Bijoy Krishna Girls' College

Spirituality reflects an individual’s connection with the transcendent, encompassing meaning, purpose, and inner peace. It may involve belief in a higher power or simply a sense of belonging and harmony with the world. Spiritual wellness can be expressed through various means, including religious beliefs, meditation, service, and finding one’s life purpose. It involves cultivating qualities such as love, joy, compassion, and contentment, which can help reduce stress and promote emotional balance. Religion may serve as a structured expression of spirituality, though one can be spiritual without being religious. Practices such as prayer, forgiveness, and volunteering contribute to spiritual well-being and have been linked to improved mental health, reduced stress, and enhanced physical health. Theories such as control, social support, and placebo explain the health benefits of spirituality. Volunteering, often viewed as a spiritual act, has also been linked to lower mortality rates and healthier lifestyle choices. This chapter examines the interplay between spiritual well-being and stress management, highlighting how a robust spiritual foundation can foster resilience and overall well-being.

One of the primary factors for death on a global scale is pneumonia as per World Health Organization. Conventional techniques for analyzing Chest X-ray (CXR) to diagnose pneumonia take a long time and call for specific medical knowledge, which increases the risk of delayed diagnosis and treatment. Immediate diagnosis and treatment of pneumonia can reduce the rate of mortality. Since CXR is a widely used, affordable, and accessible method of detecting pneumonia; a machine learning algorithm is presented in this paper to detect pneumonia using CXR. The proposed methodology extracts textural pattern features employing Completed Local Binary Pattern (CLBP) on CXR images and used those features as input to train the Convolutional Neural Network (CNN) models for classification. Experiment is carried out using a publicly available dataset named as Labeled Optical Coherence Tomography (OCT). CLBP images of CXR with proposed CNN model achieves the best average accuracy of 98.10

Protein sequence classification is a fundamental step toward functional annotation and biological analysis; however, most of the existing approaches rely on computationally expensive models or flat feature integration with limited interpretability. This paper proposes a multi-phase rule-based protein classification framework that hierarchically integrates biologically meaningful features. An early-exit decision strategy is employed. Feature extraction is performed across four progressive phases comprising chemical properties, hydropathy characteristics, behavioral sequence patterns through n-gram and six-letter exchange encoding, and functional attributes via normalized distance-based encoding. Family-specific knowledge matrices constructed exclusively from training data enable deterministic min-max rule evaluation at each phase for efficient candidate pruning and early termination. Experiments conducted on 8,555 human protein sequences spanning ten families achieve an average classification accuracy of 97.66%. 5-fold, 25-fold, and 45-fold cross-validation statistically confirm performance stability under class-imbalanced conditions. Further cross-species validation on zebrafish and mouse protein datasets yields accuracies of 96.11% and 94.89%, respectively, demonstrating robust generalization across diverse biological distributions. Comparative evaluation against machine-learning-based and transformer-based baselines shows that the proposed framework achieves a superior accuracy-efficiency trade-off while maintaining an overall time complexity of $O(n\log n)$.

The impact of artificial intelligence (AI) on the banking industry in India is increasingly pronounced, with banks leveraging AI for various functions such as fraud detection, customer service automation, creditworthiness evaluation, and personalized financial recommendations. This study employs data envelopment analysis (DEA) as a methodological approach to assess the effectiveness of public sector banks (PSBs) in promoting financial inclusion following the integration of AI into their operations. By analyzing efficiency levels during the pre- and post-introduction of AI periods, the research provides valuable insights into the evolving landscape of banking efficiency. The findings indicate a positive trend in overall technical efficiency (OTE) over time, suggesting that AI adoption has contributed to enhanced resource utilization and operational effectiveness. Moreover, the study underscores the significant potential for further improvements in efficiency, which could lead to advancements in financial inclusion initiatives. Unlike previous studies primarily focused on theoretical frameworks, this empirical examination offers practical, data-driven insights into the tangible impact of AI on PSBs in India.

MSME sector plays a significant role in Indian economy to boost up employment opportunity, entrepreneurial endeavor, and betterment of the GDP, injecting positive growth in export and enhancing per capita income of the rural families of India. But, the poor participation of the women population is one of the major challenges for the overall growth of this sector. The aim of the paper is to show the status of the women MSME entrepreneurs of West Bengal as compared to other states of India. It also aims to find out the challenges faced by the women MSME entrepreneurs during this new normal and policies adopted by the Government to combat with the challenges. In new normal, MSMEs are facing a number of challenges like liquidity crisis, issues of migrating workers, scarcity of required inputs etc. Major schemes of the West Bengal Government like Banglashree, Karma Sathi Prakalpa etc. have played a game changer for the women entrepreneurs in MSMEs especially during the new normal. The ‘Silpa Disha’ mobile app as well as the ‘Silpa Sathi’ window portal was launched.