March 2021: Top Ten Cited Article in Fuzzy Logic Systems International Journal of Fuzzy Logic Systems (IJFLS) ISSN: 1839 – 6283
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Citation Count – 165 COMPARATIVE ANALYSIS OF AHP AND FUZZY AHP MODELS FOR MULTICRITERIA INVENTORY CLASSIFICATION Golam Kabir1 and Dr. M. Ahsan Akhtar Hasin2 1
Department of Industrial and Production Engineering, Bangladesh University of Science and Technology (BUET), Dhaka-1000, Bangladesh
2
Department of Industrial and Production Engineering, Bangladesh University of Science and Technology (BUET), Dhaka-1000, Bangladesh
ABSTRACT A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. In practice, all inventories cannot be controlled with equal attention. In order to efficiently control the inventory items and to determine the suitable ordering policies for them, multicriteria inventory classification is used. Analytical Hierarchy Process (AHP) is one of the best ways for deciding among the complex criteria structure in different levels. Fuzzy Analytical Hierarchy Process (FAHP) is a synthetic extension of classical AHP method when the fuzziness of the decision makers is considered. In this paper, a comparative analysis of AHP and FAHP for multi-criteria inventory classification model has been presented. To accredit the proposed models, those were implemented for the 351 raw materials of switch gear section of Energypac Engineering Limited (EEL), a large power engineering company of Bangladesh.
KEYWORDS Analytic Hierarchy Process, Chang’s Extent Analysis, Inventory Classification
For More Details : https://wireilla.com/papers/ijfls/V1N1/1011ijfls01.pdf Volume Link : https://wireilla.com/ijfls/vol1.html
REFERENCES
[1] Bhattacharya, B. Sarkar & S.K. Mukherjee, (2007) “Distance-based consensus method forABC analysis”, International Journal of Production Research, 45(15), 3405-3420. [2] C.G.E. Boender, J.G. de Graan & F.A. Lootsma, (1989) “Multi-criteria decision analysis with fuzzy pairwise comparisons”, Fuzzy Sets and Systems, 29(2), 133–143. [3] F.T. Bozbura, A. Beskese & C. Kahraman, (2007) “Prioritization of human capital measurement indicators using fuzzy AHP”, Expert Systems with Applications, 32(4), 1100- 1112. [4] M. Braglia, A. Grassi & R. Montanari, (2004) “Multi-attribute classification method for spare parts inventory management”, Journal of Quality in Maintenance Engineering, 10(1), 55-65. [5] J.J. Buckley, (1985) “Fuzzy hierarchical analysis”, Fuzzy Sets and Systems, 17(3), 233247. [6] D.Y. Chang, (1992) “Extent analysis and synthetic decision”, Optimization Techniques and Applications, 1, 352-355. [7] D.Y. Chang, (1996) “Applications of the extent analysis method on fuzzy AHP”, European Journal of Operational Research, 95(3), 649-655. [8] R.B. Chase, F.R. Jacobs, N.J. Aquilano & N.K. Agarwal, (2006) “Operations Management for Competitive Advantag”, 11th Edition, McGraw Hill, New York, USA. [9] Y. Chen, K.W. Li, D.M. Kilgour & K.W. Hipel, (2008) “A case-based distance model for multiple criteria ABC analysis”, Computers & Operations Research, 35(3), 776-796. [10] R. Csutora & J.J Buckley, (2001) “Fuzzy hierarchical analysis: The Lambda-Max Method”, Fuzzy Sets and Systems, 120, 181–195. [11] B.E. Flores & D.C. Whybark, (1986) “Multiple Criteria ABC Analysis”, International Journal of Operations and Production Management, 6(3), 38-46. [12] B.E. Flores & D.C. Whybark, (1987) “Implementing Multiple Criteria ABC Analysis”, Journal of Operations Management, 7(1), 79-84. [13] B.E. Flores, D.L. Olson & V.K. Dorai (1992) “Management of Multicriteria Inventory Classification”, Mathematical and Computer Modeling, 16(12), 71-82. [14] H.A. Guvenir & E. Erel, (1998) “Multicriteria inventory classification using a genetic algorithm”, European Journal of Operational Research, 105(1), 29-37. [15] Hadi-Vencheh, (2010) “An improvement to multiple criteria ABC inventory classification”, European Journal of Operational Research, 201(3), 962-965. [16] H. Jamshidi & A. Jain, A. (2008) “Multi-Criteria ABC Inventory Classification: With Exponential Smoothing Weights”, Journal of Global Business Issues, Winter issue.
