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Insilico Screening of Bioactive Compounds Derived from Catharanthus Roseus against Anticancer Activity

One of the most prevalent diseases in the world is cancer. In order to stop cancer from growing, developing, and spreading, cancer treatments still need to be carefully planned and targeted. Radiation, cellular stress, and cytotoxic medications are some of the triggers that activate the mitochondrial intrinsic apoptotic pathway. Native to the Mediterranean region, Vinca roseus is a blooming perennial plant that is primarily found in the northern hemisphere. They are native to tropical regions in South Asia. There are numerous uses for the Madagascar plant, Catharanthus roseus L. The 3D crystallographic structure of the Anti-cancer receptor (PDB ID-2KCE & 2HBS ) was obtained from the Protein Data Bank and utilized as a protein target for in-silico experiments. Molecular docking was performed using Auto Dock 4 and Auto Dock Vina. A blind docking approach was employed to encompass all possible ligand binding sites. The binding free energy (kcal/mol) was utilized to calculate the binding affinity. This study reveals that Catharanthus roseus, a polyphenolic compound, may possess antioxidant, antibacterial, antifungal,&anticancer activity against the Anti-cancer receptor responsible for cancer disease, as predicted in silico. Molecular docking data suggest that Vincristine(-7.1& -9.5 Kcal/mol) and Vindesine (-9.1 & -10.5 Kcal/mol) have greater activity than Vincristine. They have good Anticancer properties. & The best protein was found to be 2HBS for Anti-cancer activity.

Published by: Rohan Anil Mali, Ranjit Jadhav, Sarika Kumbhar, Anushka Nikam

Author: Rohan Anil Mali

Paper ID: V11I5-1271

Paper Status: published

Published: November 1, 2025

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Research Paper

The Digital Catalyst for India’s Green Transition: Applying AI-Driven Recommender Systems and Natural Language Processing to Enhance Sustainable Supply Chains in Tier 2 MSMEs

This paper proposes an AI-driven marketplace that leverages Natural Language Processing (NLP), Document Intelligence, and Learning-to-Rank (LTR) models to resolve market frictions of discovery, trust, and compliance. NLP structures fragmented product data, while document AI verifies sustainability claims aligned with frameworks like BRSR, EPR, and MSME ZED certification. LTR algorithms prioritise verified green suppliers, incentivising sustainable practices. The platform also ensures inclusivity through cross-lingual conversational agents, fairness audits, and interoperability via ONDC. By enabling verifiable “green matching,” the system supports corporate Scope 3 emission reductions while advancing SDGs. The research demonstrates how AI marketplaces can serve as digital infrastructure for climate resilience and inclusive economic growth in India’s green transition.

Published by: Purab Swarup

Author: Purab Swarup

Paper ID: V11I5-1251

Paper Status: published

Published: October 31, 2025

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Research Paper

Effectiveness of Hand Reflexology upon Pain among Postcaesarean Mothers

Introduction: Caesarean section (C-section) remains a prevalent surgical intervention globally, Postoperative pain is one of the most common therapeutic problems in hospitals. The study aims to evaluate the effectiveness of hand reflexology upon post caesarean pain. Materials and Methods: A quasi-experimental pretest-post-test design was used to achieve objectives. Mothers were selected through total enumerative sampling with a sample size of 70 mothers, among them 35 were assigned to control and interventional group.The researcher collected the data using demographic variable proforma, obstetrical proforma, a Numerical pain rating scale, and a Rating scale to assess the acceptability of Hand reflexology through interview. Results: The study found that all mothers in the control group experienced severe pain before and after therapy, while in the intervention group, severe pain reduced from 100% to 54.29% at 30 minutes and to 60% at 60 minutes. Pain scores in the control group showed minimal reduction (8.71 to 8.51, t=2.02; 8.71 to 8.57, t=1.53), whereas the intervention group experienced a significant decrease (8.4 to 6.91, t=10.11; 8.4 to 6.77, t=13.97). Significant associations were found between pain levels and factors such as occupation (χ²=7.38, p<0.05), type of family (χ²=6.20, p<0.05), education (χ²=5.25, p<0.05), and gestational weeks (χ²=12.71, p<0.05) in the intervention group. Conclusion: The findings of the study indicated that the hand reflexology reduces the post operative pain. Hand reflexology over the reflexology point is a simple, easy to implement and most acceptable way to cope with pain among post caesarean mothers.

Published by: Mahima.A, Dr. Saraswathy. K, Dr. Latha Venkatesan, Dr. Vijayalakshmi . K, Dr.Dhanalakshmi.V

Author: Mahima.A

Paper ID: V11I5-1241

Paper Status: published

Published: October 31, 2025

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Research Paper

Access to Microcredit and Its Impact on Women Entrepreneurs in Rural India

This research paper investigates how microcredit access has transformed women’s entrepreneurial landscapes in rural India. Microcredit programs, often implemented through Self-Help Groups (SHGs) and microfinance institutions, have been instrumental in enhancing financial inclusion, promoting entrepreneurship, and empowering women socially and economically. The study assesses how access to small loans helps women develop enterprises, increase income, and gain greater control over family decisions. Using a mixed-method research design, the paper combines quantitative data from 100 rural entrepreneurs with qualitative interviews highlighting personal success stories and challenges. The results demonstrate a positive correlation between access to microcredit and women’s entrepreneurial growth, with implications for policy design and rural development strategies.

Published by: Keya Yash Panchmatia

Author: Keya Yash Panchmatia

Paper ID: V11I5-1250

Paper Status: published

Published: October 31, 2025

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Review Paper

Hybrid Logistic Regression and Random Forest Model for Diabetes Prediction Using Feature Elimination

The most common chronic diseases, diabetes mellitus, affect millions of people annually throughout the world. In order to lower the long-term health risk of diabetes, such as heart disease, kidney failure, and nerve damage, early detection and management are essential. The order to predict the risk of diabetes uses actual clinical data; this study presents a hybrid model that combines the Random Forest (RF) and Logistic Regression (LR) algorithms. Increase accuracy and interpretability, model also use Recursive Feature Elimination (RFE) to identify the most significant predictive features.PIMA Indian Diabetes dataset, along with World Health Organization (WHO) global health data, was used to train and validate the suggested model. The hybrid LR–RF approach obtained an accuracy of 89.2%, based on the findings and outperformed the individual model with a ROC-AUC score of 0.91. This model method shows how data-driven and interpretable artificial intelligence can help with clinical decision-making and provide patients and healthcare providers with trustworthy diagnostic tools.

Published by: Ritik Chauhan, Priyanka

Author: Ritik Chauhan

Paper ID: V11I5-1242

Paper Status: published

Published: October 31, 2025

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Research Paper

Forecasting Stock Market Prices Using Long Short-Term Memory (LSTM)

This study applies a Long Short-Term Memory (LSTM) neural network to forecast stock closing prices for selected technology companies (Apple, Google, Microsoft, and Amazon). The paper documents data collection, preprocessing, exploratory analysis (returns, volume, correlations), model architecture, and results. The aim is to evaluate LSTM’s ability to capture temporal patterns in stock prices and to provide practical insights for short-term forecasting. Key findings show that the LSTM model captures overall price trends and produces reasonable short-horizon forecasts; however, prediction accuracy is affected by market volatility, data noise, and model complexity.

Published by: Siddhi Rajput

Author: Siddhi Rajput

Paper ID: V11I5-1227

Paper Status: published

Published: October 31, 2025

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