Top Read Articles in Fuzzy Logic Systems
International Journal of Fuzzy Logic Systems (IJFLS)
ISSN: 1839 – 6283
https://wireilla.com/ijfls/index.html
COMPARATIVE ANALYSIS OF AHP AND FUZZY AHP MODELS FOR MULTICRITERIA INVENTORY CLASSIFICATION
Golam Kabir1 and Dr. M. Ahsan Akhtar Hasin2 1Department of Industrial and Production Engineering, Bangladesh University of Science and Technology (BUET), Dhaka-1000, Bangladesh 2Department 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
Full Text: https://wireilla.com/papers/ijfls/V1N1/1011ijfls01.pdf Volume Link: https://wireilla.com/ijfls/vol1.html
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[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), 249-257. [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), 29-44. [26] F.Y. Partovi & M. Anandarajan, (2002) “Classifying inventory using and artificial neural network 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.
A NEW ANALYSIS OF FAILURE MODES AND EFFECTS BY FUZZY TODIM WITH USING FUZZY TIME FUNCTION
M.Mahmoodi & A. Mirzazadeh Aboulfazl.Mirzazadeh, PHD, Department of Industrial engineering, Kharazmi University, Tehran, Iran Mahdi.Mahmoodi, Ms.Student, Department of Industrial engineering, Kharazmi University, Tehran, Iran
ABSTRACT Failure mode and effects analysis (FMEA) is awidely used engineering technique for designing, identifying and eliminating theknown and/or potential failures, problems, and errors and so on from system to other parts. The evaluating of FMEA parameters is challenging point because it’s importantfor managers to know the real risk in their systems. In this study,we used fuzzy TODIM for evaluating the potential failure modes in our system respect to factors of FMEA,which is known as; Severity(S); Occurrence (O); and detect ability (D).The final result was combined with fuzzy time function which helps to predict systems in future and solving problems in our system and it could help to avoid potential future failure modes in our systems.
KEYWORDS Fuzzy FMEA, Fuzzy set theory, Fuzzy TODIM, Fuzzy time function
Full Text: https://wireilla.com/papers/ijfls/V4N2/4214ijfls02.pdf Volume Link: https://wireilla.com/ijfls/vol4.html
REFERENCES [1] Chin. K. S. Chan. A. & Yang. J. B. Development of a fuzzy FMEA based product design system. International journal of Advanced Manufacturing Technology. 36(2008), 633-649. [2] Gilichrist, W. Modelling failure mode and effect analysis. International journal of Quality and Reliability Management.10 (5) (1993) 16-23. [3] Sharma, R.K.Kumar, D, &Kumar,P. Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modeling. International Journal of Quality and Reliability Management, 22(9) (2005)986- 1004. [4] Ben-Daya, M, & Raouf, A, .A revised failure mode and effects analysis model. International Journal of Quality and Reliability Management.13 (1) (1996), 43-47. [5] Tay, K. M., & Lim, C. P. (2006). Fuzzy FMEA with a guided rules reduction system forprioritization of failures. International Journal of Quality and ReliabilityManagement, 23(8), 1047–1066. [6] Xu, K., Tang, L. C., Xie, M., Ho, S. L., & Zhu, M. L. (2002). Fuzzy assessment of FMEA forengine systems. Reliability Engineering and System Safety, 75, 17–29. [7] Wang. Y. M. Chin. K. S., Poon. G. K. K. & Yang. J. B. Risk assessment of FMEA for engine systems. Reliability Engineering and System Saftey.75 (2009)17-29. [8] Zadeh, L, A. Fuzzy set. Information and Control.8 (1965)338-353. [9] Zimmermann, H, J. Fuzzy set theory and its application. Norwell Massachusetts. International Thomson Publishing. (2001) . [10] Klir, G. J., & Yuan, B. Fuzzy sets and fuzzy logic: theory and application. Prenticehall PTR, New Jersey.(1995) [11] Hwang, C. L., & Yoon, K.. Multiple attributes decision making methods andapplications. Berlin: Springer.(1981) [12] Ahmet Can Kutlu& Mehmet Ekmekciog˘lu, Expert systems with Applications :Fuzzy failure modes and effects analysis by using fuzzy Topsis-based fuzzy AHP.39(2012)61-67. [13] S. Zhang, S. Liu, R. Zhai, Anexrended GRA method for MCDM with interval valued triangular fuzzy assessment and unknown weights, Computers and Industrial Engineering,61(2011).1336–1341. [14] M.L. Tseng et al, Multicriteria analysis of green supply chain management using interval-valued fuzzy TODIM, Knowl.Based Syst,2012. [15] M.Mahmoodi,A.Arshadikhamseh, Advances in Fuzzy systems: New Fuzzy TopsisTodim Hybrid Method for Green Supplier SelectionUsing Fuzzy Time Function(2014).
