International Journal of Artificial Intelligence & Applications最新文献_第3页

An Authorship Identification Empirical Evaluation of Writing Style Features in Cross-Topic and Cross-genre Documents 跨主题跨体裁文献写作风格特征的作者身份鉴定实证评价

International Journal of Artificial Intelligence & Applications Pub Date : 2023-01-30 DOI: 10.5121/ijaia.2023.14101

Simisani Ndaba, E. Thuma, G. Mosweunyane

引用次数: 0

ADPP: A Novel Anomaly Detection and Privacy-Preserving Framework using Blockchain and Neural Networks in Tokenomics ADPP:一种新的异常检测和隐私保护框架，在标记经济学中使用区块链和神经网络

International Journal of Artificial Intelligence & Applications Pub Date : 2022-11-30 DOI: 10.5121/ijaia.2022.13602

Wei Yao, Jingyi Gu, Wenlu Du, Fadi P. Deek, Guiling Wang

{"title":"ADPP: A Novel Anomaly Detection and Privacy-Preserving Framework using Blockchain and Neural Networks in Tokenomics","authors":"Wei Yao, Jingyi Gu, Wenlu Du, Fadi P. Deek, Guiling Wang","doi":"10.5121/ijaia.2022.13602","DOIUrl":"https://doi.org/10.5121/ijaia.2022.13602","url":null,"abstract":"The increasing popularity of crypto assets has resulted in greater cryptocurrency investor interest and more exposure in both industry and academia. Despite the substantial socioeconomic benefits, the anonymous character of cryptocurrency trading makes it prone to abuse and a magnet for illicit purposes, which cause monetary losses for individual traders and erosion in the standing of the tokenomics industry. To regulate the illicit behavior and secure users' privacy for cryptocurrency trading, we present an Anomaly Detection and Privacy-Preserving (ADPP) Framework integrating blockchain and deep learning technologies. Specifically, ADPP leverages blockchain technologies to build a user management platform that ensures anonymity and enhances the privacy-preservation of user information. Atop the user management system, an Anomaly Detection System adapts neural networks and imbalanced learning on topological cryptocurrency flow among users to identify anomalous addresses and maintain a sanction list repository. The experiments on the real-world dataset demonstrate the effectiveness and superior performance of ADPP. The flexible framework can be easily generalized to the crypto assets with public real-time transaction (e.g., Non-fungible Token), which takes up a significant proportion of market capitalization in the domain of tokenomics.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126742420","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

Improving Explanations of Image Classifiers: Ensembles and Multitask Learning 改进图像分类器的解释:集成和多任务学习

International Journal of Artificial Intelligence & Applications Pub Date : 2022-11-30 DOI: 10.5121/ijaia.2022.13604

M. Pazzani, Severine Soltani, Sateesh Kumar, Kamran Alipour, Aadil Ahamed

{"title":"Improving Explanations of Image Classifiers: Ensembles and Multitask Learning","authors":"M. Pazzani, Severine Soltani, Sateesh Kumar, Kamran Alipour, Aadil Ahamed","doi":"10.5121/ijaia.2022.13604","DOIUrl":"https://doi.org/10.5121/ijaia.2022.13604","url":null,"abstract":"In explainable AI (XAI) for deep learning, saliency maps, heatmaps, or attention maps are commonly used to identify important regions for the classification of images of explanations. We address two important limitations of heatmaps. First, they do not correspond to type of explanations typically produced by human experts. Second, recent research has shown that many common XAI methods do not accurately identify the regions that human experts consider important. We propose using multitask learning to identify diagnostic features in images and averaging explanations from ensembles of learners to increase the accuracy of explanations. Our technique is general and can be used with multiple deep learning architectures and multiple XAI algorithms. We show that this method decreases the difference between regions of interest of XAI algorithms and those identified by human experts and the multitask learning supports the type of explanations produced by human experts. Furthermore, we show that human experts prefer the explanations produced by ensembles to those of individual networks.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"15 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127974208","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

