Pub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653696
G. Priyadharshini, K. Shyamala
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 Because of fast-moving technology and transformed individual and business behaviors, directive is outdated and is replaced with the General Data Protection Regulation (REGULATION (EU) 2016/679) Compared with Directive, GDPR is most ambitious one and it covers more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.
{"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}
Pub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653651
A. K. Rangarajan, Raja Purushothaman, H. Venkatesan
The availability of land and resource for agriculture are depleting due to urbanization and increase in population. The agricultural crops are also susceptible to pest and disease. Hence the possibility of growing crop in an artificial LED light in an indoor facility has been explored in this study. The crop namely Solanum melongena was grown in blue and red LED light fixed at a distance of 65 cm from the ground. 9 plants were grown in sunlight and 9 plants in LED light. The plant growth was monitored using manual measurement (height, number of leaves) and image processing techniques (number of plant pixels) after 1st week of placing the sapling and during the 4th week. The performance has been compared and it showed that the plants in sunlight grow better than the plants in LED light placed at 40 cm from the leaf surface.
{"title":"Evaluation of Solanum melongena crop performance in artificial LED light source for urban farming","authors":"A. K. Rangarajan, Raja Purushothaman, H. Venkatesan","doi":"10.1109/I-SMAC.2018.8653651","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653651","url":null,"abstract":"The availability of land and resource for agriculture are depleting due to urbanization and increase in population. The agricultural crops are also susceptible to pest and disease. Hence the possibility of growing crop in an artificial LED light in an indoor facility has been explored in this study. The crop namely Solanum melongena was grown in blue and red LED light fixed at a distance of 65 cm from the ground. 9 plants were grown in sunlight and 9 plants in LED light. The plant growth was monitored using manual measurement (height, number of leaves) and image processing techniques (number of plant pixels) after 1st week of placing the sapling and during the 4th week. The performance has been compared and it showed that the plants in sunlight grow better than the plants in LED light placed at 40 cm from the leaf surface.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"5 1","pages":"33-36"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80497443","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}
Pub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653758
Ananthi Sheshasaayee, L. Logeshwari
In the modern business customer response is one of the vital characteristics of services. The customer relationship management accurately predict the invaluable customer. Because attention is needed to rate low response rating customers. Most of the direct marketing sectors randomly select and reduce degree of the influencing problem. But online marketing sectors face more difficulties to identify customer responses. This paper proposes SVM model based on the RFM values and also according to the monetary value to predict recency and frequency weights.
{"title":"IMPLEMENTATION OF RFM ANALYSIS USING SUPPORT VECTOR MACHINE MODEL","authors":"Ananthi Sheshasaayee, L. Logeshwari","doi":"10.1109/I-SMAC.2018.8653758","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653758","url":null,"abstract":"In the modern business customer response is one of the vital characteristics of services. The customer relationship management accurately predict the invaluable customer. Because attention is needed to rate low response rating customers. Most of the direct marketing sectors randomly select and reduce degree of the influencing problem. But online marketing sectors face more difficulties to identify customer responses. This paper proposes SVM model based on the RFM values and also according to the monetary value to predict recency and frequency weights.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"11 1","pages":"760-763"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79059688","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}
Pub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653668
Shivani Gupta, Aaditya Jain, Priyanka Jeswani
Balanced structures are required in certain applications and clustering does not inherently aim at producing balanced partition of data. Two things are essentially required: definition of balance and modification to objective function to accommodate this definition. This paper proposes three definitions of balance in clusters: cardinality, variance and density; such that they can be directly interpreted for applications of balanced clustering. These are incorporated with objective function of standard k-means algorithm to demonstrate effect of balance in output over popular datasets. Paper also suggests method to measure the balance factor of any cluster structure.
{"title":"Generalized Method to Produce Balanced Structures Through k-means Objective Function","authors":"Shivani Gupta, Aaditya Jain, Priyanka Jeswani","doi":"10.1109/I-SMAC.2018.8653668","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653668","url":null,"abstract":"Balanced structures are required in certain applications and clustering does not inherently aim at producing balanced partition of data. Two things are essentially required: definition of balance and modification to objective function to accommodate this definition. This paper proposes three definitions of balance in clusters: cardinality, variance and density; such that they can be directly interpreted for applications of balanced clustering. These are incorporated with objective function of standard k-means algorithm to demonstrate effect of balance in output over popular datasets. Paper also suggests method to measure the balance factor of any cluster structure.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"22 1","pages":"586-590"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81442474","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}
Pub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653737
A. Balasubramani, H. Sunil Kumar, N. Madhu Kumar
An RFID & GSM based cashless automatic rationining system can be used issues a ration to most accurate, efficient & automatic distribution of ration materials now a day’s ration distribution system have a many drawback such as inaccurate, low quality and theft ration material in ration shop. Now a day’s cashless automatic ration shop is based on GSM & RFID. RFID can be used as ration card and customer data base is stored in controller. Customer want to scan the RFID card RFID reader reads the scanned card and microcontroller compares with RFID card to the government data base office after the successful verification customer wants to enter a required materials and quantity using a keyboard. After issues a ration material the microcontroller sends a information to RFID owner & Govt. office through GSM technology.
