Daily Archives: June 23, 2025

Authors: Navyashree CM, Mr. Banibrata Paul

Abstract: Effective and timely disease prediction plays a crucial role in improving healthcare outcomes. This system leverages machine learning techniques to analyze patient symptoms and accurately predict possible diseases. By utilizing a Support Vector Classifier (SVC) model trained on comprehensive symptom data, the system achieves high prediction accuracy, enabling early diagnosis and timely intervention. In addition to disease prediction, the system provides personalized recommendations, including detailed disease descriptions, precautionary measures, suitable medications, recommended workouts, and dietary guidelines. These recommendations are generated based on the predicted disease, enhancing patient awareness and supporting self-care management, thus bridging the gap between diagnosis and treatment. The integration of user-friendly symptom input and an intelligent recommendation engine makes the system a valuable tool for both patients and healthcare providers. This approach promotes informed decision-making and contributes to efficient healthcare delivery, especially in scenarios with limited immediate access to medical professionals.

 

 

Published by: vikaspatanker

Authors: Abhishek Soni

Abstract: In the era of cloud computing and continuous delivery, achieving high availability and scalability is a critical objective for modern applications. This research paper explores the integration of DevOps practices with cloud-native features such as load balancing and auto- scaling. It delves into how DevOps tools and methodologies enhance the reliability, performance, and efficiency of cloud-based services, ensuring seamless user experiences and optimized resource utilization

 

 

Published by: vikaspatanker

Authors: Aarthi, Jeevatharani shree, Kanishka, Sasikanth

Abstract: Grain quality preservation alongside loss reduction function as essential benefits of paddy drying after harvest. The traditional drying procedures are inefficient and slow while strongly depending on weather conditions thus causing substantial post-harvest losses. The research develops an IoT- based Precision Dehumidifying System that implements automatic real-time sensing along with control methods to enhance paddy drying processes. The system contains DHT sensors for temperature and humidity measurements in addition to moisture sensors that check paddy water content. The system activates the automated drying process when it detects excessive moisture through the operation of a DC fan for application of controlled airflow while utilizing a Peltier crystal for heat generation. The system operates with precise parameters to guarantee drying quality because it avoids drying the paddy too much and not enough at the same time. The IoT-based control system enables time-based observation and limited human interaction to maintain power-efficient drying processes. Optimized energy usage and minimized waste loss through implementation of this solution leads to improved overall processing efficiency together with sustainability benefits. The introduction of smart dryers delivers two benefits which are better grain quality performance alongside economical operation versus traditional drying standards. The study helps precision agriculture progress through smart sensor implementation along with automatic systems and IoT-based decision systems. This solution demonstrates adjustable characteristics which enable scalability across different climate zones to become a legitimate method for advanced grain processing units. Further development demands sensor calibration enhancement parallel to power optimization alongside machine learning model implementation for predictive moisture control.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Shailesh Bisht, Sunny Nahar

Abstract: Agriculture is a vital sector of the Indian economy, ensuring national food security and supplying essential raw materials to various industries. As agricultural productivity becomes increasingly important in the face of climate variability and resource constraints, accurate crop yield forecasting has emerged as a critical need. This paper presents a machine learning-driven framework that leverages environmental factors such as weather conditions, soil characteristics, and the Normalized Difference Vegetation Index (NDVI) for yield prediction. The proposed system is structured into three stages: (i) forecasting weather parameters, (ii) estimating NDVI using predicted weather data, and (iii) predicting crop yield by integrating both outputs. Experiments using historical agricultural datasets demonstrate that ensemble learning techniques, particularly XGBoost and Random Forest, deliver robust performance, with XGBoost achieving the highest prediction accuracy of up to 97%.

Published by: vikaspatanker

Authors: M.G Vasundhara, C M Chaithra, Chandhini K.S, G.K Kalavathi

Abstract: This review consolidates recent advancements in study of porous journal bearings under the influence of magnetic fields, surface roughness and heterogeneous slip/no-slip surfaces. Using stochastic models primarily based on Christensen's theory and incorporating magnetohydrodynamic (MHD) considerations, researchers have explored the performance variations in short, long, and finite bearings. The studies indicate that appropriate combinations of surface roughness, permeability, Hartmann number, and engineered slip conditions can enhance bearing performance, load carrying capacity, and reduce frictional losses. This paper summarizes mathematical models, numerical methodologies, and key findings, highlighting opportunities for future developments in smart bearing design.

 

 

Published by: vikaspatanker

Authors: Kaushalya Kaneson, Abiramy Kumaresan, Shanoojan Krishnasamy, Don Nandun Prabhashwara Godage, Dr. Sanika Wijayasekara, Ms. Rivoni De Zoysa

Abstract: This Research paper explores a technology- driven approach to enhancing Tamil primary education by improving pronunciation, comprehension, and creative learning. Traditional teaching methods often lack adaptability and engagement, limiting their effectiveness. This study introduces the Tamil Educational Assistant System for Primary Education (TEAS), a solution designed to support young Tamil- speaking students. The proposed approach leverages speech recognition, natural language processing, and interactive learning techniques to provide personalized educational experiences. To enable multimodal learning, TEAS integrates Tamil speech therapy for pronunciation correction, Short note generation for summarizing key concepts, Sentence formation and grammar checking for syntax improvement, and Interactive storytelling with predictive text for fostering creativity. The effectiveness of this innovative system is assessed through prototype testing and user feedback, demonstrating its potential in transforming Tamil primary education.

DOI: https://doi.org/10.5281/zenodo.15718562

Published by: vikaspatanker