Computer Journal最新文献_第8页

Severity Level Classification of Brain Tumor based on MRI Images using Fractional-Chicken Swarm Optimization Algorithm 基于分数-鸡群优化算法的MRI脑肿瘤严重程度分类

IF 1.4 4区计算机科学

Computer Journal Pub Date : 2021-06-01 DOI: 10.1093/comjnl/bxab057

R Cristin;K Suresh Kumar;P Anbhazhagan

{"title":"Severity Level Classification of Brain Tumor based on MRI Images using Fractional-Chicken Swarm Optimization Algorithm","authors":"R Cristin;K Suresh Kumar;P Anbhazhagan","doi":"10.1093/comjnl/bxab057","DOIUrl":"https://doi.org/10.1093/comjnl/bxab057","url":null,"abstract":"Brain tumor classification is highly effective in identifying and diagnosing the exact location of the tumor in the brain. The medical imaging system reported that early diagnosis and classification of the tumor increases the life of the human. Among various imaging modalities, magnetic resonance imaging (MRI) is highly used by clinical experts, as it offers contrast information of brain tumors. An effective classification method named fractional-chicken swarm optimization (fractional-CSO) is introduced to perform the severity-level tumor classification. Here, the chicken swarm behavior is merged with the derivative factor to enhance the accuracy of severity level classification. The optimal solution is obtained by updating the position of the rooster, which updates their location based on better fitness value. The brain images are pre-processed and the features are effectively extracted, and the cancer classification is carried out. Moreover, the severity level of tumor classification is performed using the deep recurrent neural network, which is trained by the proposed fractional-CSO algorithm. Moreover, the performance of the proposed fractional-CSO attained better performance in terms of the evaluation metrics, such as accuracy, specificity and sensitivity with the values of 93.35, 96 and 95% using simulated BRATS dataset, respectively.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"64 10","pages":"1514-1530"},"PeriodicalIF":1.4,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/comjnl/bxab057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49943516","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: 16

Contact Tracing Solution for Global Community 全球社区接触者追踪解决方案

IF 1.4 4区计算机科学

Computer Journal Pub Date : 2021-06-01 DOI: 10.1093/comjnl/bxab099

Hari T S Narayanan

引用次数: 3

Identifying Influential Nodes in Complex Networks Based on Neighborhood Entropy Centrality 基于邻域熵中心性的复杂网络影响节点识别

IF 1.4 4区计算机科学

Computer Journal Pub Date : 2021-06-01 DOI: 10.1093/comjnl/bxab034

Liqing Qiu;Jianyi Zhang;Xiangbo Tian;Shuang Zhang

引用次数: 4

Robust Object Detection and Localization Using Semantic Segmentation Network 基于语义分割网络的鲁棒目标检测与定位

IF 1.4 4区计算机科学

Computer Journal Pub Date : 2021-06-01 DOI: 10.1093/comjnl/bxab079

A Francis Alexander Raghu;J P Ananth

{"title":"Robust Object Detection and Localization Using Semantic Segmentation Network","authors":"A Francis Alexander Raghu;J P Ananth","doi":"10.1093/comjnl/bxab079","DOIUrl":"https://doi.org/10.1093/comjnl/bxab079","url":null,"abstract":"The advancements in the area of object localization are in great progress for analyzing the spatial relations of different objects from the set of images. Several object localization techniques rely on classification, which decides, if the object exist or not, but does not provide the object information using pixel-wise segmentation. This work introduces an object detection and localization technique using semantic segmentation network (SSN) and deep convolutional neural network (Deep CNN). Here, the proposed technique consists of the following steps: Initially, the image is denoised using the filtering to eliminate the noise present in the image. Then, pre-processed image undergoes sparking process for making the image suitable for the segmentation using SSN for object segmentation. The obtained segments are subjected as the input to the proposed Stochastic-Cat Crow optimization (Stochastic-CCO)-based Deep CNN for the object classification. Here, the proposed Stochastic-CCO, obtained by integrating stochastic gradient descent and the CCO, is used for training the Deep CNN. The CCO is designed by the integration of cat swarm optimization (CSO) and crow search algorithm and takes advantages of both optimization algorithms. The experimentation proves that the proposed Stochastic-CCO-based Deep CNN-based technique acquired maximal accuracy of 98.7.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"64 10","pages":"1531-1548"},"PeriodicalIF":1.4,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49943517","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

Energy-Efficient Cluster-Based Routing Protocol for WSN Based on Hybrid BSO–TLBO Optimization Model 基于BSO-TLBO混合优化模型的WSN节能簇路由协议

IF 1.4 4区计算机科学

Computer Journal Pub Date : 2021-06-01 DOI: 10.1093/comjnl/bxab044

Kannan Krishnan;B Yamini;Wael Mohammad Alenazy;M Nalini

引用次数: 0

Underwater Image Enhancement With Optimal Histogram Using Hybridized Particle Swarm and Dragonfly 基于混合粒子群和蜻蜓的最优直方图水下图像增强

