An Artifical Management Platform Based on Deep Learning Using Cloud Computing for Smart Cities

Yıl 2017, Special Issue (2017), 24 - 28, 24.09.2017

Yunus Santur Ebru Karaköse Mehmet Karaköse Erhan Akın

https://doi.org/10.18100/ijamec.2017SpecialIssue30466

Cited By: 4

Öz

Nowadays, deep learning is commonly used in many areas such as natural language processing, data mining, image processing and interpretation. The use of technology in city management for the purposes of effective resource management, improving the quality of service and reducing costs have led to smart city concept. The data produced by automation systems as well as internet-connected objects such as sensor, camera and mobile device are also used for smart city management. It is difficult to analyze such a big sized data by processing with conventional methods and to use them in decision-making mechanisms. In this study, deep learning based data mining was performed on big data obtained from different types of sources for smart city management and an approach to ensure that the results can be analyzed was proposed.

Anahtar Kelimeler

Big Data, Cloud Computing, Deep Learning, Deep Mining, IoT, Smart City

Kaynakça

• H. Chourabi, T. Nam, S. Walker, J.R. Gil-Garcia, S. Mellouli, K. Nahon, H.J. Scholl, “Understanding smart cities: An integrative framework”, In System Science (HICSS), 2012 45th Hawaii International Conference on (pp. 2289-2297), 2012.
• T. Nam, T. A. Pardo, “Conceptualizing smart city with dimensions of technology, people, and institutions”, In Proceedings of the 12th Annual International Digital Government Research Conf.: Digital Government Innovation in Challenging Times (pp. 282-291), 2012.
• K. Su, J. Li, H. Fu, “Smart city and the applications”, In Electronics, Communications and Control (ICECC), 2011 International Conference on (pp. 1028-1031), 2011.
• C.T. Barba, M. A. Mateos, P. R. Soto, A. M. Mezher, M. A. Igartua, “Smart city for VANETs using warning messages, traffic statistics and intelligent traffic lights”, In Intelligent Vehicles Symposium (IV), (pp. 902-907), 2012.
• Y. LeCun, Y. Bengio, G. Hinton, “Deep learning”, Nature, 521(7553), 436-444, 2015.
• Y. Santur, M. Karaköse, E. Akın, “Learning Based Experimental Approach For Condition Monitoring Using Laser Cameras In Railway Tracks”, International Journal of Applied Mathematics, Electronics and Computers (IJAMEC), 4, pp.1-5, 2016.
• L. Wang, D. Sng, “Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey”, arXiv preprint arXiv:1512.03131, 2015.
• P.Gupta,http://www.nvidia.com.tw/content/PDF/GTC/2015/smartcity/gpu-accelerated-platform-pradeep-gupta.pdf, 2015.
• Y. Santur, M. Karaköse, E. Akin, “Improving of personal educational content using big data approach for mooc in higher education”, In Information Technology Based Higher Education and Training (ITHET), 2016 15th International Conference on (pp. 1-4), 2016.
• N. Dlodlo, O. Gcaba, A. Smith,”Internet of things technologies in smart cities”, In IST-Africa Week Conference, (pp. 1-7), 2016.
• F. Paganelli, S. Turchi, D. Giuli, “A web of things framework for restful applications and its experimentation in a smart city”, 2014.
• R. Kitchin, “The real-time city? Big data and smart urbanism”, GeoJournal, 79(1), 1-14, 2014.
• F. Paganelli, S. Turchi, D. Giuli, 1A web of things framework for restful applications and its experimentation in a smart city”, 2014.
• C. Costa, M.Y. Santos, “BASIS: A big data architecture for smart cities”, In SAI Computing Conference (SAI), 2016 (pp. 1247-1256), 2016.
• N. Dlodlo, O. Gcaba, A. Smith, “Internet of things technologies in smart cities”, In IST-Africa Week Conference, pp. 1-7, 2016.
• J. Shah, B. Mishra, “IoT enabled environmental monitoring system for smart cities”, In Internet of Things and Applications (IOTA), International Conference on (pp. 383-388), 2016.
• P. Sakhardande, S. Hanagal, S. Kulkarni, “Design of disaster management system using IoT based interconnected network with smart city monitoring”, In Internet of Things and Applications (IOTA), International Conference on (pp. 185-190), 2016.
• V. Horban, “A multifaceted approach to smart energy city concept through using big data analytics”, In Data Stream Mining & Processing (DSMP), IEEE 1.Int. Conf. on (pp. 392-396), 2016.
• R.A Alshawish, S. A., Alfagih, M. S. Musbah, “Big data applications in smart cities”, In Engineering & MIS (ICEMIS), International Conference on (pp. 1-7), 2016.
• J. Dittrich, J.A. Quiané-Ruiz, “Efficient big data processing in Hadoop MapReduce”, Proceedings of the VLDB Endowment, 5(12), 2014-2015.
• Online (2016), https://cloud.google.com/solutions/iot/kit/
• Online (2016,), http://www.glennklockwood.com/data-intensive/hadoop/overview.html
• Online (2016), www.google.com
• T. White, “Hadoop: The definitive guide”, O'Reilly Media, Inc." ,2012.
• Y. Santur, S. G. Santur, M. Karaköse, “Knowledge Mining Approaach For Healthy Monitoring from Pregnancy Data with Big Volumes”, International Journal of Intelligent Systems and Applications in Engineering (IJISAE), 4, 141-145, 2016.
• Y. Santur, M. Karakose, E. Akin, “Random Forest Based Diagnosis Approach for Rail Fault Inspection in Railways”, International Conference on Electrical and Electronics Engineering (Eleco 2015), 9.th, pp.714-719, 2015.
• Y. Santur, M. Karaköse, İ. Aydın, E. Akın, “IMU based adaptive blur removal approach using image processing for railway inspection. In Systems”, Signals and Image Processing (IWSSIP), 2016 International Conference on (pp. 1-4), 2016.
• R. Girshick, J. Donahue, T. Darrell, J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation”, In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587), 2014.
• Y. Santur, M. Karaköse, E. Akın, “Chouqet fuzzy integral based condition monitoring and analysis approach using simulation framework for rail faults”,14th International Conference on Industrial Informatics (INDIN), 2016 International Conference on (pp. 345-350), 2016.
• Y. Santur, E. Karaköse, M. Karaköse, E. Akın, “Deep Learning Based Artificial Manager for Smart City”, 5th International Conference on Advanced Technology & Sciences, pp.197-201, 2017.