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Wix for Web Development and the Application of the Waterfall Model and Project Based Learning for Project Completion: A Case Study
No full textWebsite development without prior knowledge of HTML or programming experience would be a significant challenge. This study aims to share the students' experiences developing a website using Wix, a user-friendly website builder. The website was assigned as a project-based assessment of one of the courses required to complete a Foundation programme. The course was delivered using a project-based learning (PBL) approach in this context. The students worked as a group to write a project proposal, plan activities, develop the website, write reports, and present the outcome. As for the website development process, the study demonstrates the completion of the project using the Waterfall model, a sequential approach to the software development lifecycle (SDLC). In summary, this study aims to explore the use of Wix for web development, the PBL approach for course delivery, and the Waterfall model for completing the website project. The result shows that the students could complete the website development project in a timely manner by using Wix as their web design platform and adopting the Waterfall model as their project management approach. In addition, the students also benefitted from the PBL approach. The outcomes of this study will be of great benefit to educators, particularly in their role in helping students complete their website development projects
Ensemble-SMOTE: Mitigating Class Imbalance in Graduate on Time Detection
No full textIn education, detecting students graduating on time is difficult due to high data complexity. Researchers have employed various approaches in identifying on-time graduation with Machine Learning, but it remains a challenging task due to the class imbalance in the dataset. This study has aimed to (i) compare various class imbalance treatment methods with different sampling ratios, (ii) propose an ensemble class imbalance treatment method in mitigating the problem of class imbalance, and (iii) develop and evaluate predictive models in identifying the likelihood of students graduating on time during their studies in university. The dataset is collected from 4007 graduates of a university from year 2021 and 2022 with 41 variables. After feature selection, various class imbalance treatment methods were compared with different sampling ratios ranging from 50% to 90%. Moreover, Ensemble-SMOTE is proposed to aggregate the dataset generated by Synthetic Minority Oversampling Technique variants in mitigating the problem of class imbalance effectively. The dataset generated by class imbalance treatment methods were used as the input of the predictive models in detecting on-time graduation. The predictive models were evaluated based on accuracy, precision, recall, F0.5-score, F1-score, F2-score, Area under the Curve, and Area Under the Precision-Recall Curve. Based on the findings, Logistic Regression with Ensemble-SMOTE outperformed other predictive models, and class imbalance treatment methods by achieving the highest average accuracy (87.24), recall (92.50%), F1-score (91.30%), and F2-score (92.02%) from 6th until 10th trimester. To assess the effectiveness of class imbalance treatment methods, Friedman test is performed to determine on significant difference between the models after applying Shapiro-Wilk test in normality test. Consequently, Ensemble-SMOTE is ranked as the top-performers by achieving the lowest value in the average rank based on the performance metrics. Additional research could incorporate and examine more complicated approaches in mitigating class imbalance when the dataset is highly imbalanced
Social Messaging Application with Translation and Speech-to-Text Transformation
No full textUnlike traditional SMS or MMS, messaging apps offer a broader range of data transmission capabilities. The application utilizes a WIFI or internet connection and enables users to exchange information through various means such as text, voice, and multimedia files. However, popular messaging applications such as WeChat, Telegram, and WhatsApp have limitations in language translation and file uploading size. Thus, this project aims to address these limitations by developing a social messaging application that serves as a comprehensive communication tool. The application will facilitate both written and verbal communication by providing translation services for various languages, including voice messages. The proposed application intends to act as a versatile platform, functioning as a translator while enabling seamless communication between users in different languages. Translation accuracy and BLEU metric are applied to evaluate the efficacy of the enhanced social messaging application. The proposed application is able to translate voice and written messages into another language with the help of Google translation API as well as Speech to text API. The BLEU average score between English and Malay is 0.94 but the translation between Malay and Chinese is 0.82, Chinese and English is 0.70. Though not perfect, the proposed application can enhance the current social messaging application with the speech-to-text feature and message translation feature. Last but not least, a concluding remark is provided to further improve the application in future
Pi Class – A Revolutionary Step Forward in Hybrid Class Management System
No full textThe 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
