In the research paper, authors meticulously detail the development, testing, and application of an innovative deep learning model aimed at monitoring physical activities of students in real-time. Drawing upon the advanced capabilities of convolutional neural networks (CNNs), the proposed system exhibits an exceptional ability to track, analyze, and evaluate the physical exercises performed by students, thereby providing an unprecedented scope for customization in physical education strategies. This piece of scholarly work bridges the gap between physical education and cutting-edge technology, highlighting the burgeoning role of artificial intelligence in health and fitness sector. With an expansive study spanning various cohorts of physical culture students, the paper provides compelling empirical evidence that underlines the superiority of the deep learning system over conventional methods in aspects of accuracy, speed, and efficiency of monitoring. The authors demonstrate the transformative potential of their system, capable of facilitating personalized and optimized physical training strategies based on real-time feedback. Moreover, the potential implications of the study extend beyond the realm of education and into wider public health applications, with the possibility of fostering improved health outcomes on a larger scale. This research paper makes a significant contribution to the burgeoning field of AI in physical education, embodying a paradigm shift in the approach towards physical fitness and health monitoring. It underscores the potential of AI-driven technology to revolutionize traditional methods in physical education, paving the way for more personalized and effective teaching and training regimes, and ultimately contributing to enhanced health and fitness outcomes among students.
{"title":"A Novel Deep Neural Network to Analyze and Monitoring the Physical Training Relation to Sports Activities","authors":"Bakhytzhan Omarov, Nurlan Nurmash, Bauyrzhan Doskarayev, Nagashbek Zhilisbaev, Maxat Dairabayev, Shamurat Orazov, Nurlan Omarov","doi":"10.14569/ijacsa.2023.0140977","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140977","url":null,"abstract":"In the research paper, authors meticulously detail the development, testing, and application of an innovative deep learning model aimed at monitoring physical activities of students in real-time. Drawing upon the advanced capabilities of convolutional neural networks (CNNs), the proposed system exhibits an exceptional ability to track, analyze, and evaluate the physical exercises performed by students, thereby providing an unprecedented scope for customization in physical education strategies. This piece of scholarly work bridges the gap between physical education and cutting-edge technology, highlighting the burgeoning role of artificial intelligence in health and fitness sector. With an expansive study spanning various cohorts of physical culture students, the paper provides compelling empirical evidence that underlines the superiority of the deep learning system over conventional methods in aspects of accuracy, speed, and efficiency of monitoring. The authors demonstrate the transformative potential of their system, capable of facilitating personalized and optimized physical training strategies based on real-time feedback. Moreover, the potential implications of the study extend beyond the realm of education and into wider public health applications, with the possibility of fostering improved health outcomes on a larger scale. This research paper makes a significant contribution to the burgeoning field of AI in physical education, embodying a paradigm shift in the approach towards physical fitness and health monitoring. It underscores the potential of AI-driven technology to revolutionize traditional methods in physical education, paving the way for more personalized and effective teaching and training regimes, and ultimately contributing to enhanced health and fitness outcomes among students.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"73 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":"136002810","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140358
M. Vera, T. Palaoag
—Medicinal plants are a practical and cost-effective alternative for treating common ailments, especially in areas with limited access to public healthcare systems. This paper introduces a prototype of an intelligent interactive system that merges chatbot technology with artificial intelligence (AI) to address inquiries related to treatment alternatives and the application of different medicinal plants for prevalent health conditions which promote and advance alternative healing practices in the locality. The platform is a hybrid online chat service that prioritizes consumer health and encourages the responsible use of medicinal plants. This study used a survey questionnaire to gather information from traditional healers and users and concerned government agencies about how well the system prototype performed. The system's performance was assessed in terms of effectiveness, efficiency, and customer satisfaction, with respondents providing an aggregate rating of "Strongly Agree". Significantly, this study lays the groundwork for education on the use of local medicinal plants to cure illnesses and highlights the importance of providing users with accurate and reliable information on the safe use of medicinal plants. This approach empowers users to make informed decisions about the plants they use, reducing the likelihood of harmful effects and optimizing the potential benefits of medicinal plants. By supporting this effort, this study contributes to the achievement of the third Sustainable Development Goal of the UN, which aims to promote health and well-being by offering the local populace a low-cost option as a first line of defense for improving their health and wellness.
