Pub Date : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032431
B. Siddartha, G. Ravikumar
Increased use of modern advanced electronic devices rapidly increased the data collection rate, most of the advanced healthcare industries today are using updated healthcare facilities with the advanced healthcare technologies to collect and process the data. Healthcare data generated by the most of the industries are in the digital format. Provisioning protection and security to the PHI is the major concern but it is very difficult to safeguard the generated data from unauthorized users or breaches. There are many advanced techniques are in use today to protect the individuals sensitive data. Data masking approach is the advanced technique that enables security provisioning of personnel health records. This paper presented the in-depth study on current healthcare security techniques and summarized the gaps in security provisioning. Conclusion part of the paper highlights the some of the acts and policies adopted by the countries to safeguard the citizens' healthcare data.
{"title":"Analysis of Masking Techniques to Find out Security and other Efficiency Issues in Healthcare Domain","authors":"B. Siddartha, G. Ravikumar","doi":"10.1109/I-SMAC47947.2019.9032431","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032431","url":null,"abstract":"Increased use of modern advanced electronic devices rapidly increased the data collection rate, most of the advanced healthcare industries today are using updated healthcare facilities with the advanced healthcare technologies to collect and process the data. Healthcare data generated by the most of the industries are in the digital format. Provisioning protection and security to the PHI is the major concern but it is very difficult to safeguard the generated data from unauthorized users or breaches. There are many advanced techniques are in use today to protect the individuals sensitive data. Data masking approach is the advanced technique that enables security provisioning of personnel health records. This paper presented the in-depth study on current healthcare security techniques and summarized the gaps in security provisioning. Conclusion part of the paper highlights the some of the acts and policies adopted by the countries to safeguard the citizens' healthcare data.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114515483","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032429
Thulasi Bikku, V. Narayana, A. Gopi, Sk. Reshmi Khadherbhi
Nowadays the number of vehicles on the road has been expanded exponentially, but the limitations of roads and transportation frameworks have not created in a comparable method to effectively adapt with the number of vehicles going on them. Because of this, road congestion has expanded around the world. Sensor systems have increased by expanding consideration in rush hour traffic identification and maintaining a strategic distance from heavy traffic. WSNs are extremely smart because of their quicker exchange of data, simple establishment and for being more affordable contrasted with other systems. Remote sensor systems are an innovation which has assumed an enormous job empowering smarter city urban communities is utilizing this innovation to accumulate information identified with movement. The goal is to have an entire framework that empowers the observing of activity practices so choices on city advancement can be made smarter. This paper provides a survey on road traffic congestion control with the help of sensors which communicate with other vehicles nearby for avoiding traffic as well as road accidents. This paper performs a survey on various techniques on road traffic reduction methods of road accidents using sensors.
{"title":"Sensors Systems for Traffic Congestion Reduction Methodologies","authors":"Thulasi Bikku, V. Narayana, A. Gopi, Sk. Reshmi Khadherbhi","doi":"10.1109/I-SMAC47947.2019.9032429","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032429","url":null,"abstract":"Nowadays the number of vehicles on the road has been expanded exponentially, but the limitations of roads and transportation frameworks have not created in a comparable method to effectively adapt with the number of vehicles going on them. Because of this, road congestion has expanded around the world. Sensor systems have increased by expanding consideration in rush hour traffic identification and maintaining a strategic distance from heavy traffic. WSNs are extremely smart because of their quicker exchange of data, simple establishment and for being more affordable contrasted with other systems. Remote sensor systems are an innovation which has assumed an enormous job empowering smarter city urban communities is utilizing this innovation to accumulate information identified with movement. The goal is to have an entire framework that empowers the observing of activity practices so choices on city advancement can be made smarter. This paper provides a survey on road traffic congestion control with the help of sensors which communicate with other vehicles nearby for avoiding traffic as well as road accidents. This paper performs a survey on various techniques on road traffic reduction methods of road accidents using sensors.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116543811","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032435
S. Thangavelu, A. S, K C Naetra, Krishna Sathya A C, V. Lasya
Microarray databases are the most frequently used datasets for cancer analytics. Microarray databases are characterized by the presence of a very large number of genes, which exceeds the very little number of samples. So, the feature set accumulates the curse of dimensionality. Therefore, selecting a small subset of genes among thousands of genes in microarray data can potentially increase the accuracy for the classification of cancer. Many approaches, from the field of classical machine learning and soft computing, have been used to address the issue of feature selection and feature extraction for better classifications and clustering accuracy. The research outlined in this paper strives to look at a two-stage approach using minimum Redundancy Maximum Relevancy (mRMR), a feature ranking framework as the first stage followed by a hybrid genetic algorithm in the second stage that works on the features ranked by the mRMR. The proposed method is aimed to select the optimal feature subsets for better classification results in binary and multi class datasets to compensate for the curse of dimensionality in microarray datasets. The classifiers used to test the two-stage proposition are SVM, Naive-Bayes, Linear Discriminant Analysis, decision trees and random forest classifiers. The experimental results show that the gene subset selected by the mRMR-GA pipeline gives good results.
