Tuesday, April 07, 2020

Journal of Information and Communication Technology (JICT) Vol.19, No.2, April 2020

Jagadeesh Basavaiah & Chandrashekar Mohan Patil
Naseer Sanni Ajoge, Azizi Ab Aziz & Shahrul Azmi Mohd Yusof
Nooraini Yusoff, Farzana Kabir-Ahmad & Mohamad-Farif Jemili
Aradea Aradea, Iping Supriana & Kridanto Surendro

Jagadeesh Basavaiah & Chandrashekar Mohan Patil
Department of Electronics and Communication Engineering, Vidyavardhaka College of Engineering, India
jagadeesh.b, patilcm@vvce.ac.in
Human activity recognition from video scenes has become a significant area of research in the field of computer vision applications. Action recognition is one of the most challenging problems in the area of video analysis and it finds applications in human-computer interaction, anomalous activity detection, crowd monitoring and patient monitoring. Several approaches have been presented for human activity recognition using machine learning techniques. The main aim of this work is to detect and track human activity, and classify actions for two publicly available video databases. In this work, a novel approach of feature extraction from video sequence by combining Scale Invariant Feature Transform and optical flow computation are used where shape, gradient and orientation features are also incorporated for robust feature formulation. Tracking of human activity in the video is implemented using the Gaussian Mixture Model. Convolutional Neural Network based classification approach is used for database training and testing purposes. The activity recognition performance is evaluated for two public datasets namely Weizmann dataset and Kungliga Tekniska Hogskolan dataset with action recognition accuracy of 98.43% and 94.96%, respectively. Experimental and comparative studies have shown that the proposed approach outperformed state-of the art techniques.
Keywords: Action recognition, convolutional neural network, Gaussian Mixture Model, optical flow, SIFT feature extraction.

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1Naseer Sanni Ajoge, 2Azizi Ab Aziz & 2Shahrul Azmi Mohd Yusof
1Department of Computer Science, Kaduna Polytechnic, Nigeria
2School of Computing, Universiti Utara Malaysia, Malaysia
ajogenass@yahoo.com; aziziaziz, shahrulazmi@uum.edu.my
Anxiety is an aversive motivational state that occurs when an individual perceives threat at events. This condition creates harmful effects for candidates during interview sessions. An interviewee overwhelmed in such a state deploys worry as a resource to cope with the threat hence losing the ability to present the self positively for favourable assessments. Most of the digital approaches to assist interviewees in this condition are focused on coaching of verbal and non-verbal cues. The aspect of understanding interviewees’ psychological complexities that influence their behavioural tendencies is lacking in these approaches. As the first step in building an intelligent digital basedtherapy platform to overcome this issue, this article provides a building block to understanding the interviewees’ anxiety state by means of a computational model. This model is developed based on the conceptual model derived from generalized anxiety disorder theories. The formal model is valuated using mathematical analysis to determine possible equilibria state and the simulation results are tested against known cases in the literature. The simulation results showed that the degree of threats perceived at events is based on task demands and the resources to cope. Threat is the building block of anxiety through worry which is controlled by one’s personality and inherent trait anxiety. The results conform to established facts in the literature. Consequently, this model can serve as a basis to build an integrated interviewee mental state model embedded with self-efficacy and motivation constructs as a holistic approach to support interviewees in coaching environments during simulated training.
Keywords: Anxiety states, computational cognitive modeling, intelligent support agent, interviewee behavioural states

