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“A Study to Assess the Knowledge Regarding Isbar Handing and Taking Over Tool Among Staff Nurses Working at Selected Hospital in the City.”

Clinical handover is the transfer of professional responsibility and accountability for some or all aspects of care for a patient or group of patients to another person/family / legal guardian or professional group on a temporary or permanent basis. It is one of the most important skills that health professionals and students need to be taught. There are several structured formats available for clinical handover. e.g. IPASS3. I-SBAR is a mnemonic that aids in safe handover of patient information and improves communication and decision-making. This technique improves the efficiency and accuracy of Handing and Taking over the process by staff nurses4. PROBLEM STATEMENT: "A study to assess the knowledge regarding I-SBAR handing and taking over tool among staff nurses working in selected city hospitals." OBJECTIVES OF STUDY: • To assess the knowledge among staff nurses regarding the I-SBAR handing and taking over tool • To find an association of knowledge regarding the I-SBAR handing and to take over the tool with selected demographic variables. METHODS: The study was conducted at MGM of Chh. sambhajinagar city. The present study's sample size was 80 MGM hospital Chh staff nurses. sambhajinagar. A structured questionnaire regarding the ISBAR handing and taking over tool was used to assess the knowledge of staff nurses in MGM Hospital Chh. Sambhajinagar. 9 RESULTS:. The majority of samples, 57 (71.25%), have good knowledge regarding ISBAR handing and taking over the tools, 19 (23.75%) samples have average knowledge regarding ISBAR handing and taking over the tools, and 4 (5%) samples have poor knowledge regarding ISBAR handing and taking over a tool. CONCLUSION: This study assessed the knowledge regarding ISBAR handling and taking over tools. Based on the result, the investigator concluded that there was a significant association between religion and the knowledge of staff nurses regarding ISBAR handing and taking over tools.

Published by: Ms. Ashvini Chappekar, Ms. Bidyarani Yumnam

Author: Ms. Ashvini Chappekar

Paper ID: V11I1-1370

Paper Status: published

Published: April 10, 2025

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Research Paper

Encoding Digital Information in DNA: Advances, Techniques, and Applications

In 2020 approximately 64 zettabytes of Data were generated and it was predicted that by 2025 this number will be greater than twice that of it. This prediction is proving itself as every day approximately 402.74 million Terabytes of data is created and as of 2024 the number has risen to 147 Zettabytes already and it's assumed that this amount will be 181 Zettabytes by the end of 2025. This data primarily includes IoT data which is the fastest growing segment of data which is then followed by social media. The existing storage technologies cannot cater to the needs of the Zettabyte Era, as they have considerable issues like limited durability, high power consumption, and the environmental impact they cause. DNA is nature's best alternative to these problems and can store such high amounts of data for a longer period without very little decay. One gram of DNA can store up to 215 Petabytes of data. Its longevity of thousands of years and enormous information density without harming the environment by generating less e-waste makes it a promising archival storage medium.

Published by: Ananya Chandra, Mahesh Tiwari

Author: Ananya Chandra

Paper ID: V11I1-1285

Paper Status: published

Published: April 10, 2025

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Research Paper

Design and Development of V-Twin Stirling Engine

This project aims to address environmental issues like air pollution and noise generated by internal combustion (IC) engines through the development of a V-Twin Stirling engine. Stirling engines, which operate through cyclic expansion and contraction of gas via external heat sources, offer a more efficient and cleaner alternative to traditional IC engines. The design leverages a unique mechanism where one piston drives the motion of both pistons using a gear system, reducing fuel consumption and emissions. The project involves comprehensive analysis and design, with the engine components, such as flywheels, gears, and pistons, being meticulously crafted for optimized performance. The development process includes part drawings, weight and volume calculations, and precision manufacturing using aluminum. The Stirling engine’s potential to harness renewable energy, integrate into power generation systems, and recover waste heat positions it as a viable alternative for future sustainable automotive technologies. The total project budget is approximately INR 6000, covering materials, manufacturing, and necessary accessories.

Published by: Viraj Tambe, Ravi Singh, Rahul Mayekar, Tanish Tilak, Prof. Nikhil V.S.

Author: Viraj Tambe

Paper ID: V11I2-1166

Paper Status: published

Published: April 10, 2025

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Research Paper

Comparative Analysis of Machine Learning Models for Diabetes Prediction: A Performance Evaluation Study

Diabetes is a chronic disease affecting millions worldwide, necessitating early diagnosis and effective prediction models for improved healthcare outcomes. This study evaluates seven machine learning algorithms for diabetes prediction using healthcare data. We compared Logistic Regression, K-Nearest Neighbors (KNN), Random Forest, Decision Tree, AdaBoost, XGBoost, and Support Vector Machine (SVM) models. The analysis focused on key performance metrics: accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). Results showed that logistic regression achieved the highest overall performance with 79% accuracy and 0.88 AUC, suggesting its potential utility in clinical diabetes prediction applications.

Published by: Taaha Ansari, Vaishali M. Bagade

Author: Taaha Ansari

Paper ID: V11I2-1170

Paper Status: published

Published: April 10, 2025

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Research Paper

Small Businesses as the Basis of the Indian Economy

India's economic progress and GDP growth have been mostly driven by small and medium-sized businesses, or SMEs. As of March 27, 2022, there were over 7.9 million MSMEs in India, according to the Ministry of Micro, Small & Medium Enterprises. India's and the world's economies have grown because of small enterprises. In a nation with an economy the size of India, small businesses make up 95% of the industrial units, and they provide 40% of the nation's total industrial production. Once more, tiny companies account for around 45% of India's overall export earnings. This paper explores the importance of small businesses in India, their contributions, their challenges, and their evolving role in driving sustainable and inclusive economic development.

Published by: Aayaan Sardana

Author: Aayaan Sardana

Paper ID: V11I2-1140

Paper Status: published

Published: April 10, 2025

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Review Paper

Big Data Analytics for Real-Time Fraud Detection in Insurance Claims

The integration of Artificial Intelligence (AI) and Big Data Analytics is revolutionizing industries by optimizing efficiency, accuracy, and security. In healthcare and insurance, AI-driven intelligent Document Processing (IDP) automates workflows such as claims automation, medical data extraction, and regulatory compliance management. By utilizing Machine Learning (ML), Natural Language Processing (NLP), and Optical Character Recognition (OCR), IDP accelerates document classification, data validation, and anomaly detection, reducing errors by 90% and cutting processing time by 80%. In the financial sector, AI enhances fraud analytics, risk modeling, and compliance monitoring. Advanced deep learning architectures, pattern recognition, and predictive analytics improve credit risk assessment and real-time fraud mitigation. AI-powered anomaly detection techniques identify suspicious transactions, reducing cybersecurity threats and financial fraud losses.

Published by: Shaba Khatoon, Ankita Srivastava, Dr. Shish Ahmad

Author: Shaba Khatoon

Paper ID: V11I2-1151

Paper Status: published

Published: April 10, 2025

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