Refine your search
Collections
Co-Authors
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Tuna, Gurkan
- Design and Implementation of a Smart Home for the Elderly and Disabled
Abstract Views :163 |
PDF Views:1
Authors
Affiliations
1 Department of Software Engineering, Firat University, 23119, Elazig, TR
2 Department of Computer Programming, Trakya University, 22020, Edirne, TR
3 School of Foreign Languages, Trakya University, 22030, Edirne, TR
1 Department of Software Engineering, Firat University, 23119, Elazig, TR
2 Department of Computer Programming, Trakya University, 22020, Edirne, TR
3 School of Foreign Languages, Trakya University, 22030, Edirne, TR
Source
International Journal of Computer Networks and Applications, Vol 2, No 6 (2015), Pagination: 242-246Abstract
Different from the past, in recent years, an increasing number of technology solutions have been started to be designed for the elderly and disabled due to the aging population and increasing awareness about common problems of the disabled. In smart homes, a group of smart, networked devices and sensors can help make the elderly and disabled more independent by letting family members, relatives and doctors keep tabs from afar. Because there is significant interest from care providers who prefer to keep the elderly and disabled in their houses rather than caring them in assisted-living centers. Accordingly, in this study, a sample application of smart home technology is presented. In the presented application, a group of sensors are used to build an autonomous smart home. Although the presented application is just a simple example of how smart homes can be used, it has the potential of affecting all areas improving day to day life of the elderly and disabled.Keywords
Smart Homes, Networked Sensors, Intelligent Monitoring, Accessibility, Prototype Smart Home.Full Text
- Wireless Sensor Network-Based Health Monitoring System for the Elderly and Disabled
Abstract Views :164 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Programming, Trakya University, 22020, Edirne, TR
2 Department of Software Engineering, Firat University, 23119, Elazig, TR
3 School of Foreign Languages, Trakya University, 22030, Edirne, TR
1 Department of Computer Programming, Trakya University, 22020, Edirne, TR
2 Department of Software Engineering, Firat University, 23119, Elazig, TR
3 School of Foreign Languages, Trakya University, 22030, Edirne, TR
Source
International Journal of Computer Networks and Applications, Vol 2, No 6 (2015), Pagination: 247-253Abstract
Even if the elderly and disabled need the assistance of their families, parents, and healthcare providers, they prefer to live in their homes instead of assisted-living centers. Therefore, their health and activities must be remotely monitored so that in case of an urgent unexpected situation, immediate help can be provided. In this respect, this paper proposes a wireless sensor network-based health monitoring system for the elderly and disabled, and focuses on its development steps. The proposed system is composed of low-cost off-the-shelf components and enables the monitoring of important health parameters of the elderly and disabled. Since it is a wireless and portable health monitoring solution, it can be a valuable remote monitoring tool for health care service providers by reducing the cost of their services. It can be combined with data mining solutions and/or machine learning techniques to offer novel features such as pattern extraction and behavior analysis.Keywords
Wireless Sensor Network, Health Monitoring, the Elderly, the Disabled, Intelligent Monitoring.Full Text
- Machine-to-Machine Communications for Smart Homes
Abstract Views :226 |
PDF Views:0
Authors
Resul Das
1,
Gurkan Tuna
2
Affiliations
1 Department of Software Engineering, Firat University, Elazig, TR
2 Department of Computer Programming, Trakya University, Edirne, TR
1 Department of Software Engineering, Firat University, Elazig, TR
2 Department of Computer Programming, Trakya University, Edirne, TR
Source
International Journal of Computer Networks and Applications, Vol 2, No 4 (2015), Pagination: 196-202Abstract