Intelligent Abstractive Summarization of Scholarly Publications with Transfer Learning
No full textIntelligent abstractive text summarization of scholarly publications refers to machine-generated summaries that capture the essential ideas of an article while maintaining semantic coherence and grammatical accuracy. As information continues to grow at an overwhelming rate, text summarization has emerged as a critical area of research. In the past, summarization of scientific publications predominantly relied on extractive methods. These approaches involve selecting key sentences or phrases directly from the original document to create a summary or generate a suitable title. Although extractive methods preserve the original wording, they often lack the ability to produce a coherent, concise, and fluent summary, especially when dealing with complex or lengthy texts. In contrast, abstractive summarization represents a more sophisticated approach. Rather than extracting content from the source, abstractive models generate summaries using new language, often incorporating words and phrases not found in the original text. This allows for more natural, human-like summaries that better capture the key ideas in a fluid and cohesive manner. This study introduces two advanced models for generating titles from the abstracts of scientific articles. The first model employs a Gated Recurrent Unit (GRU) encoder coupled with a greedy-search decoder, while the second utilizes a Transformer model, known for its capacity to handle long-range dependencies in text. The findings demonstrate that both models outperform the baseline Long Short-Term Memory (LSTM) model in terms of efficiency and fluency. Specifically, the GRU model achieved a ROUGE-1 score of 0.2336, and the Transformer model scored 0.2881, significantly higher than the baseline LSTM model, which reported a ROUGE-1 score of 0.1033. These results underscore the potential of abstractive models to enhance the quality and accuracy of summarization in academic and scholarly contexts, offering more intuitive and meaningful summaries
DDoS Attack Detection with Machine Learning
No full textNowadays, Distributed Denial of Service (DDoS) attacks are a major issue in internet security. These attacks target servers or network infrastructure. Similar to an unanticipated traffic jam on highway (lagging/crash) that prevent normal traffic reach to destination. DDoS may prevent users to access any system services. Researchers and scientists have developed numerous methods and algorithms to improve the performance of DDoS detection. In this paper, a DDoS detection method utilizing machine learning is proposed. There are three type of supervised machine learning classification methods which are K-Nearest Neighbor, Multilayer Perceptron and Random Forest, are applied in the proposed work to assess the accuracy of the model in training and testing processes. RF classification provides robustness and interpretability, MLP offers deep learning capabilities for complex patterns, and K-NN delivers simplicity and adaptability for instance-based learning. Together, these methods can contribute to a comprehensive DDoS attack detection system using machine learning. There are two types of classification setups: binary and multi-class classification. Binary classification involves identifying traffic as either a DDoS attack or normal using the NSL-KDD dataset. Multi-class classification, on the other hand, distinguishes between various types of DDoS attacks (such as DoS, Probe, U2R, and Sybil) and normal traffic using the NSL-KDD dataset. Feature engineering is also involved in this experiment to convert the categorical features into numerical values for detecting DDoS attack. Our model's performance was effective compared to other machine learning methods. RF achieved the highest accuracy rates: 99.35% in binary classification and 97.71% in multi-class classification. K-NN followed with 99.15% in binary and 97.35% in multi-class classification, while MLP achieved 90.63% in binary and 84.33% in multi-class classification
A Cutting-Edge Hybrid Approach for Precise COVID-19 Detection using Deep Learning
No full textThe early detection of COVID-19 is essential for decision-makers to develop effective containment and treatment plans. Traditionally, researchers interpret computer tomography (CT) scans or X-ray images in order to diagnose this disease. This study aims to demonstrate that deep learning models can be applied to three common medical imaging modes: X-rays, ultrasounds, and CT scans. This study employs and enhances four convolutional neural networks for coronavirus detection, including DenseNet121, ResNet101V2, NASNetMobile, and MobileNetV2. In this study, two main experiments were carried out. In the first experiment, a model was developed by combining imagery data to detect this virus. In order to determine which model performed the best, separate models were trained using different datasets in the second experiment. Because there were only so many photos accessible, data augmentation techniques were used to enhance the amount artificially. The results indicate that the proposed models effectively accomplished the task of classifying COVID-19. The accuracy rates achieved by the combined model, utilizing DenseNet121, ResNet101V2, NASNetMobile, and MobileNetV2, were 88.21%, 93.02%, and 88.89% respectively. When using the combined imaging dataset, the CNN model employing ResNet101v2 exhibited superior accuracy compared to NASNetMobile, DenseNet121, and MobileNetV2 models.
