Journal of Computational and Theoretical Nanoscience最新文献_第7页

Pupil Detection System Using Intensity Labeling Algorithm in Field Programmable Gate Array 基于强度标记算法的现场可编程门阵列瞳孔检测系统

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9429

S. Baskaran, L. Ali, A. Anitharani, E. Rani, N. Nandhagopal

{"title":"Pupil Detection System Using Intensity Labeling Algorithm in Field Programmable Gate Array","authors":"S. Baskaran, L. Ali, A. Anitharani, E. Rani, N. Nandhagopal","doi":"10.1166/JCTN.2020.9429","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9429","url":null,"abstract":"Pupil detection techniques are an essential diagnostic technique in medical applications. Pupil detection becomes more complex because of the dynamic movement of the pupil region and it’s size. Eye-tracking is either the method of assessing the point of focus (where one sees)\u0000 or the orientation of an eye relative to the head. An instrument used to control eye positions and eye activity is the eye tracker. As an input tool for human-computer interaction, eye trackers are used in research on the visual system, in psychology, psycholinguistics, marketing, and product\u0000 design. Eye detection is one in all the applications in the image process. This is very important in human identification and it will improve today’s identification technique that solely involves the eye detection to spot individuals. This technology is still new, only a few domains\u0000 are applying this technology as their medical system. The proposed work is developing an eye pupil detection method in real-time, stable, using an intensity labeling algorithm. The proposed hardware architecture is designed using the median filter, segmentation using the threshold process,\u0000 and morphology to detect pupil shape. Finally, an intensity Labeling algorithm is done to locate an exact eye pupil region. A Real-time FPGA implementation is done by Altera Quartus II software with cyclone IV FPGA.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5364-5367"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41900363","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

Novel Multifold Secured System by Combining Multimodal Mask Steganography and Naive Based Random Visual Cryptography System for Digital Communication 结合多模态掩模隐写和基于朴素的随机视觉密码系统的数字通信多重安全系统

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9420

S. Jahnavi, C. Nandini

{"title":"Novel Multifold Secured System by Combining Multimodal Mask Steganography and Naive Based Random Visual Cryptography System for Digital Communication","authors":"S. Jahnavi, C. Nandini","doi":"10.1166/JCTN.2020.9420","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9420","url":null,"abstract":"With increase in growth of data and digital threat, demand of securing the data communicated over the internet is an essential play in the digital world. In the vision of digitalizing services with the next generation of security to the sensitive data transmitted over the internet by\u0000 hiding the existence of the data using next generation cryptography by fusing cryptography techniques is one the major technique adopted. With this the aim in traditional Least Significant Bit (LSB) is one of the widely used technique. Where the secret message or image are placed in the cover\u0000 image in the least significant bits of RGB Channels resulting in a stego image. But the drawback is, on suspecting the differences in the pixels of original and stegoimage in the secret data embedded can be guessed and extracted by attacker. The Proposed visual crypto-mask steganography method\u0000 overcomes this drawback and support good payload capacity with multi modal approach of embedding biometrics, resulting in ∞ PSNR. The authenticated person face and fingerprint information is transmitted in a cover image and mask image (magic sheet) using proposed steganography and is\u0000 combined with Random Visual Crypto Technique. Which results in enhanced and advance visual crypto steganography secured model in communicating sensitive (biometric features) information over the internet. Where the complete information cannot be extracted using only cover image. Mask image\u0000 (magic sheet) is used along with cover image that reveals the secret data in the receiving end.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5279-5295"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44079784","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: 6

Automated Detection and Classification of COVID-19 from Chest X-ray Images Using Deep Learning 利用深度学习从胸部X射线图像中自动检测和分类新冠肺炎

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9439

K. Shankar, E. Perumal

{"title":"Automated Detection and Classification of COVID-19 from Chest X-ray Images Using Deep Learning","authors":"K. Shankar, E. Perumal","doi":"10.1166/JCTN.2020.9439","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9439","url":null,"abstract":"In recent times, COVID-19 has appeared as a major threat to healthcare professionals, governments, and research communities over the world from its diagnosis to medication. Several research works have been carried out for obtaining the possible solutions for controlling the epidemic\u0000 proficiently. An effective diagnosis of COVID-19 has been carried out using computed tomography (CT) scans and X-rays to examine the lung image. But it necessitates diverse radiologists and time to examine every report, which is a tedious task. Therefore, this paper presents an automated deep\u0000 learning (DL) based COVID-19 detection and classification model. The presented model performs preprocessing, feature extraction and classification. In the earlier stage, median filtering (MF) technique is applied to preprocess the input image. Next, convolutional neural network (CNN) based\u0000 VGGNet-19 model is applied as a feature extractor. At last, artificial neural network (ANN) is employed as a classification model to identify and classify the existence of COVID-19. An extensive set of simulation analysis takes place to ensure the superior performance of the applied model.\u0000 The outcome of the experiments showcased the betterment interms of different measures.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5457-5463"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46083860","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 Ensemble of Feature Extraction with Whale Optimization Algorithm for Content Based Image Retrieval System 基于内容的图像检索系统中特征提取与Whale优化算法的集成

