Journal of Computational and Theoretical Nanoscience最新文献_第6页

First Principles Study of Structural Stability and Electronic Properties of CdTe Nanowires CdTe纳米线结构稳定性和电子性能的第一性原理研究

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9631

S. Kaushik, S. Singh, R. Thakur

引用次数: 0

An Extensive Review on Machine Learning and Deep Learning Based Cervical Cancer Diagnosis and Classification Models 基于机器学习和深度学习的癌症诊断和分类模型综述

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9437

C. Suguna, S. Balamurugan

引用次数: 3

The Usage of Social Network to Students: Does It Improve Student’s Education Quality During COVID-19 Outbreak 新冠肺炎疫情期间，社交网络对学生的使用是否提高了学生的教育质量

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9412

Indraah Kolandaisamy, Raenu Kolandaisamy

引用次数: 0

Automatic Detection of Diabetic Retinopathy Using Support Vector Machine 基于支持向量机的糖尿病视网膜病变自动检测

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9456

P. Meenal, P. Gowr, A. Ram, A. Rajini, B. Abishek, D. Ravikumar

{"title":"Automatic Detection of Diabetic Retinopathy Using Support Vector Machine","authors":"P. Meenal, P. Gowr, A. Ram, A. Rajini, B. Abishek, D. Ravikumar","doi":"10.1166/JCTN.2020.9456","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9456","url":null,"abstract":"Excess amount of insulin in human blood might affect the retina in eyes and cause abnormalities in human vision, which is generally termed as Diabetic Retinopathy (DR). Many diabetic patients are often saved by the earlier diagnosis of Diabetic Retinopathy. The surface of retinal layer\u0000 that has the earlier signs of Diabetic Retinopathy. This type of abnormalities are detected using traditional image processing methods which includes stages such as capturing fundus images, preprocessing, feature extraction and finally classification is performed to classify it as retinal\u0000 and healthy images. (The proposed system, this detection is completed by Fuzzy-C Means (FCM) clustering). The proposed automated system consists of four phases which includes, preprocessing of the captured fundus images in which the image is resized and the second stage involves CLAHE. Images\u0000 has to enhanced in order to boost up the features for which Contrast adjustment is performed in the third phase and before classification the grey and green channels of the images are extracted from the processed images. This detection process provides better results than the prevailing method.\u0000 SVM classifier has been used in the proposed framework which classified the malady level of diabetic retinopathy in eye. The proposed system manages to provide better classification rates compared to the previous methodologies. The accuracy, sensitivity and specificity of the developed automated\u0000 system was found to be 94.4%, 100% and 85.7%, which was promising than the compared methods.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5582-5589"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44628913","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

ECG Classification Framework for Cardiac Disease Prediction Using Nonlinear Vector Decomposed Neural Network 基于非线性向量分解神经网络的心电分类框架

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9453

M. Suhail, T. .. Razak

{"title":"ECG Classification Framework for Cardiac Disease Prediction Using Nonlinear Vector Decomposed Neural Network","authors":"M. Suhail, T. .. Razak","doi":"10.1166/JCTN.2020.9453","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9453","url":null,"abstract":"Early detection of heart disease may prevent myocardial infarction. Electrocardiogram (ECG) is the most widely used signal in clinical practice for the diagnosis of cardiovascular diseases such as arrhythmias and myocardial infarction. Human interpretation is time-consuming, and long-term\u0000 ECG records are difficult to detect in small differences.Therefore, automated recognition of myocardial infarction using a Computer-Aided Diagnosis (CAD) system is the research interest, which can be used effectively to reduce mortality among cardiovascular disease patients. The most important\u0000 step in the analysis of complex R-peak/QRS signals using an automated process of ECG signal. To automate the cardiovascular disease detection process, an adequate mechanism is required to characterize ECG signals, which are unknown features according to the similarities between ECG signals.\u0000 If the classification can find similarities accurately and the probability of arrhythmia detection increases, the algorithm can become an effective method in the laboratory. In this research work, a new classification strategy is proposed to all the more precisely order ECG signals dependent\u0000 on a powerful model of ECG signals. In this proposed method, a Nonlinear Vector Decomposed Neural Network (NVDN) is developed, and its simulation results show that this classifier can isolate the ECGs with high productivity. This proposed technique expands the exactness of the ECG classification\u0000 concerning increasingly exact arrhythmia discovery.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5563-5569"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43871500","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

An Artificial Bee Colony and Pigeon Inspired Optimization Hybrid Feature Selection Algorithm for Twitter Sentiment Analysis 一种基于人工蜂群和鸽子启发的推特情绪分析混合特征选择算法

