Journal of Innovation and Emerging Digital Technologies (JIEDT) https://journal.unimy.edu.my/index.php/JIEDT <p><strong>Journal of Innovation and Emerging Digital Technologies (JIEDT)</strong></p> <p><strong>eISSN 2948-4758</strong></p> <p>Indexed in Google Scholar and <a href="https://myjurnal.mohe.gov.my/public/browse-journal-view.php?id=1091" target="_blank" rel="noopener">MyJurnal</a></p> <p>Journal abbreviation: <strong>J. Inn. Emer. Digi. Tech.</strong></p> <p>Editor-in-Chief: Professor Ts. Dr. <a href="mailto:salwani.daud@unimy.edu.my" target="_blank" rel="noopener"><strong>Salwani Mohd Daud</strong></a></p> <p>The <strong>Journal of Innovation and Emerging Digital Technologies </strong>(<strong>JIEDT</strong>) is a refereed research journal managed by <strong>University Malaysia of Computer Science and Engineering</strong> (<a href="https://www.unimy.edu.my/"><strong>UNIMY</strong></a>) of Cyberjaya. The <strong>aims and scope of the journal</strong> encompass research articles, original research reports, reviews, short communications, and scientific commentaries from fundamental principles to practical applications in the broad field of <strong>emerging digital technologies.</strong></p> en-US salwani.daud@unimy.edu.my (Professor Ts. Dr. Salwani Mohd Daud) adam.yusoff@unimy.edu.my (Muhammad Adam bin Mohamed Yusoff) Sat, 31 Dec 2022 00:00:00 +0800 OJS 3.4.0.0 http://blogs.law.harvard.edu/tech/rss 60 SignBridge: Real-Time Sign Language Translator with Python and TensorFlow https://journal.unimy.edu.my/index.php/JIEDT/article/view/JIEDT.V1.NO2.ART1.DEC2022 <p><em>Sign language is the primary way for those deaf individuals to convey themselves. In real life, many people, even some deaf individuals, do not understand sign language. This research aims to develop a sign language translator using computer vision technology which helps to minimize the communication gap between normal s and deaf individuals. SignBridge mainly focuses on the user's hand gestures and body pose. SignBridge uses the MediaPipe Holistic to extract the key points of the user’s gestures and pose and perform real-time detection using OpenCV. It is trained using the Sequential model, which consists of the Long Short Term Memory (LSTM) model and the Dense layer with the set of videos as the training data stored in NumPy. After training, SignBridge can perform the prediction of the signs and show the result to the user by displaying the text on the top of the interface. The SignBridge system will be implemented on the laptop with a webcam to capture the gestures and pose of the user. A series of data analyses and comparisons have been conducted to determine the optimal model for the prediction based on four categories: alphabets, numbers, basic gestures, and a combination of three categories. With the comparison result, SignBridge successfully gained the highest accuracy among the models, and the overall accuracy will be in the range of 96.97 percent to 100 percent.</em></p> Nur Erlida Ruslan, Lee Boon Teck, Tang Shao Ming Copyright (c) 2022 Journal of Innovation and Emerging Digital Technologies (JIEDT) https://journal.unimy.edu.my/index.php/JIEDT/article/view/JIEDT.V1.NO2.ART1.DEC2022 Sun, 31 Dec 2023 00:00:00 +0800 VMouse: A Real-time Gesture-Based Virtual Mouse using Hand Landmark Model with OpenCV and Python https://journal.unimy.edu.my/index.php/JIEDT/article/view/JIEDT.V1.NO2.ART2.DEC2022 <p><em>Sign language is the primary way for those deaf individuals to convey themselves. In real life, many people, even some deaf individuals, do not understand sign language. This research aims to develop a sign language translator using computer vision technology which helps to minimize the communication gap between normal s and deaf individuals. SignBridge mainly focuses on the user's hand gestures and body pose. SignBridge uses the MediaPipe Holistic to extract the key points of the user’s gestures and pose and perform real-time detection using OpenCV. It is trained using the Sequential model, which consists of the Long Short Term Memory (LSTM) model and the Dense layer with the set of videos as the training data stored in NumPy. After training, SignBridge can perform the prediction of the signs and show the result to the user by displaying the text on the top of the interface. The SignBridge system will be implemented on the laptop with a webcam to capture the gestures and pose of the user. A series of data analyses and comparisons have been conducted to determine the optimal model for the prediction based on four categories: alphabets, numbers, basic gestures, and a combination of three categories. With the comparison result, SignBridge successfully gained the highest accuracy among the models, and the overall accuracy will be in the range of 96.97 percent to 100 percent.