Journal of Computational and Theoretical Nanoscience最新文献_第3页

Hybrid Semantic Feature Descriptor and Fuzzy C-Means Clustering for Lung Cancer Detection and Classification 混合语义特征描述符和模糊c均值聚类用于肺癌检测和分类

Journal of Computational and Theoretical Nanoscience Pub Date : 2021-04-01 DOI: 10.1166/JCTN.2021.9391

P. Priyadharshini, B. Zoraida

{"title":"Hybrid Semantic Feature Descriptor and Fuzzy C-Means Clustering for Lung Cancer Detection and Classification","authors":"P. Priyadharshini, B. Zoraida","doi":"10.1166/JCTN.2021.9391","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9391","url":null,"abstract":"Lung cancer (LC) will decrease the yield, which will have a negative impact on the economy. Therefore, primary and accurate the attack finding is a priority for the agro-dependent state. In several modern technologies for early detection of LC, image processing has become a one of the\u0000 essential tool so that it cannot only early to find the disease accurately, but also successfully measure it. Various approaches have been developed to detect LC based on background modelling. Most of them focus on temporal information but partially or completely ignore spatial information,\u0000 making it sensitive to noise. In order to overcome these issues an improved hybrid semantic feature descriptor technique is introduced based on Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Pattern (LBP) and histogram of oriented gradients (HOG) feature extraction algorithms. And also\u0000 to improve the LC segmentation problems a fuzzy c-means clustering algorithm (FCM) is used. Experiments and comparisons on publically available LIDC-IBRI dataset. To evaluate the proposed feature extraction performance three different classifiers are analysed such as artificial neural networks\u0000 (ANN), recursive neural network and recurrent neural networks (RNNs).","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46028819","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

Nyström Method to Solve Two-Dimensional Volterra Integral Equation with Discontinuous Kernel Nyström二维核不连续Volterra积分方程的求解方法

Journal of Computational and Theoretical Nanoscience Pub Date : 2021-04-01 DOI: 10.1166/JCTN.2021.9718

S. Raad, Mariam Mohammed Al-Atawi

引用次数: 1

Fog Enabled Cloud Based Intelligent Resource Management Approach Using Improved Grey Wolf Optimization Strategy and Kernel Support Vector Machine 基于改进的灰太狼优化策略和核支持向量机的基于雾的智能资源管理方法

Journal of Computational and Theoretical Nanoscience Pub Date : 2021-04-01 DOI: 10.1166/JCTN.2021.9401

R. Sudha, G. Indirani, S. Selvamuthukumaran

引用次数: 0

Combined Gray Level Transformation Technique for Low Light Color Image Enhancement 混合灰度变换技术在微光彩色图像增强中的应用

Journal of Computational and Theoretical Nanoscience Pub Date : 2021-04-01 DOI: 10.1166/JCTN.2021.9392

Durai Pandurangan, R. S. Kumar, Lukas Gebremariam, L. Arulmurugan, S. Tamilselvan

引用次数: 2

Surface Electromyographic Signal Acquisition System for Real Time Monitoring of Upper Limbs Muscles 用于上肢肌肉实时监测的表面肌电信号采集系统

Journal of Computational and Theoretical Nanoscience Pub Date : 2021-04-01 DOI: 10.1166/JCTN.2021.9716

R. Ferraz, Raiff Sales da Fonseca, Igor Thonke Rodrigues, Cláudio Bastos da Silva, H. T. Filho

引用次数: 1

Subtractive Gradient Boost Clustering for Mobile Node Authentication in Internet of Things Aware 5G Networks 物联网感知5G网络中移动节点认证的减梯度增强聚类

Journal of Computational and Theoretical Nanoscience Pub Date : 2021-04-01 DOI: 10.1166/JCTN.2021.9394

M. Haripriya, P. Venkadesh

{"title":"Subtractive Gradient Boost Clustering for Mobile Node Authentication in Internet of Things Aware 5G Networks","authors":"M. Haripriya, P. Venkadesh","doi":"10.1166/JCTN.2021.9394","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9394","url":null,"abstract":"The 5G mobile wireless network systems faces a lot of security issues due to the opening of network and its insecurity. The insecure network prone to various attacks and it disrupts secure data communications between legitimate users. Many works have addressed the security problems\u0000 in 3G and 4G networks in efficient way through authentication and cryptographic techniques. But, the security in 5G networks during data communication was not improved. Subtractive Gradient Boost Clustered Node Authentication (SGBCNA) Method is introduced to perform secure data communication.\u0000 The subtractive gradient boost clustering technique is applied to authenticate the mobile node as normal nodes and malicious nodes based on the selected features. The designed ensemble clustering model combines the weak learners to make final strong clustering results with minimum loss. Finally,\u0000 the malicious nodes are eliminated and normal mobile nodes are taken for performing the secured communication in 5G networks. Simulation is carried out on factors such as authentication accuracy, computation overhead and security level with respect to a number of mobile nodes and data packets.\u0000 The observed outcomes clearly illustrate that the SGBCNA Method efficiently improves node authentication accuracy, security level with minimum overhead than the state-of-the-art-methods.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46052184","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

Contemporary Human Activity Recognition Based Predictions by Sensors Using Random Forest Classifier 基于现代人类活动识别的随机森林分类器传感器预测

