Journal of Advances in Information Technology最新文献_第5页

A Novel Distributed Machine Learning Model to Detect Attacks on Edge Computing Network 边缘计算网络攻击检测的分布式机器学习模型

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.1.153-159

Trong-Minh Hoang, Trang-Linh Le Thi, N. M. Quy

{"title":"A Novel Distributed Machine Learning Model to Detect Attacks on Edge Computing Network","authors":"Trong-Minh Hoang, Trang-Linh Le Thi, N. M. Quy","doi":"10.12720/jait.14.1.153-159","DOIUrl":"https://doi.org/10.12720/jait.14.1.153-159","url":null,"abstract":"To meet the growing number and variety of IoT devices in 5G and 6G network environments, the development of edge computing technology is a powerful strategy for ofﬂoading processes in data servers by processing at the network and nearby the user. Besides its benefits, several challenges related to decentralized operations for improving performance or security tasks have been identified. A new research direction for distributed operating solutions has emerged from these issues, leading to applying Distributed Machine Learning (DML) techniques for edge computing. It takes advantage of the capacity of edge devices to handle increased data volumes, reduce connection bottlenecks, and enhance data privacy. The designs of DML architectures have to use optimized algorithms (e.g., high accuracy and rapid convergence) and effectively use hardware resources to overcome large-scale problems. However, the trade-off between accuracy and data set volume is always the biggest challenge for practical scenarios. Hence, this paper proposes a novel attack detection model based on the DML technique to detect attacks at network edge devices. A modified voting algorithm is applied to core logic operation between sever and workers in a partition learning fashion. The results of numerical simulations on the UNSW-NB15 dataset have proved that our proposed model is suitable for edge computing and gives better attack detection results than other state of the art solutions.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66329514","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: 2

An Optimized Machine Learning Approach for Coronary Artery Disease Detection 冠状动脉疾病检测的优化机器学习方法

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.1.66-76

S. Savita, Geeta Rani, Apeksha Mittal

引用次数: 0

Firefly with Levy Based Feature Selection with Multilayer Perceptron for Sentiment Analysis 基于Levy特征选择的萤火虫多层感知机情感分析

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.2.342-349

D. Elangovan, V. Subedha

引用次数: 0

An Efficient Model to Predict Network Packets in TVDC Using Machine Learning 基于机器学习的TVDC网络数据包预测模型

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.3.523-531

Ashmeet Kaur Duggal, Meenu Dave

{"title":"An Efficient Model to Predict Network Packets in TVDC Using Machine Learning","authors":"Ashmeet Kaur Duggal, Meenu Dave","doi":"10.12720/jait.14.3.523-531","DOIUrl":"https://doi.org/10.12720/jait.14.3.523-531","url":null,"abstract":"—Internet-based computing allows the sharing of on-demand resources. This computing technique includes data processing and storage to globally separated machines, known as Cloud Computing. Confidentiality and integrity of data on the cloud are vital. The key constraints include effective access control, accessibility, and transmission of files, in a dynamic cloud environment, seeking a Trusted Virtual Data Center (TVDC). So, to overcome challenges such as data security and integrity due to exponentially growing data size, this research paper aims to develop a prediction model using the machine learning approach, which identifies the type of incoming packet on the TVDC. Alternatively, in other words, this system predicts whether the incoming packets on the server in the cloud environment are malicious or not, using the machine learning approach. This research explored artificial intelligence verticals in building systems with learned data structures for efficient data access. This research describes the implementation of machine learning algorithms for an efficient model’s prediction of the type of incoming packet on the server. It has achieved 88% accuracy using the Gradient Boosted Tree classifier. Also, in this study, the author compares the results of two algorithms, Decision Tree and Gradient Boosted Tree, and finally selects the most optimal for this prediction.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66331631","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

Multi-speaker Speech Separation under Reverberation Conditions Using Conv-Tasnet 混响条件下的多说话人语音分离

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.4.694-700

Chunxi Wang, Maoshen Jia, Yanyan Zhang, Lu Li

引用次数: 1

Sequential Decision Making for Elevator Control 电梯控制的顺序决策

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.5.1124-1131

Emre Oner Tartan, Cebrail Ciflikli

引用次数: 0

Advances in the Development of an Algorithm for Parametric Identification of Egyptian Hieroglyphs Using Artificial Vision 埃及象形文字人工视觉参数识别算法研究进展

