A novel fuzzy similarity measure and prevalence estimation approach for similarity profiled temporal association pattern mining
Tài liệu tham khảo
https://en.wikipedia.org/wiki/Soft_computing.
http://www.soft-computing.de/def.html.
Aljawarneh, 2011, Cloud security engineering: Avoiding security threats the right way, Int. J. Cloud Appl. Comput., 1, 64
Aljawarneh, 2016, Investigations of automatic methods for detecting the polymorphic worms signatures, Future Gener. Comput. Syst., 60, 67, 10.1016/j.future.2016.01.020
Aljawarneh, 2017, A resource-efficient encryption algorithm for multimedia big data, Multimedia Tools Appl., 1
Zadeh, 1965, Fuzzy sets, Inf. Control, 8, 338, 10.1016/S0019-9958(65)90241-X
Radhakrishna, 2016, Soft Comput.
Kar, 2016, Bio inspired computing - A review of algorithms and scope of applications, Expert Syst. Appl., 59 C, 20, 10.1016/j.eswa.2016.04.018
Medathati, 2016, Bio-inspired computer vision: Towards a synergistic approach of artificial and biological vision, Comput. Vis. Image Underst., 150, 1, 10.1016/j.cviu.2016.04.009
Musilek, 2015, Review of nature-inspired methods for wake-up scheduling in wireless sensor networks, Swarm Evolut. Comput., 25, 100, 10.1016/j.swevo.2015.07.007
Lee, 2009, Bio-inspired multi-agent data harvesting in a proactive urban monitoring environment, Ad Hoc Netw., 7, 725, 10.1016/j.adhoc.2008.03.009
Das, 2016, Bio-inspired nano tools for neuroscience, Prog. Neurobiol., 142, 1, 10.1016/j.pneurobio.2016.04.008
Kheradpisheh, 2016, Bio-inspired unsupervised learning of visual features leads to robust invariant object recognition, Neurocomput., 205, 382, 10.1016/j.neucom.2016.04.029
Ashkan Zarnani, Masoud Rahgozar, Caro Lucas, Nature-Inspired approaches to mining trend patterns in spatial databases, in: Proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL’06, Springer-Verlag, Berlin, Heidelberg, 2006, pp. 1407-1414. http://dx.doi.org/10.1007/11875581_167.
Babu, 2013, Honey bee behavior inspired load balancing of tasks in cloud computing environments, Appl. Soft Comput., 13, 2292, 10.1016/j.asoc.2013.01.025
Chaturvedi, 2008
Shadi A. Aljawarneh, Vangipuram Radhakrishna, Puligadda Veereswara Kumar, Vinjamuri Janaki, G-SPAMINE: An approach to discover temporal association patterns and trends in internet of things, Future Generation Computer Systems, (ISSN 0167-739X) 2017. Available online 17 January 2017, http://dx.doi.org/10.1016/j.future.2017.01.013.
Sangaiah, 2015, An ANFIS approach for evaluation of team-level service climate in GSD projects using Taguchi-genetic learning algorithm, Appl. Soft Comput., 30, 628, 10.1016/j.asoc.2015.02.019
Gotz, 2014, A methodology for interactive mining and visual analysis of clinical event patterns using electronic health record data, J. Biomed. Inform., 48, 148, 10.1016/j.jbi.2014.01.007
Bandyopadhyay, 2011, A biologically inspired measure for coexpression analysis, IEEE/ACM Trans. Comput. Biol. Bioinform., vol. 8, 929, 10.1109/TCBB.2010.106
Xi, 2016, A biologically inspired model mimicking the memory and two distinct pathways of face perception, Neurocomputing, 205, 349, 10.1016/j.neucom.2016.04.032
Prashant Shrivastava, Anupam Shukla, Praneeth Vepakomma, Neera Bhansali, Kshitij Verma, A survey of nature-inspired algorithms for feature selection to identify parkinson’s disease, Comput. Methods Programs Biomed. (ISSN 0169-2607) (2016). Available online 12 September 2016, , http://dx.doi.org/10.1016/j.cmpb.2016.07.029.
