Optimized artificial neural network for classification of biological data

  • Authors

    • Senthilselvan Natarajan school of computingSASTRA University
    • Rajarajan S school of computingSASTRA University
    • Subramaniyaswamy V school of computingSASTRA University
    2018-05-23
    https://doi.org/10.14419/ijet.v7i2.11065
  • Breast Cancer Classification, Artificial Neural Network, Whale Swarm Optimization, Classifier.
  • Abstract

    Biological data suffers from the problem of high dimensionality which makes the process of multi-class classification difficult and also these data have elements that are incomplete and redundant. Breast Cancer is currently one of the most pre-dominant causes of death in women around the globe. The current methods for classifying a tumour as malignant or benign involve physical procedures. This often leads to mental stress. Research has now sought to implement soft computing techniques in order to classify tumours based on the data available. In this paper, a novel classifier model is implemented using Artificial Neural Networks. Optimization is done in this neural network by using a meta-heuristic algorithm called the Whale Swarm Algorithm in order to make the classifier model accurate. Experimental results show that new technique outperforms other existing models.

  • References

    1. [1] Nauck, D., & Kruse, R. (1999). Obtaining interpretable fuzzy classification rules from medical data. Artificial intelligence in medicine, 16(2), 149-169.https://doi.org/10.1016/S0933-3657(98)00070-0.

      [2] Abonyi, J., &Szeifert, F. (2003). Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recognition Letters, 24(14), 2195-2207.https://doi.org/10.1016/S0167-8655(03)00047-3.

      [3] Şahan, S., Polat, K., Kodaz, H., &Güneş, S. (2007). A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis. Computers in Biology and Medicine, 37(3), 415-423.https://doi.org/10.1016/j.compbiomed.2006.05.003.

      [4] Akay, M. F. (2009). Support vector machines combined with feature selection for breast cancer diagnosis. Expert systems with applications, 36(2), 3240-3247.https://doi.org/10.1016/j.eswa.2008.01.009.

      [5] Paulin, F., &Santhakumaran, A. (2011). Classification of breast cancer by comparing back propagation training algorithms. International Journal on Computer Science and Engineering, 3(1), 327-332.

      [6] Chen, H. L., Yang, B., Liu, J., & Liu, D. Y. (2011). A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Systems with Applications, 38(7), 9014-9022.https://doi.org/10.1016/j.eswa.2011.01.120.

      [7] Dheeba, J., Singh, N. A., &Selvi, S. T. (2014). Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach. Journal of biomedical informatics, 49, 45-52.https://doi.org/10.1016/j.jbi.2014.01.010.

      [8] Senapati, M. R., Panda, G., & Dash, P. K. (2014). Hybrid approach using KPSO and RLS for RBFNN design for breast cancer detection. Neural Computing and Applications, 24(3-4), 745-753.https://doi.org/10.1007/s00521-012-1286-6.

      [9] Mert, A., Kılıç, N., Bilgili, E., & Akan, A. (2015). Breast cancer detection with reduced feature set. Computational and mathematical methods in medicine, 2015.https://doi.org/10.1155/2015/265138.

      [10] Nahato, K. B., Harichandran, K. N., &Arputharaj, K. (2015). Knowledge mining from clinical datasets using rough sets and backpropagation neural network. Computational and mathematical methods in medicine, 2015.https://doi.org/10.1155/2015/460189.

      [11] Abdel-Zaher, A. M., &Eldeib, A. M. (2016). Breast cancer classification using deep belief networks. Expert Systems with Applications, 46, 139-144.https://doi.org/10.1016/j.eswa.2015.10.015.

      [12] Rajaguru, H., &Prabhakar, S. K. (2017, October). Bayesian linear discriminant analysis for breast cancer classification. In Communication and Electronics Systems (ICCES), 2017 2nd International Conference on (pp. 266-269). IEEE.https://doi.org/10.1109/CESYS.2017.8321279.

