پروپوزال مهندسی پزشکی - طراحی سیستم دسته‌بند فازی مبتنی بر بهینه سازی ازدحام ذرات برای تشخیص بیماری دیابت

پروپوزال مهندسی پزشکی طراحی سیستم دسته‌بند فازی مبتنی بر بهینه سازی ازدحام ذرات برای تشخیص بیماری دیابت

پروپوزال مهندسی پزشکی- 29 صفحه

((فایل  Word و  قابل ویرایش می باشد.))

بعد از پرداخت به راحتی همان لحظه می توانید آن را دانلود کنید.

Designing a fuzzy batch system based on particle swarm optimization to diagnose diabetes

  قیمت انجام پروپوزال از 250 هزار تومان تا 500 متغیر است که پروپوزال های آماده قیمت ناچیزی دارند.

پس منصف باشید و قیمت ها را با هم مقایسه کنید.

((((پروپوزال های سایت حاصل زحمت محققین سایت می باشد و  اینترنتی نیست.))))

فهرست مطالب:

بیان مسأله

اهداف پژوهش

سوالات تحقیق

فرضیات پژوهش

نوآوری‌های تحقیق

مرور منابع و پیشینه تحقیق

تعریف واژگان

روش تحقیق

PSO پیشنهادی

شرح الگوریتم

توابع برازش کیفیت قوانین

فهرست منابع و مأخذ

 

منابع:

 O. Maimon and L. Rokach, "Introduction to knowledge discovery and data mining," in Data Mining and Knowledge Discovery Handbook, ed: Springer, 2010, pp. 1-15.

    H. C. Koh and G. Tan, "Data mining applications in healthcare," Journal of Healthcare Information Management—Vol, vol. 19, p. 65, 2011.

   R. Bellazzi and B. Zupan, "Predictive data mining in clinical medicine: current issues and guidelines," international journal of medical informatics, vol. 77, pp. 81-97, 2008.

     D. Heckerman and J. S. Breese, "Causal independence for probability assessment and inference using Bayesian networks," Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, vol. 26, pp. 826-831, 1996.

    C. Olaru and L. Wehenkel, "A complete fuzzy decision tree technique," Fuzzy Sets and Systems, vol. 138, pp. 221-254, 2003.

   L. Wang, Support Vector Machines: theory and applications vol. 177: Springer, 2005.

     P. Baranyi, et al., "A generalized concept for fuzzy rule interpolation," Fuzzy Systems, IEEE Transactions on, vol. 12, pp. 820-837, 2004.

   C. Blum and A. Roli, "Hybrid metaheuristics: an introduction," in Hybrid Metaheuristics, ed: Springer, 2008, pp. 1-30.

    R. T. Santos, et al., "Extracting comprehensible rules from neural networks via genetic algorithms," in Combinations of Evolutionary Computation and Neural Networks, 2000 IEEE Symposium on, 2000, pp. 130-139.

   H. Ishibuchi, "Evolutionary multiobjective design of fuzzy rule-based systems," in Foundations of Computational Intelligence, 2007. FOCI 2007. IEEE Symposium on, 2007, pp. 9-16.

  G. G. Towell and J. W. Shavlik, "Extracting refined rules from knowledge-based neural networks," Machine learning, vol. 13, pp. 71-101, 1993.

 Z.-h. Tang and H.-y. Peng, "A Novel Rules Extraction Method Based on Clustering Analysis," in Intelligent Systems and Applications (ISA), 2010 2nd International Workshop on, 2010, pp. 1-4.

   C. Grosan, et al., "Swarm intelligence in data mining," in Swarm Intelligence in Data Mining, ed: Springer, 2006, pp. 1-20.

    U. Fayyad, et al., "From data mining to knowledge discovery in databases," AI magazine, vol. 17, p. 37, 1996.

      G. Mariscal, et al., "A survey of data mining and knowledge discovery process models and methodologies," Knowledge Engineering Review, vol. 25, p. 137, 2010.

   T.-P. Hong, et al., "An effective mining approach for up-to-date patterns," Expert systems with applications, vol. 36, pp. 9747-9752, 2009.

