References
- Ali Hassan S, Sandeep P, Zongwei L, et al. Towards an optimal resource management for IoT based green and sustainable smart cities. J Clean Prod. 2019;220:1167–1179. doi: https://doi.org/10.1016/j.jclepro.2019.01.188
- Sodhro AH, Pirbhulal S, de Albuquerque VHC. Artificial intelligence-driven mechanism for edge computing-based industrial applications. IEEE Trans Indus Inform. 2019;15(7):4235–4243. DOI:https://doi.org/10.1109/TII.2019.2902878.
- Sodhro AH, Obaidat MS, Abbasi QH, et al. Quality of service optimization in IoT driven intelligent transportation system. IEEE Wireless Communication Magazine. Dec.2019;26(6):10–17. doi: https://doi.org/10.1109/MWC.001.1900085
- Kotsiantis SB. Supervised machine learning: a review of classification techniques. In Proceedings of the 2007 Conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies (Amsterdam, The Netherlands, The Netherlands, 2007, IOS Press, p. 3–24.
- Jing L, Ng MK, Huang JZ. An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data. IEEE Trans Knowl Data Eng. 2007;19(8):1026–1041. doi: https://doi.org/10.1109/TKDE.2007.1048
- Camastra F, Verri A. A novel kernel method for clustering. IEEE Trans Pattern Anal Mach Intell. 2005;27(5):801–805. . pmid: 15875800. doi: https://doi.org/10.1109/TPAMI.2005.88
- MacQueen J. Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Statistics. Berkeley, Calif: University of California Press; 1967, vol. 1 p. 281–297.
- Wu X, Kumar V, Quinlan JR, et al. Top 10 algorithms in data mining. Knowl Inf Syst. 2008;14(1):1–37. doi: https://doi.org/10.1007/s10115-007-0114-2
- Ozdemir G, Karaboga N. A review on the cosine modulated filter bank studies using meta-heuristic optimization algorithms. Artif Intell Rev. 2019;52:1629–1653. DOI:https://doi.org/10.1007/s10462-017-9595-x.
- Aljarah I, Mafarja M, Heidari AA, et al. Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach. Knowl Inf Syst. 2020;62(2):507–539. doi: https://doi.org/10.1007/s10115-019-01358-x
- Özbakir L, Turna F. Clustering performance comparison of new generation meta-heuristic algorithms. Knowl Based Syst. 2017;130:1–16. doi: https://doi.org/10.1016/j.knosys.2017.05.023
- Hamou RM, Amine A, Boudia MA, et al. An optimal configuration of sensitive parameters of PSO applied to textual clustering. In: Exploring critical approaches of evolutionary computation (pp. 196–214). IGI Global. DOI:https://doi.org/10.4018/978-1-5225-5832-3.ch010.
- Fatahi M, Moradi S. An FPA and GA-based hybrid evolutionary algorithm for analyzing clusters. Knowl Inf Syst. 2019;62:1701–1722. DOI:https://doi.org/10.1007/s10115-019-01413-7.
- Cheng M-Y, Prayogo D. Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct. 2014;139:98–112. doi: https://doi.org/10.1016/j.compstruc.2014.03.007
- Abdullahi M, Ngadi MA, Dishing SI, et al. A survey of symbiotic organisms search algorithms and applications. Neural Comput Appl. 20192/2020):1–20. DOI:https://doi.org/10.1007/s00521-019-04170-4.
- Mohan GSSSSVK, Srinivasa Rao Y. An efficient design of fractional order differentiator using hybrid Shuffled frog leaping algorithm for handling noisy electrocardiograms. Int J Comput Appl. 2019: 1–7. DOI:https://doi.org/10.1080/1206212x.2019.1573948.
- Zhu J. Research on data mining of electric power system based on Hadoop cloud computing platform. Int J Comput Appl. 2019;41(4):289–295.
- Sodhro AH, Pirbhulal S, Sodhro GH, et al. A joint transmission power control and duty-cycle approach for smart healthcare system. IEEE Sens J. 2018;19(19):8479–8486. doi: https://doi.org/10.1109/JSEN.2018.2881611
- Sodhro AH, Malokani AS, Sodhro GH, et al. An adaptive QoS computation for medical data processing in intelligent healthcare applications. Neural Comput Appl. 2020;32(3):723–734. doi: https://doi.org/10.1007/s00521-018-3931-1
- Muzammal M, Talat R, Sodhro AH, et al. A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks. Inf Fusion. 2020;53:155–164. doi: https://doi.org/10.1016/j.inffus.2019.06.021
- Adinugroho S, Wihandika RC, Adikara PP. Newsgroup topic extraction using term-cluster weighting and Pillar K-means clustering. Int J Comput Appl. 2020DOI:https://doi.org/10.1080/1206212X.2020.1757246.
- Gad I, Hosahalli D. A comparative study of prediction and classification models on NCDC weather data. Int J Comput Appl. 2020: 1–12. DOI:https://doi.org/10.1080/1206212X.2020.1766769.
