Pub Date : 2023-01-01DOI: 10.32604/csse.2023.036179
P. Charongrattanasakul, Wimonmas Bamrungsetthapong, P. Kumam
{"title":"Designing Adaptive Multiple Dependent State Sampling Plan for Accelerated Life Tests","authors":"P. Charongrattanasakul, Wimonmas Bamrungsetthapong, P. Kumam","doi":"10.32604/csse.2023.036179","DOIUrl":"https://doi.org/10.32604/csse.2023.036179","url":null,"abstract":"","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"46 1","pages":"1631-1651"},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75952041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/csse.2023.035575
A. Shahzad, A. Altalbe
{"title":"Computing of LQR Technique for Nonlinear System Using Local Approximation","authors":"A. Shahzad, A. Altalbe","doi":"10.32604/csse.2023.035575","DOIUrl":"https://doi.org/10.32604/csse.2023.035575","url":null,"abstract":"","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"15 1","pages":"853-871"},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79148364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/csse.2023.031116
Fatih Aydemir, Aydın Çetin
Dynamic area coverage with small unmanned aerial vehicle (UAV) systems is one of the major research topics due to limited payloads and the difficulty of decentralized decision-making process. Collaborative behavior of a group of UAVs in an unknown environment is another hard problem to be solved. In this paper, we propose a method for decentralized execution of multi-UAVs for dynamic area coverage problems. The proposed decentralized decision-making dynamic area coverage (DDMDAC) method utilizes reinforcement learning (RL) where each UAV is represented by an intelligent agent that learns policies to create collaborative behaviors in partially observable environment. Intelligent agents increase their global observations by gathering information about the environment by connecting with other agents. The connectivity provides a consensus for the decision-making process, while each agent takes decisions. At each step, agents acquire all reachable agents’ states, determine the optimum location for maximal area coverage and receive reward using the covered rate on the target area, respectively. The method was tested in a multi-agent actor-critic simulation platform. In the study, it has been considered that each UAV has a certain communication distance as in real applications. The results show that UAVs with limited communication distance can act jointly in the target area and can successfully cover the area without guidance from the central command unit.
{"title":"Multi-Agent Dynamic Area Coverage Based on Reinforcement Learning with Connected Agents","authors":"Fatih Aydemir, Aydın Çetin","doi":"10.32604/csse.2023.031116","DOIUrl":"https://doi.org/10.32604/csse.2023.031116","url":null,"abstract":"Dynamic area coverage with small unmanned aerial vehicle (UAV) systems is one of the major research topics due to limited payloads and the difficulty of decentralized decision-making process. Collaborative behavior of a group of UAVs in an unknown environment is another hard problem to be solved. In this paper, we propose a method for decentralized execution of multi-UAVs for dynamic area coverage problems. The proposed decentralized decision-making dynamic area coverage (DDMDAC) method utilizes reinforcement learning (RL) where each UAV is represented by an intelligent agent that learns policies to create collaborative behaviors in partially observable environment. Intelligent agents increase their global observations by gathering information about the environment by connecting with other agents. The connectivity provides a consensus for the decision-making process, while each agent takes decisions. At each step, agents acquire all reachable agents’ states, determine the optimum location for maximal area coverage and receive reward using the covered rate on the target area, respectively. The method was tested in a multi-agent actor-critic simulation platform. In the study, it has been considered that each UAV has a certain communication distance as in real applications. The results show that UAVs with limited communication distance can act jointly in the target area and can successfully cover the area without guidance from the central command unit.","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"17 1","pages":"215-230"},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78975169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/csse.2023.034721
Abdelwahed Motwakel, Hala J. Alshahrani, Jaber S. Alzahrani, Ayman Yafoz, Heba Mohsen, Ishfaq Yaseen, Amgad Atta Abdelmageed, Mohamed I. Eldesouki
Currently, individuals use online social media, namely Facebook or Twitter, for sharing their thoughts and emotions. Detection of emotions on social networking sites’ finds useful in several applications in social welfare, commerce, public health, and so on. Emotion is expressed in several means, like facial and speech expressions, gestures, and written text. Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning (DL) and natural language processing (NLP) domains. This article proposes a Deer Hunting Optimization with Deep Belief Network Enabled Emotion Classification (DHODBN-EC) on English Twitter Data in this study. The presented DHODBN-EC model aims to examine the existence of distinct emotion classes in tweets. At the introductory level, the DHODBN-EC technique pre-processes the tweets at different levels. Besides, the word2vec feature extraction process is applied to generate the word embedding process. For emotion classification, the DHODBN-EC model utilizes the DBN model, which helps to determine distinct emotion class labels. Lastly, the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN technique. An extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach. A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.
