Читать книгу Green Internet of Things and Machine Learning - Группа авторов - Страница 80
References
Оглавление1. Tzanis, G. et al., Modern Applications of Machine Learning. Proceedings of the 1st Annual SEERC Doctoral Student Conference–DSC, 2006.
2. Horvitz, E., Machine learning, reasoning, and intelligence in daily life: Directions and challenges. IEEE Proceedings, vol. 360, 2006.
3. Mitchell, T.M., The discipline of machine learning, Carnegie Mellon University, School of Computer Science, Machine Learning Department, Pittsburgh, July 2006.
4. Ball, G.R. and Srihari, S.N., Semi-supervised learning for handwriting recognition. Document Analysis and Recognition, ICDAR’09. 10th International Conference on IEEE, 2009.
5. Valenti, R. et al., Machine learning techniques for face analysis. Mach. Learn. Techniques Int. J. Comput. Appl. (0975 – 8887), 115, 9, Springer Berlin Heidelberg, 159–187, 2008.
6. Al-Hmouz, A., An adaptive framework to provide personalisation for mobile learners, Doctor of Philosophy thesis, School of Information Systems & Technology, University of Wollongong, Australia.
7. Al-Hmouz, A., Shen, J., Yan, J., A machine learning based framework for adaptive mobile learning. Advances in Web Based Learning–ICWL 2009, Springer Berlin Heidelberg, pp. 34–43, 2009.
8. Graepel, T., Machine Learning Applications in Computer Games. ICML 2008 Tutorial, Helsinki, Finland, 5 July 2008.
9. Gabrilovich, E., Josifovski, V., Pang, B., Introduction to Computational Advertising. Association for Computational Linguistics Columbus, Ohio, USA, June 2008.
10. Cunningham, S.J., Littin, J., Witten, I.H., Applications of machine learning in information retrieval. University of Waikato, Department of Computer Science, Hamilton, New Zealand, 1997.
11. Bratko, A. et al., Spam filtering using statistical data compression models. J. Mach. Learn. Res., 7, 2673–2698, 2006.
12. Kaur, H., Singh, G., Minhas, J., A Review of Machine Learning based Anomaly Detection Techniques., Int. J. Comput. App. Technol. Res., 2, 2, 2(2), 185–187, 2013.
13. Gao, J. and Jamidar, R., Machine Learning Applications for Data Center Optimization, Google, 2014. Retrieve: https://docs.google.com/a/google.com/viewer?rl=www.google.com/about/datacenters/efficiency/internal/assets/machine-learning-applicationsfor-datacenter-optimization-finalv2.pdf
14. Haider, P., Chiarandini, L., Brefeld, U., Discriminative clustering for market segmentation. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2012.
15. Kononenko, I., Machine learning for medical diagnosis: history, state of the art and perspective. Artif. Intell. Med., 23, 1, 23(1), 89–109, 2001.
16. Sadjadi, S.O. and Hansen, J.H.L., Unsupervised Speech Activity Detection Using Voicing Measures and Perceptual Spectral Flux. IEEE Signal Proc. Let., 20, 3, March 2013.
17. Hwang, K.E., Cho, D. Y., Park, S.W., Kim, S.D., Zhan, B. T., Applying machine learning techniques to analysis of gene expression data: cancer diagnosis, Methods of Microarray Data Analysis, Kluwer Academic Publishers, Springer US, pp. 167–182, 2002.
18. Pang, B., Lee, L., Vaithyanathan, S., Thumbs up?: sentiment classification using machine learning techniques. Proceedings of the ACL-02 conference on Empirical methods in natural language processing, vol. 10, Association for Computational Linguistics, 2002.
19. Horvitz, E.J., Apacible, J., Sarin, R., Liao, L., Prediction, Expectation, and Surprise: Methods, Designs, and Study of a Deployed Traffic Forecasting Service. Microsoft Research, 2012. Retrieve: https://www.microsoft.com/en-us/research/wp-content/uploads/2014/06/horvitz_traffic_uai2005.pdf
20. Clarke, B., Fokoue, E., Zhang, H.H., Principles and theory for data mining and machine learning, Springer Series in Statistics, Springer Verlag New York, 2009.
