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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.<\/jats:p>","DOI":"10.1007\/s10462-023-10682-2","type":"journal-article","created":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T09:07:00Z","timestamp":1708592820000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A novel generalized similarity measure under intuitionistic fuzzy environment and its applications to criminal investigation"],"prefix":"10.1007","volume":"57","author":[{"given":"Palash","family":"Dutta","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhilash Kangsha","family":"Banik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,22]]},"reference":[{"issue":"1","key":"10682_CR1","doi-asserted-by":"crossref","first-page":"2789","DOI":"10.1038\/s41598-023-29932-y","volume":"13","author":"F Abbas","year":"2023","unstructured":"Abbas F, Ali J, Mashwani WK, Syam MI (2023) q-rung orthopair fuzzy 2-tuple linguistic clustering algorithm and its applications to clustering analysis. 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