optIFnet: A Capacitive Antenna Dipole Indention-Flexure Predictive Model Optimized Using Hybrid Lichtenberg Algorithm and Neural Network

In performing underground imaging surveying, applying a coating in the antenna dipole plates with robust and durable material to stay protected against rough road features is vital to consider. By doing this, the mechanical properties of the metallic antenna dipole can be improved and be shielded from deterioration. With that, this study has developed an indentation-flexure algorithm optimized using a hybrid Lichtenberg algorithm (LA) and artificial neural network (ANN) that can predict the indentation-flexure as a function of the coating material’s elastic modulus, Poisson ratio, and thickness as well as the load antenna weight. Acrylic, epoxy, nylon 101, high-density polyethylene, and polyvinyl chloride were chosen as the top five most popular coating materials. A 120° titanium cone indenter with a 0.5-inch-diameter, slightly rounded point, and a constant compressive force of 200 N in the center was employed to plot and use a nonlinear mechanical finite element analysis on an antenna dipole plate using SolidWorks. Nature-inspired and evolutionary metaheuristics such as African vultures, Lichtenberg, and gorilla troop optimization algorithm including genetic algorithm (GA) were employed as optimized models for the hardness indentation for capacitively coupled antenna dipoles. Based on the results, the hybrid LA-ANN solution with a hidden neurons of 3000 and a sigmoid activation function is the best performing model as it acquired a MSE score of 0.0061 in validation and 0.1478 in testing compare to the other model with 0.1610 for GA with 100 hidden neurons with sigmoid activation function. Thus, LA-ANN model is considered as the optIFnet as it exhibited the best prediction performance and fastest convergence among all optimizers used.

Growth Performance Analysis Using Pixel Transformation and Pattern Recognition

To achieve healthy development and optimal growth for harvest in an aquaculture system, correct determination of fish growth stages is very important. The sizes or growth stages of the fish are used by farm managers to regulate stocking densities, optimize daily feeding, and ultimately choose the ideal time for harvesting. This paper presented a vision system-based fish classification using pixel transformation and neural network pattern recognition. Morphometrics parameters are used to facilitate a supervised gathering of datasets. Before feature extraction, the images go through intensity transformation using histogram analysis and Otsu’s thresholding. Using Pearson’s correlation coefficient, the six most important characteristics of the original ten attributes were identified. The developed intelligent model using neural network pattern recognition has an overall training accuracy equal to 90.3%. The validation, test, and overall accuracy are equal to 85.7%, 85.7%, and 88.9%, respectively.