Journal of Advances in Information Technology最新文献_第2页

Corpus-Based Vocabulary List for Thai Language 基于语料库的泰语词汇表

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.2.319-327

H. Ketmaneechairat, Maleerat Maliyaem

引用次数: 1

Detection of Cookie Bomb Attacks in Cloud Computing Environment Monitored by SIEM 基于SIEM的云计算环境下Cookie炸弹攻击检测

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.2.193-203

Ryuga Kaneko, Taiichi Saito

{"title":"Detection of Cookie Bomb Attacks in Cloud Computing Environment Monitored by SIEM","authors":"Ryuga Kaneko, Taiichi Saito","doi":"10.12720/jait.14.2.193-203","DOIUrl":"https://doi.org/10.12720/jait.14.2.193-203","url":null,"abstract":"—This paper proposes a new method to detect Cookie Bomb attacks. A Cookie Bomb attack is a denial-of-service attack such that a user cannot receive a legitimate Hypertext Transfer Protocol (HTTP) response from an HTTP server because the total amount of cookies in an HTTP request exceeds the size limit accepted by the HTTP server. The new method includes our cloud architecture and detection algorithms. The cloud architecture distributes and executes a detection script, which is an implementation of the detection algorithms. This architecture uses Azure Virtual Machines, Azure Storage, Azure Automation, Azure Monitor, and Microsoft Sentinel. The virtual machines are the core components of the architecture, to which end users can connect via RDP to use their browsers. The detection script performs three tasks: obtaining paths to cookies databases generated by browsers, retrieving cookies data from a database, and comparing a threshold with the total size of all cookies a browser sends to a server. Results indicate that our proposed method 1) enables scheduled automation, 2) provides better visibility across regions, and 3) expands detection coverage for different Windows users, browsers, and browser profiles.","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":"66330529","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

Masked Face Detection and Recognition System Based on Deep Learning Algorithms 基于深度学习算法的蒙面人脸检测与识别系统

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.2.224-232

Hayat Al-Dmour, Afaf Tareef, A. Alkalbani, A. Hammouri, B. Alrahmani

{"title":"Masked Face Detection and Recognition System Based on Deep Learning Algorithms","authors":"Hayat Al-Dmour, Afaf Tareef, A. Alkalbani, A. Hammouri, B. Alrahmani","doi":"10.12720/jait.14.2.224-232","DOIUrl":"https://doi.org/10.12720/jait.14.2.224-232","url":null,"abstract":"Coronavirus (COVID-19) pandemic and its several variants have developed new habits in our daily lives. For instance, people have begun covering their faces in public areas and tight quarters to restrict the spread of the disease. However, the usage of face masks has hampered the ability of facial recognition systems to determine people's identities for registration authentication and dependability purpose. This study proposes a new deep-learning-based system for detecting and recognizing masked faces and determining the identity and whether the face is properly masked or not using several face image datasets. The proposed system was trained using a Convolutional Neural Network (CNN) with cross-validation and early stopping. First, a binary classification model was trained to discriminate between masked and unmasked faces, with the top model achieving a 99.77% accuracy. Then, a multi-class model was trained to classify the masked face images into three labels, i.e., correctly, incorrectly, and non-masked faces. The proposed model has achieved a high accuracy of 99.5%. Finally, the system recognizes the person's identity with an average accuracy of 97.98%. The visual assessment has proved that the proposed system succeeds in locating and matching faces. © 2023 by the authors.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"19 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66330580","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-class Classification Approach for Retinal Diseases 视网膜疾病的多分类方法

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.3.392-398

Mario G. Gualsaqui, Stefany M. Cuenca, Ibeth L. Rosero, D. A. Almeida, C. Cadena, Fernando Villalba, Jonathan D. Cruz

引用次数: 1

Utilizing Word Index Approach with LSTM Architecture for Extracting Adverse Drug Reaction from Medical Reviews 基于LSTM结构的词索引方法在医学评论中药物不良反应提取中的应用

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.3.543-549

Asmaa J. M. Alshaikhdeeb, Y. Cheah

{"title":"Utilizing Word Index Approach with LSTM Architecture for Extracting Adverse Drug Reaction from Medical Reviews","authors":"Asmaa J. M. Alshaikhdeeb, Y. Cheah","doi":"10.12720/jait.14.3.543-549","DOIUrl":"https://doi.org/10.12720/jait.14.3.543-549","url":null,"abstract":"— Adverse Drug Reaction (ADR) detection from social reviews refers to the task of exploring medical online stores and social reviews for extracting any mention of abnormal reactions that occur after consuming a particular medical product by the consumers themselves. A variety of approaches have been used for extracting ADR from social/medical reviews. These approaches include machine learning, dictionary-based and statistical approaches. Yet, these approaches showed either a high dependency on using an external knowledge source for ADR detection or relying on domain-dependent mechanisms that might lose contextual information. This study aims to propose word sequencing with Long Short-Term Memory (LSTM) architecture. A benchmark dataset of MedSyn has been used in the experiments. Then, a word indexing, mapping, and padding method have been used to represent the words within the reviews as fixed sequences. Such sequences have been fed into the LSTM consequentially. Experimental results showed that the proposed LSTM could achieve an F1 score of up to 92%. Comparing such a finding to the baseline studies reveals the superiority of LSTM. The demonstration of the efficacy of the proposed method has taken different forms including the examination of word indexing with different classifiers, the examination of different features with LSTM, and through the comparison against the baseline studies.","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":"66331918","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