[17] G. Kabir & M.A.A. Hasin, (2011a) “Multi-Criteria Inventory Classification through Integration of Fuzzy Analytic Hierarchy Process and Artificial Neural Network”, International Journal of Industrial and System Engineering (IJISE), Article in Press. [18] G. Kabir & M.A.A. Hasin, (2011b) “Evaluation of Customer Oriented Success Factors in Mobile Commerce Using Fuzzy AHP”, Journal of Industrial Engineering and Management, 4(2), 361- 386. [19] G. Kabir & R.S. Sumi, (2010) “An Ontology-Based Intelligent System with AHP to Support Supplier Selection”, Suranaree Journal of Science and Technology, 17(3), 249257. [20] Kahraman, U. Cebeci & Ruan, D. (2004) “Multi-attribute comparison of catering service companies using fuzzy AHP: the case of Turkey”, International Journal of Production Economics, 87(2), 171-184. [21] Q.S. Lei, J. Chen & Q. Zhou, (2005) “Multiple criteria inventory classification based on principal components analysis and neural network”, Proceedings of Advances in neural networks, Berlin, 1058-1063. [22] Q. Liu & D. Huang, (2006) “Classifying ABC Inventory with Multicriteria Using a Data Envelopment Analysis Approach”, Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06), Jian, China, 01, 1185-1190. [23] L. Mikhailov, (2003) “Deriving priorities from fuzzy pairwise comparison judgements”, Fuzzy Sets and Systems, 134(3), 365-385. [24] W.L. Ng, (2007) “A simple classifier for multiple criteria ABC analysis”, European Journal of Operational Research, 177(1), 344-353. [25] F.Y. Partovi & J. Burton, (1993) “Using the analytic hierarchy process for ABC analysis”, International Journal of Production and Operations Management, 13(9), 2944. [26] F.Y. Partovi & M. Anandarajan, (2002) “Classifying inventory using and artificial neuralnetwork approach”, Computers & Industrial Engineering, 41(4), 389-404. [27] Ramanathan, R. (2006) “ABC inventory classification with multiple-criteria using weighted linear optimization”, Computers & Operations Research, 33(3), 695-700. [28] T.L. Saaty, (1980) “The analytic hierarchy process”, New York, NY: McGraw-Hill. [29] T.L. Saaty, (2000) “Fundamentals of Decision Making and Priority Theory”, 2nd ed. Pittsburgh, PA: RWS Publications. [30] K. Šimunović, G. Šimunović & T. Šarić, (2009) “Application of Artificial Neural Networks to Multiple Criteria Inventory Classification”, Strojarstvo, 51(4), 313-321. [31] L.G. Vargas, (1990) “An overview of the analythic hierarchy its process and applications”, European Journal of Operational Research, 48(1), 2-8. [32] Y.M. Wang, J.B. Yang & D.L. Xu, (2005) “A two-stage logarithmic goal programming method for generating weights from interval comparison matrices”, Fuzzy Sets Systems, 152, 475-498.
[33] R. Xu, (2000) “Fuzzy least square priority method in the analytic hierarchy process”, Fuzzy Sets and Systems, 112(3), 395-404. [34] M.C. Yu (2011) “Multi-criteria ABC analysis using artificial-intelligence-based classification techniques”, Expert Systems with Applications, 38(4), 3416-3421. [35] P. Zhou & L. Fan, (2007) “A note on multi-criteria ABC inventory classification using weighted linear optimization”, European Journal of Operational Research, 182(3), 14881491.
Citation Count – 114 BIPOLAR FUZZY HYPERGRAPHS
Sovan Samanta and Madhumangal Pal Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore - 721 102, India
ABSTRACT In this paper, we define some basic concepts of bipolar fuzzy hypergraphs, cut level bipolar fuzzy hypergraphs, dual bipolar fuzzy hypergraphs and bipolar fuzzy transversal. Also some basic theorems related to the stated graphs have been presented.
KEYWORDS Bipolar fuzzy hypergraphs, cut level bipolar fuzzy hypergraphs, bipolar fuzzy transversal.
For More Details : https://wireilla.com/papers/ijfls/V2N1/2112ijfls03.pdf Volume Link : https://wireilla.com/ijfls/vol2.html
REFERENCES
[1] M. Akram, Bipolar fuzzy graphs, Information Sciences, doi:10:1016/j.ins.2011:07:037, 2011. [2] R. H. Goetschel, Introduction to fuzzy hypergraphs and Hebbian structures, Fuzzy Setsand Systems, 76; 113 -130, 1995. [3] R. H. Goetschel, Fuzzy colorings of fuzzy hypergraphs, Fuzzy Sets and Systems, 94; 18 - 204, 1998. [4] R. H. Goetschel and W. Voxman, Intersecting fuzzy hypergraphs, Fuzzy Sets and Systems, 99, 81 - 96, 1998. [5] A. Rosenfeld, Fuzzy graphs, in: L.A. Zadeh, K.S. Fu, M. Shimura (Eds.), Fuzzy Sets andTheir Applications, Academic Press, New York, 77 - 95, 1975. [6] S. Samanta and M. Pal, Fuzzy threshold graphs, CIIT International Journal of Fuzzy Systems, 3(12), 360 - 364, 2011. [7] S. Samanta and M. Pal, Fuzzy tolerance graphs, International Journal of Latest Trends inMathematics, 1(2), 57 - 67, 2011: [8] Samanta and M. Pal, Bipolar fuzzy intersection and line graphs, Communicated. [9] S. Samanta and M. Pal, Fuzzy k-competition graphs and p-competition fuzzy graphs, Communicated. [10] L.A. Zadeh, Fuzzy sets, Information and Control, 8, 338 - 353, 1965. [11] W.R. Zhang, Bipolar fuzzy sets and relations: a computational frame work for cognitive modelling and multiagent decision analysis, Proceedings of IEEE Conf., 305 - 309, 1994.
