References

Adeyanju, I.A., O.O. Awodoye and E.O. Omidiora, 2016. Performance evaluation of an improved self-organizing feature map and modified counter propagation network in face recognition. Br. J. Math. Comput. Sci., 14: 1-12.
CrossRef  |  Direct Link  |  

Aggarwal, K.K., Y. Singh and J.K. Chhabra, 2002. An integrated measure of software maintainability. Proceedings of the 2002 Annual Symposium on Reliability and Maintainability, January 28-31, 2002, IEEE, Seattle, Washington, pp: 235-241.

Akanmu, T.A., S.O. Olabiyisi, E.O. Omidiora, C.A. Oyeleye and M.A. Mabayoje et al., 2010. Comparative study of complexities of breadth-first search and depth-first search algorithms using software complexity measures. Proceedings of the 2010 World Congress on Engineering (WCE’10), June 30-July 2, 2010, London, England, UK., ISBN:978-988-17012-9-9, pp: 1-6.

Akinwale, C., S. Olatunde, E. Olusayo and J. Babalola, 2012. Performance evaluation of simulated annealing and genetic algorithm in solving examination timetabling problem. Sci. Res. Essays, 7: 1727-1733.
CrossRef  |  Direct Link  |  

Akinwale, O.C., O.S. Olatunde, O.E. Olusayo and F. Temitayo, 2014. Hybrid metaheuristic simulated annealing and genetic algorithm for solving examination timetabling problem. Intl. J. Comput. Sci. Eng., 3: 7-22.

Anderson, D. and G. McNeill, 1992. Artificial neural networks technology. MSc Thesis, Data & Analysis Center for Software (DACS), New York, USA.

Anonymous, 2012. Code complexity-software complexity. Verifysoft Technology GmbH, Germany. https://www.verifysoft.com/en_code_complexity.html

Anonymous, 2015. Causes of software crisis. UK Essays, Nottingham, England, UK. https://www.ukessays.com/essays/information-technology/symptoms-and-primary-causes-of-software-crisis-information-technology-essay.php

Ayodele, O., F. Temitayo, S.O. Olabiyisi, E.O. Omidiora and J. Oladosu, 2016. Development of a modified local binary pattern-gabor wavelet transform aging invariant face recognition system. Proceedings of the International Conference on Computing Research and Innovations (OcRI’16), September 7-9, 2016, University of Ibadan, Ibadan, Nigeria, pp: 108-114.

Baharudin, B., L.H. Lee and K. Khan, 2010. A review of machine learning algorithms for text-documents classification. J. Adv. Inform. Technol., 1: 4-20.
CrossRef  |  Direct Link  |  

Belabbes, S. and G. Richard, 2008. On using SVM and Kolmogorov complexity for spam filtering. Proceedings of the 21st International Conference on Florida Artificial Intelligence Research Society (FLAIRS’08), May 15-17, 2008, Association for the Advancement of Artificial Intelligence, Menlo Park, California, USA., pp: 130-135.

Bennett, C.H., P. Gacs, M. Li, P.M. Vitanyi and W.H. Zurek, 1998. Information distance. IEEE. Trans. Inf. Theor., 44: 1407-1423.
CrossRef  |  Direct Link  |  

Biradar, S. and M. Raikar, 2017. Performance analysis of text classifiers based on news articles-a survey. Indian J. Sci. Res., 15: 156-161.
Direct Link  |  

Chan, C.H., A. Sun and E.P. Lim, 2001. Automated online news classification with personalization. Proceedings of the 4th International Conference on Asian Digital Library (ICADL’01), December 10-12, 2001, Bangalore, India, pp: 320-329.

Chandra, A., M. Suaib and D. Beg, 2015. Web spam classification using supervised artificial neural network algorithms. Adv. Comput. Intell. Intl. J., 2: 21-30.
Direct Link  |  

Chelf, B. and A. Chou, 2008. Controlling software complexity: The business case for static source code analysis. Technical report, Coverity, San Francisco, California, USA. https://www.bitpipe.com/detail/RES/1204657851_780.html

Cilibrasi, R.L. and P.M.B. Vitanyi, 2007. The Google similarity distance. IEEE Trans. Knowl. Data Eng., 19: 370-383.
CrossRef  |  

Dagan, I., Y. Karov and D. Roth, 1997. Mistake-driven learning in text categorization. Proceedings of the 2nd Conference on Empirical Methods in Natural Language Processing (EMNLP’97), August 1-2, 1997, ACL, Providence, Rhode Island, USA., pp: 1-9.

Damasevicius, R. and V. Stuikys, 2010. Metrics for evaluation of metaprogram complexity. Comput. Sci. Inf. Syst., 7: 769-787.
CrossRef  |  Direct Link  |  

Debbarma, M.K., S. Debbarma, N. Debbarma, K. Chakma and A. Jamatia, 2013. A review and analysis of software complexity metrics in structural testing. Intl. J. Comput. Commun. Eng., 2: 129-133.

