The following manuscripts will be appeared in October 2024 as vol. 3, issue no. 3. Articles in this forthcoming issue are articles that have been accepted for publication but have not been formally published and are not yet the complete volume (still in progress), issue, page information, and DOI. The articles will be added periodically after the copyediting process.

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Proofread No. 1.  MobiTest – A Software for Mobile-Based Testing

Ayesha Anees Zaveri^1*, Ramsha Mashood², Nabiha Faisal³, Misbah Parveen⁴, Naveera Sami⁵, Mobeen Nazar⁶, Saba Imtiaz⁷

 

^{1, 2,3,4,6,7} University of Kuala Lumpur, Kuala Lumpur, Malaysia

⁵ NED University of Engineering & Technology, Karachi, Pakistan

*corresponding author: (zaveri.ayesha@s.unikl.edu.my, ORCiD: 0009-0007-7230-1696)

https://doi.org/10.33093/jiwe.2024.3.3.1, pp. 1-20

 

 

Abstract - MobiTest is an application that serves as a valuable tool in the fast-growing field of software testing. Efficiency is crucial in this industry, where testers, quality assurance teams, and others must meticulously test each application, avoiding the need to repeat the entire cycle to identify bugs. This application is a breeze thanks to its intuitive features and educational content. Thanks to continuous integration, testers can easily keep up with the fast-paced development cycle and start automating tasks as soon as the user interface development is completed. This saves valuable time and ensures a smoother and more efficient process. During the development of this application, a need arose for manual testing, which unfortunately resulted in the inefficient use of resources. MobiTest was designed to overcome these limitations by providing the ability to generate generic test scripts for any application as needed. It can efficiently and adaptively handle intricate tasks according to predefined parameters. This application thoroughly examines every possible detail, allowing the hacker to exploit the system.

Keywords— MobiTest, Codeless, Automation, E-commerce, Leverage, Quality Assurance Teams, Integration, UI development, Generic, Test Scripts, Hacker.

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Proofread No. 2.  Gamification: Design an Interactive Cybersecurity Learning Platform for Multimedia University Students

Adlil Khaliq bin Abdul Razack¹, Mohamad Firdaus bin Mat Saad²

^1,2 Faculty of Computing and Informatics, Multimedia University, Malaysia
*corresponding Author: (firdaus.matsaad@mmu.edu.my, ORCiD: 0000-0003-3768-4808)

https://doi.org/10.33093/jiwe.2024.3.3.2, pp. 21-40

 

Abstract - Cybersecurity has emerged as a critical imperative in contemporary digital landscapes, necessitating heightened awareness and proficiency across all demographic segments. Accordingly, this research has been meticulously crafted to delve into the complexities of cybersecurity awareness, with a specific focus on university students. The study embarks on an exhaustive analysis encompassing the evaluation of cybersecurity awareness levels within the targeted groups, the identification of prevailing issues and practices, and an exploration of novel methodologies, notably gamification, to fortify cybersecurity knowledge and skills among diverse user cohorts. Central to this investigation is the efficacy of gamified learning environments tailored expressly for augmenting cybersecurity awareness among university students. Through a comprehensive examination of existing platforms, methodological frameworks, and user interactions, this research outlines critical trends, challenges, and latent opportunities within the cybersecurity awareness domain, with a specific emphasis on gamification's transformative potential. The study not only identifies key areas for improvement but also proposes innovative solutions rooted in gamified learning paradigms, with the overarching goal of fostering engaging, effective, and sustainable cybersecurity awareness initiatives among students. Drawing upon a synthesis of theoretical constructs, empirical insights, and pragmatic recommendations, this research significantly contributes to the evolving discourse on cybersecurity education. By underscoring the transformative efficacy of gamification as a pivotal tool in cybersecurity awareness initiatives, this study overlays the way for substantial advancements in cybersecurity education paradigms, offering a roadmap for enhancing cybersecurity awareness levels among university students and beyond.

