Journal of Informatics and Web Engineering最新文献

Modelling of Virtual Campus Tour in Minecraft 用 Minecraft 制作虚拟校园参观模型

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.2

Liyana Tan Lin, Han-Foon Neo

{"title":"Modelling of Virtual Campus Tour in Minecraft","authors":"Liyana Tan Lin, Han-Foon Neo","doi":"10.33093/jiwe.2024.3.1.2","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.2","url":null,"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.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"16 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139777396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementation of Grover’s Algorithm & Bernstein-Vazirani Algorithm with IBM Qiskit 用 IBM Qiskit 实现格罗弗算法和伯恩斯坦-瓦齐拉尼算法

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.6

Yang-Che Liu, Mei-Feng Liu

引用次数: 0

Adaptive Gaussian Wiener Filter for CT-Scan Images with Gaussian Noise Variance 用于具有高斯噪声方差的 CT 扫描图像的自适应高斯维纳滤波器

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.11

Kai Liang Lew, Chung Yang Kew, Kok-Swee Sim, Shing Chiang Tan

{"title":"Adaptive Gaussian Wiener Filter for CT-Scan Images with Gaussian Noise Variance","authors":"Kai Liang Lew, Chung Yang Kew, Kok-Swee Sim, Shing Chiang Tan","doi":"10.33093/jiwe.2024.3.1.11","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.11","url":null,"abstract":"Medical imaging plays an important role in modern healthcare, with Computed Tomography (CT) being essential for high-resolution cross-sectional imaging. However, Gaussian noise often occurs within the CT scan images and makes it difficult for image interpretation and reduces the diagnostic accuracy, creating a significant obstacle to fully utilizing CT scanning technology. Existing denoising techniques have a hard time balance between noise reduction and preserving the important image details, failing to enable the optimal diagnostic precision. This study introduces Adaptive Gaussian Wiener Filter (AGWF), a novel filter aims to denoise CT scan images that have been corrupted with various Gaussian noise variance without compromising the image details. The AGWF combines the Gaussian filter for initial noise reduction, followed by the implementation of Wiener filter, which can adaptively estimate noise variance and signal power in localized regions. This approach not only outperforms other existing techniques but also showcases a remarkable balance between noise reduction and image detail preservation. The experiment evaluates 300 images from the dataset and each image is corrupted with Gaussian noise variance to ensure a comprehensive evaluation of the AGWF’s performance. The evaluation indicated that AGWF can improve the Signal-to-Noise Ratio (SNR) value and reduce the Root Mean Square Error (RMSE) and Mean Square Error (MSE) value, showing a qualitative improvement in CT scan imagery. The proposed method holds promising potential for advancing medical imaging technology with the implementation of deep learning.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"67 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139779468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Crow Search and RBFNN: A Novel Approach to Medical Data Classification 混合乌鸦搜索和 RBFNN：医疗数据分类的新方法

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.17

Marai Ali, Faisal Khan, Muhammad Nouman Atta, Abdullah Khan, Asfandyar Khan

{"title":"Hybrid Crow Search and RBFNN: A Novel Approach to Medical Data Classification","authors":"Marai Ali, Faisal Khan, Muhammad Nouman Atta, Abdullah Khan, Asfandyar Khan","doi":"10.33093/jiwe.2024.3.1.17","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.17","url":null,"abstract":"The Radial Basis Function Neural Network (RBFNN) is frequently employed in artificial neural networks for diverse classification tasks, yet it encounters certain limitations, including issues related to network latency and local minima. To tackle these challenges, researchers have explored various algorithms to enhance learning performance and alleviate local minima problems. This study introduces a novel approach that integrates the Crow Search Algorithm (CSA) with RBFNN to augment the learning process and address the local minima issue associated with RBFNN. The study evaluates the performance of this innovative model by comparing it to state-of-the-art models like Flower-pollination-RBNN (FP-NN), Artificial Neural Network (ANN), and the conventional RBFNN. To assess the efficacy of the proposed model, the study employs specific datasets, such as the Breast Cancer and Thyroid Disease datasets from the UCI Machine Repository. The simulation results illustrate that the proposed model surpasses other models in terms of accuracy, exhibiting lower Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. Specifically, for the Breast Cancer dataset, the proposed model attains an accuracy of 99.9693%, MSE of 0.000307024, and MAE of 0.00789449. Likewise, for the Thyroid Disease dataset, the proposed model achieves an accuracy of 99.9535%, along with MSE of 0.000464932 and MAE of 0.0057098. For the diabetes dataset, the proposed model demonstrates an accuracy of 98.8073%, MSE of 0.003024, and MAE of 0.009449. In summary, this analysis underscores the enhanced accuracy and effectiveness of the proposed model when compared to traditional approaches.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"173 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139837422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Electric Vehicle Health Monitoring with Electric Vehicle Range Prediction and Route Planning 电动汽车健康监测与电动汽车续航里程预测和路线规划

