Wednesday, November 30, 2022

Journal of Information and Communication Technology (JICT) Vol.18, No.2, April 2019

Karthikeyan Shanmugasundaram, Ahmad Sufril Azlan Mohmed & Nur Intan Raihana Ruhaiyem

Manar Abduljabbar Ahmad Mizher, Ang Mei Choo, Siti Norul Huda Sheikh Abdullah & Kok Weng Ng

Husna Jamal Abdul Nasir, Ku Ruhana Ku-Mahamud & Eiji Kamioka
Mohamed Jalaldeen Mohamed Razi, Md Habibullah & Husnayati Hussin

Karthikeyan Shanmugasundaram, Ahmad Sufril Azlan Mohmed & Nur Intan Raihana Ruhaiyem
School of Computer Sciences, Universiti Sains Malaysia, Malaysia
This paper proposes a Hybrid Improved Bacterial Swarm (HIBS) optimization algorithm for the minimization of Equal Error Rate (EER) as a performance measure in a hand-based multimodal biometric authentication system. The hybridization of the algorithm was conducted by incorporating Bacterial Foraging Optimization (BFO) and Particle Swarm Optimization (PSO) algorithm to mitigate weaknesses in slow and premature convergence. In the proposed HIBS algorithm, the slow convergence of BFO algorithm was mitigated by using the random walk procedure of Firefly algorithm as an adaptive varying step size instead of using fixed step size. Concurrently, the local optima trap (i.e. premature convergence) of PSO algorithm was averted by using mutation operator. The HIBS algorithm was tested using benchmark functions and compared against classical BFO, PSO and other hybrid algorithms like Genetic Algorithm-Bacterial Foraging Optimization (GA-BFO), Genetic Algorithm-Particle Swarm Optimization (GA-PSO) and other BFO-PSO algorithms to prove its exploration and exploitation ability. It was observed from the experimental results that the EER values, after the influence of the proposed HIBS algorithm, dropped to 0.0070% and 0.0049% from 1.56% and 0.86% for the right and left hand images of the Bosphorus database, respectively. The results indicated the ability of the proposed HIBS in optimization problem where it optimized relevant weights in an authentication system.
Keywords: Bacterial Foraging, Particle Swarm Optimization, Firefly Algorithm, Biometric authentication system.
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1Manar Abduljabbar Ahmad Mizher, 2Ang Mei Choo , 3Siti Norul Huda Sheikh Abdullah & 4Kok Weng Ng
1'2Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Malaysia
3Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Malaysia
4Faculty of Engineering, University of Nottingham Malaysia Campus

Key frame extraction is one of the critical techniques in computer vision fields such as video search, video identification and video forgeries detection. The extracted key frames should be sufficient key frames that preserve main actions in a video with compact representation. The objective of this work is to improve our previous action key frames extraction algorithm (AKF) by adapting a threshold which selects action key frames as final key frames. The threshold adaptation was achieved by using the mean value, the standard deviation, and the L1-norm instead of the comparison of user summaries evaluation method to obtain a fully automatic video summarisation algorithm, and by eliminating the conditions in selecting the final key frames to reduce the complexity of the algorithm. We have validated our proposed Improved AKF on complex colour video shots instead of the simple grey level video shots. The Improved AKF algorithm was able to extract a compact number of action key frames by preventing redundant key frames, reduce processing complexity, and preserve sufficient information about the main actions in a video shot. We then evaluated the Improved AKF algorithm with the-state-of-the-art algorithms in terms of compression ratio using Paul videos and Shih-Tang dataset. The evaluation results showed that the Improved AKF algorithm achieved better compression ratio and retained sufficient information in the extracted action key frames under different testing video shots. Therefore, the improved AKF algorithm is a suitable technique for applications in computer vision fields such as passive object-based video authentication systems
Keywords:Basic action, blocks differential, L1-norm, motion estimation, optical flow.
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1Husna Jamal Abdul Nasir, 2Ku Ruhana Ku-Mahamud &3 Eiji Kamioka
1Universiti Malaysia Perlis, Malaysia
2Universiti Utara Malaysia, Malaysia
3Shibaura Institute of Technology, Japan
The Ant Colony System (ACS) algorithm has been applied in solving packet routing problems in Wireless Sensor Networks (WSNs). Solving these problems is complicated as packets need to be submitted through sensor nodes which are spatially distributed and heterogeneous by nature. Without an effective packet routing algorithm, energy consumption will be increased while network lifetime will be reduced. Most researches are focused on optimizing the routing process by using predefined parameters within a certain range. However, this approach will not guarantee optimal performance. This paper presents the parameter adaptation values for ACS experimental set-up in validating its performance. Possible values of each parameter within a defined range were employed. Experiments were conducted to obtain the best value of each parameter to be used for throughput, energy consumption, and latency. Results of this study can be adopted to achieve optimal performance for the packet routing process.
Keywords: Ant Colony Optimization, Parameter Tuning, Performance Evaluation.
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1Mohamed Jalaldeen Mohamed Razi, 2Md Habibullah & 3Husnayati Hussin
1Department of Commerce and Financial Management, University of Kelaniya, Sri Lanka
2Department of Computer Science and Engineering, Uttara University, Bangladesh
3Department of Information Systems, International Islamic University Malaysia, Malaysia
Proper knowledge management (KM) is vital for any organization to perform to the expectation including higher learning institutions (HLIs). Hence, struggling to perform is an indication of lack of KM initiatives. Though many facets of KM have been investigated in HLIs, more studies are needed as the previous empirical works have focused only on knowledge sharing behavior among academicians. An intensive literature review exposes that nonexistence of works employing KM-related theories. Therefore, this study seeks to investigate academicians’ perceived intention (KM Intention) and involvement in KM initiatives (KM Behavior) and its predictors in a Malaysian HLI, which is struggling to perform, by relating theory of knowledge creation. KM intention explains the perception and the attitudes towards KM while the KM behavior illustrates the real behavior. Both these variables were operationalized based on knowledge creation theory through the socialization, externalization, combination, and internalization (SECI) process. A conceptual framework was developed based on the theory of reasoned action and the theory of planned behavior. Six independent variables representative of the socio-cultural nature of KM - trust, management support, decentralization, IT support, performance expectancy, and effort expectancy - were considered as the predictors of KM intention, which in turn, predict KM behavior. Data were collected from 156 academicians from an HLI in Malaysia using questionnaires. The questionnaire items were adapted from previous studies. The structural model analysis confirmed that out of seven proposed hypotheses, four are supported: Trust, performance expectancy, and effort expectancy influence KM Intention, while KM Intention influences KM Behavior. Even though further research works are needed to generalize the findings, the current research and the findings can enrich the KM literature and provide some insights to the decision makers of the selected HLI on the appropriate KM implementation strategies.
Keywords: knowledge management, knowledge sharing behavior, faculty members, theory of reasoned action, theory of planned behavior.

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