Converting Real Human Avatar to Cartoon Avatar Utilizing CycleGAN 利用CycleGAN将真人化身转换为卡通化身

International Journal of Artificial Intelligence & Applications Pub Date : 2022-11-30 DOI: 10.5121/ijaia.2022.13601

Wenxin Tian

引用次数: 0

A Systematic Study of Deep Learning Architectures for Analysis of Glaucoma and Hypertensive Retinopathy 青光眼和高血压视网膜病变分析的深度学习架构系统研究

International Journal of Artificial Intelligence & Applications Pub Date : 2022-11-30 DOI: 10.5121/ijaia.2022.13603

Madhura Prakash M, Deepthi K Prasad, Meghna S Kulkarni, Spoorthi K, V. S.

{"title":"A Systematic Study of Deep Learning Architectures for Analysis of Glaucoma and Hypertensive Retinopathy","authors":"Madhura Prakash M, Deepthi K Prasad, Meghna S Kulkarni, Spoorthi K, V. S.","doi":"10.5121/ijaia.2022.13603","DOIUrl":"https://doi.org/10.5121/ijaia.2022.13603","url":null,"abstract":"Deep learning models are applied seamlessly across various computer vision tasks like object detection, object tracking, scene understanding and further. The application of cutting-edge deep learning (DL) models like U-Net in the classification and segmentation of medical images on different modalities has established significant results in the past few years. Ocular diseases like Diabetic Retinopathy (DR), Glaucoma, Age-Related Macular Degeneration (AMD / ARMD), Hypertensive Retina (HR), Cataract, and dry eyes can be detected at the early stages of disease onset by capturing the fundus image or the anterior image of the subject’s eye. Early detection is key to seeking early treatment and thereby preventing the disease progression, which in some cases may lead to blindness. There is a plethora of deep learning models available which have established significant results in medical image processing and specifically in ocular disease detection. A given task can be solved by using a variety of models and or a combination of them. Deep learning models can be computationally expensive and deploying them on an edge device may be a challenge. This paper provides a comprehensive report and critical evaluation of the various deep learning architectures that can be used to segment and classify ocular diseases namely Glaucoma and Hypertensive Retina on the posterior images of the eye. This review also compares the models based on complexity and edge deployability.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117045365","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

Difference of Probability and Information Entropy for Skills Classification and Prediction in Student Learning 学生学习中技能分类与预测的概率与信息熵差异

International Journal of Artificial Intelligence & Applications Pub Date : 2022-09-30 DOI: 10.5121/ijaia.2022.13501

Kennedy E. Ehimwenma, Safiya Al Sharji, M. Raheem

{"title":"Difference of Probability and Information Entropy for Skills Classification and Prediction in Student Learning","authors":"Kennedy E. Ehimwenma, Safiya Al Sharji, M. Raheem","doi":"10.5121/ijaia.2022.13501","DOIUrl":"https://doi.org/10.5121/ijaia.2022.13501","url":null,"abstract":"The probability of an event is in the range of [0, 1]. In a sample space S, the value of probability determines whether an outcome is true or false. The probability of an event Pr(A) that will never occur = 0. The probability of the event Pr(B) that will certainly occur = 1. This makes both events A and B thus a certainty. Furthermore, the sum of probabilities Pr(E1) + Pr(E2) + … + Pr(En) of a finite set of events in a given sample space S = 1. Conversely, the difference of the sum of two probabilities that will certainly occur is 0. Firstly, this paper discusses Bayes’ theorem, then complement of probability and the difference of probability for occurrences of learning-events, before applying these in the prediction of learning objects in student learning. Given the sum total of 1; to make recommendation for student learning, this paper submits that the difference of argMaxPr(S) and probability of student-performance quantifies the weight of learning objects for students. Using a dataset of skill-set, the computational procedure demonstrates: i) the probability of skill-set events that has occurred that would lead to higher level learning; ii) the probability of the events that has not occurred that requires subject-matter relearning; iii) accuracy of decision tree in the prediction of student performance into class labels; and iv) information entropy about skill-set data and its implication on student cognitive performance and recommendation of learning [1].","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126956287","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 Deep Learning Approach for Defect Detection and Segmentation in X-Ray Computed Tomography Slices of Additively Manufactured Components 基于深度学习的增材制造部件x射线计算机断层扫描缺陷检测与分割方法

