Category Archives: Uncategorized

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

CFD Analysis on Car Rear Spoiler
Authors:-Turlapati Siva Krishna, M. Hari Sai, K. Naresh

Abstract-:The stability of a car refers to its ability to maintain its trajectory and resist external disturbances, such as wind, road irregularities, or sudden maneuvers. Several key factors affect a car’s stability, including its center of gravity, weight distribution, suspension system, tire characteristics, and aerodynamics. A spoiler is an aerodynamic device attached to a vehicle, typically on the rear deck lid or trunk, designed to improve its stability and reduce drag at high speeds. By altering airflow around the vehicle, a spoiler can enhance its overall performance and handling. There are various types of spoilers, including rear spoilers, front spoilers, and side skirts, each serving a specific purpose. The benefits of spoilers include improved stability, reduced drag, and enhanced performance, making them a popular choice for racing cars, high-performance vehicles, and aftermarket accessories.

DOI: 10.61137/ijsret.vol.11.issue2.454

Published by: Kajal Tripathi

Application of Carbon Nanomaterials in Energy Production and Storage
Authors:-Aryan Kuhar

Abstract-:In this modern world the demand for more sustainable energy production and storage solutions has elevated the interest in nanotechnology, in which carbon-based nanomaterials are particularly interesting in improving energy production systems. This review paper explores the application of carbon nanomaterials, including nanomaterials like carbon nanotubes (CNTs), graphene, fullerene etc., in many energy production methods. Their unique properties such as large surface area, high electrical conductivity and high mechanical strength, make these carbon nanomaterials optimal candidates for improving energy storage and generation processes. In energy devices such as lithium-ion batteries, solar cells and fuel cells, these carbon nanomaterials have demonstrated improvement in better charge transport, energy density, catalytic performance and charge/discharge efficiency. These nanomaterials are also developed so there are more cost-effective alternatives to current technology.

DOI: 10.61137/ijsret.vol.11.issue2.453

Published by: Kajal Tripathi

A Supervised Learning Framework for Predicting GSC Antibody Seropositivity in Guillain–Barré Syndrome Using Multivariate Clinical and Demographic Indicators
Authors:-Ms. Sangeetha Raj S, Ayushi Negi, Ekta Kumari, Christina S

Abstract-:This paper proposes a robust supervised learning framework for predicting ganglioside complex (GSC) antibody seropositivity in patients with Guillain–Barré Syndrome (GBS) using multivariate clinical and demographic features. Drawing from a comprehensive dataset encompassing 129 GBS patients, we employed advanced machine learning methods support vector machines, random forests, decision trees, and k-nearest neighbours to predict seropositivity for six key anti-ganglioside antibodies (GM1, GM2, GD1a, GD1b, GT1b, GQ1b). Rigorous feature selection, cross-validation, and class imbalance handling were implemented to ensure robustness. Results show that routine clinical data can deliver accurate antibody seropositivity predictions, supporting GBS management where serological assays are delayed or unavailable.

DOI: 10.61137/ijsret.vol.11.issue2.452

Published by: Kajal Tripathi