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.18

Jayapradha Jayaram, J. Chetan, Barun Nayak

{"title":"Electric Vehicle Health Monitoring with Electric Vehicle Range Prediction and Route Planning","authors":"Jayapradha Jayaram, J. Chetan, Barun Nayak","doi":"10.33093/jiwe.2024.3.1.18","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.18","url":null,"abstract":"The automotive industry is experiencing a revolutionary wave due to the rapid spread of electric vehicles (EVs), which is paving the way for a fundamental and long-lasting revolution in the way we approach transportation. The global movement to reduce greenhouse gas emissions and lessen the environmental impact of traditional internal combustion engine vehicles has seen a significant boost in the popularity of electric vehicles as people come together to support environmentally conscious and sustainable mobility solutions. But the ecology surrounding electric vehicles must continue to flourish if the particular problems that EVs present are to be successfully addressed. Chief among these are the formidable foes of range anxiety and battery health management. Range anxiety is a real issue felt by many potential EV owners worry about becoming stuck because their battery has run out before reaching their destination. This psychological barrier is very noticeable and makes present and future EV owners doubtful. In addition, the longevity and health of EV batteries are essential to their continued effectiveness and affordability. The driving range and operating efficiency of the vehicle are directly affected by the gradual degradation of the battery due to several factors like aging, charging patterns, and temperature. This research presents an integrative and holistic approach to address these pressing issues, enhancing and elevating the whole EV ownership experience by combining Electric Vehicle Health Monitoring (EVHM) with Electric Vehicle Range Prediction (EVRP) and Route Planning (EVRP). Combining these three essential elements creates an all-encompassing plan created to not only lessen these enormous obstacles but also accelerate the switch to electric vehicles by giving consumers the knowledge and assurance they require for a smooth, eco-friendly, and sustainable mobility in the future.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"38 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139778481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of Student’s Academic Performance through Data Mining Approach 通过数据挖掘方法预测学生的学习成绩

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/2024.3.1.16

Muhammad Mubashar Hussain, Shahzad Akbar, Syed Ale Hassan, Muhammad Waqas Aziz, Farwa Urooj

{"title":"Prediction of Student’s Academic Performance through Data Mining Approach","authors":"Muhammad Mubashar Hussain, Shahzad Akbar, Syed Ale Hassan, Muhammad Waqas Aziz, Farwa Urooj","doi":"10.33093/2024.3.1.16","DOIUrl":"https://doi.org/10.33093/2024.3.1.16","url":null,"abstract":"The universities and institutes produce a large amount of student data that can be used in a disciplinary way and useful information can be extracted by using an automated approach. Educational Data Mining (EDM) is an emerging discipline used in the educational environment to deal with big student data and extract useful information. The data mining of students’ data can help the At-risk students as well as the stakeholders by the early warning. This study aims to predict the performance of the students based on student-related data to increase the overall performance. In existing studies, insufficient attributes and complexity of network models is a problem. The student’s current records and grades need to be analyzed. In this approach, the Levenberg Marquardt Algorithm (MLA) deep learning algorithm is used. The data consists of the class test, attendance, assignment and midterm scores. The neural network model consists of four input variables, three hidden and one output layer. The performance of the deep neural network is evaluated by accuracy, precision, recall and F1 score. The proposed model gained a higher accuracy of 88.6% than existing studies. The study successfully predicts the student's final grades using current academic records. This research will be beneficial to the students, educators and educational authorities as a whole.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"65 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139836643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Lung Cancer Detection with Pre-Trained CNN Models 利用预先训练的 CNN 模型检测肺癌

