International Journal of Artificial Intelligence & Applications最新文献

Wavelet Scattering Transform for ECG Cardiovascular Disease Classification 用于心电图心血管疾病分类的小波散射变换

International Journal of Artificial Intelligence & Applications Pub Date : 2024-01-29 DOI: 10.5121/ijaia.2024.15107

Islam D. S. Aabdalla, D. Vasumathi

{"title":"Wavelet Scattering Transform for ECG Cardiovascular Disease Classification","authors":"Islam D. S. Aabdalla, D. Vasumathi","doi":"10.5121/ijaia.2024.15107","DOIUrl":"https://doi.org/10.5121/ijaia.2024.15107","url":null,"abstract":"Classifying the ECG dataset is the main technique for diagnosing heart disease. However, the focus of this field is increasingly on prediction, with a growing dependence on machine learning techniques. This study aimed to enhance the accuracy of cardiovascular disease classification using data from the PhysioNet database by employing machine learning (ML). The study proposed several multi-class classification models that accurately identify patterns within three classes: heart failure rhythm (HFR), normal heart rhythm (NHR), and arrhythmia (ARR). This was accomplished by utilizing a database containing 162 ECG signals. The study employed a variety of techniques, including frequency-time domain analysis, spectral features, and wavelet scattering, to extract features and capture unique characteristics from the ECG dataset. The SVM model produced a training accuracy of 97.1% and a testing accuracy of 92%. This work provides a reliable, effective, and human error-free diagnostic tool for identifying heart disease. Furthermore, it could prove to be a valuable resource for future medical research projects aimed at improving the diagnosis and treatment of cardiovascular diseases.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"35 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140488494","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

Review of AI Maturity Models in Automotive SME Manufacturing 汽车中小企业制造业人工智能成熟度模型回顾

International Journal of Artificial Intelligence & Applications Pub Date : 2024-01-29 DOI: 10.5121/ijaia.2024.15104

Dharmender Salian

引用次数: 0

Ensemble Learning Approach for Digital Communication Modulation’s Classification 数字通信调制分类的集合学习方法

International Journal of Artificial Intelligence & Applications Pub Date : 2024-01-29 DOI: 10.5121/ijaia.2024.15103

Yahya Benremdane, Said Jamal, Oumaima Taheri, Jawad Lakziz, Said Ouaskit

{"title":"Ensemble Learning Approach for Digital Communication Modulation’s Classification","authors":"Yahya Benremdane, Said Jamal, Oumaima Taheri, Jawad Lakziz, Said Ouaskit","doi":"10.5121/ijaia.2024.15103","DOIUrl":"https://doi.org/10.5121/ijaia.2024.15103","url":null,"abstract":"This work uses artificial intelligence to create an automatic solution for the modulation's classification of various radio signals. This project is a component of a lengthy communications intelligence process that aims to find an automated method for demodulating, decoding, and deciphering communication signals. As a result, the work we did involved selecting the database required for supervised deep learning, assessing the performance of current methods on unprocessed communication signals, and suggesting a deep learning network-based method that would enable the classification of modulation types with the best possible ratio between computation time and accuracy. In order to use the current automatic classification models as a guide, we first conducted study on them. As a result, we suggested an ensemble learning strategy based on Transformer Neural Network and adjusted ResNet that takes into account the difficulty of forecasting in low Signal Noise Ratio (SNR) scenarios while also being effective at extracting multiscale characteristics from the raw I/Q sequence data. Ultimately, we produced an architecture for communication signals that is simple to work with and implement. With an accuracy of up to 95%, this solution's optimum and sturdy architecture decides the type of modulation on its own.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"63 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140486910","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

Passive Sonar Detection and Classification Based on Demon-Lofar Analysis and Neural Network Algorithms 基于声纳分析和神经网络算法的被动声纳探测与分类

International Journal of Artificial Intelligence & Applications Pub Date : 2024-01-29 DOI: 10.5121/ijaia.2024.15106

