Dynamic Touchstroke Analysis with Explainable Artificial Intelligence Tree-Based Learners
Main Article Content
Keywords
Behavioural Biometrics, Touch-stroke dynamic, Explainable Artificial Intelligence (XAI), Random forest (RF), Decision tree (DT)
Abstract
As mobile devices become integral to daily life, robust authentication methods are essential for ensuring security. Traditional methods like personal identification numbers and swipe patterns remain vulnerable to social engineering attacks. To address these risks, this study investigates behavioural biometrics, specifically touch-stroke dynamics, as a transparent and secure alternative. By leveraging unique user interaction patterns, such as touch speed and pressure, this approach provides a distinctive means of authentication. Although various machine learning techniques are available for touch-stroke analysis, the interpretability of classification decisions is vital. This paper implements explainable artificial intelligence with tree-based learners, specifically decision trees and random forests, to enhance the transparency and effectiveness of touch-stroke dynamic authentication. Performance evaluations show that random forests achieve equal error rates (EER) between 0.03% and 0.05%, and decision trees yield EERs between 0.02% and 0.07%, demonstrating a balance between security and interpretability for mobile authentication.
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References
Acien, A., Morales, A., Fierrez, J., Vera-Rodriguez, R., & Delgado-Mohatar, O. (2021). BeCAPTCHA: Behavioral bot detection using touchscreen and mobile sensors benchmarked on HuMIdb. Engineering Applications of Artificial Intelligence, 98, 104058. https://doi.org/10.1016/J.ENGAPPAI.2020.104058
Alwahaishi, S., & Zdralek, J. (2020). Biometric Authentication Security: An Overview. Proceedings - 2020 IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2020, 87–91. https://doi.org/10.1109/CCEM50674.2020.00027
Bajaber, A., Fadel, M., & Elrefaei, L. (n.d.). Evaluation of Deep Learning Models for Person Authentication Based on Touch Gesture. https://doi.org/10.32604/csse.2022.022003
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Bello, A. A., Chiroma, H., Gital, A. Y., Gabralla, L. A., Abdulhamid, S. M., & Shuib, L. (2020). Machine learning algorithms for improving security on touch screen devices: A survey, challenges and new perspectives. Neural Computing & Applications, 32(17), 13651–13678. https://doi.org/10.1007/S00521-020-04775-0/FIGURES/9
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189–215. https://doi.org/10.1016/J.NEUCOM.2019.10.118
Chapter 7. Learning (II): SVM & Ensemble Learning | Data Analytics: A Small Data Approach. (n.d.). Retrieved 1 November 2024, from https://dataanalyticsbook.info/chapter-7.-learning-ii-svm-ensemble-learning.html
Classifier Definition | DeepAI. (n.d.). Retrieved 28 November 2023, from https://deepai.org/machine-learning-glossary-and-terms/classifier
Decision Tree Tutorials & Notes | Machine Learning | HackerEarth. (n.d.). Retrieved 1 November 2024, from https://www.hackerearth.com/practice/machine-learning/machine-learning-algorithms/ml-decision-tree/tutorial/
Decision Trees - Graphical Explainable AI • Aggregata. (n.d.). Retrieved 19 April 2024, from https://aggregata.de/en/blog/supervised-learning/decision-trees/
Deep neural network architecture | Download Scientific Diagram. (n.d.). Retrieved 1 November 2024, from https://www.researchgate.net/figure/Deep-neural-network-architecture_fig2_347813247
Delgado-Santos, P., Tolosana, R., Guest, R., Lamb, P., Khmelnitsky, A., Coughlan, C., & Fierrez, J. (2024). SwipeFormer: Transformers for mobile touchscreen biometrics. Expert Systems with Applications, 237, 121537. https://doi.org/10.1016/J.ESWA.2023.121537