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9432

P. Sasikumar, K. Venkatachalapathy

{"title":"An Ensemble of Feature Extraction with Whale Optimization Algorithm for Content Based Image Retrieval System","authors":"P. Sasikumar, K. Venkatachalapathy","doi":"10.1166/JCTN.2020.9432","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9432","url":null,"abstract":"In recent days, content based image retrieval (CBIR) becomes a hot research area, which aims to determine the relevant images to the query image (QI) from the available large sized database. This paper presents an optimal hybrid feature extraction with similarity measure (OHFE-SM) for\u0000 CBIR. Initially, histogram equalization of images takes place as a preprocessing step. Then, texture, shape and color features are extracted. The texture features include Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) is extracted, where the optimal number\u0000 of features will be chosen by whale optimization algorithm (WOA). Afterwards, the shape feature extraction takes place by Crest lines and color feature extraction process will be carried out using Quaternion moments. Finally, Euclidean distance will be applied as a similarity measure to determine\u0000 the distance among the feature vectors exist in the database and QI. The images with higher similarity index will be considered as relevant images and is retrieved from the database. A detailed experimental validation takes place against Corel10K dataset. The simulation results showed that\u0000 the proposed OHFE-SM model has outperformed the existing methods with the higher average precision of 0.915 and recall of 0.780.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5386-5398"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45769328","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

Public Key Cryptosystem Based on Optimized Chaos-Based Image Encryption 基于混沌优化图像加密的公钥密码系统

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9411

Supriya Khaitan, Shrddha Sagar, Rashi Agarwal

引用次数: 0

Content Based Medical Image Retrieval Using Multilevel Hybrid Clustering Segmentation with Feed Forward Neural Network 基于前馈神经网络的多层次混合聚类分割医学图像检索

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9452

R. Inbaraj, G. Ravi

{"title":"Content Based Medical Image Retrieval Using Multilevel Hybrid Clustering Segmentation with Feed Forward Neural Network","authors":"R. Inbaraj, G. Ravi","doi":"10.1166/JCTN.2020.9452","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9452","url":null,"abstract":"Content-Based Image Retrieval (CBIR) is another yet broadly recognized method for distinguishing images from monstrous and unannotated image databases. With the improvement of network and mixed media headways ending up being increasingly famous, customers are not content with the regular\u0000 information retrieval progresses. So nowadays, Content-Based Image Retrieval (CBIR) is the perfect and fast recovery source. Lately, various strategies have been created to improve CBIR execution. Data clustering is an overlooked method of hiding formatting extraction from large data blocks.\u0000 With large data sets, there is a possibility of high dimensionality Models are a challenging domain with both massive numerical accuracy and efficiency for multidimensional data sets. The calibration and rich information dataset contain the problem of recovery and handling of medical images.\u0000 Every day, more medical images were converted to digital format. Therefore, this work has applied these data to manage and file a novel approach, the “Clustering (MHC) Approach Using Content-Based Medical Image Retrieval Hybrid.” This work is implemented as four levels. With each\u0000 level, the effectiveness of job retention is improved. Compared to some of the existing works that are being done in the analysis of this work’s literature, the results of this work are compared. The classification and learning features are used to retrieve medical images in a database.\u0000 The proposed recovery system performs better than the traditional approach; with precision, recall, F-measure, and accuracy of proposed method are 97.29%, 95.023%, 4.36%, and 98.55% respectively. The recommended approach is most appropriate for recuperating clinical images for various\u0000 parts of the body.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5550-5562"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47429339","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

Hybrid Feature Extraction and Classification for Alzheimer’s Disease Detection 用于阿尔茨海默病检测的混合特征提取与分类

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9455

P. Sharmila, C. Rekha, D. Devi, K. Revathi, K. Sornalatha

引用次数: 0

Analysis of Plant Disease Detection and Classification Models: A Computer Vision Perspective 植物病害检测与分类模型分析:计算机视觉视角

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9435

K. Jayaprakash, S. Balamurugan

{"title":"Analysis of Plant Disease Detection and Classification Models: A Computer Vision Perspective","authors":"K. Jayaprakash, S. Balamurugan","doi":"10.1166/JCTN.2020.9435","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9435","url":null,"abstract":"Presently, rapid and precise disease identification process plays a vital role to increase agricultural productivity in a sustainable manner. Conventionally, human experts identify the existence of anomaly in plants occurred due to disease, pest, nutrient deficient, weather conditions.\u0000 Since manual diagnosis process is a tedious and time consuming task, computer vision approaches have begun to automatically detect and classify the plant diseases. The general image processing tasks involved in plant disease detection are preprocessing, segmentation, feature extraction and\u0000 classification. This paper performs a review of computer vision based plant disease detection and classification techniques. The existing plant disease detection approaches including segmentation and feature extraction techniques have been reviewed. Additionally, a brief survey of machine\u0000 learning (ML) and deep learning (DL) models to identify plant diseases also takes place. Furthermore, a set of recently developed DL based tomato plant leaf disease detection and classification models are surveyed under diverse aspects. To further understand the reviewed methodologies, a detailed\u0000 comparative study also takes place to recognize the unique characteristics of the reviewed models.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5422-5428"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43485106","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

Blockchain Solution for Evidence Forgery Detection 区块链证据伪造检测解决方案

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9454

B. Kumar, K. R. Kumar

引用次数: 0

Improved Encryption Towards Data Security in Serverless Computing 无服务器计算中数据安全的改进加密

Journal of Computational and Theoretical Nanoscience Pub Date : 2020-12-01 DOI: 10.1166/JCTN.2020.9417

A. Arulprakash, K. Sampathkumar

引用次数: 1