      [13] Tan, Y. J., Sim, K. S., & Ting, F. F. (2017, November). Breast cancer detection using convolutional neural networks for mammogram imaging system.In Robotics, Automation and Sciences (ICORAS), 2017 International Conference on (pp. 1-5).IEEE.https://doi.org/10.1109/ICORAS.2017.8308076.

      [14] Arya, C., &Tiwari, R. (2016, January). Expert system for breast cancer diagnosis: a survey. In Computer Communication and Informatics (ICCCI), 2016 International Conference on (pp. 1-9). IEEE.https://doi.org/10.1109/ICCCI.2016.7479940.

      [15] UCI Machine Learning Repository. [http://archive.ics.uci.edu/ml/]. Irvine, CA: University of California, Center for Machine Learning and Intelligent Systems.

      [16] Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., Gao, X. Z., &Indragandhi, V. (2017). A hybrid quantum-induced swarm intelligence clustering for the urban trip recommendation in smart city. Future Generation Computer Systems, 83, 653-673. https://doi.org/10.1016/j.future.2017.08.060.

      [17] Subramaniyaswamy, V., &Logesh, R. (2017). Adaptive KNN based Recommender System through Mining of User Preferences. Wireless Personal Communications, 97(2), 2229-2247.https://doi.org/10.1007/s11277-017-4605-5.

      [18] Logesh, R., &Subramaniyaswamy, V. (2017). A Reliable Point of Interest Recommendation based on Trust Relevancy between Users. Wireless Personal Communications, 97(2), 2751-2780. https://doi.org/10.1007/s11277-017-4633-1.

      [19] Logesh, R., &Subramaniyaswamy, V. (2017). Learning Recency and Inferring Associations in Location Based Social Network for Emotion Induced Point-of-Interest Recommendation. Journal of Information Science & Engineering, 33(6), 1629–1647.

      [20] Subramaniyaswamy, V., Logesh, R., Abejith, M., Umasankar, S., &Umamakeswari, A. (2017). Sentiment Analysis of Tweets for Estimating Criticality and Security of Events. Journal of Organizational and End User Computing (JOEUC), 29(4), 51-71. https://doi.org/10.4018/JOEUC.2017100103.

      [21] Indragandhi, V., Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., Siarry, P., &Uden, L. (2018). Multi-objective optimization and energy management in renewable based AC/DC microgrid. Computers & Electrical Engineering.

      [22] Subramaniyaswamy, V., Manogaran, G., Logesh, R., Vijayakumar, V., Chilamkurti, N., Malathi, D., &Senthilselvan, N. (2018). An ontology-driven personalized food recommendation in IoT-based healthcare system. The Journal of Supercomputing, 1-33. https://doi.org/10.1007/s11227-018-2331-8.

      [23] Arunkumar, S., Subramaniyaswamy, V., &Logesh, R. (2018). Hybrid Transform based Adaptive Steganography Scheme using Support Vector Machine for Cloud Storage. Cluster Computing.

      [24] Indragandhi, V., Subramaniyaswamy, V., &Logesh, R. (2017). Resources, configurations, and soft computing techniques for power management and control of PV/wind hybrid system. Renewable and Sustainable Energy Reviews, 69, 129-143.https://doi.org/10.1016/j.rser.2016.11.209.

      [25] Ravi, L., &Vairavasundaram, S. (2016). A collaborative location based travel recommendation system through enhanced rating prediction for the group of users. Computational intelligence and neuroscience, 2016, Article ID: 1291358. https://doi.org/10.1155/2016/1291358.

      [26] Logesh, R., Subramaniyaswamy, V., Malathi, D., Senthilselvan, N., Sasikumar, A., &Saravanan, P. (2017). Dynamic particle swarm optimization for personalized recommender system based on electroencephalography feedback. Biomedical Research, 28(13), 5646-5650.

      [27] Arunkumar, S., Subramaniyaswamy, V., Karthikeyan, B., Saravanan, P., &Logesh, R. (2018). Meta-data based secret image sharing application for different sized biomedical images. Biomedical Research, 29.