Journal of Computational and Theoretical Nanoscience Pub Date : 2021-04-01 DOI: 10.1166/JCTN.2021.9404

S. Anand, S. Magesh, I. Arockiamary

{"title":"Contemporary Human Activity Recognition Based Predictions by Sensors Using Random Forest Classifier","authors":"S. Anand, S. Magesh, I. Arockiamary","doi":"10.1166/JCTN.2021.9404","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9404","url":null,"abstract":"The task of recognizing human activities directs extensive divergence of various functions and applications. Despite analysing the intricate activity it endures demanding requirements in contemporary field of research. A subject performs a definite task at a particular time by determining\u0000 the activity by using sensor data. In this research task we appraise a unique way by using data with supervised learning techniques by placing sensors on the human body by contingent upon classification process at different stages. The State-of-art machine learning approach random forests\u0000 are widely discussed in terms of covering practical and theoretical aspects of body sensing. The eventual target is the superior rate of accurate predictions effecting Human Activity Recognition further effective for behavioural monitoring, medical and healthcare sectors. Classification processes\u0000 are deployed for pairs of activities that are distracted often and this work attempts to analyse the essential sensors for the improved prediction. The results shows the best accuracy scores and the remaining of our findings we expose the outline, exhibiting the degree of distraction between\u0000 features of ranking and human activities which renders back to sensor ranking.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41956662","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

Optimized Design of Low Power Complementary Metal Oxide Semiconductor Low Noise Amplifier for Zigbee Application Zigbee低功耗互补金属氧化物半导体低噪声放大器的优化设计

Journal of Computational and Theoretical Nanoscience Pub Date : 2021-04-01 DOI: 10.1166/JCTN.2021.9387

S. Manjula, R. Karthikeyan, S. Karthick, N. Logesh, M. Logeshkumar

引用次数: 0

Hymenopteran Colony Stream Clustering Algorithm and Comparison with Particle Swarm Optimization and Genetic Optimization Clustering 膜壳虫群体流聚类算法及其与粒子群优化和遗传优化聚类的比较

Journal of Computational and Theoretical Nanoscience Pub Date : 2021-04-01 DOI: 10.1166/JCTN.2021.9402

Nikhil Parafe, M. Venkatesan, Prabhavathy Panner

{"title":"Hymenopteran Colony Stream Clustering Algorithm and Comparison with Particle Swarm Optimization and Genetic Optimization Clustering","authors":"Nikhil Parafe, M. Venkatesan, Prabhavathy Panner","doi":"10.1166/JCTN.2021.9402","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9402","url":null,"abstract":"Stream is endlessly inbound sequence of information, streamed information is unbounded and every information are often examined one time. Streamed information are often noisy and therefore the variety of clusters within the information and their applied mathematics properties will change\u0000 over time, wherever random access to the information isn’t possible and storing all the arriving information is impractical. When applying data set processing techniques and specifically stream clustering Algorithms to real time information streams, limitation in execution time and memory\u0000 have to be oblige to be thought-about carefully. The projected hymenopteran colony stream clustering Algorithmic is a clustering Algorithm which forms cluster according to density variation, in which clusters are separated by high density features from low density feature region with mounted\u0000 movement of hymenopteran. Result shows that it created denser cluster than antecedently projected Algorithmic program. And with mounted movement of ants conjointly it decreases the loss of data points. And conjointly the changed radius formula of cluster is projected so as to increase performance\u0000 of model to create it a lot of dynamic with continuous flow of information. And also we changed probability formula for pick up and drop to reduce oulier. Results from hymenopteran experiments conjointly showed that sorting is disbursed in 2 phases, a primary clustering episode followed by\u0000 a spacing part. In this paper, we have also compared proposed Algorithm with particle swarm optimization and genetic optimization using DBSCAN and k -means clustering.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49421103","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

Double Clustering Based Neural Feedback Method for Unstructured Text Data 基于双聚类的非结构化文本数据神经反馈方法

Journal of Computational and Theoretical Nanoscience Pub Date : 2021-04-01 DOI: 10.1166/JCTN.2021.9385

S. Sarannya, M. Venkatesan, Prabhavathy Panner

{"title":"Double Clustering Based Neural Feedback Method for Unstructured Text Data","authors":"S. Sarannya, M. Venkatesan, Prabhavathy Panner","doi":"10.1166/JCTN.2021.9385","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9385","url":null,"abstract":"Text clustering has now a days become a very major technique in many fields including data mining, Natural Language Processing etc. It’s also broadly used for information retrieval and assimilation of textual data. Majority of the works which were carried out previously focuses\u0000 on the clustering algorithms where feature extraction is done without considering the semantic meaning of word based on its context. In the given work, we introduce a double clustering algorithm using K -Means, by using in conjuction, a Bi-directional Long Short-Term Memory and a Convolutional\u0000 Neural Network for the purpose of feature extraction, so that the semantic meaning is also considered. Recurrent neural network (RNN) has the ability to study long-term dependencies prevailing in input whereas CNN models are for long known to be effective in feature extraction of local features\u0000 of given input data. Unlike all the works previously carried out, this proposed work considers and carries out extraction of features and clustering of documents as one combined mechanism. Here result of clustering is send back to the model as feedback information thereby optimizing the parameters\u0000 of the network model dynamically. Clustering in a double-clustering manner is implemented, which increases the time efficiency.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41550733","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