Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kunwar, Fateh Bahadur; John, Tegil J.; Krishna, G. Venkata; Reddy, Kotte Amaranadha; Srinivas, T. Aditya Sai; Bharti, Sandeep Title A name given to the resource Fuzzy Logic-AHP Hybrid Model for Faculty Performance Evaluation to Enhance Educational Quality in Higher Education Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE International Conference on Emerging Trends in Computing and Communication, ETCOM 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ETCOM66606.2025.11436403" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ETCOM66606.2025.11436403</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037395939?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037395939?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kunwar F.B., Greater Noida Institute of Technology, Department of Computer Science and Engineering, Uttar Pradesh, Greater Noida, India; John T.J., Christ University, Department of Computer Science, Karnataka, Bengaluru, India; Krishna G.V., Prasad V Potluri Siddhartha Institute of Technology, Department of CSE (AI&amp;ML), Vijayawada, India; Reddy K.A., School of Advanced Sciences, Kalasalingam Academy of Research and Education, Department of Mathematics, Tamil Nadu, Virudhunagar, India; Srinivas T.A.S., Ravindra College of Engineering for Women, Department of CSE, Kurnool, India; Bharti S., Deewan College, Department of Computer Science and Engineering (CSE), Uttar Pradesh, Meerut, India Description An account of the resource Guaranteeing equitable and precise evaluation of teacher performance is a continual challenge in higher education, as subjective discrimination, uneven metrics, and absence of cohesive frameworks frequently obstruct informed decision-making. A hybrid Fuzzy Logic-Analytic Hierarchy Process (AHP) model is developed to integrate systematic requirement weighting through AHP with the uncertainty management features of fuzzy logic. The method assesses faculty performance across various dimensions, including classroom effectiveness, research output, service involvement, and professional advancement. The integration guarantees impartiality in criterion weighing and adaptability in managing qualitative assessments, resulting in a balanced and thorough evaluation method. The proposed hybrid framework, in contrast to standard models, reduces subjectivity, improves interpretability, and provides greater accuracy in prediction. Experimental findings indicate that the model attains an Accuracy of 96.8%, Precision of 97.2%, Recall of 96.5%, F1score of 96.8%, and AUC of 0.98, surpassing baseline methods like Decision Trees, Logistic Regression, and Support Vector Machines. These findings confirm the resilience and flexibility of the proposed methodology in practical teacher evaluation contexts. The research enhances educational quality and facilitates the integration of hybrid decision-support systems into institutional policy-making and future academic performance evaluations. 2025 IEEE. Subject The topic of the resource Analytic Hierarchy Process (AHP); Criteria Weighting; Educational Quality; Faculty Performance Evaluation; Fuzzy Inference System; Fuzzy Logic; Higher Education Analytics; Hybrid Model; Intelligent Decision Support System; Machine Learning in Education; Membership Functions; Multi-Criteria Decision Making; Robustness Analysis; Sensitivity Analysis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158508-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online