. The minority class is oversampled in these methods, synthetic samples are created adaptively, and different prices are placed on misclassification mistakes in order to solve the issue of class imbalance. As a result, rigorous assessment utilizing pertinent metrics and cost considerations are required. The efficacy of these strategies, however, depends on dataset features and problem-specific factors. Class imbalance is still a hot topic for study, and there has been constant innovation in novel methods that are adapted to certain dataset characteristics and application fields. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Edufusion: Integrating Artificial Intelligence With Teaching Practices to Enhance Learning Experiences
The present research explores the influence of artificial intelligence (AI) on traditional teaching methods in the education sector. Through a comprehensive review of existing literature and empirical studies, the present research aims to elucidate the transformative potential of AI technologies in reshaping pedagogical practices and enhancing learning outcomes. The study aims to provide the integrated perspective on the ever changing dynamics influenced by the incorporation of AI in the current education system through a meticulously conducted questionnaire- based investigation involving a diverse pool of huge participants, primarily students from the region of Delhi NCR. 2025 by IGI Global Scientific Publishing. -
MobileNetV3-Based Fine-Tuned Facial Emotion Recognition with Targeted Class Balancing
Facial emotion recognition (FER) is a pillar of affective computing and augmented human computer interaction, but has been stymied by the problem of class imbalance and lack of prevalence of subtle emotional differences. This paper presents a lightweight FER framework based on the MobileNetV3 architecture with a fine-tuned and weighted dataset that applies class balance and class weighting as strategies that optimized the three-class classification of three discrete emotions Angry, Happy, and Sad. The characteristics of the dataset were assembled comprising a total of 7,305 labelled facial images, based on the KDEF, Kaggle, and Face Expression Dataset hence inheriting the heterogeneity of subjects and imaging conditions. The pre-processing of all of the images carried out as the RGB input and after resizing (224 x 224 pixels) a massive data augmentation done to encourage generalization. Transfer learning in the training pipeline is done through progressive unfreezing and the weight of the loss on the minority classes (Angry and Sad) are boosted to improve the performance of detection. The achieved model resulted in an accuracy of 87% on the test set, and had equal accuracy in preciseness, recall, and F1-scores over all emotion types. Extended error analysis revealed that the majority of cases that were misclassified fell between the categories Angry and Sad because they were mistaken due to combining visual cues. Even then, the performance showed stability despite the variable lighting as well as in variable positional context. In Comparison, MobileNetV3 outperforms state-of-art-lightweight models with respect to accuracy and computation of similar computational complexity. 2025 IEEE. -
Computing isogeny on Edwards curves for quantum safe cryptography
In recent years, cryptographic research has seen a surge of interest in post-quantum cryptography driven by the potential threat that quantum computers pose to traditional public-key cryptosystems. Isogeny-based cryptography is a promising method in post-quantum cryptography, relying on the computational challenge of calculating isogenies, which are specific mappings between elliptic curves. The efficiency of isogeny computations is vital for real-world cryptographic applications. However, computing isogenies, especially with large parameters, can be very resource intensive. To overcome this challenge, we purpose an efficient method for computing odd-degree isogenies on certain form of an elliptic curves by employing an auxiliary coordinate. Our work appears to bridge the gap in computational efficiency for odd-degree isogenies, especially in terms of reducing the complexity of the isogeny computations when compared to traditional affine and projective methods. The derived formula is more efficient than affine and projective cases. We also analyse the algebraic complexity of these calculations and compare them to alternative formulae. Additionally, we evaluate the runtimes for isogeny computation across different prime numbers and compare them with other elliptic curve model to check the performance. At last, we suggest potential avenues for future work. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
Wave scattering and dynamic stress concentration in piezoelectric half-planes with semi-elliptical notches under SH-wave excitation
This study presents a comprehensive analytical framework for investigating the scattering and dynamic stress response of semi-elliptical notches in piezoelectric half-planes subjected to anti-plane shear (SH) waves. The primary objective is to unify the treatment of notches, cracks, and circular holes within a rigorous wavedefect interaction model while explicitly incorporating piezoelectric coupling and nanoscale surface/interface effects. The methodology employs the complex function method in conjunction with the Helmholtz equation and wavefield superposition theory, leading to an infinite system of equations that rigorously satisfies continuity and boundary conditions; a systematic truncation strategy is then applied to achieve convergent solutions. Results demonstrate that surface/interface effects significantly suppress the dynamic stress concentration factor, particularly under vertical SH-wave disturbance, while resonance peaks become sharper at low modulus ratios and higher piezoelectric constants such as PZT-5H and BaTiO3. Importantly, the formulation naturally recovers classical elasticity results in the absence of piezoelectric effects, providing strong theoretical