21. Mowry, M., A Survey of RFID in the medical industry with emphasis on applications to surgery and surgical devices. MAE188, Introduction to RFID, Dr. Rajit Gadh, UCLA, p. 22, Jun. 9, 2008. Retrieve: https://silo.tips/download/a-survey-of-rfid-in-the-medical-industry-contents#
22. Namboodiri, V. and Gao, L., Energy-aware tag anti-collision protocols for RFID systems. IEEE Trans. Mob. Comput., 9, 1, 44–59, 2010.
23. Xu, X., Gu, L., Wang, J., Xing, G., Cheung, S., Read more with less: An adaptive approach to energy-efficient RFID systems. IEEE J. Sel. Areas Commun., 29, 8, 1684–1697, 2011.
24. Li, T., Wu, S., Chen, S., Yang, M., Generalized energy-efficient algorithms for the RFID estimation problem. IEEE ACM Trans. Netw., 20, 6, 1978–1990, 2012.
25. Amin, Y., Printable green RFID antennas for embedded sensors. PhD dissertation, KTH School of Information and Communication Technology, Kista, Sweden, 2013.
26. Lee, C., Kim, D., Kim, J., An energy efficient active RFID protocol to avoid over heading problem. IEEE Sens. J., 14, 1, 15–24, 2014.
27. Shaikh, F., Zeadally, S., Exposito, E., Enabling Technologies for GreenInternet of Things. IEEE Syst. J., 11, 2, 983–994, 2017.
28. Minerva, R., Biru, A., Rotondi, D., Towards a definitionof the Internet of Things (IoT), IEEE Internet initiative, Telecom Italia S.P.A., May 2015.
29. Atzori, L., Iera, A., Morabito, G., The Internet of Things: A survey. Comput. Network, Elsevier, 54, 15, 2787–2805, Oct. 2010.
30. López, T.S. et al., Adding sense to the IOT-An architecture framework for smart object systems. Pers. Ubiquit. Comput., 16, 3, 291–308, Mar. 2012.
31. Gershenfeld, N., Krikorian, R., Cohen, D., The Internet of Things. Sci. Am., 291, 4, 76–81, 2004.
32. Murugesan, S., Harnessing green IT: Principles and practices. IEEE IT Prof., 10, 1, 24–33, Jan.-Feb. 2008.
33. Xu, L.D., He, W., Li, S., Internet of Things in industries: A survey. IEEE Trans. Ind. Inf., 10, 4, 2233–2243, Nov. 2014.
34. Perera, C., Liu, C.H., Jayawardena, S., The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey. IEEE Trans. Emerg. Topics Comput., 3, 4, 2015.
35. Zhu, C., Leung, V.C.M., Shu, L., Ngai, E.C.-H., Green Internet of Things for Smart World. IEEE Access, 3, 2151–2162, 2015.
36. Rose, K., Eldridge, S., Chapin, L., The Internet of Things (IoT): An Overview, Understanding the issues of more connected world, Karen Rose, Scott Eldridge, Lyman Chapin, Internet Society, 2015.
37. Gershenfeld, N., Krikorian, R., Cohen, D., The Internet of Things. Sci. Am., 291, 4, 76–81, 2004.
38. Rawashdeh, S., Eyadat, W., Magableh, A., Mardini, W., Yasin, M.B., Sustainable Smart World. 10th International Conference on Information and Communication Systems (ICICS), 2019.
39. Albreem, M.A.M., El-Saleh, A.A., Isa, M., Salah, W., Jusoh, M., Azizan, M.M., Ali, A., Green internet of things (IoT): An overview. IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), 2017.
40. Poongodi, T., Ramya, S.R., Suresh, P., Balusamy, B., Application of IoT in Green Computing, Advances in Greener Energy Technologies, Springer Singapore, 2020.
41. Lohan, V. and Singh, R.P., Research challenges for Internet of Things: A review. International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN), 2017.
42. Haldorai, A., Ramu, A., Murugan, S., Computing and Communication Systems in Urban Development, Urban Computing, Springer Nature Switzerland AG, 2019.
43. Mohana Sundaram, K., Hussain, A., Sanjeevikumar, P., Holm-Nielsen, J.B., Kaliappan, V.K., Kavya Santhoshi, B., Deep Learning for Fault Diagnostics in Bearings, Insulators, PV Panels, Power Lines, and Electric Vehicle Applications—The State-of-the-Art Approaches. IEEE Access, 9, 4124641260, 2021.
1 * Corresponding author: k.verma2006@gmail.com