Pub Date : 2024-01-29DOI: 10.5121/ijaia.2024.15107
Islam D. S. Aabdalla, D. Vasumathi
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.
{"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}
Pub Date : 2024-01-29DOI: 10.5121/ijaia.2024.15104
Dharmender Salian
This study reviews studies on Artificial Intelligence (AI) maturity models (MM) in automotive manufacturing. To stay competitive, SMEs in the automotive industry need to embrace digitalization. SMEs employ a large segment of the USA's workforce. The benefits of operational efficiency, quality improvement, cost reduction, and innovative culture have made SMEs more aggressive about digitalization. Digitalizing operations with Artificial Intelligence are on the rise. In this paper, AI applications in SMEs are examined through the lens of an AI maturity model.
{"title":"Review of AI Maturity Models in Automotive SME Manufacturing","authors":"Dharmender Salian","doi":"10.5121/ijaia.2024.15104","DOIUrl":"https://doi.org/10.5121/ijaia.2024.15104","url":null,"abstract":"This study reviews studies on Artificial Intelligence (AI) maturity models (MM) in automotive manufacturing. To stay competitive, SMEs in the automotive industry need to embrace digitalization. SMEs employ a large segment of the USA's workforce. The benefits of operational efficiency, quality improvement, cost reduction, and innovative culture have made SMEs more aggressive about digitalization. Digitalizing operations with Artificial Intelligence are on the rise. In this paper, AI applications in SMEs are examined through the lens of an AI maturity model.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"40 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140487834","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}
Pub Date : 2024-01-29DOI: 10.5121/ijaia.2024.15103
Yahya Benremdane, Said Jamal, Oumaima Taheri, Jawad Lakziz, Said Ouaskit
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.
{"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}
Pub Date : 2024-01-29DOI: 10.5121/ijaia.2024.15106
Said Jamal, Jawad Lakziz, Yahya Benremdane, Said Ouaskit
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.
{"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}
Pub Date : 2024-01-29DOI: 10.5121/ijaia.2024.15101
Islam D. S. Aabdalla, D. Vasumathi
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.
{"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}
Pub Date : 2024-01-29DOI: 10.5121/ijaia.2024.15102
Miftah Asharaf Najeeb, Alhaam Alariyibi
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.
人脸识别是安全系统、社交媒体平台和增强现实应用等众多现代应用不可或缺的一部分。这些系统的成功在很大程度上取决于它们所使用的人脸识别模型的性能，特别是卷积神经网络（CNN）的性能。然而，现实世界中的许多分类任务都会遇到数据集不平衡的问题，有些类别的代表性明显不足。没有解决这种类不平衡问题的人脸识别模型往往表现不佳，尤其是在涉及大量人脸识别（多类问题）的任务中。本研究探讨了数据集中的类不平衡如何影响人脸识别神经网络分类器的创建。最初，我们设计了一个用于人脸识别的卷积神经网络模型，整合了混合重采样方法（过采样和欠采样）来解决数据集的不平衡问题。此外，我们还采用了增强技术来提高泛化能力和整体性能。通过综合实验，我们评估了不平衡数据集对基于 CNN 的分类器性能的影响。我们使用 Pins 人脸数据进行了实证研究，根据准确率、精确度、召回率和 F1 分数测量结果评估了结论。对比分析表明，在数据集类别不平衡的情况下，所提出的卷积神经网络分类器的性能会下降。相反，建议的系统利用数据重采样技术，显著提高了不平衡数据集的分类性能。这项研究强调了数据重采样方法在提高人脸识别模型性能方面的功效，为未来更可靠、更高效的系统开辟了前景。
{"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}
Pub Date : 2024-01-29DOI: 10.5121/ijaia.2024.15105
Shahriyar Guliyev
Artificial Narrow Intelligence is in the phase of moving towards the AGN, which will attempt to decide as a human being. We are getting closer to it by each day, but AI actually is indefinite to many, although it is no different than any other set of mathematically defined computer operations in its core. Generating new data from a pre-trained model introduces new challenges to science & technology. In this work, the design of such an architecture from scratch, solving problems, and introducing alternative approaches are what has been conducted. Using a deep thinker, Tolstoy, as an object of study is a source of motivation for the entire research.
