Daily Archives: May 15, 2025

Machine Learning in Nutritional Science: Personalizing Diets with Precision Algorithms
Authors:-Lubna Tabasum

Abstract-:Machine learning (ML) is revolutionizing nutritional science by enabling the personalization of diets based on individual health conditions, genetic profiles, and lifestyle factors. This paper explores the role of machine learning in enhancing dietary recommendations and improving health outcomes. It discusses how ML algorithms can analyze large datasets of nutritional information, medical histories, and genomic data to create tailored dietary plans that optimize nutrition for individuals. The paper highlights the integration of AI technologies in the development of precision nutrition models, the benefits of personalized diets in managing chronic diseases, and the emerging trends in nutrition prediction. The challenges of data privacy, model interpretability, and the need for interdisciplinary collaboration between nutritionists, data scientists, and clinicians are also examined. The paper concludes by discussing the future of machine learning in nutritional science, emphasizing the potential of combining AI with wearable technologies to continuously monitor and adjust diets for long-term health optimization.

DOI: 10.61137/ijsret.vol.11.issue2.466

Published by: Kajal Tripathi

AI in Marine Conservation: Monitoring Oceans with Machine Learning and Remote Sensing
Authors:-Bhumika. M

Abstract-:Marine ecosystems are among the most biologically diverse yet vulnerable environments on the planet, facing significant threats from climate change, pollution, and overexploitation. Artificial Intelligence (AI) is increasingly being leveraged to monitor and protect these fragile ecosystems through innovations in machine learning and remote sensing. This paper explores the integration of AI technologies in marine conservation, detailing their applications in species identification, coral reef monitoring, illegal fishing detection, and marine habitat mapping. Drawing from recent advancements, the study highlights how satellite imagery, autonomous underwater vehicles (AUVs), and acoustic sensors, coupled with AI algorithms, are enabling more precise and timely environmental assessments. Ethical and regulatory considerations, including data privacy in territorial waters, fairness in conservation resource allocation, and inclusivity in global biodiversity goals, are discussed. Challenges such as limited annotated data, sensor constraints, and interpretability of AI models are examined. The paper concludes with a forward-looking view on emerging technologies, collaborative platforms, and policy developments that aim to scale and democratize AI-based marine conservation globally.

DOI: 10.61137/ijsret.vol.11.issue2.465

Published by: Kajal Tripathi

Artificial Intelligence in Game Theory: Learning Strategy in Competitive and Cooperative Systems
Authors:-Ashish Kumar

Abstract-:Artificial Intelligence (AI) and game theory have converged into a powerful interdisciplinary domain that focuses on strategic interaction among intelligent agents. This paper explores how AI systems, particularly through reinforcement learning and multi-agent environments, are transforming the way game-theoretic strategies are learned, adapted, and executed. It begins by outlining the foundational principles of game theory—especially concepts like Nash equilibrium, zero-sum games, and cooperation models—and explores how AI extends these concepts by learning optimal strategies from experience. Through detailed case studies, including applications in autonomous vehicle coordination, online auctions, and cybersecurity defense mechanisms, the paper shows how AI-driven agents can dynamically adapt to competitive and cooperative scenarios. Ethical and regulatory considerations surrounding fairness, transparency, and accountability in automated decision-making are critically examined. Challenges such as scalability, interpretability, and emergent behavior in complex multi-agent systems are discussed, along with prospective solutions. Finally, the paper considers the future landscape, highlighting trends like quantum game theory, hybrid learning models, and self-organizing AI systems that promise to expand the role of game theory in intelligent decision-making.

DOI: 10.61137/ijsret.vol.11.issue2.464

Published by: Kajal Tripathi

A Comparative Study on Additive Cross-Modal Attention Network (ACMA) for Depression Detection Based on Audio and Textual Features
Authors:-Asif S Majeed, Evelyn Treasa Jaison, Fathima S, Arunlal M L, Dr. Jyothi R L, Swathi S

Abstract-:This study introduces an approach for depression detection through an Additive Cross-Modal Attention Network (ACMA) that integrates audio and textual data to improve diagnostic accuracy without relying on self-report questionnaires. Traditional depression assessments often depend on patient- disclosed information, which may not always be accurate due to stigma or personal reluctance, leading to potential underdiagno- sis. The ACMA model addresses these limitations by leveraging cross-modal attention mechanisms within a Bidirectional Long Short-Term Memory (BiLSTM) and Transformer model to cap- ture and assign optimal weights to relevant features across audio and text modalities. This enables the model to effectively detect depressive symptoms by analyzing both linguistic and acoustic cues. The model is designed for both binary classification (depressed vs. non-depressed) and regression tasks to estimate depression severity, utilizing the DAIC-WOZ dataset for evaluation. ACMA demonstrates significant improvements over baseline models, achieving high accuracy, recall, and F1 scores. Additionally, the model’s adaptability across different datasets underscores its potential as a robust, non-intrusive tool for clinical applications in mental health diagnostics. This work advances the field of au- tomated depression detection, providing a foundation for further research in cross-modal mental health assessment systems.

DOI: 10.61137/ijsret.vol.11.issue2.463

Published by: Kajal Tripathi