Citation Count – 50 A NEW OPERATION ON HEXAGONAL FUZZY NUMBER P. Rajarajeswari1, A.Sahaya Sudha2 and R.Karthika3 1
Department of Mathematics, Chikkanna Government Arts College, Tirupur-641 602 2
3
Department of Mathematics, Nirmala College for women, Coimbatore-641018
Department of Mathematics, Hindustan Institute of Technology, Coimbatore-641028.
ABSTRACT The Fuzzy set Theory has been applied in many fields such as Management, Engineering etc. In this paper a new operation on Hexagonal Fuzzy number is defined where the methods of addition, subtraction, and multiplication has been modified with some conditions. The main aim of this paper is to introduce a new operation for addition, subtraction and multiplication of Hexagonal Fuzzy number on the basis of alpha cut sets of fuzzy numbers.
KEYWORDS Fuzzy arithmetic, Hexagonal fuzzy numbers, Function principles
For More Details : https://wireilla.com/papers/ijfls/V3N3/3313ijfls02.pdf Volume Link : https://wireilla.com/ijfls/vol3.html
REFERENCES
[1] Abhinav Bansal (2011) Trapezoidal Fuzzy numbers (a,b,c,d):Arithmetic behavior. International Journal of Physical and Mathematical Sciences, ISSN-2010-1791. [2] Bansal,A.,(2010)Some non linear arithmetic operations on triangular fuzzy numbers(m,B, a). Advances in fuzzy mathematics, 5,147-156. [3] Dubois.D and Prade.H,(1978) Operations on fuzzy numbers ,International Journal of Systems Science, vol.9, no.6.,pp.613-626. [4] Dwyer.,(1965), P.S. Fuzzy sets. Information and Control, No.8: 338–353. [5] Fuller.R and Majlender.P.,(2003), On weighted possibilistic mean and variance of fuzzynumbers, Fuzzy Sets and Systems, vol.136, pp.363-374 [6] Heilpern.S.,(1997), Representation and application of fuzzy numbers, Fuzzy sets andSystems, vol.91, no.2, pp.259-268. [7] Klaua.D.,(1965) ,Über einen Ansatz zur mehrwertigen Mengenlehre. Monatsb. Deutsch. Akad. Wiss. Berlin 7, 859–876 [8] Klir.G.J., (2000), Fuzzy Sets: An Overview of Fundamentals, Applications, and Personalviews. Beijing Normal University Press, pp.44-49. [9] Klir., (1997) Fuzzy arithmetic with requisite constraints, Fuzzy Sets System, vol. 91, ,pp. 165– 175. [10] Kauffmann,A.,(1980) Gupta,M., Introduction to Fuzzy Arithmetic :Theory and Applications,Van Nostrand Reinhold, New York. [11] Malini.S.U,Felbin.C.Kennedy.,(2013), An approach for solving Fuzzy Transportation using Octagonal Fuzzy numbers,Applied Mathematical Sciences,no.54,2661-2673 [12] Nasseri.H(2008) Positive and non-negative, International Mathematical Forum,3,17771780. [13] Rezvani .S.,(2011).,Multiplication Operation on Trapezoidal Fuzzy numbers, Journal of Physical Sciences,Vol no-15,17-26 [14] Yager.R.,(1979) control,41,29-55.
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[15] Zadeh,L.A.,(1965) Fuzzy Sets, Information and Control.,No.8 pp.338-353. [16] Zadeh,L.A.,(1978) Fuzzy set as a basis for a theory of possibility, Fuzzy sets and systems, No.1,pp.3-28. [17] Zimmermann,H. J,(1996) Fuzzy Set Theory and its Applications, Third Edition,Kluwer Academic Publishers, Boston, Massachusetts. [18] http://debian.fmi.uni-sofia.bg/~cathy/SoftCpu/FUZZY_BOOK/chap5-3.pdf. Triangular fuzzy numbers.
Citation Count – 36 INTERVAL-VALUED INTUITIONISTIC FUZZY CLOSED IDEALS OF BG-ALGEBRA AND THEIR PRODUCTS Tapan Senapati1, Monoranjan Bhowmik2, Madhumangal Pal3 1,3
Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore -721 102, India 2
Department of Mathematics, V. T. T. College, Midnapore- 721 101, India
ABSTRACT In this paper, we apply the concept of an interval-valued intuitionistic fuzzy set to ideals and closed ideals in BG-algebras. The notion of an interval-valued intuitionistic fuzzy closed ideal of a BG-algebra is introduced, and some related properties are investigated. Also, the product of interval-valued inntuitionistic fuzzy BG-algebra is investgated.
