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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Proofread No. 7. Comparative Analysis of Linear and Nonlinear sEMG Methods for Detecting Muscle Fatigue During Dynamic Biceps Curls

Tang Ming^1,2*, Ling Weay Ang¹, Sellappan Palaniappan¹

¹ Faculty of Information Technology, Malaysia University of Science and Technology, Jalan PJU 5/5, Kota Damansara, 47810 Petaling Jaya, Malaysia.

²Intelligent Science & Information Engineering College, Xi'an Peihua University, China

*corresponding author: (tangming@phd.must.edu.my; ORCiD: 0009-0000-4978-6663)

https://doi.org/10.33093/jiwe.2024.3.3.7, pp. 121-132

 

Abstract - Muscle fatigue, a key concern in sports science, rehabilitation, and occupational health, influences performance, injury risk, and provides insights into muscle functionality and endurance. Surface electromyography (sEMG) has emerged as a vital tool for non-invasively tracking muscle electrical activity and gauging health. As its application for muscle fatigue assessment grows, identifying the most accurate analytical methods is essential. Current sEMG analyses employ both linear and nonlinear metrics to measure fatigue onset and progression, yet research is ongoing to determine which method is most effective in the context of dynamic contractions. The study was aimed to evaluate the efficacy of established linear and nonlinear methods in measuring muscle fatigue caused by dynamic contractions through surface electromyography (sEMG) signals. A group of twelve healthy individuals completed biceps curls at a consistent pace of one repetition per four seconds, which constituted 75% of their 10-repetition maximum. Concurrently, sEMG signals were captured from the biceps brachii muscle at 1000 Hz. To assess the sEMG signals during the initial, middle, and final sets of 10 repetitions, three linear metrics—mean frequency, median frequency, and spectral moment ratio (SMR)—along with two nonlinear approaches, namely sample entropy and detrended fluctuation analysis (DFA), were utilized. The study's outcomes indicated notable shifts in the SMR values and the two DFA-derived scaling exponents across the exercise sets. These results indicated that SMR, sample entropy, and DFA are effective in gauging muscle fatigue, with sample entropy and DFA demonstrating heightened sensitivity to the fatigue levels when compared to the linear metrics.

Keywords—Muscle Fatigue, Surface Electromyography (sEMG), Dynamic Contractions, Linear and Nonlinear Metrics, Fatigue Assessment, Biceps Curl Exercise

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Proofread No. 8.  Pi Class –A Revolutionary Step Forward in Hybrid Class Management System

Qasim Hassan¹, Nabiha Faisal², Ayesha Anees Zaveri³*

^1,2 Bahria University Karachi, 13 National Stadium Rd, Karsaz Faisal Cantonment, Karachi, Karachi City, Sindh, Pakistan.

³ Malaysian Institute of Information Technology, Universiti Kuala Lumpur, 1016, Jln Sultan Ismail, Bandar Wawasan, 50250 Kuala Lumpur, Malaysia.

*Corresponding author: (zaveri.ayesha@s.unikl.edu.pk; ORCiD: 0009-0007-7230-1696)

https://doi.org/10.33093/jiwe.2024.3.3.8, pp. 133-146

 

 

Abstract - The COVID-19 pandemic has caused widespread disruptions globally, resulting in a state of health emergency with numerous deaths reported and an overall implementation of quarantine and isolation. Strict lockdowns have been implemented to curb the spread of the virus, requiring social distancing and limiting physical interactions. These measures had far-reaching impacts on all aspects of life, including education. Education was one of the most affected sectors, with difficulty delivering quality education in schools, colleges, and universities. It was hard to provide quality education, a basic need of humanity. In this research paper, we propose an adequate solution to overcome the difficulties of maintaining educational quality during and after the COVID-19 pandemic and its multiple variants. The proposed solution is a hybrid model for an autonomous lecture recording system that facilitates students to attend physical classes and attend lectures virtually. The solution proposed is a cost-effective and convenient way for students to access lectures. The application involves hardware and software components that record and preserve lectures' audio and visual aspects. The system will allow lectures to be delivered directly to the students' devices. The major modules of the project include Python scripting, model training, UI/UX design, and app development.

Keywords— Remote education, online lecturers, lecture backups, self-generated lecture links, cloud-based class management, automated recordings, smart applications, hybrid classrooms.

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Proofread No. 9.  Comparative Evaluation of Machine Learning Models for Mobile Phone Price Prediction: Assessing Accuracy, Robustness, and Generalization Performance

Saima Anwar Lashari¹, M. Muntazir Khan², Abdullah Khan^3*, Sana Salahuddin⁴, M. Noman Atta⁵

¹College of Computing and Informatics, Saudi Electronic University, 4552 Prince Mohammed Ibn Salman Ibn Abdulaziz Rd, 6867, Ar Rabi, Riyadh 13316, Saudi Arabia.

^2,3,4,5Institute of Computer Sciences and Information Technology, The University of Agriculture Peshawar, Peshawar, Khyber Pakhtunkhwa, Pakistan.