{"title":"Implementation of a Smarter Herbal Medication Delivery System Employing an AI-Powered Chatbot","authors":"M. Vera, T. Palaoag","doi":"10.14569/ijacsa.2023.0140358","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140358","url":null,"abstract":"—Medicinal plants are a practical and cost-effective alternative for treating common ailments, especially in areas with limited access to public healthcare systems. This paper introduces a prototype of an intelligent interactive system that merges chatbot technology with artificial intelligence (AI) to address inquiries related to treatment alternatives and the application of different medicinal plants for prevalent health conditions which promote and advance alternative healing practices in the locality. The platform is a hybrid online chat service that prioritizes consumer health and encourages the responsible use of medicinal plants. This study used a survey questionnaire to gather information from traditional healers and users and concerned government agencies about how well the system prototype performed. The system's performance was assessed in terms of effectiveness, efficiency, and customer satisfaction, with respondents providing an aggregate rating of \"Strongly Agree\". Significantly, this study lays the groundwork for education on the use of local medicinal plants to cure illnesses and highlights the importance of providing users with accurate and reliable information on the safe use of medicinal plants. This approach empowers users to make informed decisions about the plants they use, reducing the likelihood of harmful effects and optimizing the potential benefits of medicinal plants. By supporting this effort, this study contributes to the achievement of the third Sustainable Development Goal of the UN, which aims to promote health and well-being by offering the local populace a low-cost option as a first line of defense for improving their health and wellness.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"14 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73509702","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140589
Qinyan Gao
—With the development of virtual technology, posture recognition technology has been integrated into virtual technology. This new technology allows users to further understand and observe the activities carried out in life scenes based on their original observation of the external world. And it enables them to make intelligent decisions. Existing posture recognition cannot meet the requirements of precise positioning in virtual environments. Therefore, a two-stage three-dimensional pose recognition model is proposed. The experiment illustrates that the three-dimensional gesture recognition performance is excellent. In addition, under the ablation experiment, the error accuracy of the research model decreased by more than 5 mm, and the overall error accuracy decreased by 10%. In the P-R curve, the accuracy rate of the model reaches 0.741, and the recall rate reaches 0.65. When conducting empirical analysis, the virtual posture disassembly action is complete; the disassembly error is less than 5%, and the disassembly error accuracy is good. The fit degree of the leg bending amplitude reaches over 96%, and the fit degree of the arm bending amplitude reaches over 95%. When the model is applied to actual teaching, the overall satisfaction score of teachers and students reaches 94.6 points. This has effectively improved the teaching effect of art design and is of great significance to the development of education in China.
{"title":"The Application of Virtual Technology Based on Posture Recognition in Art Design Teaching","authors":"Qinyan Gao","doi":"10.14569/ijacsa.2023.0140589","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140589","url":null,"abstract":"—With the development of virtual technology, posture recognition technology has been integrated into virtual technology. This new technology allows users to further understand and observe the activities carried out in life scenes based on their original observation of the external world. And it enables them to make intelligent decisions. Existing posture recognition cannot meet the requirements of precise positioning in virtual environments. Therefore, a two-stage three-dimensional pose recognition model is proposed. The experiment illustrates that the three-dimensional gesture recognition performance is excellent. In addition, under the ablation experiment, the error accuracy of the research model decreased by more than 5 mm, and the overall error accuracy decreased by 10%. In the P-R curve, the accuracy rate of the model reaches 0.741, and the recall rate reaches 0.65. When conducting empirical analysis, the virtual posture disassembly action is complete; the disassembly error is less than 5%, and the disassembly error accuracy is good. The fit degree of the leg bending amplitude reaches over 96%, and the fit degree of the arm bending amplitude reaches over 95%. When the model is applied to actual teaching, the overall satisfaction score of teachers and students reaches 94.6 points. This has effectively improved the teaching effect of art design and is of great significance to the development of education in China.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"90 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72593355","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140569
Aatila Mustapha, Lachgar Mohamed, Hrimech Hamid, Kartit Ali
—Machine learning (ML) algorithms are being integrated into several disciplines. Ophthalmology is one field of health sector that has benefited from the advantages and capacities of ML in processing of different types of data. In a large number of studies, the detection and classification of various diseases, such as keratoconus, was carried out by analyzing corneal characteristics, in different data types (images, measurements, etc.), using ML tools. The main objective of this study was to conduct a rigorous systematic review of the use of ML techniques in the detection and classification of keratoconus. Papers considered in this study were selected carefully from Scopus and Web of Science digital databases, according to their content and to the adoption of ML methods in the classification of keratoconus. The selected studies were reviewed to identify different ML techniques implemented and the data types handled in the diagnosis of keratoconus. A total of 38 articles, published between 2005 and 2022, were retained for review and discussion of their content.