KEYWORDS BG-algebras, interval-valued intuitionistic fuzzy sets (IVIFSs), IVIF-ideals, IVIFC-ideals, homomorphism, equivalence relation, upper(lower)-level cuts, product of BG-algebra.
For More Details : https://wireilla.com/papers/ijfls/V2N2/2212ijfls03.pdf Volume Link : https://wireilla.com/ijfls/vol2.html
REFERENCES
[1] S.S. Ahn and H.D. Lee, Fuzzy subalgebras of BG-algebras, Commun. Korean Math. Soc. 19(2) (2004), 243-251. [2] K.T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20 (1986), 87-96. [3] K.T. Atanassov and G. Gargo, Interval-valued intuitionistic fuzzy sets, Fuzzy Sets and Systems, 31(1) (1989), 343-349. [4] K.T. Atanassov, Operations over interval-valued fuzzy set, Fuzzy Sets and Systems, 64 (1994), 159-174. [5] K.T. Atanassov, More on intuitionistic fuzzy sets, Fuzzy Sets and Systems, 33(1) (1989), 37-46. [6] K.T. Atanassov, New operations defined over the intuitionistic fuzzy sets, Fuzzy Sets and Systems, 61 (1994), 137-142. [7] R. Biswas, Rosenfeld's fuzzy subgroups with interval valued membership function, FuzzySets and Systems, 63 (1994), 87-90. [8] J.R. Cho and H.S. Kim, On B-algebras and quasigroups, Quasigroups and Related Systems 7 (2001), 1–6. [9] G. Deschrijver, Arithmetic operators in interval-valued fuzzy theory, Information Sciences, 177 (2007), 2906-2924. [10] D.H. Foster, Fuzzy topological groups, Journal of Mathematical Analysis and Applications, 67(2) (1979), 549-564. [11] Q.P. Hu and X. Li, On BCH-algebras, Mathematics Seminar Notes, 11 (1983), 313-320. [12] Q.P. Hu and X. Li, On proper BCH-algebras, Math Japonica, 30 (1985), 659-661. [13] Y. Imai and K. Iseki, On axiom system of propositional calculi, XIV Proc. Japan Academy, 42 (1966), 19-22. [14] Y.B. Jun, E.H. Roh and H.S. Kim, On fuzzy B-algebras, Czech. Math. J., 52(127) (2002), 375-384. [15] K.H. Kim, Intuitionistic fuzzy ideals of semigroups, Indian J. Pure Appl. Math, 33(4) (2002), 443- 449. [16] N.K. Jana and M. Pal, Some operators defined over interval-valued intuitionistic fuzzy sets, Fuzzy Logic and Optimization, Editor: S. Nanda, Narosa Publishing House, New Delhi, India, (2006), 113- 126. [17] C.B. Kim and H.S. Kim, On BG-algebras, Demonstratio Mathematica, 41 (2008), 497505. [18] W.J. Liu, Fuzzy invariant subgroups and fuzzy ideals, Fuzzy Sets and Systems, 8 (1982), 132-139.
[19] R. Muthuraj, M. Sridharan and P.M. Sitharselvam, Fuzzy BG-ideals in BG-algebra, International Journal of Computer Applications, 2(1) (2010), 26-30. [20] J. Neggers and H.S. Kim, On -algebras, Math. Vensik, 54 (2002), 21-29. [21] H.K. Park and H.S. Kim, On quadratic B-algebras, Quasigroups and Related Systems 7 (2001), 67–72. [22] A. Rosenfeld, Fuzzy Groups, Journal of Mathematical Analysis and Applications, 35 (1971), 512- 517. [23] A.B. Saeid, Some results on interval-valued fuzzy BG-algebra, World Academy of Science, Engineering and Technology, 5 (2005), 183-186. [24] A.B. Saeid, Fuzzy topological BG-algebra, International Mathematical Journal, 6(2) (2005), 121- 127. [25] A.B. Saeid, Interval-valued fuzzy BG-algebra,, Kangweon-Kyungki Math. Jour., 14(2) (2006), 203-215. [26] T. Senapati, M. Bhowmik and M. Pal, Fuzzy closed ideals of B-algebras, International Journal of Computer Science Engineering and Technology, 1(10) (2011), 669-673. [27] T. Senapati, M. Bhowmik and M. Pal, On intuitionistic fuzzy subalgebras in BGalgebras, (Submitted). [28] T. Senapati, M. Bhowmik and M. Pal, Interval-valued intuitionistic fuzzy BGsubalgebras, Journal of Fuzzy Mathematics (Accepted). [29] L.A. Zadeh, Fuzzy sets, Information and Control, 8 (1965), 338-353. [30] L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning. I, Information Sciences, 8 (1975), 199-249. [31] L.A. Zadeh, Toward a generalized theory of uncertainty (GTU)-an outline, Information Sciences, 172 (2005), 1-40. [32] A. Zarandi and A.B. Saeid, Intuitionistic fuzzy ideals of BG-algebras, World Academy of Science, Engineering and Technology, 5 (2005), 187-189.