*corresponding author: (abdullah_khan@aup.edu.pk; ORCiD: 0000-0003-1718-7038)

https://doi.org/10.33093/jiwe.2024.3.3.9, pp. 147-163

 

 

Abstract - These days, mobile phones are the most commonly purchased goods. Thousands of new models with improved features, designs, and specifications are released yearly. An autonomous mobile price prediction system is required to assist customers in determining whether or not they can afford these devices. Many machine learning models exhibit varying performance degrees based on their architecture and learning properties. Ten widely used classifiers were assessed in this study: Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Decision Tree (DT), Naïve Bayes (NB), Linear Discriminant Analysis (LDA), AdaBoost, and Light Gradient Boosting (LGB). The F1-score, recall, accuracy, and precision of these models were evaluated. According to the findings, the results indicated that LR, with its use of the Elastic Net parameter, outperformed the others with 96% accuracy, 97% precision, 94% recall, and 96% F1-score. Other models like XGBoost, LGB, and SVM also showed strong performance, whereas KNN had the poorest performance. The study highlights the importance of selecting the appropriate model for accurate mobile price prediction. Among all the machine learning used in this paper, the LR classifier outperforms the other state-of-the-art models because of the elastic Net parameter used for mobile phone price prediction.   

Keywords— Logistic Regression, Random Forest, K-Nearest Neighbor, Support Vector Machine, Extreme Gradient Boosting, Decision Tree, Naïve Bayes, Linear Discriminant Analysis, AdaBoost, Light Gradient Boosting

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Proofread No. 10. Development of Robot Feature for Stunting Analysis Using Long-Short Term Memory (LSTM) Algorithm

Muhammad Rahadian Abdurrahman^1*, Halim Al-Aziz², Farras Adhani Zayn³, Muhammad Agus Purnomo⁴, Heru Agus Santoso⁵

^1,2.3,5 Department of Informatics Engineering, Faculty of Computer Science, Dian Nuswantoro University, Semarang, Indonesia.

⁴Department of Electrical Engineering, Faculty of Engineering, Universitas Dian Nuswantoro, Semarang, Indonesia.

^*corresponding author: (heru.agus.santoso@dsn.dinus.ac.id; ORCID:0000-0002-5436-1739)

https://doi.org/10.33093/jiwe.2024.3.3.10, pp. 164-175

 

Abstract  - Stunting prevalence in Indonesia persists as a significant challenge, necessitating concerted efforts from all stakeholders. We developed a robot for stunting analysis using a deep learning algorithm. It aligns with the Sustainable Development Goal (SDG) agenda, specifically targeting SDG 3, which focuses on ensuring good health and well-being for all.  Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) developed to address the issue of vanishing gradient in RNNs. In general, either LSTM can be used in analysis. This study aims to classify stunting based on age and height using LSTM. The LSTM model was trained with 50 epochs using datasets collected from the health office and robots. The evaluation results show a training accuracy of 96.65% and training validation of 96.61%, with precision, recall, and f1-score varying in relevance to the f1-score and support value. This research illustrates the potential for using data classification methods in stunting diagnosis. However, it is necessary to adjust parameters and augment the training dataset to enhance model performance. With good convergence at epoch 50, these results show the model's ability to classify stunting based on age and height. However, further validation and testing on larger datasets is needed to thoroughly test the reliability and generalization of the model. This research can contribute to the development of deep learning regarding robots as a means of testing stunting. This research provides initial evidence of the potential of stunting classification methods using robots. However, parameter adjustments and increasing the amount of training data need to be done to improve the overall model performance.

Keywords—Long short-term memory, LSTM, Stunting, Robot, Deep learning, Classification

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Proofread No. 11. Investigation on Understanding the Numeracy Capacity of Intellectual Disabled Students using Enabling Technology Tools:  Augmented Reality and UI/UX

Vishvjit Thakar^1*, Romany Thakar², Pratik Vyas³

¹ Computer Science and Engineering, Indrashil University, At. & Po Rajpur Taluka Kadi, Rajpur, Gujarat 382715, India.

² Digital Q-A Analyst, Framework Design, Top Floor, 48-52 Canal St, Nottingham NG1 7EH, United Kingdom.

³ Nottingham Trent University, 50 Shakespeare St, Nottingham NG1 4FQ, United Kingdom.

*correspondingauthor: (vishvjitkthakar@gmail.com; ORCiD:0000-0002-0164-6423)

https://doi.org/10.33093/jiwe.2024.3.3.11, pp. 176-189

 

Abstract - The population of individuals with intellectual disabilities (ID) is increasing, necessitating assistance with a wide range of daily activities. Acquiring and assessing numeracy and communication skills are critical for this demographic, requiring tools and techniques tailored to their specific needs. Effective educational tools must employ multi-modal and multi-sensory approaches to cater to diverse learning styles and incorporate assistive technological solutions. Despite the availability of numerous tools, there is a need to enhance their utility and effectiveness. This study aims to identify and refine the requirements for an innovative educational tool that employs Two-dimensional (2D) and Augmented Reality (AR) technologies. To achieve this, we conducted semi-structured interviews and surveys with teachers working with students with ID, gaining insights into the current solutions, advantages, and limitations. Additionally, we used physical props as design probes in a co-design methodology to better understand and elicit the true needs of individuals with ID. The findings from this research will inform the development of a 2D/AR tool designed to make learning mathematics more engaging and effective for individuals with ID, contributing to the advancement of inclusive education practices. Enabling Technology plays a significant role in the numeracy ability among people with ID. Generative AI and Explainable AI shall further improve learning ability in the years to come.

 

Keywords—Intellectually Disabled, Augmented Reality, Multimodal, User Experience, Enabling Technology.