 

Keywords- Cybersecurity Awareness, Gamification Education, Cyber Threats, Transformative Learning, Cybersecurity Platform

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Proofread No. 3.  Treatment Recommendation using BERT Personalization

J Jayapradha^1*, Yukta Kulkarni², Lakshmi Vadhanie G³, Palanichamy Naveen⁴, Elham Abdulwahab Anaam⁵

^1,2,3 Department of Computing Technologies, SRM Institute of Science and Technology,

Kattankulathur, Tamil Nadu 6030203, India

^4,5 Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia
63100, Cyberjaya, Selangor.

*corresponding author: (jayapraj@srmist.edu.in; ORCiD: 0000-0002-2548-9135)

https://doi.org/10.33093/jiwe.2024.3.3.3, pp. 41-62

 

 

Abstract - This research work develops a new framework that combines patient feedback with evidence-based best practices across disease states to improve drug recommendations. It employs BERT as its free-text processing engine to deal with sentiment judgment and classification. The functionality of the system, named `PharmaBERT`, includes acceptance of drug review data as a comprehensive input, drug categorization when dealing with a wide range of treatments and fine-tuning the BERT-based model for gaining positive or negative sentiment towards specific medications. PharmaBERT categorizes various drugs and fine-tunes the BERT structure to perceive lots of possible sentiments for specific medications. Consequently, PharmaBERT brings all its training and optimization capabilities together and through this, the system reaches a higher accuracy of up to 91% thus showcasing the potency of the model in capturing patient sentiments. While being a BERT spin-off, PharmaBERT utilizes its own set of experienced techniques to comprehend and sense the health-related text input given by the patient, doctor, or pharmacist. It uses transfer learning, that is, it learns from language representations to adapt quickly to the intricacies of drug reviewing. Through PharmaBERT, healthcare professionals may expand their diagnoses with insights from patient feedback to constitute more neutral decisions.

 

Keywords - Bidirectional Encoder Representation from Transformers (BERT), Machine Learning (ML), Artificial Intelligence (AI), Large Language Models (LLMs), Deep Neural Network (DNN), Natural Language Processing (NLP).

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Proofread No. 4.  Performance Evaluation on E-Commerce Recommender System based on KNN, SVD, CoClustering and Ensemble Approaches

Wan-Er Kong^1*, Tong-Ern Tai², Palanichamy Naveen³, Heru Agus Santoso⁴

^1,2.3 Faculty of Computing and Informatics, Multimedia University, 63100 Cyberjaya, Malaysia.

⁴Department of Informatics Engineering, Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia.

*corresponding author: (kong.wan.er@student.mmu.edu.my; ORDIC:0009-0003-4221-1850)

https://doi.org/10.33093/jiwe.2024.3.3.4, pp. 63-76

 

 

Abstract - E-commerce recommender systems (RS) nowadays are essential for promoting products. These systems are expected to offer personalized recommendations for users based on the user preference. This can be achieved by employing cutting-edge technology such as artificial intelligence (AI) and machine learning (ML). Tailored recommendations for users can boost user experience in using the application and hence increase income as well as the reputation of a company. The purpose of this study is to investigate popular ML methods for e-commerce recommendation and study the potential of ensemble methods to combine the strengths of individual approaches. These recommendations are derived from a multitude of factors, including users' prior purchases, browsing history, demographic information, and others. To forecast the interests and preferences of users, several techniques are chosen to be investigated in this study, which include Singular Value Decomposition (SVD), k-Nearest Neighbor Baseline (KNN Baseline) and CoClustering. In addition, several evaluation metrics including the fraction of concordant pairs (FCP), mean absolute error (MAE), root mean square error (RMSE) and normalized discounted cumulative gain (NDCG) will be used to assess how well different techniques work. To provide a better understanding, the outcomes produced in this study will be incorporated into a graphical user interface (GUI).