Citation Count – 36 A FUZZY MODEL FOR ANALOGICAL PROBLEM SOLVING
Michael Gr. Voskoglou School of Technological Applications Graduate Technological Educational Institute, Patras, Greece
ABSTRACT
In this paper we develop a fuzzy model for the description of the process of Analogical Reasoning by representing its main steps as fuzzy subsets of a set of linguistic labels characterizing the individuals’ performance in each step and we use the Shannon- Wiener diversity index as a measure of the individuals’ abilities in analogical problem solving. This model is compared with a stochastic model presented in author’s earlier papers by introducing a finite Markov chain on the steps of the process of Analogical Reasoning. A classroom experiment is also presented to illustrate the use of our results in practice.
KEYWORDS Fuzzy Sets, Analogical Reasoning, Problem Solving
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[10] Voskoglou, M. Gr. (2003), “Analogical Problem Solving and Transfer”, Proceedings of the 3d Mediterranean Conference on Mathematical Education, pp.295-303, Athens. [11] Voskoglou, M. Gr. (2009), “Fuzziness or probability in the process of learning: A general question illustrated by examples from teaching mathematics”, The Journal of Fuzzy Mathematics, 17(3), pp. 679-686, International Fuzzy Mathematics Institute (Los Angeles). [12] Voskoglou, M. Gr. (2009), “Fuzzy sets in Case-Based Reasoning”, Fuzzy Systems and Knowledge Discovery, Vol.6, pp. 252-256, IEEE Computer Society. [13] Voskoglou, M. Gr. (2011), “Measuring students modelling capacities: A fuzzy approach, Iranian Journal of Fuzzy Systems, 8(3), pp.23-33 [14] Voskoglou, M. Gr. (2011), Stochastic and fuzzy models in Mathematics Education, Artificial Intelligence and Management, Lambert Academic Publishing, Saarbrucken, Germany. [15] Voskoglou, M. Gr. (2011) Case-Based Reasoning: “History, Methodology and Development Trends”, International Journal of Psychology Research, 6(5), pp. 505530, Nova Publishers, N Y.
Citation Count – 35 Brain Tumor Segmentation using hybrid Genetic Algorithm and Artificial Neural Network Fuzzy Inference System (ANFIS) Minakshi Sharma1, Dr. Sourabh Mukharjee2 1 2
Assistant Professor in the Department of IT in GIMT kanipla, kurukshetra, India.
Associate Professor in the Department of Computer science in Banasthali university, Rajasthan
ABSTRACT Medical image segmentation plays an important role in treatment planning, identifying tumors, tumor volume, patient follow up and computer guided surgery. There are various techniques for medical image segmentation. This paper presents a image segmentation technique for locating brain tumor(AstrocytomaA type of brain tumor).Proposed work has been divided in two phases-In the first phase MRI image database(Astrocytoma grade I to IV) is collected and then preprocessing is done to improve quality of image. Second-phase includes three steps-Feature extraction, Feature selection and Image segmentation. For feature extraction proposed work uses GLCM (Grey Level co-occurrence matrix).To improve accuracy only a subset of feature is selected using hybrid Genetic algorithm(Genetic Algorithm+fuzzy rough set) and based on these features fuzzy rules and membership functions are defined for segmenting brain tumor from MRI images of .ANFIS is a adaptive network which combines benefits of both fuzzy and neural network .Finally, a comparative analysis is performed between ANFIS, neural network, Fuzzy ,FCM,K-NN, DWT+SOM,DWT+PCA+KN, Texture combined +ANN, Texture Combined+ SVM in terms of sensitivity ,specificity ,accuracy.
KEYWORDS ANFIS, Brain tumor(Astrocytoma), sensitivity, specificity, accuracy, MR images, Neural network, Fuzzy, ANFIS,FCM,K-NN, GLCM, Genetic algorithm.