Machine to Machine (M2M) can be described as technologies which allow both wired and wireless systems to communicate with other devices of the same ability.M2M brings several benefits to industry and business, since it can be used in a wide range of applications for monitoring and control purposes. It is expected that M2M technologies when combined with smart phones will become integral elements in smart homes. Accordingly, in this study, a sample application of M2M technologies is presented. In the presented application, using temperature data provided by sensors, the smart air conditioner automatically adjusts itself. Although the presented application is just a simple example of how M2M can be used, it has the potential of affecting all areas improving our day to day life.Keywords
Machine-to-Machine Communications, M2M, Smart Homes, Sip.Full Text
- Big Data Analysis for M2M Networks: Research Challenges and Open Research Issues
Abstract Views :217 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Programming, Trakya University, Edirne, 22020, TR
2 Department of Software Engineering, Firat University, Elazig, 23119, TR
3 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, Tamilnadu, IN
4 Department of Computer Engineering, Adnan Menderes University, Aydin, 09010, TR
1 Department of Computer Programming, Trakya University, Edirne, 22020, TR
2 Department of Software Engineering, Firat University, Elazig, 23119, TR
3 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, Tamilnadu, IN
4 Department of Computer Engineering, Adnan Menderes University, Aydin, 09010, TR
Source
International Journal of Computer Networks and Applications, Vol 4, No 1 (2017), Pagination: 27-34Abstract
In recent years, solutions based on machine-to-machine (M2M) communications have started to support us in many areas of our life and work. However, the amount of data collected by M2M has increased tremendously and surpassed our expectations. This makes it necessary to investigate data mining methodologies and machine learning techniques in order to efficiently utilize large amounts of data gathered by M2M devices. In this paper, we first review existing data mining and machine-learning techniques specifically designed and proposed for M2M networks. Then, we discuss Big Data concept, investigate Big Data analysis techniques, and the importance of Big Data for M2M networks. Finally, we investigate research challenges and open research issues in M2M to provide an insight into future research opportunities.Keywords
Machine-to-Machine (M2M), Machine Learning, Data Mining, Big Data.Full Text
References
- J. N. Al-Karaki, K. -C. Chen, G. Morabito and J. de Olieveira, “From M2M communications to the Internet of Things: Opportunities and challenges,” Ad Hoc Networks, vol. 18, pp. 1-2, 2014.
- G. Wu, S. Talwar, K. Johnsson, N. Himayat, and K. D. Johnson, “M2M: From Mobile to Embedded Internet,” IEEE Communications Magazine, vol. 49, no. 4, pp. 36-43, April 2011.
- J. Holler, V. Tsiatsis, C. Mulligan, S. Avesand, S. Karnouskos and D. Boyle, “From Machine-to-Machine to the Internet of Things: Introduction to a New Age of Intelligence,” Academic Press: MA, USA, 2014.
- A. Biral, M. Centenaro, A. Zanella, L. Vangelista, and M. Zorzi, “The challenges of M2M massive access in wireless cellular networks,” Digital Communications and Networks, vol. 1, no. 1, pp. 1-19, February 2015.
- ETSI, “Machine to Machine Communications,” TS 102 689.
- D. Boswarthick, O. Elloumi, and O. Hersent (Eds), M2M Communications: A Systems Approach, Wiley: West Sussex, UK, 2012.
- Z. Fan, Q. Chen, G. Kalogridis, S. Tan, and D. Kaleshi, “The power of data: Data analytics for M2M and smart grid,” 2012 3rd IEEE PES international conference and exhibition on Innovative Smart Grid Technologies (ISGT Europe), pp. 1-8, 2012.
- G. Suciu, A. Vulpe, A. Martian, S. Halunga, and D. N. Vizireanu, “Big Data Processing for Renewable Energy Telemetry Using a Decentralized Cloud M2M System,” Wireless Personal Communications, 2015.
- G. Suciu, V. Suciu, A. Martian, R. Craciunescu, A. Vulpe, I. Marcu, S. Halunga, and O. Fratu, “Big Data, Internet of Things and Cloud Convergence – An Architecture for Secure E-Health Applications,” Journal of Medical Systems, vol. 39, no. 141, 2015.
- A. J. Jara, D. Genoud and Y. Bocchi, “Big Data for Cyber Physical Systems: An Analysis of Challenges, Solutions and Opportunities,” 2014 Eighth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 376-380, 2014.
- I. A. T. Hashem, I. Yaqoob, N. B. Anuar, S. Mokhtar, A. Gani, and S. U. Khan, “The rise of "big data" on cloud computing: Review and open research issues,” Information Systems, vol. 47, pp. 98-115, 2015.
- M.-S. Chen, J. Han, and P. S. Yu, “Data Mining: An Overview from a Database Perspective,” IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 866-883, December 1996.
