Dr Palash Dutta
@dibru.ac.in
Assist Professor, Dept of Mathematics
Dibrugarh University
RESEARCH INTERESTS
Fuzzy Set Theory, Decision Making
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Palash Dutta and Alakananda Konwar
Elsevier BV
Palash Dutta and Abhilash Kangsha Banik
Springer Science and Business Media LLC
AbstractIn our contemporary world, where crime prevails, the expeditious conduct of criminal investigations stands as an essential pillar of law and order. However, these inquiries often grapple with intricate complexities, particularly uncertainties stemming from the scarcity of reliable evidence, which can significantly hinder progress. To surmount these challenges, the invaluable tools of crime linkage and psychological profiling of offenders have come to the forefront. The advent of Intuitionistic Fuzzy Sets (IFS) has proven pivotal in navigating these uncertain terrains of decision-making, and at the heart of this lies the concept of similarity measure-an indispensable tool for unraveling intricate problems of choice. While a multitude of similarity measures exists for gauging the likeness between IFSs, our study introduces a novel generalized similarity measure firmly rooted in the IFS framework, poised to surpass existing methods with enhanced accuracy and applicability. We then extend the horizon of practicality by employing this pioneering similarity measure in the domain of clustering for crime prediction-a paramount application within the realm of law enforcement. Furthermore, we venture into the domain of psychological profiling, a potent avenue that has the potential to significantly fortify the arsenal of crime investigations. Through the application of our proposed similarity measure, we usher in a new era of efficacy and insight in the pursuit of justice. In sum, this study not only unveils a groundbreaking similarity measure within the context of an Intuitionistic fuzzy environment but also showcases its compelling applications in the arena of criminal investigation, marking a significant stride toward swifter and more informed decisions in the realm of law and order.
Palash Dutta, Bornali Saikia, and Gourangajit Borah
Elsevier BV
Palash Dutta and Gourangajit Borah
Springer Science and Business Media LLC
Palash Dutta, Gourangajit Borah, Brindaban Gohain, and Rituparna Chutia
Springer Science and Business Media LLC
Abstract Background The concept of Pythagorean fuzzy sets (PFSs) is an utmost valuable mathematical framework, which handles the ambiguity generally arising in decision-making problems. Three parameters, namely membership degree, non-membership degree, and indeterminate (hesitancy) degree, characterize a PFS, where the sum of the square of each of the parameters equals one. PFSs have the unique ability to handle indeterminate or inconsistent information at ease, and which demonstrates its wider scope of applicability over intuitionistic fuzzy sets. Results In the present article, we opt to define two nonlinear distances, namely generalized chordal distance and non-Archimedean chordal distance for PFSs. Most of the established measures possess linearity, and we cannot incorporate them to approximate the nonlinear nature of information as it might lead to counter-intuitive results. Moreover, the concept of non-Archimedean normed space theory plays a significant role in numerous research domains. The proficiency of our proposed measures to overcome the impediments of the existing measures is demonstrated utilizing twelve different sets of fuzzy numbers, supported by a diligent comparative analysis. Numerical examples of pattern recognition and medical diagnosis have been considered where we depict the validity and applicability of our newly constructed distances. In addition, we also demonstrate a problem of suitable medicine selection for COVID-19 so that the transmission rate of the prevailing viral pandemic could be minimized and more lives could be saved. Conclusions Although the issues concerning the COVID-19 pandemic are very much challenging, yet it is the current need of the hour to save the human race. Furthermore, the justifiable structure of our proposed distances and also their feasible nature suggest that their applications are not only limited to some specific research domains, but decision-makers from other spheres as well shall hugely benefit from them and possibly come up with some further extensions of the ideas.