</em></p> Nur Erlida Ruslan, Tang Shao Ming, Lee Boon Teck Copyright (c) 2022 Journal of Innovation and Emerging Digital Technologies (JIEDT) https://journal.unimy.edu.my/index.php/JIEDT/article/view/JIEDT.V1.NO2.ART2.DEC2022 Sun, 31 Dec 2023 00:00:00 +0800 An Experimental Comparison of Supervised Machine Learning Models for Health-based Classification of Remaining Useful of Life in Turbofan Engines https://journal.unimy.edu.my/index.php/JIEDT/article/view/JIEDT.V1.NO2.ART3.DEC2022 <p><em>Prediction of remaining useful life (RUL) of a component in a manufacturing line is important to predictive maintenance. In this paper, we describe a data driven approach to using machine learning-based techniques for automating the failure prediction of equipment. The performance of the machine learning models are measured on their precision, recall, F1 scores and predicting the health of the engine from 21 features. This experimental analysis based on a new dataset from NASA on turbofan engines shows that KNN classifier performs the best in modeling a health indicator for this problem.</em></p> Hazeem Ahmad Taslim, Nor Adnan Yahaya, Nor Akmar Mohd Yahya Copyright (c) 2022 Journal of Innovation and Emerging Digital Technologies (JIEDT) https://journal.unimy.edu.my/index.php/JIEDT/article/view/JIEDT.V1.NO2.ART3.DEC2022 Sun, 31 Dec 2023 00:00:00 +0800 A Study of Comparison Analysis Tools for Children’s Food Nutrition Recommendation among Parent using Mobile Application https://journal.unimy.edu.my/index.php/JIEDT/article/view/JIEDT.V1.NO2.ART4.DEC2022 <p><em>A supplying children nutrition allowed children have good development and health for supporting development in future. However, to ensure that the children have sufficient nutrition, parents are the most concerned about preparing the food whether in security and insecurity of the food. Therefore, preparation of food recommendation is an essential process to prepare food with nutrition for children. As result, this report addresses a research gap in previous work on food nutrition for the market, commercial, and research objectives. Then, investigation of various research focusing on evaluation and analysis are conducted. To obtain the limitations of the tool study, the strengths and weaknesses of its features and functionality are counted. Finally, these tools still need to be improved, particularly for children food nutrition development and recommendation.</em></p> Noorrezam Yusop, Norrlaili Shapiee Copyright (c) 2022 Journal of Innovation and Emerging Digital Technologies (JIEDT) https://journal.unimy.edu.my/index.php/JIEDT/article/view/JIEDT.V1.NO2.ART4.DEC2022 Sat, 31 Dec 2022 00:00:00 +0800 A Review on Students’ Performance Analysis and Feedback System in the Virtual Learning Environment and e-Learning Environment https://journal.unimy.edu.my/index.php/JIEDT/article/view/JIEDT.V1.NO2.ART5.DEC2022 <p>In today's learning environment, educators must be able to forecast student performance and provide feedback to students in order for them to fully understand their academic position. As a result, they must adapt their teaching practices to enhance their learning outcomes. To overcome this problem many alternative and advanced methods have been proposed and implemented by research scholars. This study is based on the systematic review of previous research work based on the e-learning system and virtual learning environment to recognize significant research in the field of education systems. The research work is based on machine learning, deep learning models, and dataset used for the system. The research has contributed to highlighting the related challenges and solutions in virtual tutor and performance analysis implementation and presented a solution that will help to manage the huge data in the e-learning platforms.</p> Sireesha Prathi Gadapa, Salwani Mohd Daud Copyright (c) 2022 Journal of Innovation and Emerging Digital Technologies (JIEDT) https://journal.unimy.edu.my/index.php/JIEDT/article/view/JIEDT.V1.NO2.ART5.DEC2022 Sat, 31 Dec 2022 00:00:00 +0800