Career Selection Using Single-Valued NeutrosophicSets and Normalized Euclidean Distance Method

Abstract

Career selection involves incomplete, indeterminate, and sometimes conflicting information. This article represents both student profiles and career requirements by single-valued neutrosophic triples and compares them with a normalized Euclidean distance. The normalization is corrected to use all three coordinates, ensuring that every reported distance lies in [0, 1]. A four-student, four-career illustration is recomputed from the supplied profile tables. The resulting nearest-profile recommendations are anatomy for student s1, surgery for s2, and pharmacy for both s3 and s4. In particular, the recalculation corrects the earlier assignment of medicine to s3. A decision-margin certificate is also introduced: if the difference between the two smallest distances exceeds twice the maximum distance-estimation error, the recommendation cannot change under that perturbation. The example is intended as a transparent decision-support demonstration, not as a validated psychological or educational assessment. Real use requires expert elicitation of the career profiles, appropriate subject weights, and validation against independent student outcomes.