Bornali Saikia, Palash Dutta, and Pranjal Talukdar
Springer Science and Business Media LLC
Palash Dutta and Abhilash Kangsha Banik
Springer Science and Business Media LLC
Palash Dutta and Gourangajit Borah
Springer Science and Business Media LLC
Palash Dutta and Sonom Shome
Springer Science and Business Media LLC
Gourangajit Borah and Palash Dutta
Elsevier BV
Brindaban Gohain, Rituparna Chutia, and Palash Dutta
Elsevier BV
Palash Dutta and Gourangajit Borah
World Scientific Pub Co Pte Ltd
This paper’s core objective is to introduce a novel notion called hyperbolic fuzzy set (HFS) where, the grades follow the stipulation that the product of optimistic and pessimistic degree must be less than or equal to one (1), rather than their sum not exceeding one (1) as in case of IFSs. The concept of HFS originates from a hyperbola, which provides extreme flexibility to the decision makers in the representation of vague and imprecise information. It is observed that IFSs, Pythagorean fuzzy sets (PFSs), and q-rung orthopair fuzzy sets (Q-ROFSs) often failed to express the uncertain information properly under some specific situations, while HFS tends to overcome such limitations by being applicable under those perplexed situations too. In this paper, we first define some basic operational laws and few desirable properties of HFSs. Second, we define a novel score function, accuracy function, and also establish some of their properties. Third, a novel similarity and distance measure is proposed for HFSs that are capable of distinguishing between different physical objects or alternatives based on the grounds of “similitude degree” and “farness coefficient”, respectively. Later, the advantages of all of these newly defined measures have been showcased by performing a meticulous comparative analysis. Finally, these measures have been successfully applied in various COVID-19 associated problems such as medical decision-making, antivirus face-mask selection, efficient sanitizer selections, and effective medicine selection for COVID-19. The final results obtained with our newly defined measures comply with several other existing methods that we considered and the decision strategy adopted is simple, logical, and efficient. The significant findings of this study are certain to aid the healthcare department and other frontline workers to take necessary measures to reduce the intensity of the coronavirus transmission, so that we can hopefully progress toward the end of this ruthless pandemic.
Palash Dutta and Gourangajit Borah
Springer Science and Business Media LLC
Multicriteria Decision Making (MCDM) has a huge role to play while ruling out one suitable alternative among a pool of alternatives governed by predefined multiple criteria. Some of the factors like imprecision, lack of information/data, etc., which are present in traditional MCDM processes have showcased their lack of efficiency and hence eventually it has paved the ways for the development of Fuzzy multicriteria decision making (FMCDM). In FMCDM processes, the decision makers can model most of the real-life phenomena by fuzzy information-based preferences. The availability of a wide literature on similarity measure (SM) emphasizes the vital role of SM of generalized fuzzy numbers (GFNs) to conduct accurate and precise decision making in FMCDM problems. Despite having few advantages, most of the existing approaches possessed a certain degree of counter intuitiveness and discrepancies. Thus, we have attempted to propose a novel SM for generalized trapezoidal fuzzy numbers (GTrFNs) which could deliberately overcome the impediments associated with the earlier existing approaches. Moreover, a meticulous comparative study with the existing approaches is also presented. This paper provides us with an improved method to obtain the similarity values between GTrFNs and the proposed SM consists of calculating the prominent features of fuzzy numbers such as expected value and variance. We use fourteen different sets of GTrFNs, to compare the fruition of the present approach with the existing SM approaches. Furthermore, to show the utility and applicability of our proposed measure, we illustrate few practical scenarios such as the launching of an electronic gadget by a company, a problem of medical diagnosis and finally, a proper anti-virus mask selection in light of the recent COVID-19 pandemic. The obtained results with our proposed SM, for the mentioned FMCDM problems, are analytically correct and they depict the efficiency and novelty of the present article.