{"title":"Cashless automatic rationing system by using GSM and RFID Technology","authors":"A. Balasubramani, H. Sunil Kumar, N. Madhu Kumar","doi":"10.1109/I-SMAC.2018.8653737","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653737","url":null,"abstract":"An RFID & GSM based cashless automatic rationining system can be used issues a ration to most accurate, efficient & automatic distribution of ration materials now a day’s ration distribution system have a many drawback such as inaccurate, low quality and theft ration material in ration shop. Now a day’s cashless automatic ration shop is based on GSM & RFID. RFID can be used as ration card and customer data base is stored in controller. Customer want to scan the RFID card RFID reader reads the scanned card and microcontroller compares with RFID card to the government data base office after the successful verification customer wants to enter a required materials and quantity using a keyboard. After issues a ration material the microcontroller sends a information to RFID owner & Govt. office through GSM technology.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"15 1","pages":"719-722"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81831335","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}
Pub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653654
M. Sughasiny, J. Rajeshwari
The triumphant utilization of data mining in extremely evident areas like trade, commerce, and e-business has directed to its application in another industry. The medical conditions are still knowledge rich but information low. There is an abundance of information feasible inside the medical practices. Still, there is a shortage of essential investigation mechanisms to recognize hidden trends and relationships in data. Many researchers have applied Data Mining methods for the prognosis and diagnosis of several diseases. Machine Learning methods have broadly utilized in the prognostication of different diseases at the beginning stages. The current decade has observed an abnormal development in the variety and volume of electronic data associated with the development and research, patient self-tracking, and health records together suggested to as Big Data. This paper presents a comprehensive literature survey on the importance of Feature Selection methods, Supervised Machine Learning methods, Unsupervised Machine Learning methods and big data for the healthcare industry.
{"title":"Application Of Machine Learning Techniques, Big Data Analytics In Health Care Sector – A Literature Survey","authors":"M. Sughasiny, J. Rajeshwari","doi":"10.1109/I-SMAC.2018.8653654","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653654","url":null,"abstract":"The triumphant utilization of data mining in extremely evident areas like trade, commerce, and e-business has directed to its application in another industry. The medical conditions are still knowledge rich but information low. There is an abundance of information feasible inside the medical practices. Still, there is a shortage of essential investigation mechanisms to recognize hidden trends and relationships in data. Many researchers have applied Data Mining methods for the prognosis and diagnosis of several diseases. Machine Learning methods have broadly utilized in the prognostication of different diseases at the beginning stages. The current decade has observed an abnormal development in the variety and volume of electronic data associated with the development and research, patient self-tracking, and health records together suggested to as Big Data. This paper presents a comprehensive literature survey on the importance of Feature Selection methods, Supervised Machine Learning methods, Unsupervised Machine Learning methods and big data for the healthcare industry.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"10 1","pages":"741-749"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80928944","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}
Pub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653699
Xin-xin Xie, Wenzhun Huang, H. Wang, Zhe Liu
In this paper, we conduct research on the image retrieval algorithm based on the support vector machine and the decision tree. Image database retrieval system is the core part of the image database, the system uses a certain algorithm of image to transform the image data in the database, operation and organization, and connecting with the complete image database retrieval algorithm of the image retrieval function, in order to obtain the retrieval results, to meet the needs of users to meet the needs of its users. Have the feature such as shape, texture, color data, which determines the image database has a different way of conventional database retrieval. In order to improve the efficiency of the image database retrieval, must be carefully designed the structure of image database retrieval system, adopt efficient image retrieval method quickly. Our research proposes the novel perspectives of the related issues that obtain the feasible and effective.
{"title":"Image Retrieval with Adaptive SVM and Random Decision Tree","authors":"Xin-xin Xie, Wenzhun Huang, H. Wang, Zhe Liu","doi":"10.1109/I-SMAC.2018.8653699","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653699","url":null,"abstract":"In this paper, we conduct research on the image retrieval algorithm based on the support vector machine and the decision tree. Image database retrieval system is the core part of the image database, the system uses a certain algorithm of image to transform the image data in the database, operation and organization, and connecting with the complete image database retrieval algorithm of the image retrieval function, in order to obtain the retrieval results, to meet the needs of users to meet the needs of its users. Have the feature such as shape, texture, color data, which determines the image database has a different way of conventional database retrieval. In order to improve the efficiency of the image database retrieval, must be carefully designed the structure of image database retrieval system, adopt efficient image retrieval method quickly. Our research proposes the novel perspectives of the related issues that obtain the feasible and effective.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"34 1","pages":"784-787"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78472809","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}
Pub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653759
Latika A. Thamke, M. Vaidya
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.
{"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}
Pub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653714
Anupama Shetter, S. N. Prajwalasimha, Swapna Havalgi
In this paper, a collective median filtering and histogram equalization based de-noising technique is proposed for images. Initial noise detection is performed by considering neighboring pixel values then median filtering is performed to remove high density noise. The filtered image is then subjected for histogram equalization to regain correlation between adjacent pixels. The final image enhancement is done by contrast adjustment method. The experimental results show that the proposed algorithm provides high quality restored images compared to existing ones.
{"title":"Image De-Noising Algorithm based on Filtering and Histogram Equalization","authors":"Anupama Shetter, S. N. Prajwalasimha, Swapna Havalgi","doi":"10.1109/I-SMAC.2018.8653714","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653714","url":null,"abstract":"In this paper, a collective median filtering and histogram equalization based de-noising technique is proposed for images. Initial noise detection is performed by considering neighboring pixel values then median filtering is performed to remove high density noise. The filtered image is then subjected for histogram equalization to regain correlation between adjacent pixels. The final image enhancement is done by contrast adjustment method. The experimental results show that the proposed algorithm provides high quality restored images compared to existing ones.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"41 1","pages":"325-328"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81759357","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}
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