Devi, N. B., Kavida, A. C., & Murugan, R. (2022). Feature Extraction and Object Detection Using Fast-Convolutional Neural Network for Remote Sensing Satellite Image. Journal of the Indian Society of Remote Sensing, 50(6), 961–973. https://doi.org/10.1007/S12524-022-01506-X/TABLES/2
Explainable AI | Royal Society. (n.d.). Retrieved 22 March 2024, from https://royalsociety.org/news-resources/projects/explainable-ai/
Explainable AI (XAI) with a Decision Tree | by Idit Cohen | Towards Data Science. (n.d.). Retrieved 3 April 2024, from https://towardsdatascience.com/explainable-ai-xai-with-a-decision-tree-960d60b240bd
Feature Extraction Explained - MATLAB & Simulink. (n.d.). Retrieved 26 March 2024, from https://www.mathworks.com/discovery/feature-extraction.html
Garg, S. (2019). Face Recognition System: A Review. Proceedings of DHE Sponsored 1 Day National Seminar on Recent Advancement in IT & E-Commerce: Present Scenario & Future Prospects RAITECOM-2019
Gavisiddappa, G., Mahadevappa, S., & Mohan Patil, C. (2020). Multimodal Biometric Authentication System Using Modified ReliefF Feature Selection and Multi Support Vector Machine. International Journal of Intelligent Engineering and Systems, 13(1). https://doi.org/10.22266/ijies2020.0229.01
Goh, T.-J., Chong, L.-Y., Chong, S.-C., & Goh, P.-Y. (2024). A Campus-based Chatbot System using Natural Language Processing and Neural Network. Journal of Informatics and Web Engineering, 3(1), 96–116. https://doi.org/10.33093/jiwe.2024.3.1.7
Keykhaie, S., & Pierre, S. (2020). Mobile Match on Card Active Authentication Using Touchscreen Biometric. IEEE Transactions on Consumer Electronics, 66(4), 376–385. https://doi.org/10.1109/TCE.2020.3029955
Khoh, W. H., Pang, Y. H., Ooi, S. Y., Wang, L.-Y.-K., & Poh, Q. W. (2023). Predictive Churn Modeling for Sustainable Business In The Telecommunication Industry: Optimized Weighted Ensemble Machine Learning. Sustainability, 15(11), 8631. https://doi.org/10.3390/su15118631
Liu, S., Shao, W., Li, T., Xu, W., & Song, L. (2022). Recent advances in biometrics-based user authentication for wearable devices: A contemporary survey. Digital Signal Processing, 125, 103120. https://doi.org/10.1016/J.DSP.2021.103120
Ma, J., Chen, J., Chen, L., Zhou, X., Qin, X., Tang, Y., Sun, G., & Chen, J. (2021). Gaussian mixture model-based target feature extraction and visualization. Journal of Visualization, 24(3), 545–563. https://doi.org/10.1007/S12650-020-00724-0/TABLES/4
Miraoui, M., & El-Etriby, S. (2019). A context-aware authentication approach for smartphones. 2019 International Conference on Computer and Information Sciences, ICCIS 2019. https://doi.org/10.1109/ICCISCI.2019.8716453
Montgomery, M., Chatterjee, P., Jenkins, J., & Roy, K. (2019). Touch Analysis: An Empirical Evaluation of Machine Learning Classification Algorithms on Touch Data. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11611 LNCS, 147–156. https://doi.org/10.1007/978-3-030-24907-6_12/FIGURES/6
Neural Networks with R. (n.d.). Retrieved 24 March 2024, from https://subscription.packtpub.com/book/data/9781788397872/1/ch01lvl1sec27/pros-and-cons-of-neural-networks
Ooi, S. Y., Tan, S. C. & Cheah, W. P. (2017) Temporal Sampling Forest (TS-F): An Ensemble Temporal Learner. Soft Computing, 21, 7039–7052. https://doi.org/10.1007/s00500-016-2242-7
Ooi, S. Y., Tan, S. C., & Cheah, W. P. (2018). Temporal Sleuth Machine with Decision Tree for Temporal Classification. Soft Computing, 22(24), 8077–8095. https://doi.org/10.1007/s00500-017-2747-8
Ooi, S. Y., & Teoh, A. B. J. (2019). Touch-Stroke Dynamics Authentication Using Temporal Regression Forest. IEEE Signal Processing Letters, 26(7), 1001–1005. https://doi.org/10.1109/LSP.2019.2916420
Qin, D., Amariucai, G.T., Qiao, D., & Guan, Y. (2024). Improving behavior based authentication against adversarial attack using XAI. ArXiv. https://arxiv.org/abs/2402.16430 .