机器学习(ML)算法正在被整合到几个学科中。眼科是卫生部门的一个领域，受益于机器学习在处理不同类型数据方面的优势和能力。在大量的研究中，通过使用ML工具分析不同数据类型(图像、测量等)的角膜特征，对圆锥角膜等各种疾病进行检测和分类。本研究的主要目的是对ML技术在圆锥角膜的检测和分类中的应用进行严格的系统回顾。本研究考虑的论文是根据其内容和采用ML方法对圆锥角膜进行分类，从Scopus和Web of Science数字数据库中精心挑选出来的。对所选的研究进行回顾，以确定在圆锥角膜诊断中实施的不同ML技术和处理的数据类型。2005年至2022年间发表的总共38篇文章被保留下来，以对其内容进行审查和讨论。
{"title":"Machine Learning Techniques in Keratoconus Classification: A Systematic Review","authors":"Aatila Mustapha, Lachgar Mohamed, Hrimech Hamid, Kartit Ali","doi":"10.14569/ijacsa.2023.0140569","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140569","url":null,"abstract":"—Machine learning (ML) algorithms are being integrated into several disciplines. Ophthalmology is one field of health sector that has benefited from the advantages and capacities of ML in processing of different types of data. In a large number of studies, the detection and classification of various diseases, such as keratoconus, was carried out by analyzing corneal characteristics, in different data types (images, measurements, etc.), using ML tools. The main objective of this study was to conduct a rigorous systematic review of the use of ML techniques in the detection and classification of keratoconus. Papers considered in this study were selected carefully from Scopus and Web of Science digital databases, according to their content and to the adoption of ML methods in the classification of keratoconus. The selected studies were reviewed to identify different ML techniques implemented and the data types handled in the diagnosis of keratoconus. A total of 38 articles, published between 2005 and 2022, were retained for review and discussion of their content.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72795760","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140356
Giat Karyono, Asmala Ahmad, S. A. Asmai
—Removing as much noise as possible in an image while preserving its fine details is a complex and challenging task. We propose a wavelet-based and non-local means (NLM) denoising method to overcome the problem. Two well-known wavelets: dual-tree complex wavelet transform (DT-CWT) and discrete wavelet transform (DWT), have been used to change the noise image into several wavelet coefficients sequentially. NLM filtering and universal hard thresholding with cycle spinning have been used for thresholding on its approximation and detail coefficients, respectively. The inverse two-dimensional DWT was applied to the modified wavelet coefficients to obtain the denoised image. We conducted experiments with twelve test images on the set12 data set, adding the additive Gaussian white noise with variances of 10 to 90 in increments of 10. Three evaluation metrics, such as peak signal noise to rate (PSNR), structural similarity index metric (SSIM), and mean square error (MSE), have been used to evaluate the effectiveness of the proposed denoising method. From these measurement results, the proposed denoising method outperforms DT-CWT, DWT, and NLM almost in all noise levels except for the noise level of 10. At that noise level, the proposed denoising method is lower than NLM but better than DT-CWT and DWT.