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.4.788-795

Rafael Bolívar León, C. Peña, G. G. Moreno

引用次数: 0

Fuzzy Based Butterfly Life Cycle Algorithm for Measuring Company's Growth Performance 基于模糊蝴蝶生命周期算法的公司成长性评价

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.1.1-6

Gregoryus Imannuel Perdana, M. Devanda, D. N. Utama

引用次数: 0

Comparative Analysis of Machine Learning in Predicting the Treatment Status of COVID-19 Patients 机器学习预测COVID-19患者治疗状况的对比分析

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.1.56-65

Anthony Anggrawan, Mayadi Mayadi, Christofer Satria, B. K. Triwijoyo, R. Rismayati

{"title":"Comparative Analysis of Machine Learning in Predicting the Treatment Status of COVID-19 Patients","authors":"Anthony Anggrawan, Mayadi Mayadi, Christofer Satria, B. K. Triwijoyo, R. Rismayati","doi":"10.12720/jait.14.1.56-65","DOIUrl":"https://doi.org/10.12720/jait.14.1.56-65","url":null,"abstract":"COVID-19 has become a global pandemic that causes many deaths, so medical treatment for COVID-19 patients gets special attention, whether hospitalized or self-isolated. However, the problem in medical action is not easy, and the most frequent mistakes are due to inaccuracies in medical decision-making. Meanwhile, machine learning can predict with high accuracy. For that, or that's why this study aims to propose a data mining classification method as a machine learning model to predict the treatment status of COVID-19 patients accurately, whether hospitalized or self-isolated. The data mining method used in this research is the Random Forest (RF) and Support Vector Machine (SVM) algorithm with Confusion Matrix and k-fold Cross Validation testing. The finding indicated that the machine learning model has an accuracy of up to 94% with the RF algorithm and up to 92% with the SVM algorithm in predicting the COVID-19 patient's treatment status. It means that the machine learning model using the RF algorithm has more accurate accuracy than the SVM algorithm in predicting or recommending the treatment status of COVID-19 patients. The implication is that RF machine learning can help/replace the role of medical experts in predicting the patient's care status.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66329656","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: 1

A Generational Cohort Comparison of Icon Selection Accuracy under Varying Conditions of Icon Entropy and Concreteness 不同图标熵和具体条件下的代际队列图标选择准确性比较

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.2.250-256

Kleddao Satcharoen, Pikulkaew Tangtisanon

{"title":"A Generational Cohort Comparison of Icon Selection Accuracy under Varying Conditions of Icon Entropy and Concreteness","authors":"Kleddao Satcharoen, Pikulkaew Tangtisanon","doi":"10.12720/jait.14.2.250-256","DOIUrl":"https://doi.org/10.12720/jait.14.2.250-256","url":null,"abstract":"—The objective of this research was to compare icon selection accuracy under varying icon entropy and concreteness conditions between different generational cohorts ( Millennial, Generation X, and Baby Boomers ). These generational cohorts have different levels of experience with technology, with younger generations often being framed as “digital natives” and holding stronger technological experience and competence in comparison to older groups. Generational groups also have variations in physiological factors including visual acuity and reaction time. Despite these differences between user groups, many user interaction systems and processes are designed for a single user, rather than considering differences in user processing between different groups. Therefore, this study compares generational cohorts in their icon selection accuracy under varying icon conditions, to help identify what generational differences can be observed in this task. The study selected a sample of 150 participants ( n = 50 for each generational cohort ). The experiment was a 2  2  3 design ( entropy ( high / low )  abstractness ( abstract / concrete )  time ( 9 / 6 / 3 seconds ) , with each participant completing 60 trials ( five questions per entropy / abstractness pair over three timed runs ). Results showed that there were significant differences in mean accuracy per trial under all of the time conditions and icon entropy and concreteness conditions . Mean differences showed that under most conditions, Millennial and Generation X participants did not have a significant mean difference, but Baby Boomers were significantly slower under almost all conditions . The implication of this finding is that Baby Boomers are more sensitive to icon abstractness and entropy conditions than other age groups tested .","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66330152","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