Journal of Computational Science and Intelligent Technologies最新文献_第2页

Classification of Diabetic Retinopathy using Stacked Autoencoder-Based Deep Neural Network 基于堆叠自编码器的深度神经网络对糖尿病视网膜病变的分类

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa.jcsit1102

Yasir Eltigani Ali Mustaf, Bashir Hassan Ismail

引用次数: 22

Classification of Lung Nodules using Improved Residual Convolutional Neural Network 基于改进残差卷积神经网络的肺结节分类

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa.jcsit1103

Salah Eldeen Babiker

引用次数: 19

A Hybrid Genetic-Neuro Algorithm for Cloud Intrusion Detection System 云入侵检测系统的遗传-神经混合算法

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa.jcsit20201203

Suresh Adithya Nallamuthu

{"title":"A Hybrid Genetic-Neuro Algorithm for Cloud Intrusion Detection System","authors":"Suresh Adithya Nallamuthu","doi":"10.53409/mnaa.jcsit20201203","DOIUrl":"https://doi.org/10.53409/mnaa.jcsit20201203","url":null,"abstract":"The security for cloud network systems is essential and significant to secure the data source from intruders and attacks. Implementing an intrusion detection system (IDS) for securing from those intruders and attacks is the best option. Many IDS models are presently based on different techniques and algorithms like machine learning and deep learning. In this research, IDS for the cloud computing environment is proposed. Here in this model, the genetic algorithm (GA) and back propagation neural network (BPNN) is used for attack detection and classification. The Canadian Institute for Cyber-security CIC-IDS 2017 dataset is used for the evaluation of performance analysis. Initially, from the dataset, the data are preprocessed, and by using the genetic algorithm, the attack was detected. The detected attacks are classified using the BPNN classifier for identifying the types of attacks. The performance analysis was executed, and the results are obtained and compared with the existing machine learning-based classifiers like FC-ANN, NB-RF, KDBN, and FCM-SVM techniques. The proposed GA-BPNN model outperforms all these classifying techniques in every performance metric, like accuracy, precision, recall, and detection rate. Overall, from the performance analysis, the best classification accuracy is achieved for Web attack detection with 97.90%, and the best detection rate is achieved for Brute force attack detection with 97.89%.","PeriodicalId":125707,"journal":{"name":"Journal of Computational Science and Intelligent Technologies","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114785325","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

A New Neural Network-Based Intrusion Detection System for Detecting Malicious Nodes in WSNs 基于神经网络的wsn恶意节点入侵检测系统

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa.jcsit20201301

C. Narmatha

引用次数: 5

A Survey on Cloud Computing for Information Storing 信息存储的云计算研究综述

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa.jcsit20201202

S. Ravichandran

引用次数: 6

Face Recognition Framework based on Convolution Neural Network with modified Long Short Term memory Method 基于改进长短期记忆方法的卷积神经网络人脸识别框架

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa.jcsit20201304

Sushmitha Parikibanda

{"title":"Face Recognition Framework based on Convolution Neural Network with modified Long Short Term memory Method","authors":"Sushmitha Parikibanda","doi":"10.53409/mnaa.jcsit20201304","DOIUrl":"https://doi.org/10.53409/mnaa.jcsit20201304","url":null,"abstract":"For real-world applications, such as video monitoring, interaction between human machines and safety systems, face recognition is very critical. Deep learning approaches have demonstrated better results in terms of precision and processing speed in image recognition compared to conventional methods. In comparison to traditional methods. While facial detection problems with different commercial applications have been extensively studied for several decades, they still face problems with many specific scenarios, due to various problems such as severe facial occlusions, very low resolutions, intense lighting and exceptional changes in image or video compression artifacts, etc. The aim of this work is to robustly solve the issues listed above with a facial detection approach called Convolution Neural Network with Long short-term Model (CNN-mLSTM). This method first flattened the original frame, calculating the gradient image with Gaussian filter. The edge detection algorithm Canny-Kirsch Method will then be used to identify edge of the human face. The experimental findings suggest that the technique proposed exceeds the current modern methods of face detection.","PeriodicalId":125707,"journal":{"name":"Journal of Computational Science and Intelligent Technologies","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132592211","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 Data Aggregation Technique for WSNs Using Lightweight Cryptographic Algorithm 基于轻量级加密算法的无线传感器网络数据聚合技术