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1Nooraini Yusoff, 2Farzana Kabir-Ahmad & 3Mohamad-Farif Jemili
1Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Malaysia
2School of Computing, Universiti Utara Malaysia, Malaysia
3Department of Information Technology and Communication, Sultan Abdul Halim Mu’adzam Shah Polytechnic, Malaysia
nooraini.y@umk.edu.my; farzana58@uum.edu.my; mfarif@polimas.edu.my
Motion trajectory prediction is one of the key areas in behaviour and surveillance studies. Many related successful applications have been reported in the literature. However, most of the studiesare based on sigmoidal neural networks in which some dynamic properties of the data are overlooked due to the absence of spatiotemporal encoding functionalities. Even though some sequential (motion) learning studies have been proposed using spatiotemporal neural networks, as in those sigmoidal neural networks, the approach used is mainly supervised learning. In such learning, it requires a target signal, in which this is not always available in some applications. For this study, motion learning using spatiotemporal neural network is proposed. The learning is based onreward-modulated spike-timing-dependent plasticity (STDP), whereby the learning weight adjustment provided by the standard STDP is modulated by the reinforcement. The implementationof reinforcement approach for motion trajectory can be regarded as a major contribution of this study. In this study, learning is implemented on a reward basis without the need for learningtargets. The algorithm has shown good potential in learning motion trajectory particularly in noisy and dynamic settings. Furthermore, the learning uses generic neural network architecture, whichmakes learning adaptable for many applications.
Keywords: Motion learning, reinforcement learning, reward-modulated spike-timing-dependent plasticity, spatio-temporal neural network.

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Aradea Aradea, Iping Supriana & Kridanto Surendro
School of Electrical Engineering and Informatics, Bandung Institute of Technology, Indonesia
aradea@unsil.ac.id; iping,endro@informatika.org
Service system is currently facing environmental complexity problems, such as the need of a distributed, heterogeneous, decentralized, and interdependent system which operatesdynamically and unpredictably. This condition requires the service system to have an ability to adapt in order to realize sustainable functions. The success of service adaptation is determined by its ability to handle variability at runtime. The purpose of this research is to realize service flexibility through variability modeling, which is an extension of previous work to enrich the adaptability view. The methodology was developed through the monitor-analyse-plan-execute-knowledge control loops approach integrated into the adaptive service (service level) element within the adaptive enterprise service system metamodel based on goal-oriented requirements engineering. Service adaptation scenario was prepared through proactive and reactive adaptation mechanisms. For evaluation, the model was applied to the case of a configuration management system. The experimental results showed that the model is able to adapt to runtime variability and accomodates the growth of the service component items shown by the description of the system scalability. The proposed model has a better alternative design in analyzing variability with a total response that can be applicable in normal operations and overload. It also meets the expected level (level-5: adapting) of the adaptive capability maturity model as a standard for assessment of a service system adaptation.
Keywords: Self-adaptive systems, service variability, goal-based, MAPE-K, rule-based systems.

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Nurul Liyana Hairuddin, Lizawati Mi Yusuf & Mohd Shahizan Othman
School of Computing, Universiti Teknologi Malaysia, Malaysia
nurulliyanadin1803@gmail; lizawati, shahizan@utm.my
In forensic anthropology, gender classification is one of the crucial steps involved in developing the biological profiles of skeleton remains. There are several different parts of skeleton remains and every part contains several features. However, not all features can contribute to gender classification in forensic anthropology. Besides that, another limitation that exists in previous researches is the absence of parameter optimization for the classifier. Thus, this paper proposed metaheuristic algorithms such as Particle Swarm Optimization, Ant Colony Algorithm and Harmony Search Algorithm based feature selection to identify the most significant features of skeleton remains. Once the set of significant features was obtained, the learning rate and momentum of Back Propagation Neural Network (BPNN) were optimized. This was to obtain a good combination of parameters in order to produce a better gender classification. This study used 1,538 data samples from Goldman Osteometric Dataset which consisted of femur, humerus and tibia parts. Based on the feature selection results, the Optimized BPNN outperformed other methods for all datasets. The Ant Colony Algorithm-Optimized Back Propagation Neural Network produced the highest accuracy for all parts of the skeleton where for femur was 89.44%, the humerus with 88.97% and tibia with 87.52% accuracy. Hence, it can be concluded that optimized parameter is capable of providing a better gender classification performance with the best set of features. Due to good gender classification techniques, the implication of this study is evident in the area of forensic anthropology where the process of developing a biological profile can be shortened which in turn enhances the productivity of anthropologists.
Keywords: Back propagation neural network, metaheuristic algorithms, optimization, gender classification.

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