consistency. Validation is achieved through analytical recovery of benchmark cases (semicircular notch and edge crack), graphical comparisons with established results, and rapid convergence of the truncated system, confirming both accuracy and robustness. The practical implications of these findings extend to structural health monitoring, non-destructive evaluation, and the optimal design of advanced piezoelectric composites, where accurate prediction of defect evolution and stress amplification is critical. While the present work is restricted to semi-elliptical notches under SH-wave excitation in half-plane geometries, the approach is readily extensible to more general defect shapes and mixed-mode disturbances. The novelty of this study lies in its ability to capture piezoelectric surface/interface effects within an exact analytical framework, providing predictive capability for defect-induced stress concentrations and offering a reliable basis for the design and reliability assessment of high-performance piezoelectric materials. The Author(s), under exclusive licence to SocietItaliana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Occupational Exposure to Cooking Oil Fumes : Biochemical, Cytogenetic and Molecular Signatures
Occupational exposure to Cooking Oil Fumes (COFs) is a widespread concern in the newlineculinary industry, and it has raised significant health apprehensions due to its potential adverse effects on individuals working in kitchens. This current research presents a comprehensive analysis of the biochemical, cytogenetic, and molecular analysis observed in individuals exposed to COFs in their workplace. The study employed a cross-sectional approach, involving a cohort of kitchen personnel working in diverse culinary settings. Biochemical assessments focused on analyzing blood parameters, such as lipid profiles, liver enzymes, and markers of oxidative stress, to gauge the impact of COFs on the participants systemic health. Cytogenetic investigations encompassed the assessment of chromosomal aberrations and micronuclei frequency in peripheral blood lymphocytes, shedding light on potential genotoxicity associated with COF exposure. Moreover, molecular analyses involved the examination of ApoE and BMAL1 gene expression patterns related to inflammation, oxidative stress response, and detoxification pathways also this aspect aimed to uncover the newlineunderlying molecular mechanisms influenced by COFs. Preliminary results suggest a significant association between COF exposure and alterations in biochemical parameters, newlineparticularly an increase in oxidative stress markers and changes in lipid profiles, indicative of potential cardiovascular risks. Cytogenetic assessments revealed an elevated frequency of chromosomal aberrations and micronuclei formation, highlighting genotoxic effects linked to COF exposure. Molecular investigations demonstrated differential expression patterns of ApoE and BMAL1 genes involved in inflammation and oxidative stress responses, further corroborating the adverse effects of COFs on cellular processes. The findings of this research underscore the importance of addressing occupational exposure to COFs and implementing appropriate safety measures in cooking area. -
Studies on phase transitions and dielectric properties of biowaste synthesized porous carbon nanoparticlesferroelectric liquid crystal mixture
Ferroelectric liquid crystals(FLCs), an exciting class of liquid crystals(LCs), found potential applications in the display as well as non-display regimes due to their fast response, low driving voltage and nonvolatile memory. The amalgamation of nanoparticles into FLCs has opened up new avenues in the LCs research field by alterations/modification of the existing properties of LCs. In this work, porous carbon nanoparticles (PCNPs) were dispersed into FLC mixture (W206E) and investigated their doping effect on FLCs textural, phase transition temperatures and dielectric studies in planar-aligned cells. Dielectric spectroscopy was carried out in the frequency range of 20 Hz to 10 MHz to explore the frequency as well as the temperature dependent of FLC in the entire SmC* region. The transition temperature of FLC mixture is increased by 4 C in PCNPs doped FLC sample then undoped FLC sample. Nearly 8.42% increase in permittivity is observed. A Gold stone relaxation mode at ?627 Hz was observed at lower frequency. 2024 Taylor & Francis Group, LLC. -
An Assessment of Farmers' Perception and Adaptive Capacity for Climate Change
In the past decades, various regions in U.P. had experienced severe floods. The effects of climate change also affected agricultural production. This study investigated the farmers' perception of climate change and suggested strategies for mitigating its effects using a primary survey with the help of a pre-structured schedule. Change in rainfall pattern, problems in seed quality, the emergence of new pests and diseases, changes in the crop cycle were the few effects that farmers' perceived due to climate change. Even the most mitigation efforts by the farmers cannot prevent some of the impacts of climate change within the following decades. It makes adaptation a must-have for addressing these impacts. 2022, The Society of Economics and Development. All rights reserved. -
Intelligent Manufacturing and Industry 4.0: Impact, Trends, and Opportunities
