Back Propagation Neural Network and Chicken Swarm Optimization for Yoruba Indigenous Food Recognition.
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Date
2023-07-13
Journal Title
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Publisher
University of Ibadan Journal of Science and Logics in ICT Research.
Abstract
Back Propagation Neural Networks (BPNNs) are widely used to model complex systems in a steady state.
However, BPNNs have a slow convergence rate. Several meta-heuristic algorithms have been used to speed up
its convergence. Though, the modifications increased the number of parameters which affect the convergence
rate, but more work needs to be done on BPNN in order to improve the performance of BPNN. This work
introduced Chicken Swarm Optimization (CSO) technique to improve the performance of BPNNs in order to
recognize Yoruba indigenous food. 1251 varieties of Yoruba indigenous foods such as Amala ogede, Iyan
gbere, Puguru, Akara, Dele, Ekuru, Monu, Aseke, Abari, Sapala, and Egbo were prepared. The prepared foods
were captured using a digital camera and were digitalized. The digitalized foods were subjected to preprocessing
using Image resizing, Morphological, Edge scaling, Histogram, normalization and Sobel edge. All images were
subjected to feature extraction using Local Binary Pattern (LBP) and the feature vectors gotten were optimized
with Chicken Swarm Optimization (CSO) algorithm. The result of the optimized parameters were classified
using Back Propagation Neural Network (BPNN). The recognition accuracy using BPNN yields 82.22 %, 87.59
%, 83.67 %, and 85.34 % for the four categories of food respectively, whereas BPNN-CSO yields 85.34 %,
91.90 %, 91.02 %, and 90.23 %, respectively. Sensitivity using BPNN yields 87.96%, 92.22%, 90.22%, 90.13%
while BPNN-CSO delivers 90.13%, 97.78%, 97.74%, 96.71% respectively using recognition time and
sensitivity standard term that are included in the result table. It was observed that, the recognition accuracy,
sensitivity, specificity, and false positive ratio values of BPNN-CSO gives improved performance result
compared to BPNN.
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Citation
Agbeniyi, M.T., Ajao, J. F., and Babatunde, R.S. (2023)