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In mathematical logic, the Boolean satisfiability problem (SAT) is an important decision problems solved by determining if a solution exists to a Boolean formula. A SAT instance is satisfiable when there exists an assignment of Boolean variables (each either TRUE or FALSE) that results in the Boolean formula returning TRUE. Artificial neural network (ANN) possesses a comprehensive structure of training and testing stages thus, ANN has emerged as one of the most efficient tools extend in finding patterns and extract information to solve real-life applications.  The work presents an inclusion of random three satisfiability of Boolean logical problem in Hopfield neural network (HNN). The main purpose is to utilize the optimization capacity of the Lyapunov energy function of Hopfield neural network (HNN) for optimal representation of Random kSatistibaility for COVID-19 Surveillance Data Set (CSDS) classification. The  COVID-19 Surveillance Data Set (CSDS) was obtained from an UCI , with the aim of extracting the relationship of dominant attributes that contribute to COVID-19 detections among the based on the data. The logical mining task was carried based on the data mining technique of the energy minimization technique of HNN. The computational simulations have been carried using the different number of clauses in validating the efficiency of the proposed model in the training COVID-19 Surveillance Data Set (CSDS) classification, the findings reveal the effectiveness and robustness of k satisfiability reverse analysis with Hopfield neural network in extracting the dominant attributes toward  COVID-19 Surveillance Data Set (CSDS) logic.

Artificial Neural Network Logic-Based Reverse Analysis with Application to COVID-19 Surveillance Dataset