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.
Full Text: 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 Sets and Systems, 76; 113 -130, 1995. [3] R. H. Goetschel, Fuzzy colorings of fuzzy hypergraphs, Fuzzy Sets and Systems, 94; 185 - 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 and Their 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 in Mathematics, 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 modeling and multiagent decision analysis, Proceedings of IEEE Conf., 305 - 309, 1994.
A NEW OPERATION ON HEXAGONAL FUZZY NUMBER
P. Rajarajeswari1, A.Sahaya Sudha2 and R.Karthika3 1Department of Mathematics, Chikkanna Government Arts College, Tirupur-641 602 2Department of Mathematics, Nirmala College for women, Coimbatore-641018 3Department 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
Full Text: 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 fuzzy numbers, Fuzzy Sets and Systems, vol.136, pp.363-374 [6] Heilpern.S.,(1997), Representation and application of fuzzy numbers, Fuzzy sets and Systems, 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 Personal views. 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.
On Solving
Fuzzy Mathematical relationships,
Information
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PROPERTIES OF FUZZY INNER PRODUCT SPACES
Asit Dey1 and Madhumangal Pal2 1Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore-721102, India 2Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore-721102, India
ABSTRACT In this paper, natural inner product structure for the space of fuzzy n−tuples is introduced. Also we have introduced the ortho vector, stochastic fuzzy vectors, ortho- stochastic fuzzy vectors, ortho-stochastic fuzzy matrices and the concept of orthogonal complement of fuzzy vector subspace of a fuzzy vector space.
KEYWORDS Ortho vector, Stochastic vector, Ortho-Stochastic vector, Orthogonal Complement, Orthostochastic matrix, Reflection.
Full Text: http://wireilla.com/papers/ijfls/V4N2/4214ijfls03.pdf Volume Link: https://wireilla.com/ijfls/vol4.html
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THINK FUZZY SYSTEM: DEVELOPING NEW PRICING STRATEGY METHODS FOR CONSUMER GOODS USING FUZZY LOGIC
Antonio Morim*, Eduardo Sá Fortes*, Paulo Reis+, Carlos Cosenza+ , FranciscoDoria+, Armando Gonçalves+ * Production Engineering Master’s Program, Federal University of Rio de Janeiro, Instituto Alberto Luiz Coimbra de Pós–Graduação e Pesquisa de Engenharia ,COPPE, Bloco G, Ilha do Fundão, 21945-970, Rio de Janeiro – Brasil
ABSTRACT The main purpose of this article is to present and explore potential applications in marketing administration related to pricingstrategyusingfuzzylogic. Considering the new trends in consumer behavior in Brazil’s economy and the consistent growth of C and D social classes an application was developed by the authors to better understand and adjust pricing strategies: The Think Fuzzy System that combines fuzzy logic (COPPE Cosenza Model), and some other related strategic concepts, supported by mathematical microeconomic modeling, utility factor, indifference curves and an experiential hierarchic clustering model.
