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Full-Text Articles in Engineering
Multi-Objective Evolutionary Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns
Multi-Objective Evolutionary Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns
Engineering Management and Systems Engineering Faculty Research & Creative Works
This paper presents an evolutionary neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A pareto-based, multi-objective evolutionary algorithm utilizing the Strength Pareto Evolutionary Algorithm (SPEA2) fitness evaluation scheme simultaneously evolves connection weights and identifies the neural network topology using network complexity and classification accuracy as objective functions. A combined vector-matrix representation scheme and differential evolution recombination operators are employed. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. The inputs to the evolutionary neural network model are used to classify …
Exploring The Effect Of Different Numbers Of Convolutional Filters And Training Loops On The Performance Of Alphazero, Jared Prince
Exploring The Effect Of Different Numbers Of Convolutional Filters And Training Loops On The Performance Of Alphazero, Jared Prince
Masters Theses & Specialist Projects
In this work, the algorithm used by AlphaZero is adapted for dots and boxes, a two-player game. This algorithm is explored using different numbers of convolutional filters and training loops, in order to better understand the effect these parameters have on the learning of the player. Different board sizes are also tested to compare these parameters in relation to game complexity. AlphaZero originated as a Go player using an algorithm which combines Monte Carlo tree search and convolutional neural networks. This novel approach, integrating a reinforcement learning method previously applied to Go (MCTS) with a supervised learning method (neural networks) …
Comparison Of Deep Convolutional Neural Networks And Edge Detectors For Image-Based Crack Detection In Concrete, Sattar Dorafshan, Robert J. Thomas, Marc Maguire
Comparison Of Deep Convolutional Neural Networks And Edge Detectors For Image-Based Crack Detection In Concrete, Sattar Dorafshan, Robert J. Thomas, Marc Maguire
Civil and Environmental Engineering Faculty Publications
This paper compares the performance of common edge detectors and deep convolutional neural networks (DCNN) for image-based crack detection in concrete structures. A dataset of 19 high definition images (3420 sub-images, 319 with cracks and 3101 without) of concrete is analyzed using six common edge detection schemes (Roberts, Prewitt, Sobel, Laplacian of Gaussian, Butterworth, and Gaussian) and using the AlexNet DCNN architecture in fully trained, transfer learning, and classifier modes. The relative performance of each crack detection method is compared here for the first time on a single dataset. Edge detection methods accurately detected 53–79% of cracked pixels, but they …