Int. J. Commun. Networks Inf. Secur.最新文献

Radar Based Activity Recognition using CNN-LSTM Network Architecture 基于CNN-LSTM网络结构的雷达活动识别

Int. J. Commun. Networks Inf. Secur. Pub Date : 2023-01-11 DOI: 10.17762/ijcnis.v14i3.5630

A. Victoria, S. V. Manikanthan, R. VaradarajuH., Muhammad Alkirom Wildan, K. Kishore

引用次数: 1

An Optimized Deep Learning Based Optimization Algorithm for the Detection of Colon Cancer Using Deep Recurrent Neural Networks 一种优化的基于深度学习的基于深度递归神经网络的结肠癌检测优化算法

Int. J. Commun. Networks Inf. Secur. Pub Date : 2023-01-08 DOI: 10.17762/ijcnis.v14i1s.5589

V. T. R. P. Ku, M. Arulselvi, K. Sastry

{"title":"An Optimized Deep Learning Based Optimization Algorithm for the Detection of Colon Cancer Using Deep Recurrent Neural Networks","authors":"V. T. R. P. Ku, M. Arulselvi, K. Sastry","doi":"10.17762/ijcnis.v14i1s.5589","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i1s.5589","url":null,"abstract":"Colon cancer is the second leading dreadful disease-causing death. The challenge in the colon cancer detection is the accurate identification of the lesion at the early stage such that mortality and morbidity can be reduced. In this work, a colon cancer classification method is identified out using Dragonfly-based water wave optimization (DWWO) based deep recurrent neural network. Initially, the input cancer images subjected to carry a pre-processing, in which outer artifacts are removed. The pre-processed image is forwarded for segmentation then the images are converted into segments using Generative adversarial networks (GAN). The obtained segments are forwarded for attribute selection module, where the statistical features like mean, variance, kurtosis, entropy, and textual features, like LOOP features are effectively extracted. Finally, the colon cancer classification is solved by using the deep RNN, which is trained by the proposed Dragonfly-based water wave optimization algorithm. The proposed DWWO algorithm is developed by integrating the Dragonfly algorithm and water wave optimization.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127484421","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

A DDoS Attack Detection using PCA Dimensionality Reduction and Support Vector Machine 基于PCA降维和支持向量机的DDoS攻击检测

Int. J. Commun. Networks Inf. Secur. Pub Date : 2023-01-08 DOI: 10.17762/ijcnis.v14i1s.5586

Bhargavi Goparaju, Bandla Sreenivasa Rao

引用次数: 1

A Broadband Meta surface Based MIMO Antenna with High Gain and Isolation For 5G Millimeter Wave Applications 5G毫米波应用中基于宽带元表面的高增益隔离MIMO天线

Int. J. Commun. Networks Inf. Secur. Pub Date : 2023-01-08 DOI: 10.17762/ijcnis.v14i1s.5592

N. Rao, Lalitha Bhavani Konkyana, V. Raju, M.S.R. Naidu, Chukka Ramesh Babu

引用次数: 1

Design and Performance Analysis of Low Latency Routing Algorithm based NoC for MPSoC 基于NoC的MPSoC低延迟路由算法设计与性能分析

Int. J. Commun. Networks Inf. Secur. Pub Date : 2023-01-08 DOI: 10.17762/ijcnis.v14i1s.5590