For More Details : https://wireilla.com/papers/ijfls/V2N4/2412ijfls03.pdf Volume Link : https://wireilla.com/ijfls/vol2.html
REFERENCES
[1] D. Jude Hemanth, C.Kezi Selva Vijila and J.Anitha,(2010) ,“Application of Neuro-Fuzzy Model for MR Brain Tumor Image Classification “,Biomedical Soft Computing and Human Sciences, vol.16,No.1, pp.95-102 [2] Bose, N.K. and Liang, P. (2004). Neural Network Fundamentals with Graphs, Algorithms, and Applications, Edition ,TMH, India. [3] Gonzalez, R.C. Richard, E.W. (2004), Digital ImageProcessing, II Indian Edition, Pearson Education, New Delhi,India. [5] Monireh Sheikh Hosseini, Maryam Zekri1(2012),” A review of medical image classification using Adaptive Neuro-Fuzzy Inference System (ANFIS)”, Journal of Medical Signals and Sensors, pp 51-62 [6] NOOR ELAIZA ABDUL KHALID, SHAFAF IBRAHIM, MAZANI MANAF,” Brain Abnormalities Segmentation Performances contrasting : Adaptive Network-Based Fuzzy Inference System (ANFIS) vs K-Nearest Neighbors (k-NN) vs Fuzzy c-Means (FCM)”,Recent researches in computer science,pp285-290 [7] T. Logeswaria,M. Karnan,” An improved implementation of brain tumor detection using segmentation based on soft computing”, Journal of Cancer Research and Experimental Oncology Vol.2(1) pp. 006-014, March, 2010 [8] Haarlick(1979),”R.M. Statistical and structural approaches to texture”, Proceedings of the IEEE, vol.67, pp.786-804. [9] S. K. Saha, A.K. Das, Bhabatosh Chanda(2004), “ CBIR using Perception based Texture and Color Measures”, in Proc. of 17th International Conference on Pattern recognition(ICPR’04), Vol. 2, [10] MATLAB, User’s Guide, The Math Works [11] Rafel C.Gonzalez,Richard E.Woods,Steven L.Eddins,”Digital image processing using MATLAB”,pp82-83,338-339,336-351 [12] Rami J. Oweis and Muna J.Sunna’(2005), “A Combined Neuro Fuzzy Approach for Classifying Image Pixels in Medical Applications”. Journal of Electrical Engineering, Vol.56, pp 146-150 [13] N.Benamrane, A. Aribi, L.Kraoula(2006), “Fuzzy Neural Networks and Genetic Algorithms for Medical Images Interpretation”. IEEE Proceedings of the Geometric Modeling and Imaging-new trends,.pp 259-264 [14] Ramiro Castellanos and Sunanda Mitra, “Segmentation of magnetic resonance images using a neurofuzzy algorithm”. IEEE Symposium on Computer-Based Medical Systems, 2000. [15] Chin-Ming Hong (2006)l,“A Novel and Efficient Neuro-Fuzzy Classifier for Medical Diagnosis”. IEEE International Joint Conference on Neural Networks pp 735-741
[17] MATLAB, User’s Guide, The Math Works, Inc., [18] Albayrak.S and Amasyal.F.: Fuzzy C-means clustering on medical diagnostic systems, international Turkish symposium on Artificial Intelligence and Neural Networks, 2003. [19] S. K. Saha, A.K. Das, Bhabatosh Chanda(2004), “ CBIR using Perception based Texture and Color Measures”, in Proc. of 17th International Conference on Pattern Recognition(ICPR’04), Vol. 2. [20] Ho-Duck Kim; Chang-Hyun Park; Hyun-Chang Yang(2006) ,”Genetic Algorithm Based Feature Selection Method Development for Pattern Recognition “,SICE-ICASE,pp 1020 1025. [21] Jang, J.-S.R(1993),” ANFIS: adaptive-network-based fuzzy inference system”, Systems, Man and Cybernetics, IEEE Transactions,pp665 – 685 [22] Jang, J.-S.R(1993),” Hybrid Genetic Algorithms for Feature Selection”, Il-Seok Oh, , JinSeon Lee, and Byung-Ro Moon, ,pp 1424-1437.
Citation Count – 24 Application of Neuro-Fuzzy Expert System for the Probe and Prognosis of Thyroid Disorder Imianvan Anthony Agboizebeta1 and Obi Jonathan Chukwuyeni2 1
2
Department of Computer Science, Faculty of Physical Sciences, University of Benin, Benin City, Edo State, Nigeria.
Department of Computer Science, Faculty of Physical Sciences, University of Benin, Benin City, Edo State, Nigeria.
ABSTRACT Thyroid disorders are common disorders of the thyroid gland. Thyroid disorders include such diseases and conditions as graves disease, thyroid nodules, Hashimoto's thyroiditis, trauma to the thyroid, thyroid cancer and birth defects. These include being born with a defective thyroid gland or without a thyroid gland. Thyroid disorder can be caused by hyperthyroidism, thyroid cancer, goiter, hyperparathyroidism and postpartum thyroiditis. Thyroid disorder are usually characterized by life threatening symptoms such as insomnia, irritability, nervousness, unexplained weight loss, heat sensitivity, increased perspiration, thinning of skin, warm skin, fine hair, brittle hair and thinning hair. Neuro-Fuzzy Logic explores approximation techniques from neural networks to finds the parameter of a fuzzy system. This paper which demonstrates the practical application of Information Technology (IT) in the health sector, has presented a hybrid neuro-fuzzy Expert System to help in diagnosis of thyroid disorder using a set of symptoms. The system designed is an interactive system that tells the patient his current condition as regards thyroid disorder.