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa/jcsit/e202203010116

引用次数: 1

A New Modified Recurrent Extreme Learning with PSO Machine Based on Feature Fusion with CNN Deep Features for Breast Cancer Detection 一种基于CNN深度特征融合的改进循环极限学习PSO机用于乳腺癌检测

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa.jcsit20201303

S. M, Manimurugan S

{"title":"A New Modified Recurrent Extreme Learning with PSO Machine Based on Feature Fusion with CNN Deep Features for Breast Cancer Detection","authors":"S. M, Manimurugan S","doi":"10.53409/mnaa.jcsit20201303","DOIUrl":"https://doi.org/10.53409/mnaa.jcsit20201303","url":null,"abstract":"Breast cancer is a prevalent cause of death, and is the only form of cancer that is common among women worldwide and mammograms-based computer-aided diagnosis (CAD) program that allows early detection, diagnosis and treatment of breast cancer. But the performance of the current CAD systems is still unsatisfactory. Early recognition of lumps will reduce overall breast cancer mortality. This study investigates a method of breast CAD, focused on feature fusion with deep features of the Convolutional Neural Network (CNN). First, present a scheme of mass detection based on CNN deep features and modified clustering of the Extreme Learning Machine (MRELM). It forecasts load through Recurrent Extreme Learning Machine (RELM) and utilizes Artificial Bee Colony (ABC) to optimize weights and biases. Second, a collection of features is constructed that relays deep features, morphological features, texture features, and density features. Third, MRELM classifier is developed to distinguish benign and malignant breast masses using the fused feature set. Extensive studies show the precision and efficacy of the proposed method of mass diagnosis and classification of breast cancer.","PeriodicalId":125707,"journal":{"name":"Journal of Computational Science and Intelligent Technologies","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127753885","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: 7

A Distributed Control System for Monitoring and Controlling Different Processes 一种用于监控不同过程的分布式控制系统

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa/jcsit/e202203014350

Pamela D, Gerard Joe Nigel K, Kingston Stanley P, Stefy Mol Mathew

{"title":"A Distributed Control System for Monitoring and Controlling Different Processes","authors":"Pamela D, Gerard Joe Nigel K, Kingston Stanley P, Stefy Mol Mathew","doi":"10.53409/mnaa/jcsit/e202203014350","DOIUrl":"https://doi.org/10.53409/mnaa/jcsit/e202203014350","url":null,"abstract":"A set of controllers connected by a data network and appearing to function as one unit is known as a distributed control system (DCS). Despite having diverse roles, locations, configurations, and specifications, they are all connected by a common language of communication. In applications like chemical plants or oil refineries, where there are many input-output modules engaged in communication, DCS is favoured. They are multitasking systems that can handle sizable common databases and support numerous control loops through graphical function block representation. The necessity of the hour is process monitoring and control from a distance. One such option is DCS, which is used in most process control sectors. This idea is based on applying the Supervisory Control and Data Acquisition (SCADA) concept in the stations and using DCS to control the existing process stations in the lab (level and flow process). The process control panel for each station’s monitoring and control is developed with a user-friendly Front Panel. The Proportional Integral Derivative (PID) Controller is programmed using functional blocks for the level and flow process.","PeriodicalId":125707,"journal":{"name":"Journal of Computational Science and Intelligent Technologies","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130967449","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 Survey of the Impact of Digital Twin Technology on IoT, Industries, and Other Smart Environments 数字孪生技术对物联网、工业和其他智能环境的影响调查

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa/jcsit/e202203011723

Mathupriya S, Saira Banu S, S. S, Arthi B

引用次数: 1