Recent Advances in Computer Science and Communications最新文献_第10页

An Image Recognition Method Based On Dynamic System Synchronization 基于动态系统同步的图像识别方法

Recent Advances in Computer Science and Communications Pub Date : 2022-12-01 DOI: 10.2174/2666255816666221201155914

Xiaoran Chen, Wanbo Yu, Xiang Li

{"title":"An Image Recognition Method Based On Dynamic System Synchronization","authors":"Xiaoran Chen, Wanbo Yu, Xiang Li","doi":"10.2174/2666255816666221201155914","DOIUrl":"https://doi.org/10.2174/2666255816666221201155914","url":null,"abstract":"\u0000\u0000At present, image recognition technology first classifies images and outputs category information through the neural network. Then search. Before retrieval, the feature database needs to be established first, and then one-to-one correspondence. This method is tedious, time-consuming and low accuracy.In the field of computer vision research, researchers have given various image recognition methods to be applied in various fields, and made many research achievements. But at present, the accuracy, stability and time efficiency can't meet the needs of practical work. In terms of UAV image recognition, high accuracy and low consumption are required. Previous methods require huge databases, which increases the consumption of UAVs. Taking aerial transmission line images as the research object, this paper proposes a method of image recognition based on chaotic synchronization. Firstly, the image is used as a function to construct a dynamic system, and the function structure and parameters are adjusted to realize chaos synchronization. In this process, different types of images are identified. At the same time, we research this dynamic system characteristics，and realize the mechanism of image recognition. Compared with other methods, the self-built aerial image data set for bird's nest identification, iron frame identification and insulator identification has the characteristics of high identification rate and less calculation time. It is preliminarily proved that the method of synchronous image recognition is practical, and also worthy of further research, verification and analysis. This article is divided into the following sections:\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48353330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Threat of Adversarial Attacks within Deep Learning: Survey 深度学习中对抗性攻击的威胁:调查

Recent Advances in Computer Science and Communications Pub Date : 2022-11-25 DOI: 10.2174/2666255816666221125155715

Roshni singh, Ataussamad

{"title":"Threat of Adversarial Attacks within Deep Learning: Survey","authors":"Roshni singh, Ataussamad","doi":"10.2174/2666255816666221125155715","DOIUrl":"https://doi.org/10.2174/2666255816666221125155715","url":null,"abstract":"\u0000\u0000In today’s era, Deep Learning has become the center of recent ascent in the field of artificial intelligence and its models. There are various Artificial Intelligence models that can be viewed as needing more strength for adversely defined information sources. It also leads to a high potential security concern in the adversarial paradigm; the DNN can also misclassify inputs that appear to expect in the result. DNN can solve complex problems accurately. It is empaneled in the vision research area to learn deep neural models for many tasks involving critical security applications. We have also revisited the contributions of computer vision in adversarial attacks on deep learning and discussed its defenses. Many of the authors have given new ideas in this area, which has evolved significantly since witnessing the first-generation methods. For optimal correctness of various research and authenticity, the focus is on peer-reviewed articles issued in the prestigious sources of computer vision and deep learning. Apart from the literature review, this paper defines some standard technical terms for non-experts in the field. This paper represents the review of the adversarial attacks via various methods and techniques along with their defenses within the deep learning area and future scope. Lastly, we bring out the survey to provide a viewpoint of the research in this Computer Vision area.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45067645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Design And Challenges In Energy OptimizingCr-Wireless Sensor Networks 无线传感器网络能量优化的设计与挑战

Recent Advances in Computer Science and Communications Pub Date : 2022-11-04 DOI: 10.2174/2666255816666221104115024

