Pub Date : 2018-07-31DOI: 10.14257/IJGDC.2018.11.7.02
Yong-kyu Lee, D. Park
The competition of Go between AlphaGo and Lee Sedol attracted global interest; AlphaGo was victorious. The core function of AlphaGo is a deep-learning system, in which the computer learns by itself. It is now said that the utility of deep-learning systems using artificial intelligence is verified. Recently, the government of South Korea passed the lnternet of Things (IoT) Act with a view to developing a business model to promote loT. In this paper, we identify IoT market sales through IoT market trend analysis and extract an IoT business model. Then, we apply the findings to Deep learning AI technology in order to design an internet business model for Deep learning AI. We look at Deep Learning as it is used in smart home technology, autonomous vehicles, and a healthcare wearable device. This paper will be fundamental for social development using the technologies of the 4th industry.
{"title":"Design of Internet of Things Business Model with Deep Learning Artificial Intelligence","authors":"Yong-kyu Lee, D. Park","doi":"10.14257/IJGDC.2018.11.7.02","DOIUrl":"https://doi.org/10.14257/IJGDC.2018.11.7.02","url":null,"abstract":"The competition of Go between AlphaGo and Lee Sedol attracted global interest; AlphaGo was victorious. The core function of AlphaGo is a deep-learning system, in which the computer learns by itself. It is now said that the utility of deep-learning systems using artificial intelligence is verified. Recently, the government of South Korea passed the lnternet of Things (IoT) Act with a view to developing a business model to promote loT. In this paper, we identify IoT market sales through IoT market trend analysis and extract an IoT business model. Then, we apply the findings to Deep learning AI technology in order to design an internet business model for Deep learning AI. We look at Deep Learning as it is used in smart home technology, autonomous vehicles, and a healthcare wearable device. This paper will be fundamental for social development using the technologies of the 4th industry.","PeriodicalId":46000,"journal":{"name":"International Journal of Grid and Distributed Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14257/IJGDC.2018.11.7.02","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44843520","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 : 2018-07-31DOI: 10.14257/ijgdc.2018.11.7.03
Yunsik Son, Seman Oh, Yangsun Lee
The IoT-Cloud virtual machine system is a cloud-based execution solution for IoT devices with offloading techniques that delegate tasks requiring high computing power from low-performance IoT devices to a high-performance cloud environment as a service. The IoT devices with the IoT-Cloud virtual machine system can perform complex tasks using the computing power of high-performance cloud. The offloading technique can reduce the execution performance depending on the workload of the IoT devices and the clouds. Therefore, it is necessary to decide offloading execution considering the workload of the IoT devices and the clouds. In this paper, CPU utilization trend, which is one of the workload indices, is predicted through deep learning in order to decide offloading execution considering the workload of the IoT devices and clouds. In this paper, we present four CPU usage models and introduce a technique for predicting server load based on hybrid deep neural network. The predicted CPU utilization trend is indicative of future CPU utilization information and is therefore an indicator for offloading execution decisions. Through experiments, we confirmed that the proposed method estimates the load of the model very similar, and it can apply the offloading adaptively according to the load of the server.
