Engineering Commons^™

Improved Capability Of A Computational Foot/Ankle Model Using Artificial Neural Networks, Ruchi D. Chande

Theses and Dissertations

Computational joint models provide insight into the biomechanical function of human joints. Through both deformable and rigid body modeling, the structure-function relationship governing joint behavior is better understood, and subsequently, knowledge regarding normal, diseased, and/or injured function is garnered. Given the utility of these computational models, it is imperative to supply them with appropriate inputs such that model function is representative of true joint function. In these models, Magnetic Resonance Imaging (MRI) or Computerized Tomography (CT) scans and literature inform the bony anatomy and mechanical properties of muscle and ligamentous tissues, respectively. In the case of the latter, literature reports …

Pattern Recognition In Class Imbalanced Datasets, Nahian A. Siddique

Theses and Dissertations

Class imbalanced datasets constitute a significant portion of the machine learning problems of interest, where recog­nizing the ‘rare class’ is the primary objective for most applications. Traditional linear machine learning algorithms are often not effective in recognizing the rare class. In this research work, a specifically optimized feed-forward artificial neural network (ANN) is proposed and developed to train from moderate to highly imbalanced datasets.

The proposed methodology deals with the difficulty in classification task in multiple stages—by optimizing the training dataset, modifying kernel function to generate the gram matrix and optimizing the NN structure. First, the training dataset is extracted …