T. Nagalaxmi, E. S. Rao, P. Chandrasekhar

{"title":"Design and Performance Analysis of Low Latency Routing Algorithm based NoC for MPSoC","authors":"T. Nagalaxmi, E. S. Rao, P. Chandrasekhar","doi":"10.17762/ijcnis.v14i1s.5590","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i1s.5590","url":null,"abstract":"The Network on Chip is appropriate where System-on-Chip technology is scalable and adaptable. The Network on Chip is a new communication architecture with a number of benefits, including scalability, flexibility, and reusability, for applications built on Multiprocessor System on a Chip (MPSoC). However, the design of efficient NoC fabric with high performance is critically complex because of its architectural parameters. Identifying a suitable scheduling algorithm to resolve arbitration among ports to obtain high-speed data transfer in the router is one of the most significant phases while designing a Network on chip based Multiprocessor System on a Chip. Low latency, throughput, space utilization, energy consumption, and reliability for Network on chip fabric are all determined by the router. The performance of the NoC system is hampered by the deadlock issues that plague conventional routing algorithms. This work develops a novel routing algorithm to address the deadlock problem. In this paper, a deterministic shortest path deadlock-free routing method is developed based on the analysis of the Turn Model. In the 2D-mesh structure, the algorithm uses separate routing methods for the odd and even columns. This minimizes the number of paths for a single channel, congestion, and latency. Two test scenarios—one with and one without a load test—were used to evaluate the proposed model. For a zero-load network, three clock cycles are utilized to transfer the packets. For the load network, five clocks are utilized to transfer the packets. The latency is measured for both cases without load and with load test and the corresponding latency is 3ns and 7ns respectively.The proposed method has an 18.57Mbps throughput. The area and power utilization for the proposed method are 69% (IO utilization) and 0.128W respectively. In order to validate the proposed method, the latency is compared with existing work and 50% latency is reduced both with and without congestion load.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133744135","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

Alzheimer's And Parkinson's Disease Classification Using Deep Learning Based On MRI: A Review 基于MRI深度学习的阿尔茨海默病和帕金森病分类研究综述

Int. J. Commun. Networks Inf. Secur. Pub Date : 2023-01-08 DOI: 10.17762/ijcnis.v14i1s.5588

A. Suganya., S. Aarthy

引用次数: 0

Edge Computing in Centralized Data Server Deployment for Network Qos and Latency Improvement for Virtualization Environment 集中式数据服务器部署中的边缘计算:虚拟化环境下网络Qos和时延提升

Int. J. Commun. Networks Inf. Secur. Pub Date : 2023-01-03 DOI: 10.17762/ijcnis.v14i3.5607

A. Yadav, Bhanu Sharma, Akash Kumar Bhagat, Harshal Shah, C. Manjunath, Aishwarya Awasthi

{"title":"Edge Computing in Centralized Data Server Deployment for Network Qos and Latency Improvement for Virtualization Environment","authors":"A. Yadav, Bhanu Sharma, Akash Kumar Bhagat, Harshal Shah, C. Manjunath, Aishwarya Awasthi","doi":"10.17762/ijcnis.v14i3.5607","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i3.5607","url":null,"abstract":"With the advancement of Internet of Things (IoT), the network devices seem to be raising, and the cloud data centre load also raises; certain delay-sensitive services are not responded to promptly which leads to a reduced quality of service (QoS). The technique of resource estimation could offer the appropriate source for users through analyses of load of resource itself. Thus, the prediction of resource QoS was important to user fulfillment and task allotment in edge computing. This study develops a new manta ray foraging optimization with backpropagation neural network (MRFO-BPNN) model for resource estimation using quality of service (QoS) in the edge computing platform. Primarily, the MRFO-BPNN model makes use of BPNN algorithm for the estimation of resources in edge computing. Besides, the parameters relevant to the BPNN model are adjusted effectually by the use of MRFO algorithm. Moreover, an objective function is derived for the MRFO algorithm for the investigation of load state changes and choosing proper ones. To facilitate the enhanced performance of the MRFO-BPNN model, a widespread experimental analysis is made. The comprehensive comparison study highlighted the excellency of the MRFO-BPNN model.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131489727","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: 2

Linguistic Based Emotion Detection from Live Social Media Data Classification Using Metaheuristic Deep Learning Techniques 使用元启发式深度学习技术从实时社交媒体数据分类中进行基于语言的情感检测

Int. J. Commun. Networks Inf. Secur. Pub Date : 2022-12-31 DOI: 10.17762/ijcnis.v14i3.5604