KEYWORDS Consumer behavior, think fuzzy system, fuzzy logic, pricing methods, microeconomic mathematical models
Full Text: https://wireilla.com/papers/ijfls/V7N1/7117ijfls01.pdf Volume Link: https://wireilla.com/ijfls/vol7.html
REFERENCES [1] ALAMGIR, M., et al.Influence of brand name on consumer decision making process - an empirical study on car buyers (2010). Ann. “Ştefancel Mare” Univ. Suceava. Fascicle Fac. Econ. Public Admin., 10 (2) (2010), pp. 142–153; [2] BAGOOZI, et al. (1999). The role of emotions in marketing. Journal of Academic Marketing Science, 27 (2) (1999), pp. 184–206 [3] BIDIN, NUZARARHIA et al (2016);7th. International Economics & Business Management Conference; [4] AAKER, David A.(2009), Marketing Research; Wiley & Sons, New York; [5] CHANG .C.,(2015). A hybrid decision-making model for factors influencing the purchase intentions of technology products: the moderating effect of lifestyle. Behaviour & Information Technology. Volume 34, Issue 12; [6] COSENZA, Carlos A. (2011), Notas de aula disciplina - Introdução à Lógica Fuzzy – COPPE – UFRJ; Rio de Janeiro; [7] COSENZA, Carlos A. (1981), - "A Industrial Location Model"- Working paper, Martin Centre for Architectural and Urban Studies, Cambridge University [8] DORIA, Francisco, A. (2011) – Notas de aula disciplina- Limites Computacionais e Modelagem Matemática – COPPE – UFRJ; Rio de Janeiro; [9] DORIA, Francisco A. & COSENZA, Carlos A. (2009), Crise na Economia. Editora Revan, Rio de janeiro; [10] GANIDEH, S. et al, 2011. Can Fuzzy Logic Predict Consumer Ethnocentric Tendencies? An Empirical Analysis in Jordan. Journal of Physical Science and Application, 100-106; [11] HENDERSON, James M. & QUANDT, Richard E. (1968), Teoria Microeconomica – Uma Abordagem Matemática. Biblioteca Pioneira de Ciências Sociais, São Paulo; [12] KOTLER P. (2012), Marketing Management, New Jersey, Simon & Schuster Co. [13] LAISoon, W., et al, (2013). Hybrid vehicle adoption - a conceptual study. J. Educ. Vocat. Res. 4 (6), 165e168. [14] KLIR, George J.(1995)Fuzzy Sets and Fuzzy Logic: theory and applications. Prentice Hall, New Jersey; [15] NAEINI, A. et al,(2016), Prioritizing Lifestyles in Shopping Centers, Using Fuzzy Logic Inference System ( Case Study: Shopping Centers in Zanjan). Intal Management Journal 6776; [16] PINDYCK, Robert S. (2009), Microeconomics. Prentice Hall,New Jersey; [17] SAMUELSON, Paul A. (1997), Fundamentos da Análise, Editora Nova Cultural. São Paulo;
[18] SARLI, A. &Tat, H(2011). Attracting Consumers by Finding out Their Psychographic Traits, International Journal of Fundamental Psychology & Social Sciences, Vol 1, No.1, pp.6-10 [19] SOLOMON, Michael R. (2002), Consumer Behaviour: buying, having, and being. Prentice Hall, New Jersey; [20] SOLOMON, Michael R.. et al. (2006). Consumer behavior: A Europeanperspective. England: Pearson Education Limited. 731 p. [21] NAGLE, T., (1987). The Strategy & Tactics of Pricing : A Guide to Profitable Decision Making, New Jersey. Prentice Hall. [22] VALÁSKOVÁ, K., & KLIESTIK, T., (2015). Behavioral Reactions of Consumers To Economic Recession. Journal Business: Theory and Practice, Slovakia; [23] YOGI K.,(2015). An Empirical and Fuzzy Logic Approach to Product Quality and Purchase Intention of Customers in two Whelers, Pacific Science Review B: Humanities and Social Sciences, 57 -69; [24] ZADEH, L.A. (1975), "The concept of a linguistic variable and its application to approximate reasoning", Parts 1 and 2, Information Sciences 8,199-249; 301-357; [25] ZLATEVA, P. et al., A Model of Intention to Purchase as a Component of Social CRM System, 2011 International Conference on E-business, Management and Economics IPEDR Vol.25 (2011) © (2011) IACSIT Press, Singapore;
A COMBINATION OF PALMER ALGORITHM AND GUPTA ALGORITHM FOR SCHEDULING PROBLEM IN APPAREL INDUSTRY
Cecilia E. Nugraheni1, Luciana Abednego1 and Maria Widyarini2 1Dept. of Computer Science, Parahyangan Catholic University, Bandung, Indonesia 2Dept. of Business Adm., Parahyangan Catholic University, Bandung, Indonesia
ABSTRACT The apparel industry is a class of textile industry. Generally, the production scheduling problem in the apparel industry belongs to Flow Shop Scheduling Problems (FSSP). There are many algorithms/techniques/heuristics for solving FSSP. Two of them are the Palmer Algorithm and the Gupta Algorithm. Hyper-heuristic is a class of heuristics that enables to combine of some heuristics to produce a new heuristic. GPHH is a hyper-heuristic that is based on genetic programming that is proposed to solve FSSP [1]. This paper presents the development of a computer program that implements the GPHH. Some experiments have been conducted for measuring the performance of GPHH. From the experimental results, GPHH has shown a better performance than the Palmer Algorithm and Gupta Algorithm.