- D. Talia, P. Trunfio, and F. Marozzo, “Introduction to Data Mining,” in Data Analysis in the Cloud, pp. 1-25, 2016.
- I. Kononenko and M. Kukar, “Machine Learning Basics,” in Machine Learning and Data Mining, pp. 59-105, 2007.
- T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, “An Efficient k-Means Clustering Algorithm: Analysis and Implementation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881-892, July 2002.
- G. Holmes, A. Donkin, and I. H. Witten, “WEKA: a machine learning workbench,” Proceedings of the 1994 Second Australian and New Zealand Conference on Intelligent Information Systems, pp. 357-371, 1994.
- Z. He, “An overview of data mining,” in Data Mining for Bioinformatics Applications, pp. 1-10, 2015.
- A. Lausch, Andreas Schmidt, and Lutz Tischendorf, “Data mining and linked open data – New perspectives for data analysis in environmental research,” Ecological Modelling, vol. 295, pp. 5-17, 2015.
- C. C. Aggarwal, Data Mining, Springer International Publishing: New Delhi, India, May 2015. ISBN: 978-3-319-14141-1.
- Sapphire: Large Scale Data Mining and Pattern Recognition. Available at: http:// computation.llnl.gov/casc/sapphire/overview/overview.html. Accessed: October 7, 2015.
- S. M. Weiss and C. A. Kulikowski, Computer Systems that Learn. Morgan Kaufmann: San Mateo, CA, 1991.
- R. Duda and P. Hart, Pattern Recognition and Scene Analysis. Wiley, New York, 1973.
- R. E. Abdel-Aal, “Comparison of Algorithmic and Machine Learning Approaches for the Automatic Fitting of Gaussian Peaks,” Neural Computing & Applications, vol. 11, no. 1, pp. 17-29, 2002.
- P. D. Wasserman, Neural Computing: Theory and Practice. Van Nostrand Reinhold, New York, 1989.
- G. Krempl, I. Žliobaite, D. Brzeziński, E. Hüllermeier, M. Last, V. Lemaire, T. Noack, A. Shaker, S. Sievi, M. Spiliopoulou, and J. Stefanowski, “Open challenges for data stream mining research,” SIGKDD Explor. Newsl., vol. 16, no.1, pp. 1-10, September 2014.
- H. Mannila. Methods and Problems in Data Mining. In Proceedings of the 6th International Conference on Database Theory (ICDT '97), F. N. Afrati and P.G. Kolaitis (Eds.). Springer-Verlag, London, UK, 1997. pp. 41-55.
- C. E. Brodley, U. Rebbapragada, K. Small, and B. C. Wallace, “Challenges and Opportunities in Applied Machine Learning,” AI Magazine, vol. 33, no. 1, pp. 11-24, March 2012.
- C. Parker “Unexpected challenges in large scale machine learning,” in Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine '12). ACM, New York, NY, USA, 2012. pp. 1-6.
- R. Genuer, J.-M. Poggi, C. Tuleau-Malot, N. Villa-Vialaneix, “Random forests and big data,” 47`eme Journ´ees de Statistique de la SFdS, Jun 2015, Lille, France. 2015.
- M. I. Jordan, “On statistics, computation and scalability,” Bernoulli, vol. 19, no. 4, pp. 1378-1390, 2013.
- L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.
- A. Verikas, A. Gelzinis, and M. Bacauskiene, “Mining data with random forests: a survey and results of new tests,” Pattern Recognition, vol. 44, no. 2, pp. 330-349, 2011.
- A. Ziegler and I. R. König, “Mining data with random forests: current options for real-world applications,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 4, no. 1, pp. 55-63, 2014.
- I. Arel, D. C. Rose, and T. P. Karnowski, “Research frontier: deep machine learning--a new frontier in artificial intelligence research,” IEEE Computational Intelligence Magazine, vol. 5, no. 4, pp. 13-18, November 2010.
- C. L. Philip Chen and Chung-Yang Zhang, “Data-intensive applications, challenges, techniques and technologies: A survey on Big Data,” Information Sciences, vol. 275, pp. 314-347, 10 August 2014.