Random Forest Algorithm for Absolute Beginners in Data Science. (n.d.). Retrieved 24 March 2024, from https://www.analyticsvidhya.com/blog/2021/10/an-introduction-to-random-forest-algorithm-for-beginners/
Random forest Algorithm in Machine learning | Great Learning. (n.d.). Retrieved 24 March 2024, from https://www.mygreatlearning.com/blog/random-forest-algorithm/
Random Forest in Machine Learning. (n.d.). Retrieved 1 November 2024, from https://www.analyticsvidhya.com/blog/2021/06/understanding-random-forest/
Rawal, A., McCoy, J., Rawat, D. B., Sadler, B. M., & Amant, R. S. (2022). Recent Advances in Trustworthy Explainable Artificial Intelligence: Status, Challenges, and Perspectives. IEEE Transactions on Artificial Intelligence, 3(6), 852–866. https://doi.org/10.1109/TAI.2021.3133846
Seek, C. Y., Ooi, S. Y., Pang, Y. H., Lew, S. L., & Heng, X. Y. (2023). Elderly and Smartphone Apps: Case Study with Lightweight MySejahtera. Journal of Informatics and Web Engineering, 2(1), 13–24. https://doi.org/10.33093/jiwe.2023.2.1.2
Spaling, M. M., & Singh Uppal, A. (2021). Multi-factor authentication in network security [Master’s thesis, University of Alberta]. https://doi.org/10.7939/r3-ftat-7h78 .
Tolosana, R., Vera-Rodriguez, R., & Fierrez, J. (2020). BioTouchPass: Handwritten Passwords for Touchscreen Biometrics. IEEE Transactions on Mobile Computing, 19(7), 1532–1543. https://doi.org/10.1109/TMC.2019.2911506
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., & Ortega-Garcia, J. (2020). BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks. IEEE Transactions on Information Forensics and Security, 15, 2616–2628. https://doi.org/10.1109/TIFS.2020.2973832
Top 4 advantages and disadvantages of Support Vector Machine or SVM | by Dhiraj K | Medium. (n.d.). Retrieved 23 March 2024, from https://dhirajkumarblog.medium.com/top-4-advantages-and-disadvantages-of-support-vector-machine-or-svm-a3c06a2b107
Types of explainable AI. (n.d.). Retrieved 19 April 2024, from https://courses.minnalearn.com/en/courses/trustworthy-ai/preview/explainability/types-of-explainable-ai/
Voege, P., Abu Sulayman, I. I. M., & Ouda, A. (2022). Smart Chatbot for User Authentication. Electronics 2022, 11(23), 4016. https://doi.org/10.3390/ELECTRONICS11234016
Voege, P., & Ouda, A. (2022). An Innovative Multi-Factor Authentication Approach. 2022 International Symposium on Networks, Computers and Communications, ISNCC 2022. https://doi.org/10.1109/ISNCC55209.2022.9851710
Wang, C., Wang, Y., Chen, Y., Liu, H., & Liu, J. (2020). User authentication on mobile devices: Approaches, threats and trends. Computer Networks, 170, 107118. https://doi.org/10.1016/j.comnet.2020.107118
Wang, Z., Chen, F., Zhou, N., Ma, M., Li, X., Guo, Y., & Chen, D. (2021). Identity authentication based on dynamic touch behavior on smartphone. 2021 6th International Conference on Image, Vision and Computing, ICIVC 2021, 469–474. https://doi.org/10.1109/ICIVC52351.2021.9527023
Alwahaishi, S., & Zdralek, J. (2020). Biometric Authentication Security: An Overview. Proceedings - 2020 IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2020, 87–91. https://doi.org/10.1109/CCEM50674.2020.00027
Bajaber, A., Fadel, M., & Elrefaei, L. (n.d.). Evaluation of Deep Learning Models for Person Authentication Based on Touch Gesture. https://doi.org/10.32604/csse.2022.022003
