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S. Dhanasekar

Bio: S. Dhanasekar is an academic researcher from VIT University. The author has contributed to research in topics: Computer science & Fuzzy logic. The author has an hindex of 2, co-authored 12 publications receiving 21 citations. Previous affiliations of S. Dhanasekar include SRM University & Bharathiar University.

Papers
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Journal ArticleDOI
TL;DR: The introduced method together with Yager’s ranking technique gives the optimal solution of the problem, which satisfies the conditions of optimality, feasibility, and positive allocation of cells using the elementwise subtraction of fuzzy numbers.
Abstract: In this paper, a new method is proposed to solve fully fuzzy transportation problems using the approach of the Hungarian and MODI algorithm. The objective of the proposed algorithm, namely, fuzzy Hungarian MODI algorithm, is to obtain the solution of fully fuzzy transportation problems involving triangular and trapezoidal fuzzy numbers. The introduced method together with Yager’s ranking technique gives the optimal solution of the problem. It also satisfies the conditions of optimality, feasibility, and positive allocation of cells using the elementwise subtraction of fuzzy numbers. A comparative study of the proposed method with existing procedure reveals that the solution of the proposed method satisfies the necessary conditions of a Transportation Problem (TP) to be an optimal solution in which the other methods do not guarantee. The proposed method is the extension of the Hungarian MODI method with fuzzy values. It is easy to understand and implement, as it follows the standard steps of the regular transportation problems. The method can be extended to other kinds of fuzzy transportation problems, such as unbalanced fuzzy TP, fuzzy degeneracy problem, fuzzy TP with prohibited routes, and many more.

15 citations

Journal ArticleDOI
TL;DR: Bellman RE, Zadeh LA, and Hannan EL 1981: Linear programming with multiple fuzzy goals.
Abstract: Bellman RE, Zadeh LA . 1970. Decision-making in a fuzzy environment, Manage. Sci. , 17: 141-164. Hannan EL 1981. Linear programming with multiple fuzzy goals. Fuzzy Sets Syst. , 6: 235-248 Hansen MP 2000. Use of substitute Scalarizing Functions to guide Local Search based Heuristics: The case of MOTSP, J. Heuristics, 6: 419-431 Jaszkiewicz A 2002. Genetic Local Search for Multiple Objectives Combinatorial

8 citations

Proceedings ArticleDOI
21 Apr 2022
TL;DR: An optimized area for a 64-point FFT processor using a Vedic multiplier with a modified compressor adder with modified 4:2 Compressor adder to speed up the vedic multipliers, minimizing carry delay.
Abstract: Fast Fourier Transform (FFT) technique has been used for processing the images, signals. It is used in spectrum analysis, sound filtering, image filtering, and data compression to name but a few. In the mentioned applications, FFT is used to translate the time domain signals into a spectrum representation. An optimized area for a 64-point FFT processor using a Vedic multiplier with a modified compressor adder has been proposed. Multi-radix approaches such as radix-23, radix-22, and radix-24 decrease the computational cost of the FFT processor. In the FFT processor, 8-bit Urdhva Tiryakbhyam vedic multipliers have been introduced, which reduces the total length of multiplication operations. A modified 4:2 Compressor adder will be used to speed up the vedic multipliers, minimizing carry delay. Urdhva Tiryakbhyam vedic multipliers uses modified compressor adders in the multi-radix FFT processor to reduce the silicon chip hardware area. The proposed multi-radix FFT processor uses a Field Programmable Gate Array (FPGA) chip named Virtex-6 XC6VLX75T device with 354S slice registers and 19247 slice Look up Tables (LUTs). It operates with a clock frequency of 148.57 MHz as its maximum. The FFT algorithm developed decreases the slice register to 87.8y% and slice LUTs to 48%. It operates by the clock frequency up to 148.57 MHz.

4 citations

Journal ArticleDOI
03 Nov 2020
TL;DR: An improved Hungarian method is introduced for solving fuzzy assignment problem and can be applied for finding the Hamiltonian circuit with minimum fuzzy cost in the fuzzy traveling salesman problem.
Abstract: In this paper an improved Hungarian method is introduced for solving fuzzy assignment problem. When we apply Hungarian method to solve fuzzy assignment problem, if the minimum number of lines crossing the fuzzy zeros are not equal to the order of the fuzzy cost matrix, this method can be used to get the optimal solution with less computational work. This method reduces the computational work of getting the optimal solution. Further this method can also be applied for finding the Hamiltonian circuit with minimum fuzzy cost in the fuzzy traveling salesman problem. Some numerical examples are furnished to understand the algorithm.