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9431

S. Kasthuri, A. N. Jebaseeli

{"title":"An Artificial Bee Colony and Pigeon Inspired Optimization Hybrid Feature Selection Algorithm for Twitter Sentiment Analysis","authors":"S. Kasthuri, A. N. Jebaseeli","doi":"10.1166/JCTN.2020.9431","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9431","url":null,"abstract":"Twitter Sentiment Study is a difficult task that comprises the various kind of preprocessing phases, including reduction in dimensionality. The reduction in dimensionality ensures minimum computational complexity and improved performance in the classification course. In Twitter data,\u0000 each tweet has functionality values that may or may not reflect an individual’s response. As a result, when tweets are signified as feature matrices, many sparse data points are created and possibly overhead and error rates increase in sentiment analysis on Twitter. This paper proposes\u0000 a novel kind of algorithm as Artificial Bee Colony and Pigeon Inspired Optimization Hybrid Feature Selection Algorithm. The ABC-PIO combines with the characteristics that ABC can produce various samples, PIO can reach the best value rapidly and Cauchy perturbation strategy can improve optimal\u0000 solution. The proposed technique archive Accuracy of 99.01% for Decision tree, 77.34% for Navy Bias and 60.89% Random Forest. The comparative analysis show that the proposed ABC-PIO with Decision tree archive much better results compared to other existing techniques.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5378-5385"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49432500","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: 5

Light Weight Cryptography Based Encrypted Multiple Secret Share Creation for Biometrics Images 基于轻量级密码学的生物特征图像加密多秘密共享创建

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9441

Elavarasi Gunasekaran, Vanitha Muthuraman

引用次数: 0

Internet of Things and Cloud Enabled Hybrid Feature Extraction with Adaptive Neuro Fuzzy Inference System for Diabetic Retinopathy Diagnosis 基于物联网和云的自适应神经模糊推理系统混合特征提取在糖尿病视网膜病变诊断中的应用

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9418

K. Parthiban, K. Venkatachalapathy

{"title":"Internet of Things and Cloud Enabled Hybrid Feature Extraction with Adaptive Neuro Fuzzy Inference System for Diabetic Retinopathy Diagnosis","authors":"K. Parthiban, K. Venkatachalapathy","doi":"10.1166/JCTN.2020.9418","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9418","url":null,"abstract":"At present times, the diabetic retinopathy (DR) become high and it is required to design an Internet of Things (IoT) enabled DR diagnosis tool to assist the diagnosis process of remote patients. This study designs and develops IoT and cloud computing based Hybrid Feature Extraction\u0000 (HFE) with Adaptive Neuro Fuzzy Inference System (ANFIS) for DR detection and classification model, abbreviated as HFE-ANFIS model. The proposed model initially captures the retinal fundus image of the patient using the IoT enabled head mounted camera and transmit the images to the cloud server,\u0000 which executes the diagnosis process. The image preprocessing takes place using three stages namely color space conversion, filtering, and contrast enhancement. Next, segmentation process takes place using fuzzy c-means (FCM) model to identify the diseased portions in the fundus image. Then,\u0000 HFE based feature extraction and ANFIS based classification processes are carried out to grade the different levels of DR. The performance validation of the HFE-ANFIS model takes place against MESSIDOR dataset and the results are investigated under different dimensions. The simulation outcome\u0000 indicated that the HFE-ANFIS model has offered superior performance to other methods with the maximum average sensitivity of 94.55%, specificity of 96.41%, precision of 94.66% and accuracy of 95.97%.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5261-5269"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45963024","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

Counter Based Authentication Verification to Secure Patient Data in Cloud 基于计数器的云端患者数据安全验证

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9447

A. Vikram, G. Gopinath

引用次数: 0

Implementation of Internet of Things-Based Sentiment Analysis for Farming System 基于物联网的农业系统情感分析实现

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9426

Dadheech Pankaj, R. Sheeba, R. Vidya, P. Rajarajeswari, P. Srinivasan, C. Kumar, Sudhakar Sengan

{"title":"Implementation of Internet of Things-Based Sentiment Analysis for Farming System","authors":"Dadheech Pankaj, R. Sheeba, R. Vidya, P. Rajarajeswari, P. Srinivasan, C. Kumar, Sudhakar Sengan","doi":"10.1166/JCTN.2020.9426","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9426","url":null,"abstract":"The Internet is slowly shaping to be the primary information source that fulfils all the needs of a person. Whenever someone plans to buy a product, they tend to consult with the reviews online to get a clear idea of the product in terms of its various aspects. The problem is that the\u0000 information available about a single product is so much in volume that the users not be able to extract the information they require from this massive amount of data. The paper proposes a system that generates a temporal aspect based text summary of user opinions that are collected from different\u0000 sources across the Internet with their time-stamp. These comments are broken into sentences and sub-sentences after predefined based classification. Then, Sentiment analysis is performed. The time relationship is taken into account, and the causal relationship is identified at the deflection\u0000 points or the time frames during which there is a significant opinion change. The major advantage of this system is that the changes in user opinions with time can be traced and the cause of this sentiment change can be found out in addition to offering customers a quick, convenient and easy\u0000 way to consume information about a product to help them decide whether or not to purchase it. It also helps enterprises to get relevant insights related to their products based on the customer reviews online.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5339-5345"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42105272","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