{"title":"Feature Selection in Cancer Genetics using Hybrid Soft Computing","authors":"S. Thangavelu, A. S, K C Naetra, Krishna Sathya A C, V. Lasya","doi":"10.1109/I-SMAC47947.2019.9032435","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032435","url":null,"abstract":"Microarray databases are the most frequently used datasets for cancer analytics. Microarray databases are characterized by the presence of a very large number of genes, which exceeds the very little number of samples. So, the feature set accumulates the curse of dimensionality. Therefore, selecting a small subset of genes among thousands of genes in microarray data can potentially increase the accuracy for the classification of cancer. Many approaches, from the field of classical machine learning and soft computing, have been used to address the issue of feature selection and feature extraction for better classifications and clustering accuracy. The research outlined in this paper strives to look at a two-stage approach using minimum Redundancy Maximum Relevancy (mRMR), a feature ranking framework as the first stage followed by a hybrid genetic algorithm in the second stage that works on the features ranked by the mRMR. The proposed method is aimed to select the optimal feature subsets for better classification results in binary and multi class datasets to compensate for the curse of dimensionality in microarray datasets. The classifiers used to test the two-stage proposition are SVM, Naive-Bayes, Linear Discriminant Analysis, decision trees and random forest classifiers. The experimental results show that the gene subset selected by the mRMR-GA pipeline gives good results.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116957671","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032701
B. C. Naik, B. Anuradha
Recently, the remote sensing data is widely used for the extraction of water body from the satellite images. The accuracy assessment of the extracted water features from the satellite images is highly correlated with the real time data. Spatiotemporal changes in nagarjunasagar reservoir, located in India in a period of 2014 to 2019 time series and analysis using multi temporal Landsat-8 (OLI) images. Unsupervised classification (Isodata) and spectral water indexing methods, including NDVI, NDWI, MNDWI and AWEI were evaluated for surface water body extraction and change detection. The overall accuracy and kappa coefficients were evaluated for water indexing methods. The statistical parameters of the accuracy results show that AWEI achieved 96.26% overall accuracy, 0.94 kappa coefficient and MNDWI achieved 96.94% overall accuracy, 0.95 kappa coefficient. The AWEI and MNDWI water indexes performed better results as compared to other water indexing methods.
{"title":"Time Series Analysis of Water Feature Extraction using Water Index Techniques from Landsat Remote Sensing Images","authors":"B. C. Naik, B. Anuradha","doi":"10.1109/I-SMAC47947.2019.9032701","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032701","url":null,"abstract":"Recently, the remote sensing data is widely used for the extraction of water body from the satellite images. The accuracy assessment of the extracted water features from the satellite images is highly correlated with the real time data. Spatiotemporal changes in nagarjunasagar reservoir, located in India in a period of 2014 to 2019 time series and analysis using multi temporal Landsat-8 (OLI) images. Unsupervised classification (Isodata) and spectral water indexing methods, including NDVI, NDWI, MNDWI and AWEI were evaluated for surface water body extraction and change detection. The overall accuracy and kappa coefficients were evaluated for water indexing methods. The statistical parameters of the accuracy results show that AWEI achieved 96.26% overall accuracy, 0.94 kappa coefficient and MNDWI achieved 96.94% overall accuracy, 0.95 kappa coefficient. The AWEI and MNDWI water indexes performed better results as compared to other water indexing methods.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116253832","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032542
Timmana Hari Krishna, C. Rajabhushnam
In recently observed that breast related diseases affects women present all over the globe, where it emerges as the second most common disease in the world. In 2012, 12 % cancer patients were present and from these patients 25 % are breast cancer patients. In the traditional method to cure the breast cancer is malignant tumor. Most of the doctors manually presumed the bosom malignant growth region. Various examinations have referred that this manual presumed requires more time and it relies upon the operation and machine. Therefore, it is necessary to design a perfect algorithm for the identification of bosom diseases. In this report, we have developed an algorithm to identify the breast cancer patient automatically. This algorithm can automatically detect the tumor of breast cancer by observing the biopsy pictures. Also, the calculation must be very precise, as the lives of individuals are at risk. All the performance operations are done on the microscopy pictures and the data set for this microscopy pictures is designed for the clustering analysis of a picture. The experimental results of the proposed scheme show accuracy 98.3 %, precision 0.65, Recall 0.95, F1 score 0.77 and ROC - AUC 0.692.
