Enhancing Physical Education Quality Evaluation with Probabilistic Simplified Neutrosophic Analysis and Network Security Frameworks

Abstract

Integrating sophisticated probabilistic analysis and guaranteeing data security with strong network frameworks, the goal is to improve physical education (PE) assessments. Limitations of traditional methods include poor handling of uncertainty, complex computations, lack of flexibility in evaluations, and inadequate data protection mechanisms. Research aims to improve PE quality evaluations through probabilistic simplified neutrosophic analysis (PSNA) while integrating network security frameworks to ensure secure data handling and enhance the accuracy and reliability of assessments. The proposed framework combines PSNA with enhanced network security (NS), further enhanced by various optimization techniques such as ant colony optimization (ACO), posture optimization, particle swarm optimization (PSO) and genetic algorithm (GA) for efficient evaluation and prediction. The combination ensures accurate PE assessments and provides secure, adaptive data management systems. Data collection involves student assessments, performance metrics, feedback, and security logs. Missing values will be assigned using probabilistic and preprocessing techniques. Security protocols such as encryption, multi-factor authentication, secure data storage, and real-time threat detection will safeguard sensitive educational data. The results demonstrate improved accuracy in PE assessments through advanced evaluation techniques and secure data management, with the PSNA-NS performance ensuring reliable, adaptive, and safe educational systems.