{"title":"Image Denoising using Wavelet Cycle Spinning and Non-local Means Filter","authors":"Giat Karyono, Asmala Ahmad, S. A. Asmai","doi":"10.14569/ijacsa.2023.0140356","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140356","url":null,"abstract":"—Removing as much noise as possible in an image while preserving its fine details is a complex and challenging task. We propose a wavelet-based and non-local means (NLM) denoising method to overcome the problem. Two well-known wavelets: dual-tree complex wavelet transform (DT-CWT) and discrete wavelet transform (DWT), have been used to change the noise image into several wavelet coefficients sequentially. NLM filtering and universal hard thresholding with cycle spinning have been used for thresholding on its approximation and detail coefficients, respectively. The inverse two-dimensional DWT was applied to the modified wavelet coefficients to obtain the denoised image. We conducted experiments with twelve test images on the set12 data set, adding the additive Gaussian white noise with variances of 10 to 90 in increments of 10. Three evaluation metrics, such as peak signal noise to rate (PSNR), structural similarity index metric (SSIM), and mean square error (MSE), have been used to evaluate the effectiveness of the proposed denoising method. From these measurement results, the proposed denoising method outperforms DT-CWT, DWT, and NLM almost in all noise levels except for the noise level of 10. At that noise level, the proposed denoising method is lower than NLM but better than DT-CWT and DWT.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"22 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74449077","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140416
Amit Moondra, P. Chahal
org
org
{"title":"Improved Speaker Recognition for Degraded Human Voice using Modified-MFCC and LPC with CNN","authors":"Amit Moondra, P. Chahal","doi":"10.14569/ijacsa.2023.0140416","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140416","url":null,"abstract":"org","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"191 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74458282","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.01403103
Pardeep Kumar, Ankit Kumar
—In recent decades, cardiovascular diseases have eclipsed all others as the main reason for death in both low and middle income countries. Early identification and continuous clinical monitoring can reduce the death rate associated with heart disorders. Neither service is yet accessible, as it requires more intellect, time, and skill to effectively detect cardiac disorders in all circumstances and to advise a patient for 24 hours. In this study, researchers suggested a Machine Learning-based approach to forecast the development of cardiac disease. For precise identification of cardiac disease, an efficient ML technique is required. The proposed method works on five classes, one normal and four diseases. In the research, all classes were assigned a primary task, and recommendations were made based on that. The proposed method optimises feature weighting and selects efficient features. Following feature optimization, adaptive boost learning using tree and KNN bases is used. In the trial, sensitivity improved by 3-4%, specificity by 4-5%, and accuracy by 3-4% compared to the previous approach.
{"title":"Heart Disease Classification and Recommendation by Optimized Features and Adaptive Boost Learning","authors":"Pardeep Kumar, Ankit Kumar","doi":"10.14569/ijacsa.2023.01403103","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01403103","url":null,"abstract":"—In recent decades, cardiovascular diseases have eclipsed all others as the main reason for death in both low and middle income countries. Early identification and continuous clinical monitoring can reduce the death rate associated with heart disorders. Neither service is yet accessible, as it requires more intellect, time, and skill to effectively detect cardiac disorders in all circumstances and to advise a patient for 24 hours. In this study, researchers suggested a Machine Learning-based approach to forecast the development of cardiac disease. For precise identification of cardiac disease, an efficient ML technique is required. The proposed method works on five classes, one normal and four diseases. In the research, all classes were assigned a primary task, and recommendations were made based on that. The proposed method optimises feature weighting and selects efficient features. Following feature optimization, adaptive boost learning using tree and KNN bases is used. In the trial, sensitivity improved by 3-4%, specificity by 4-5%, and accuracy by 3-4% compared to the previous approach.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"37 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74665844","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140808
Mohamed Elashmawy, I. Elamvazuthi, L. I. Izhar, S. Paramasivam, Steven W. Su
—The disease, tuberculosis (TB) is a serious health concern as it primarily affects the lungs and can lead to fatalities. However, early detection and treatment can cure the disease. One potential method for detecting TB is using Computer Aided Diagnosis (CAD) systems, which can analyze Chest X-Ray Images (CXR) for signs of TB. This paper proposes a new approach for improving the performance of CAD systems by using a hybrid pre-processing method for Convolutional Neural Network (CNN) models. The goal of the research is to enhance the accuracy and Area Under Curve (AUC) of detection for TB in CXR images by combining two different pre-processing methods and multi-classifying different manifestations of the disease. The hypothesis is that this approach will result in more accurate detection of TB in CXR images. To achieve this, this research used augmentation and segmentation techniques to pre-process the CXR images before feeding them into a pre-trained CNN model for classification. The VGG16 model managed to achieve an AUC of 0.935, an accuracy of 90% and a 0.8975 F1-score with the proposed pre-processing method.
