Template-Type: ReDIF-Article 1.0 Author-Name: Mamta Brahmbhatt Title: Unpacking Green Quality: A structural Model Analysis of Eco-Awareness and Trust Publication-Status: Published Abstract: Green purchasing behaviour refers to the inclination of consumers to choose and prioritise a variety of environmentally friendly products that minimise negative impacts on the environment. This facet of buying has recently acquired significance, since many consumers engaging in green purchasing behaviour are motivated by a profound commitment to environmental conservation, waste reduction, and ethical company practices. The paper explores the role of eco-awareness and trust on consumer perceptions of the quality of green products to cover a vacuum in the available literature regarding the interdependence between these variables when consumers are shopping online. Based on 292 respondents in Gujarat with structural equation modelling being used to measure these dependencies, the study establishes that the dimension of eco-centricity perceptions has a consequential influence on green quality cognition (β = 0.45, p < 0.01) and trust (β = 0.38, p < 0.01). The model has great predictive power with its contribution about 62 percent (R2= 0.62) explaining the variance in green quality cognition. The results prove the significance of eco-consciousness and consumer trust in driving the green buying behaviour and may serve well in planning the proper eco-friendly marketing. Incorporating trustworthy connection, effective brand associations and Quality Cognition is essential for enterprises in the green market. Keywords: Eco-centric Perceptions; Green Brand Awareness; Green Quality Cognition; Trust; Sustainability Journal: Milky Way Scientific Journal Pages: 141-155 Volume: 2 Issue: 1 Year: 2026 Month: March DOI: 10.65021/mwsj.v2.i1.31 File-URL: https://mwsjournal.com/index.php/mws/article/view/31 File-Function: Abstract page File-Format: text/html File-URL: https://mwsjournal.com/index.php/mws/article/download/31/14 File-Function: Full text File-Format: application/pdf Handle: RePEc:gyr:mwsjou:v:2:y:2026:i:1:id:31 Template-Type: ReDIF-Article 1.0 Author-Name: Sanketkumar Vachhani Author-Name: Dineshkumar R Chavda Author-Name: Harshitaben Suva Title: Indian Knowledge Systems and Environmental Disclosure for Corporate Sustainability: A Conceptual Framework Publication-Status: Published Abstract: Environmental disclosure has become an important mechanism for companies to communicate information about environmental impacts, sustainability initiatives, and ecological responsibilities. At the same time, Indian Knowledge Systems (IKS) offer ethical and ecological perspectives that emphasise harmony with nature, responsible resource utilisation, and social well-being. This conceptual study, based entirely on secondary sources including academic literature, policy documents, sustainability frameworks, and corporate sustainability reports, examines the integration of IKS with environmental disclosure practices in the Indian corporate context. The study discusses how principles such as dharma, panchatatva, vasudhaiva kutumbakam, and ahimsa may strengthen stakeholder accountability, ethical governance, and environmental responsibility within sustainability reporting practices. It further develops a conceptual framework explaining how indigenous knowledge traditions can complement contemporary environmental disclosure mechanisms by supporting broader ecological awareness and long-term sustainability orientation. The study concludes that integrating traditional Indian values with modern sustainability reporting frameworks may contribute to more transparent, socially responsive, and ethically informed approaches to corporate sustainability while also presenting practical implementation and operational challenges. Keywords: Corporate Sustainability; Environmental Disclosure; Indian Knowledge Systems; Sustainable Development Goals Journal: Milky Way Scientific Journal Pages: 111-127 Volume: 2 Issue: 1 Year: 2026 Month: March DOI: 10.65021/mwsj.v2.i1.33 File-URL: https://mwsjournal.com/index.php/mws/article/view/33 File-Function: Abstract page File-Format: text/html File-URL: https://mwsjournal.com/index.php/mws/article/download/33/12 File-Function: Full text File-Format: application/pdf Handle: RePEc:gyr:mwsjou:v:2:y:2026:i:1:id:33 Template-Type: ReDIF-Article 1.0 Author-Name: Abdulazeez Mousa Title: Foundations of Machine Learning and Its Applications in Healthcare: A Review of Methods, Clinical Impact, and Open Challenges Publication-Status: Published Abstract: Healthcare systems generate large volumes of imaging, laboratory, genomic, medication, sensor, and narrative clinical data. Machine learning provides methods for learning patterns from these data and converting them into predictions that may support diagnosis, prognosis, workflow prioritization, and biomedical discovery. This narrative review explains the principles of machine learning for healthcare readers and connects them to major clinical applications. It begins with an introduction to supervised, unsupervised, semi-supervised, and reinforcement learning, followed by common algorithms, deep-learning architectures, model training, generalization, evaluation metrics, and calibration. The review then examines medical imaging, electronic health records analytics, risk prediction, decision support, genomics, drug discovery, drug-drug interaction prediction, and clinical natural language processing. Evidence from landmark studies is discussed alongside recent guidance on external validation, reporting, foundation models, federated learning, explainability, and implementation. The review concludes that machine learning has achieved clinically meaningful performance in selected tasks, but safe adoption requires prospective testing, fairness assessment, privacy protection, workflow integration, and human oversight. Keywords: clinical decision support; deep learning; electronic health records; healthcare; machine learning Journal: Milky Way Scientific Journal Pages: 128-140 Volume: 2 Issue: 1 Year: 2026 Month: March DOI: 10.65021/mwsj.v2.i1.35 File-URL: https://mwsjournal.com/index.php/mws/article/view/35 File-Function: Abstract page File-Format: text/html File-URL: https://mwsjournal.com/index.php/mws/article/download/35/13 File-Function: Full text File-Format: application/pdf Handle: RePEc:gyr:mwsjou:v:2:y:2026:i:1:id:35