{"title":"Bosom Malignant Diseases (Cancer) Identification by using Deep Learning Technique","authors":"Timmana Hari Krishna, C. Rajabhushnam","doi":"10.1109/I-SMAC47947.2019.9032542","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032542","url":null,"abstract":"In recently observed that breast related diseases affects women present all over the globe, where it emerges as the second most common disease in the world. In 2012, 12 % cancer patients were present and from these patients 25 % are breast cancer patients. In the traditional method to cure the breast cancer is malignant tumor. Most of the doctors manually presumed the bosom malignant growth region. Various examinations have referred that this manual presumed requires more time and it relies upon the operation and machine. Therefore, it is necessary to design a perfect algorithm for the identification of bosom diseases. In this report, we have developed an algorithm to identify the breast cancer patient automatically. This algorithm can automatically detect the tumor of breast cancer by observing the biopsy pictures. Also, the calculation must be very precise, as the lives of individuals are at risk. All the performance operations are done on the microscopy pictures and the data set for this microscopy pictures is designed for the clustering analysis of a picture. The experimental results of the proposed scheme show accuracy 98.3 %, precision 0.65, Recall 0.95, F1 score 0.77 and ROC - AUC 0.692.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123413856","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032627
Sunil Annareddy, Srikanth Tammina
Since the last decade, internet plays an imperative and vital role in the creation and retrieval of colossal amounts of information. With ever-increasing advancements in technological field and creation of data at an exponential rate, impertinent or irrelevant data is proliferating at a vast scale in commensuration with relevant data. Moreover, the usage of mobile phones has increased drastically, and phones are becoming an evident part of everyone's lives. With this, there is a notable increase in the number of spam messages from spammers. According to recent statistics, 96% of Indians receive unsolicited text messages every day. SMS spam is any unwanted or unsolicited text note in the form of weblink, promotional message or irrelevant text sent uncritically and non-selectively to your mobile phone, regularly for advertising purposes. The surge in unsolicited information across all platforms including mobile text messages and emails has created an expedited need for the advancement and refinement of more reliable filters to counteract the spam in these messages. Traditionally, rule-based approach is employed to counteract spam messages. According to this approach, a set of rules are employed on the messages by some authority manually. By this method, no favorable or assuring results will be shown because the rules need to regularly be restructured based on the source of spam messages, which is an arduous process. Instead, we use deep learning methods that are efficient and does not require any rules. Deep learning models require a set of training dataset samples to learn the rules from these SMS messages and build a text classifier that efficiently classifies spam from these messages. This paper presents a systematic review of employing deep learning methods namely, convolutional neural network and recurrent neural network on huge corpus of SMS texts to build a spam classifier that classifies messages as ham or spam.
{"title":"A Comparative Study of Deep Learning Methods for Spam Detection","authors":"Sunil Annareddy, Srikanth Tammina","doi":"10.1109/I-SMAC47947.2019.9032627","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032627","url":null,"abstract":"Since the last decade, internet plays an imperative and vital role in the creation and retrieval of colossal amounts of information. With ever-increasing advancements in technological field and creation of data at an exponential rate, impertinent or irrelevant data is proliferating at a vast scale in commensuration with relevant data. Moreover, the usage of mobile phones has increased drastically, and phones are becoming an evident part of everyone's lives. With this, there is a notable increase in the number of spam messages from spammers. According to recent statistics, 96% of Indians receive unsolicited text messages every day. SMS spam is any unwanted or unsolicited text note in the form of weblink, promotional message or irrelevant text sent uncritically and non-selectively to your mobile phone, regularly for advertising purposes. The surge in unsolicited information across all platforms including mobile text messages and emails has created an expedited need for the advancement and refinement of more reliable filters to counteract the spam in these messages. Traditionally, rule-based approach is employed to counteract spam messages. According to this approach, a set of rules are employed on the messages by some authority manually. By this method, no favorable or assuring results will be shown because the rules need to regularly be restructured based on the source of spam messages, which is an arduous process. Instead, we use deep learning methods that are efficient and does not require any rules. Deep learning models require a set of training dataset samples to learn the rules from these SMS messages and build a text classifier that efficiently classifies spam from these messages. This paper presents a systematic review of employing deep learning methods namely, convolutional neural network and recurrent neural network on huge corpus of SMS texts to build a spam classifier that classifies messages as ham or spam.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123974624","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032468
R. G, Thasleena V. A, Liloja, Mohammed Shahzad
Monitoring the quality of water with conservative methods is testing the water samples which we collected manually in the laboratories or testing centers is a time consuming process. It will result in the wastage of cost, man power and time. Inorder to make the process economical and effective, we introduced a water quality monitoring system with the help of various sensors which checks the water quality in real time. We used pH, conductivity, temperature and turbidity sensors to measure the pH value, conductivity, temperature and turbidity of water. The presence of impurities in the water could be detected by the values obtained in the sensors. The Arduino transferred the information collected from the various sensors to the microcontroller and then passed to the android application with a Wi-Fi module. As it is a user friendly application, the results can be easily viewed and understandable by the user. The water quality monitoring system keep on testing the impurity content of water resources to provide for a well surrounding with pollution free water.