Keywords— Machine Learning, Filtering Technique, E-Commerce System, Recommender System, Collaborative Filtering

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Proofread No. 5.  Review on Automated Storage and Retrieval System for Warehouse

Alex Low Kai Jie¹, Sim Kok Swee ^1*, Lew Kai Liang ¹

¹Faculty of Engineering and Technology, Multimedia University, Malaysia

*corresponding author: (kssim@mmu.edu.my; ORCiD: 0000-0003-2976-8825)

https://doi.org/10.33093/jiwe.2024.3.3.5, pp. 77-97

 

 

Abstract - The swift expansion of e-commerce and supply chain operations has significantly enhanced the efficiency of warehouse management systems, establishing them as vital components in augmenting organizations' competitiveness. This paper delves into warehouse sorting systems to enhance the sorting process, reduce error rates, and simplify internal warehouse procedures. It aims to develop a scalable, adaptable, and efficient warehouse sorting system that can maximize sorting efficiency while effectively responding to changing market demands through the use of advanced automation technologies. The study provides an in-depth review of the literature that has explored the Automated Storage and Retrieval System (ASRS) within the context of warehouse operations. It offers a comprehensive introduction to the operational systems of warehouses, detailing each type of ASRS along with the technologies that can improve the efficiency and accuracy of these systems. Moreover, the paper thoroughly investigates and classifies the ASRS design decision problem and compares multiple types of ASRS. The analysis aims to delineate the distinctions among various ASRS configurations, assessing their scalability, adaptability, and their impact on operational efficacy in warehouse environments Through this comparative review, the paper emphasizes the potential enhancements in sorting processes that modern ASRS can provide, ensuring that warehouse operations can rapidly adapt to market changes and demands. The goal is to highlight best practices and technological innovations that can lead to more responsive and efficient warehouse management systems. This exploration contributes to a better understanding of how cutting-edge automation and adaptable system designs can significantly influence the efficiency of warehouse sorting processes.

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Keywords— Warehouse Automation, ASRS Review, Smart Warehouse, Material handling systems, Automated Storage and Retrieval System

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Proofread No. 6.  Development of Virtual Reality-Based Left Brain System

Kok An Teo¹, Kok Swee Sim^1*, Chean Khim Toa², Kai Liang Lew¹

¹Faculty of Engineering and Technology, Multimedia University, Malaysia

²School of Computing and Data Science, Xiamen University, Malaysia

*corresponding author: (kssim@mmu.edu.my; ORCiD: 0000-0003-2976-8825)

https://doi.org/10.33093/jiwe.2024.3.3.6, pp. 98-120

 

 

Abstract - This paper proposes a virtual reality based Left Brain System named BrainUp World to improve left brain thinking. The left-brain system was employed with mobile virtual reality technology, hand motion tracking and haptic feedback system. The implementation of these systems is to enhance the experience and sense of embodiment. BrainUp World includes virtual reality-based brain training games to improve a person’s attention level, reasoning skill, auditory cognition, arithmetic skill, sequencing skill and memory. The hand tracking system was utilized with an IR camera to capture the hand orientation and return the gesture data to the VR application. A low-cost and lightweight haptic glove was invented which provides the sense of touch using vibrations while interacting with VR contents. An experimental study was conducted to assess the efficacy of BrainUp World compared to traditional PC-based training approaches. Participants were randomly assigned to either the VR-based or PC-based training group and underwent 6 different games to test a person’s attention level, reasoning skill, auditory cognition, arithmetic skill, sequencing skill, and memory. The results revealed a statistically significant improvement in left brain function in the VR-based training group compared to the PC-based group, with a T-score growth of 4.73. Analysis using ANOVA confirmed the significance of this difference (p-Value = 0.04). Notably, the study also identified age-related differences in thinking fluency, highlighting the importance of personalized cognitive training interventions. In conclusion, BrainUp World demonstrates the potential of VR technology in promoting left brain development, as evidenced by empirical findings from the conducted study. By offering immersive and interactive cognitive training experiences, VR-based systems hold promise for enhancing various aspects of cognitive function associated with left hemisphere dominance. Further research in this area is encouraged to explore the full potential of VR-based interventions for cognitive enhancement.

Keywords— Left Brain, Training, Virtual Reality, Leap Motion, Haptic Glove

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