Using Artificial Neural Network to Test Image Covert Communication Effect 利用人工神经网络测试图像隐蔽通信效果

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.4.741-748

Caswell Nkuna, Ebenezer Esenogho, R. Heymann, E. Matlotse

{"title":"Using Artificial Neural Network to Test Image Covert Communication Effect","authors":"Caswell Nkuna, Ebenezer Esenogho, R. Heymann, E. Matlotse","doi":"10.12720/jait.14.4.741-748","DOIUrl":"https://doi.org/10.12720/jait.14.4.741-748","url":null,"abstract":"—Hacking social or personal information is rising, and data security is given serious attention in any organization. There are several data security strategies depending on what areas it is applied to, for instance, voice, image, or video. Image is the main focus of this paper; hence, this paper proposed and implemented an image steganography (covert communication) technique that does not break existing image recognition neural network systems. This technique enables data to be hidden in a cover image while the image recognition Artificial Neural Network (ANN) checks the presence of any visible alterations on the stego-image. Two different image steganography methods were tested: Least Significant Bit (LSB) and proposed Discrete Cosine Transform (DCT) LSB-2. The resulting stego-images were analyzed using a neural network implemented in the Keras TensorFlow soft tool. The results showed that the proposed DCT LSB-2 encoding method allows a high data payload and minimizes visible alterations, keeping the neural network’s efficiency at a maximum. An optimum ratio for encoding data in an image was determined to maintain the high robustness of the steganography system. This proposed method has shown improved stego-system performance compared to the previous techniques.","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":"66333271","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

Improved Model to Detect Cancer from Cervical Histopathology Images by Optimizing Feature Selection and Ensemble Classifier 基于优化特征选择和集成分类器的宫颈癌组织病理学图像癌症检测改进模型

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.4.777-787

R. Laxmi, B. Kirubagari, Lakshmana Pandian

引用次数: 0

Demystifying Blockchain: A Critical Analysis of Application Characteristics in Different Domains 揭开区块链的神秘面纱:不同领域应用特征的批判性分析

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.4.718-728

Vedika Jorika, Nagaratna Medishetty

引用次数: 1

Improving Autonomous Vehicle Performance through Integration of an Image Deraining and a Deep Learning-Based Network for Lane Following 通过集成图像训练和基于深度学习的车道跟踪网络来提高自动驾驶汽车的性能

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.6.1159-1168

Hoang Tran Ngoc, Phuc Phan Hong, Anh Nguyen Quoc, Luyl-Da Quach

{"title":"Improving Autonomous Vehicle Performance through Integration of an Image Deraining and a Deep Learning-Based Network for Lane Following","authors":"Hoang Tran Ngoc, Phuc Phan Hong, Anh Nguyen Quoc, Luyl-Da Quach","doi":"10.12720/jait.14.6.1159-1168","DOIUrl":"https://doi.org/10.12720/jait.14.6.1159-1168","url":null,"abstract":"—Lane-keeping is a vital component of autonomous driving that requires multiple artificial intelligence technologies and vision systems. However, maintaining a vehicle’s position within the lane is challenging when there is low visibility due to rain. In this research, a combination of image deraining and a deep learning-based network is proposed to improve the performance of the autonomous vehicle. First, a robust progressive Residual Network (ResNet) is used for rain removal. Second, a deep learning-based network architecture of the Convolutional Neural Networks (CNNs) is applied for lane-following on roads. To assess its accuracy and rain-removal capabilities, the network was evaluated on both synthetic and natural Rainy Datasets (RainSP), and its performance was compared to that of earlier research networks. Furthermore, the effectiveness of using both deraining and non-deraining networks in CNNs is evaluated by analyzing the predicted steering angle output. The experimental results show that the proposed model generates safe and accurate motion planning for lane-keeping in autonomous vehicles.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135610446","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

Research on Substation Engineering Estimates Based on BIM-DE-RF 基于BIM-DE-RF的变电站工程评估研究

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.5.892-896

Songsong Wang, Wenxuan Qiao, Lei Wang, Zhewei Shen, Pengju Yang, Li Bian

引用次数: 0