Digital Twin (DT) technology combined with the Internet of Things (IoT) can be used to provide new solutions to real-time monitoring and management of agriculture. This paper introduces an IoT-based digital twin platform that will help in streamlining agricultural operations by incorporating different sensor technologies to monitor vital soil and crop conditions, such as moisture, temperature, pH, and nitrogen concentrations. The system offers predictive analytics to inform irrigation control, pest control, and fertilizer application, to help in making agricultural activities more sustainable. The effectiveness of the model is tested based on real time data integration and predictive modeling with 92% accuracy in monitoring soil moisture and an 87 percent accuracy in predicting crop yields. Although the system shows a high potential in terms of resource optimization and productivity, issues like sensor calibration, network connectivity and scalability to bigger operations exist. The future direction must be aimed at making sensors more reliable, more scalable, and adding AI and automation to make the system even more efficient and applicable in precision farming.

In this paper, a hybrid multi-layer machine learning model is proposed for early prediction of PD using voice data. The suggested technique combines a number of base learners namely Random Forest, XGBoost, LightGBM, and CatBoost through ensemble methods like Voting Classifier, and Stacking Classifier. A thorough feature engineering, from correlation analysis to feature selection and polynomial feature extraction, was applied to improve the model’s quality. Experiments results show that the hybrid model obtained accuracy of 91.30% with high precision and recall especially for Class-1(Parkinson’s disease). Although very strong in its entirety the limited ability to discriminate healthy subjects (Class 0) (recall around 0.75). Among the selected models, Voting Classifier and CatBoost were robust and presented generalization well for all performance measures. The contribution of this work is two-folds: it demonstrates that ensemble models can be used to enhance diagnostic accuracy, and it addresses three main issues inherent in Parkinson's disease detection. Optimization of the performance of the model in discriminating healthy from Parkinson’s disease individuals is highly valuable, with an emphasis on the generalization and clinical usage.

Stress has become a significant issue in today's society, affecting a person's emotions, thoughts, actions, and communication. Although the human body is designed to cope with stress, prolonged stress can lead to serious health problems such as cancer, cardiovascular disease, depression, and diabetes. However, identifying and tracking a person's stress levels is difficult. Many machine learning methods have been proposed in the literature to address this issue. The primary aim of this study is to develop an accurate and rapid framework for detecting a person's stress levels and estimating their degree of stress. In this proposed study, a machine learning-based approach using an integrated feature selection technique is proposed for human stress detection. The dataset is preprocessed using Label Encoding, and the imbalanced dataset is addressed using SMOTE. The dataset is normalized to ensure consistency. The integrated feature selection technique, Recursive Feature Elimination (RFE), is then applied to select the most crucial features for building the system. Finally, several classification algorithms, such as Decision Tree (DT), Random Forest, Gradient Boosting, AdaBoost, XGBoost, and Extra Tree Classifier, are utilized to identify the stress level of humans. The experiments are conducted on the SWELL-KW dataset.