Journal of Computing Theories and Applications最新文献

Learning through Simulation: A Role-Based Learning Exercise to Equip Students in Big Data Requirement Elicitation Skills and Challenges 通过模拟学习：基于角色的学习练习，让学生掌握大数据需求激发技能并应对挑战

Journal of Computing Theories and Applications Pub Date : 2024-07-28 DOI: 10.62411/jcta.10847

Ppg Dinesh Asanka, Thilini V. Mahanama

{"title":"Learning through Simulation: A Role-Based Learning Exercise to Equip Students in Big Data Requirement Elicitation Skills and Challenges","authors":"Ppg Dinesh Asanka, Thilini V. Mahanama","doi":"10.62411/jcta.10847","DOIUrl":"https://doi.org/10.62411/jcta.10847","url":null,"abstract":"Bridging the gap between theoretical knowledge and practical application remains challenging in big data education. Our paper proposes a platform where students actively participate in a role-based learning (RBL) exercise designed to simulate real-world big data projects. This RBL exercise assigns each student participant to a business analyst role tasked with eliciting requirements or a customer role that provides those big data requirements. We conducted RBL sessions for postgraduate and undergraduate students learning big data analytics/data warehousing. Through role-playing in a collaborative environment, students face challenges such as unclear objectives, data privacy concerns, and evolving requirements. We evaluated the effectiveness of RBL simulations through brainstorming sessions, which verified that the students achieved the learning outcomes through RBL exercises. We also collected students’ feedback through a survey and found the RBL experience helped us understand the big data requirement elicitation skills, highlighting the significance of communication and collaboration skills. Further, we have evaluated students through a final exam question, and identified that students who participated in the RBL exercise outperformed in the big data requirement elicitation question. In summary, our research demonstrates that this RBL approach offers a valuable learning experience by enabling students to directly experience the complexities of big data requirement elicitation and identify the future requirements or challenges that encourage them to acquire the required skills.","PeriodicalId":397752,"journal":{"name":"Journal of Computing Theories and Applications","volume":"4 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797028","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

Development of a Model to Classify Skin Diseases using Stacking Ensemble Machine Learning Techniques 利用堆叠集合机器学习技术开发皮肤病分类模型

Journal of Computing Theories and Applications Pub Date : 2024-05-20 DOI: 10.62411/jcta.10488

Oluwayemisi Jaiyeoba, E. Ogbuju, Owolabi Temitope Yomi, Francisca Oladipo

{"title":"Development of a Model to Classify Skin Diseases using Stacking Ensemble Machine Learning Techniques","authors":"Oluwayemisi Jaiyeoba, E. Ogbuju, Owolabi Temitope Yomi, Francisca Oladipo","doi":"10.62411/jcta.10488","DOIUrl":"https://doi.org/10.62411/jcta.10488","url":null,"abstract":"Skin diseases are highly prevalent and transmissible. It has been one of the major health problems that most people face. The diseases are dangerous to the skin and tend to spread over time. A patient can be cured of these skin diseases if they are detected on time and treated early. However, it is difficult to identify these diseases and provide the right medications. This study's research objectives involve developing an ensemble machine learning based model for classifying Erythemato-Squamous Diseases (ESD). The ensemble techniques combine five different classifiers, Naïve Bayes, Support Vector Classifier, Decision Tree, Random Forest, and Gradient Boosting, by merging their predictions and utilizing them as input features for a meta-classifier during training. We tested and validated the ensemble model using the dataset from the University of California, Irvine (UCI) repository to assess its effectiveness. The Individual classifiers achieved different accuracies: Naïve Bayes (85.41%), Support Vector Machine (98.61%), Random Forest (97.91%), Decision Tree (95.13%), Gradient Boosting (95.83%). The stacking method yielded a higher accuracy of 99.30% and a precision of 1.00, recall of 0.96, F1 score of 0.97, and specificity of 1.00 compared to the base models. The study confirms the effectiveness of ensemble learning techniques in classifying ESD.","PeriodicalId":397752,"journal":{"name":"Journal of Computing Theories and Applications","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120330","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

Integrating Convolutional Neural Network and Weighted Moving Average for Enhanced Human Fall Detection Performance 整合卷积神经网络和加权移动平均法，提高人体跌倒检测性能

Journal of Computing Theories and Applications Pub Date : 2024-05-16 DOI: 10.62411/jcta.10428