KEYWORDS Hyper-heuristic, Genetic Programming, Palmer Algorithm, Gupta Algorithm, Flow Shop Scheduling Problem, Apparel Industry
Full Text: https://wireilla.com/papers/ijfls/V11N1/11121ijfls01.pdf Volume Link: https://wireilla.com/ijfls/vol11.html
REFERENCES [1] Cecilia E. Nugraheni and Luciana Abednego. On the Development of Hyper Heuristics Based Framework for Scheduling Problems in Textile Industry. International Journal of Modeling and Optimization, Vol. 6, No. 5, October 2016. [2] Robert, N. Tomastik, Peter, B. Luh, and Guandong, Liu. Scheduling Flexible Manufacturing System for Apparel Production. IEEE Transaction on Robotics and Automation. 12(5): 789-799. [3] Scholz-Retter Bernd et al. 2015. Applying Autonomous Control in Apparel Manufacturing. Proc. Of 9th WSEAS Int. Conference on Robotics, Control and Manufacturing Technology. 73-78. [4] C. E. Nugraheni and L. Abednego, “A survey on heuristics for scheduling problem in textile industry,” in Proc. ICEAI 2015. [5] C. E. Nugraheni and L. Abednego, “A comparison of heuristics for scheduling problems in textile industry,” Jurnal Teknologi, vol. 78, no. 6-6. 2016. [6] Said Aqil and Karam Allali. Three metaheuristics for solving the flow shop problem with permutation and sequence dependent setup time. Proc. Of Conference: 2018 4th International Conference on Optimization and Applications (ICOA). 2019. [7] Peter Bamidele Shola and Asaju La’aro Bolaji. A metaheuristic for solving flowshop problem. International Journal of Advanced Computer Research, Vol 8(37). [8] Le Zhang and Jinnan Wu. A PSO-Based Hybrid Metaheuristic for Permutation Flowshop Scheduling Problems. The Scientific World Journal. Vol. 2014. [9] Ochoa G., Rodriguez J.A.V, Petrovic S., and Burke E. K. 2009. Dispatching Rules for Production Scheduling: a Hyper-heuristic Landscape Analysis. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2009), Montreal, Norway. [10] C. E. Nugraheni and L. Abednego, “Collaboration of multi-agent and hyper-heuristics systems for production scheduling problem,”International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 7, no. 8, pp. 1136-1141, 2013. [11] C. E. Nugraheni and L. Abednego, “A combined meta-heuristic with hyper-heuristic approach to single machine production scheduling,” International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 8, no. 8, pp. 1322-1326, 2014. [12] C.E. Nugraheni, L. Abednego, and M. Widyarini. A Genetic Programming based HyperHeuristic for Production Scheduling in Apparel Industry. International Conference on Machine Learning Techniques and NLP (MLNLP 2020), October 24-25, 2020, Sydney, Australia Volume Editors : David C. Wyld, Dhinaharan Nagamalai (Eds) ISBN : 978-1925953-26-8. [13] E. Taillard. Some efficient heuristic methods for the flow shop sequencing problem. European Journal of Operational Research 47 (1990) pp. 65-74.
FUZZY CLUSTERING BASED SEGMENTATION OF VERTEBRAE IN T1-WEIGHTED SPINAL MR IMAGES
Jiyo.S.Athertya and G.Saravana Kumar Department of Engineering Design, IIT-Madras, Chennai, India
ABSTRACT Image segmentation in the medical domain is a challenging field owing to poor resolution and limited contrast. The predominantly used conventional segmentation techniques and the thresholding methods suffer from limitations because of heavy dependence on user interactions. Uncertainties prevalent in an image cannot be captured by these techniques. The performance further deteriorates when the images are corrupted by noise, outliers and other artifacts. The objective of this paper is to develop an effective robust fuzzy C- means clustering for segmenting vertebral body from magnetic resonance image owing to its unsupervised form of learning. The motivation for this work is detection of spine geometry and proper localisation and labelling will enhance the diagnostic output of a physician. The method is compared with Otsu thresholding and K-means clustering to illustrate the robustness.The reference standard for validation was the annotated images from the radiologist, and the Dice coefficient and Hausdorff distance measures were used to evaluate the segmentation.
