- Surapati Pramanik has been working as an assistant professor in mathematics since 2006 at Nandalal Ghosh B.T. College... moreSurapati Pramanik has been working as an assistant professor in mathematics since 2006 at Nandalal Ghosh B.T. College, Panpur, Narayanpur, Dist- North 24 Parganas, West Bengal, India, PIN-743126.
• He is enlisted in the World's Top cited 2% Scientists, 2022 ( prepared by Stanford University, USA in association with Elsevier BV).
• Googlescholar: https://scholar.google.com/citations?user=vLGVDYgAAAAJ&hl=en
• AD Scientific index: https://www.adscientificindex.com/scientist.php?id=4305829
• He received a Ph. D. in Mathematics from the Indian Institute of Engineering Science and Technology (IIEST), Shibpur and Ph. D. in Education, an M. Sc. and an M. Ed. from University of Kalyani.
• He acts as a Ph. D. supervisor in Mathematics for Jadavpur University and IIEST, Shibpur.
• His research focuses on fuzzy Multi-Criteria Decision Making (MCDM), mathematics education, Soft computing, and neutrosophic fuzzy sets.
• His research earned outstanding paper awards several times in West Bengal State Science and Technology Congress in mathematics (2011) and Social Sciences (2010, 2013, 2019).
• He is an editorial board member of “Neutrosophic Sets and Systems”, and “Current Chinese Science: Artificial Intelligence & Robotics”.
• His publication includes 31 book chapters, 154 research papers, and 03 editorial books.
• He acts as a reviewer for more than 65 international journals and reviewed more than 260 manuscripts for international journals.
• He is a life member of thirteen scientific organizations including Calcutta Mathematical Society, and ISI, Kolkata. Currently, he is working on neutrosophic and fuzzy hybrid sets.edit
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Bipolar neutrosophic set theory and rough neutrosophic set theory are emerging as powerful tool for dealing with uncertainty, and indeterminate, incomlete, and inprecise information. In the present study we develop a hybrid structure... more
Bipolar neutrosophic set theory and rough neutrosophic set theory are emerging as powerful tool for dealing with uncertainty, and indeterminate, incomlete, and inprecise information. In the present study we develop a hybrid structure called “rough bipoar neutrsophic set”. In the study, we define rough bipoar neutrsophic set and define union, complement, intersection and containment of rough bipolar neutrosophic sets.
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Rough neutrosophic set, a hybrid intelligent structure of rough set and neutrosophic set, is a powerful mathematical tool to deal with indeterminate, inconsistent and incomplete information, which has caught attention to the researchers.... more
Rough neutrosophic set, a hybrid intelligent structure of rough set and neutrosophic set, is a powerful mathematical tool to deal with indeterminate, inconsistent and incomplete information, which has caught attention to the researchers. We present a brief review of decision making models in rough neutrosophic environment. In this chapter, we propose two aggregation operators, namely, a rough neutrosophic arithmetic mean operator (RNAMO) and a rough neutrosophic geometric mean operator (RNGMO). We establish some basic properties of the proposed operators. In the decision making situation, the rating of all alternatives is expressed with the upper and lower approximation operators and the pair of neutrosophic sets, which are characterized by truth-membership degree, indeterminacy-membership degree, and falsity membership degree. Weight of each criterion is completely unknown to the decision maker. We define a cosine function to obtain the unknown criteria weights in rough neutrosophic environment. We develop four new multi-criteria decision making methods based on the proposed operators. Finally, we solve a numerical example to illustrate the feasibility, applicability and efficiency of the proposed methods.
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We propose an approach for multi-attribute group decision-making (MAGDM) problems under neutrosophic information, where the preference values of alternatives over the attributes and the importance of attributes are expressed in terms of... more
We propose an approach for multi-attribute group decision-making (MAGDM) problems under neutrosophic information, where the preference values of alternatives over the attributes and the importance of attributes are expressed in terms of single-valued neutrosophic sets. Firstly, we develop a nonlinear programming approach based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to determine relative closeness intervals of alternatives. Secondly, we aggregate closeness intervals to find out the ranking order of all alternatives by computing their optimal membership degrees based on the ranking method of interval numbers. Finally, we provide an illustrative example to show the effectiveness of the proposed approach.
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The purpose of the study is to examine the performances of international Mathematical Olympiad (IMO) between China, India and the United States of America (USA) and make a comparison. It presents an overview of the preparation of IMO in... more
The purpose of the study is to examine the performances of international Mathematical Olympiad (IMO) between China, India and the United States of America (USA) and make a comparison. It presents an overview of the preparation of IMO in China, India and the USA. The methodology of the study is based on an interpretative and analytical study of document. This study presents the differences of performances of students of China, India and USA at IMO. This study also presents the comparison between the performances of women of China, India and the USA. The performances of China and the USA at IMO are outstanding. The women performances of China are better in terms of participation and obtaining medals than India and the USA.
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Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is an effective tool for multi-attribute decision making. We present a general overview about the development of TOPSIS strategies in neutrosophic environments. In... more
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is an effective tool for multi-attribute decision making. We present a general overview about the development of TOPSIS strategies in neutrosophic environments. In this chapter, we extend TOPSIS strategy to solve multi-attribute group decision making problems in single valued neutrosophic set as well as interval neutrosophic set environments. To develop the proposed TOPSIS strategies, the weights of decision makers are determined by using similarity measure based on Hamming distance. We aggregate each decision maker’s ratings to make common decision using aggregation operators. Employing revised closeness coefficient, we select the best option in the proposed TOPSIS strategies. To demonstrate the applicability and effectiveness of the proposed TOPSIS strategy, we solve two numerical examples of multi-attribute group decision making problems.
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Abstract Selection of a suitable alternative among the possible options is a difficult activity for decision-makers. Because of the indeterminate information and the complexity in periodic form of decision problems, it is difficult to... more
Abstract Selection of a suitable alternative among the possible options is a difficult activity for decision-makers. Because of the indeterminate information and the complexity in periodic form of decision problems, it is difficult to express attributes in terms of crisp sets, fuzzy sets, and neutrosophic sets. However, complex neutrosophic set (CNS)—an important mathematical tool—can tackle uncertainty and indeterminate situations that are in periodic form. CNS generalizes the neutrosophic set whose truth membership function (complex-valued), indeterminacy membership function (complex-valued), and falsity membership functions (complex-valued) are the accumulations of truth amplitude term (real-valued) with phase term, indeterminate amplitude term (real-valued) with phase term, and false amplitude term (real-valued) with phase term, respectively. This chapter presents three similarity measures between CNSs. We propose complex neutrosophic cosine, Jaccard, and Dice similarity measures, and prove some important results of the proposed measures. We also propose weighted complex neutrosophic cosine, Jaccard, and Dice similarity measures, and prove some basic properties of the proposed weighted measures. We define a tangent function for determining unknown attributes weights under CNS environment. We develop cosine, Jaccard, and Dice similarity-based measures as three new strategies for multi-attribute decision-making problems. A numerical example of stream selection of students after secondary examination is given to demonstrate the proposed strategies.