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Research Paper

Impact of Artificial Intelligence Tools on Academic Performance and Psychological Well-Being Among Undergraduate Students in Selected Colleges in Kollam District

Background & Objectives: The present study was undertaken to assess the impact of Artificial Intelligence (AI) tools on academic performance and psychological well-being among undergraduate students in selected colleges of Kollam district. Methodology: A quantitative research approach with a descriptive research design was used. A sample of 100 undergraduate students was selected from Younus College of Engineering using a non-probability convenience sampling technique. Data were collected through structured questionnaires for measuring AI tool usage, academic performance, and psychological well-being. For the analysis descriptive and inferential statistics were used. Results: The findings revealed that majority of students that is 58% had a high level of AI tool usage, 26% had moderate usage, 13% had very high usage, and 3% had low usage. Regarding the academic performance, 63% achieved excellent level and 37% an average level. Psychological well-being was moderate in 56%, mild in 31%, good in 12%, and poor in 1%. Statistical analysis shows that there is a significant weak positive correlation between AI tool usage and academic performance (r = 0.240, p = 0.016) and a significant weak negative correlation between AI tool usage and psychological well-being (r = -0.270, p = 0.007). Conclusion: The study highlights that the usage of AI tools enhance academic performance of students and higher usage of AI tools is reduces the psychological well-being of the student which emphasizing the need for balanced digital habits.

Published by: Sajna Sajeer, Prof. Amal James, Asha Raichal Sam, Jeena Varghese, Mahima Murali, Irfana Hussain, Devika, Alan Lalal

Author: Sajna Sajeer

Paper ID: V12I4-1191

Paper Status: published

Published: August 25, 2026

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A study to assess the effectiveness of structured teaching program on knowledge regarding home maneuver for foreign body ingestion among mothers of toddlers in selected villages of district Mandi, Himachal Pradesh

Foreign body ingestion is a common and potentially life-threatening emergency among toddlers. Mothers play a vital role in prevention and immediate management, and adequate knowledge of home manoeuvres can help reduce complications and improve child safety. The main aim of the study was to assess the effectiveness of a structured teaching programme on knowledge regarding home manoeuvres for foreign body ingestion among mothers of toddlers in selected villages of District Mandi, Himachal Pradesh. A quantitative research approach with quasi-experimental non-randomized control group design was used. The study was conducted on 60 mothers of toddlers, with 30 in the experimental group and 30 in the control group. Samples were selected using non-probability convenient sampling technique from selected villages of District Mandi, Himachal Pradesh. Data were analyzed using descriptive and inferential statistics and presented through tables and bar diagrams. The pretest mean knowledge score was 10.13 in the experimental group and 10.03 in the control group, whereas the posttest mean scores were 19.76 and 10.46, respectively. The calculated paired 't' value in the experimental group was 14.82, higher than the table value of 2.05 at p≤0.05, while in the control group it was 1.12, lower than 2.05. Thus, the structured teaching programme had a significant impact on mothers' knowledge. A significant association was also found between posttest knowledge score and selected socio-demographic variables in the experimental group at p≤0.05 level of significance.

Published by: Yamini Sharma, Shephali Walia, Pallavi Mehra, Priyanka Sharma

Author: Yamini Sharma

Paper ID: V12I4-1187

Paper Status: published

Published: August 25, 2026

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Research Paper

Detection of Eye Diseases Using Machine Learning Techniques

Eye disorders have become one of the leading causes of vision loss around the globe. This highlights the importance of early accurate diagnosis of these disorders. Most of the current methods for diagnosing eye diseases use manual examination of retinal images and depend primarily on experienced ophthalmologists. These manual methods can take a lot of time to examine each eye(s) and can also be highly subjective in their determination of a diagnosis.In this project, we will develop an automated framework that uses machine-learning techniques to automatically detect and classify multiple eye disorders from retinal images.We only used traditional machine-learning methods in this work and did not consider using deep-learning models. The proposed framework includes systematic image preprocessing for standardising input images, automated feature extraction using feature extraction methods, and dataset balancing using the synthetic minority oversampling technique (SMOTE) to adjust for class imbalance in each of the datasets used for training and testing our classification system. The proposed framework includes the use of supervised machine-learning algorithms for the classification of all images in each class. The performance of the proposed machine-learning framework was evaluated thoroughly using various performance metrics including confusion matrices, ROC curves, precision–recall analysis, learning curve graphs, PCA visualisation, and cross-validation stability analysis. The results from the experimental evaluations indicated that the proposed machine-learning framework can generate very high classification accuracy and good generalisability for the various types of diseases evaluated using the proposed machine-learning classification system. Overall, the experimental results show that the proposed machine-learning framework can provide good quality decision support tools to use for early eye disorder diagnosis in clinical settings.