     K. P. Murphy, Machine Learning: A Probabilistic Perspective: The MIT Press, 2012.

   S. S. Haykin, Neural networks: a comprehensive foundation: Prentice Hall Englewood Cliffs NJ, 2007.

     P. Benardos and G.-C. Vosniakos, "Optimizing feedforward artificial neural network architecture," Engineering Applications of Artificial Intelligence, vol. 20, pp. 365-382, 2007.

     G. P. Zhang, "Neural networks for classification: a survey," Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 30, pp. 451-462, 2000.

  A. P. Engelbrecht, Computational intelligence: an introduction: Wiley, 2007.

   C. Jin, et al., "Attribute selection method based on a hybrid BPNN and PSO algorithms," Applied Soft Computing, vol. 12, pp. 2147-2155, 2012.

   J. R. Quinlan, "C4. 5: Programs for machine learning (morgan kaufmann series in machine learning)," Morgan Kaufmann, 1993.

   S. B. Kotsiantis, et al., Supervised machine learning: A review of classification techniques, 2007.

    R. O. Duda, et al., Pattern classification: John Wiley & Sons, 2012.

   C. M. Bishop and N. M. Nasrabadi, Pattern recognition and machine learning vol. 1: springer New York, 2006.

    R. S. Parpinelli, et al., "Data mining with an ant colony optimization algorithm," Evolutionary Computation, IEEE Transactions on, vol. 6, pp. 321-332, 2002.

    O. Cordón, et al., "Genetic fuzzy systems. New developments," Fuzzy Sets and Systems, vol. 141, pp. 1-3, 2004.

    R. C. Green, et al., "Training neural networks using central force optimization and particle swarm optimization: insights and comparisons," Expert systems with applications, vol. 39, pp. 555-563, 2012.

    R. Eberhart and J. Kennedy, "A new optimizer using particle swarm theory," in Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on, 1995, pp. 39-43.

     T. Krink, et al., "Particle swarm optimisation with spatial particle extension," in Evolutionary Computation, 2002. CEC'02. Proceedings of the 2002 Congress on, 2002, pp. 1474-1479.

    J. Kennedy and R. Mendes, "Population structure and particle swarm performance," in Evolutionary Computation, 2002. CEC'02. Proceedings of the 2002 Congress on, 2002, pp. 1671-1676.

      E. Peer, et al., "Using neighbourhoods with the guaranteed convergence PSO," in Swarm Intelligence Symposium, 2003. SIS'03. Proceedings of the 2003 IEEE, 2003, pp. 235-242.

     F. Van den Bergh and A. P. Engelbrecht, "A cooperative approach to particle swarm optimization," Evolutionary Computation, IEEE Transactions on, vol. 8, pp. 225-239, 2004.

    R. Brits, et al., "A niching particle swarm optimizer," in Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning, 2002, pp. 692-696.

      P. N. Suganthan, "Particle swarm optimiser with neighbourhood operator," in Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, 1999.

   Y. Shi and R. Eberhart, "A modified particle swarm optimizer," in Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, 1998, pp. 69-73.

      Y. Shi, "Particle swarm optimization: developments, applications and resources," in Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, 2001, pp. 81-86.

     J. Peng, et al., "Battery pack state of charge estimator design using computational intelligence approaches," in Battery Conference on Applications and Advances, 2000. The Fifteenth Annual, 2000, pp. 173-177.

    D. Tsou and C. MacNish, "Adaptive particle swarm optimisation for high-dimensional highly convex search spaces," in Evolutionary Computation, 2003. CEC'03. The 2003 Congress on, 2003, pp. 783-789.

   A. Ratnaweera, et al., "Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients," Evolutionary Computation, IEEE Transactions on, vol. 8, pp. 240-255, 2004.

     I. De Falco, et al., "Facing classification problems with particle swarm optimization," Applied Soft Computing, vol. 7, pp. 652-658, 2007.

     I. De Falco, et al., "Evaluation of particle swarm optimization effectiveness in classification," in Fuzzy Logic and Applications, ed: Springer, 2006, pp. 164-171.

   N. Nouaouria and M. Boukadoum, "Particle swarm classification for high dimensional data sets," in Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on, 2010, pp. 87-93.