- Y. Bengio, A. Courville, and P. Vincent, “Representation learning: a review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798-1828, 2013.
- D. Yu and L. Deng, “Deep learning and its applications to signal and information processing,” IEEE Signal Processing Magazine, vol. 28, no. 1, pp. 145-154, 2011.
- J. X. Dong, A. Krzyzak, and C. Y. Suen, “Fast SVM training algorithm with decomposition on very large data sets,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 4, pp. 603-618, 2005.
- P. Pääkkönen and D. Pakkala, “Reference Architecture and Classification of Technologies, Products and Services for Big Data Systems,” Big Data Research, vol. 2, pp. 166-186, 2015.
- X. Jin, B. W. Wah, X. Cheng, and Y. Wang, “Significance and Challenges of Big Data Research,” Big Data Research, vol. 2, pp. 166-186, 2015.
- Y. Ma, H. Wu, L. Wang, B. Huang, R. Ranjan, A. Zomaya, and W. Jie, “Remote sensing big data computing,” Future Generation Computing Systems, vol. 51, pp. 45-60, October 2015.
- J. Cervantes, F. G. Lamont, A. López-Chau, L. R. Mazahua, J. Sergio Ruíz, “Data selection based on decision tree for SVM classification on large data sets,” Applied Soft Computing, vol. 37, pp. 787-798, December 2015.
- L. Bottou, “Large-Scale Machine Learning with Stochastic Gradient Descent,” in Proceedings of COMPSTAT'2010, pp. 177-186, 30 September 2010.
- K. Sopyła and P. Drozda, “Stochastic Gradient Descent with Barzilai–Borwein update step for SVM,” Information Sciences, vol. 316, pp. 218-233, 2015.
- T. D. Kulkarni, P. Kohli, J. B. Tenenbaum, and V. Mansinghka, “Picture: A probabilistic programming language for scene perception,” 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4390-4399, 2015.
- K. Kumar and Yung-Hsiang Lu, “Cloud Computing for Mobile Users: Can Offloading Computation Save Energy?” Computer, vol. 43, no. 4, pp. 51-56.
- A. Botta, W.de Donato, V. Persico, and A. Pescapé, “Integration of Cloud computing and Internet of Things: A survey,” Future Generation Computer Systems, 2015.
- Y. Yang and X. Liu, “A robust semi-supervised learning approach via mixture of label information,” Pattern Recognition Letters, vol. 68, part 1, pp. 15-21, 15 December 2015.
- Y. Zhu, E. Zhong, Z. Lu, and Q. Yang, “Feature engineering for semantic place prediction,” Pervasive and Mobile Computing, vol. 9, no. 6, pp. 772-783, December 2013.
- R. Hu, B. M. Namee, and S. J. Delany, “Active learning for text classification with reusability,” Expert Systems with Applications, vol. 45, pp. 438-449, 1 March 2016.
- H. Rajaona, F. Septier, P. Armand, Y. Delignon, C. Olry, A. Albergel, and J. Moussafir, “An adaptive Bayesian inference algorithm to estimate the parameters of a hazardous atmospheric release,” Atmospheric Environments, vol. 122, pp. 748-762, December 2015.
- N. V. Chawla, Data mining for imbalanced datasets: An overview. In Data Mining and Knowledge Discovery Handbook, Springer US, 2010, pp. 875-886.
- J. Gama, R. Sebastião, and P. P. Rodrigues, “Issues in evaluation of stream learning algorithms,” in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, June 2009, pp. 329-338.
- A. Ahmad, A. Paul, and M. M. Rathore, “An efficient divide-and-conquer approach for big data analytics in machine-to-machine communication,” Neurocomputing, vol 174, part A, pp. 439-453, 22 January 2016.
- G. Suciu, A. Vulpe, O. Fratu, and V. Suciu, “M2M remote telemetry and cloud IoT big data processing in viticulture,” 2015 International Wireless Communications and Mobile Computing Conference (IWCMC), 2015, pp. 1117-1121.
- R. T. Kouzes, G. A. Anderson, S. T. Elbert, I. Gorton, and D. K. Gracio, “The changing paradigm of data-intensive computing,” Computer, vol. 42, no. 1, pp. 26-34, 2009.