S. Mubeen, Nandini Kulkarni, Manuel R. Tanpoco, R. D. Kumar, M. Naidu, T. Dhope

引用次数: 3

Centralized Cloud Service Providers in Improving Resource Allocation and Data Integrity by 4G IoT Paradigm 集中式云服务提供商通过4G物联网模式改善资源分配和数据完整性

Int. J. Commun. Networks Inf. Secur. Pub Date : 2022-12-31 DOI: 10.17762/ijcnis.v14i3.5601

Rahul Bhatt, Rishi Shikka, R. ManjunathC., S. Sharma, Arvind Kumar Pandey, K. Bala

{"title":"Centralized Cloud Service Providers in Improving Resource Allocation and Data Integrity by 4G IoT Paradigm","authors":"Rahul Bhatt, Rishi Shikka, R. ManjunathC., S. Sharma, Arvind Kumar Pandey, K. Bala","doi":"10.17762/ijcnis.v14i3.5601","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i3.5601","url":null,"abstract":"Due to the expansion of Internet of Things (IoT), the extensive wireless, and 4G networks, the rising demands for computing calls and data communication for the emergent EC (EC) model. By stirring the functions and services positioned in the cloud to the user proximity, EC could offer robust transmission, networking, storage, and transmission capability. The resource scheduling in EC, which is crucial to the accomplishment of EC system, has gained considerable attention. This manuscript introduces a new lighting attachment algorithm based resource scheduling scheme and data integrity (LAARSS-DI) for 4G IoT environment. In this work, we introduce the LAARSS-DI technique to proficiently handle and allot resources in the 4G IoT environment. In addition, the LAARSS-DI technique mainly relies on the standard LAA where the lightning can be caused using the overall amount of charges saved in the cloud that leads to a rise in electrical intensity. Followed by, the LAARSS-DI technique designs an objective function for the reduction of cost involved in the scheduling process, particularly for 4G IoT environment. A series of experimentation analyses is made and the outcomes are inspected under several aspects. The comparison study shown the improved performance of the LAARSS-DI technology to existing approaches.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115021081","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

An Investigation on Disease Diagnosis and Prediction by Using Modified K-Mean clustering and Combined CNN and ELM Classification Techniques 基于改进k -均值聚类和CNN与ELM联合分类技术的疾病诊断与预测研究

Int. J. Commun. Networks Inf. Secur. Pub Date : 2022-12-31 DOI: 10.17762/ijcnis.v14i1s.5639

S. Waris, S. Koteeswaran

{"title":"An Investigation on Disease Diagnosis and Prediction by Using Modified K-Mean clustering and Combined CNN and ELM Classification Techniques","authors":"S. Waris, S. Koteeswaran","doi":"10.17762/ijcnis.v14i1s.5639","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i1s.5639","url":null,"abstract":"Data analysis is important for managing a lot of knowledge in the healthcare industry. The older medical study favored prediction over processing and assimilating a massive volume of hospital data. The precise research of health data becomes advantageous for early disease identification and patient treatment as a result of the tremendous knowledge expansion in the biological and healthcare fields. But when there are gaps in the medical data, the accuracy suffers. The use of K-means algorithm is modest and efficient to perform. It is appropriate for processing vast quantities of continuous, high-dimensional numerical data. However, the number of clusters in the given dataset must be predetermined for this technique, and choosing the right K is frequently challenging. The cluster centers chosen in the first phase have an impact on the clustering results as well. To overcome this drawback in k-means to modify the initialization and centroid steps in classification technique with combining (Convolutional neural network) CNN and ELM (extreme learning machine) technique is used. To increase this work, disease risk prediction using repository dataset is proposed. We use different types of machine learning algorithm for predicting disease using structured data. The prediction accuracy of using proposed hybrid model is 99.8% which is more than SVM (support vector machine), KNN (k-nearest neighbors), AB (AdaBoost algorithm) and CKN-CNN (consensus K-nearest neighbor algorithm and convolution neural network).","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125700232","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