Said Jamal, Jawad Lakziz, Yahya Benremdane, Said Ouaskit

{"title":"Passive Sonar Detection and Classification Based on Demon-Lofar Analysis and Neural Network Algorithms","authors":"Said Jamal, Jawad Lakziz, Yahya Benremdane, Said Ouaskit","doi":"10.5121/ijaia.2024.15106","DOIUrl":"https://doi.org/10.5121/ijaia.2024.15106","url":null,"abstract":"This paper focuses on an experimental study that used passive sonar sensors as the primary information source for the submerged target in order to identify, classify, and recognize naval targets. Surface vessels and submarine generate a specific sound either by propulsion systems, auxiliary equipment or blades of their propellers, producing information known as the \"acoustic signature\" that is unique to each type of target. Consequently, the analysis and classification of targets depend on the processing of the frequencies produced by these vibrations (sound). utilizing the TPWS (Two-Pass-Split Windows) filter, this work aims to develop a novel technique for target identification and classification utilizing passive sonars. This technique involves processing the target's signal in the time-frequency domain. subsequently, in order to improve the frequency lines of the target noise and decrease the background noise, a TPSW algorithm is implemented in the frequency domain. By integrating narrowband and broadband analysis as inputs of an artificial intelligence model that can classify a target into one of the categories given in the training phase, the target has finally been classified. Our findings demonstrated that the suggested approach is dependent upon the size of the target noise data collection and the noise-to-effective-signal ratio.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"66 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140486499","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 Comprehensive Systematic Review for Cardiovascular Disease using Machine Learning Techniques 利用机器学习技术对心血管疾病进行全面系统综述

International Journal of Artificial Intelligence & Applications Pub Date : 2024-01-29 DOI: 10.5121/ijaia.2024.15101

Islam D. S. Aabdalla, D. Vasumathi

{"title":"A Comprehensive Systematic Review for Cardiovascular Disease using Machine Learning Techniques","authors":"Islam D. S. Aabdalla, D. Vasumathi","doi":"10.5121/ijaia.2024.15101","DOIUrl":"https://doi.org/10.5121/ijaia.2024.15101","url":null,"abstract":"The global upswing in cardiovascular disease (CVD) cases presents a critical challenge. While the ultimate goal remains elusive, improving CVD prediction accuracy is vital. Machine learning and deep learning are crucial for decoding complex health data, enhancing cardiac imaging, and predicting disease outcomes in clinical practice. This systematic literature review meticulously analyses CVD using machine learning techniques, with a particular emphasis on algorithms for classification and prediction. The metaanalysis covers 343 references from 2020 to November 2023, preceding a thorough examination of 65 selected references. Acknowledging current hurdles in CVD classification methods that impede practical use, this systematic literature review (SLR) is conducted. The study provides valuable insights for researchers and healthcare professionals, facilitating the integration of clinical applications in machine learning settings related to CVD. It also aids in promptly identifying potential threats and implementing precautionary measures. The study also recognizes prevalent classical machine learning methods, emphasizing their clinically relevant diagnostic outcomes. Deliberating on current trends, algorithms, and potential areas for future research offers a comprehensive insight into the present state of affairs.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"36 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140487861","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

Imbalanced Dataset Effect on CNN-Based Classifier Performance for Face Recognition 不平衡数据集对基于 CNN 的人脸识别分类器性能的影响

International Journal of Artificial Intelligence & Applications Pub Date : 2024-01-29 DOI: 10.5121/ijaia.2024.15102