The use of intelligence in manufacturing has emerged as a fascinating subject for academics and businesses everywhere. This book focuses on various manufacturing operations and services which are provided to customers to achieve greater manufacturing flexibility, as well as widespread customization and improved quality with the help of advanced and smart technologies. It describes cyber-physical systems and the whole product life cycle along with a variety of smart sensors, adaptive decision models, high-end materials, smart devices, and data analytics. Intelligent Manufacturing and Industry 4.0: Impact, Trends, and Opportunities focuses on Intelligent Manufacturing and the design of smart devices and products that meet the demand of Industry 4.0, manufacturing and cyber-physical systems, along with real-time data analytics for Intelligent Manufacturing. The usage of advanced smart and sensing technologies in Intelligent Manufacturing for healthcare solutions is discussed as well. Popular use cases and case studies related to Intelligent Manufacturing are addressed to provide a better understanding of this topic. This publication is ideally designed for use by technology development practitioners, academicians, data scientists, industry professionals, researchers, and students interested in uncovering the latest innovations in the field of Intelligent Manufacturing. Features: Presents cutting-edge manufacturing technologies and information to maximise product exchanges and production Discusses the improvement in service quality, product quality, and production effectiveness Conveys how a manufacturing companys competitiveness can increase if it can manage the turbulence and changes in the global market Presents how intelligence production is essential in Industry 4.0 and how Industry 4.0 offers greater manufacturing flexibility, as well as widespread customisation, improved quality, and increased productivity Covers the ways businesses handle the challenges of generating an increasing number of customised items with quick time to market and greater quality Includes popular use cases and case studies related to intelligent manufacturing to provide a better understanding of this discipline. 2025 selection and editorial matter, Alka Chaudhary, Vandana Sharma, and Ahmed Alkhayyat individual chapters, the contributors. -
Influence of Coronavirus Disease 2019 on human biological timekeeping
To stay in sync with environmental cues, the body's metabolic activities must be rhythmic, and these rhythmic functions are known as circadian rhythms, which repeat every 24 h. People's sleep-wake and eating patterns were interrupted as a result of house confinement, making them more vulnerable to noncommunicable chronic diseases during the coronavirus disease 2019 (COVID-19) period. During the epidemic, there was a greater degree of misalignment with this synchronization. The effects of severe acute respiratory syndrome coronavirus 2 (COVID-19) on the human circadian clock are studied in depth. The literature review was conducted fully online, with the website utilized to collect all of the papers from PubMed, and duplicates were handled only in the first phase. Researchers found that individuals of all ages who are pushed to adjust their daily routines shift to the later chronotype, resulting in lifestyle modifications and an altered biological timing system that contributes to noncommunicable chronic illnesses. Chronic illnesses have bidirectional conductance, which means they can be caused by both environmental and self-modification in daily activity, as was the case during the COVID-19 outbreak, which forced people to stay at home. This review comes to the conclusion that fighting the pandemic may be best done by changing medications and focusing on immune health. 2023 Wolters Kluwer Medknow Publications. All rights reserved. -
Tuning the output of the higher plants Circadian Clock
The circadian clock is an ascribed regulator found in the cells of creatures, that keeps biological and behavioral processes in stnc with dailt environmental changes throughout the 24-hour ctcles. When the circadian clock in humans malfunctions or is misaligned with environmental signals, the timing of the sleep-wake ctcle is altered and several circadian rhtthm sleep disorders result. Due to the Earth's rotation on its axis, predictable environmental changes are anticipated bt complex processes. The combined term for these ststems is the circadian clock. The circadian rhtthm regulates photostnthesis and photoperiodism, making it the "primart controller of plant life." The circadian clock is made up of post-translational alterations to core oscillators, epigenetic tweaks to DNA and histones, and auto regulatort feedback loops in transcription. In addition, the circadian clock is cell-autonomous and regulates the circadian rhtthms of distinct organs. Biochemical elements such as photostnthetic products, mineral nutrients, calcium ions, and hormones are used bt the core oscillators to communicate with one another. Arabidopsis is utilized to identift clock-related genes that govern plant growth, germination, pollination, flowering, abiotic and biotic stress responses, and more. The biological ctcles of all species, notablt humans, are undoubtedlt impacted bt other elements, including high altitude and changing ecoststems, in addition to the ones alreadt stated. Although it hasn't tet published ant experimental or scientific evidence to support them, the implication that living things have lives does appear inescapable. Hence, the present studt elaborates on the higher plants related to the circadian clock. The Author(s). -
Predicting Financial Market Volatility Using Regression and Machine Learning Techniques
In standard Simple Linear Regression (SLR), one of the major assumptions is that the error terms have constant variance (homoscedasticity). However, this assumption is frequently violated in many real-world datasets, resulting in inefficient estimates and reduced predictive accuracy. To overcome this shortcoming, we propose a hybrid modeling platform that combines SLR with statistical and machine learning methods. The approach starts with SLR to identify the main linear relationship. Whenever residual diagnostics report the presence of heteroskedasticity, an Autoregressive Conditional Heteroskedasticity (ARCH) model is used to estimate time-varying variance. Such estimated variances are utilized in a Weighted Generalized Least Squares (WGLS) model, which stabilizes the error structure. Finally, to capture any remaining nonlinear patterns, an Artificial Neural Network (ANN) is applied on the residuals of the WGLS model. By layering these techniques, the hybrid framework improves both stability and predictive power. Simulation studies and empirical tests on Apple Inc. stock data confirmed that the hybrid framework yields reduced MAE and RMSE values and greater explanatory strength than individual approaches. 2025 IEEE. -
