Mahatma Gandhi Kashi Vidyapith is a public university located in Varanasi, Uttar Pradesh, India. Established in 10 February 1921 as Kashi Vidyapith and later renamed, it is administered under the state legislature of the government of Uttar Pradesh. It got University status in 1974 as Deemed to be University and State University status in 2009 by The Uttar Pradesh State Universities (Amendment) Act, 2008 (act no. 6 of 2009). The university has more than 400+ affiliated colleges spread over six districts. It is one of the largest state universities in Uttar Pradesh, with hundreds of thousands of students, both rural and urban. It offers a range of professional and academic courses in arts, science, commerce, agriculture science, law, computing and management.
This study investigates the critical factors influencing the financial competitiveness and performance of multinational corporations (MNCs) operating in India, addressing the complexity of evaluating diverse performance drivers. Using the best-worst method (BWM), this study systematically prioritizes determinants such as human resource management practices (HRMP), capital structure, cost management, operational efficiency, and organizational behavior based on insights from academic and industry experts. The findings reveal that HRMP is the most influential factor, followed by capital structure and operational efficiency, underscoring the importance of internal organizational and financial drivers over external macro-level variables, such as political stability and global trade policies. These results suggest that firms seeking long-term competitiveness in India should strengthen their internal capabilities along with financial resilience. This study provides practical guidance for MNC leaders and HR professionals to optimize resource allocation, enhance talent management, and improve strategic decision-making, while also offering implications for policymakers to support a favorable business ecosystem. Academically, this research demonstrates the application of BWM in analyzing MNC performance and positioning internal drivers as key enablers of sustained competitiveness in emerging markets.
Cancer disease classification using high dimensional microarray datasets has become an important research area in healthcare analytics, bioinformatics, and intelligent clinical decision support systems because conventional machine learning approaches frequently experience challenges related to feature redundancy, noisy attributes, overfitting, computational complexity, and reduced predictive stability. This research paper presents an efficient hybrid and ensemble machine learning framework for accurate cancer disease classification using binary and multiclass cancer microarray datasets. The proposed framework integrates advanced feature selection techniques including Recursive Feature Elimination, Maximum Relevance Minimum Redundancy, Boruta, Correlation Feature Selection, and Principal Component Analysis with metaheuristic optimization algorithms such as Ant Colony Optimization, Particle Swarm Optimization, Improved Grey Wolf Optimization, Ant Lion Optimization, and Salp Swarm Optimization for identifying the most informative gene expression features and reducing dimensionality. Furthermore, multiple machine learning classifiers including Support Vector Machine, Random Forest, AdaBoost, XG Boost, Extreme Learning Machine, and ensemble voting approaches are incorporated to improve predictive reliability, robustness, and generalization capability. Experimental analysis performed on lung cancer, colon cancer, prostate cancer, leukemia, breast cancer, ALL-AML, lymphoma, and SRBCT microarray datasets demonstrated significant improvements in classification accuracy, sensitivity, specificity, precision, recall, Matthews Correlation Coefficient, and F1 score compared with conventional machine learning classifiers. The proposed hybrid ensemble framework effectively minimizes misclassification, enhances feature optimization, improves classification stability, and provides a reliable computational approach for intelligent cancer diagnosis, healthcare analytics, and precision clinical decision support systems [1], [2].
India confronts a twofold problem in combating cybercrime: the increasing diversification of cyber offences and a persistent lack of public awareness and institutional capacity to report them. India has surpassed the United States and Canada as the most common target of mobile malware attacks. Using data from the 80th Round of the National Sample Survey on Comprehensive Modular Survey-Telecom (January–March 2025), this paper investigates cybercrime typologies and the public’s ability to report such instances. The findings indicate that the reporting landscape remains relatively unequal. Only 15
Bibliometric analysis is a widely used technique for analyzing large quantities of academic literature and evaluating its impact in a particular academic field. This paper used bibliometric analysis to analyze the academic research on yoga therapy for non-communicable diseases from 1995 to 2024. This study used SCOPUS to find related publications on yoga therapy for non-communicable diseases. “Yoga Therapy”, “Therapeutic Yoga”, “Pranayama”, “Yoga”, “NCDs”, “Non-Communicable Diseases”, and keywords related to various Non-Communicable Diseases were used for gathering the relevant articles. 2313 publications in total were selected for this research. In this study, four different bibliometric parameters, performance analysis, trend analysis, citation analysis, and network analysis, were used to evaluate the performance of these articles. According to this analysis, the three countries with the highest number of publications and citations regarding Yoga Therapy for Non-Communicable Diseases are the USA, India, and Canada. The three most significant researchers in this field are Nagendra, H.R., Cohen L. and Nagarathna, R. 'Yoga,' 'cancer,' 'breast cancer,' and ‘quality of life,' and 'exercise are the three most frequently used keywords. A further finding of the study indicates that the popular topics for Yoga Therapy for Non-Communicable Diseases are mind-body therapy, COVID-19, and Psycho-oncology. This research provides insight into the origins, current status, and future direction of Yoga Therapy for Non-Communicable Diseases research.
In today's fast paced experience driven society, gastronomy has become more than just a necessity- it is a cultural phenomenon deeply tied to emotions, identity, and impulse. Impulsive consumption through gastronomy reflects how food choices are often made spontaneously, driven by emotion, social media influence and pursuit of pleasure rather than basic nutritional needs. The purpose of this study is to identify the factors influencing impulsive consumption through gastronomy. Sample size of the study is 329 collected from Delhi-NCR. Exploratory factor analysis and multiple regression is used as statistical tool to analysis of data. Finding of the study reveals that there is a significant positive relationship between gamification, perceived professionalism and, telepresence and impulsive consumption through gastronomy. Strongest relationship reflects between gamification and impulsive consumption through gastronomy while telepresence has least. At last discussions, limitations and future scope of the study reported at the end.