- Gupta S, Chandra S, MaheswarI N, et al. A model for screening eye diseases using optical coherence tomography images. Int J Comput Appl. 2020: 1–5. DOI:https://doi.org/10.1080/1206212X.2020.1759857.
- Huang H. Research on feature classification method of network text data based on association rules. Int J Comput Appl. 2020;42(2):157–163.
- Wang L, Zheng K, Tao X, et al. Affinity propagation clustering algorithm based on large-scale data-set. Int J Comput Appl. 2018;40(3):1–6. DOI:https://doi.org/10.1080/1206212X.2018.1425184.
- Sodhro AH, Luo Z, Sodhro GH, et al. Artificial intelligence based QoS optimization for multimedia communication in IoV systems. Future Gener Comput Syst. 2019;95:667–680. doi: https://doi.org/10.1016/j.future.2018.12.008
- Wu H, Zhou Y, Luo Q, et al. Training feedforward neural networks using symbiotic organisms search algorithm. Comput Intell Neurosci. 2016;2016:1–14. DOI:https://doi.org/10.1155/2016/9063065.
- Govender P, Ezugwu AE. A symbiotic organisms search algorithm for optimal allocation of blood products. IEEE Access. 2018;7:2567–2588. doi: https://doi.org/10.1109/ACCESS.2018.2886408
- Govender P, Ezugwu AE. A symbiotic organisms search algorithm for blood assignment problem. In: Blesa MJ, Blum C, Gambini Santos H, et al., editors. International workshop on hybrid metaheuristics. Cham: Springer; 2019. p. 200–208.
- Chauvie S, De Maggi A, Baralis I, et al. Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial. Eur Radiol. 2020: 1–7. DOI:https://doi.org/10.1007/s00330-020-06783-z.
- Cheng M-Y, Prayogo D, Wu Y-W. Prediction of permanent deformation in asphalt pavements using a novel symbiotic organisms search–least squares support vector regression. Neural Computing and Applications. 2019;31(10):6261–6273. doi: https://doi.org/10.1007/s00521-018-3426-0
- Zhang B, Sun L, Yuan, H. et al. An improved regularized extreme learning machine based on symbiotic organisms search. In: 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2016. p. 1645-1648.
- Bozorg-Haddad O, Azarnivand A, Hosseini-Moghari S-M, et al. Optimal operation of reservoir systems with the symbiotic organisms search (SOS) algorithm. Journal of Hydroinformatics. 2017;19(4):507–521. doi: https://doi.org/10.2166/hydro.2017.085
- Karaboga D, Akay B. Artificial bee colony (ABC), harmony search and bees algorithms on numerical optimization. In: Innovative production machines and systems virtual conference. 2009.
- Reynolds G, Peng B. Cultural algorithms: computational modeling of how cultures learn to solve problems: an engineering example. Cybern Syst. December 2005;36(8):753–771. doi: https://doi.org/10.1080/01969720500306147
- Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Softw. 2014;69:46–61. doi: https://doi.org/10.1016/j.advengsoft.2013.12.007
- Chen K, Zhou F, Liu A. Chaotic dynamic weight particle swarm optimization for numerical function optimization. Knowl Based Syst. 2018;139:23–40. doi: https://doi.org/10.1016/j.knosys.2017.10.011
- Kanan HR, Nazeri B. A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm. Expert Syst Appl. 2014;41(14):6123–6130. doi: https://doi.org/10.1016/j.eswa.2014.04.022
- Opara KR, Arabas J. Differential Evolution: A survey of theoretical analyses. Swarm Evol Comput. 2019;44:546–558. doi: https://doi.org/10.1016/j.swevo.2018.06.010
- Langdon WB, Poli R. Evolving problems to learn about particle swarm optimizers and other search algorithms. IEEE Trans Evol Comput. 2007;11(5):561–578. doi: https://doi.org/10.1109/TEVC.2006.886448
- Arora S, Singh S. Butterfly optimization algorithm: a novel approach for global optimization. Soft comput. 2019;23(3):715–734. doi: https://doi.org/10.1007/s00500-018-3102-4
- Helou GE, Abou khalil C. Data mining knowledge extraction techniques. Models of the digital economy, project supported, University Paris II, February 16, 2004.
- Lichman M. UCI machine learning repository; University of California, School of Information and Computer Science: Irvine, CA, USA, 2013 [cited 2016 March 13]; Available from online: http://archive.ics.uci.edu/ml.
- Krzywinski M, Altman N. Points of significance: visualizing samples with box plots. Nat Methods. 2014;11:119–120. doi: https://doi.org/10.1038/nmeth.2813
- https://docs.microsoft.com/fr-fr/sql/analysis-services/data-mining/classification-matrix-analysis-services-data-mining?view=sql-analysis-services-2017
- Aljarah I, Mafarja M, Heidari AA, et al. Clustering analysis using a novel locality-informed gray wolf-inspired clustering approach. Knowl Inf Syst. 2020;62(2):507–539. doi: https://doi.org/10.1007/s10115-019-01358-x
- Kummar R, Indrawn A. Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatr. 2011;48:277–289. doi: https://doi.org/10.1007/s13312-011-0055-4