AI and the Evolution of Creative Writing: From Generative Text to Literary Innovation
Authors:-Selva Kumar

Abstract-:Artificial Intelligence (AI) has made significant strides in the field of creative writing, transforming the way stories are conceived, written, and even read. This paper examines the evolution of AI’s role in creative writing, from early generative text models to more sophisticated AI-driven literary tools that push the boundaries of artistic expression. The study explores how AI can be used as a tool for generating text, assisting in narrative structure, and enhancing the creative process. Case studies of AI-generated works, such as those produced by GPT-3 and other natural language processing (NLP) models, are discussed to demonstrate the capabilities and limitations of AI in literary contexts. The paper also addresses the ethical and philosophical implications of AI-driven creative writing, including the role of human authorship, originality, and the future of creativity. Finally, the paper explores the potential of AI in revolutionizing the literary world, offering new possibilities for authors, readers, and the publishing industry.

DOI: 10.61137/ijsret.vol.11.issue2.468

Published by: Kajal Tripathi

Autonomous AI in Space Exploration: Navigating the Challenges of Off-Earth Missions
Authors:-Prabhu Prasad

Abstract-:Autonomous Artificial Intelligence (AI) is playing an increasingly pivotal role in space exploration, especially in off-Earth missions. With the growing ambition to explore and colonize other planets, such as Mars and the Moon, AI-driven autonomous systems are essential for overcoming the challenges posed by the vast distances, extreme environments, and limited human presence in space. This paper explores the applications and future potential of autonomous AI in space missions, focusing on the use of robotics, autonomous navigation, and decision-making algorithms to enhance mission efficiency, reduce human risk, and optimize operational capabilities. Additionally, the paper addresses the technological hurdles, ethical considerations, and regulatory frameworks that must be navigated to ensure the safe and effective use of AI in space exploration.

DOI: 10.61137/ijsret.vol.11.issue2.467

Published by: Kajal Tripathi

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

The Growing Reliance on Artificial Intelligence in Everyday Human Activities: An Analytical Perspective
Authors:-Assistant Professor Nitin S Bheemalli

Abstract-:In recent years, the swift incorporation of Artificial Intelligence (AI) into various aspects of everyday life has profoundly transformed the ways in which individuals work, communicate, and manage their daily activities. This paper delves into the intricate relationships that have emerged between humans and AI across multiple domains, including healthcare, education, transportation, communication, domestic life, and decision- making processes. Through a comprehensive literature review and detailed analysis of case studies, this research aims to elucidate the degree of AI integration in these fields and assess its benefits as well as potential risks. The paper concludes by identifying key areas where policy intervention is necessary and addressing ethical considerations that must be taken into account during the development and deployment of AI systems intended for routine use.

DOI: 10.61137/ijsret.vol.11.issue2.462

Published by: Kajal Tripathi

Artificial Intelligence Data Centers Efficiency and Performance Enhancements through Liquid Cooling
Authors:-Girish Kishor Ingavale

Abstract-:The rapid expansion of artificial intelligence (AI) applications, such as machine learning, deep learning, and neural networks, has led to an unprecedented surge in the computational demands placed on data centers. Traditional air-cooling methods, which have served well in the past, are becoming increasingly inadequate for the high-density computing environments necessitated by AI workloads. This inadequacy is primarily due to the significant heat generation associated with AI computations, which can lead to reduced system performance and increased energy consumption. Liquid cooling emerges as a promising alternative, leveraging the superior thermal conductivity of liquids to more effectively dissipate heat. This article presents a comprehensive analysis of the implementation of liquid cooling systems in AI data centers, with a specific focus on their impact on energy efficiency, Power Usage Effectiveness (PUE), and overall system performance. Through a detailed comparative analysis of air and liquid cooling systems, this study demonstrates the substantial benefits of adopting liquid cooling technologies in AI data centers. Key findings indicate that liquid cooling can reduce energy consumption by up to 40% compared to traditional air-cooling methods. Additionally, PUE improvements ranging from 15% to 30% were observed, highlighting the enhanced energy efficiency achieved through liquid cooling. Furthermore, the study reveals a 20% decrease in server failure rates and a 10-15% improvement in computational performance due to the superior thermal management provided by liquid cooling. These enhancements are critical for maintaining the high availability and performance required by AI applications. The initial investment in liquid cooling infrastructure is justified by the long-term savings in energy costs and reduced maintenance requirements. This article contributes to the growing body of literature advocating for the adoption of liquid cooling in modern data centers, particularly those focused on AI workloads. The findings underscore the importance of liquid cooling in ensuring the sustainable growth and operational efficiency of AI data centers.