Brindaban Gohain, Surabhi Gogoi, Rituparna Chutia, and Palash Dutta
Springer Science and Business Media LLC
Pranjal Talukdar and Palash Dutta
IGI Global
The Entropy measure of an intuitionistic fuzzy set (IFS) plays a significant role in decision making sciences, for instance, medical diagnosis, pattern recognition, criminal investigation, etc. The inadequate nature of an entropy measure may lead to some invalid results. Therefore, it is significant to use an efficient entropy measure for studying various decision-making problems under IFS environment. This paper first proposes a novel similarity measure for IFS. Based on the proposed similarity measure, an advanced entropy measure is defined with a different axiomatic approach. This axiomatic approach allows us to measure an IFS's entropy with the help of a similarity measure. To show the efficiency of the proposed similarity measure, a comparative study is performed with the existing similarity measures. Some structural linguistic variables are taken as examples to show the validity and consistency of the proposed entropy measure along with the existing entropy measures. Finally, based on the proposed entropy measure, a multi-criteria decision-making problem is performed.
Pranjal Talukdar, Palash Dutta, and Soumendra Goala
Springer Science and Business Media LLC
Palash Dutta and Bornali Saikia
Springer Nature Singapore
Brindaban Gohain, Rituparna Chutia, and Palash Dutta
Wiley
Palash Dutta and Bulendra Limboo
Springer Science and Business Media LLC
Palash Dutta and Gourangajit Borah
World Scientific Pub Co Pte Ltd
Background: Mega multinational companies are highly dependent on robots to handle the maximum of their machinery workload, which significantly reduces human labor and saves valuable time as well. However, as vital as the role of robots is, a much more challenging task is its selection. Moreover, the robots need to be evaluated on the grounds of different specifications and their ease of handling, which results in a smooth and work-efficient environment. Objective: The prime objective of this paper is to devise a fruitful decision-making model for a robot selection problem, which utilizes a multi-criteria decision-making method known as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The TOPSIS method is based on the newly defined distance measure involving generalized fuzzy numbers with unequal heights (GFNUHs). Methodology/Approach: At first, we define a novel distance measure based on the “expected value” and “variance” of GFNUHs, where both the parameters are evaluated with the help of the [Formula: see text]-cut method. We then also give the expression for the distance-based similarity measure and investigate some of their properties. Both the distance and the similarity measure(s) are then validated for their effectiveness through a hypothetical case study of pattern recognition. Moreover, we consider 10 different bunches of generalized fuzzy numbers (GFNs) and present a comparative study with the already established measures to establish the efficiency and superiority of our proposed measures. Finally, the distance measure is deployed in the TOPSIS method, which facilitates suitable robot selection by an automobile company. Findings/Results: A comparison of results for the proposed distance measure and the similarity measure with the existing ones is presented which proves that the proposed measure(s) are effective and usable. Novelty/Value: The evaluation of expected value and variance of GFNUHs with the help of [Formula: see text]-cut technique is a completely original idea showcased in this paper and its improved version of TOPSIS for GFNUHs as discussed shall add a new direction in the realm of decision-making.
Palash Dutta and Sayesta Akhtari
SAGE Publications
Evaluation of humans’ health risk is an essential and most demanding aid in relevance to the process of decision-making. Accumulation of quality knowledge on the attributes of each and every available data, information and model parameters, involved in risk assessment, plays a crucial role in the process of evaluation. It is important to note that, most frequently, model parameters are imprecise due to the availability of limited data and knowledge. Under such circumstances, probability theory (PT) and the theory of fuzzy sets can be brought forth to deal with the emerging uncertainties. There is also a need to devise an amalgamate technique to perform health risk assessment under uncertainty. Although some different approaches are available in this regard, all approaches are situation or problem dependent and fail to address some specific issues. Therefore, this article presents a general amalgamate technique to address all the concerned issues, and, finally, health risk is carried out using this approach.
Brindaban Gohain, Rituparna Chutia, Palash Dutta, and Surabhi Gogoi
Wiley
Palash Dutta, Gourangajit Borah, and Surajit Borkotokey
Springer Science and Business Media LLC
Soumendra Goala, Deo Prakash, Palash Dutta, Pranjal Talukdar, K. D. Verma, and G. Palai
Springer Science and Business Media LLC
Brindaban Gohain, Rituparna Chutia, and Palash Dutta
Wiley