Dinari, F., 2015. Halstead complexity metrics in software engineering. J. Renewable Nat. Resour. Bhutan, 3: 418-424.

Fagbola, T.M., S.O. Olabiyisi, F.I. Egbetola and A. Oloyede, 2017. Review of technical approaches to face recognition with varying pose and illumination in unconstrained scenes. FUOYE. J. Eng. Technol., 2: 1-8.
Direct Link  |  

Greene, D. and P. Cunningham, 2006. Practical solutions to the problem of diagonal dominance in kernel document clustering. Proceedings of the 23rd International Conference on Machine Learning, June 25-29, 2006, ACM, Pittsburgh, Pennsylvania, USA., ISBN:1-59593-383-2, pp: 377-384.

Haynes, K., R. Kulkarni, R. Stough and L. Schintler, 2010. Exploring region classifier based on Kolmogorov complexity. MSc Thesis, GMU School of Public, George Mason University, Fairfax, Virginia.

Herraiz, I. and A.E. Hassan, 2010. Beyond Lines of Code: Do We Need More Complexity Metrics?. In: Making Software: What Really Works and Why We Believe it, Oram, A. and G. Wilson (Eds.). O’Reilly Media, Sebastopol, California, USA., ISBN:978-0-596-80832-7, pp: 125-141.

Ho, T.K. and M. Basu, 2000. Measuring the complexity of classification problems. Proceedings of the 15th International Conference on Pattern Recognition Vol. 2, September 3-7, 2000, IEEE, Barcelona, Spain, pp: 43-47.

Huang, Y., 2009. Advances in artificial neural networks-methodological development and application. Algorithms, 2: 973-1007.
CrossRef  |  Direct Link  |  

Joho, H. and M. Sanderson, 2007. Document frequency and term specificity. Proceedings of the Conference on Large Scale Semantic Access to Content (Text, Image, Video and Sound), May 30-June 01, 2007, ACM, Pittsburgh, Pennsylvania, USA., pp: 350-359.

Kaur G. and K. Bajaj, 2016. News classification and its techniques: A review. IOSR. J. Comput. Eng., 18: 22-26.
Direct Link  |  

Kinnunen, M.J., 2006. Complexity measures for system architecture models. Ph.D Thesis, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

Kolmogorov, A.N., 1965. Three approaches to the quantitative definition ofinformation. Problems Inform. Transmission, 1: 1-7.
Direct Link  |  

Krenker, A., J. Bester and A. Kos, 2011. Introduction to the Artificial Neural Networks. In: Artificial Neural Networks-Methodological Advances and Biomedical Applications, Suzuki, K. (Ed.). InTech, Rijeka, Croatia, ISBN:978-953-307-243-2, pp: 3-18.

Lai, S., L. Xu, K. Liu and J. Zhao, 2015. Recurrent convolutional neural networks for text classification. Proceedings of the 29th International Conference on Artificial Intelligence Vol. 333, January 25-30, 2015, Association for the Advancement of Artificial Intelligence, Menlo Park, California, USA., pp: 2267-2273.

Li, L., 2006. Data complexity in machine learning and novel classification algorithms. Ph.D Thesis, California Institute of Technology, California, USA.

Linders, B., 2014. Using complexity measurements to improve software quality. InfoQ., London, England, UK. https://www.infoq.com/news/2014/10/complexity-software-quality

Littlestone, N., 1988. Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Mach. Learn., 2: 285-318.
Direct Link  |  

Mandal, A.K. and R. Sen, 2014. Supervised learning methods for bangla web document categorization. Intl. J. Artif. Intell. Appl., 5: 93-105.
CrossRef  |  Direct Link  |  

May, R., G. Dandy and H. Maier, 2011. Review of Input Variable Selection Methods for Artificial Neural Networks. In: Artificial Neural Networks-Methodological Advances and Biomedical Applications, Suzuki, K. (Ed.). InTech, Rijeka, Croatia, ISBN:978-953-307-243-2, pp: 19-45.

Meeuwen, F.V., 2013. Multi-label text classification of news articles for ASDMedia. MSc Thesis, Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands.

Ming, L. and R. Sleep, 2004. Melody classification using a similarity metric based on Kolmogorov complexity. Sound Music Comput., 1: 1-5.
Direct Link  |  

Mourino-Garcia, M., R. Perez-Rodriguez and L.E. Anido-Rifon, 2015. Bag-of-concepts document representation for textual news classification. Intl. J. Comput. Ling. Appl., 6: 173-188.
Direct Link  |  

Ng, H.T., W.B. Goh and K.L. Low, 1997. Feature selection, perceptron learning and a usability case study for text categorization. Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Philadelphia, Pennsylvania, United States, July 27-31, 1997, ACM, New York, USA., pp: 67-73.