-结核病是一种严重的健康问题，因为它主要影响肺部并可导致死亡。然而，早期发现和治疗可以治愈这种疾病。检测结核病的一种潜在方法是使用计算机辅助诊断(CAD)系统，该系统可以分析胸部x射线图像(CXR)以寻找结核病的迹象。本文提出了一种利用卷积神经网络(CNN)模型的混合预处理方法来提高CAD系统性能的新方法。本研究的目的是通过结合两种不同的预处理方法，对结核病的不同表现进行多分类，提高CXR图像对结核病检测的准确性和曲线下面积(Area Under Curve, AUC)。假设这种方法将导致在CXR图像中更准确地检测结核病。为了实现这一点，本研究使用增强和分割技术对CXR图像进行预处理，然后将其输入预训练的CNN模型进行分类。采用该预处理方法，VGG16模型的AUC为0.935，准确率为90%，f1分数为0.8975。
{"title":"Detection of Tuberculosis Based on Hybridized Pre-Processing Deep Learning Method","authors":"Mohamed Elashmawy, I. Elamvazuthi, L. I. Izhar, S. Paramasivam, Steven W. Su","doi":"10.14569/ijacsa.2023.0140808","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140808","url":null,"abstract":"—The disease, tuberculosis (TB) is a serious health concern as it primarily affects the lungs and can lead to fatalities. However, early detection and treatment can cure the disease. One potential method for detecting TB is using Computer Aided Diagnosis (CAD) systems, which can analyze Chest X-Ray Images (CXR) for signs of TB. This paper proposes a new approach for improving the performance of CAD systems by using a hybrid pre-processing method for Convolutional Neural Network (CNN) models. The goal of the research is to enhance the accuracy and Area Under Curve (AUC) of detection for TB in CXR images by combining two different pre-processing methods and multi-classifying different manifestations of the disease. The hypothesis is that this approach will result in more accurate detection of TB in CXR images. To achieve this, this research used augmentation and segmentation techniques to pre-process the CXR images before feeding them into a pre-trained CNN model for classification. The VGG16 model managed to achieve an AUC of 0.935, an accuracy of 90% and a 0.8975 F1-score with the proposed pre-processing method.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"32 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77542027","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.01401105
Adisak Intana, Kanjana Laosen, Thiwatip Sriraksa
—Change Impact Analysis (CIA) is essential to the software development process that identifies the potential effects of changes during the development process. The changing of requirements always impacts on the software testing because some parts of the existing test cases may not be used to test the software. This affects new test cases to be entirely generated from the changed version of software requirements specification that causes a considerable amount of time and effort to generate new test cases to re-test the modified system. Therefore, this paper proposes a novel automatic impact analysis approach of test cases based on changes of use case based requirement specification. This approach enables a framework and CIA algorithm where the impact of test cases is analysed when the requirement specification is changed. To detect the change, two versions as before-change and after-change of the use case model are compared. Consequently, the patterns representing the cause of variable changes are classified and analysed. This results in the existing test cases to be analysed whether they are completely reused, partly updated as well as additionally generated. The new test cases are generated automatically by using the Combination of Equivalence and Classification Tree Method (CCTM). This benefits the level of testing coverage with a minimised number of test cases to be enabled and the redundant test cases to be eliminated. The automation of this approach is demonstrated with the developed prototype tool. The validation and evaluation result with two real case studies from Hospital Information System (HIS) together with perspective views of practical specialists confirms the contribution of this tool that we seek.
{"title":"An Automated Impact Analysis Approach for Test Cases based on Changes of Use Case based Requirement Specifications","authors":"Adisak Intana, Kanjana Laosen, Thiwatip Sriraksa","doi":"10.14569/ijacsa.2023.01401105","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01401105","url":null,"abstract":"—Change Impact Analysis (CIA) is essential to the software development process that identifies the potential effects of changes during the development process. The changing of requirements always impacts on the software testing because some parts of the existing test cases may not be used to test the software. This affects new test cases to be entirely generated from the changed version of software requirements specification that causes a considerable amount of time and effort to generate new test cases to re-test the modified system. Therefore, this paper proposes a novel automatic impact analysis approach of test cases based on changes of use case based requirement specification. This approach enables a framework and CIA algorithm where the impact of test cases is analysed when the requirement specification is changed. To detect the change, two versions as before-change and after-change of the use case model are compared. Consequently, the patterns representing the cause of variable changes are classified and analysed. This results in the existing test cases to be analysed whether they are completely reused, partly updated as well as additionally generated. The new test cases are generated automatically by using the Combination of Equivalence and Classification Tree Method (CCTM). This benefits the level of testing coverage with a minimised number of test cases to be enabled and the redundant test cases to be eliminated. The automation of this approach is demonstrated with the developed prototype tool. The validation and evaluation result with two real case studies from Hospital Information System (HIS) together with perspective views of practical specialists confirms the contribution of this tool that we seek.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77746108","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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