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.3

Chee Chiet Chai, W. Khoh, Ying Han Pang, Hui-Yen Yap

{"title":"A Lung Cancer Detection with Pre-Trained CNN Models","authors":"Chee Chiet Chai, W. Khoh, Ying Han Pang, Hui-Yen Yap","doi":"10.33093/jiwe.2024.3.1.3","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.3","url":null,"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%.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"62 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139836715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementation of Grover’s Algorithm & Bernstein-Vazirani Algorithm with IBM Qiskit 用 IBM Qiskit 实现格罗弗算法和伯恩斯坦-瓦齐拉尼算法

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.6

Yang-Che Liu, Mei-Feng Liu

引用次数: 0

Vision-Based Gait Analysis for Neurodegenerative Disorders Detection 基于视觉的步态分析用于神经退行性疾病检测

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.9

Vincent Wei Sheng Tan, Wei Xiang Ooi, Yi Fan Chan, Connie Tee, Michael Kah Ong Goh

{"title":"Vision-Based Gait Analysis for Neurodegenerative Disorders Detection","authors":"Vincent Wei Sheng Tan, Wei Xiang Ooi, Yi Fan Chan, Connie Tee, Michael Kah Ong Goh","doi":"10.33093/jiwe.2024.3.1.9","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.9","url":null,"abstract":"Parkinson’s Disease (PD) is a debilitating neurodegenerative disorder that affects a significant portion of aging population. Early detection of PD symptoms is crucial to prevent the progression of the disease. Research has revealed that gait attributes can provide valuable insights into PD symptoms. The gait acquisition techniques used in current research can be broadly divided into two categories: vision-based and sensor-based. The markerless vision-based classification model has become a prominent research trend due to its simplicity, low cost and patient comfort. In this study, we propose a novel markerless vision-based approach to obtain gait features from participants' gait videos. A dataset containing gait videos from normal subjects and PD patients were collected, along with a control group of 25 healthy adults. The participants were requested to perform a Timed Up and Go (TUG) test, during which their walking sequences were recorded using two smartphones positioned at different angles, namely side and front. A multi-person pose estimator is used to estimate human skeletal joint points from the collected gait videos. Different gait features associated with PD including stride length, number of steps taken during turn, turning duration, speed and cadence are derived from these key point information to perform PD detection. Experimental results show that the proposed solution achieves an accuracy of 89.39%. The study's findings demonstrate the potential of markerless vision-based gait acquisition techniques for early detection of PD symptoms.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"208 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139836676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Electric Vehicle Health Monitoring with Electric Vehicle Range Prediction and Route Planning 电动汽车健康监测与电动汽车续航里程预测和路线规划

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.18

Jayapradha Jayaram, J. Chetan, Barun Nayak

{"title":"Electric Vehicle Health Monitoring with Electric Vehicle Range Prediction and Route Planning","authors":"Jayapradha Jayaram, J. Chetan, Barun Nayak","doi":"10.33093/jiwe.2024.3.1.18","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.18","url":null,"abstract":"The automotive industry is experiencing a revolutionary wave due to the rapid spread of electric vehicles (EVs), which is paving the way for a fundamental and long-lasting revolution in the way we approach transportation. The global movement to reduce greenhouse gas emissions and lessen the environmental impact of traditional internal combustion engine vehicles has seen a significant boost in the popularity of electric vehicles as people come together to support environmentally conscious and sustainable mobility solutions. But the ecology surrounding electric vehicles must continue to flourish if the particular problems that EVs present are to be successfully addressed. Chief among these are the formidable foes of range anxiety and battery health management. Range anxiety is a real issue felt by many potential EV owners worry about becoming stuck because their battery has run out before reaching their destination. This psychological barrier is very noticeable and makes present and future EV owners doubtful. In addition, the longevity and health of EV batteries are essential to their continued effectiveness and affordability. The driving range and operating efficiency of the vehicle are directly affected by the gradual degradation of the battery due to several factors like aging, charging patterns, and temperature. This research presents an integrative and holistic approach to address these pressing issues, enhancing and elevating the whole EV ownership experience by combining Electric Vehicle Health Monitoring (EVHM) with Electric Vehicle Range Prediction (EVRP) and Route Planning (EVRP). Combining these three essential elements creates an all-encompassing plan created to not only lessen these enormous obstacles but also accelerate the switch to electric vehicles by giving consumers the knowledge and assurance they require for a smooth, eco-friendly, and sustainable mobility in the future.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"9 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139838154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0