DOI: 10.61137/ijsret.vol.11.issue2.461

Published by: Kajal Tripathi

From Manual Input to Intelligent Execution: RPA-Driven Data Management in Camstar MES Environments
Authors:-Satish Kumar Nalluri, Varun Teja Bathini

Abstract-:RPA is being utilized in the manufacturing industry for data management and increased operational efficiency. The authors discuss the potential of Robotics Process Automation, or RPA, to revolutionize the automation of data entry and processing in Camstar Manufacturing Execution Systems (MES) systems. Many manufacturing systems, such as Camstar MES, are very manually input dependent – a factor that causes inefficiencies and errors and raises operational costs. Plus, RPA leads to automation of repetitive tasks, like data entry and data validation, thus minimizing errors and improving accuracy of data. By analyzing a semiconductor manufacturing firm, this paper assesses the concrete advantages of RPA such as more efficient production cycles, increased accuracy of data, and lower overall costs. It also looks into the quality and quantity of results obtained with RPA before and after its implementation. Any disadvantages, such as issues with system integration, employee buy-in, and upfront costs are discussed. Towards the end of the study recommendations are made for successful implementation of RPA, such as gradual or staged implementation of RPA, adequate training of staff using RPA, and ongoing monitoring and refining of RPA. The results show how RPA-based data management can inform smart advancements in manufacturing and improve manufacturing operations.

DOI: 10.61137/ijsret.vol.11.issue2.460

Published by: Kajal Tripathi

Enhancing Healthcare Accessibility, Risk Prediction, and Digital Record Management – Maternal and Child Health Monitoring System
Authors:-Shapna Rani E, Associate Nandhini S, Shwetha B, Sree Suvetha G, Thanzia Z

Abstract-:The Maternal and Child Health Monitoring System is an AI-driven solution designed to improve maternal and newborn healthcare by tracking essential health data, predicting risks, and streamlining administrative processes. Its mission is to “Empower mothers and ensure child well-being through personalized health tracking and AI-powered risk assessments.” The digital health monitoring application is designed to improve maternal and child health outcomes by tracking essential health data, predicting risks, and streamlining administrative processes. For pregnant women, the allows users to input health metrics such as blood pressure, weight, and glucose levels, using machine learning to predict potential health risks like gestational diabetes and preeclampsia. Post-birth, the records essential child details (e.g., birth time, date, gender) and assigns a unique ID to track developmental milestones, vaccinations, and growth metrics. This ID also facilitates the issuance of digital birth certificates, integrating seamlessly with government systems for legal registration. The sends reminders for checkups and vaccinations to ensure timely healthcare for both mothers and children. Data is securely stored in a database, providing authorized users such as parents and healthcare providers with accessible, real-time information. The system also offers recommendations for personalized health, guidance, and mental health support. By combining health monitoring, predictive analytics, and administrative automation, the application offers a comprehensive solution that improves maternal and child health, simplifies birth registration, and ensures efficient healthcare management. Key Features include AI-powered risk prediction, real-time health tracking, Unique ID-based record management, vaccination reminders, digital birth certification, and multi-language support for broader accessibility.

DOI: 10.61137/ijsret.vol.11.issue2.459

Published by: Kajal Tripathi