Journal of Advances in Information Technology最新文献_第9页

Regression-Based Machine Learning Framework for Customer Churn Prediction in Telecommunication Industry 基于回归的电信行业客户流失预测机器学习框架

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.5.1046-1055

Sylvester Igbo Ele, Uzoma Rita Alo, Henry Friday Nweke, Ofem Ajah Ofem

引用次数: 0

CRL+: A Novel Semi-Supervised Deep Active Contrastive Representation Learning-Based Text Classification Model for Insurance Data CRL+:一种基于半监督深度主动对比表示学习的保险数据文本分类模型

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.5.1056-1062

Amir Namavar Jahromi, Ebrahim Pourjafari, Hadis Karimipour, Amit Satpathy, Lovell Hodge

引用次数: 0

A Model for Deployment of Dedicated Connected Autonomous Vehicle Lanes Considering User Fairness 一种考虑用户公平性的互联自动驾驶专用车道部署模型

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.5.1012-1018

Hongfei Jia, Yunpeng Qi, Chao Liu, Ruiyi Wu

{"title":"A Model for Deployment of Dedicated Connected Autonomous Vehicle Lanes Considering User Fairness","authors":"Hongfei Jia, Yunpeng Qi, Chao Liu, Ruiyi Wu","doi":"10.12720/jait.14.5.1012-1018","DOIUrl":"https://doi.org/10.12720/jait.14.5.1012-1018","url":null,"abstract":"—The dedicated Connected Autonomous Vehicle (CAV) lanes can avoid the interference of human-driven vehicles and create relatively safe operating conditions for CAVs. Besides, the dedicated CAV lanes can give full advantages of the connectivity and controllability to further improve the capacity of links. However, the consequent problem is unfairness among the traffic network users due to the higher priority right of CAVs in some links. This paper develops a bi-level programming model to design the CAV dedicated lanes deployment scheme considering the user fairness issue. In the lower-level model, we define the road resistance functions under various scenarios by investigating the effect of the dedicated lane on link capacity and construct the traffic assignment model which is solved by the diagonalized Frank-Wolfe method. The upper-level model aims to solve the multi-objective optimization problem that integrates user fairness and total system travel cost. The user fairness problem determines the fairness index using the Wilson entropy model, and the travel cost problem considers different users’ travel time value coefficients.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"21 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":"136305462","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

Face Detection in Close-up Shot Video Events Using Video Mining 基于视频挖掘的特写视频事件人脸检测

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.2.160-167

Amjad Rehman Khan, M. Harouni, Sepideh Sharifi, Saeed Ali Omer Bahaj, T. Saba

{"title":"Face Detection in Close-up Shot Video Events Using Video Mining","authors":"Amjad Rehman Khan, M. Harouni, Sepideh Sharifi, Saeed Ali Omer Bahaj, T. Saba","doi":"10.12720/jait.14.2.160-167","DOIUrl":"https://doi.org/10.12720/jait.14.2.160-167","url":null,"abstract":"—Face detection and recognition in abrupt dynamic images is still challenging due to high complexity of images. To tackle this issue, we employed Gray-Level Co-occurrence Matrix (GLCM) to convert large video into smaller consequential sections containing sequence information from a series of images. GLCM is a matrix associated with the relationship between the values of adjacent pixels in an image. The proposed method is composed of two stages. First, the video is taken as input using the histogram difference method. Features are extracted using co-occurrence matrix of images, statistical methods, and the border of sudden shots extracted from the video. Second, face recognition with the Viola-Jones algorithm is performed on the sudden shots extracted in the first step. Thus, the face is extracted by video data mining in output in close shots. In this method, we compared the parameter model in three windows (3, 5 and 7) and threshold limit for detecting abrupt cuts between values (0.1, 0.5, 1.5, 1.5 and 2) for each window. The highest percentage of face detection is attained by considering the maximum percentage of abrupt cuts in the 5×5 window with a threshold value of 1.","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":"66329972","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

Clickbait Detection in Indonesian News Title with Gray Unbalanced Class Based on BERT 基于BERT的印尼新闻标题灰色不平衡类标题党检测

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.2.233-241

P. Andono, Pieter Santoso Hadi, Muljono Muljono, Catur Supriyanto

{"title":"Clickbait Detection in Indonesian News Title with Gray Unbalanced Class Based on BERT","authors":"P. Andono, Pieter Santoso Hadi, Muljono Muljono, Catur Supriyanto","doi":"10.12720/jait.14.2.233-241","DOIUrl":"https://doi.org/10.12720/jait.14.2.233-241","url":null,"abstract":"—Bahasa Indonesia is used by about 263 million people in the world but it is classified as an under-resourced language. The problem of clickbait in news analysis has gained attention in recent years. However, for Indonesian, there is still a lack of resources for clickbait tasks. Clickbait attracts the attention of readers, even though the content is not informative and misleading. The imbalance of the clickbait dataset means unequal distribution of classes within the dataset which affects the classification result. In this research, focal loss is proposed to improve classification accuracy without reducing the number of original data. Normally, clickbait data are separated into two classes, namely clickbait, and non-clickbait. However, some titles are difficult to categorize, even by humans. Therefore, this study categorizes the titles into three categories, namely clickbait, non-clickbait, and gray-clickbait. The proposed method achieves an accuracy of 93.4% in the classification of two classes, which is better than previous studies. However, the proposed method achieves an accuracy of 73.3% in the classification of three classes. Our research shows a high similarity between gray-clickbait and clickbait data, making classification more challenging. On the other hand, the use of titles on three categorizations in clickbait is not enough to provide better classification performance.","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":"66330587","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

