Computer Systems Science and Engineering最新文献_第8页

Designing Adaptive Multiple Dependent State Sampling Plan for Accelerated Life Tests 加速寿命试验自适应多相关状态采样方案设计

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.036179

P. Charongrattanasakul, Wimonmas Bamrungsetthapong, P. Kumam

引用次数: 0

Computing of LQR Technique for Nonlinear System Using Local Approximation 非线性系统LQR技术的局部逼近计算

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.035575

A. Shahzad, A. Altalbe

引用次数: 0

Multi-Agent Dynamic Area Coverage Based on Reinforcement Learning with Connected Agents 基于连接智能体强化学习的多智能体动态区域覆盖

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.031116

Fatih Aydemir, Aydın Çetin

{"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}

引用次数: 4

Deer Hunting Optimization with Deep Learning Enabled Emotion Classification on English Twitter Data 基于深度学习的英语推特数据情感分类的猎鹿优化

4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 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

{"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}

引用次数: 0

Optical Based Gradient-Weighted Class Activation Mapping and Transfer Learning Integrated Pneumonia Prediction Model 基于光学梯度加权类激活映射和迁移学习的肺炎预测模型

4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.042078

Chia-Wei Jan, Yu-Jhih Chiu, Kuan-Lin Chen, Ting-Chun Yao, Ping-Huan Kuo

{"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}

引用次数: 0

Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data 基于参数调优机器学习的阿拉伯语Twitter数据情感识别

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 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

引用次数: 0

RO-SLAM: A Robust SLAM for Unmanned Aerial Vehicles in a Dynamic Environment RO-SLAM:无人机动态环境下的鲁棒SLAM

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.039272

Ji Peng

引用次数: 0

Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System for Visually Impaired People 马鹿优化视障人士人工智能图像字幕系统

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.035529

A. Hilal, Fadwa M. Alrowais, F. Al-Wesabi, Radwa Marzouk

引用次数: 1

A Novel Soft Clustering Method for Detection of Exudates 一种新的渗出液软聚类检测方法

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.034901

K. Wisaeng

引用次数: 1

EfficientNetV2 Model for Plant Disease Classification and Pest Recognition 植物病害分类与害虫识别的高效netv2模型

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.032231

R. Devi, V. R. Vijayakumar, P. Sivakumar

引用次数: 0