      [28] Vairavasundaram, S., Varadharajan, V., Vairavasundaram, I., & Ravi, L. (2015). Data miningâ€based tag recommendation system: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(3), 87-112. https://doi.org/10.1002/widm.1149.

      [29] Logesh, R., Subramaniyaswamy, V., &Vijayakumar, V. (2018). A personalised travel recommender system utilising social network profile and accurate GPS data. Electronic Government, an International Journal, 14(1), 90-113. https://doi.org/10.1504/EG.2018.089538.

      [30] Vijayakumar, V., Subramaniyaswamy, V., Logesh, R., &Sivapathi, A. (2018). Effective Knowledge Based Recommeder System for Tailored Multiple Point of Interest Recommendation. International Journal of Web Portals.

      [31] Subramaniyaswamy, V., Logesh, R., &Indragandhi, V. (2018). Intelligent sports commentary recommendation system for individual cricket players. International Journal of Advanced Intelligence Paradigms, 10(1-2), 103-117. https://doi.org/10.1504/IJAIP.2018.089492.

      [32] Indragandhi, V., Subramaniyaswamy, V., &Logesh, R. (2017). Topological review and analysis of DC-DC boost converters. Journal of Engineering Science and Technology, 12 (6), 1541–1567.

      [33] Saravanan, P., Arunkumar, S., Subramaniyaswamy, V., &Logesh, R. (2017). Enhanced web caching using bloom filter for local area networks. International Journal of Mechanical Engineering and Technology, 8(8), 211-217.

      [34] Arunkumar, S., Subramaniyaswamy, V., Devika, R., &Logesh, R. (2017). Generating visually meaningful encrypted image using image splitting technique. International Journal of Mechanical Engineering and Technology, 8(8), 361–368.

      [35] Subramaniyaswamy, V., Logesh, R., Chandrashekhar, M., Challa, A., &Vijayakumar, V. (2017). A personalised movie recommendation system based on collaborative filtering. International Journal of High Performance Computing and Networking, 10(1-2), 54-63. https://doi.org/10.1504/IJHPCN.2017.083199.

      [36] Senthilselvan, N., UdayaSree, N., Medini, T., SubhakariMounika, G., Subramaniyaswamy, V., Sivaramakrishnan, N., &Logesh, R. (2017). Keyword-aware recommender system based on user demographic attributes. International Journal of Mechanical Engineering and Technology, 8(8), 1466-1476.

      [37] Subramaniyaswamy, V., Logesh, R., Vijayakumar, V., &Indragandhi, V. (2015). Automated Message Filtering System in Online Social Network. Procedia Computer Science, 50, 466-475.https://doi.org/10.1016/j.procs.2015.04.016.

      [38] Subramaniyaswamy, V., Vijayakumar, V., Logesh, R., &Indragandhi, V. (2015). Unstructured data analysis on big data using map reduce. Procedia Computer Science, 50, 456-465. https://doi.org/10.1016/j.procs.2015.04.015.

      [39] Subramaniyaswamy, V., Vijayakumar, V., Logesh, R., &Indragandhi, V. (2015). Intelligent travel recommendation system by mining attributes from community contributed photos. Procedia Computer Science, 50, 447-455.https://doi.org/10.1016/j.procs.2015.04.014.

      [40] Vairavasundaram, S., &Logesh, R. (2017). Applying Semantic Relations for Automatic Topic Ontology Construction. Developments and Trends in Intelligent Technologies and Smart Systems, 48.

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  • How to Cite

    Natarajan, S., S, R., & V, S. (2018). Optimized artificial neural network for classification of biological data. International Journal of Engineering & Technology, 7(2), 817-822. https://doi.org/10.14419/ijet.v7i2.11065

    Received date: 2018-04-04

    Accepted date: 2018-05-04

    Published date: 2018-05-23