{"title":"Deer Hunting Optimization with Deep Learning Enabled Emotion Classification on English Twitter Data","authors":"Abdelwahed Motwakel, Hala J. Alshahrani, Jaber S. Alzahrani, Ayman Yafoz, Heba Mohsen, Ishfaq Yaseen, Amgad Atta Abdelmageed, Mohamed I. Eldesouki","doi":"10.32604/csse.2023.034721","DOIUrl":"https://doi.org/10.32604/csse.2023.034721","url":null,"abstract":"Currently, individuals use online social media, namely Facebook or Twitter, for sharing their thoughts and emotions. Detection of emotions on social networking sites’ finds useful in several applications in social welfare, commerce, public health, and so on. Emotion is expressed in several means, like facial and speech expressions, gestures, and written text. Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning (DL) and natural language processing (NLP) domains. This article proposes a Deer Hunting Optimization with Deep Belief Network Enabled Emotion Classification (DHODBN-EC) on English Twitter Data in this study. The presented DHODBN-EC model aims to examine the existence of distinct emotion classes in tweets. At the introductory level, the DHODBN-EC technique pre-processes the tweets at different levels. Besides, the word2vec feature extraction process is applied to generate the word embedding process. For emotion classification, the DHODBN-EC model utilizes the DBN model, which helps to determine distinct emotion class labels. Lastly, the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN technique. An extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach. A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"43 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":"135563636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/csse.2023.042078
Chia-Wei Jan, Yu-Jhih Chiu, Kuan-Lin Chen, Ting-Chun Yao, Ping-Huan Kuo
Pneumonia is a common lung disease that is more prone to affect the elderly and those with weaker respiratory systems. However, hospital medical resources are limited, and sometimes the workload of physicians is too high, which can affect their judgment. Therefore, a good medical assistance system is of great significance for improving the quality of medical care. This study proposed an integrated system by combining transfer learning and gradient-weighted class activation mapping (Grad-CAM). Pneumonia is a common lung disease that is generally diagnosed using X-rays. However, in areas with limited medical resources, a shortage of medical personnel may result in delayed diagnosis and treatment during the critical period. Additionally, overworked physicians may make diagnostic errors. Therefore, having an X-ray pneumonia diagnosis assistance system is a significant tool for improving the quality of medical care. The result indicates that the best results were obtained by a ResNet50 pretrained model combined with a fully connected classification layer. A retraining procedure was designed to improve accuracy by using gradient-weighted class activation mapping (Grad-CAM), which detects the misclassified images and adds weights to them. In the evaluation tests, the final combined model is named Grad-CAM Based Pneumonia Network (GCPNet) out performed its counterparts in terms of accuracy, precision, and F1 score and reached 97.2% accuracy. An integrated system is proposed to increase model performance where Grad-CAM and transfer learning are combined. Grad-CAM is used to generate the heatmap, which shows the region that the model is focusing on. The outcomes of this research can aid in diagnosing pneumonia symptoms, as the model can accurately classify chest X-ray images, and the heatmap can assist doctors in observing the crucial areas.
