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A review on feature selection algorithms
A large number of data are increasing in multiple fields such as social media, bioinformatics and health care. These data contain redundant, irrelevant or noisy data which causes high dimensionality. Feature selection is generally used in data mining to define the tools and techniques available for reducing inputs to a controllable size for processing and analysis. Feature selection is also used for dimension reduction, machine learning and other data mining applications. A survey of different feature selection methods are presented in this paper for obtaining relevant features. It also introduces feature selection algorithm called genetic algorithm for detection and diagnosis of biological problems. Genetic algorithm is mainly focused in the field of medicines which can be beneficial for physicians to solve complex problems. Finally, this paper concludes with various challenges and applications in feature selection. Springer Nature Singapore Pte Ltd 2019. -
Analyzing Corporate Social Responsibility (CSR) Practices and Ethical Leadership in Promoting Sustainable Business: A Structured Equation Modelling Approach
This study investigates the pivotal role of Corporate Social Responsibility (CSR) practices and ethical leadership in promoting sustainable business development across diverse regions of India, specifically focusing on Goa, Kerala, and Gujarat. Employing a quantitative approach, data was collected from a sample of 300 respondents through a structured questionnaire using a five-point Likert scale. The survey assessed perceptions of CSR initiatives, the influence of ethical leadership, and their combined effect on sustainability outcomes within the regional business landscape. To analyze the data, Partial Least Squares Structural Equation Modeling (PLS-SEM) was conducted using Smart PLS software, enabling the evaluation of complex relationships and the validation of hypotheses. The findings reveal a strong, positive correlation between ethical leadership and CSR engagement, both of which significantly contribute to sustainable business practices. The study highlights that organizations adopting responsible and transparent business practices, guided by ethical leadership, are more likely to enhance their long-term sustainability and contribute to regional development. Furthermore, the research underscores the growing relevance of integrating CSR into core business strategies to align with sustainable development goals (SDGs). By incorporating a machine learning perspective, the study also suggests avenues for future research in predictive modeling and CSR impact assessment. This research adds value to the existing literature by contextualizing CSR within Indias varied socio-economic environments and offers practical insights for policymakers, business leaders, and sustainability advocates aiming to foster ethical and responsible business ecosystems across the country. The Author(s) 2025 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits the use, sharing, adaptation, distribution and reproduction in any medium or format, as long as appropriate credit to the original author(s) and the source is given by providing a link to the Creative Commons license and changes need to be indicated if there are any. The images or other third-party material in this article are included in the articles Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the articles Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. -
Diabe Maigre and Diabe Gras Revisited
[No abstract available] -
Universality of black hole thermodynamics beyond the thermal approximation
Mathur and Mehta were awarded the third prize in the 2023 Gravity Research Foundation Essay Competition for their work demonstrating the universality of black hole (BH) thermodynamics. Their research established that any Extremely Compact Object (ECO) exhibits the same thermodynamic properties as a BH, irrespective of the presence of an event horizon. This significant finding was derived under the assumption that the BH emission spectrum behaves thermally. However, compelling arguments rooted in energy conservation and the BH back reaction indicate that the spectrum of Hawking radiation cannot be perfectly thermal. In this paper, we explore the extension of Mathur and Mehtas findings to scenarios where the radiation spectrum deviates from exact thermal behavior, utilizing the concept of the BH dynamical state. 2025 World Scientific Publishing Company. -
Quantum corrections in general relativity explored through a GUP-inspired maximal acceleration analysis
A maximun acceleration analysis by Pati dating back to 1992 is here improved by replacing the traditional Heisenberg Uncertainty Principle (HUP) with the Generalized Uncertainty Principle (GUP), which predicts the existence of a minimum length in Nature. This new approach allows one to find a numerical value for the maximum acceleration existing in Nature for a physical particle that turns out to be [Formula presented] that is, a function of two fundamental physical quantities such as the speed of light c and the Planck length lp. An application of this result to black hole (BH) physics allows one to estimate a new quantum limit to general relativity. It is indeed shown that, for every real Schwarzschild BH, the maximum gravitational acceleration occurs, without becoming infinite, when the Schwarzschild radial coordinate reaches the gravitational radius. This means that quantum corrections to general relativity become necessary not at the Planck scale, as the majority of researchers in the field think, but at the Schwarzschild scale, in agreement with recent interesting results in the literature. In other words, the quantum nature of physics, which in this case manifests itself through the GUP, appears to prohibit the existence of real singularities, in this current case forbiddiing the gravitational acceleration of a Schwarzschild BH from becoming infinite. 2025 The Authors -