     N. Nouaouria, et al., "Particle swarm classification: A survey and positioning," Pattern Recognition, 2013.

    J. Fan and Y. Fan, "High dimensional classification using features annealed independence rules," Annals of statistics, vol. 36, p. 2605, 2008.

   I. K. Fodor, "A survey of dimension reduction techniques," ed: Technical Report UCRL-ID-148494, Lawrence Livermore National Laboratory, 2002.

 H. Liu, et al., "A fuzzy adaptive turbulent particle swarm optimisation," International Journal of Innovative Computing and Applications, vol. 1, pp. 39-47, 2007.

      L. d. S. Coelho and V. C. Mariani, "A novel chaotic particle swarm optimization approach using Hénon map and implicit filtering local search for economic load dispatch," Chaos, Solitons & Fractals, vol. 39, pp. 510-518, 2009.

   X. Wang, et al., "Distributed particle swarm optimization and simulated annealing for energy-efficient coverage in wireless sensor networks," Sensors, vol. 7, pp. 628-648, 2007.

   M. Kessentini, et al., "Search-based model transformation by example," Software & Systems Modeling, vol. 11, pp. 209-226, 2012.

   V. M. Saffarzadeh, et al., "A Hybrid Approach Using Particle Swarm Optimization and Simulated Annealing For N-queen Problem," Journal of World Academy of Science, Engineering and Technology, vol. 67, pp. 974-978, 2010.

      N. Nouaouria and M. Boukadoum, "A particle swarm optimization approach to mixed attribute data-set classification," in Swarm Intelligence (SIS), 2011 IEEE Symposium on, 2011, pp. 1-8.

  J. Kennedy and R. C. Eberhart, "A discrete binary version of the particle swarm algorithm," in Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on, 1997, pp. 4104-4108.

     M. G. Omran, et al., "Dynamic clustering using particle swarm optimization with application in image segmentation," Pattern Analysis and Applications, vol. 8, pp. 332-344, 2006.

    J. Moreno-Pérez, et al., "Discrete Particle Swarm Optimization for the p-median problem," in Proceedings of the 7th metaheuristics international conference, Montréal, Canada, 2007.

  S. Consoli, et al., "Discrete Particle Swarm Optimization for the minimum labelling Steiner tree problem," Natural Computing, vol. 9, pp. 29-46, 2010.

   N. Nouaouria and M. Boukadoum, "A Particle Swarm Optimization Approach for the Case Retrieval Stage in CBR," in Research and Development in Intelligent Systems XXVII, ed: Springer, 2011, pp. 209-222.

   D. Braendler and T. Hendtlass, "The suitability of particle swarm optimisation for training neural hardware," in Developments in Applied Artificial Intelligence, ed: Springer, 2002, pp. 190-199.

   J. Kennedy and R. Mendes, "Neighborhood topologies in fully informed and best-of-neighborhood particle swarms," Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 36, pp. 515-519, 2006.

     I. C. Trelea, "The particle swarm optimization algorithm: convergence analysis and parameter selection," Information processing letters, vol. 85, pp. 317-325, 2003.

     F. Van den Bergh and A. P. Engelbrecht, "A study of particle swarm optimization particle trajectories," Information sciences, vol. 176, pp. 937-971, 2006.

     M. Jiang, et al., "Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm," Information processing letters, vol. 102, pp. 8-16, 2007.

    B. R. Secrest and G. B. Lamont, "Visualizing particle swarm optimization-Gaussian particle swarm optimization," in Swarm Intelligence Symposium, 2003. SIS'03. Proceedings of the 2003 IEEE, 2003, pp. 198-204.

    P. J. Angeline, "Using selection to improve particle swarm optimization," in Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, 1998, pp. 84-89.

     C. A. Koay and D. Srinivasan, "Particle swarm optimization-based approach for generator maintenance scheduling," in Swarm Intelligence Symposium, 2003. SIS'03. Proceedings of the 2003 IEEE, 2003, pp. 167-173.

  M. Løvbjerg, "Improving particle swarm optimization by hybridization of stochastic search heuristics and self-organized criticality," Aarhus Universitet, Datalogisk Institut, 2002.

