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Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

References [1] Alharbi, A., Tchier, F.: Using a genetic-fuzzy algorithm as a computer aided diagnosis tool on saudi arabian breast cancer database. Mathematical Biosciences 286, 39 – 48 (2017) [2] Antonelli, M., Ducange, P., Marcelloni, F.: A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers. Information Sciences 283, 36 – 54 (2014). New Trend of Computational Intelligence in Human-Robot Interaction [3] Awad, N.H., Ali, M.Z., Suganthan, P.N., Reynolds, R.G.: An ensemble sinusoidal parameter

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Design of Fuzzy Rule-based Classifiers through Granulation and Consolidation

concept formation, Artificial Intelligence, 40(1–3):11–61, 1989 [16] S. Guillaume and B. Charnomordic, Learning interpretable fuzzy inference systems with FisPro, Information Sciences, 180(20):4409–4427, 2011 [17] J. Hühn and E. Hüllermeier, FURIA: an algorithm for unordered fuzzy rule induction, Data Mining and Knowledge Discovery, 19(3):293–319, 2009 [18] H. Ishibuchi, T. Nakashima, and T. Murata, Three-objective genetic-based machine learning for linguistic rule extraction, Information Sciences, 136(1–4):109–133, 2001 [19] H. Ishibuchi, K

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A new approach to nonlinear modelling of dynamic systems based on fuzzy rules

–1711. Cordón, O. (2011). A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems, International Journal of Approximate Reasoning 52 (6): 894–913. Cordón, O., Herrera, F., Hoffmann, F. and Magdalena, L. (2001). Genetic Fuzzy Systems , World Scientific Publishing Company, Singapore. Cpałka, K. (2009a). A new method for design and reduction of neuro-fuzzy classification systems, IEEE Transactions on Neural Networks 20 (4): 701–714. Cpałka, K. (2009b). On evolutionary

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Error reduction in promoted confidence factor of a rule using improved fuzzy rule promotion technique

, 2009, Issue 3, No 1, 5119-5136. 5. Huan g, Y., Y. La n, S. J. Th omson, A. Fang, W. C. Hoffmann, R. E. Lace y. Development of Soft Computing and Applications in Agricultural and Biological Engineering. - Journal of Computers and Electronics in Agriculture, Vol. 71, 2010, No 2, 107-127. 6. Sile r, W., J. J. Buckle y. Fuzzy Expert Systems and Fuzzy Reasoning. USA, John Wiley & Sons, Inc., 2005. ISBN-9-7804-7138-8593. 7. Reshmidevi, T. V., T. I. Eldh o, R. Jana. A GIS-Integrated Fuzzy Rule-Based Inference System for Land

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Using Fuzzy Logic to Solve Bioinformatics Tasks

References P. J. Woolf, Y. Wang , "A fuzzy logic approach to analyzing gene expression data", Physiological Genomics, vol. 3, pp. 9-13, 2000. J. Casillas, O. Cordon, M. J. Del Jesus, F. Herrera , "Genetic feature selection in a fuzzy rule - based classification system learning process for high-dimensional problems," Information Sciences, vol. 136, pp. 135-157, 2001. H. Ressom, R. Reynolds, R. S. Varghese , "Increasing the efficiency of fuzy logic-based gene expression data analysis

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Comparison of Fuzzy System with Neural Aggregation FSNA with Classical TSK Fuzzy System in Anti-Collision Problem of USV

Abstract

The paper presents the research whose the main goal was to compare a new Fuzzy System with Neural Aggregation of fuzzy rules FSNA with a classical Takagi-Sugeno-Kanga TSK fuzzy system in an anti-collision problem of Unmanned Surface Vehicle USV. Both systems the FSNA and the TSK were learned by means of Cooperative Co-evolutionary Genetic Algorithm with Indirect Neural Encoding CCGA-INE.

The paper includes an introduction to the subject, a description of the new FSNA and the tuning method CCGA-INE, and at the end, numerical research results with a summary. The research includes comparison of the FSNA with the classical TSK system in the anti-collision problem of the USV.

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A comparative study of fuzzy logic-based models for groundwater quality evaluation based on irrigation indices

explanation. University of Texas at El Paso DigitalCommons@UTEP Departmental Technical Reports (CS). Paper 783. D ahiya S., S ingh B., G aur S., G arg V.K., K ushwaha H.S. 2007. Analysis of groundwater quality using fuzzy synthetic evaluation. Journal of Hazardous Materials. Vol. 147. No. 3 p. 938–946. DOI 10.1016/j.jhazmat.2007.01.119. D ange P.S., L ad R.K. 2017. A fuzzy rule based system for an environmental acceptability of Sewage Treatment Plant. KSCE Journal of Civil Engineering, Vol. 21. Iss. 7 p. 2590–2595. D oneen L.D. 1962. The influence

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A New Mechanism for Data Visualization with Tsk-Type Preprocessed Collaborative Fuzzy Rule Based System

information granules, A JohnWiley & Sons, Inc., Publication, 2005. [8] W. Pedrycz, Collaborative fuzzy clustering, Pattern Recognition Letters, vol. 23, no. 14, pp. 1675-1686, 2002. [9] W. Pedrycz and P. Rai, Collaborative Fuzzy Clustering with the use of Fuzzy C-Means and its Quantification, Fuzzy Sets and System, vol. 159, no. 18, pp. 2399-2427, 2008. [10] C. T. Lin, M. Prasad, and J. Y Chang, Designing Mamdani Type Fuzzy Rule Using a Collaborative FCM Scheme, International Conference on Fuzzy Theory and Its Application

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A Fuzzy Aproach For Facial Emotion Recognition

Abstract

This article deals with an emotion recognition system based on the fuzzy sets. Human faces are detected in images with the Viola - Jones algorithm and for its tracking in video sequences we used the Camshift algorithm. The detected human faces are transferred to the decisional fuzzy system, which is based on the variable fuzzyfication measurements of the face: eyebrow, eyelid and mouth. The system can easily determine the emotional state of a person.

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Application of Fuzzy Logic for Risk Assessment/ Izplūdušās loģikas pielietojums risku analīzē/ Применение нечеткой логики для анализа рисков

Abstract

Risk assessment is an important task in many areas of human activity: economic, technical, ecological etc. Preliminary data adequacy in risk assessments is carried out on the basis of statistical methods and experts’ evaluation on potential losses and probabilities of the event. But in many cases, risk assessment must be carried out under the conditions of lack of initial information or uncertainty of information. For that reason, special risk assessment approaches (methods) are necessary. One of them is the usage of fuzzy logic approach. In this paper, fuzzy logic approach is used to manage this uncertainty in information concerning accidental releases of toxic chemicals at chemical plants. This approach can be used by plant risk advisers in Latvia to make right decisions in the situations where chemical releases can harm not only the environment but also human health.

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