Psychosocial correlates of resilience among older adults in Mexico
There is a tremendousglobal increase in the older adultspopulation. Mental health in older age is as important in as it is for other age categories. Majority of older adults show healthy states, vitality, good humor and enthusiasm in performing various activities, interest in continuing to contribute to their family and society despite the difficulties of this stage of life due to large part to resilience they have. The aim of the study was to establish social and psychosocial factors associated with resilience.A cross-sectional and correlation study was conducted on older adults who were hospitalized in a public General Hospital of Mexico in 2013. Resilience, gender, occupation, family environment, self-esteem, presence of critical life events, and the presence of significant persons were assessed. 186 older adults participated. Higher levels of resilience were found in males and employed people. Participants with a functional family and high self-esteem had the highest levels of resilience. Besides, 15% of the variance of the total resilience score was explained by family environment, and 27% was explained by self-esteem (p<0.05).Although all participants were older adults, individual characteristics such as gender, occupation and self-esteem; besides family environment, were found to be associated to the levels of resilience in this population. Specific programs- -enhancing these factorsare needed to improve resilience. 2019 Oriental Scientific Publishing Company. All rights reserved. -
Validity of a Short Version Scale of Knowledge, Attitudes and Practices of Sexual and Reproductive Health in Indigenous Women in Mexico
The study evaluated the validity and reliability of the Scale of Knowledge, Attitudes and Practices of Sexual and Reproductive Health (ECAPSSR) in indigenous women in Mexico. A total of 177 women aged 14 to 48 participated, and signed and authorized their participation. Three dimensions were evaluated: knowledge, attitudes and practices. A final version of 78 questions of the original scale was obtained with acceptable reliability. Confirmatory Factor Analysis suggests an acceptable fit in three subscales. A valid, reliable and structured version was obtained as a screening option that allowed the evaluation of sexual and reproductive health in indigenous women. 2025 Society for Menstrual Cycle Research. -
Translation and Validation of the Malayalam Version of the Subjective Happiness Scale
The subjective happiness scale (SHS) is a brief instrument used to measure global subjective happiness that has been translated from its original English to many other languages. To date, there is no reported translation of this scale into Malayalam, a language spoken by over 32 million people especially in the southern state of Kerala, India. In the present study, 656 community-dwelling older adults participating in the Kerala Einstein study (KES) completed the Malayalam version of the SHS. The Malayalam version demonstrated high internal consistency and good convergent validity, as assessed by comparison to measures of depression and anxiety. We also used factor analysis to determine that the Malayalam version of the SHS has a unidimensional structure, akin to the original English as well as other language adaptations. Our study adds to the repertoire of tools to measure happiness in non-English-speaking populations, enabling future research to explore the foundations of well-being across diverse cultures. The Author(s) 2024. -
Translation and Validation of the Malayalam Version of the Subjective Happiness Scale
The subjective happiness scale (SHS) is a brief instrument used to measure global subjective happiness that has been translated from its original English to many other languages. To date, there is no reported translation of this scale into Malayalam, a language spoken by over 32 million people especially in the southern state of Kerala, India. In the present study, 656 community-dwelling older adults participating in the Kerala Einstein study (KES) completed the Malayalam version of the SHS. The Malayalam version demonstrated high internal consistency and good convergent validity, as assessed by comparison to measures of depression and anxiety. We also used factor analysis to determine that the Malayalam version of the SHS has a unidimensional structure, akin to the original English as well as other language adaptations. Our study adds to the repertoire of tools to measure happiness in non-English-speaking populations, enabling future research to explore the foundations of well-being across diverse cultures. The Author(s) 2024. -
Considering Cultural Responsiveness in the Creation of the International Competences for Undergraduate Psychology (ICUP) Model: What Can Psychology Learn?