Stock Market Prediction Techniques Using Artificial Intelligence: A Systematic Review
This paper systematically reviews the literature related to stock price prediction systems. The reviewers collected 6222 research works from 12 databases. The reviewers reviewed the full-text of 10 studies in preliminary search and 70 studies selected based on PRISMA. This paper uses the PRISMA-based Python framework systematic-reviewpy to conduct this systematic review and browser-automationpy to automate downloading of full-texts. The programming code with comprehensive documentation, citation data, input variables, and reviews spreadsheets is provided, making this review replicable, open-source, and free from human errors in selecting studies. The reviewed literature is categorized based on type of prediction systems to demonstrate the evolution of techniques and research gaps. The reviewed literature is 7 % statistical, 9% machine learning, 23% deep learning, 20% hybrid, 25% combination of machine learning and deep learning, and 14% studies explore multiple categories of techniques. This review provides detailed information on prediction techniques, competitor techniques, performance metrics, input variables, data timing, and research gap to enable researchers to create prediction systems per technique category. The review showed that stock trading data is most used and collected from Yahoo! Finance. Studies showed that sentiment data improved stock prediction, and most papers used tweets from Twitter. Most of the reviewed studies showed significant improvements in predictions to previous systems. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Hybrid Subset Feature Selection and Importance Framework
Feature selection algorithms are used in high-dimensional data to remove noise, reduce model overfitting, training and inference time, and get the importance of features. Features subset selection is choosing the subset with the best performance. This research provides a Hybrid subset feature selection and importance (HSFSI) framework that provides a pipeline with customization for choosing feature selection algorithms. The authors propose a hybrid algorithm in the HSFSI framework to select the best possible subset using an efficient exhaustive search. The framework is tested using the Bombay stock exchange IT index's companies' data collected quarterly for 16 years consisting of 71 financial ratios. The experimental results demonstrate that models created using 12 features chosen by the proposed algorithm outperform models with all features with up to 6% accuracy. The importance-based ranks of all features are generated using the framework calculated using 13 implemented feature selection techniques. All selected feature subsets are cross-validated using prediction models such as support vector machine, logistic regression, KNeighbors classffier, random forest, and deep neural network. The HSFSI framework is available as an open-source Python software package named ''feature-selectionpy'' available at GitHub and Python package index. 2023 IEEE. -
Impact of digital payment trends: Unraveling consumer behavior patterns (preand post-COVID-19)
The COVID- 19 pandemic has accelerated the adoption of digital payments worldwide, fundamentally altering consumer behavior patterns. This study investigates the impact of digital payment trends on consumer behavior, comparing pre-and post-pandemic scenarios. It delves into the factors which drove the shift towards digital payments, exploring the changing preferences and perceptions of consumers. The study examines the influence of demographics, socio-economic factors, and technological advancements on consumer adoption of digital payment methods. It also analyzes the effect of the pandemic on e-commerce, online shopping, and mobile payments, highlighting the emergence of new trends and preferences. The findings provide valuable insights into the evolving landscape of digital payments and consumer behavior, offering implications for businesses, policymakers, and researchers 2025 by IGI Global Scientific Publishing. -
Efficient Method for Tomato Leaf Disease Detection and Classification based on Hybrid Model of CNN and Extreme Learning Machine
Through India, most people make a living through agriculture or a related industry. Crops and other agricultural output suffer significant quality and quantity losses when plant diseases are present. The solution to preventing losses in the harvest and quantity of agricultural products is the detection of these illnesses. Improving classification accuracy while decreasing computational time is the primary focus of the suggested method for identifying leaf disease in tomato plant. Pests and illnesses wipe off thousands of tons of tomatoes in India's harvest every year. The agricultural industry is in danger from tomato leaf disease, which generates substantial losses for producers. Scientists and engineers can improve their models for detecting tomato leaf diseases if they have a better understanding of how algorithms learn to identify them. This proposed approaches a unique method for detecting diseases on tomato leaves using a five-step procedure that begins with image preprocessing and ends with feature extraction, feature selection, and model classification. Preprocessing is done to improve image quality. That improved K-Means picture segmentation technique proposes segmentation as a key intermediate step. The GLCM feature extraction approach is then used to extract relevant features from the segmented image. Relief feature selection is used to get rid of the categorization results. finally, classification techniques such as CNN and ELM are used to categorize infected leaves. The proposed approach to outperforms other two models such as CNN and ELM. 2023 IEEE.