International Journal of Artificial Intelligence & Applications Pub Date : 2022-07-31 DOI: 10.5121/ijaia.2022.13401

P. Acharya, Tsuchin P. Chu, Khaled R. Ahmed, S. Kharel

{"title":"A Deep Learning Approach for Defect Detection and Segmentation in X-Ray Computed Tomography Slices of Additively Manufactured Components","authors":"P. Acharya, Tsuchin P. Chu, Khaled R. Ahmed, S. Kharel","doi":"10.5121/ijaia.2022.13401","DOIUrl":"https://doi.org/10.5121/ijaia.2022.13401","url":null,"abstract":"Additive manufacturing is an emerging and crucial technology that can overcome the limitations of traditional manufacturing techniques to accurately manufacture highly complex parts. X-ray Computed Tomography (XCT) is a widely used method for non-destructive testing of AM parts. However, detection and segmentation of defects in XCT images of AM have many challenges due to contrast, size, and appearance of defects. This study developed deep learning techniques for detecting and segmenting defects in XCT images of AM. Due to a large number of required defect annotations, this paper applied image processing techniques to automate the defect labeling process. A single-stage object detection algorithm (YOLOv5) was applied to the problem of defect detection in image data. Three different variants of YOLOv5 were implemented and their performances were compared. U-Net was applied for defect segmentation in XCT slices. Finally, this research demonstrates that deep learning techniques can improve the automatic defect detection and segmentation in XCT data of AM.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"294 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124221540","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

New Local Binary Pattern Feature Extractor with Adaptive Threshold for Face Recognition Applications 基于自适应阈值的局部二值模式特征提取方法在人脸识别中的应用

International Journal of Artificial Intelligence & Applications Pub Date : 2022-07-31 DOI: 10.5121/ijaia.2022.13406

Soroosh Parsai, M. Ahmadi

引用次数: 1

Management of Unplanned Changes in Production Processes: AI Control Systems 生产过程中计划外变化的管理:人工智能控制系统

International Journal of Artificial Intelligence & Applications Pub Date : 2022-07-31 DOI: 10.5121/ijaia.2022.13407

Zilvinas Svigaris

{"title":"Management of Unplanned Changes in Production Processes: AI Control Systems","authors":"Zilvinas Svigaris","doi":"10.5121/ijaia.2022.13407","DOIUrl":"https://doi.org/10.5121/ijaia.2022.13407","url":null,"abstract":"Quality risk management in industrial plants involves big calculations, the scale of which is often not only incomprehensible but also difficult to manage due to many parameters that affect the quality of production. Unsurprisingly, artificial intelligence-based quality management models are being introduced in manufacturing, only in niche, narrow areas, mostly for tracking product defects or identifying local quality defects. However, detecting the defect stage already is a late stage of the problem, which is almost always associated with a loss. Here comes the importance of prediction of problems or identifying of problematic patterns at an early stage before having production losses. Such attempts are rare and require a special approach. This type of module is needed for wide range problem forecasting in manufacturing. It should be configurable and clear not only by narrow area professionals, but also by medium-sized factory technologists who can configure such a system themselves to control their production quality risks. So here we are developing an approach whose strengths would be its simplicity, comprehensibility, fastness, and accessibility in its training, allowing us to understand why in one case or another the system predicts one decision or another.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129790492","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

Predicting more Infectious Virus Variants for Pandemic Prevention through Deep Learning 通过深度学习预测更多传染性病毒变体以预防大流行

International Journal of Artificial Intelligence & Applications Pub Date : 2022-07-31 DOI: 10.5121/ijaia.2022.13403

Glenda Tan Hui En, K. Erhn, Shen Bingquan

引用次数: 0

International Journal of Artificial Intelligence &amp; Applications最新文献

International Journal of Artificial Intelligence & Applications最新文献