This article aims to describe the development of foundational competencies relevant to cultural responsiveness (CR), within the context of the International Competences for Undergraduate Psychology (ICUP) model (Nolan et al., 2025). The underlying premise of the ICUP model is that the acquisition of undergraduate-level foundational psychology competences can and should have high value in personal, work, and community contextsregardless of graduate career destination. A targeted background on CR is given, followed by brief descriptions of the International Collaboration on Undergraduate Psychology Outcomes (ICUPO) project (which created the ICUP model; International Collaboration on Undergraduate Psychology Outcomes, n.d.) and of the CR competences themselves. Then, procedural aspects of the ICUPO project relevant to CR are described, followed by quantitative and qualitative approaches to exploring the CR of the diverse ICUPO Committee members. The findings are discussed in terms of implications for (a) psychology educatorsin particular, they need to possess the capacity to be culturally responsive in order to be able to support students in acquiring or improving their own CR; (b) psychology education leaders undertaking undergraduate curricular renewal; and (c) the sustainable future of the discipline of psychology. 2025 American Psychological Association -
Collaborative Processes in the Development of the International Competences for Undergraduate Psychology (ICUP) Model
Across all nations, undergraduate psychology programmes aim to promote the acquisition of foundational psychology competences. Yet, until recently, a universally recognised model outlining essential competences did not exist. The International Collaboration on Undergraduate Psychology Outcomes (ICUPO) addressed this gap by developing the International Competences for Undergraduate Psychology (ICUP) Model. The aim of this article is to provide guidance about how other groups might successfully approach similar efforts to delineate discipline-specific key competences. We describe the processes that led to the development of the ICUP Model, framed by group development theory (Preparing, Forming, Storming, Norming, and Performing Stages), with additional consideration of individual ICUPO Committee member psychological needs for competence, relatedness, and autonomy. Each group development Stage section (a) describes project activities relevant to the characteristics of that Stage, and (b) lists key strategies employed and lessons learned, as well as commentary on psychological needs. To further enhance the value of this endeavour, the Discussion includes (a) commentary on the strengths and limitations of these theories for understanding and enhancing the effectiveness of such project processes, and (b) actionable insights for educational leaders undertaking similar projects. 2025 The Author(s). International Journal of Psychology published by John Wiley & Sons Ltd on behalf of International Union of Psychological Science. -
k-Domination Vertex Connectivity in Internet of Things Networks
The Internet of Things refers to a collection of closely connected devices that form a network through wireless or wired communication technology that work together to achieve common goals for their users. The IoT devices that are distributed in nature may cause the system to suffer from server crashes, server omissions, incorrect responses, and arbitrary errors. In this paper, we present a method of fault tolerance in IoT networks using graph theory approach to ensure the robustness of the network in case of attacks or disconnections through the concept of domination vertex connectivity in graphs. We further study this parameter in case of the Tensor product and Lexicographic product of specific graph classes, which have major implications in IoT networks. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2026. -
k-Domination Connectivity in Graphs
The conditional connectivity of G with respect to a graph theoretic property P is the smallest cardinality of a set S of vertices (edges), if any, such that every component Hi of the disconnected graph G ? S has property P. Connectivity and domination are two major areas of graph theory having numerous applications in computer and information sciences. In this paper, we study a type of conditional connectivity that combines connectivity and domination, called k-domination connectivity. Further, we investigate the k-domination connectivity of some standard graph classes. 2025 World Scientific Publishing Company. -
Analysing the impact of the taxation law amendment of 2019 on corporate taxation in India
The Taxation Law (Amendment) Act, 2019 in India has brought major changes in the taxation revenue as well as in legal provisions. The actual ground reality of the Amendment on a microeconomic level is unknown, but a correlation analysis on macroeconomic indicators show that there is a high positive correlation between the corporate tax revenue and the GDP growth. The author also interlinks the effects of tax cuts on the economy with privatization and how it can mitigate the risks of tax evasion. There is a generalized misconception with privatization that it leads to a significant loss in taxation revenue. The study shows that in fact, privatization helps to expand the earnings of the Government by widening the taxation structure and slab, which the author has found through statistics. It is high time to have strong regulatory measures to prevent tax evasion by encouraging more corporate entities to become a part of the tax base. Indian Institute of Finance. -
Interaction of Generational Differences with Gender and Residential Nature in Attitudes Toward Interfaith Marriages
The present study examined the interaction effects of generations, gender, and residential nature on attitudes toward interfaith marriage in a sample of 1190 Indian participants from iGen, Xennials and Millennials, and Baby Boomers generations. Data were collected using a socio-demographic response sheet and the Attitude Scale, with lower ratings indicating positive attitudes and higher ratings indicating negative attitudes. The results of this study demonstrated that generational differences are significantly associated with gender and residential nature. There was a significant interaction between generation and gender and generation and residential nature on attitudes toward interfaith marriages. 2024 Taylor & Francis Group, LLC. -