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Bello, A. A., Chiroma, H., Gital, A. Y., Gabralla, L. A., Abdulhamid, S. M., & Shuib, L. (2020). Machine learning algorithms for improving security on touch screen devices: A survey, challenges and new perspectives. Neural Computing & Applications, 32(17), 13651–13678. https://doi.org/10.1007/S00521-020-04775-0/FIGURES/9
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189–215. https://doi.org/10.1016/J.NEUCOM.2019.10.118
Chapter 7. Learning (II): SVM & Ensemble Learning | Data Analytics: A Small Data Approach. (n.d.). Retrieved 1 November 2024, from https://dataanalyticsbook.info/chapter-7.-learning-ii-svm-ensemble-learning.html
Classifier Definition | DeepAI. (n.d.). Retrieved 28 November 2023, from https://deepai.org/machine-learning-glossary-and-terms/classifier
Decision Tree Tutorials & Notes | Machine Learning | HackerEarth. (n.d.). Retrieved 1 November 2024, from https://www.hackerearth.com/practice/machine-learning/machine-learning-algorithms/ml-decision-tree/tutorial/
Decision Trees - Graphical Explainable AI • Aggregata. (n.d.). Retrieved 19 April 2024, from https://aggregata.de/en/blog/supervised-learning/decision-trees/
Deep neural network architecture | Download Scientific Diagram. (n.d.). Retrieved 1 November 2024, from https://www.researchgate.net/figure/Deep-neural-network-architecture_fig2_347813247
Delgado-Santos, P., Tolosana, R., Guest, R., Lamb, P., Khmelnitsky, A., Coughlan, C., & Fierrez, J. (2024). SwipeFormer: Transformers for mobile touchscreen biometrics. Expert Systems with Applications, 237, 121537. https://doi.org/10.1016/J.ESWA.2023.121537
Devi, N. B., Kavida, A. C., & Murugan, R. (2022). Feature Extraction and Object Detection Using Fast-Convolutional Neural Network for Remote Sensing Satellite Image. Journal of the Indian Society of Remote Sensing, 50(6), 961–973. https://doi.org/10.1007/S12524-022-01506-X/TABLES/2
Explainable AI | Royal Society. (n.d.). Retrieved 22 March 2024, from https://royalsociety.org/news-resources/projects/explainable-ai/
Explainable AI (XAI) with a Decision Tree | by Idit Cohen | Towards Data Science. (n.d.). Retrieved 3 April 2024, from https://towardsdatascience.com/explainable-ai-xai-with-a-decision-tree-960d60b240bd
Feature Extraction Explained - MATLAB & Simulink. (n.d.). Retrieved 26 March 2024, from https://www.mathworks.com/discovery/feature-extraction.html
Garg, S. (2019). Face Recognition System: A Review. Proceedings of DHE Sponsored 1 Day National Seminar on Recent Advancement in IT & E-Commerce: Present Scenario & Future Prospects RAITECOM-2019
Gavisiddappa, G., Mahadevappa, S., & Mohan Patil, C. (2020). Multimodal Biometric Authentication System Using Modified ReliefF Feature Selection and Multi Support Vector Machine. International Journal of Intelligent Engineering and Systems, 13(1). https://doi.org/10.22266/ijies2020.0229.01
Goh, T.-J., Chong, L.-Y., Chong, S.-C., & Goh, P.-Y. (2024). A Campus-based Chatbot System using Natural Language Processing and Neural Network. Journal of Informatics and Web Engineering, 3(1), 96–116. https://doi.org/10.33093/jiwe.2024.3.1.7
Keykhaie, S., & Pierre, S. (2020). Mobile Match on Card Active Authentication Using Touchscreen Biometric. IEEE Transactions on Consumer Electronics, 66(4), 376–385. https://doi.org/10.1109/TCE.2020.3029955