Employee Reimbursement System for a Manufacturing Company 制造企业员工报销制度

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.2.350-354

R. A. C. Roque, Dhan Joseph P. Praga, G. L. Intal

引用次数: 0

Automatic Diagnosis of Rice Leaves Diseases Using Hybrid Deep Learning Model 基于混合深度学习模型的水稻叶片病害自动诊断

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.3.418-425

Amjad Rehman Khan, I. Abunadi, Bayan I. Alghofaily, Haider Ali, T. Saba

引用次数: 0

Optimized Deep Neural Networks Audio Tagging Framework for Virtual Business Assistant 优化的深度神经网络音频标记框架的虚拟商务助理

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.3.550-558

Fatma Sh. El-metwally, Ali I. Eldesouky, Nahla B. Abdel-Hamid, Sally M. Elghamrawy

{"title":"Optimized Deep Neural Networks Audio Tagging Framework for Virtual Business Assistant","authors":"Fatma Sh. El-metwally, Ali I. Eldesouky, Nahla B. Abdel-Hamid, Sally M. Elghamrawy","doi":"10.12720/jait.14.3.550-558","DOIUrl":"https://doi.org/10.12720/jait.14.3.550-558","url":null,"abstract":"— A virtual assistant has a huge impact on business and an organizations development. It can be used to manage customer relations and deal with received queries, automatically reply to e-mails and phone calls.Audio signal processing has become increasingly popular since the development of virtual assistants. Deep learning and audio signal processing advancements have dramatically enhanced audio tagging. Audio Tagging (AT) is a challenge that requires eliciting descriptive labels from audio clips. This study proposes an Optimized Deep Neural Networks Audio Tagging Framework for Virtual Business Assistant to categorize and analyze audio tagging. Each input signal is used to extract the various audio tagging features. The extracted features are input into a neural network to carry out a multi-label classification for the predicted tags. Optimization techniques are used to improve the quality of the model fit for neural networks. To test the efficiency of the framework, four comparison experiments have been conducted between it and some of the others. From these results, it was concluded that this framework is better than the others in terms of efficiency. When the neural network was trained, Mel-Frequency Cepstral Coefficient (MFCC) features with Adamax achieved the best results with 93% accuracy and a 0.17% loss. When evaluating the performance of the model for seven labels, it achieved an average of precision 0.952, recall 0.952, F-score 0.951, accuracy 0.983, and an equal error rate of 0.015 in the evaluation set compared to the provided Detection and Classification of Acoustic Scenes and Events (DSCASE) baseline where he achieved and accuracy of 72.5% and","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":"66332105","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

Empirical Evaluation of Machine Learning Performance in Forecasting Cryptocurrencies 机器学习预测加密货币性能的实证评估

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.4.639-647

Lauren Al Hawi, S. Sharqawi, Q. A. Al-Haija, A. Qusef

引用次数: 0

Improvised Explosive Device Detection Using CNN With X-Ray Images 利用CNN x射线图像检测简易爆炸装置

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.4.674-684

Chakkaphat Chamnanphan, S. Vorapatratorn, Khwunta Kirimasthong, Tossapon Boongoen, Natthakan Iam-on

{"title":"Improvised Explosive Device Detection Using CNN With X-Ray Images","authors":"Chakkaphat Chamnanphan, S. Vorapatratorn, Khwunta Kirimasthong, Tossapon Boongoen, Natthakan Iam-on","doi":"10.12720/jait.14.4.674-684","DOIUrl":"https://doi.org/10.12720/jait.14.4.674-684","url":null,"abstract":"—The concept of a smart city and its associated services have been extensively explored in terms of innovation development and the application of technological concepts. One of the significant concerns in promoting smart living is the security of personal lives and assets, which are at risk from organized crime and acts of terrorism. A considerable amount of attention is paid to preventing bomb attacks in public places, especially the detection of an Improvised Explosive Device (IED). This research focuses on developing an analysis model that can accurately classify instances of x-ray images of baggage or objects as containing IEDs or not. The model provides an alternative to conventional techniques that fail to detect concealed or hidden devices. For this specific project, sample images are generated by experts to cover a range of cases encountered in operations during the past decade. These images are then used to develop a deep learning model, employing several data augmentation methods to overcome the issue of a limited number of training samples. As compared to a related work that exploits neural networks, the proposed model usually achieves higher accuracy rates for unseen samples, with the best accuracy rate being 0.985. Furthermore, an empirical study is conducted to determine the optimal size of the training set that exhibits good predictive performance. The study reveals that a large training set, apart from using a lot of resources, may not yield the best results as it may indicate overfitting.","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":"66332923","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