Pichai Shanmugavadivu

Cardiovascular diseases (CVDs) pose a major global health challenge despite medical advancements. Machine Learning (ML) offers potential for early detection and prediction of CVD risk. This research utilized two datasets, Kaggle’s CVD dataset (70,000 instances) and UCI’s CHD dataset (303 instances), for ML model evaluation. After Exploratory Data Analysis (EDA) and data cleansing, the CVD dataset was reduced by 2.67%. Feature engineering introduced new variables like BMI, MAP, and PP to enhance predictive accuracy. The goal is to identify the best ML models. A set of Regression Models in the categories of Linear Regression, Tree Regression, Optimizable Neural Network, Kernel Regression, and Ensemble Regression were deployed on both CVD and CHD datasets. Boosted Tree Ensemble Regression gave an accuracy of 97.37% for CVD dataset, and Robust Linear Regression returned an accuracy of 98.52% for CHD dataset.

This research presents an AI-driven student performance analysis system that integrates Machine Learning (ML), Explainable AI (XAI), and Rule-Based Reasoning to provide personalized study plans. The system analyzes career aspirations, study hours, and academic performance to generate targeted study recommendations, helping students optimize their learning strategies. By identifying underperforming students, the system enables timely interventions to improve academic progress. This study introduces a hybrid explainable AI (XAI) model that combines ML-based predictions, SHAP-based explainability, and forward-chaining rule-based reasoning. A Random Forest model with hyperparameter tuning was employed to predict weekly study hours, achieving a Mean Absolute Error (MAE) of 2.89, Mean Squared Error (MSE) of 12.32, and R ² Score of 0.74. Additionally, a forward-chaining rule-based system was implemented to generate interpretable study recommendations along with recommended study hours, though it exhibited higher errors (MAE: 3.85, MSE: 18.42, R ² : 0.61) compared to ML. To validate model reliability, a K-Fold Cross-Validation approach was applied, yielding MAE of 2.95, MSE of 13.25, and R ² of 0.72, further confirming the stability of ML predictions. The results demonstrated that ML-based predictions outperformed rule-based reasoning in accuracy, while SHAP-based XAI techniques enhanced transparency. This hybrid approach bridges the gap between accuracy and interpretability, ensuring that AI-driven educational recommendations are trustworthy, explainable, and actionable. By combining predictive analytics with rule-based reasoning, this system empowers educators with data-driven, personalized learning interventions to enhance student success.

The health and productivity of dairy cows are critical factors in sustainable livestock management. Along with the rapid rise in intelligence and technology, applying intelligence in livestock management helps in monitoring and provide precise and effective care for the cattle herd. This research designs an intelligent system that can assist the farmers and predict Gir cows’ diseases and a support system powered by Artificial Intelligence (AI). The proposed system integrates Internet of Things (IoT) and sensors to track and monitor critical health parameters of the Gir cow, which includes the step count, lying time, rumination time, heart rate, and various environmental factors contributing to the well-being of the cow. The data points that are gathered from the sensors is then processed and analysed using machine learning (ML) algorithms, including Random Forest (RF), decision tree (DT), Logistic Regression, K-Neighbours, and Support Vector Machine (SVM) to predict abnormalities including diseases such as lameness, mastitis, heat stress, and digestive problems. The AI techniques used in the system involve complex data processing and pattern recognition to identify early signs of diseases. The RF and DT ML models achieved the highest accuracy (100%), while SVM demonstrated robust performance with 94% accuracy. Integrating real-time monitoring with predictive analytics enables early detection of health issues, allowing timely interventions and improving overall herd management. The proposed system enhances cow welfare and optimises farm productivity but also has the potential to revolutionise the dairy industry. The complex intelligent system provides a reliable and efficient platform for disease prediction and herd management, and can significantly contribute to the sustainability and profitability of dairy farming, thereby shaping the future of the industry.