Forthcoming Issue on March 2024, Vol. 3 No. 1
The following manuscripts will be appeared in February 2024 as vol. 3, issue no. 1:
Proofread No. 1. Term Standardisation With LDA Model To Detect Service Disruption Events Using English And Manglish Tweets
Noraysha Yusuf 1, Maizatul Akmar Ismail2, Tasnim M. A. Zayet3*, Kasturi Dewi Varathan4, Rafidah MD Noor5
1,2,3,4,5 Universiti Malaya, Malaysia
*corresponding author:(email@example.com; ORCiD: 0000-0001-5755-5953)
Abstract - Rapid transit is one of Malaysia's most important transportation modes, where commuters use public transportation to travel. Any disruption in the rapid transit service affects their daily routines. Therefore, detecting such service disruption has become fundamental. In this study, the disruption in Malaysia's rapid transit service was assessed using English and Manglish (a combination of English and Malay) tweets through Latent Dirichlet Allocation (LDA). The gathered tweets were classified into event and non-event tweets and LDA was applied to the event tweets. Manglish event tweets were pre-processed using the proposed term standardisation technique. As a result, LDA has proved its efficiency in topic detection for both English and Manglish tweets with better performance for Manglish tweets; The best event detection rate of the LDA_English model was at the likelihood of 80% while the best detection rate of the LDA_Manglish model was at a likelihood of 60%.
Keywords— Rapid Transit, LDA, Manglish, Multilingual, Twitter
Received: 21 June 2023; Accepted: 9 August 2023; Published: 16 February 2024
Proofread No. 2. Modelling of Virtual Campus Tour in Minecraft
Liyana Tan Lin1, Han-Foon Neo2*
1, 2 Faculty of Information Science and Technology,
Multimedia University, Jalan Ayer Keroh Lama,
Bukit Beruang, 75450, Malaysia
*corresponding author: (firstname.lastname@example.org; ORCiD: 0000-0001-5054-3351)
Abstract - Virtual tours have revolutionized the way to explore and experience places from the comfort of our own home. Through advanced technology and immersive digital platforms, virtual tours offer a compelling alternative to tradition face-to-face visits. Whether a famous landmark, museum, real estate or natural wonders, virtual tours offer a unique opportunity to navigate and discover these places form a distance. Meanwhile, creating a virtual tour in Minecraft can provide a unique and immersive experience that sets the users apart from other virtual tour platforms. Minecraft is one of the most popular video games in the world and boasts a large and dedicated community of players. Using Minecraft for a virtual tour allow users to reach a larger audience who are already familiar with the game, increasing the likelihood of engagement and participation. In this paper, the aim is to create a virtual campus tour in Minecraft to give the visitors an immersive and interactive experience with creative freedom. A series of buildings have been built such as Siti Hasmah Digital Library, Common Lecture Complex (CLC) and Smart Lab. Visitors can move around the campus with some gameplay mechanics using mouse and keyboard. Building information was also integrated so visitors can see details about each building during the virtual tour. The virtual tour provides access, comfort and a sense of connection to prospective students, their families and international visitors. Additionally, it serves as a low-cost marketing tool that increases engagement, attracts potential students, researchers and staff and ultimately benefits the University’s recruitment efforts.
Keywords—Campus, Virtual Tour, Minecraft, Modelling, Engaging experience, Interactivity
Received: 20 July 2023; Accepted: 29 August 2023; Published: 16 February 2024
Proofread No. 3. A Lung Cancer Detection With Pre-Trained CNN Models
Chai Chee Chiet1, Khoh Wee How2*, Pang Ying Han3 and Yap Hui Yen4
1,2,3,4 Faculty of Information Science and Technology, Multimedia University, Malaysia
*corresponding author:( email@example.com；ORCiD: 0000-0002-7338-8427）
Abstract - Lung cancer is a common cancer in Malaysia, affecting the majority of male citizens. The early detection of lung cancer will decrease its death rate. The only way to detect lung cancer is with a CT scan, and it also requires the doctor to check the scan to confirm the disease. In another way, the computer's support for the detection and diagnosis tool will assist doctors in determining lung cancer more accurately and efficiently. There are three main objectives for this research work. The first target is to study state-of-the-art research work to detect and recognize lung cancer from CT scan images. Then, the article will aim to adopt pre-trained convolutional neural network models in lung cancer detection. It also evaluates the performance of convolutional models on lung cancer imagery data. Then, the pre-trained models with a few added layers and modifications to parameters such as epochs, batch size, optimizer, etc. to conduct model training in this article. After that, Python Pylidc is used in image pre-processing to filter the dataset. Overall, pre-trained models such as ResNet-50, VGG-16, Xception, and MobileNet achieve above-state-of-the-art performance in classifying lung cancer from CT scan images in the range of 78% to 86% accuracy. The best detection accuracy result is the pre-trained VGG-16 model with the addition of some fully connected layers, 16 batch sizes, and the Adam optimizer, which achieved 86.71%.
Keywords— Convolution neural network, Lung Cancer, Image Processing, pre-trained CNN models
Received: 14 June 2023; Accepted: 31 August 2023; Published: 16 February 2024
Proofread No. 4. Optimizing Medical IoT Disaster Management with Data Compression
Nunudzai Mrewa1, Athirah Mohd Ramly1,2,3, Angela Amphawan1,2, Tse-Kian Neo4*
1Department of Computing and Information Systems, School of Engineering, Sunway University,
47500 Petaling Jaya, Malaysia.
2Smart Photonics Research Laboratory, School of Engineering and Technology, Sunway University,
47500 Petaling Jaya, Malaysia
3Research Centre for Human-Machine Collaboration (HUMAC), Department of Computing and Information Systems, Sunway University, 47500 Petaling Jaya, Malaysia.
4CAMELOT, Faculty of Creative Multimedia, Multimedia University, Cyberjaya 63100 Selangor, Malaysia
*Corresponding author: (firstname.lastname@example.org; ORCiD: 0000-0002-5991-7409)
Abstract - In today's technological landscape, the convergence of the Internet of Things (IoT) with various industries showcases the march of progress. This coming together involves combining diverse data streams from different sources and transmitting processed data in real-time. This empowers stakeholders to make quick and informed decisions, especially in areas like smart cities, healthcare, and industrial automation, where efficiency gains are evident. However, with this convergence comes a challenge – the large amount of data generated by IoT devices. This data overload makes processing and transmitting information efficiently a significant hurdle, potentially undermining the benefits of this union. To tackle this issue, we introduce "Beyond Orion," a novel lossless compression method designed to optimize data compression in IoT systems. The algorithm employs advanced techniques such as Lempel Ziv-Welch and Huffman encoding, while also integrating strategies like pipelining, parallelism, and serialization for greater efficiency and lower resource usage. Through rigorous experimentation, we demonstrate the effectiveness of Beyond Orion. The results show impressive data reduction, with up to 99% across various datasets, and compression factors as high as 7694.13. Comparative tests highlight the algorithm's prowess, achieving savings of 72% and compression factor of 3.53. These findings have significant implications. They promise improved data handling, more effective decision-making, and optimized resource allocation across a range of IoT applications. By addressing the challenge of data volume, Beyond Orion emerges as a significant advancement in IoT data management, marking a substantial step towards realizing the full potential of IoT technology.
Keywords—Data Compression, Internet of Things (IoT), Disaster Management, Enhanced Lempel Ziv-Welch and Huffman, Pipelining, Parallelism, Serialization
Received: 29 May 2023; Accepted: 12 September 2023; Published: 16 February 2024