Koomesh Pub Date : 2018-08-01 DOI: 10.1109/I-SMAC.2018.8653696

G. Priyadharshini, K. Shyamala

{"title":"Strategy and Solution to comply with GDPR : Guideline to comply major articles and save penalty from non-compliance","authors":"G. Priyadharshini, K. Shyamala","doi":"10.1109/I-SMAC.2018.8653696","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653696","url":null,"abstract":"General Data Protection Regulation (GDPR) is no more buzz word and it sets new standard on security across globe. Every organization who deals with data started doing self-assessment to check how it has impact on their business and what are all the ways they can prepare themselves to comply with GDPR. Since 1995, Europe Union (EU) followed \"Data Protective Directive\" (Directive) on Data privacy. Fourth Industrial Revolution (4IR) has range of new technologies covering digital, artificial, biological and big data and impacting all discipline from aeronautical to economies and industries. Because of fast-moving technology and transformed individual and business behaviors, directive is outdated and is replaced with the General Data Protection Regulation (REGULATION (EU) 2016/679) Compared with Directive, GDPR is most ambitious one and it covers more operators under this act. The regulation completely changes the groundwork for how organizations can manage personal data of EU citizens. GDPR gives more control on Personally Identifiable Information (PII), Protected Health Information (PHI) or other sensitive information and imposes new rules on organization who manage and process PII or PHI. Objective of this white paper is to give broad overview of forthcoming GDPR and it doesn’t focus on legal clause or penalty details. This covers the difference between Directive and GDPR, who are all covered under these new regulations. This also gives idea about consequences of the GDPR if an organization don’t comply with GDPR and how organization to prepare themselves so that they can continue their business as usual without any impact and guide to avoid data breach and penalty.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"165 1","pages":"190-195"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80415298","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: 9

Evaluation of Solanum melongena crop performance in artificial LED light source for urban farming 城市人工LED光源下龙葵作物生长性能评价

Koomesh Pub Date : 2018-08-01 DOI: 10.1109/I-SMAC.2018.8653651

A. K. Rangarajan, Raja Purushothaman, H. Venkatesan

引用次数: 1

IMPLEMENTATION OF RFM ANALYSIS USING SUPPORT VECTOR MACHINE MODEL 使用支持向量机模型实现RFM分析

Koomesh Pub Date : 2018-08-01 DOI: 10.1109/I-SMAC.2018.8653758

Ananthi Sheshasaayee, L. Logeshwari

引用次数: 0

Generalized Method to Produce Balanced Structures Through k-means Objective Function 利用k-均值目标函数生成平衡结构的广义方法

Koomesh Pub Date : 2018-08-01 DOI: 10.1109/I-SMAC.2018.8653668

Shivani Gupta, Aaditya Jain, Priyanka Jeswani

引用次数: 2

Cashless automatic rationing system by using GSM and RFID Technology 采用GSM和RFID技术的无现金自动配给系统

Koomesh Pub Date : 2018-08-01 DOI: 10.1109/I-SMAC.2018.8653737

A. Balasubramani, H. Sunil Kumar, N. Madhu Kumar

引用次数: 3

Application Of Machine Learning Techniques, Big Data Analytics In Health Care Sector – A Literature Survey 机器学习技术、大数据分析在医疗保健领域的应用——文献综述

Koomesh Pub Date : 2018-08-01 DOI: 10.1109/I-SMAC.2018.8653654

M. Sughasiny, J. Rajeshwari

引用次数: 11

I-SMAC 2018 Messages I-SMAC 2018信息

Koomesh Pub Date : 2018-08-01 DOI: 10.1109/i-smac.2018.8653754

引用次数: 0

Image Retrieval with Adaptive SVM and Random Decision Tree 基于自适应支持向量机和随机决策树的图像检索

Koomesh Pub Date : 2018-08-01 DOI: 10.1109/I-SMAC.2018.8653699

Xin-xin Xie, Wenzhun Huang, H. Wang, Zhe Liu

引用次数: 6

Classification of Lung Diseases Using a Combination of Texture, Shape and Pixel Value by K-NN Classifier 基于纹理、形状和像素值的K-NN分类器肺部疾病分类

Koomesh Pub Date : 2018-08-01 DOI: 10.1109/I-SMAC.2018.8653759

Latika A. Thamke, M. Vaidya

{"title":"Classification of Lung Diseases Using a Combination of Texture, Shape and Pixel Value by K-NN Classifier","authors":"Latika A. Thamke, M. Vaidya","doi":"10.1109/I-SMAC.2018.8653759","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653759","url":null,"abstract":"Lung diseases are the disorder, issues that affect the lungs, the organs that permit us to breathe and it is the most frequent medical conditions worldwide especially in India. In this work, the problem of lung diseases like the difficulty encountered while classifying the disease in radiography can be solved. In this work, we propose Features Extraction Techniques for classification of Lung Computed Tomography Images. A Combination of Texture, Shape and Pixel Coefficient Feature are developed for Classifying the CT images of lung disease. The proposed system can classify lung images automatically as Normal Lung, Pleural Effusion, Emphysema and Bronchitis. The proposed System contains four steps. In the initial step, the images are pre-processed. In the second step, the images are segmented by Thresholding and Edge Detection. In the third step, the Texture, Shape and Pixel Coefficient Feature are calculated using the GLCM (Gray Level Co-occurrence Matrix), Moment Invariant and WHT (Walsh Hadamard Transform) and combined to form the single descriptor. In the final step, the K-NN, Multiclass-SVM and Decision Tree classifiers are used for classification of Lung images. The images are the CT scan images. The total datasets contain 400 images, 100 images of each disease like the Normal, Pleural Effusion, Emphysema and Bronchitis. The 280 images are used for Training and 120 images are used for Testing. The classification accuracy of folding method accomplished by the K-NN classifier with Global Thresholding is 97.50% for WHT +GLCM, 97.50% for WHT + MI, 94.45% for GLCM + MI, 97.50% for WHT +GLCM+MI. The K-NN classifier with Global Thresholding reduces the time and also gives better results as compared to other methods and classifiers.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"37 1","pages":"235-240"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80183837","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

Image De-Noising Algorithm based on Filtering and Histogram Equalization 基于滤波和直方图均衡化的图像去噪算法

Koomesh Pub Date : 2018-08-01 DOI: 10.1109/I-SMAC.2018.8653714

Anupama Shetter, S. N. Prajwalasimha, Swapna Havalgi

引用次数: 6

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