Proofread No. 1.  Facial Skin Analysis in Malaysians using YOLOv5: A Deep Learning Perspective

Ying Huey Gan1, Shih Yin Ooi2 *, Ying Han Pang2, Yi Hong Tay3 and Quan Fong Yeo2

1 Public Mutual Berhad, Menara Public Bank 2, No.78, Jalan Raja Chulan, 50200 Kuala Lumpur, Malaysia

2 Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia

3 365 Production, B1-13A-01, Soho Suite @KLCC, 20, Jalan Perak, 50450 Kuala Lumpur, Malaysia

*corresponding author: (syooi@mmu.edu.my, ORCiD: 0000-0002-3024-1011)

https://doi.org/10.33093/jiwe.2024.3.2.1, pp. 1-18



Abstract - Nowadays, people are more concerned about their skin conditions and are more willing to spend money and time on facial care routines. The beauty sector market is increasing, and more skin type readers are being created to help people determine their skin type. While various skin type readers are in the market, each is invented and tested abroad. Those skin type readers in the beauty market are not applied well on Malaysian skin. Therefore, this paper proposes a facial skin analysis system tailored primarily for Malaysian skin. This paper integrated object detection and deep learning algorithms in developing skin-type readers. A unique dataset consisting solely of facial images of Malaysian skin was created from scratch for the model. Additionally, You Only Look Once version 5 (YOLOv5) is employed to detect users' facial skin conditions, such as acne, pigment, enlarged pores, uneven skin, blackheads, etc. Then, based on the detected skin conditions, it further classifies the user's skin type into the normal, oily, sensitive, or dry groups.

Keywords— Skin Type Classification, Image Processing, Object Detection, Deep Learning, YOLOv5

Received: 14 June 2023; Accepted: 23 December 2023; Published: 16 June 2024


Proofread No. 2.  Classroom Environment Analysis Via Internet of Things

Kai-Yuan Tan1, Kok-Why Ng1*, Kanesaraj Ramasamy1

1 Faculty of Computing and Informatics, Multimedia University, Malaysia

*corresponding author: (kwng@mmu.edu.my; ORCiD: 0000-0003-4516-4634)

https://doi.org/10.33093/jiwe.2024.3.2.2, pp. 19-36


Abstract - In this era of rapid technological advancement, the potential of the digital age has opened up numerous possibilities for our society. However, despite these advancements, traditional classrooms still lack the necessary technology to create an optimal learning environment for students. Consequently, students may struggle to effectively acquire knowledge within classrooms. This paper aims to conduct a classroom environment analysis using Internet of Things technology to gather data and uncover valuable insights. The proposed solution involves an embedded system for controlling and monitoring the classroom environment, as well as exporting historical data for further research. By ensuring accurate data collection, this paper seeks to facilitate meaningful improvements in the classroom environment, aligning with the principle of "garbage in, garbage out" in computer science.

Keywords—Classroom, Environment, Arduino, Sensors, Internet of Things

Received: 14 June 2023; Accepted: 01 October 2023; Published: 16 June 2024


Proofread No. 3.  Real Time 3D Internal Building Directory Map

Zi Yang Chia1, Pey Yun Goh1 *

1 Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia

*corresponding author: (pygoh@mmu.edu.my, ORCiD: 0000-0003-2060-3223)

https://doi.org/10.33093/jiwe.2024.3.2.3, pp. 37-56


Abstract - Global Positioning System (GPS) is a famous technology around the world in identifying the real time precise location of any object with the assistance of satellites. The most common application of GPS is the use of outdoor maps. GPS offers efficient, scalable and cost-effective location services. However, this technology is not reliable when the position is in an indoor environment. The signal is very weak or totally lost due to signal attenuation and multipath effects. Among the indoor positioning technologies, WLAN is the most convenient and cost effective. In recent research, machine learning algorithms have become popular and utilized in wireless indoor positioning to achieve better performance. In this paper, different machine learning algorithms are employed to classify different positions in the real-world environment (e.g., Ixora Apartment - House and Multimedia University Malacca – FIST building). Received Signal Strength Indication (RSSI) is collected at each reference point. This data is then used to train the model with hyperparameter tuning. Based on the experiment result, Random Forest achieved 82% accuracy in Ixora Apartment and 84% accuracy in one of the buildings in Multimedia University Malacca. These results outperformed the other models, i.e., K-Nearest Neighbors (KNN) and Support Vector Machine (SVM).

