Category Archives: Uncategorized

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

Authors: Research Scholor Mr. Arun Kumar Pyasi, Assistant Professor Mr. Hariram Sahu

Abstract: This study evaluates the extended performance and environmental benefits of Warm Mix Asphalt (WMA) prepared using a medium-setting bitumen emulsion and VG 30 binder. Building on prior mix design optimization, this work investigates moisture susceptibility and tensile strength performance across varying conditions. Indirect Tensile Strength (ITS) tests were conducted at 5°C to 40°C and showed that mixes with a 70:30 bitumen-emulsion ratio at 120°C achieved a peak ITS of 1.14 MPa at 25°C. Tensile Strength Ratio (TSR) values exceeded 80%, indicating strong resistance to moisture damage. Retained Stability tests confirmed the durability of the mix with a value of 85.6%, well above the minimum threshold. Additionally, fuel efficiency analysis for a hypothetical pavement section demonstrated a 25–30% reduction in diesel consumption when using WMA instead of HMA. This translated to a 28% reduction in CO₂ emissions per ton of mix. These findings reinforce the potential of emulsion-based WMA as a technically viable and environmentally superior alternative for sustainable pavement construction in India.

DOI: https://doi.org/10.5281/zenodo.15876742

Published by: vikaspatanker

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

Integrated Approaches to Computer System Validation Within GxP-Compliant Pharmaceutical Quality Management Systems
Authors:-Aditi Akundi, Dr. Pavithra G, Dr. Swapnil SN

Abstract-:The pharmaceutical industry is heavily regulated due to the direct impact of its products on human health and safety. To ensure compliance and maintain data integrity, regulatory authorities such as the U.S. Food and Drug Administration (FDA), European Medicines Agency (EMA), and others require that computerized systems used in Good Practice (GxP) environments undergo rigorous validation. Computer System Validation (CSV) plays a pivotal role in ensuring that such systems consistently perform according to their intended use and comply with applicable regulations. This paper provides an in-depth conceptual overview of CSV within the framework of pharmaceutical Quality Management Systems (QMS). It explores its regulatory basis, the validation lifecycle, risk-based approaches, common challenges, and industry best practices, while highlighting the significance of CSV in maintaining quality, compliance, and patient safety.

DOI: 10.61137/ijsret.vol.11.issue2.458

Published by: Kajal Tripathi

Artificial Intelligence in Energy Management: A Comprehensive Literature Review on Methods, Applications, and Challenges
Authors:-Jayendra Jadhav, Aashirwad Mehare, Aditya Wandhekar, Sanyukta Pawar, Pranjal Chavan, Vedant Nigade

Abstract-:The mounting pressure for efficient and sustainable energy solutions has driven the adoption of Artificial Intelligence (AI) in contemporary energy systems. This literature review consolidates evidence from more than 20 recent studies on AI-based approaches for renewable energy and smart grid management. It discusses AI methods like machine learning, deep learning, reinforcement learning, and optimization techniques applied in energy forecasting, load management, fault detection, and demand response. The review emphasizes AI’s application in improving energy efficiency, lowering costs, and facilitating decentralized energy systems. It also touches on the most important hardware devices involved, e.g., photovoltaic panels, smart meters, IoT devices, and battery storage systems. Although it has the potential to transform, the use of AI in energy systems is confronted with various challenges such as high infrastructure expenditure, data needs, system integration problems, and regulatory issues. This paper concludes by establishing research gaps and outlining future directions for the complete utilization of AI to achieve a sustainable and intelligent energy ecosystem.

DOI: 10.61137/ijsret.vol.11.issue2.457

Published by: Kajal Tripathi

Fake News Detection Using Natural Language Processing
Authors:-Professor Kirti Randhe, Nameet Vyavahare, Rajkumar Vishwakarma, Sai Kudale

Abstract-:In the digital age, the rapid dispersal of information through social media and online platforms has increased the spread of fake and exaggerated news, posing serious challenges and threats to public trust, societal stability, democratic processes and national security and peace. This research explores the application of Natural Language Processing (NLP) techniques for the automatic detection of fake news, aiming to enhance the reliability of information consumed by the public. By leveraging and applying machine learning and deep learning models in conjunction with NLP methods such as text preprocessing, tokenization, feature extraction, and sentiment analysis, this study investigates effective strategies for distinguishing between factual, genuine and misleading content. Various algorithms, including Support Vector Machines, Random Forest, Naïve Bayes and deep learning approaches like LSTM and BERT, are evaluated using benchmark datasets. The results demonstrate the potential of NLP-driven solutions to accurately classify news articles, highlighting their significance in combating misinformation. This paper contributes to the growing field of automated fake news detection and offers insights into building more trustworthy digital information ecosystems.

DOI: 10.61137/ijsret.vol.11.issue2.456

Published by: Kajal Tripathi

J.A.R.V.I.S: AI ASSISTANT
Authors:-Bhuneshwar Singh Chauhan, Swati Kumari, D Sai Divya Reddy, Dhananjay Sahu, Professor Neha Soni

Abstract-:This paper examines the rapidly evolving field of modern technology, with a particular focus on virtual assistants developed through Python. It shows how it changes these assistants are having on human-computer interactions by using advanced technologies such as Natural Language Processing (NLP) and Artificial Intelligence (AI). The literature review consolidates key research findings on the functions, capabilities, and design strategies of virtual assistants. In the system architecture section, a clear structure is presented for desktop virtual assistants, detailing key components like the user interface, speech recognition modules, dialogue management, and more. The methodology section outlines a structured approach to designing and building such systems. The conclusion emphasizes the significant advancements in virtual assistant technology while also addressing ongoing challenges such as ensuring system stability and safeguarding data security. Ultimately, the paper underscores the importance of continued innovation in this field to fully unlock the potential of virtual assistants in various industries.

DOI: 10.61137/ijsret.vol.11.issue2.455

Published by: Kajal Tripathi