Pundru Chandra Shaker Reddy, Y. Sucharitha

{"title":"A Design And Challenges In Energy Optimizing\u0000Cr-Wireless Sensor Networks","authors":"Pundru Chandra Shaker Reddy, Y. Sucharitha","doi":"10.2174/2666255816666221104115024","DOIUrl":"https://doi.org/10.2174/2666255816666221104115024","url":null,"abstract":"\u0000\u0000The progress of the Cognitive Radio-Wireless Sensor Network is being influenced by advancements in wireless sensor networks (WSNs), which significantly have unique features of cognitive radio technology (CR-WSN). Enhancing the network lifespan of any network requires better utilization of the available spectrum as well as the selection of a good routing mechanism for transmitting informational data to the base station from the sensor node without data conflict.\u0000\u0000\u0000\u0000Cognitive radio methods play a significant part in achieving this, and when paired with WSNs, the above-mentioned objectives can be met to a large extent.\u0000\u0000\u0000\u0000A unique energy-saving Distance- Based Multi-hop Clustering and Routing (DBMCR) methodology in association with spectrum allocation is proposed as a heterogeneous CR-WSN model. The supplied heterogeneous CR-wireless sensor networks are separated into areas and assigned a different spectrum depending on the distance. Information is sent over a multi-hop connection after dynamic clustering using distance computation.\u0000\u0000\u0000\u0000The findings show that the suggested method achieves higher stability and ensures the energy-optimizing CR-WSN. The enhanced scalability can be seen in the First Node Death (FND). Additionally, the improved throughput helps to preserve the residual energy of the network which helps to address the issue of load balancing across nodes.\u0000\u0000\u0000\u0000Thus, the result acquired from the above findings shows that the proposed heterogeneous model achieves the enhanced network lifetime and ensures the energy optimizing CR-WSN.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44973667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

An Effective COVID-19 CT Image Denoising Method Based on a Deep Convolutional Neural Network 基于深度卷积神经网络的新型冠状病毒CT图像去噪方法

Recent Advances in Computer Science and Communications Pub Date : 2022-09-20 DOI: 10.2174/2666255816666220920150916

Xiaojing Fan, Hanyue Liu, Chunsheng Zhang, Zichao Wang, Qingming Lin, Zhanjiang Lan, Mingyang Jiang, Jie Lian, Xueyan Chen

{"title":"An Effective COVID-19 CT Image Denoising Method Based on a Deep Convolutional Neural Network","authors":"Xiaojing Fan, Hanyue Liu, Chunsheng Zhang, Zichao Wang, Qingming Lin, Zhanjiang Lan, Mingyang Jiang, Jie Lian, Xueyan Chen","doi":"10.2174/2666255816666220920150916","DOIUrl":"https://doi.org/10.2174/2666255816666220920150916","url":null,"abstract":"\u0000\u0000Faced with the global threat posed by SARS-CoV-2 (COVID-19), low-dose Computed tomography (LDCT), as the primary diagnostic tool, is often accompanied by high levels of noise. And this can easily interfere with the radiologist's assessment. Convolutional Neural Networks (CNN), as a method of deep learning, have been shown to have excellent effects in image denoising.\u0000\u0000\u0000\u0000Modified convolutional neural network algorithm to train the denoising model. Make the model to extract the highlighted features of the lesion region better and ensure its effectiveness in removing noise from COVID-19 lung CT images, preserving more important detail information of the images and reducing the adverse effects of denoising.\u0000\u0000\u0000\u0000We propose a CNN-based deformable convolutional denoising neural network (DCDNet). By combining deformable convolution methods with residual learning on the basis of CNN structure, more image detail features are retained in CT image denoising.\u0000\u0000\u0000\u0000According to the noise reduction evaluation index of PSNR, SSIM and RMSE, DCDNet shows excellent denoising performance for COVID-19 CT images. From the visual effect of denoising, DCDNet can effectively remove image noise and preserve more detailed features of lung lesions.\u0000\u0000\u0000\u0000The experimental results indicate that the DCDNet-trained model is more suitable for image denoising of COVID-19 than traditional image denoising algorithms under the same training set.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42043447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

YARN Schedulers for Hadoop MapReduce Jobs: Design Goals, Issues and Taxonomy Hadoop MapReduce作业的YARN调度器:设计目标、问题和分类

Recent Advances in Computer Science and Communications Pub Date : 2022-08-31 DOI: 10.2174/2666255816666220831125012

Gnanendra Kotikam, S. Lokesh

引用次数: 0

A Retrieval Method for Spatiotemporal Information of Chorography Based on Deep Learning 基于深度学习的地理时空信息检索方法

Recent Advances in Computer Science and Communications Pub Date : 2022-08-29 DOI: 10.2174/2666255816666220829103359

