Daily Archives: June 25, 2026

Authors: Atul Dubey, Vikash, Ashish Juneja

Abstract: The paper is a study on the use of ISO shipping containers to build sustainable Urban Economical weaker section housing society for Slum living in Metropolitan cities in India. As per world bank report nearly 35.2 % of urban population of India living in Slums (2018). In Japan, China and Vietnam the use of container housing solved their housing problems. Shigeru Ban , a house hold name in architecture in Japan has designed high end homes, biennials and museums by using containers. Room heating is a primary concern of using container as housing material because steel boxes are good heat conductors. But by using passive or active cooling technology we provide insulation. Also it leaves a low carbon foot print too. As per right to shelter in U. P. Avas Vikas Parishad v. friends coop. Housing society limited, the right to shelter has been fundamental right to residence secured under article 19(1)e and the right to life guaranteed under article 21.The state has to provide facilities and opportunities to build houses to standardize life of poor people. Container housing is a new trend of green construction technology, also it is made up of a heavy, good engineering property steel which is corrosion free and has adequate life to use as shelter. Average cost of shipping container houses are ranging from 5-15 lakh INR. India uses about 4 to 5 % of total shipping containers used in the world. This paper covers the literature review, methodology adopted for research, challenges and opportunity to use container as housing material.

DOI: http://doi.org/10.5281/zenodo.20838604

Published by: vikaspatanker

Authors: Dr. Praveen B. Hulloli, Dr.Venugopal Jalihal

Abstract: This research focuses on determining the applicability of Bradford’s Law of Scattering within the corpus of Indian medical research on Artificial Intelligence (AI) in healthcare. The study analyzing 9,774 papers that amassed 123,043 citations, the data was retrieved from the Web of Science citation database covering the period from 2005 to 2024. The study examined annual research output trends, researcher communication preferences, and the productivity of journals. The results show that year 2023 recorded the highest volume of publications, totaling 2104 (21.53%. The year 2021 demonstrated the peak research influence, as measured by total citations 26356 (21.42%) and an h-index of 69. Furthermore, journal impact was not solely linked to publication volume. Despite publishing fewer papers than volume leaders, the highly specialized “Expert Systems with Applications” demonstrated superior quality, achieving the highest Average Citations per Paper (ACPP) of 36.03. The Bradford’s analysis strongly confirmed the law’s applicability, with the core zone consisting of 1.90% of journals contributing 33.22% of the literature. This clear stratification, supported by a negligible negative error (0.49%), confirms the concentration of key AI medical literature in a small core of highly productive journals.

DOI: http://doi.org/10.5281/zenodo.20837913

Published by: vikaspatanker

Authors: Krishna Prisad Bajgai, Dr. Bhojraj Ghimire, Niraj Kumar Shah, Netra Prasad Joshi

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) technologies are increasingly applied in financial institutions for credit scoring, fraud detection, algorithmic trading, and risk management. Although these techniques offer high predictive performance, many models operate as complex “black-box” systems whose decision-making processes are difficult to interpret. This lack of transparency creates challenges related to trust, fairness, and regulatory compliance. Explainable Artificial Intelligence (XAI) aims to provide transparency and interpretability to AI-based models by offering explanations for their predictions. This paper explores the role of explainable AI in financial decision systems, focusing on its applications in credit risk assessment, fraud detection, and financial forecasting. The study reviews existing explainability techniques such as SHAP, LIME, and interpretable models, and proposes a conceptual framework for integrating explainable AI into financial decision-making systems. The findings highlight that integrating explainability mechanisms improves trust, transparency, and regulatory compliance while maintaining model performance. The paper concludes with future research directions for developing trustworthy AI-driven financial systems.

DOI: http://doi.org/10.5281/zenodo.20837802

Published by: vikaspatanker