Ogheneovo, E.E., 2014. On the relationship between software complexity and maintenance costs. J. Comput. Commun., 2: 1-16.
Direct Link  |  

Olabiyisi S.O. and O.A. Adewole, 2008. On the software complexity measures of bubble sort algorithm. Res. J. Appl. Sci., 3: 5-9.

Olabiyisi S.O., O.O. Elijah and O.I. Esther, 2012. Performance evaluation of procedural cognitive complexity metric and other code based complexity metrics. Intl. J. Sci. Eng. Res., 3: 1-7.

Olabiyisi, S.O., A.B. Adetunji and T.R. Olusi, 2013. Using software requirement specification as complexity metric for multi-paradigm programming languages. Intl. J. Emerging Technol. Adv. Eng., 3: 562-569.

Olabiyisi, S.O., E.O. Omidiora and M.O. Balogun, 2014. A complexity metric for multi-paradigm programming languages. Intl. J. Emerging Technol. Adv. Eng., 4: 59-65.
Direct Link  |  

Olabiyisi, S.O., R.O. Ayeni, E.O. Omidiora and O.A. Bello, 2008. Universal scan machine for complexity measurement of computer programs. J. Comput. Applic., 15: 73-81.

Omidiora, E.O., A.O. Fakolujo, R.O. Ayeni, I.A. Adeyanju and E.O. Oyetunji et al., 2008. Optimised fisher discriminant analysis for recognition of faces having black features. J. Eng. Appl. Sci., 3: 524-531.

Oyewole, S.A. and O.O. Olugbara, 2017. Product image classification using Eigen Colour feature with ensemble machine learning. Egypt. Inf. J., 30: 1-18.
Direct Link  |  

Pao, H.K., Y.L. Chou and Y.J. Lee, 2012. Malicious URL detection based on Kolmogorov complexity estimation. Proceedings of the 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT’12) Vol. 1, December 4-7, 2012, IEEE, Macau, China, ISBN:978-1-4673-6057-9, pp: 380-387.

Patterson, S., J. Strasburg and J. Bunge, 2012. Knight upgrade triggered old trading system, big losses. The Wall Street Journal Newspaper, New York, USA. https://www.wsj.com/articles/SB10000872396390444318104577589694289838100

Ramil, J.F. and M.M. Lehman, 2000. Metrics of software evolution as effort predictors-a case study. Proceedings of the 2000 International Conference on Software Maintenance (ICSM), October 11-14, 2000, IEEE, San Jose, California, pp: 163-172.

Revolle, M., N. Le Bihan and F. Cayre, 2016. Algorithmic Information Theory for Automatic Classification. University of Grenoble, Grenoble, France,.

Richard, G. and A. Doncescu, 2008. Spam filtering using Kolmogorov complexity analysis. Intl. J. Web Grid Serv., 4: 136-148.
Direct Link  |  

Schutze, H., D.A. Hull and J.O. Pedersen, 1995. A comparison of classifiers and document representations for the routing problem. Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 09-13, 1995, ACM, Seattle, Washington, USA., ISBN:0-89791-714-6, pp: 229-237.

Skjennum, P.L., 2016. Multilingual news article classification. Master’s Thesis, Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway.

Sun, Y., D. Wendi, D.E. Kim and S.Y. Liong, 2016. Application of artificial neural networks in groundwater table forecasting-a case study in a Singapore swamp forest. Hydrology Earth Syst. Sci., 20: 1405-1412.
Direct Link  |  

Tan, G., 2016. A collection of well-known software failures. Ph.D Thesis, Pennsylvania State University, Pennsylvania, USA.

Temitayo, F., O. Stephen and A. Abimbola, 2012. Hybrid GA-SVM for efficient feature selection in E-mail classification. Comput. Eng. Intell. Syst., 3: 17-28.

Wang, Y. and X.J. Wang, 2005. A new approach to feature selection in text classification. Proceedings of the 2005 International Conference on Machine Learning and Cybernetics Vol. 6, August 18-21, 2005, IEEE, Guangzhou, China, pp: 3814-3819.

Wiener, E., J.O. Pedersen and A.S. Weigend, 1995. A neural network approach to topic spotting. Proceedings of the 4th Annual Symposium on Document Analysis and Information Retrieval, April 24-26, 1995, Las Vegas, Nevada, pp: 317-332.

Zhang, X., J. Zhao and Y. LeCun, 2015. Character-Level Convolutional Networks for Text Classification. In: Advances in Neural Information Processing Systems, Cortes, C., N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett (Eds.). Curran Associates, Inc., New York, USA., pp: 649-657.