Journal of Computational Science and Intelligent Technologies最新文献_第3页

A Review on Prostate Cancer Detection using Deep Learning Techniques 基于深度学习技术的前列腺癌检测研究综述

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa.jcsit20201204

C. Narmatha, M. SurendraPrasad, Salem Hospitals

{"title":"A Review on Prostate Cancer Detection using Deep Learning Techniques","authors":"C. Narmatha, M. SurendraPrasad, Salem Hospitals","doi":"10.53409/mnaa.jcsit20201204","DOIUrl":"https://doi.org/10.53409/mnaa.jcsit20201204","url":null,"abstract":"The second most diagnosed disease of men throughout the world is Prostate cancer (PCa). 28% of cancers in men result in the prostate, making PCa and its identification an essential focus in cancer research. Hence, developing effective diagnostic methods for PCa is very significant and has critical medical effect. These methods could improve the advantages of treatment and enhance the patients' survival chance. Imaging plays a significant role in the identification of PCa. Prostate segmentation and classification is a difficult process, and the difficulties fundamentally vary with one imaging methodology then onto the next. For segmentation and classification, deep learning algorithms, specifically convolutional networks, have quickly become an optional technique for medical image analysis. In this survey, various types of imaging modalities utilized for diagnosing PCa is reviewed and researches made on the detection of PCa is analyzed. Most of the researches are done in machine learning based and deep learning based techniques. Based on the results obtained from the analysis of these researches, deep learning based techniques plays a significant and promising part in detecting PCa. Most of the techniques are based on computer aided detection (CAD) systems, which follows preprocessing, segmentation, feature extraction, and classification processes, which yield efficient results in detecting PCa. As a conclusion from the analysis of some recent works, deep learning based techniques are adequate for the detection of PCa.","PeriodicalId":125707,"journal":{"name":"Journal of Computational Science and Intelligent Technologies","volume":"1 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":"122663763","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 Survey on Security Risks in Internet of Things (IoT) Environment 物联网(IoT)环境下安全风险调查

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa.jcsit20201201

Mugesh Ravi

引用次数: 8

Performance Analysis of Deep Belief Neural Network for Brain Tumor Classification 深度信念神经网络在脑肿瘤分类中的性能分析

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa.jcsit20201305

Sreenivas Eeshwaroju, Novi Michigan Usa Harman Connected Services, Praveena Jakula

{"title":"Performance Analysis of Deep Belief Neural Network for Brain Tumor Classification","authors":"Sreenivas Eeshwaroju, Novi Michigan Usa Harman Connected Services, Praveena Jakula","doi":"10.53409/mnaa.jcsit20201305","DOIUrl":"https://doi.org/10.53409/mnaa.jcsit20201305","url":null,"abstract":"The brain tumors are by far the most severe and violent disease, contributing to the highest degree of a very low life expectancy. Therefore, recovery preparation is a crucial step in improving patient quality of life. In general , different imaging techniques such as computed tomography ( CT), magnetic resonance imaging ( MRI) and ultrasound imaging have been used to examine the tumor in the brain, lung , liver, breast , prostate ... etc. MRI images are especially used in this research to diagnose tumor within the brain with classification results. The massive amount of data produced by the MRI scan, therefore, destroys the manual classification of tumor vs. non-tumor in a given period. However for a limited number of images, it is presented with some constraint that is precise quantitative measurements. Consequently, a trustworthy and automated classification scheme is important for preventing human death rates. The automatic classification of brain tumors is a very challenging task in broad spatial and structural heterogeneity of the surrounding brain tumor area. Automatic brain tumor identification is suggested in this research by the use of the classification with Deep Belief Network (DBN). Experimental results show that the DBN archive rate with low complexity seems to be 97 % accurate compared to all other state of the art methods.","PeriodicalId":125707,"journal":{"name":"Journal of Computational Science and Intelligent Technologies","volume":"229 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":"133401173","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

Compression Methodologies for Columnar Database Optimization 列式数据库优化的压缩方法

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa/jcsit/e202203012432

Praveen Kumar Sadineni

{"title":"Compression Methodologies for Columnar Database Optimization","authors":"Praveen Kumar Sadineni","doi":"10.53409/mnaa/jcsit/e202203012432","DOIUrl":"https://doi.org/10.53409/mnaa/jcsit/e202203012432","url":null,"abstract":"Today’s life is completely dependent on data. Conventional relational databases take longer to respond to queries because they are built for row-wise data storage and retrieval. Due to their efficient read and write operations to and from hard discs, which reduce the time it takes for queries to produce results, columnar databases have recently overtaken traditional databases. To execute Business Intelligence and create decision-making systems, vast amounts of data gathered from various sources are required in data warehouses, where columnar databases are primarily created. Since the data are stacked closely together, and the seek time is reduced, columnar databases perform queries more quickly. With aggregation queries to remove unnecessary data, they allow several compression techniques for faster data access. To optimise the efficiency of columnar databases, various compression approaches, including NULL Suppression, Dictionary Encoding, Run Length Encoding, Bit Vector Encoding, and Lempel Ziv Encoding, are discussed in this work. Database operations are conducted on the compressed data to demonstrate the decrease in memory needs and speed improvements.","PeriodicalId":125707,"journal":{"name":"Journal of Computational Science and Intelligent Technologies","volume":"10 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":"125418776","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