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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics
Mohamad Khairi Ishak
Universiti Sains Malaysia
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Spike neuron optimization using deep reinforcement learning Tan Szi Hui; Mohamad Khairi Ishak
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp175-183

Abstract

Deep reinforcement learning (DRL) which involved reinforcement learning and artificial neural network allows agents to take the best possible actions to achieve goals. Spiking Neural Network (SNN) faced difficulty in training due to the non-differentiable spike function of spike neuron. In order to overcome the difficulty, Deep Q network (DQN) and Deep Q learning with normalized advantage function (NAF) are proposed to interact with a custom environment. DQN is applied for discrete action space whereas NAF is implemented for continuous action space. The model is trained and tested to validate its performance in order to balance the firing rate of excitatory and inhibitory population of spike neuron by using both algorithms. Training results showed both agents able to explore in the custom environment with OpenAI Gym framework. The trained model for both algorithms capable to balance the firing rate of excitatory and inhibitory of the spike neuron. NAF achieved 0.80% of the average percentage error of rate of difference between target and actual neuron rate whereas DQN obtained 0.96%. NAF attained the goal faster than DQN with only 3 steps taken for actual output neuron rate to meet with or close to target neuron firing rate.
Performance evaluation of embedded ethernet and Controller Area Network (CAN) in real time control communication system Ching Chia Leong; Mohamad Khairi Ishak
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1353.942 KB) | DOI: 10.11591/ijres.v8.i1.pp36-50

Abstract

Real-time communication is important in control network. In real-time communication, message need to be delivered from source to destination within specification. Embedded Ethernet and Controller Area Network (CAN) protocol can be used in control network to achieve hard real-time communication. For embedded Ethernet protocol, Carrier Sense Multiple Access with Collision Detection (CSMA/CD) is the media access control (MAC) used to control data transmission between nodes in network. Back-off algorithm in CSMA/CD is used to handle packet collisions and retransmission. For CAN protocol, it is communication protocol developed mainly for automotive application. It has priority arbitration to handle collisions and retransmission. In this project, embedded Ethernet network models and CAN network models are developed and simulated in MATLAB Simulink software. Several back-off algorithms, which are Binary Exponential Backoff (BEB), Linear Back-off Algorithm, Exponential-Linear back-off Algorithm and Logarithm Back-off Algorithm are proposed and implemented into Embedded Ethernet network model to evaluate the performance. Both embedded Ethernet and CAN network models are extended to 3 nodes, 10 nodes, and 15 nodes to evaluate performance at different network condition. The performance criteria evaluated and discussed are average delay and jitter of packets. The results show that in network with high number of nodes, Linear Back-off Algorithm and Exponential-Linear back-off Algorithm shows improvement in packets delay and jitter. For CAN network, the packet jitter is relatively low.
A java servlet based transaction broker for internet of things edge device communications Zainatul Yushaniza Mohamed Yusoff; Mohamad Khairi Ishak; Lukman AB Rahim
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3455

Abstract

Internet of things (IoT) technology is growing exponentially in almost every sphere of life. IoT offers several innovation capabilities and features, but they are also prone to security vulnerabilities and risks. These vulnerabilities must be studied to protect these technologies from being exploited by others. Cryptography techniques and approaches are commonly used to address and deal with security vulnerabilities. In general, the message queuing telemetry transport (MQTT) is an application layer protocol vulnerable to various known and unknown security issues. One possible solution is to introduce an encryption algorithm into the MQTT communication protocol for secure transmission. This study aims to solve the security problem of IoT traffic by using a secure and lightweight communication proxy. The strategy behind this communication broker acts as a network gateway providing secure transaction keys to all IoT nodes in the network. This task uses a java servlet and elliptic curve cryptography (ECC) algorithm to generate identity encryption keys in a component-based web transaction infrastructure. This approach encrypts the data before it is sent via the MQTT protocol to secure the communication channel and raise the security device and network transactions. 
Who danced better? ranked tiktok dance video dataset and pairwise action quality assessment method Irwandi Hipiny; Hamimah Ujir; Aidil Azli Alias; Musdi Shanat; Mohamad Khairi Ishak
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.919

Abstract

Video-based action quality assessment (AQA) is a non-trivial task due to the subtle visual differences between data produced by experts and non-experts. Current methods are extended from the action recognition domain where most are based on temporal pattern matching. AQA has additional requirements where order and tempo matter for rating the quality of an action. We present a novel dataset of ranked TikTok dance videos, and a pairwise AQA method for predicting which video of a same-label pair was sourced from the better dancer. Exhaustive pairings of same-label videos were randomly assigned to 100 human annotators, ultimately producing a ranked list per label category. Our method relies on a successful detection of the subject’s 2D pose inside successive query frames where the order and tempo of actions are encoded inside a produced String sequence. The detected 2D pose returns a top-matching Visual word from a Codebook to represent the current frame. Given a same-label pair, we generate a String value of concatenated Visual words for each video. By computing the edit distance score between each String value and the Gold Standard’s (i.e., the top-ranked video(s) for that label category), we declare the video with the lower score as the winner. The pairwise AQA method is implemented using two schemes, i.e., with and without text compression. Although the average precision for both schemes over 12 label categories is low, at 0.45 with text compression and 0.48 without, precision values for several label categories are comparable to past methods’ (median: 0.47, max: 0.66).
Co-Authors Aidil Azli Alias Ching Chia Leong Hamimah Ujir Irwandi Hipiny Lukman AB Rahim Musdi Shanat Tan Szi Hui Zainatul Yushaniza Mohamed Yusoff