{"title":"IOT Based Water Quality Monitoring with Android Application","authors":"R. G, Thasleena V. A, Liloja, Mohammed Shahzad","doi":"10.1109/I-SMAC47947.2019.9032468","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032468","url":null,"abstract":"Monitoring the quality of water with conservative methods is testing the water samples which we collected manually in the laboratories or testing centers is a time consuming process. It will result in the wastage of cost, man power and time. Inorder to make the process economical and effective, we introduced a water quality monitoring system with the help of various sensors which checks the water quality in real time. We used pH, conductivity, temperature and turbidity sensors to measure the pH value, conductivity, temperature and turbidity of water. The presence of impurities in the water could be detected by the values obtained in the sensors. The Arduino transferred the information collected from the various sensors to the microcontroller and then passed to the android application with a Wi-Fi module. As it is a user friendly application, the results can be easily viewed and understandable by the user. The water quality monitoring system keep on testing the impurity content of water resources to provide for a well surrounding with pollution free water.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125310731","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032666
Sachin Kumar, S. V., Vijayalaxmi
Indirect Immunfluorsece method (IFA) is one of the important laboratory procedures for the diagnosis of the autoimmune disease, but it suffers from low throughput and subjectivity due to manual interpretation. The Human Epithelial type-2 (HEp-2) pattern, such as homogeneous, speckled, centromere, Nucleolar pattern images, gives the diagnosis of different autoimmune diseases. For the current study, different patterns are obtained from the publicly available datasets A.I.D.A ((Auto- Immunity Diagnosis by Computer) project of 1000 images. The images pre-processed and features such as statistical and textural features extracted and explored to find the appropriate one for the detection and the classification of ANA HEp2 cells pattern. The paper uses the Analysis of Variance (ANOVA) for the identification of appropriate features and Artifical Neural network (ANN) for classification. The result obtained indicates that textural features are the better features in comparison with other extracted features, with the results obtained average accuracy around 92% using ANN as the classifier. The outcome thus produced is useful for the further design of cost-effective image analysis in the autoimmune diagnosis
{"title":"Automatic classification of ANA HEp-2 Immunofluorescence images based on the texture features using artificial neural network","authors":"Sachin Kumar, S. V., Vijayalaxmi","doi":"10.1109/I-SMAC47947.2019.9032666","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032666","url":null,"abstract":"Indirect Immunfluorsece method (IFA) is one of the important laboratory procedures for the diagnosis of the autoimmune disease, but it suffers from low throughput and subjectivity due to manual interpretation. The Human Epithelial type-2 (HEp-2) pattern, such as homogeneous, speckled, centromere, Nucleolar pattern images, gives the diagnosis of different autoimmune diseases. For the current study, different patterns are obtained from the publicly available datasets A.I.D.A ((Auto- Immunity Diagnosis by Computer) project of 1000 images. The images pre-processed and features such as statistical and textural features extracted and explored to find the appropriate one for the detection and the classification of ANA HEp2 cells pattern. The paper uses the Analysis of Variance (ANOVA) for the identification of appropriate features and Artifical Neural network (ANN) for classification. The result obtained indicates that textural features are the better features in comparison with other extracted features, with the results obtained average accuracy around 92% using ANN as the classifier. The outcome thus produced is useful for the further design of cost-effective image analysis in the autoimmune diagnosis","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121109031","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032678
Veeramalai Sankaradass, P. Karthikeyan, T. Ravishankar, J. Murugan
Content Based Image Retrieval (CBIR) aims the system to compel the recovery of pictures from a very large store of collected pictures. The recovered picture approaches shading, color, texture and size. In this paper, a privacy saving substance based on picture recovery computes by utilizing Earth Moveable Distance (EMD) which is proposed because of the administrations of information proprietor to reappropriate picture from the database that is powerfully accessible in the cloud without extracting the entire substance from the database that should be given to the client's precise query. The proposed scheme supports the neighborhood highlight based CBIR with EMD as closeness metric. The EMD matches perceptual similarity for substance based picture recovery. It is additionally dependent on transportation issue from straight improvement, for which proficient calculations are accessible and to get comparability metric effectively. The sensitive (LSH) Local Sensitive Hash is improved for search efficiency. We look at the recovery execution of EMD and examine the protection and security of pictures dependent on client query.
