Shehab Abdulhabib Alzaeemi

Work place: School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang Malaysia



Research Interests: Network Architecture, Data Mining, Data Structures and Algorithms, Logic Calculi, Logic Circuit Theory


Shehab Abdulhabib Alzaeemi received a Bachelor Degree of Education (Science) from Taiz Universiti in 2004 and Master of Science (Mathematics) from Universiti Sains Malaysia in 2016 and now student PhD in Universiti Sains Malaysia. He was fellow under the Academic Staff Training System of Sana'a Community College from 2005-2014. His research interests mainly focus on neural network, logic programming, and data mining.

Author Articles
Mean-Field Theory in Hopfield Neural Network for Doing 2 Satisfiability Logic Programming

By Saratha Sathasivam Shehab Abdulhabib Alzaeemi Muraly Velavan

DOI:, Pub. Date: 8 Aug. 2020

The artificial neural network system's dynamical behaviors are greatly dependent on the construction of the network. Artificial Neural Network's outputs suffered from a shortage of interpretability and variation lead to severely limited the practical usability of artificial neural networks for doing the logical program. The goal for implementing a logical program in Hopfield neural network rotates rounding minimizing the energy function of the network to reaching the best global solution which ordinarily fetches local minimum solution also. Nevertheless, this problem can be overcome by utilizing the hyperbolic tangent activation function and the Boltzmann Machine in the Hopfield neural network. The foremost purpose of this article is to explore the solution quality obtained from the Hopfield neural network to solve 2 Satisfiability logic (2SAT) by using the Mean-Field Theory algorithm. We want for replacing the real unstable prompt local field for the separate neurons into the network by its average local field utility. By using the solution to the deterministic Mean-Field Theory (MFT) equation, the system will derive the training algorithms in which time-consuming stochastic measures of collections are rearranged. By evaluating the outputs of global minima ratio (zM), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) with computer processing unit (CPU) time as benchmarks, we find that the MFT theory successfully captures the best global solutions by relaxation effects energy function.

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