Computer Systems Science and Engineering最新文献

Faster RCNN Target Detection Algorithm Integrating CBAM and FPN 结合CBAM和FPN的快速RCNN目标检测算法

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-06-07 DOI: 10.3390/app13126913

Wenshun Sheng, Xiongfeng Yu, Jiayan Lin, Xin Chen

{"title":"Faster RCNN Target Detection Algorithm Integrating CBAM and FPN","authors":"Wenshun Sheng, Xiongfeng Yu, Jiayan Lin, Xin Chen","doi":"10.3390/app13126913","DOIUrl":"https://doi.org/10.3390/app13126913","url":null,"abstract":"In the process of image shooting, due to the influence of angle, distance, complex scenes, illumination intensity, and other factors, small targets and occluded targets will inevitably appear in the image. These targets have few effective pixels, few features, and no obvious features, which makes it difficult to extract their effective features and easily leads to false detection, missed detection, and repeated detection, thus affecting the performance of target detection models. To solve this problem, an improved faster region convolutional neural network (RCNN) algorithm integrating the convolutional block attention module (CBAM) and feature pyramid network (FPN) (CF-RCNN) is proposed to improve the detection and recognition accuracy of small-sized, occluded, or truncated objects in complex scenes. Firstly, it incorporates the CBAM attention mechanism in the feature extraction network in combination with the information filtered by spatial and channel attention modules, focusing on local efficient information of the feature image, which improves the detection ability in the face of obscured or truncated objects. Secondly, it introduces the FPN feature pyramid structure, and links high-level and bottom-level feature data to obtain high-resolution and strong semantic data to enhance the detection effect for small-sized objects. Finally, it optimizes non-maximum suppression (NMS) to compensate for the shortcomings of conventional NMS that mistakenly eliminates overlapping detection frames. The experimental results show that the mean average precision (MAP) of target detection of the improved algorithm on PASCAL VOC2012 public datasets is improved to 76.2%, which is 13.9 percentage points higher than those of the commonly used Faster RCNN and other algorithms. It is better than the commonly used small-sample target detection algorithm.","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89604886","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: 3

SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition. SNELM:用于COVID-19识别的挤压引导ELM。

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-20 DOI: 10.32604/csse.2023.034172

Yudong Zhang, Muhammad Attique Khan, Ziquan Zhu, Shuihua Wang

{"title":"SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition.","authors":"Yudong Zhang, Muhammad Attique Khan, Ziquan Zhu, Shuihua Wang","doi":"10.32604/csse.2023.034172","DOIUrl":"https://doi.org/10.32604/csse.2023.034172","url":null,"abstract":"<p><p>(Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due to its simplicity of usage, quick learning speed, and great generalization performances. The number of hidden neurons in ELM is set to 2000. Ten runs of 10-fold cross-validation are implemented to generate impartial results. (Result) For the 296-image dataset, our SNELM model attains a sensitivity of 96.35 ± 1.50%, a specificity of 96.08 ± 1.05%, a precision of 96.10 ± 1.00%, and an accuracy of 96.22 ± 0.94%. For the 640-image dataset, the SNELM attains a sensitivity of 96.00 ± 1.25%, a specificity of 96.28 ± 1.16%, a precision of 96.28 ± 1.13%, and an accuracy of 96.14 ± 0.96%. (Conclusion) The proposed SNELM model is successful in diagnosing COVID-19. The performances of our model are higher than seven state-of-the-art COVID-19 recognition models.</p>","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9784682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

ELM-Based Shape Adaptive DCT Compression Technique for Underwater Image Compression 基于elm的水下图像形状自适应DCT压缩技术

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.028713

M. Jamunarani, C. Vasanthanayaki

引用次数: 0

Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network 无线传感器网络能量最小化的遗传鸡群算法

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.025503

A. Jameer Basha, S. Aswini, S. Aarthini, Yun-Seung Nam, M. Abouhawwash

引用次数: 0

Development of Pandemic Monitoring System Based on Constellation of Nanosatellites 基于纳米卫星星座的流行病监测系统研制

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.032677

Omar Ben Bahri, Abdullah Alhumaidi Alotaibi

{"title":"Development of Pandemic Monitoring System Based on Constellation of Nanosatellites","authors":"Omar Ben Bahri, Abdullah Alhumaidi Alotaibi","doi":"10.32604/csse.2023.032677","DOIUrl":"https://doi.org/10.32604/csse.2023.032677","url":null,"abstract":"Covid-19 is a global crisis and the greatest challenge we have faced. It affects people in different ways. Most infected people develop a mild to moderate form of the disease and recover without hospitalization. This presents a problem in spreading the pandemic with unintentionally manner. Thus, this paper provides a new technique for COVID-19 monitoring remotely and in wide range. The system is based on satellite technology that provides a pivotal solution for wireless monitoring. This mission requires a data collection technique which can be based on drones' technology. Therefore, the main objective of our proposal is to develop a mission architecture around satellite technology in order to collect information in wide range, mostly, in areas suffer network coverage. A communication method was developed around a constellation of nanosatellites to cover Saudi Arabia region which is the area of interest in this paper. The new proposed architecture provided an efficient monitoring application discussing the gaps related to thermal imaging data. It reached 15.8 min as mean duration of visibility for the desired area. In total, the system can reach a coverage of 5.8 h/day, allowing to send about 21870 thermal images. © 2023 CRL Publishing. All rights reserved.","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69724920","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: 1

Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet 基于无批处理归一化深度卷积神经网络的拥挤场景鲁棒计数

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.037706

S. Zahir, R. Khan, M. Ullah, Muhammad Ishaq, Naqqash Dilshad, Amin Ullah, Mi Young Lee

引用次数: 1

Human Personality Assessment Based on Gait Pattern Recognition Using Smartphone Sensors 基于智能手机传感器步态模式识别的人格评估

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.036185

Kainat Ibrar, Abdul Muiz Fayyaz, M. A. Khan, Majed Alhaisoni, U. Tariq, Seob Jeon, Yun-Seong Nam

引用次数: 0

On Layout Optimization of Wireless Sensor Network Using Meta-Heuristic Approach 基于元启发式方法的无线传感器网络布局优化研究

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.032024

Abeeda Akram, K. Zafar, A. Mian, Abdul Rauf Baig, R. Almakki, Lulwah Alsuwaidan, Shakir Khan

引用次数: 0

Latency Minimization Using an Adaptive Load Balancing Technique in Microservices Applications 在微服务应用中使用自适应负载平衡技术最小化延迟

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.032509

G. Selvakumar, L. Jayashree, S. Arumugam

引用次数: 0

Biometric Verification System Using Hyperparameter Tuned Deep Learning Model 基于超参数调优深度学习模型的生物特征验证系统

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.034849

Mohammad Yamin, Saleh Bajaba, Sarah B. Basahel, E. Lydia

引用次数: 0