Adaptive Fuzzy Neural Framework for Task Efficiency and Fitness Prediction Analysis - Kim, Dr. Lukas Kazlauskas; Petrauskaite , Dr. Ruta (The American Journal of Interdisciplinary Innovations and Research, 2026)
The increasing integration of intelligent systems into human-centered performance evaluation has created a demand for adaptive computational models capable of analyzing complex relationships between cognitive capacity, task execution, and fitness-related factors. Traditional predictive approaches often struggle with uncertainty, nonlinear interactions, and dynamic variations in human performance characteristics. This research presents an Adaptive Fuzzy Neural Framework for Task Efficiency and Fitness Prediction Analysis, designed to combine the reasoning capability of fuzzy systems with the learning ability of neural networks for improved prediction accuracy and adaptability. The framework conceptualizes task efficiency as a multidimensional outcome influenced by individual resources, environmental conditions, technological interaction, and performance constraints. The proposed approach utilizes fuzzy inference mechanisms to manage imprecise behavioral and operational variables while neural learning techniques optimize predictive relationships from collected performance patterns. The study is theoretically supported by previous research on automation impacts, data-driven analysis, workplace psychological resources, and intelligent system adoption. Existing studies demonstrate that technological transformation changes task requirements and creates new demands for adaptive human performance models (Acemoglu & Restrepo, 2019). Similarly, algorithmic approaches in healthcare and behavioral analysis highlight the potential of computational models for extracting meaningful patterns from complex human data (Alonso et al., 2018). The proposed framework provides a structured pathway for predicting task performance efficiency while considering fitness-related indicators, offering potential applications in workforce optimization, intelligent training systems, and adaptive decision-support environments. The research contributes a conceptual model that bridges artificial intelligence, fuzzy reasoning, and human performance analytics while identifying limitations related to data dependency, interpretability, and real-world deployment challenges.