KEYWORDS Neural network, Fuzzy logic, Diagnosis, Prognosis, Thyroid Disorder
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REFERENCES
[1] Ahsan A. H. M. and Golam K. (2011), “Analytic Hierarchy Process, Chang’s Extent Analysis, Inventory Classification”, International Journal of Fuzzy Logic Systems (IJFLS), 1(1), 1 - 16. [2] Akinyokun O.C. (2002), “Neuro-fuzzy expert system for evaluation of human Resource performance”, First Bank of Nigeria Endowment Fund lecture Federal University of technology, Akure, Nigeria. [3] Aleksander I. and Morton H. (1998), “An introduction to neural computing” 2nd Edition Computer Science press. [4] Andreas N. (2001), “Neuro-Fuzzy system”, retrieved from http//:Neuro-Fuzzy System, html. [5] Bart K. and Satoru I. (1993), “Fuzzy http//:Fortunecity.com/emachines/e11/86/fuzzylog.html
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Citation Count – 20 Framing Fuzzy Rules using Support Sets for Effective Heart Disease Diagnosis E.P.Ephzibah1, Dr. V. Sundarapandian2 1
School of Information Technology and Engineering, VIT University, Vellore, TamilNadu, India, 2
Research and Development Centre, Vel Tech Dr. RR & Dr. SR Technical University, Chennai, Tamil Nadu, INDIA.
ABSTRACT Significance and relevance of certain features are obtained by various techniques. Feature subset selection involves summarizing mutual associations between class decisions and attribute values in a pre-classified database. In this paper genetic algorithm is used to find the relevant set of features by optimizing the fitness function and using the operators like crossover and mutation. Fuzzy logic is a form of knowledge representation suitable for notions that cannot be defined precisely, but which depend upon their contexts. In this work the fuzzy rules are framed with the help of support sets. The classification done using fuzzy inference system provides results that are better than other techniques.
KEYWORDS Genetic Algorithms, Fuzzy logic, Medical data, Disease diagnosis.
For More Details : https://wireilla.com/papers/ijfls/V2N1/2112ijfls02.pdf Volume Link : https://wireilla.com/ijfls/vol2.html
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Isabelle Guyon, Andre Elisseeff: An introduction to variable and feature selection, Journal of Machine learning research 3 1157-1182, (2003)
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Zimmermann, H.-J.: “Fuzzy Set Theory - And its Applications,” 3rd Ed, Kluwer AcademicPublishers, (1997)
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[8] Novruz Allahverdi, Serhat Torun & Ismail Saritas: Design of a fuzzy expert system for determination of coronary heart disease risk, International Conference on Computer Systems and Technologies - CompSysTech’07. [9]
Kwong, C. K., Chang, K.Y. & Tsim, Y. C.: A genetic algorithm based knowledge discovery system for the design of fluid dispensing processes for electronic packaging, Expert Systems with Applications, 36(2), 3829–3838. (2008)
[10] J.Shapiro: Genetic algorithms in machine learning. Lecture notes in computer science (Vol. 2049/2001, pp. 146–168). (2001) [11] Zhu, F., & Guan, S.: Feature selection for modular GA-based classification. Applied Soft Computing, pp.381–393.(2004) [12] Booker, L. B., Goldberg, D. E., & Holland, J. H.: Classifier systems and genetic algorithms, Artificial Intelligence, 40(1–3), 235–282.(1989) [13] Soler, V., Roig, J., Prim, M.: Finding Exceptions to Rules in Fuzzy Rule Extraction, KES 2002, Knowledge-based Intel. Information Engineering Systems, Part 2,11151119.( 2002) [14] Latha Parthiban, Subramanian, R:Intelligent heart disease prediction system using CANFIS and Genetic Algorithm, International Jouranl of Biological Life Sciences, (2007)
[15] Sellappan Palaniappan, Rafiah Awang “Intelligent Heart Disease Prediction SystemUsing Data Mining Techniques” International Conference on Computer Systems and Applications, April 2008. pp.108-115, AICCSA 2008. IEEE/ACS.(2008)
Citation Count – 18 A NEW METHOD FOR RANKING IN AREAS OF TWO GENERALIZED TRAPEZOIDAL FUZZY NUMBERS Salim Rezvani1 1
Department of Mathematics, Imam Khomaini Mritime University of Nowshahr, Nowshahr, Iran
ABSTRACT In this paper, we want proposed a new method for ranking in areas of two generalized trapezoidal fuzzy numbers. A simpler and easier approach is proposed for the ranking of generalized trapezoidal fuzzy numbers. For the confirmation this results, we compared with different existing approaches. 2010 AMS CLASSIFICATION: 47S20, 03E72.
KEYWORDS Generalized Trapezoidal Fuzzy Numbers, Ranking Method.
For More Details : https://wireilla.com/papers/ijfls/V3N1/3113ijfls02.pdf Volume Link : https://wireilla.com/ijfls/vol3.html
REFERENCES
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L. A. Zadeh, Fuzzy set ,(1965) Information and Control, vol.8,no.3, pp.338-353.
