Daily Archives: June 5, 2026

Authors: K. Chaitanya, Assistant Professor Nekuri Jyothsna

Abstract: Diabetes mellitus is a growing chronic health condition that needs to be detected at an early stage to avoid complications. Machine learning (ML) is proving to be an efficient solution for developing Clinical Decision Support Systems (CDSS), which aid doctors in diagnosing and predicting diseases. The research aims to develop an artificial intelligence-based CDSS for diabetes prediction using supervised machine learning algorithms such as Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB). The results of the experiments prove that the ensemble methods are better than traditional methods. The proposed CDSS is a solution for predicting diabetes mellitus and shows its potential for providing accurate insights for decision-making in the health care industry. The use of such artificial intelligence-based CDSS is significant for decision-making in health care.

DOI: https://doi.org/10.5281/zenodo.20555588

Published by: vikaspatanker

Authors: Assistant Professor Prajina V K, Assistant Professor Bhargavi M R

Abstract: Despite the technological advancements in agriculture, it continues to be vulnerable to climate change effects, and poor crop choice due to unfavorable conditions results in low yields and monetary hardship to farmers. This paper proposes a hybrid machine learning approach for crop recommendation that takes into account not only the weather forecast (rainfall, temperature, humidity) but also soil characteristics (pH, nitrogen, phosphorus, potassium). It consists of two stages: the Random Forest algorithm for feature selection and prediction followed by the XGBoost algorithm for correction of predicted values. Applying the approach to the data set of 50,000 crop images tagged by location for 15 main crops within a period of 10 years (2015-2025) in India, the hybrid algorithm reaches the accuracy level of 94.2% compared to Random Forest (89.3%), XGBoost (91.6%), SVM (84.2%), and KNN (81.5%). Rainfall and minimum temperature were recognized as crucial features by the algorithm. The proposed algorithm is implemented in a smartphone application for farmers that provides recommendations based on weather forecasts for the next 5 days, which allows increasing crop yields up to 20-30%.

DOI: https://doi.org/10.5281/zenodo.20555541

Published by: vikaspatanker

Authors: Assistant Professor Gargi Mishra, Assistant Professor S Anantha Priyadharsini

Abstract: Access to quality healthcare in developing nations often remains challenging due to several factors including geographical distance, shortage of competent medical professionals, and lack of diagnostic facilities. This study proposes an efficient cloud-based smart health kiosk that facilitates the delivery of cost-effective, easy-to-access, and quality diagnostic services. The design of the kiosk involves the use of IoT enabled medical sensors (digital stethoscope, infrared thermometer, pulse oximeter, blood pressure measurement device, glucometer, ECG, and urinalysis dipstick reader) and edge computing gateway for capturing the data and pre-processing the acquired data. Telemedicine is used for establishing a video connection between the patient and remote physician. Medical data is transferred to the cloud storage through an HIPAA compliant network for long-term storage and initial triaging using artificial intelligence. After deployment at 50 rural areas in India serving 250,000 patients in 18 months, average travel time decreased from 32 km to 1.5 km and out-of-pocket costs were minimized by 68%. Patient satisfaction rate was recorded to be 94%.

DOI: https://doi.org/10.5281/zenodo.20555450

Published by: vikaspatanker