Kyi Pyar

{"title":"Integrating Convolutional Neural Network and Weighted Moving Average for Enhanced Human Fall Detection Performance","authors":"Kyi Pyar","doi":"10.62411/jcta.10428","DOIUrl":"https://doi.org/10.62411/jcta.10428","url":null,"abstract":"This study proposes an approach for human fall classification utilizing a combination of Weighted Moving Average (WMA) and Convolutional Neural Networks (CNN) on the SisFall dataset. Falls among elderly individuals pose a significant public health concern, necessitating effective automated detection systems for timely intervention and assistance. The SisFall dataset, comprising accelerometer data collected during simulated falls and activities of daily living, serves as the basis for training and evaluating the proposed classification system. The proposed method begins by preprocessing accelerometer data using a WMA technique to enhance signal quality and reduce noise. Subsequently, the preprocessed data are fed into a CNN architecture optimized for feature extraction and fall classification. The CNN leverages its ability to automatically learn discriminative features from raw sensor data, enabling robust and accurate classification of fall and non-fall events. Experimental results demonstrate the efficacy of the proposed approach in accurately distinguishing between fall and non-fall activities, achieving high classification performance metrics such as accuracy, precision, recall, and F1-score. Comparative analysis with existing methods showcases the WMA-CNN hybrid approach's superiority in classification accuracy and robustness. Overall, the proposed methodology presents a promising framework for real-time human fall classification using sensor data, offering potential applications in wearable devices, ambient assisted living systems, and healthcare monitoring technologies to enhance safety and well-being among elderly individuals.","PeriodicalId":397752,"journal":{"name":"Journal of Computing Theories and Applications","volume":" 921","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127610","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 Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis 三维车辆轮毂生成与合成的生成模型综述

Journal of Computing Theories and Applications Pub Date : 2024-03-21 DOI: 10.62411/jcta.10125

Timileyin Opeyemi Akande, O. O. Alabi, Julianah B. Oyinloye

{"title":"A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis","authors":"Timileyin Opeyemi Akande, O. O. Alabi, Julianah B. Oyinloye","doi":"10.62411/jcta.10125","DOIUrl":"https://doi.org/10.62411/jcta.10125","url":null,"abstract":"Integrating deep learning methodologies is pivotal in shaping the continuous evolution of computer-aided design (CAD) and computer-aided engineering (CAE) systems. This review explores the integration of deep learning in CAD and CAE, particularly focusing on generative models for simulating 3D vehicle wheels. It highlights the challenges of traditional CAD/CAE, such as manual design and simulation limitations, and proposes deep learning, especially generative models, as a solution. The study aims to automate and enhance 3D vehicle wheel design, improve CAE simulations, predict mechanical characteristics, and optimize performance metrics. It employs deep learning architectures like variational autoencoders (VAEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs) to learn from diverse 3D wheel designs and generate optimized solutions. The anticipated outcomes include more efficient design processes, improved simulation accuracy, and adaptable design solutions, facilitating the integration of deep learning models into existing CAD/CAE systems. This integration is expected to transform design and engineering practices by offering insights into the potential of these technologies.","PeriodicalId":397752,"journal":{"name":"Journal of Computing Theories and Applications","volume":" 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140221806","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

Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer’s Drug Discovery with LightGBM 利用 LightGBM 对β-泌乳素酶 1 抑制剂的活性进行分类，以发现阿尔茨海默氏症药物

Journal of Computing Theories and Applications Pub Date : 2024-03-10 DOI: 10.62411/jcta.10129

T. R. Noviandy, Khairun Nisa, Ghalieb Mutig Idroes, Irsan Hardi, N. R. Sasmita

{"title":"Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer’s Drug Discovery with LightGBM","authors":"T. R. Noviandy, Khairun Nisa, Ghalieb Mutig Idroes, Irsan Hardi, N. R. Sasmita","doi":"10.62411/jcta.10129","DOIUrl":"https://doi.org/10.62411/jcta.10129","url":null,"abstract":"This study explores the utilization of LightGBM, a gradient-boosting framework, to classify the inhibitory activity of beta-secretase 1 inhibitors, addressing the challenges of Alzheimer's disease drug discovery. The study aims to enhance classification performance by focusing on overcoming the limitations of traditional statistical models and conventional machine-learning techniques in handling complex molecular datasets. By sourcing a dataset of 7298 compounds from the ChEMBL database and calculating molecular descriptors for each compound as features, we employed LightGBM in conjunction with a set of carefully selected molecular descriptors to achieve a nuanced analysis of compound activities. The model's efficiency was benchmarked against traditional machine-learning algorithms, revealing LightGBM's superior accuracy (84.93%), precision (87.14%), sensitivity (89.93%), specificity (77.63%), and F1-score (88.17%) in classifying beta-secretase 1 inhibitor activity. The study underscores the critical role of molecular descriptors in understanding drug efficacy, highlighting LightGBM's potential in streamlining the virtual screening process. Conclusively, the findings advocate for LightGBM's adoption in computational drug discovery, offering a promising avenue for advancing Alzheimer's disease therapeutic development by facilitating the identification of potential drug candidates with enhanced precision and reliability.","PeriodicalId":397752,"journal":{"name":"Journal of Computing Theories and Applications","volume":"17 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140255287","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