{"title":"Foundations of ANNs: Tolstoy’s Genius Explored using Transformer Architecture","authors":"Shahriyar Guliyev","doi":"10.5121/ijaia.2024.15105","DOIUrl":"https://doi.org/10.5121/ijaia.2024.15105","url":null,"abstract":"Artificial Narrow Intelligence is in the phase of moving towards the AGN, which will attempt to decide as a human being. We are getting closer to it by each day, but AI actually is indefinite to many, although it is no different than any other set of mathematically defined computer operations in its core. Generating new data from a pre-trained model introduces new challenges to science & technology. In this work, the design of such an architecture from scratch, solving problems, and introducing alternative approaches are what has been conducted. Using a deep thinker, Tolstoy, as an object of study is a source of motivation for the entire research.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"41 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140487712","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}
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.
{"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}
Pub Date : 2023-11-29DOI: 10.5121/ijaia.2023.14604
Abdullah A. Al-Shaher, Yusef S. AlKhawari
This paper demonstrates how cubic Spline (B-Spline) models can be used to recognize 2-dimension nonrigid handwritten isolated characters. Each handwritten character is represented by a set of nonoverlapping uniformly distributed landmarks. The Spline models are constructed by utilizing cubic order of polynomial to model the shapes under study. The approach is a two-stage process. The first stage is learning, we construct a mixture of spline class parameters to capture the variations in spline coefficients using the apparatus Expectation Maximization algorithm. The second stage is recognition, here we use the Fréchet distance to compute the variations between the spline models and test spline shape for recognition. We test the approach on a set of handwritten Arabic letters
{"title":"Learning Spline Models with the EM Algorithm for Shape Recognition","authors":"Abdullah A. Al-Shaher, Yusef S. AlKhawari","doi":"10.5121/ijaia.2023.14604","DOIUrl":"https://doi.org/10.5121/ijaia.2023.14604","url":null,"abstract":"This paper demonstrates how cubic Spline (B-Spline) models can be used to recognize 2-dimension nonrigid handwritten isolated characters. Each handwritten character is represented by a set of nonoverlapping uniformly distributed landmarks. The Spline models are constructed by utilizing cubic order of polynomial to model the shapes under study. The approach is a two-stage process. The first stage is learning, we construct a mixture of spline class parameters to capture the variations in spline coefficients using the apparatus Expectation Maximization algorithm. The second stage is recognition, here we use the Fréchet distance to compute the variations between the spline models and test spline shape for recognition. We test the approach on a set of handwritten Arabic letters","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"90 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139211044","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}
Voice Recognition Technology (VRT) has played a crucial role in technology development, finding extensive use in the development of humanitarian assistance applications, including assistance programs for individuals with disabilities to use smart vehicles and smart homes, as well as websites. This paper discusses implementing a Computer Application (PC-App) for humanitarian assistance written in Python to enable Arabic-speaking elderly and handicapped employees to access and navigate webmail accounts using Arabic Voice Commands (AVC). Furthermore, a survey was conducted for elderly and disabled employees to assess the effectiveness of the application, with participants evaluating that it was useful in addition to improving their interaction with their accounts in Webmail. Ultimately, this application promotes independence and functionality for Arabic-speaking individuals, regardless of their mobility disability levels, by allowing them to independently use the Webmail interface using AVC.
{"title":"Building Python Application for Webmail Interfaces Navigation using Voice Recognition Technology","authors":"Mokhtar Alkhattali, Mostafa Dow, Khawla Azwee, Mohamed Sayah","doi":"10.5121/ijaia.2023.14601","DOIUrl":"https://doi.org/10.5121/ijaia.2023.14601","url":null,"abstract":"Voice Recognition Technology (VRT) has played a crucial role in technology development, finding extensive use in the development of humanitarian assistance applications, including assistance programs for individuals with disabilities to use smart vehicles and smart homes, as well as websites. This paper discusses implementing a Computer Application (PC-App) for humanitarian assistance written in Python to enable Arabic-speaking elderly and handicapped employees to access and navigate webmail accounts using Arabic Voice Commands (AVC). Furthermore, a survey was conducted for elderly and disabled employees to assess the effectiveness of the application, with participants evaluating that it was useful in addition to improving their interaction with their accounts in Webmail. Ultimately, this application promotes independence and functionality for Arabic-speaking individuals, regardless of their mobility disability levels, by allowing them to independently use the Webmail interface using AVC.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139215017","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}
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