IF 1.4 4区计算机科学

Computer Journal Pub Date : 2021-06-01 DOI: 10.1093/comjnl/bxab056

R Prasath;T Kumanan

{"title":"Underwater Image Enhancement With Optimal Histogram Using Hybridized Particle Swarm and Dragonfly","authors":"R Prasath;T Kumanan","doi":"10.1093/comjnl/bxab056","DOIUrl":"https://doi.org/10.1093/comjnl/bxab056","url":null,"abstract":"Typically, underwater image processing is mainly concerned with balancing the color change distortion or light scattering. Various researches have been processed under these issues. This proposed model incorporates two phases, namely, contrast correction and color correction. Moreover, two processes are involved within the contrast correction model, namely: (i) global contrast correction and (ii) local contrast correction. For the image enhancement, the main target is on the histogram evaluation, and therefore, the optimal selection of histogram limit is very essential. For this optimization purpose, a new hybrid algorithm is introduced namely, swarm updated Dragonfly Algorithm, which is the hybridization of Particle Swarm Optimization (PSO) and Dragonfly Algorithm (DA). Further, this paper mainly focused on Integrated Global and Local Contrast Correction (IGLCC). The proposed model is finally distinguished over the other conventional models like Contrast Limited Adaptive Histogram, IGLCC, dynamic stretching IGLCC-Genetic Algorithm, IGLCC-PSO, IGLCC- Firefly and IGLCC-Cuckoo Search, IGLCC-Distance-Oriented Cuckoo Search and DA, and the superiority of the proposed work is proved.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"64 10","pages":"1494-1513"},"PeriodicalIF":1.4,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49943518","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

Be Scalable and Rescue My Slices During Reconfiguration 在重新配置期间可伸缩并挽救我的切片

IF 1.4 4区计算机科学

Computer Journal Pub Date : 2021-06-01 DOI: 10.1093/comjnl/bxab108

Adrien Gausseran;Frederic Giroire;Brigitte Jaumard;Joanna Moulierac

引用次数: 0

Pruning of Health Data in Mobile-Assisted Remote Healthcare Service Delivery 移动辅助远程医疗服务提供中的健康数据修剪

IF 1.4 4区计算机科学

Computer Journal Pub Date : 2021-06-01 DOI: 10.1093/comjnl/bxab083

Safikureshi Mondal;Nandini Mukherjee

{"title":"Pruning of Health Data in Mobile-Assisted Remote Healthcare Service Delivery","authors":"Safikureshi Mondal;Nandini Mukherjee","doi":"10.1093/comjnl/bxab083","DOIUrl":"https://doi.org/10.1093/comjnl/bxab083","url":null,"abstract":"The use of cloud computing and mobile devices is increasing in healthcare service delivery primarily because of the huge storage capacity of cloud, the heterogeneous structure of health data and the user-friendly interfaces on mobile devices. We propose a healthcare delivery scheme where a large knowledge base is stored in the cloud and user responses from mobile devices are input to this knowledge base to reach a preliminary diagnosis of diseases based on patients' symptoms. However, instead of sending every response to the cloud and getting data from cloud server, it may often be desirable to prune a portion of the knowledge base that is stored in a graph form and download in to the mobile devices. Downloading data from cloud depends on the storage, battery power, processor of a mobile device, wireless network bandwidth and cloud processor capacity. In this paper, we focus on developing mathematical expressions involving the above mentioned criteria and show how these parameters are dependent on each other. The expressions built in this paper can be used in real-life scenarios to take decisions regarding the amount of data to be pruned in order to save energy as well as time.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"64 10","pages":"1549-1564"},"PeriodicalIF":1.4,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49943520","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

SS6: Online Short-Code RAID-6 Scaling by Optimizing New Disk Location and Data Migration SS6:通过优化新磁盘位置和数据迁移的在线短码RAID-6扩展

IF 1.4 4区计算机科学

Computer Journal Pub Date : 2021-06-01 DOI: 10.1093/comjnl/bxab134

Zhu Yuan;Xindong You;Xueqiang Lv;Ping Xie

{"title":"SS6: Online Short-Code RAID-6 Scaling by Optimizing New Disk Location and Data Migration","authors":"Zhu Yuan;Xindong You;Xueqiang Lv;Ping Xie","doi":"10.1093/comjnl/bxab134","DOIUrl":"https://doi.org/10.1093/comjnl/bxab134","url":null,"abstract":"Thanks to excellent reliability, availability, flexibility and scalability, redundant arrays of independent (or inexpensive) disks (RAID) are widely deployed in large-scale data centers. RAID scaling effectively relieves the storage pressure of the data center and increases both the capacity and I/O parallelism of storage systems. To regain load balancing among all disks including old and new, some data usually are migrated from old disks to new disks. Owing to unique parity layouts of erasure codes, traditional scaling approaches may incur high migration overhead on RAID-6 scaling. This paper proposes an efficient approach based Short-Code for RAID-6 scaling. The approach exhibits three salient features: first, SS6 introduces \u0000<tex>$tau $</tex>\u0000 to determine where new disks should be inserted. Second, SS6 minimizes migration overhead by delineating migration areas. Third, SS6 reduces the XOR calculation cost by optimizing parity update. The numerical results and experiment results demonstrate that (i) SS6 reduces the amount of data migration and improves the scaling performance compared with Round-Robin and Semi-RR under offline, (ii) SS6 decreases the total scaling time against Round-Robin and Semi-RR under two real-world I/O workloads (iii) the user average response time of SS6 is better than the other two approaches during scaling and after scaling.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"64 10","pages":"1600-1616"},"PeriodicalIF":1.4,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49984585","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: 2

A Collaborative Learning-Based Algorithm for Task Offloading in UAV-Aided Wireless Sensor Networks 一种基于协同学习的无人机辅助无线传感器网络任务分流算法

IF 1.4 4区计算机科学

Computer Journal Pub Date : 2021-06-01 DOI: 10.1093/comjnl/bxab100

Rama Al-Share;Mohammad Shurman;Abdallah Alma'aitah

引用次数: 6