UNMASKING FRAUDSTERS: Ensemble Features Selection to Enhance Random Forest Fraud Detection 揭开欺诈者的面纱：选择集合特征以增强随机森林欺诈检测能力

Journal of Computing Theories and Applications Pub Date : 2023-12-25 DOI: 10.33633/jcta.v1i2.9462

Maureen Ifeanyi Akazue, Irene Alamarefa Debekeme, Abel Efe Edje, Clive Asuai, Ufuoma John Osame

{"title":"UNMASKING FRAUDSTERS: Ensemble Features Selection to Enhance Random Forest Fraud Detection","authors":"Maureen Ifeanyi Akazue, Irene Alamarefa Debekeme, Abel Efe Edje, Clive Asuai, Ufuoma John Osame","doi":"10.33633/jcta.v1i2.9462","DOIUrl":"https://doi.org/10.33633/jcta.v1i2.9462","url":null,"abstract":"Fraud detection is used in various industries, including banking institutes, finance, insurance, government agencies, etc. Recent increases in the number of fraud attempts make fraud detection crucial for safeguarding financial information that is confidential or personal. Many types of fraud problems exist, including card-not-present fraud, fake Marchant, counterfeit checks, stolen credit cards, and others. An ensemble feature selection technique based on Recursive feature elimination (RFE), Information gain (IG), and Chi-Squared (X2) in concurrence with the Random Forest algorithm, was proposed to give research findings and results on fraud detection and prevention. The objective was to choose the essential features for training the model. The Receiver Operating Characteristic (ROC) Score, Accuracy, F1 Score, and Precision are used to evaluate the model's performance. The findings show that the model can differentiate between fraudulent transactions and those that are not, with an ROC Score of 95.83% and an Accuracy of 99.6%. The F1 Score of 99.6%% and precision of 100% further sustain the model's ability to detect fraudulent transactions with the least false positives correctly. The ensemble feature selection technique reduced training time and did not compromise the model's performance, making it a valuable tool for businesses in preventing fraudulent transactions.","PeriodicalId":397752,"journal":{"name":"Journal of Computing Theories and Applications","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139157564","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 Quantum Key Distribution Protocol with Chaotic System for Securing Data Transmission 利用混沌系统确保数据传输安全的混合量子密钥分发协议

Journal of Computing Theories and Applications Pub Date : 2023-12-20 DOI: 10.33633/jcta.v1i2.9547

De Rosal Ignatius Moses Setiadi, Muhamad Akrom

引用次数: 0

Evaluation of University Websites in Nigeria using the Web Content Accessibility Guidelines 使用《网页内容可访问性指南》评估尼日利亚大学网站

Journal of Computing Theories and Applications Pub Date : 2023-12-11 DOI: 10.33633/jcta.v1i2.9381

E. Ogbuju, Olalekan Ihinkalu, Emmanuel Ajulo, Oluwayemisi Jaiyeoba, V. Yemi-Peters

{"title":"Evaluation of University Websites in Nigeria using the Web Content Accessibility Guidelines","authors":"E. Ogbuju, Olalekan Ihinkalu, Emmanuel Ajulo, Oluwayemisi Jaiyeoba, V. Yemi-Peters","doi":"10.33633/jcta.v1i2.9381","DOIUrl":"https://doi.org/10.33633/jcta.v1i2.9381","url":null,"abstract":"Providing accessible open educational resources (OER) is essential for users with impairments to access university resources. To achieve this, web content accessibility guidelines (WCAG) have been developed. In this study, we used the AChecker web accessibility evaluation tool to assess the content of 42 federal university websites in Nigeria and recorded their conformance level to the WCAG. The findings show that at Level A (Minimal Compliance), there were 855 known problems, 55 likely problems, and 7536 potential problems. At Level AA (Acceptable Compliance), 2516 known problems, 58 likely problems, and 15537 potential problems were identified. At Level AAA (Optimal Compliance), 2679 known problems were found, while there were no likely problems, and 16772 potential problems. The results indicated that most websites did not conform to the accessibility guidelines, highlighting the need for educational institutions to comply with WCAG2.1 content standard. The study recommends introducing accessibility training courses in web design and development to ensure effective OER creation for people with diverse abilities. Furthermore, enforcing the implementation of these guidelines by flagging down non-compliant educational websites was suggested. There is a problem of lack of accessibility in federal university websites in Nigeria, leading to unequal access to web content for users with varying abilities. The study aimed to identify aspects of the websites where accessibility needs to be improved and promote diversity and inclusiveness for users with different abilities to have equal access to web content.","PeriodicalId":397752,"journal":{"name":"Journal of Computing Theories and Applications","volume":"14 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138979287","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

