eman salah, Mona Shokair, Fathi Abd El-Samie, wafaa ahmed
{"title":"利用卷积神经网络检测注意缺陷多动障碍","authors":"eman salah, Mona Shokair, Fathi Abd El-Samie, wafaa ahmed","doi":"10.21608/ijt.2023.315782","DOIUrl":null,"url":null,"abstract":": Attention deficit hyperactivity disorder (ADHD) is a neurological disease that is very common in recent times, and many attempts have been made to overcome it. ADHD is diagnosed in boys more than girls. Girls are more likely to have only symptoms of inattention, and less likely to exhibit disruptive behavior that makes ADHD symptoms more noticeable. This means that girls with ADHD may not always be diagnosed. Artificial intelligence has played a very important role in eliminating this disorder using deep learning technology. Deep learning has three algorithms as Deep Neural Network (DNN), convolution neural network (CNN), Recurrent Neural Network (RNN). The disease is diagnosed using functional magnetic resonance imaging (fMRI) to determine whether the person is affected or not by taking some snapshots of brain images. A convolutional neural network (CNN) was chosen to extract the specifications or features of fMRI images.There were an optimization technique of the fMRI datasets namely, Nesterov-Accelerated Adaptive Moment Estimation (Nadam). Using these optimization techniques for adapting the classification system for three CNN network or models for ADHD cases, it was concluded that the accuracy for CNN NET 1 is 97.5%, accuracy for CNN NET 2 is 95% and accuracy for CNN NET 3 is 98.75 %. Finally, it’s found that CNN NET 3 is the best as its high accuracy so the system is improved.","PeriodicalId":42285,"journal":{"name":"International Journal of Interdisciplinary Telecommunications and Networking","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2023-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detection Attention Deficit Hyperactivity Disorder by using Convolution Neural Network\",\"authors\":\"eman salah, Mona Shokair, Fathi Abd El-Samie, wafaa ahmed\",\"doi\":\"10.21608/ijt.2023.315782\",\"DOIUrl\":null,\"url\":null,\"abstract\":\": Attention deficit hyperactivity disorder (ADHD) is a neurological disease that is very common in recent times, and many attempts have been made to overcome it. ADHD is diagnosed in boys more than girls. Girls are more likely to have only symptoms of inattention, and less likely to exhibit disruptive behavior that makes ADHD symptoms more noticeable. This means that girls with ADHD may not always be diagnosed. Artificial intelligence has played a very important role in eliminating this disorder using deep learning technology. Deep learning has three algorithms as Deep Neural Network (DNN), convolution neural network (CNN), Recurrent Neural Network (RNN). The disease is diagnosed using functional magnetic resonance imaging (fMRI) to determine whether the person is affected or not by taking some snapshots of brain images. A convolutional neural network (CNN) was chosen to extract the specifications or features of fMRI images.There were an optimization technique of the fMRI datasets namely, Nesterov-Accelerated Adaptive Moment Estimation (Nadam). Using these optimization techniques for adapting the classification system for three CNN network or models for ADHD cases, it was concluded that the accuracy for CNN NET 1 is 97.5%, accuracy for CNN NET 2 is 95% and accuracy for CNN NET 3 is 98.75 %. Finally, it’s found that CNN NET 3 is the best as its high accuracy so the system is improved.\",\"PeriodicalId\":42285,\"journal\":{\"name\":\"International Journal of Interdisciplinary Telecommunications and Networking\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2023-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Interdisciplinary Telecommunications and Networking\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21608/ijt.2023.315782\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"TELECOMMUNICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Interdisciplinary Telecommunications and Networking","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21608/ijt.2023.315782","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
Detection Attention Deficit Hyperactivity Disorder by using Convolution Neural Network
: Attention deficit hyperactivity disorder (ADHD) is a neurological disease that is very common in recent times, and many attempts have been made to overcome it. ADHD is diagnosed in boys more than girls. Girls are more likely to have only symptoms of inattention, and less likely to exhibit disruptive behavior that makes ADHD symptoms more noticeable. This means that girls with ADHD may not always be diagnosed. Artificial intelligence has played a very important role in eliminating this disorder using deep learning technology. Deep learning has three algorithms as Deep Neural Network (DNN), convolution neural network (CNN), Recurrent Neural Network (RNN). The disease is diagnosed using functional magnetic resonance imaging (fMRI) to determine whether the person is affected or not by taking some snapshots of brain images. A convolutional neural network (CNN) was chosen to extract the specifications or features of fMRI images.There were an optimization technique of the fMRI datasets namely, Nesterov-Accelerated Adaptive Moment Estimation (Nadam). Using these optimization techniques for adapting the classification system for three CNN network or models for ADHD cases, it was concluded that the accuracy for CNN NET 1 is 97.5%, accuracy for CNN NET 2 is 95% and accuracy for CNN NET 3 is 98.75 %. Finally, it’s found that CNN NET 3 is the best as its high accuracy so the system is improved.
期刊介绍:
The International Journal of Interdisciplinary Telecommunications and Networking (IJITN) examines timely and important telecommunications and networking issues, problems, and solutions from a multidimensional, interdisciplinary perspective for researchers and practitioners. IJITN emphasizes the cross-disciplinary viewpoints of electrical engineering, computer science, information technology, operations research, business administration, economics, sociology, and law. The journal publishes theoretical and empirical research findings, case studies, and surveys, as well as the opinions of leaders and experts in the field. The journal''s coverage of telecommunications and networking is broad, ranging from cutting edge research to practical implementations. Published articles must be from an interdisciplinary, rather than a narrow, discipline-specific viewpoint. The context may be industry-wide, organizational, individual user, or societal. Topics Covered: -Emerging telecommunications and networking technologies -Global telecommunications industry business modeling and analysis -Network management and security -New telecommunications applications, products, and services -Social and societal aspects of telecommunications and networking -Standards and standardization issues for telecommunications and networking -Strategic telecommunications management -Telecommunications and networking cultural issues and education -Telecommunications and networking hardware and software design -Telecommunications investments and new ventures -Telecommunications network modeling and design -Telecommunications regulation and policy issues -Telecommunications systems economics