基于内容的图像检索(CBIR)的目的是迫使系统从非常大的收集图像存储中恢复图像。恢复的图像接近阴影、颜色、纹理和大小。本文提出了一种基于图像恢复的隐私保存物质，利用地球可移动距离(Earth mobile Distance, EMD)进行计算，因为信息所有者的管理需要从云中可强大访问的数据库中重新获取图像，而无需从数据库中提取应提供给客户端精确查询的整个物质。该方案支持基于邻域突出的CBIR，并以EMD作为接近度度量。EMD匹配基于物质的图像恢复的感知相似性。此外，它还依赖于直接改进的运输问题，对此可以进行熟练的计算，并有效地获得可比性度量。提高了敏感(LSH)局部敏感散列的搜索效率。我们将查看EMD的恢复执行，并检查依赖于客户机查询的图片的保护和安全性。
{"title":"An Enhanced Content Based Image Retrieval in Cloud Computing with Privacy Towards EMD","authors":"Veeramalai Sankaradass, P. Karthikeyan, T. Ravishankar, J. Murugan","doi":"10.1109/I-SMAC47947.2019.9032678","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032678","url":null,"abstract":"Content Based Image Retrieval (CBIR) aims the system to compel the recovery of pictures from a very large store of collected pictures. The recovered picture approaches shading, color, texture and size. In this paper, a privacy saving substance based on picture recovery computes by utilizing Earth Moveable Distance (EMD) which is proposed because of the administrations of information proprietor to reappropriate picture from the database that is powerfully accessible in the cloud without extracting the entire substance from the database that should be given to the client's precise query. The proposed scheme supports the neighborhood highlight based CBIR with EMD as closeness metric. The EMD matches perceptual similarity for substance based picture recovery. It is additionally dependent on transportation issue from straight improvement, for which proficient calculations are accessible and to get comparability metric effectively. The sensitive (LSH) Local Sensitive Hash is improved for search efficiency. We look at the recovery execution of EMD and examine the protection and security of pictures dependent on client query.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125101736","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032537
M. Selvaperumal, D. Kirubakaran
Three-phase asymmetric 9 level inverter is presented with another configuration proposed uneven staggered inverter has topsy-turvy voltage source 1:2:4. To expand the come to of level by the advance get nearer to of intensity electronic parts it is recommended to use by including the number of switches. The planned circuit exchanging gadget is reduced, three-stage inverter circuit control technique and exchanging design is Mat lab extremely hard for this reason switches are supplanted by a diode. The stockpile recurrence adjustment procedure is anything but difficult to control the yield capability of an inverter. The recurrence regulation strategy is anything but difficult to produce the reasonable exchanging gate signal additionally Configuration can be made as got by the equipment and recreation results guarantees the similarity of this recurrence balance technique.
{"title":"Novel Harmonic Diminution of 3phase Asymmetric Cascaded Multilevel Inverter","authors":"M. Selvaperumal, D. Kirubakaran","doi":"10.1109/I-SMAC47947.2019.9032537","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032537","url":null,"abstract":"Three-phase asymmetric 9 level inverter is presented with another configuration proposed uneven staggered inverter has topsy-turvy voltage source 1:2:4. To expand the come to of level by the advance get nearer to of intensity electronic parts it is recommended to use by including the number of switches. The planned circuit exchanging gadget is reduced, three-stage inverter circuit control technique and exchanging design is Mat lab extremely hard for this reason switches are supplanted by a diode. The stockpile recurrence adjustment procedure is anything but difficult to control the yield capability of an inverter. The recurrence regulation strategy is anything but difficult to produce the reasonable exchanging gate signal additionally Configuration can be made as got by the equipment and recreation results guarantees the similarity of this recurrence balance technique.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124064232","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：