     T. Hendtlass, "A combined swarm differential evolution algorithm for optimization problems," in Engineering of Intelligent Systems, ed: Springer, 2001, pp. 11-18.

     S. Das, et al., "Particle swarm optimization and differential evolution algorithms: technical analysis, applications and hybridization perspectives," in Advances of Computational Intelligence in Industrial Systems, ed: Springer, 2008, pp. 1-38.

     M. R. AlRashidi and M. E. El-Hawary, "A survey of particle swarm optimization applications in electric power systems," Evolutionary Computation, IEEE Transactions on, vol. 13, pp. 913-918, 2009.

     W.-J. Zhang and X.-F. Xie, "DEPSO: hybrid particle swarm with differential evolution operator," in Systems, Man and Cybernetics, 2003. IEEE International Conference on, 2003, pp. 3816-3821.

   K. Chandramouli and E. Izquierdo, "Image classification using chaotic particle swarm optimization," in Image Processing, 2006 IEEE International Conference on, 2006, pp. 3001-3004.

    H. Gao, et al., "Training RBF neural network with hybrid particle swarm optimization," in Advances in Neural Networks-ISNN 2006, ed: Springer, 2006, pp. 577-583.

   B. Al-kazemi and C. K. Mohan, "Multi-phase generalization of the particle swarm optimization algorithm," in Evolutionary Computation, 2002. CEC'02. Proceedings of the 2002 Congress on, 2002, pp. 489-494.

   B. Al-Kazemi and C. Mohan, "Discrete multi-phase particle swarm optimization," in Information Processing with Evolutionary Algorithms, ed: Springer, 2005, pp. 305-327.

    L. I. Kuncheva, "Combining classifiers: Soft computing solutions," Pattern Recognition: From Classical to Modern Approaches, pp. 427-451, 2001.

     H. Ishibuchi, et al., Classification and modeling with linguistic information granules: advanced approaches advanced approaches to linguistic data mining: Springer, 2005.

     F. Herrera, "Genetic fuzzy systems: taxonomy, current research trends and prospects," Evolutionary Intelligence, vol. 1, pp. 27-46, 2008.

    H. Roubos and M. Setnes, "Compact and transparent fuzzy models and classifiers through iterative complexity reduction," Fuzzy Systems, IEEE Transactions on, vol. 9, pp. 516-524, 2001.

   M. A. Kbir, et al., "Hierarchical fuzzy partition for pattern classification with fuzzy if-then rules," Pattern Recognition Letters, vol. 21, pp. 503-509, 2000.

  P. Jaganathan, et al., "Classification rule discovery with ant colony optimization and improved Quick Reduct algorithm," IAENG International Journal of Computer Science, vol. 33, pp. 50-55, 2007.

    M. S. Abadeh, et al., "Induction of Fuzzy Classification systems via evolutionary ACO-based algorithms," computer, vol. 35, p. 37, 2008.

     K. Nozaki, et al., "Adaptive fuzzy rule-based classification systems," Fuzzy Systems, IEEE Transactions on, vol. 4, pp. 238-250, 1996.

     H. Ishibuchi, et al., "Voting in fuzzy rule-based systems for pattern classification problems," Fuzzy Sets and Systems, vol. 103, pp. 223-238, 1999.

      J. Han, et al., Data mining: concepts and techniques: Morgan kaufmann, 2006.

      L. A. Zadeh, "Fuzzy sets," Information and control, vol. 8, pp. 338-353, 1965.

    M. F. Ganji and M. S. Abadeh, "Using fuzzy ant colony optimization for diagnosis of diabetes disease," in Electrical Engineering (ICEE), 2010 18th Iranian Conference on, 2010, pp. 501-505.

   C. Zhou, et al., "Evolving accurate and compact classification rules with gene expression programming," Evolutionary Computation, IEEE Transactions on, vol. 7, pp. 519-531, 2003.

   C. Merz, et al., "UCI repository of machine learning databases " in http://www.archive.ics.uci.edu/ml/datasets.html, ed. University of California, Department of Information and Computer Science, Irvine, CA, 1996.