Keywords— Indoor Positioning, Wi-Fi, KNN, SVM, Random Forest

Received: 13 June 2023; Accepted: 18 December 2023; Published: 16 June 2024


Proofread No. 4. Assessing the Efficiency of Deep Learning Methods for Automated Vehicle Registration Recognition for University Entrance

Muhammad Syaqil Irsyad1, Zarina Che Embi1 *and Khairil Imran Bin Ghauth1

1 Faculty of Computing and Informatics, Multimedia University, Malaysia

*corresponding author: (zarina.embi@mmu.edu.my, ORCiD: 0000-0001-9378-7380)

https://doi.org/10.33093/jiwe.2024.3.2.4, pp. 57-69


Abstract - With the ever-increasing number of vehicles on the road, a faster reliable security system for university entry is needed. This paper presents an approach for Automatic Number Plate Recognition (ANPR) using deep learning and PP-OCRv3. The proposed approach utilizes a pre-trained object detection model to locate license plates, extracts a single frame of the license plate, performs license plate recognition, applies pre-processing techniques, and employs PP-OCRv3 for text extraction in real time. The system was tested with Malaysian vehicle plates, and its accuracy and speed of detection were evaluated. The results show the system's potential to be easily adapted to different camera systems, angles, and lighting conditions by retraining the deep learning model. The paper also explores various deep learning methods, such as CenterNet, EfficientDet, and Faster R-CNN, and their effectiveness in automated vehicle registration detection. The research methodology involves creating a dataset from Open Images Dataset V4, converting label text into XML files, and utilizing the TensorFlow model trained on the COCO dataset. The paper concludes with the synthetic evaluation of the trained models, comparing their performance based on precision, recall, and F1-score. Overall, the proposed approach highlights the potential of deep learning and PP-OCRv3 in achieving accurate and efficient ANPR systems.

Keywords— number plate detection, optical character recognition, license plate recognition, TensorFlow, CenterNet, EfficientDet, Faster R-CNN, PP-OCRv3

Received: 09 September 2023; Accepted: 18 December 2023; Published: 16 June 2024


Proofread No. 5.  Streamlining Dental Clinic Management for Effective Digitisation Productivity and Usability

Sin-Ban Ho1*, En-Yu Chew2, Chuie-Hong Tan3

1,2 Faculty of Computing and Informatics, Multimedia University, 63100 Cyberjaya, Malaysia

3 Faculty of Management, Multimedia University, 63100 Cyberjaya, Malaysia

*corresponding author: (sbho@mmu.edu.my, ORCiD: 0000-0003-2995-2120)

https://doi.org/10.33093/jiwe.2024.3.2.5, pp. 70-85



Abstract - Oral health is an integral part of overall health, and poor oral hygiene can lead to a variety of health problems. Modern oral care has greatly improved our quality of life, but the increasing demand for routine dental checkups and treatments calls for improved systems for managing patient records and appointments. While technology has significantly enhanced the efficacy and experience of dental care, many dental clinics still rely on paper records to record the patient’s oral condition, but these are not easily accessible to the patients for viewing. This study aims to address the issue by developing a Dental Clinic Management System to manage patient appointments and records. This system will allow patients to manage their appointments, view their dental history, and receive comments from dentists. Dentists will be able to view appointments, perform treatments, and provide feedback to patients, while the administrator or receptionist will be able to manage appointments, view records, and create invoices. By streamlining dental clinic management, this system aims to improve the overall quality of oral healthcare.

