Determinants of School Orientation in Urban and Rural Areas of Analamanga Antananarivo, Madagascar
School orientation constitutes a decisive stage in students’ educational trajectories, influencing their motivation, academic achievement, and future professional prospects. Although this choice should ideally be guided by each student’s personal aspirations, abilities, and interests, it is often shaped by a range of external factors, notably familial, economic, cultural, and geographical influences. In many contexts, orientation decisions do not result from a free and informed process but rather from sometimes constraining influences exerted by parents, teachers, or the social environment. This study examines the dynamics of school orientation in two contrasting contexts: urban and rural settings. It aims to highlight the impact of variables such as family income, parents’ level of education, their educational attitudes and expectations, as well as students’ perceptions of their own abilities and career prospects. Through a comparative analysis, the study seeks to understand how these factors, depending on the context, contribute to either enhancing or constraining students’ freedom of choice in their educational pathways.
Published by: Radanielina Tsaranto Evatiana Rahaliva, Andrianarimanana Jean Claude Omer, Rakotoson Olivia, Andrianjary Myriam
Author: Radanielina Tsaranto Evatiana Rahaliva
Paper ID: V11I6-1318
Paper Status: published
Published: December 30, 2025
Transforming Library Management, Leveraging Data Analytics through A Strategic Approach
In the era of digital transformation, analytics has emerged as a critical enabler across industries, including library management. Libraries, now evolving into knowledge management centres, face dynamic user needs and operational challenges. This paper explores the role of data analytics in enhancing library services, improving decision-making, and addressing contemporary challenges. It outlines the types of analytics applicable to libraries, the analytical environment, and strategies for overcoming operational constraints. Practical examples and case studies illustrate how analytics can be effectively integrated into library operations to optimise resources and strengthen user engagement.
Published by: Mr. Kishore Ramdas Ingale, Dr. Anil P Sarode, Ms. Shubhada Sachin Apte
Author: Mr. Kishore Ramdas Ingale
Paper ID: V11I6-1299
Paper Status: published
Published: December 24, 2025
Autonomous Drone Navigation Using Computer Vision: Challenges and Future Directions
Unmanned Aerial Vehicles (UAVs), commonly known as drones, have emerged as versatile platforms revolutionizing diverse fields such as precision agriculture, environmental monitoring, infrastructure inspection, and disaster management. Their growing impact is driven by advances in autonomy that enable efficient, scalable, and intelligent operations. Among the enabling technologies, computer vision plays a pivotal role by allowing drones to perceive, interpret, and interact with their environment through visual sensing. This capability has significantly improved tasks such as obstacle detection, localization, mapping, and scene understanding, even in GPS-denied or visually degraded conditions. Despite these advances, achieving full autonomy remains a major challenge. Vision-based navigation is often hindered by adverse weather, illumination changes, dynamic obstacles, and computational limitations, alongside issues of domain shift and long-tail edge cases that compromise reliability and safety. This review paper presents a comprehensive analysis of recent progress in vision-based autonomous drone navigation, spanning classical computer vision, deep learning, and emerging paradigms such as Vision Transformers, reinforcement learning, and self-supervised learning. It further highlights open challenges and outlines future research directions, including multi-sensor fusion, domain adaptation, collaborative perception, neuromorphic computing, and explainable AI—aiming to guide the development of resilient and robust UAV systems capable of dependable real-world autonomy.