Khoh, W. H., Pang, Y. H., Ooi, S. Y., Wang, L.-Y.-K., & Poh, Q. W. (2023). Predictive Churn Modeling for Sustainable Business In The Telecommunication Industry: Optimized Weighted Ensemble Machine Learning. Sustainability, 15(11), 8631. https://doi.org/10.3390/su15118631
Liu, S., Shao, W., Li, T., Xu, W., & Song, L. (2022). Recent advances in biometrics-based user authentication for wearable devices: A contemporary survey. Digital Signal Processing, 125, 103120. https://doi.org/10.1016/J.DSP.2021.103120
Ma, J., Chen, J., Chen, L., Zhou, X., Qin, X., Tang, Y., Sun, G., & Chen, J. (2021). Gaussian mixture model-based target feature extraction and visualization. Journal of Visualization, 24(3), 545–563. https://doi.org/10.1007/S12650-020-00724-0/TABLES/4
Miraoui, M., & El-Etriby, S. (2019). A context-aware authentication approach for smartphones. 2019 International Conference on Computer and Information Sciences, ICCIS 2019. https://doi.org/10.1109/ICCISCI.2019.8716453
Montgomery, M., Chatterjee, P., Jenkins, J., & Roy, K. (2019). Touch Analysis: An Empirical Evaluation of Machine Learning Classification Algorithms on Touch Data. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11611 LNCS, 147–156. https://doi.org/10.1007/978-3-030-24907-6_12/FIGURES/6
Neural Networks with R. (n.d.). Retrieved 24 March 2024, from https://subscription.packtpub.com/book/data/9781788397872/1/ch01lvl1sec27/pros-and-cons-of-neural-networks
Ooi, S. Y., Tan, S. C. & Cheah, W. P. (2017) Temporal Sampling Forest (TS-F): An Ensemble Temporal Learner. Soft Computing, 21, 7039–7052. https://doi.org/10.1007/s00500-016-2242-7
Ooi, S. Y., Tan, S. C., & Cheah, W. P. (2018). Temporal Sleuth Machine with Decision Tree for Temporal Classification. Soft Computing, 22(24), 8077–8095. https://doi.org/10.1007/s00500-017-2747-8
Ooi, S. Y., & Teoh, A. B. J. (2019). Touch-Stroke Dynamics Authentication Using Temporal Regression Forest. IEEE Signal Processing Letters, 26(7), 1001–1005. https://doi.org/10.1109/LSP.2019.2916420
Qin, D., Amariucai, G.T., Qiao, D., & Guan, Y. (2024). Improving behavior based authentication against adversarial attack using XAI. ArXiv. https://arxiv.org/abs/2402.16430 .
Random Forest Algorithm for Absolute Beginners in Data Science. (n.d.). Retrieved 24 March 2024, from https://www.analyticsvidhya.com/blog/2021/10/an-introduction-to-random-forest-algorithm-for-beginners/
Random forest Algorithm in Machine learning | Great Learning. (n.d.). Retrieved 24 March 2024, from https://www.mygreatlearning.com/blog/random-forest-algorithm/
Random Forest in Machine Learning. (n.d.). Retrieved 1 November 2024, from https://www.analyticsvidhya.com/blog/2021/06/understanding-random-forest/
Rawal, A., McCoy, J., Rawat, D. B., Sadler, B. M., & Amant, R. S. (2022). Recent Advances in Trustworthy Explainable Artificial Intelligence: Status, Challenges, and Perspectives. IEEE Transactions on Artificial Intelligence, 3(6), 852–866. https://doi.org/10.1109/TAI.2021.3133846
Seek, C. Y., Ooi, S. Y., Pang, Y. H., Lew, S. L., & Heng, X. Y. (2023). Elderly and Smartphone Apps: Case Study with Lightweight MySejahtera. Journal of Informatics and Web Engineering, 2(1), 13–24. https://doi.org/10.33093/jiwe.2023.2.1.2
Spaling, M. M., & Singh Uppal, A. (2021). Multi-factor authentication in network security [Master’s thesis, University of Alberta]. https://doi.org/10.7939/r3-ftat-7h78 .