KEYWORDS Vertebra segmentation, MRI, fuzzy clustering, labeling
Full Text: https://wireilla.com/papers/ijfls/V6N2/6216ijfls02.pdf Volume Link: https://wireilla.com/ijfls/vol6.html
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INTERVAL-VALUED INTUITIONISTIC FUZZY CLOSED IDEALS OF BG-ALGEBRA AND THEIR PRODUCTS
Tapan Senapati#1, Monoranjan Bhowmik*2, Madhumangal Pal#3 #Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore -721 102, India. *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 AND PHRASES BG-algebras, interval-valued intuitionistic fuzzy sets (IVIFSs), IVIF-ideals, IVIFC-ideals, homomorphism, equivalence relation, upper(lower)-level cuts, product of BG-algebra.
Full Text: http://wireilla.com/papers/ijfls/V2N2/2212ijfls03.pdf Volume Link: https://wireilla.com/ijfls/vol2.html
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[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.
A NEW APPROACH FOR RANKING OF OCTAGONAL INTUITIONISTIC FUZZY NUMBERS
Dr.P.Rajarajeswari1 and G.Menaka2 1Department of Mathematics, Chikkanna Govt Arts College, Tirupur. 2Department of Mathematics, Park College of Technology, Coimbator.
ABSTRACT In this paper we introduce Octagonal Intuitionistic fuzzy numbers with its membership and nonmembership functions. A new method is proposed for finding an optimal solution for intuitionistic fuzzy transportation problem, in which the costs are octagonal intuitionistic fuzzy numbers. The procedure is illustrated with a numerical example.
KEYWORDS Intuitionistic fuzzy transportation problems, Octagonal Intuitionistic fuzzy numbers, Ranking method, Modi method, Initial Basic Feasible Solution, Optimal Solution.
Full Text: http://wireilla.com/papers/ijfls/V7N2/7217ijfls01.pdf Volume Link: https://wireilla.com/ijfls/vol7.html
REFERENCES [1] Fuzzy sets and K.Atanassov.1989. More on Intuitionistic Fuzzy sets, Fuzzy sets and systems, 33, pp.37-46. [2] Atanassov .K.T. “Intuitionistic Fuzzy Sets”, Fuzzy sets and systems, Vol.20 (1), pp: 8796,(1986) [3] A.Thamaraiselvi and R. Santhi,“On Intuitionistic Fuzzy Transportation Problem Using Hexagonal Intuitionistic Fuzzy Numbers”, International Journal of Fuzzy Logic systems (IJFLS) Vol.5, No.1, January 2015. [4] Thangaraj Beaula – M. Priyadharshini, “ A New Algorithm for Finding a Fuzzy Optimal Solution for Intuitionistic Fuzzy Transportation Problems, International Journalof Applications of Fuzzy Sets and Artificial Intelligence ( ISSN 2241-1240), Vol.5(2015),183192. [5] Dr.S.Ismail Mohideen, K.Prasanna Devi, M. Devi Durga, “Fuzzy Transportation Problem of Octagon Fuzzy Numbers with α-Cut and Ranking Technique”, Dr.Ismail Mohideen et al, Journal of Computer – JoC, Vol.1 Issue.2, July-2016, pg-60-67. [6] Dr.Chandrasekaran,G.Gokila, Juno Saju, “ Ranking of Octagonal Fuzzy Numbers for Solving Multi Objective Fuzzy Linear Programming Problem with Simplex Method and Graphical Method, International Journal of Scientific Engineering and Applied Science (IJSEAS) – Volume -1, Issue-5, August-2015. [7] Dr.M.S.Annie Christi Int. “Transportation Problem with Pentagonal Intuitionistic Fuzzy Numbers Solved Using Ranking Technique and Russell’s Method, Journal of Engineering Research and Applications, ISSN: 2248 – 9622, Vol.6.Issue 2, (part-4), Feb 2016, pp.82-86. [8] Nagoor Gani.A, Abbas. S,(2013) “A New method for solving in Fuzzy Transportation Problem”, Applied Mathematics Sciences, vol.7,No.28, pp.1357 – 1365. [9] O’heigeartaigh.H,(1982) “A Fuzzy Transportation Algorithm” Fuzzy Sets and Systems, pp.235-243.