Miftah Asharaf Najeeb, Alhaam Alariyibi

{"title":"Imbalanced Dataset Effect on CNN-Based Classifier Performance for Face Recognition","authors":"Miftah Asharaf Najeeb, Alhaam Alariyibi","doi":"10.5121/ijaia.2024.15102","DOIUrl":"https://doi.org/10.5121/ijaia.2024.15102","url":null,"abstract":"Facial Recognition is integral to numerous modern applications, such as security systems, social media platforms, and augmented reality apps. The success of these systems heavily depends on the performance of the Face Recognition models they use, specifically Convolutional Neural Networks (CNNs). However, many real-world classification tasks encounter imbalanced datasets, with some classes significantly underrepresented. Face Recognition models that do not address this class imbalance tend to exhibit poor performance, especially in tasks involving a wide range of faces to identify (multi-class problems). This research examines how class imbalance in datasets impacts the creation of neural network classifiers for Facial Recognition. Initially, we crafted a Convolutional Neural Network model for facial recognition, integrating hybrid resampling methods (oversampling and under-sampling) to address dataset imbalances. In addition, augmentation techniques were implemented to enhance generalization capabilities and overall performance. Through comprehensive experimentation, we assess the influence of imbalanced datasets on the performance of the CNN-based classifier. Using Pins face data, we conducted an empirical study, evaluating conclusions based on accuracy, precision, recall, and F1-score measurements. A comparative analysis demonstrates that the performance of the proposed Convolutional Neural Network classifier diminishes in the presence of dataset class imbalances. Conversely, the proposed system, utilizing data resampling techniques, notably enhances classification performance for imbalanced datasets. This study underscores the efficacy of data resampling approaches in augmenting the performance of Face Recognition models, presenting prospects for more dependable and efficient future systems.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"44 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140487370","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

Foundations of ANNs: Tolstoy’s Genius Explored using Transformer Architecture 人工神经网络的基础：利用变压器架构探索托尔斯泰的天才思想

International Journal of Artificial Intelligence & Applications Pub Date : 2024-01-29 DOI: 10.5121/ijaia.2024.15105

Shahriyar Guliyev

引用次数: 0

Smart Crosswalk: Machine Learning and Image Processing based Pedestrian and Vehicle Monitoring System 智能人行横道：基于机器学习和图像处理的行人和车辆监控系统

International Journal of Artificial Intelligence & Applications Pub Date : 2023-11-29 DOI: 10.5121/ijaia.2023.14603

Hiruni J.M.D.K, Weerakoon L.M.R, Weerasinghe T.R, Jayasinghe S.J.A.S.M.S, Jenny Krishara, S. Chandrasiri

{"title":"Smart Crosswalk: Machine Learning and Image Processing based Pedestrian and Vehicle Monitoring System","authors":"Hiruni J.M.D.K, Weerakoon L.M.R, Weerasinghe T.R, Jayasinghe S.J.A.S.M.S, Jenny Krishara, S. Chandrasiri","doi":"10.5121/ijaia.2023.14603","DOIUrl":"https://doi.org/10.5121/ijaia.2023.14603","url":null,"abstract":"The conventional pedestrian crossing system's shortcomings require urgent reform to enhance the safety of pedestrians and improve urban mobility. Issues such as insufficient time for pedestrians to cross, prolong waiting times, neglection of emergency vehicles, and the absence of effective 24/7 response mechanisms at traditional crosswalks present significant safety concerns in urban areas. Our primary intention is to develop a cutting-edge pedestrian crossing system that relies on deep learning and image processing technologies as its foundation. This research addresses to innovate an advanced smart crosswalk consisting of four essential components: a real-time Pedestrian Detection and Priority System customized for individuals with special needs, a responsive system for detecting road conditions, vehicle availability and speed near crosswalks, a real-time Emergency Vehicle Detection and Priority System strengthened by rigorous verification procedures, and a robust framework for identifying pedestrian accidents and violations of crosswalk rules. The entire system has been meticulously designed not only to enhance pedestrian safety by identifying potential dangers but also to optimize traffic flow. In essence, it aims to provide an improved pedestrian crossing experience characterized by increased safety and efficiency.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"10 9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139210049","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

Learning Spline Models with the EM Algorithm for Shape Recognition 利用 EM 算法学习用于形状识别的样条曲线模型

International Journal of Artificial Intelligence & Applications Pub Date : 2023-11-29 DOI: 10.5121/ijaia.2023.14604

Abdullah A. Al-Shaher, Yusef S. AlKhawari

引用次数: 0

Building Python Application for Webmail Interfaces Navigation using Voice Recognition Technology 利用语音识别技术构建用于网络邮件界面导航的 Python 应用程序

International Journal of Artificial Intelligence & Applications Pub Date : 2023-11-29 DOI: 10.5121/ijaia.2023.14601

Mokhtar Alkhattali, Mostafa Dow, Khawla Azwee, Mohamed Sayah

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