Keywords— Dental, Clinic Management System, Appointment, Oral Health, Productivity and Usability

Received: 26 May 2023; Accepted: 16 December 2023; Published: 16 June 2024


Proofread No. 6.  Knowledge-based Word Tokenization System for Urdu

Asif Khan1, Khairullah Khan1, Wahab Khan1,2*, Sadiq Nawaz Khan1, Rafiul Haq3

1Department of Computer Science, University of Science & Technology Bannu, Pakistan

2Department of Computer Science, International Islamic University Islamabad, Pakistan

3College of Intelligence and Computing, Tianjin University, Tianjin, 300350 China

*Corresponding Author: (wahbshri@gmail.com, ORCiD: 0000-0002-5694-0419)

https://doi.org/10.33093/jiwe.2024.3.2.6, pp. 86-97



Abstract - Word tokenization, a foundational step in natural language processing (NLP), is critical for tasks like part-of-speech tagging, named entity recognition, and parsing, as well as various independent NLP applications. In our tech-driven era, the exponential growth of textual data on the World Wide Web demands sophisticated tools for effective processing. Urdu, spoken widely across the globe, is experiencing a surge in, presents unique challenges due to its distinct writing style, the absence of capitalization features, and the prevalence of compound words. This study introduces a novel knowledge-based word tokenization system tailored for Urdu. Central to this system is a maximum matching model with forward and reverse variants, setting it apart from conventional approaches. The novelty of our system lies in its holistic approach, integrating knowledge-based techniques, dual-variant maximum matching, and heightened adaptability to low-resource language speakers, emphasizing the urgent need for advanced Urdu Language Processing (ULP) systems. However, Urdu, labeled as a low-resource language challenges compared to traditional machine learning (ML) approaches. Significantly, our system eliminates the need for a features file and pre-labelled datasets, streamlining the tokenization process. To evaluate the proposed model's efficacy, a comprehensive analysis was conducted on a dataset comprising 100 sentences with 5,000 Urdu words, yielding an impressive accuracy of 97%. This research makes a substantial contribution to Urdu language processing, providing an innovative solution to the complexities posed by the unique linguistic attributes of Urdu tokenization.

Keywords— Natural Language Processing (NLP), Urdu Language Processing (ULP), Forward Maximum Matching (FMM), Reverse Maximum Matching (RMM), Part-of-speech tagging (POS)

Received: 22 November 2023; Accepted: 24 January 2024; Published: 16 June 2024


Proofread No. 7. Sentiment Analysis in Social Media: A Case Study of Hike in University School Fees in Selected Nigerian Universities

Abdulahi Olarewaju Aremu1*, Isah Muhammad2

1,2Department of Linguistics, Usmanu Danfodiyo University, Sokoto, Nigeria

*Corresponding author: (aremuoabdulahi@gmail.com; ORCiD: 0009-0009-9812-9542)

https://doi.org/10.33093/jiwe.2024.3.2.7, pp. 98-104



Abstract- Faced with escalating operational costs and government disinvestment, Nigerian public universities are implementing tuition fee increases to maintain institutional functionality. This necessary fiscal measure comes in the wake of 2022 industrial action, which exacerbated pre-existing financial strain through extended work stoppages and potentially higher costs associated with resuming activities, while leaving unaddressed the longstanding demands of academics for improved welfare and working conditions. The court-mandated resumption of academic activities without resolution of these core issues further strained university finances, leading to a significant increase in tuition fees. Using VADER, this study investigated social media sentiments related to the increase in university school fees at Usmanu Danfodiyo University, Sokoto, and the University of Maiduguri. The results revealed that students' sentiments regarding the rise in tuition fees at the two universities were largely neutral, with 4.6% positive sentiment, 7.9% negative sentiment, and 87.5% neutral sentiment identified for Usmanu Danfodiyo University, Sokoto. In contrast, the University of Maiduguri had 0% positive sentiment, 19.8% negative sentiment, and 80.2% neutral sentiment. The study recommends seeking feedback through surveys or student leaders and offering scholarships to indigent students to address fee hike concerns at the two universities. While VADER is designed to handle social media textual data, few misclassifications of sentiments were noted and discussed.

KeywordsNigerian universities, School fees, Hike, Social media, Vader

Received: 21 December 2023; Accepted: 22 February 2024; Published: 16 June 2024