Published by: Vaishali Ashok Barse, Dr. Kavita Yogesh Suryawanshi

Author: Vaishali Ashok Barse

Paper ID: V12I4-1190

Paper Status: published

Published: August 19, 2026

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Research Paper

Altered Pattern of Calcium, Phosphorus, and Calcium/Phosphorus Ratio in Serum and Synovial Fluid of Rheumatoid Arthritis Patients

Abstract Rheumatoid Arthritis is a systemic autoimmune inflammatory disease characterized by synovitis and serositis (inflammation of the linning surfaces of the joints, pericardium, pleura and rheumatoid nodules). This study was carried to determine the status of biochemical parameters i.e. calcium, phosphorus and calcium/phosphorus ratio in serum and synovial fluid of RA patients. A total of 66 RA patients were included in the study. Out of which 48(72.7%) were female patients of RA and 18(27.3%) were male patients of RA. Some normal individuals (n=25) were also investigated. The levels of serum calcium and phosphorus were found to be significantly (p<0.001) lowered as compared to normal individuals. A significant (p<0.001) decrease in levels of calcium and phosphorus in synovial fluid of RA patients was observed. Calcium/phosphorus ratio was also found to be lowered in RA patients. It was also observed that calcium/phosphorus ratio was more decreased in females as compared to males.

Published by: Bhanoo Priya, Jai Bharti

Author: Bhanoo Priya

Paper ID: V12I4-1175

Paper Status: published

Published: August 18, 2026

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Research Paper

MedConnect: An AI-Based Health Companion with Medical Report Simplification and 3D Anatomy Visualization

The increasing demand for accessible and continuous healthcare has motivated the development of intelligent digital health platforms. This paper presents MedConnect, an AI-powered health companion that integrates real-time vitals monitoring using IoT devices, simplification of complex medical reports using large language models, interactive 3D anatomy visualization, and an automated emergency response system. Users can upload medical reports in PDF, image, or text format. The system employs Optical Character Recognition (OCR) with Tesseract, Medical Named Entity Recognition using SpaCy and Med7, and a locally hosted LLaMA-3 8B model via Ollama to generate easy-to-understand plain-language summaries. Identified conditions are highlighted on an interactive 3D human anatomy model built with Three.js and React-Three-Fiber. The platform further supports voice input and output in English, Telugu, and Hindi, real-time emotion detection through webcam, and a reliable SOS mechanism that auto-dials 108 while alerting registered caretakers. Evaluation on a custom dataset of 150 de-identified medical reports yielded a SARI score of 47.6, BERTScore of 73.2, anatomy-mapping accuracy of 91.4%, and emergency response reliability between 96–100%. MedConnect offers a low-cost, privacy-preserving, and scalable solution suitable for preventive healthcare in resource-constrained settings such as India.

Published by: Dr. Kanigiri Suresh, Vanguri Shiva Ram, S. Ruchitha, Manchalla VLN Sri Ganesh, Shaviv Ebenezer Medhari

Author: Dr. Kanigiri Suresh

Paper ID: V12I4-1177

Paper Status: published

Published: August 18, 2026

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Research Paper

Smart Solar EV Charging Station Using IoT

Due to the growing use of electric vehicles, there is a rise in the demand for intelligent charging systems. Conventional wired charging systems rely on physical connection through wires, which results in wear and tear of cables, electricity risks, and inconvenience to the user. In this paper, we will be designing IoT-based smart wireless EV charging with an auto detection and battery monitoring system, which will help us charge the EVs in a smart and automatic manner without any physical connection with the charger, along with the battery monitoring of the vehicle. Our proposed system includes a wireless power transfer arrangement having a transmitter and receiver coil in order to transfer energy without any physical link. The charging will start automatically through the IR sensor when the vehicle is detected in the charging region and will be stopped once the vehicle is taken away from the charging zone. Charging station and vehicle will communicate with each other wirelessly through two ESP32 modules. Charging parameters will be continuously monitored with the help of the current sensor INA219, which will measure the voltage, current, and power consumption of the battery, and their values will be displayed on a 16x2 LCD screen. All the collected data will be sent wirelessly to an IoT-based mobile application for remote monitoring of battery percentage and charging states of the system.

Published by: Banothu Varun, Addetla Nithin, Ch. Rishika Sagar, D. Karthik Yadav

Author: Banothu Varun

Paper ID: V12I4-1188

Paper Status: published

Published: August 18, 2026

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