Manuscript received: 23 Jan 2024 | Revised: 26 Feb 2024 | Accepted: 25 Mar 2024 | Published: : 30 Apr 202
Ranking Up The Social Media Factors on Purchasing Decision of Malaysian Generation Y Customers: DOI: https://doi.org/10.33093/ijomfa.2024.5.1.8
No full textSocial media platforms offer marketing, information dissemination, direct customer contact and optimised availability of product and service information, which influences consumer behaviour, especially among Generation Y. Therefore, this study ranking up the social media factors such as electronic word of mouth (e-WOM), customer engagement, trust, brand image and advertising on the purchasing decisions of Generation Y consumers in Malaysia. 193 data were collected using Google Forms and face-to-face questionnaires. The data was then analysed using the Best-Worst method, which ranks the most important factors according to respondents' gender, income, and education level. One response was rejected because the value of consistency ratio (CR) was above the associated threshold value of 0.3062. Across all demographics, trust was found to be the most important factor in the purchase decision. Brand image and advertisement, on the other hand, are the least important, indicating a departure from traditional marketing strategies. A detailed explanation of these findings is presented in the paper. This study provides further insight and deepens knowledge for businesses by enabling them to adapt their marketing approaches and gain an understanding of the changing buying patterns and behaviours of Malaysian Generation Y consumers
Factors Influencing Non-Performing Loans between Islamic and Conventional Banks in Malaysia: DOI: https://doi.org/10.33093/ijomfa.2024.5.2.10
No full textThis study reveals the long-term effects of several variables on Non-Performing Loans (NPLs) in Islamic banks (IBs) and conventional banks (CBs) in Malaysia. Using the Autoregressive Distributed Lag (ARDL) technique, the study supports a proactive approach and finds a persistent association between NPLs and asset quality, loan quality, unemployment rate, and inflation rate. Using the ARDL approach, the analysis covers the monthly period from 2018 to early 2021. This study aims to investigate NPLs at two different times: before and after the COVID-19 epidemic. The findings indicate that The ARDL model identifies Loan Quality 1 and 2 as significant influencers of NPLs in Malaysia's CBs. At the same time, asset quality and the unemployment rate show no significant impact. In contrast, IBs show a strong positive correlation between Asset Quality and NPL, with economic factors like the Unemployment Rate and Inflation Rate significantly affecting NPL, reflecting the unique risk-sharing nature of Islamic finance. These findings necessitate improved risk management strategies in both banking sectors
Awareness and perception of alternative dispute resolution methods in Penang
No full textAlternative dispute resolution (ADR) methods serve as cheaper and faster options to settle disputes in place of litigation. Although the local government and relevant ADR bodies in Malaysia have been taking initiatives to promote ADR to lighten overloaded courts’ load and create a peaceful and harmonious society, the awareness level still leaves room for improvement. This study aimed to investigate the perception and awareness level of ADR in Penang. Questionnaires were distributed to the course participants of an ADR training course in Penang. The analysis shows that while a large majority of the participants were aware of ADR before enrolling in the course, more than half of them believed that ADR methods were not actively utilised in Malaysia while about two-thirds believed that the general public was not aware of such options. Actions such as more media campaigns and greater institutional support should be undertaken to educate and heighten the public’s awareness about the ADR’s availability, processes and functions so that they could be translated into greater usage