Daily Archives: April 18, 2025

Company Inventory Management System Using Appian
Authors:-Balaji S, Dr. Krithika. D. R

Abstract- The product in every company decides the availability of resources according to the user needs. Each product must be useful to the user in certain ways to decide as per the demands. This paper speaks about how the products are handled by different departments from storage team to user by choosing the control of each product for the supply and flow in a company. This paper also conveys that this will tell all the activities happens in a single company for deciding how the storage team is very important in storing the products, each team decides the product supply to make it useful for users. When a product gets requested by user it must be decided by the team to inform the availability. The communication mechanism in this application is very useful in understanding the entire system by each team very easily. So, every activity in this application completely named as Inventory to explain about the management of this application.

DOI: 10.61137/ijsret.vol.11.issue2.352

Published by: Anju Tripathi

Disease Prediction Model Using Multi-Modal Data Fusion
Authors:-Shruti Deokule, Dipti Kause, Suhani Korde, Suhani Korde

Abstract- With recent developments in machine learning and healthcare informatics Strong disease prediction models have been made possible . In order to improve the accuracy and dependability of early disease diagnosis, we present a multi-modal data fusion system in this paper. Advanced fusion techniques that can lessen the drawbacks of single-modality models are used to integrate heterogeneous data sources, such as wearable sensor readings, genomic data, medical images, and electronic health records (EHR). Our method integrates crucial information from multiple datasets by combining feature selection, preprocessing, and ensemble learning. In comparison to the traditional models, we find that the experimental results produce 15% higher prediction accuracy and lower error rates—down to 2.3% for cases of chronic disease.

href=”https://doi.org/10.61137/ijsret.vol.11.issue2.351″>10.61137/ijsret.vol.11.issue2.351

Published by: Anju Tripathi

Automated Student Attendance Using Computer Vision
Authors:-Reshi, Merinkanth, Yogeswari, Gayathri, HOD/IT Dr.P.Sachidhanandam

Abstract-The implementation of an Automated Student Attendance System utilizing Computer Vision optimizes attendance management by leveraging facial recognition technology to automate the marking process. This approach minimizes manual intervention while enhancing accuracy and efficiency. A web-based interface facilitates seamless attendance tracking, record maintenance, and real-time monitoring. Furthermore, the system incorporates email notifications to provide timely updates and allows direct downloads of attendance logs for administrative convenience. Security measures are reinforced through the identification and image capture of unauthorized individuals, with automated email alerts dispatched to administrators for enhanced surveillance. By integrating artificial intelligence, this system ensures a robust, reliable, and autonomous attendance tracking solution, significantly improving record-keeping efficiency within educational institutions and organizations.

DOI: 10.61137/ijsret.vol.11.issue2.350

Published by: Kajal Tripathi

Bianchi Type-III Cosmological Model with f(R, T) Gravity Based on Lyra Geometry
Authors:-L. S. Ladke, B.V.Bansole, V. P. Tripade

Abstract- This paper is devoted to the study of Bianchi type-III cosmological model with gravity in the presence of perfect fluid based on Lyra geometry. We formalize the gravity equations based on Lyra geometry. To solve the field equations, obtained by considering Bianchi type-III space-time, we used physical condition that the shear scalar σ2 is proportional to scalar expansion . The behavior of the model has been discussed by studying the physical and kinematical properties of the model.

DOI: 10.61137/ijsret.vol.11.issue2.331

Published by: Kajal Tripathi

AI-Powered SQL Assistant: Transforming Natural Language into Optimized SQL Queries
Authors:-Atharv Deshmukh, Manali Gawade, Ronit Fulari

Abstract-Databases have a steep learning curve, littered with schema design, SQL di- alects, and performance optimizations. Formulating efficient SQL queries is a chal- lenging process to tinker with, and this is one of the reasons many developers and analysts are blocked. Here we present an AI-based SQL Assistant that utilizes cutting-edge AI models to transform natural language requests into fast SQL code. It lives on top of a variety of SQL dialects, including Spark SQL, PostgreSQL, and MySQL, and provides schema suggestions and smart executor queries. It can use a feedback loop with machine learning methods to improve performance after the sys- tem is deployed based on users adapting the system to query patterns. We provide experimental evidence to show that the proposed solution not only improves query performance and execution time but also accuracy so that the database interactions are smooth for non-experts.

DOI: 10.61137/ijsret.vol.11.issue2.349

Published by: Kajal Tripathi

Harnessing Computer Vision for Precision Agriculture: Advancements in Crop Monitoring, Yield Prediction, and Disease Identification
Authors:-Swaraj Kawade, Anurag Kawade, Rohit Kashid, Aditya Jedhe, Rajdeep Jagtap

Abstract-The use of modern technologies such as computer vision and artificial intelligence individually or in tandem are modifying farming methods. These technologies are assisting in agricultural practices as the world population is expected to peak at 9.7 billion by 2050. The objective of this paper is to analyze major advancements within the years 2020 to 2024 regarding the role of computer vision in agriculture, specifically in crop health monitoring, yield prediction, and early detection of plant diseases. Some impressive progress includes: Autonomous weed management systems with 96% accuracy (Praveenraj et al., 2024). Near 99% accuracy in crop yield predictions from machine learning models (Sharma et al., 2023). Deep learning algorithms correctly identifying plant diseases at a rate of 99.35% (Li et al., 2021). Additionally, the development of autonomous vehicles has contributed to safety within agricultural fields, while AI image generation contributes to predicting potential yield imagery, along with real-time field monitoring robotic systems. These developments are positive strides towards sustainable agriculture. On the other hand, these systems always carry limitations like dealing with dynamic field conditions, insufficient amounts of data, and the need for high-end processing units. This paper evaluates the advancement to date, what it means in terms of practical agriculture, and how we can further progress:

DOI: 10.61137/ijsret.vol.11.issue2.348

Published by: Kajal Tripathi

Image Fusion of MRI and CT Scan for Brain Tumor Detection Using VGG-19
Authors:-Professor Kirti Digholkar, Shreyas Depura, Adwait Mali, Vedant Latthe, Rohan Patil

Abstract-For a patient’s prognosis, the careful examination of image bio-analytics, including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) imaging, is crucial for the patient’s tumor detection. Moreover, interpreting these images manually remains challenging owing to the required expertise and time needed to properly analyze the images. To address this issue, we propose an image improvement model that enhances the accuracy of MRI and CT scans using Wavelet- based fusion and the VGG-19 architecture. Image fusion, or the merging of medical images, synergistically uses and adapts the various modalities’ strengths and weaknesses. In our research, we apply the Wavelet approach to MRI and CT images by splitting them into frequency sub-bands. Structural details are important for the image’s low-frequency LL band. The VGG- 19 network which consists of several convolutional layers and pooling layers is then used to merge the LL bands and form the fused images. Our method undergoes a series of preprocessing, feature extraction, and fusion stages on brain MRI and CT scans. This method saves time for medical practitioners and enables efficient tumor identification through automation, improving the overall quality of patient care.

DOI: 10.61137/ijsret.vol.11.issue2.347

Published by: Kajal Tripathi