{"title":"Hybrid Deep Neural Network based Performance Estimation Method for Efficient Offloading on IoT-Cloud Environments","authors":"Yunsik Son, Seman Oh, Yangsun Lee","doi":"10.14257/ijgdc.2018.11.7.03","DOIUrl":"https://doi.org/10.14257/ijgdc.2018.11.7.03","url":null,"abstract":"The IoT-Cloud virtual machine system is a cloud-based execution solution for IoT devices with offloading techniques that delegate tasks requiring high computing power from low-performance IoT devices to a high-performance cloud environment as a service. The IoT devices with the IoT-Cloud virtual machine system can perform complex tasks using the computing power of high-performance cloud. The offloading technique can reduce the execution performance depending on the workload of the IoT devices and the clouds. Therefore, it is necessary to decide offloading execution considering the workload of the IoT devices and the clouds. In this paper, CPU utilization trend, which is one of the workload indices, is predicted through deep learning in order to decide offloading execution considering the workload of the IoT devices and clouds. In this paper, we present four CPU usage models and introduce a technique for predicting server load based on hybrid deep neural network. The predicted CPU utilization trend is indicative of future CPU utilization information and is therefore an indicator for offloading execution decisions. Through experiments, we confirmed that the proposed method estimates the load of the model very similar, and it can apply the offloading adaptively according to the load of the server.","PeriodicalId":46000,"journal":{"name":"International Journal of Grid and Distributed Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44623895","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 : 2018-07-31DOI: 10.14257/IJGDC.2018.11.7.01
Suraj Srivastava, Dinesh Grover
{"title":"Fuzzy-based Cluster Head Selection for Pegasis Protocol in WSN","authors":"Suraj Srivastava, Dinesh Grover","doi":"10.14257/IJGDC.2018.11.7.01","DOIUrl":"https://doi.org/10.14257/IJGDC.2018.11.7.01","url":null,"abstract":"","PeriodicalId":46000,"journal":{"name":"International Journal of Grid and Distributed Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46669704","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 : 2018-07-31DOI: 10.14257/ijgdc.2018.11.7.08
Youngbok Cho, Sunghee Woo
Automatic segmentation of the area of interest in medical image processing is a very important but difficult problem. Deep learning algorithms can help clinicians and radiologists determine diagnosis and treatment plans. We propose and evaluate a probabilistic approach for automated region of interest ROIs detection using convolutional neural networks (CNNs). The proposed algorithm is simple and can be divide into regions and features can be extracted for the divided regions. We also propose a preprocessing algorithm based on CNN and RNN to automatically classify ROIs that are finely adjusted through image standardization based on TW3. The result is 20%-40% more accurate than those obtained using the conventional method. In addition, input image sensitivity is approximately 40% greater and the specificity was equal to or greater than 96%.
{"title":"Automated ROI Detection in Left Hand X-ray Images using CNN and RNN","authors":"Youngbok Cho, Sunghee Woo","doi":"10.14257/ijgdc.2018.11.7.08","DOIUrl":"https://doi.org/10.14257/ijgdc.2018.11.7.08","url":null,"abstract":"Automatic segmentation of the area of interest in medical image processing is a very important but difficult problem. Deep learning algorithms can help clinicians and radiologists determine diagnosis and treatment plans. We propose and evaluate a probabilistic approach for automated region of interest ROIs detection using convolutional neural networks (CNNs). The proposed algorithm is simple and can be divide into regions and features can be extracted for the divided regions. We also propose a preprocessing algorithm based on CNN and RNN to automatically classify ROIs that are finely adjusted through image standardization based on TW3. The result is 20%-40% more accurate than those obtained using the conventional method. In addition, input image sensitivity is approximately 40% greater and the specificity was equal to or greater than 96%.","PeriodicalId":46000,"journal":{"name":"International Journal of Grid and Distributed Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14257/ijgdc.2018.11.7.08","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49154370","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 : 2018-07-31DOI: 10.14257/IJGDC.2018.11.7.07
Madani Youness, Erritali Mohammed, Ben Jamâa
{"title":"Semantic Indexing of a Corpus","authors":"Madani Youness, Erritali Mohammed, Ben Jamâa","doi":"10.14257/IJGDC.2018.11.7.07","DOIUrl":"https://doi.org/10.14257/IJGDC.2018.11.7.07","url":null,"abstract":"","PeriodicalId":46000,"journal":{"name":"International Journal of Grid and Distributed Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14257/IJGDC.2018.11.7.07","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48864043","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 : 2018-07-31DOI: 10.14257/IJGDC.2018.11.7.04
