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

Segmentation of Tissue-Injured Melanoma Convolution Neural Networks 组织损伤黑色素瘤的卷积神经网络分割

Journal of Computational and Theoretical Nanoscience Pub Date : 2021-04-01 DOI: 10.1166/JCTN.2021.9389

C. Hemalatha, S. Satheesh, N. Kamal, C. Devi, A. Vinothkumar, A. Kannan

引用次数: 0

An Experimental Investigation on Cooling Performance of Battery Pack by Using Nano-Enhanced Phase Change Material 纳米增强相变材料对电池组冷却性能的实验研究

Journal of Computational and Theoretical Nanoscience Pub Date : 2021-04-01 DOI: 10.1166/JCTN.2021.9380

G. Murali, G. Sravya, A. Srinath, J. Jaya

{"title":"An Experimental Investigation on Cooling Performance of Battery Pack by Using Nano-Enhanced Phase Change Material","authors":"G. Murali, G. Sravya, A. Srinath, J. Jaya","doi":"10.1166/JCTN.2021.9380","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9380","url":null,"abstract":"A rechargeable lithium ion battery has captured prime importance in the modern era due to its cycle life and energy density. In the present study hexagonal shaped 18650 lithium ion cylindrical cell battery pack was designed and fabricated with paraffin wax as a Phase Change Material\u0000 (PCM). But low thermal conductivity of the PCM causes impediment to the development of Electrical Vehicles (EV’s) which remains as a significant challenge. In the cylindrical cell battery module the maximum temperature is obtained in the mid region which causes uneven temperature distribution\u0000 among cell. In order to overcome the limitation and to achieve efficient performance of battery module, the nano enhanced Phase Change Material (Ne-PCM) was incorporated in middle four cells by using graphene platelet nano powder (GPN), Multi Walled Carbon Nano Tube (MWCNT) and Graphite Synthetic\u0000 Powder (GSP). Experiments on the battery module were conducted without any cooling, with PCM cooling and with Ne-PCM cooling. The results revealed that Battery pack with Ne-PCM has shown successful performance by minimizing the temperature below 50 °C in all considered discharge rates\u0000 i.e., (1C, 2C and 3C) and maintained even temperature distribution among cells.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1213-1220"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44634724","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

Enhanced Load Balancing Approach for Cloud Data Center Load Optimization 云数据中心负载优化的增强型负载均衡方法

Journal of Computational and Theoretical Nanoscience Pub Date : 2021-04-01 DOI: 10.1166/JCTN.2021.9399

J. Prassanna, V. Neelanarayanan

{"title":"Enhanced Load Balancing Approach for Cloud Data Center Load Optimization","authors":"J. Prassanna, V. Neelanarayanan","doi":"10.1166/JCTN.2021.9399","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9399","url":null,"abstract":"Cloud computing is a most popular technology that has huge response in markets. Cloud computing has the potential to access applications and their related data via the Internet anywhere. Most companies already pay for the use of cloud resources for storage purposes and ultimately reduce\u0000 the costs of infrastructure spending. They can make use of this technology for accessing to company applications like pay-as-you-go approach. One of the major obstacles associated with cloud computing technology is to better optimization of resource allocation. Assigning of workloads to the\u0000 servers using load balancing techniques is used to achieve less response time and better resource optimization across the server. Resource control and balance of load are the major conflicts in the cloud environment, which is why there are different load balancing algorithms, each with its\u0000 own advantages and disadvantage. In order to achieve a better economy and mutual benefit, efficient algorithms can be derived simultaneously by optimizing servers, green computing and better utilization of resources. The objective of this paper is to analyze and enhance existing load balancing\u0000 algorithms.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1270-1274"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46099422","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

Fuzzy C-Means (FCM) Clustering with Probabilistic Neural Network (PNN) Model for Detection and Classification of Rice Plant Diseases in Internet of Things-Cloud Centric Precision Agriculture 基于概率神经网络(PNN)模型的模糊c均值聚类在物联网云中心精准农业中的水稻病害检测与分类

Journal of Computational and Theoretical Nanoscience Pub Date : 2021-04-01 DOI: 10.1166/JCTN.2021.9400

P. Sindhu, G. Indirani, P. Dinadayalan

{"title":"Fuzzy C-Means (FCM) Clustering with Probabilistic Neural Network (PNN) Model for Detection and Classification of Rice Plant Diseases in Internet of Things-Cloud Centric Precision Agriculture","authors":"P. Sindhu, G. Indirani, P. Dinadayalan","doi":"10.1166/JCTN.2021.9400","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9400","url":null,"abstract":"Presently, the field of Internet of Things (loT) has been employed in diverse applications like Smart Grid, Surveillance, Smart homes, and so on. Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop health. Recognition\u0000 of diseases from the plant images is an active research topic which makes use of machine learning (ML) approaches. This paper introduces an effective rice plant disease identification and classification model to identify the type of disease from infected rice plants. The proposed method aims\u0000 to detect three rice plant diseases such as Bacterial leaf blight, Brown spot, and Leaf smut. The proposed method involves a set of different processes namely image acquisition, preprocessing, segmentation, feature extraction and classification. At the earlier stage, IoT devices will be used\u0000 to capture the image and stores it with a cloud server, which executes the classification process. In the cloud, the rice plant images under preprocessing to improvise the quality of the image. Then, fuzzy c-means (FCM) clustering method is utilized for the segmentation of disease portion\u0000 from a leaf image. Afterwards, feature extraction takes place under three kinds namely color, shape, and texture. Finally, probabilistic neural network (PNN) is applied for multi-class classification. A detailed experimental analysis ensured the effective classification performance of the\u0000 proposed method under all the test images applied.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1194-1200"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41742667","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

An Advanced Image Processing Prototype for Corrosion Finding Using Image Processing 利用图像处理技术进行腐蚀检测的先进图像处理原型

Journal of Computational and Theoretical Nanoscience Pub Date : 2021-04-01 DOI: 10.1166/JCTN.2021.9388

M. Malathi, P. Sinthia

引用次数: 0

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":"18 1","pages":"1263-1269"},"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