Daily Archives: January 31, 2026

Authors: P. Vijetha, Sk Maqbool basha

Abstract: The risk–return relationship is a fundamental concept in finance, guiding investment decisions and portfolio management. This study empirically examines the relationship between risk and return among 10 actively traded equity stocks over a five-year period (2019–2024). Both systematic risk (beta) and total risk measures (standard deviation and variance) are analyzed to determine their influence on equity returns. Secondary data from NSE, BSE, and financial databases were used, and statistical techniques including descriptive statistics, correlation analysis, regression analysis, and t-tests were employed. The findings reveal a positive and statistically significant relationship between risk and return, with beta emerging as the strongest predictor. Regression results indicate that risk measures collectively explain over 50% of the variance in returns. The study validates the traditional risk–return tradeoff and highlights the importance of incorporating multiple risk metrics for informed investment decisions. Implications for investors, portfolio managers, and policymakers are discussed, emphasizing strategies for optimizing returns while managing risk in dynamic equity markets.

Published by: vikaspatanker

Authors: Vishal Singh, Hemant Singh, Ajay Rawat, Shivam Kumar Jha

Abstract: Nutrition-analysis applications traditionally provide qualitative, binary guidance such as “healthy,” “unhealthy,” or “avoid.” However, recent advances in generative artificial intelligence (AI) enable hyper-personalized, quantitative moderation advice that recommends specific serving sizes, risk thresholds, and actionable alternatives. This paper investigates whether quantitative, personalized recommendations enhance user comprehension, confidence, and dietary intent compared to generic, qualitative warnings. We conduct a randomized controlled A/B user study with 100 participants and compare a qualitative control interface against a quantitative, generative-AI- powered interface offering explicit serving guidance and alternatives. Results show that quantitative moderation advice significantly improves comprehension accuracy, user confidence, trust, and positive dietary intent. These findings provide strong HCI evidence supporting the integration of precise, personalized guidance in digital nutrition applications.

Published by: vikaspatanker

Authors: Krishna Prisad Bajgai, Dr. Bhoj Raj Ghimire

Abstract: Early identification of unrecoverable loans is a critical requirement for financial institutions to maintain portfolio quality, comply with regulatory provisioning standards, and minimize credit losses. In Nepal, microfinance institutions and banks are mandated to report loan performance using the Nepal Rastra Bank (NRB) N002 monitoring framework, which contains borrower demographics, loan characteristics, delinquency behavior, and provisioning information. Despite the availability of structured regulatory data, most institutions continue to rely on rule-based aging mechanisms that fail to capture complex nonlinear risk patterns. This study proposes a machine learning-based framework for predicting unrecoverable loans using NRB N002-compliant datasets. A supervised classification problem is formulated, where loans are labeled as unrecoverable based on regulatory delinquency thresholds (Days Past Due >180 or Provision ≥50%). Three models—Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost)—are implemented and evaluated using recall, precision, F1-score, and ROC-AUC metrics, with special emphasis on recall to minimize false negatives in high-risk loan identification. Experimental results demonstrate that XGBoost achieves superior performance with near-perfect recall for unrecoverable loans and an ROC-AUC exceeding 0.97, significantly outperforming traditional statistical approaches. Explainability is ensured using SHAP-based feature attribution. highlighting delinquency duration, overdue principal, outstanding exposure, and provisioning ratios as dominant predictors. The findings confirm that machine learning models can substantially enhance early warning credit risk systems within Nepalese financial institutions while maintaining regulatory transparency and operational interpretability.

DOI: https://doi.org/10.5281/zenodo.18440690

 

Published by: vikaspatanker

Authors: Sahil Shinde, Pushkar Rahane, Sudarshan Suryavanshi, Krishna Tayde, Prof. Bhagawat S. Mohite

Abstract: This research Security is a major concern in homes, offices, and restricted areas. Traditional lock systems using mechanical keys have limitations such as key loss, duplication, and lack of access control. To overcome these issu es, this project presents the design and implementation of an RFID Door Lock using Arduino. The proposed system uses Radio Frequency Identification (RFID) technology to allow only authorized users to access the door. An RFID reader reads the unique ID of the RFID card or tag and sends it to the Arduino microco ntroller. The Arduino compares the scanned ID with the pre-stored authorized IDs. If the ID matches, the system.

 

 

Published by: vikaspatanker