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Information Seminar Paper

Title

PREDICTION OF EQUILIBRIUM WATER LOSS DURING OSMOTIC DEHYDRATION IN GREEN BEAN USING ARTIFICIAL NEURAL NETWORK

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Abstract

 IN THIS PAPER, ESTIMATION EQUILIBRIUM WATER LOSS OF GREEN BEAN IN OSMOTIC DEHYDRATION USING ARTIFICIAL NEURAL NETWORK HAS BEEN PRESENTED. PROCESSING FACTORS WERE SOLUTE CONCENTRATIONS AND PROCESS TEMPERATURES. FEED FORWARD NEURAL NETWORK WITH LEVENBERG–MARQUARDT TRAINING ALGORITHM WAS USED TO CALCULATE THE OUTPUT VALUES OF THE NEURONS OF THE HIDDEN LAYER. ACCORDING TO THE NETWORK'S TRAINING, VALIDATION AND TESTING RESULTS, A TWO LAYER NEURAL NETWORK WITH EIGHT NEURONS IN THE HIDDEN LAYER IS SELECTED AS THE BEST ARCHITECTURE FOR ACCURATE PREDICTION OF THE WATER LOSS. THE ACCEPTABLE AGREEMENT BETWEEN THE RESULTS OF ANN MODEL AND EXPERIMENTAL DATA DEMONSTRATES THE CAPABILITY OF THE NEURAL NETWORK TECHNIQUE FOR ESTIMATING EQUILIBRIUM WATER LOSS DURING OSMOTIC DEHYDRATION.

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