Application of LSTM Model for Western Music Composition
Music is one of the innate creative expressions of human beings. Music composition approaches have always been a focal point of music-based research and there has been an increasing interest in Artificial Intelligence (AI) based music composition methods in recent times. Developing an accurate algorithm and neural network architecture is imperative to the success of an AI-based approach to music composition. The present work explores the composition of western music through neural network using a Long Short-Term Memory (LSTM) algorithm. Compositions from seminal western composers such as J.S. Bach, W.A. Mozart, L.V. Beethoven, and F. Chopin were used as the dataset to train the neural network. Seven compositions were generated by the LSTM model and these outputs were presented to a group of thirty volunteers between 18-24 years of age. They were surveyed to identify the music piece as composed by a human or AI and how interesting they found the melodies of each piece. It was found that the LSTM model generated compositions that were thought to be made by a human and create melodies of interest from the perception of the volunteers. It is expected that through this study, more AI-based composition approaches can be developed which encompass more and more of the musical phenomenon. 2022 IEEE. -
Seismic Activity-based Human Intrusion Detection using Deep Neural Networks
Human intrusion detection systems have found their applications in many sectors including the surveillance of critical infrastructures. Generally, these systems make use of cameras mounted on strategic locations for surveillance purposes. Cameras based detection systems are limited by line-of-sight, need regular maintenance and dependence of electricity for operations. These are all detrimental to the efficiency of these detection systems, especially in remote locations. To overcome these challenges, intrusion detection systems based on seismic activities have been in use. The seismic activities collected through geophones from the human footfalls can act as the input for these detection systems. This also poses a challenge as the data generated by the geophones for the seismic activities produced from footsteps are not always identical and hence not accurate. In this proposed work, a Deep Neural Network based approach has been used on the dataset collected from the geophones to effectively predict the presence of humans. The results gave a success rate with 94.86% accuracy with testing data and 92.00% accuracy with real-time data with the geophones deployed on an area covered with grass. 2022 IEEE. -
Pneumonia Detection using Ensemble Transfer Learning
Pneumonia is among the most common illnesses and causes to death among the young children worldwide. It is more serious in under-developed countries as it is hard to diagnose due to the absence of specialists. Chest X-ray images have essentially been utilized in the diagnosis of this disease. Examining chest X-rays is a difficult task, even for an experienced radiologist. Information Technology, especially Artificial Intelligence, have started contributing to accurate diagnosis of pneumonia from chest X-ray images. In this work, we used deep learning, transfer learning, and ensemble voting to increase the accuracy of pneumonia detection. The models utilized are VGG16, MobileNetV2, and InceptionV3, all pre-trained on ImageNet, and used the Kaggle RSNA CXR image dataset. The results from these models are ensembled using the weighted average ensemble approach to achieve better accuracy and obtained 98.63% test accuracy. The results are promising, and the proposed model can assist doctors in detecting pneumonia quickly and accurately from Chest X-Ray. 2022 IEEE. -
Comparison of Full Training and Transfer Learning in Deep Learning for Image Classification
The deep learning algorithms on a small dataset are often not efficient for image classification problems. Make use of the features learned by a model trained on large similar dataset and saved for future reference is a method to solve this problem. In this work, we present a comparison of full training and transfer learning for image classification using Deep Learning. Three different deep learning architectures namely MobileNetV2, InceptionV3 and VGG16 were used for this experiment. Transfer learning showed higher accuracy and less loss than full-training. According to transfer learning results, MobileNetV2 model achieved 98.96%, InceptionV3 model achieved 98.44% and VGG16 model achieved 97.405 as highest test accuracies. The full-trained models did not achieve as much accuracy as that of transfer learning models on the same dataset. The accuracies achieved by full-training for MobileNetV2, InceptionV3 and VGG16 are 79.08%, 73.44% and 75.62% respectively. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Cardiovascular Disease Prediction through Ensembled Transfer Learning on Cardiac Magnetic Resonance Imaging
Cardiovascular Diseases (CVD) cause more deaths worldwide than most of the other diseases. The diagnosis of cardiovascular disease from Magnetic Resonance Imaging plays a major role in the medical field. The technological revolution contributed a lot to increase the effectiveness of CVD diagnosis. Many Artificial Intelligence methods using Deep Learning models are available to assist the cardiologist in the diagnosis of CVD from Magnetic Resonance Imaging (MRI). In this study, we leverage on the merits of deep learning, transfer learning, and ensemble voting to improve the accuracy of Artificial Intelligence-based CVD detection. VGG16, MobileNetV2, and InceptionV3, trained on ImageNet, are the models used and the dataset is the Automatic Cardiac Diagnosis Challenge dataset. We customized the classification layers of all three models to suit the CVD detection problem. The results from these models are ensembled using the soft-voting and hard-voting approaches. Test accuracies obtained are 97.94% and 98.08% from hard-voting and soft-voting respectively. The experimental results demonstrated that the ensemble of outputs from transfer learning-based Deep Learning models produces much improved results for CVD diagnosis from MRI images. 2022 Sibu Cyriac, Sivakumar R. and Nidhin Raju. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.