Tolosana, R., Vera-Rodriguez, R., & Fierrez, J. (2020). BioTouchPass: Handwritten Passwords for Touchscreen Biometrics. IEEE Transactions on Mobile Computing, 19(7), 1532–1543. https://doi.org/10.1109/TMC.2019.2911506
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., & Ortega-Garcia, J. (2020). BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks. IEEE Transactions on Information Forensics and Security, 15, 2616–2628. https://doi.org/10.1109/TIFS.2020.2973832
Top 4 advantages and disadvantages of Support Vector Machine or SVM | by Dhiraj K | Medium. (n.d.). Retrieved 23 March 2024, from https://dhirajkumarblog.medium.com/top-4-advantages-and-disadvantages-of-support-vector-machine-or-svm-a3c06a2b107
Types of explainable AI. (n.d.). Retrieved 19 April 2024, from https://courses.minnalearn.com/en/courses/trustworthy-ai/preview/explainability/types-of-explainable-ai/
Voege, P., Abu Sulayman, I. I. M., & Ouda, A. (2022). Smart Chatbot for User Authentication. Electronics 2022, 11(23), 4016. https://doi.org/10.3390/ELECTRONICS11234016
Voege, P., & Ouda, A. (2022). An Innovative Multi-Factor Authentication Approach. 2022 International Symposium on Networks, Computers and Communications, ISNCC 2022. https://doi.org/10.1109/ISNCC55209.2022.9851710
Wang, C., Wang, Y., Chen, Y., Liu, H., & Liu, J. (2020). User authentication on mobile devices: Approaches, threats and trends. Computer Networks, 170, 107118. https://doi.org/10.1016/j.comnet.2020.107118
Wang, Z., Chen, F., Zhou, N., Ma, M., Li, X., Guo, Y., & Chen, D. (2021). Identity authentication based on dynamic touch behavior on smartphone. 2021 6th International Conference on Image, Vision and Computing, ICIVC 2021, 469–474. https://doi.org/10.1109/ICIVC52351.2021.9527023
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Dec 30, 2024
Article Details
How to Cite
Lim, W. P., Ooi, S. Y., Pang, Y. H., Ramalingam, S., & Chew, Y. J. (2024). Dynamic Touchstroke Analysis with Explainable Artificial Intelligence Tree-Based Learners. Journal of Telecommunications and the Digital Economy, 12(4), 137–161. https://doi.org/10.18080/jtde.v12n4.1023
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Telecommunications
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Author Biographies
Wun Puo Lim, Multimedia University, Melaka, Malaysia
Faculty of Information Science and Technology (FIST), Multimedia University, Jalan Ayer Keroh Lama, Melaka, 75450, Malaysia
Shih Yin Ooi, Multimedia University, Melaka, Malaysia
Faculty of Information Science and Technology (FIST), Multimedia University, Jalan Ayer Keroh Lama, Melaka, 75450, Malaysia
Ying Han Pang, Multimedia University, Melaka, Malaysia
Faculty of Information Science and Technology (FIST), Multimedia University, Jalan Ayer Keroh Lama, Melaka, 75450, Malaysia
Soodamani Ramalingam, University of Hertfordshire, United Kingdom
School of Physics, Engineering & Computer Science, Department of Engineering and Technology, University of Hertfordshire, College Ln, Hatfield AL10 9AB, United Kingdom
Yee Jian Chew, Multimedia University, Melaka, Malaysia
Faculty of Information Science and Technology (FIST), Multimedia University, Jalan Ayer Keroh Lama, Melaka, 75450, Malaysia
