Analysis of WEKA data mining algorithms Bayes net, random forest, MLP and SMO for heart disease prediction system: A case study in Iraq

Data mining is defined as a search through large amounts of data for valuable information. The association rules, grouping, clustering, prediction, sequence modeling is some essential and most general strategies for data extraction. The processing of data plays a major role in the healthcare industry's disease detection. A variety of disease evaluations should be required to diagnose the patient. However, using data mining strategies, the number of examinations should be decreased. This decreased examination plays a crucial role in terms of time and results. Heart disease is a death-provoking disorder. In this recent instance, health issues are immense because of the availability of health issues and the grouping of various situations. Today, secret information is important in the healthcare industry to make decisions. For the prediction of cardiovascular problems, (Weka 3.8.3) tools for this analysis are used for the prediction of data extraction algorithms like sequential minimal optimization (SMO), multilayer perceptron (MLP), random forest and Bayes net. The data collected combine the prediction accuracy results, the receiver operating characteristic (ROC) curve, and the PRC value. The performance of Bayes net (94.5%) and random forest (94%) technologies indicates optimum performance rather than the sequential minimal optimization (SMO) and multilayer perceptron (MLP) methods

Assessing the Performance of Handcrafted Features for Human action Recognition

Recognition of Human action such as running, punching, bending, kicking etc. plays an vital role in futuristic applications like intelligent video surveillance, health care monitoring, robotics, smart automation system, computer gaming etc. This field relies on various approaches based on hand crafted features like PCA, HOG, LBPH, DWT, STIP, SWF, SWFHOG and deep learning techniques like CNN, RNN and their variants. Though many approaches have been proposed and implemented by researchers, the literature survey suggests that a detailed understanding of the approaches and a comparison of advantages and limitations is required to develop more accurate action recognition method. This paper focuses on this issue and gives detailed analysis of results obtained by implementing algorithms on standardize open source datasets of varying complexity namely Weizmann, KTH, UT Interaction and UCF sports.  The results are compared based on the classification accuracy as it is one of the performance measure for checking reliability of the method. The comparison shows that, SHFHOG feature gives the best classification accuracy as compared to other handcrafted features and also outperforms the simple CNN

A Review of Physical Human Activity Recognition Chain Using Sensors

In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, encouraging healthy behaviours, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical cases/conditions of elderly and patients is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.