In this paper, a new method is proposed for people tracking using the human skeleton provided by the Kinect sensor, Our method is based on skeleton data, which includes the coordinate value of each joint in the human body. For data classification, the Support Vector Machine (SVM) and Random Forest techniques are used. To achieve this goal, 14 classes of movements are defined, using the Kinect Sensor to extract data containing 46 features and then using them to train the classification models. The system was tested on 12 subjects, each of whom performed 14 movements in each experiment. Experiment results show that the best average accuracy is 90.2 % for the SVM model and 99 % for the Random forest model. From the experiments, we concluded that the best distance between the Kinect sensor and the human body is one meter.
Health Information Technology (HIT) provides many opportunities for transforming and improving health care systems. HIT enhances the quality of health care delivery, reduces medical errors, increases patient safety, facilitates care coordination, monitors the updated data over time, improves clinical outcomes, and strengthens the interaction between patients and health care providers. Living in modern large cities has a significant negative impact on people's health, for instance, the increased risk of chronic diseases such as diabetes. According to the rising morbidity in the last decade, the number of patients with diabetes worldwide will exceed 642 million in 2040, meaning that one in every ten adults will be affected. All the previous research on diabetes mellitus indicates that early diagnoses can reduce death rates and overcome many problems. In this regard, machine learning (ML) techniques show promising results in using medical data to predict diabetes at an early stage to save people's lives. In this paper, we propose an intelligent health care system based on ML methods as a real-time monitoring system to detect diabetes mellitus and examine other health issues such as food and drug allergies of patients. The proposed system uses five machine learning methods: K-Nearest Neighbors, Naïve Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM). The system selects the best classification method with high accuracy to optimize the diagnosis of patients with diabetes. The experimental results show that in the proposed system, the SVM classifier has the highest accuracy of 83%.
Due to the changing flow conditions during the pipeline's operation, several locations of erosion, damage, and failure occur. Leak prevention and early leak detection techniques are the best pipeline risk mitigation measures. To reduce detection time, pipeline models that can simulate these breaches are essential. In this study, numerical modeling using COMSOL Multiphysics is suggested for different fluid types, velocities, pressure distributions, and temperature distributions. The system consists of 12 meters of 8-inch pipe. A movable ball with a diameter of 5 inches is placed within. The findings show that dead zones happen more often in oil than in gas. Pipe insulation is facilitated by the gas phase's thermal inefficiency (thermal conductivity). The fluid mixing is improved by 2.5 m/s when the temperature is the lowest. More than water and gas, oil viscosity and dead zones lower maximum pressure. Pressure decreases with maximum velocity and vice versa. The acquired oil data set is utilized to calibrate the Support Vector Machine and Decision Tree techniques using MATLAB R2021a, ensuring the precision of the measurement. The classification result reveals that the Support Vector Machine (SVM) and Decision Tree (DT) models have the best average accuracy, which is 98.8%, and 99.87 %, respectively.
The brain tumors are among the common deadly illness that requires early, reliable detection techniques, current identification, and imaging methods that depend on the decisions of neuro-specialists and radiologists who can make possible human error. This takes time to manually identify a brain tumor. This work aims to design an intelligent model capable of diagnosing and predicting the severity of magnetic resonance imaging (MRI) brain tumors to make an accurate decision. The main contribution is achieved by adopting a new multiclass classifier approach based on a collected real database with new proposed features that reflect the precise situation of the disease. In this work, two artificial neural networks (ANNs) methods namely, Feed Forward Back Propagation neural network (FFBPNN) and support vector machine (SVM), used to expectations the level of brain tumors. The results show that the prediction result by the (FFBPN) network will be better than the other method in time record to reach an automatic classification with classification accuracy was 97% for 3-class which is considered excellent accuracy. The software simulation and results of this work have been implemented via MATLAB (R2012b).