Sitanggang, D., Siregar, S.D., Situmang, S.M., Indra, E., Sagala, A.R., Sihombig, O., Nababan, M., Pasaribu, H., Damanik, R.R., Turnip, M. and Saragh, R.I. (2018) Application of Forward Chaining Method to Diagnosis of Onion Plant Diseases. Journal of Physics: Conference Series, 1007, Article ID: 012048. https://doi.org/10.1088/1742-6596/1007/1/012048

has been cited by the following article:

• TITLE: Developing and Evaluating a Neuro-Fuzzy Expert System for Improved Food and Nutrition in Nigeria

  AUTHORS: Oluwatoyin Mary Yerokun, Moses Okechukwu Onyesolu

  KEYWORDS: Neuro-Fuzzy, Leghemoglobin, Expert System, Confusion Matrix, Dataset

  JOURNAL NAME: Open Access Library Journal, Vol.8 No.4, April 27, 2021

  ABSTRACT: Protein is a class of food needed daily by humans and even animals. Protein is rated as 20% to 30% of daily food requirements making it a very significant part of daily needs in compliance with the International Labour Organization (ILO) order on food. This class of food from animals has been threatened and carries a lot of health risks unlike protein from plant sources. The need for an alternative to plant protein led to this work. A neuro-fuzzy expert system for detection of leghemoglobin in legumes was developed and evaluated. Knowledge acquisition was done by oral interview of prominent biochemists and botanists that provided key technical facts on leghemoglobin and visits to botanical gardens of Society for Underutilized Legumes (SUL) in Nigeria. Production rule-base technique and forward-chaining mechanisms with linguistic antecedent conditions were used. MATLAB platform was employed for the development of the system. Confusion matrix was employed for the performance evaluation of the developed system. The result is a neuro-fuzzy expert system with gaussian membership functions with accuracy of 100% as against 99.56% for triangular, trapezoidal and gaussian combination functions, precision of 100% for all the membership functions evaluated and recall of 100% for gaussian membership functions and 99.53% for triangular, trapezoidal and Gaussian combination functions.