Gatsoulis, 2015, Intrinsically motivated learning systems based on biologically-inspired novelty detection, Robot. Auton. Syst., 68, 12, 10.1016/j.robot.2015.02.006
Nomura, 2015, Image coding and pooling with a bio-inspired reaction-diffusion algorithm, Proc. Comput. Sci., 71, 125, 10.1016/j.procs.2015.12.175
Yusoff, 2012, Biologically inspired temporal sequence learning, Procedia Eng., 41, 319, 10.1016/j.proeng.2012.07.179
Banković, 2013, Bio-inspired enhancement of reputation systems for intelligent environments, Inform. Sci., 222, 99, 10.1016/j.ins.2011.07.032
Kerr, 2015, A biologically inspired spiking model of visual processing for image feature detection, Neurocomputing, 158, 268, 10.1016/j.neucom.2015.01.011
Rui, 2012, Nature-inspired clustering algorithms for web intelligence data, vol. 3, 147
Cruz-Aceves, 2016, On the performance of nature inspired algorithms for the automatic segmentation of coronary arteries using Gaussian matched filters, Appl. Soft Comput., 46, 665, 10.1016/j.asoc.2016.01.030
Bong, 2011, Multi-objective nature-inspired clustering and classification techniques for image segmentation, Appl. Soft Comput., 11, 3271, 10.1016/j.asoc.2011.01.014
Senthilnath, 2016, Chapter 9 - Multitemporal remote sensing image classification by nature- inspired techniques, 187, 10.1016/B978-0-12-804536-7.00009-0
Rolanía, 2013, Bacterially inspired evolving system with an application to time series prediction, Appl. Soft Comput., 13, 1136, 10.1016/j.asoc.2012.10.012
Lifeng Nai, Yinglong Xia, Ilie G. Tanase, Hyesoon Kim, Exploring big graph computing — An empirical study from architectural perspective, J. Parallel Distrib. Comput. (ISSN 0743-7315) (2016). Available online 16 August 2016, http://dx.doi.org/10.1016/j.jpdc.2016.07.006.
Sangaiah, 2015, An ANFIS approach for evaluation of team-level service climate in GSD projects using Taguchi-genetic learning algorithm, Appl. Soft Comput., 30, 628, 10.1016/j.asoc.2015.02.019
Sangaiah, 2014, An adaptive neuro-fuzzy approach to evaluation of team level service climate in GSD projects, Neural Comput. Appl., 25, 573, 10.1007/s00521-013-1521-9
Sangaiah, 2015, A Fuzzy DEMATEL approach based on intuitionistic fuzzy information for evaluating knowledge transfer effectiveness in GSD projects, Int. J. Innovative Comput. Appl., 6, 203, 10.1504/IJICA.2015.073006
V. Radhakrishna, P.V. Kumar, V. Janaki, An approach for mining similar temporal association patterns in single database scan, in: Proceedings of 1st International Conference on Information and Communication Technology for Intelligent Systems, Vol. 2, Published in Smart Innovation, Systems and Technologies, vol. 51, 2016, pp. 607–617,.
V. Radhakrishna, P.V. Kumar, V. Janaki, A novel approach to discover similar temporal association patterns in a single database scan, in: IEEE International Conference on Computational Intelligence and Computing Research, ICCIC, Madurai, 2015, pp. 1–8.
Yoo, 2009, Similarity-profiled temporal association mining, IEEE Trans. Knowl. Data Eng., 21, 147
Radhakrishna, 2015, A novel approach for mining similarity profiled temporal association patterns, Revista Tecnicade La Facultad de Ingenieria Universidad del Zulia, 38, 80
Radhakrishna, 2016, An efficient approach to find similar temporal association patterns performing only single database scan, Revista Tecnicade La Facultad de Ingenieria Universidad del Zulia, 39, 241
Vangipuram Radhakrishna, P.V. Kumar, V. Janaki, A novel approach for mining similarity profiled temporal association patterns using venn diagrams, in: Proceedings of the International Conference on Engineering and MIS, ICEMIS 15, 2015, http://dx.doi.org/10.1145/2832987.2833071.
Lin, 2014, A similarity measure for text classification and clustering, IEEE Trans. Knowl. Data Eng., 26, 1575, 10.1109/TKDE.2013.19
Yoo, 2012, Temporal data mining: Similarity profiled association pattern, Data Min. Found Intel Paradigms, 23, 29, 10.1007/978-3-642-23166-7_3
soung Yoo, 2008, Mining temporal association patterns under a similarity constraint, vol. 5069, 401