Shuliang Huan

{"title":"A Retrieval Method for Spatiotemporal Information of Chorography Based on Deep Learning","authors":"Shuliang Huan","doi":"10.2174/2666255816666220829103359","DOIUrl":"https://doi.org/10.2174/2666255816666220829103359","url":null,"abstract":"\u0000\u0000On the retrieval of spatiotemporal information of chorography (STIC), one of the most important topics is how to quickly pinpoint the desired STIC text out of the massive chorography databases. Domestically, there are not diverse means to retrieve the spatiotemporal information from chorography database. Emerging techniques like data mining, artificial intelligence (AI), and natural language processing (NLP) should be introduced into the informatization of chorography.\u0000\u0000\u0000\u0000This study intends to devise an information retrieval method for STIC based on deep learning, and fully demonstrates its feasibility.\u0000\u0000\u0000\u0000Firstly, the authors explained the flow for retrieving and analyzing the data features of STIC texts, and established a deep hash model for STIC texts. Next, the data matching flow was defined for STIC texts, the learned hash code was adopted as the memory address of STIC texts, and the hash Hamming distance of the text information was computed through linear search, thereby completing the task of STIC retrieval.\u0000\u0000\u0000\u0000Our STIC text feature extraction model learned better STIC text features than the contrastive method. It learned many hash features, and differentiated between different information well, when there were many hash bits.\u0000\u0000\u0000\u0000In addition, our hash algorithm achieved the best retrieval accuracy among various methods. Finally, the hash features acquired by our algorithm can accelerate the retrieval speed of STIC texts. These experimental results demonstrate the effectiveness of the proposed model and algorithm.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47014983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Literature review on devlopment of feature selection and learning mechanism for fuzzy rule based system 基于模糊规则的系统特征选择与学习机制研究进展综述

Recent Advances in Computer Science and Communications Pub Date : 2022-08-23 DOI: 10.2174/2666255816666220823163913

Ankur Kumar, Avinash Kaur

引用次数: 0

Assessment of various scheduling and load balancing algorithms in integrated cloud-fog environment 综合云雾环境下各种调度和负载均衡算法的评估

Recent Advances in Computer Science and Communications Pub Date : 2022-08-19 DOI: 10.2174/2666255816666220819124133

Jyotsna, P. Nand

{"title":"Assessment of various scheduling and load balancing algorithms in integrated cloud-fog environment","authors":"Jyotsna, P. Nand","doi":"10.2174/2666255816666220819124133","DOIUrl":"https://doi.org/10.2174/2666255816666220819124133","url":null,"abstract":"\u0000\u0000It is required to design a suitable scheduling algorithm that enhances the timely execution of goals such as load distribution, cost monitoring, and minimal time lag to react, increased security awareness, optimized energy usage, dependability, and so on. In order to attain these criteria, a variety of scheduling strategies based on hybrid, heuristic, and meta-heuristic techniques are under consideration.\u0000\u0000\u0000\u0000IoT devices and a variety of network resources make up the integrated cloud-fog environment. Every fog node has devices that release or request resources. A good scheduling algorithm is required in order to maintain the requests for resources made by various IoT devices.\u0000\u0000\u0000\u0000This research focuses on analysis of numerous scheduling challenges and techniques employed in a cloud-fog context. This work evaluates and analyses the most important fog computing scheduling algorithms.\u0000\u0000\u0000\u0000The survey of simulation tools used by the researchers is done. From the compared results, the highest percentage in the literature has 60% of scheduling algorithm which is related to task scheduling and 37% of the researchers have used iFogSim simulation tool for the implementation of the proposed algorithm defined in their research paper.\u0000\u0000\u0000\u0000The findings in the paper provide a roadmap of the proposed efficient scheduling algorithms and can help researches to develop and choose algorithms close to their case studies.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45366445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Secure Virtual Machine Live Migration using Advanced Metric Encryption 使用高级度量加密的安全虚拟机实时迁移

Recent Advances in Computer Science and Communications Pub Date : 2022-08-15 DOI: 10.2174/2666255816666220815145203

R. Saravanaguru, Gokul Geetha Narayanan

引用次数: 0

Comparison Of Soft Computing And Optimization Techniques In Classification Of Ecg Signal 心电信号分类的软计算与优化技术比较

Recent Advances in Computer Science and Communications Pub Date : 2022-08-04 DOI: 10.2174/2666255816666220804161549

P. Mathur, Pooja, K. Veer

引用次数: 1