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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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Research Paper

Factors Considered while Choosing a Life Insurance Company

This paper evaluates the factors considered for the selection of a Life Insurance Company by individuals. Life Insurance is an important part of one’s life, which gives security to the family of that individual future security in the absence of that individual. Many people take a policy on the recommendation of friends or family members, which can prove wrong as that individual doesn’t consider the important factors related to the policy, its terms and conditions, etc. So, it is important to consider the factors related to a life insurance company, like the Claim Settlement Ratio of the company, Solvency Margins, Reputation, etc. Therefore, this paper evaluates the behaviour of consumers while selecting a company by way of a survey with a structured questionnaire where people are asked about their choices about a company. The research focuses more on the salaried people, who were the samples for this survey, as they are primarily responsible for protection themselves and their families. The main objective of this research is to check the knowledge of people about different terms associated with life insurance, as well as the importance of these terms, while selecting a life Insurance Company.

Published by: Chaitrali Gaidhani, Arya Mohite, Ayush Deshmukh, Adit Bhoite, Kanchan Kawale, Kashish Khandelwal

Author: Chaitrali Gaidhani

Paper ID: V11I5-1235

Paper Status: published

Published: October 29, 2025

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Research Paper

Intellectual Property Rights and Competition Law: A Critical Analysis

This research paper examines the complex interface between Intellectual Property Rights (IPR) and Competition Law, analyzing the delicate balance between promoting innovation through temporary monopolies and preventing anti-competitive practices that harm consumer welfare. The study critically evaluates the legal framework governing this interface in India, primarily through Section 3(5) of the Competition Act, 2002, and examines landmark judicial precedents that have shaped the jurisprudence in this domain. Through comparative analysis of international approaches and examination of contemporary challenges, this paper argues that while IPR and competition law serve complementary objectives of promoting innovation and consumer welfare, their intersection requires careful judicial and regulatory navigation to prevent abuse of monopoly power while preserving incentives for innovation. Through a comprehensive examination of case law, statutory frameworks, and emerging trends in digital markets, this paper argues that a balanced approach is essential to foster innovation while maintaining competitive markets. The analysis includes recent developments in the technology sector antitrust enforcement, particularly focusing on major cases involving Google, Apple, Microsoft, and other tech giants that illustrate the contemporary challenges at this legal intersection.

Published by: Vanshika Nakra, Estuti kumari

Author: Vanshika Nakra

Paper ID: V11I5-1210

Paper Status: published

Published: October 28, 2025

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Review Paper

An AI-Based Framework for Early Cancer Detection Using Machine Learning Technique

Cancer detection using machine learning has emerged as a promising approach for improving early diagnosis and patient outcomes. This research focuses on applying advanced algorithms such as Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and ensemble models to analyze medical imaging and histopathological data. The system automates feature extraction and classification, enhancing diagnostic accuracy and reducing human error. Data from breast, lung, and oral cancer datasets were used for model training and validation. Preprocessing techniques were applied to ensure image clarity and consistency. The proposed model achieved high precision and recall in identifying cancerous patterns. Limitations include data imbalance and interpretability challenges. Future work aims to integrate real-time diagnostics and multi-modal data for broader clinical use.

Published by: Ms. Rashida Bano, Ms. Noorishta Hashmi, Ms. Umaima fatima

Author: Ms. Rashida Bano

Paper ID: V11I5-1214

Paper Status: published

Published: October 25, 2025

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Research Paper

Detecting Money Laundering through Artificial Intelligence: A Commercial and Predictive Perspective

Money laundering—the concealment and integration of illicit proceeds into the formal financial system—undermines the trust and fairness of global financial systems, presenting enormous challenges to investors, regulators, and commercial enterprises. Traditional detection methods based on rigid rule-based systems and manual auditing have proven insufficient in combating increasingly sophisticated laundering schemes. This paper demonstrates how data science, commercial domain knowledge, and machine learning—specifically, decision tree models—can be synthesized to enhance real-time detection of suspicious financial activities. Through a comprehensive workflow involving synthetic transaction data generation, exploratory data analysis, and predictive modeling, critical patterns such as transaction amount, timing, customer risk profiles, and transaction type emerge as powerful indicators of money laundering behavior. Bar diagrams and visual analytics visually support the findings, illustrating feature importance rankings and identifying high-risk transaction segments. The commercial impact of this approach includes proactive regulatory compliance, significant workload reduction for compliance analysts, and minimal customer friction through reduced false positives. This research highlights how student-level expertise combined with interpretable AI tools can effectively bridge the gap between traditional commerce education and modern financial technology compliance solutions. The decision tree model achieved 99.93% testing accuracy with a precision and recall of 99.82% each, demonstrating the viability of automated AML detection systems in real-world banking environments.

Published by: Nimit Jain, Kaashvi Soni

Author: Nimit Jain

Paper ID: V11I5-1212

Paper Status: published

Published: October 25, 2025

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Research Paper

Margins and Gateways: The Economic Struggles of Emerging Fine Artists in India’s Contemporary Art Landscape

This paper examines the key challenges faced by emerging artists in India, including limited access to markets, professional networks, and financial stability. It explores how gatekeeping in galleries, intense competition for exposure, and the scarcity of grants and patrons restrict opportunities for new talent. The instability of freelance and teaching work further compounds these difficulties. Through analysis of current conditions and available support systems, the paper highlights the need for more inclusive, transparent, and decentralized frameworks to support emerging artists and ensure a more equitable future for India’s creative community.

Published by: Kaavya Mittal

Author: Kaavya Mittal

Paper ID: V11I5-1209

Paper Status: published

Published: October 24, 2025

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