肺炎是一种常见的肺部疾病，更容易影响老年人和呼吸系统较弱的人。然而，医院的医疗资源是有限的，有时医生的工作量过大，会影响他们的判断。因此，良好的医疗救助制度对于提高医疗服务质量具有重要意义。本研究提出了迁移学习与梯度加权类激活映射(Grad-CAM)相结合的集成系统。肺炎是一种常见的肺部疾病，通常用x射线诊断。然而，在医疗资源有限的地区，医务人员的短缺可能导致关键时期的诊断和治疗延误。此外，过度劳累的医生可能会犯诊断错误。因此，拥有x线肺炎诊断辅助系统是提高医疗质量的重要工具。结果表明，采用ResNet50预训练模型与全连接分类层相结合的方法获得了最好的分类效果。采用梯度加权类激活映射(gradient-weighted class activation mapping, Grad-CAM)方法检测误分类图像并对其添加权重，从而提高再训练的准确率。在评估测试中，最终的组合模型被命名为基于Grad-CAM的肺炎网络(GCPNet)，其准确度、精密度和F1得分均优于同类模型，准确率达到97.2%。为了提高模型的性能，提出了一种将梯度建模和迁移学习相结合的集成系统。使用Grad-CAM生成热图，热图显示模型所关注的区域。这项研究的结果可以帮助诊断肺炎的症状，因为该模型可以准确地对胸部x射线图像进行分类，并且热图可以帮助医生观察关键区域。
{"title":"Optical Based Gradient-Weighted Class Activation Mapping and Transfer Learning Integrated Pneumonia Prediction Model","authors":"Chia-Wei Jan, Yu-Jhih Chiu, Kuan-Lin Chen, Ting-Chun Yao, Ping-Huan Kuo","doi":"10.32604/csse.2023.042078","DOIUrl":"https://doi.org/10.32604/csse.2023.042078","url":null,"abstract":"Pneumonia is a common lung disease that is more prone to affect the elderly and those with weaker respiratory systems. However, hospital medical resources are limited, and sometimes the workload of physicians is too high, which can affect their judgment. Therefore, a good medical assistance system is of great significance for improving the quality of medical care. This study proposed an integrated system by combining transfer learning and gradient-weighted class activation mapping (Grad-CAM). Pneumonia is a common lung disease that is generally diagnosed using X-rays. However, in areas with limited medical resources, a shortage of medical personnel may result in delayed diagnosis and treatment during the critical period. Additionally, overworked physicians may make diagnostic errors. Therefore, having an X-ray pneumonia diagnosis assistance system is a significant tool for improving the quality of medical care. The result indicates that the best results were obtained by a ResNet50 pretrained model combined with a fully connected classification layer. A retraining procedure was designed to improve accuracy by using gradient-weighted class activation mapping (Grad-CAM), which detects the misclassified images and adds weights to them. In the evaluation tests, the final combined model is named Grad-CAM Based Pneumonia Network (GCPNet) out performed its counterparts in terms of accuracy, precision, and F1 score and reached 97.2% accuracy. An integrated system is proposed to increase model performance where Grad-CAM and transfer learning are combined. Grad-CAM is used to generate the heatmap, which shows the region that the model is focusing on. The outcomes of this research can aid in diagnosing pneumonia symptoms, as the model can accurately classify chest X-ray images, and the heatmap can assist doctors in observing the crucial areas.","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"44 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":"135563953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/csse.2023.033834
Ibrahim M. Alwayle, Badriyya Alonazi, Jaber S. Alzahrani, Khaled M. Alalayah, Khadija M. Alaidarous, I. A. Ahmed, Mahmoud Othman, Abdelwahed Motwakel
{"title":"Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data","authors":"Ibrahim M. Alwayle, Badriyya Alonazi, Jaber S. Alzahrani, Khaled M. Alalayah, Khadija M. Alaidarous, I. A. Ahmed, Mahmoud Othman, Abdelwahed Motwakel","doi":"10.32604/csse.2023.033834","DOIUrl":"https://doi.org/10.32604/csse.2023.033834","url":null,"abstract":"","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"124 1","pages":"3423-3438"},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88025039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/csse.2023.039272
Ji Peng
{"title":"RO-SLAM: A Robust SLAM for Unmanned Aerial Vehicles in a Dynamic Environment","authors":"Ji Peng","doi":"10.32604/csse.2023.039272","DOIUrl":"https://doi.org/10.32604/csse.2023.039272","url":null,"abstract":"","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"57 1","pages":"2275-2291"},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91299753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/csse.2023.035529
A. Hilal, Fadwa M. Alrowais, F. Al-Wesabi, Radwa Marzouk
{"title":"Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System for Visually Impaired People","authors":"A. Hilal, Fadwa M. Alrowais, F. Al-Wesabi, Radwa Marzouk","doi":"10.32604/csse.2023.035529","DOIUrl":"https://doi.org/10.32604/csse.2023.035529","url":null,"abstract":"","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"20 1","pages":"1929-1945"},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75594324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/csse.2023.034901
K. Wisaeng
{"title":"A Novel Soft Clustering Method for Detection of Exudates","authors":"K. Wisaeng","doi":"10.32604/csse.2023.034901","DOIUrl":"https://doi.org/10.32604/csse.2023.034901","url":null,"abstract":"","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"362 1","pages":"1039-1058"},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76510453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/csse.2023.032231
R. Devi, V. R. Vijayakumar, P. Sivakumar
{"title":"EfficientNetV2 Model for Plant Disease Classification and Pest Recognition","authors":"R. Devi, V. R. Vijayakumar, P. Sivakumar","doi":"10.32604/csse.2023.032231","DOIUrl":"https://doi.org/10.32604/csse.2023.032231","url":null,"abstract":"","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"2 1","pages":"2249-2263"},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76777169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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