Dynamic and Static Handwriting Assessment in Parkinson's Disease: A Synergistic Approach with C-Bi-GRU and VGG19 帕金森病的动态和静态笔迹评估：与 C-Bi-GRU 和 VGG19 协同作用的方法

Journal of Computing Theories and Applications Pub Date : 2023-12-06 DOI: 10.33633/jcta.v1i2.9469

Sohaib Ali, Adeel Hashmi, Ali Hamza, Umar Hayat, Hamza Younis

{"title":"Dynamic and Static Handwriting Assessment in Parkinson's Disease: A Synergistic Approach with C-Bi-GRU and VGG19","authors":"Sohaib Ali, Adeel Hashmi, Ali Hamza, Umar Hayat, Hamza Younis","doi":"10.33633/jcta.v1i2.9469","DOIUrl":"https://doi.org/10.33633/jcta.v1i2.9469","url":null,"abstract":"Parkinson's disease (PD) is a neurodegenerative disorder causing a decline in dopamine levels, impacting the peripheral nervous system and motor functions. Current detection methods often identify PD at advanced stages. This study addresses early-stage detection using handwriting analysis, specifically exploring the PaHaW dataset for pen pressure and stroke movement data. Evaluating online and offline features, the research employs pre-trained CNN models (VGG 19 and AlexNet) for offline datasets, achieving an overall accuracy of 0.53. For online datasets, velocity, and acceleration features are extracted and classified using Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and recurrent neural networks (RNN), with GRU yielding the highest accuracy at 0.57. Notably, the convolution-based model C-Bi-GRU surpasses other architectures with a remarkable 0.75 accuracy, emphasizing its efficacy in early PD detection. These findings underscore the potential of handwriting analysis as a diagnostic tool for PD, contributing valuable insights for further research and development in medical diagnostics.","PeriodicalId":397752,"journal":{"name":"Journal of Computing Theories and Applications","volume":"58 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138595048","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

CoSoGMIR: A Social Graph Contagion Diffusion Framework using the Movement-Interaction-Return Technique CoSoGMIR：使用移动-互动-返回技术的社会图谱传染扩散框架

Journal of Computing Theories and Applications Pub Date : 2023-12-06 DOI: 10.33633/jcta.v1i2.9355

A. Ojugo, P. Ejeh, Maureen Ifeanyi Akazue, N. Ashioba, Christopher Chukwufunaya Odiakaose, Rita Erhovwo Ako, Blessing Nwozor, F. Emordi

{"title":"CoSoGMIR: A Social Graph Contagion Diffusion Framework using the Movement-Interaction-Return Technique","authors":"A. Ojugo, P. Ejeh, Maureen Ifeanyi Akazue, N. Ashioba, Christopher Chukwufunaya Odiakaose, Rita Erhovwo Ako, Blessing Nwozor, F. Emordi","doi":"10.33633/jcta.v1i2.9355","DOIUrl":"https://doi.org/10.33633/jcta.v1i2.9355","url":null,"abstract":"Besides the inherent benefits of exchanging information and interactions between nodes on a social graph, they can also become a means for the propagation of knowledge. Social graphs have also become a veritable structure for the spread of disease outbreaks. These and its set of protocols are deployed as measures to curb its widespread effects as it has also left network experts puzzled. The recent lessons from the COVID-19 pandemic continue to reiterate that diseases will always be around. Nodal exposure, adoption/diffusion of disease(s) among interacting nodes vis-a-vis migration of nodes that cause further spread of contagion (concerning COVID-19 and other epidemics) has continued to leave experts bewildered towards rejigging set protocols. We model COVID-19 as a Markovian process with node targeting, propagation and recovery using migration-interaction as a threshold feat on a social graph. The migration-interaction design seeks to provision the graph with minimization and block of targeted diffusion of the contagion using seedset(s) nodes with a susceptible-infect policy. The study results showed that migration and interaction of nodes via the mobility approach have become an imperative factor that must be added when modeling the propagation of contagion or epidemics.","PeriodicalId":397752,"journal":{"name":"Journal of Computing Theories and Applications","volume":"5 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138596939","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