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S-H. Chen, (1985) Ranking fuzzy numbers withmaximizing set and minimizing set, Fuzzy Sets and Systems, vol. 17, no. 2, pp. 113 129. [10] C. Liang, J. Wu and J. Zhang, (2006) Ranking indices and rules for fuzzy numbers based on gravity center point, Paper presented at the 6th world Congress on Intelligent Control and Automation, Dalian, China, pp.21-23 [11] Y. J.Wang and H. S.Lee, (2008) The revised method of ranking fuzzy numbers with an area between the centroid and original points, Computers and Mathematics with Applications, vol. 55, pp.2033- 2042. [12] S. j. Chen and S. M. Chen, (2007) Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers, Applied Intelligence, vol. 26, pp. 1-11. [13] S. Abbasbandy and T. Hajjari, (2009) A new approach for ranking of trapezoidal fuzzy numbers, Computers and Mathematics with Applications, vol. 57, pp. 413-419. [14] S.M Chen and J. H. Chen, (2009) Fuzzy risk analysis based on ranking generalized fuzzy numbers with different heights and different spreads, Expert Systems with Applications, vol. 36. pp. 6833- 6842. [15] Shan-Hou Chen and Guo-Chin Li, (2000) Representation, Ranking, ang Distance of Fuzzy Number eith Exponential Membership Function Using Graded mean Integration method, Tamsui Oxford journal of Mathematical Sciences 16, 123-131. [16] Cheng, CH., (1998) A new approach for ranking fuzzy numbers by distance method. Fuzzy Sets and Systems, 95 (3), 307-317.
[17] Chu, TC., Tsao, CT., (2002) Ranking fuzzy numbers with an area between the centroid point and original point. Computers and Mathematics with Applications, 43 (1-2), 111117. [18] Jiang Wen et al, (2011) A modified similarity measure of generalized fuzzy numbers, Procedia Engineering 15. 2773- 2777. [19] Pushpinder Singh et al, (2010) Ranking of Generalized Trapezoidal Fuzzy Numbers Based on Rank, Mode, Divergence and Spread, Turkish Journal of Fuzzy Systems (eISSN: 13091190), Vol.1, No.2, pp. 141-152. [20] Kumar, A., Singh P., Kaur A., Kaur, P., (2010) RM approach for ranking of generalized trapezoidal fuzzy numbers. Fuzzy Information and Engineering, 2 (1), 37-47. [21] Babu et al, Ranking Generalized Fuzzy Numbers using centroid of centroids, International Journal of Fuzzy Logic Systems (IJFLS) Vol.2, No.3, July 2012.
Citation Count – 17 COMPARISON OF DIFFERENT T-NORM OPERATORS IN CLASSIFICATION PROBLEMS Fahimeh Farahbod1 and Mahdi Eftekhari2 1
Department of Computer Engineering, Shahid Bahonar University, Kerman, Iran
2
Department of Computer Engineering, Shahid Bahonar University, Kerman, Iran
ABSTRACT Fuzzy rule based classification systems are one of the most popular fuzzy modeling systems used in pattern classification problems. This paper investigates the effect of applying nine different T-norms in fuzzy rule based classification systems. In the recent researches, fuzzy versions of confidence and support merits from the field of data mining have been widely used for both rules selecting and weighting in the construction of fuzzy rule based classification systems. For calculating these merits the product has been usually used as a Tnorm. In this paper different T-norms have been used for calculating the confidence and support measures. Therefore, the calculations in rule selection and rule weighting steps (in the process of constructing the fuzzy rule based classification systems) are modified by employing these T-norms. Consequently, these changes in calculation results in altering the overall accuracy of rule based classification systems. Experimental results obtained on some well-known data sets show that the best performance is produced by employing the AczelAlsina operator in terms of the classification accuracy, the second best operator is DuboisPrade and the third best operator is Dombi. In experiments, we have used 12 data sets with numerical attributes from the University of California, Irvine machine learning repository (UCI).
KEYWORDS Pattern classification, Fuzzy systems, T-norm operators.
For More Details : https://wireilla.com/papers/ijfls/V2N3/2312ijfls03.pdf Volume Link : https://wireilla.com/ijfls/vol2.html
REFERENCES
[1] H. Ishibuchi, T. Yamamoto, Rule Weight Specification in Fuzzy Rule-Based Classification Systems, IEEE Trans. on Fuzzy Systems (2005), vol. 13, no. 4, pp. 428435. [2] http://archive.ics.uci.edu/ml/. [3] O. T. Yílíz, ö. Aslan, E. Alpaydin, Multivariate Statistical Tests for Comparing Classification Algorithms, C.A. Coello Coello (Ed.): LION 5, LNCS 6683, pp. 1–15, (2011). (©Springer-Verlag Berlin Heidelberg 2011.) [4] D. Sheskin, Handbook of parametric and nonparametric statistical procedures. Chapman & Hall/CRC (2006). [5] S. García, A. Fernández, J. Luengo, F. Herrera, Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power, Information Sciences 180 (2010) 2044–2064. [6] J. Demšar, Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7:1–30 (2006).