Today, machine learning and deep learning have paved the way for vital and critical applications such as abnormal detection. Despite the modernity of transfer learning, it has proved to be one of the crucial inventions in the field of deep learning because of its promising results. For the purpose of this study, transfer learning is utilized to extract human motion features from RGB video frames to improve detection accuracy. A convolutional neural network (CNN) based on Visual Geometry Group network 19 (VGGNet-19) pre-trained model is used to extract descriptive features. Next, the feature vector is passed into Binary Support Vector Machine classifier (BSVM) to construct a binary-SVM model. The performance of the proposed framework is evaluated by three parameters: accuracy, area under the curve, and equal error rate. Experiments performed on two different datasets comprising highly different context abnormalities accomplished an accuracy of 97.44% and an area under the curve (AUC) of 0.9795 for University of Minnesota (UMN) dataset and accomplished an accuracy of 86.69% and an AUC of 0.7987 for University of California, San Diego Pedistrain1 (UCSD-PED1) dataset. Moreover, the performance of the pre-trained network VGGNet-19 with handcrafted feature descriptors and with other CNN pre-trained networks, respectively, has been investigated in this study for abnormal behavior detection. The results demonstrated that VGGNet-19 has better performance than histogram of oriented gradients, background subtraction, and optical flow. In addition, the VGGNet-19 shows higher detection accuracy than other pre-trained networks: GoogleNet, ResNet50, AlexNet, and VGGNet-16. INDEX TERMS Abnormal behavior detection, transfer learning, convolutional neural network, VGGNet-19, handcrafted feature descriptors, pre-trained networks.

Abstract-Electromyography

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This study investigates and acts as a trial clinical outcome for human motion and behaviour analysis in consensus of health related quality of life in Malaysia. The proposed technique was developed to analyze and access the quality of human motion that can be used in hospitals, clinics and human motion researches. It aims to establish how to widespread the quality of life effects of human motion. Reliability and validity are needed to facilitate subject outcomes. An experiment was set up in a laboratory environment with conjunction of analyzing human motion and its behaviour. Five classifiers and algorithms were used to recognize and classify the motion patterns. The proposed PCA-K-Means clustering took 0.058 seconds for classification process. Resubstitution error for the proposed technique was 0.002 and achieved 94.67% of true positive for total confusion matrix of the classification accuracy. The proposed clustering algorithm achieved higher speed of processing, higher accuracy of performance and reliable cross validation error

A bipolar membrane fuel cell (BPMFC) is a novel hydrogen/oxygen (H 2 /O 2 ) fuel cell consisting of two-layer membranes. The design of BPMFC is still in an early stage, and it requires profound research to explore its functions, working operations, and improve its performance. This review article systematically described the previous manipulations made in developing BPMFC in terms of process design and electrolyte materials. These two criteria are the most important in the design of BPMFC. Several modifications and manipulations were made, and the improvements observed over the years are also presented in this study in terms of electrochemical performance and properties. For instance, modifications and rearrangements of BPMFC components, new electrolyte materials, and different membrane layer integration techniques have been proposed. Different effects on BPMFC properties and performance were discovered when modifications were made. Some of the BPMFC managed to perform without any issues, whereas some encountered water management issues, lack of cell stability, and degradation of power output. To date, the optimal reported power density of the BPMFC was about 327 mW/cm 2 and it managed to operate successfully for 40 h without showing any signs of degradation. In this regard, the commercialization of BPMFC for fuel cell performance is recommended as it displays a high potential for improving electrochemical cell performance and ensuring high cell durability.