Published by: Muhammad Jamil Sani, Jamal Nasser Alotaibi
Author: Muhammad Jamil Sani
Paper ID: V11I6-1282
Paper Status: published
Published: December 24, 2025
AI-Driven Portfolio Optimisation Strategies in High-Inflation Macroeconomic Conditions
High inflation significantly affects asset prices, risk premiums, and investor behaviour, making traditional portfolio optimisation models less effective. This study explores the application of artificial intelligence (AI) techniques—specifically machine learning (ML) models and heuristic optimisation algorithms—to enhance portfolio performance during periods of high inflation. Using historical macroeconomic and financial market data, the project trains models to identify inflation-sensitive assets, predict returns, and construct optimal asset allocations. Methods such as Random Forest regression, LSTM neural networks, and Genetic Algorithms are compared with classical approaches like Modern Portfolio Theory (MPT). Performance is evaluated using metrics including Sharpe ratio, risk-adjusted returns, and inflation-adjusted returns. The findings aim to determine whether AI-driven strategies can outperform traditional models when inflation is elevated. This project contributes to the growing domain of AI-based financial modelling and offers practical insights for investors seeking resilience against inflationary volatility. In addition to evaluating performance during inflationary spikes, the study examines how AI models respond to shifting macroeconomic signals such as interest rate hikes, currency fluctuations, and commodity price volatility. By incorporating these variables into the learning framework, the models aim to provide more stable predictions and adaptive asset allocation decisions. This helps assess whether AI can truly capture inflation-driven market distortions better than conventional statistical models, which often assume linear relationships and stable correlations. Furthermore, the project highlights the practical implications of AI-driven optimisation for investors, financial planners, and policymakers operating in inflation-sensitive economies. By demonstrating how machine learning outputs can be integrated into investment decision-making, the study contributes to the growing domain of predictive financial analytics. The broader goal is to understand whether AI can create more resilient and inflation-hedged portfolios in real-world scenarios. The findings are expected to offer valuable insights into designing future-ready investment strategies that remain robust even during prolonged periods of macroeconomic uncertainty.
Published by: Aadya Goyal
Author: Aadya Goyal
Paper ID: V11I6-1310
Paper Status: published
Published: December 24, 2025
Evaluating the Proliferative and Inhibitory Effects of Selected Indian Spices and Herbs on Vigna Radiata Cell Growth
Scientists have always been on the hunt for a therapeutic chemical with the potential to treat deadly diseases. There is a growing interest in using natural compounds derived from plants as a natural cancer cell treatment. Herbs and spices such as turmeric, garlic, cinnamon, clove, and tulsi are rich in bioactive compounds and have long been studied for their medicinal value in humans. However, their potential role in modulating cancer cell proliferation is underutilized. By exploring the proliferative and inhibitory effects of these traditional Indian herbs on Vigna radiata, this study contributes new knowledge to medical science and phytochemistry. It also opens new avenues for applying culturally significant, easily accessible, sustainable and inexpensive natural resources in modern cancer treatments. This study focuses on finding the specific herb extracts which are potent inhibitors of cell proliferation, in turn reducing the cancerous cell growth, leading to an invaluable impact on cancer treatment worldwide. Furthermore, this research aligns with global goals for sustainable development, particularly those related to accessible, low-cost and sustainable healthcare.
Published by: Seema Bajpai, Aarav Chetan Jain, Dhairya Milin Shah, Nishka Sachin Koneri, Pahal Kayur Shah, Ms. Ranjana Yadav
Author: Seema Bajpai
Paper ID: V11I6-1309
Paper Status: published
Published: December 23, 2025
Women at Work: The Economics of Gender Inequality
In the last two decades, there has been a significant change in the social and economic horizon of India, with the Female Labor Force participation rate reaching 41.7% in 2023-2024. While the growing FLFPR indicates increased economic participation of women, it does not always mean that women are getting quality employment opportunities or becoming empowered. In fact, this boost in FLFPR is because most women, especially rural women, are compelled to work because of unfavorable circumstances and not because there are doors opening for them. As FLFPR is an inadequate economic metric, this study also explores other economic indicators like the global gender gap index, female entrepreneurship rate, access to financial services, women’s asset management, female unemployment rate, etc. While the participation of women is increasing exponentially across all professions, they still have to face an uphill battle to move forward in their careers and gain respect. To collect empirical evidence on workplace barriers, this study has circulated a survey filled out by 121 anonymous women belonging to all age groups from both the formal and informal sectors. The FLFPR of India has also been compared with that of emerging economies to evaluate India’s current scenario in light of global standards. Secondary data has been collected from sources such as the World Bank, the World Economic Forum, and Indian National newspapers. Thematic patterns of gender pay gap, pre-conceived notions, hostile work environment, negative and prejudiced attitude towards women, lack of mentorship programmes, and inadequate provision of childcare and maternity benefits have emerged. These become obstructions not just for women but also for organizations, India, its economy, and the Gross Domestic Product of this country. If half of the populace remains dormant in the workforce, the Indian economy will never be able to reach its maximum potential.
Published by: Myra Khurana
Author: Myra Khurana
Paper ID: V11I6-1315
Paper Status: published
Published: December 23, 2025