S. Madhavi
{"title":"Power Optimized Quantum Data Aggregation Scheduling","authors":"S. Madhavi","doi":"10.14257/IJGDC.2018.11.7.04","DOIUrl":"https://doi.org/10.14257/IJGDC.2018.11.7.04","url":null,"abstract":"","PeriodicalId":46000,"journal":{"name":"International Journal of Grid and Distributed Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42087912","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 : 2018-06-30DOI: 10.14257/IJGDC.2018.11.6.04
Jaehyun Kim, Yangsun Lee
{"title":"A Study on Abstract Syntax Tree for Development of a JavaScript Compiler","authors":"Jaehyun Kim, Yangsun Lee","doi":"10.14257/IJGDC.2018.11.6.04","DOIUrl":"https://doi.org/10.14257/IJGDC.2018.11.6.04","url":null,"abstract":"","PeriodicalId":46000,"journal":{"name":"International Journal of Grid and Distributed Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44408630","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 : 2018-06-30DOI: 10.14257/ijgdc.2018.11.6.01
V. Ragu, Seunghyun Yang, Kang-Suk Chae, Jangwoo Park, Changsun Shin, Su Yang, Yongyun Cho
{"title":"Analysis and Forecasting of Electric Power Energy Consumption in IoT Environments","authors":"V. Ragu, Seunghyun Yang, Kang-Suk Chae, Jangwoo Park, Changsun Shin, Su Yang, Yongyun Cho","doi":"10.14257/ijgdc.2018.11.6.01","DOIUrl":"https://doi.org/10.14257/ijgdc.2018.11.6.01","url":null,"abstract":"","PeriodicalId":46000,"journal":{"name":"International Journal of Grid and Distributed Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44309584","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 : 2018-06-30DOI: 10.14257/IJGDC.2018.11.6.03
Seong-Muk Choi, J. Bok, Hyung-Taek Lee, Gwangyong Gim
{"title":"A Study on the Crawler-based Security Model for Improving Modulation Monitoring of Websites","authors":"Seong-Muk Choi, J. Bok, Hyung-Taek Lee, Gwangyong Gim","doi":"10.14257/IJGDC.2018.11.6.03","DOIUrl":"https://doi.org/10.14257/IJGDC.2018.11.6.03","url":null,"abstract":"","PeriodicalId":46000,"journal":{"name":"International Journal of Grid and Distributed Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14257/IJGDC.2018.11.6.03","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44547501","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 : 2018-06-30DOI: 10.14257/ijgdc.2018.11.6.08
Md. Abdullah Al-Shafi, A. Bahar
Today, data centers power utilization has massive influences on environments. Data centers are energy-starved, crucial structures that direct big-scale internet-based facilities. The extreme energy utilization and green contamination of data centers have turned into a serious concern. Energy expenditure archetypes are decisive in planning and improving energy-resourceful functions to control extreme energy utilization in data center. Experts are looking for locating efficient explanations to build data centers diminish energy expenditure whereas retaining the preferred feature of service objectives. Hence, GreenCloud or Internet-based processing answers are desired that cannot merely lessen operating expenses but also prevent energy for the natural environment. This study organizes architectural foundations, resource distribution for data Centre and challenges for energy proficient organization of cloud computing atmospheres. Besides energyeconomy fashions for data centers in future are presented in this paper.
{"title":"Efficient Organization of Data Center for Cloud Computing: A Survey","authors":"Md. Abdullah Al-Shafi, A. Bahar","doi":"10.14257/ijgdc.2018.11.6.08","DOIUrl":"https://doi.org/10.14257/ijgdc.2018.11.6.08","url":null,"abstract":"Today, data centers power utilization has massive influences on environments. Data centers are energy-starved, crucial structures that direct big-scale internet-based facilities. The extreme energy utilization and green contamination of data centers have turned into a serious concern. Energy expenditure archetypes are decisive in planning and improving energy-resourceful functions to control extreme energy utilization in data center. Experts are looking for locating efficient explanations to build data centers diminish energy expenditure whereas retaining the preferred feature of service objectives. Hence, GreenCloud or Internet-based processing answers are desired that cannot merely lessen operating expenses but also prevent energy for the natural environment. This study organizes architectural foundations, resource distribution for data Centre and challenges for energy proficient organization of cloud computing atmospheres. Besides energyeconomy fashions for data centers in future are presented in this paper.","PeriodicalId":46000,"journal":{"name":"International Journal of Grid and Distributed Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14257/ijgdc.2018.11.6.08","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47337863","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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