3 citations

Proceedings ArticleDOI
21 Apr 2022
TL;DR: The application of transfer learning and deep learning in the Computed Aided Diagnosis (CAD) system to aid doctors in classifying lung nodules is proposed in this paper and the performance of these deep features is explored.
Abstract: Cancer is one of the deadliest diseases that affect people worldwide, regardless of their socioeconomic situation. Head and neck cancer, brain cancer, stomach cancer, breast cancer, and pancreatic cancer are some of the different types of cancer. Lung cancer has the highest occurrence and fatality rate among all cancer diseases worldwide. It has become more prevalent in developing countries due to increased air pollution. As a result of this terrible sight, lung cancer screening and early diagnosis are now more important than ever. Taking Computed Tomography (CT) images of the entire chest region and analyzing them for any abnormalities is the most common method for detecting lung cancer at its initial stage. In the clinical diagnosis of many diseases, transfer learning and deep learning are becoming increasingly important. Lung nodules, categorized as malignant or benign, are radiographic indications of lung cancer. The application of transfer learning and deep learning in the Computed Aided Diagnosis (CAD) system to aid doctors in classifying lung nodules is proposed in this paper. Convolutional Neural Networks (CNNs) such as VGG16, VGG19, and ResNet50 are used as feature extractors in this study. In the classification of lung nodules, the performance of these deep features is explored.

3 citations


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Book
01 Jan 1980
TL;DR: Fuzzy Logic in Data Modeling Semantics, Constraints and Database Design, Kluwer Academic Publ.
Abstract: R. Babuska, Fuzzy Modeling for Control, Kluwer Academic Publ., Dordrecht, 1998, 288 p. B. Bouchon-Meunier, L. Foulloy, M. Ramdani, Logique Floue Exercices Corriges et Exemples d' Applications, Cepadues Editions. Toulouse, 199B, 200 p. G.Q. Chen, Fuzzy Logic in Data Modeling Semantics, Constraints and Database Design, Kluwer Academic Publ., Boston. 1998, 240 p. K.J. Cios. W. Pedrycz, R.S. Swiniarski. Data Mining Methods for Knowledge Discovery, Kluwer Academic Publ., Dordrecht. 199B. J. Godjevac, Idees Nettes sur la Logique Floue, Presses Polytechniques et Universitaires Romandes. Lausanne, 1997,200 p. E. Hisdal, Logical structures for representation of knowledge and uncertainty, Physica-Verlag, Heidelberg, Germany, 199B. M. Jamshidi, A. Titli, L.A. Zadeh, S. Boverie, Applications of Fuzzy LogiC Towards High Machine Intelligence Quotient Systems, Prentice-Hall, Englewood Cliffs, NJ, 1997,423 p. F. Lootsma, Fuzzy Logic for Planning and Decision Making Applied Optimization 8. Kluwer Academic Publ., Dordrecht, 1998. W. Mielczarski, Fuzzy Logic Techniques in Power Systems, Physica-Verlag, Berlin, 1998, 456 p. J.N. Mordeson, P.S. Nair, Fuzzy Mathematics An Introduction for Engineers and Scientists. Physica-Verlag, Berlin, 1998,270 p. W. Pedrycz, Computational Intelligence An Introduction, CRC Press, Boca Raton, FL, 1997,304 p. M. Reghis, E. Roventa, Classical and Fuzzy Concepts in Mathematical Logic and Applications, CRC Press, Boca Raton, FL, 1998. L. Reznik, Fuzzy Controllers, Newness Press, UK, 1997. Schindler, Fuzzy-Datenanalyse durch kontextbasierte Datenbankanfragen, Deutscher Universitiits Verlag, Leverkusen, Germany, 1998. O. Wolkenhauer, Possibility Theory with Applications to Data Analysis. Research Studies Press, Hertfordshire, UK, 1998, 290 p. Edited volumes

197 citations

Journal ArticleDOI
TL;DR: The aim of this study is to make general users aware that many of these fuzzy arithmetic operations are incorrectly used and can lead to untrue and misleading consequences.

21 citations

Journal ArticleDOI
01 Mar 2019
TL;DR: A novel direct solution approach for fully fuzzy transportation problems in which all of the model parameters as well as decision variables are considered as fuzzy numbers and embedded into a metaheuristic, namely priority-based PSO algorithm for generating new solution vectors and seeking for better fuzzy acceptable solutions.
Abstract: This paper presents a novel direct solution approach for fully fuzzy transportation problems in which all of the model parameters as well as decision variables are considered as fuzzy numbers. In d...

16 citations

Journal ArticleDOI
TL;DR: The introduced method together with Yager’s ranking technique gives the optimal solution of the problem, which satisfies the conditions of optimality, feasibility, and positive allocation of cells using the elementwise subtraction of fuzzy numbers.
Abstract: In this paper, a new method is proposed to solve fully fuzzy transportation problems using the approach of the Hungarian and MODI algorithm. The objective of the proposed algorithm, namely, fuzzy Hungarian MODI algorithm, is to obtain the solution of fully fuzzy transportation problems involving triangular and trapezoidal fuzzy numbers. The introduced method together with Yager’s ranking technique gives the optimal solution of the problem. It also satisfies the conditions of optimality, feasibility, and positive allocation of cells using the elementwise subtraction of fuzzy numbers. A comparative study of the proposed method with existing procedure reveals that the solution of the proposed method satisfies the necessary conditions of a Transportation Problem (TP) to be an optimal solution in which the other methods do not guarantee. The proposed method is the extension of the Hungarian MODI method with fuzzy values. It is easy to understand and implement, as it follows the standard steps of the regular transportation problems. The method can be extended to other kinds of fuzzy transportation problems, such as unbalanced fuzzy TP, fuzzy degeneracy problem, fuzzy TP with prohibited routes, and many more.

15 citations