Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 104 for “"Artificial Neural Networks (ANNs)"”.
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Deriving statistical inference from the application of artificial neural networks to clinical metabolomics data
… Non-linear projections methods, typified by Artificial Neural Networks (ANNs) may be more appropriate to model potential nonlinear latent covariance; however, they are not widely used due to difficulty in deriving statistical inference, and thus biological interpretation. Herein, we …
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Exploration of the Application of Machine Learning to the Improvement of Interatomic Potentials
… of a specific machine learning process, artificial neural networks (ANNs), to the improvement of IPs and MD simulation is discussed.
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Artificial Neural Network-Based Robotic Control
<p>Artificial neural networks (ANNs) are highly-capable alternatives to traditional problem solving schemes due to their ability to solve non-linear systems with a nonalgorithmic approach. The applications of ANNs range from process control to pattern recognition and, with increasing importance, …
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Modeling helicopter dynamic loads using artificial neural networks
In this thesis, artificial neural networks (ANNs) are used to model helicopter main rotor dynamic loads as a function of flight variables. The motivation to develop an accurate model of such loads is to reduce maintenance and replacement costs by eliminating excessive conservatism currently …
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Scalability Analysis of Synchronous Data-Parallel Artificial Neural Network (ANN) Learners
Artificial Neural Networks (ANNs) have been established as one of the most important algorithmic tools in the Machine Learning (ML) toolbox over the past few decades. ANNs' recent rise to widespread acceptance can be attributed to two developments: (1) the availability of large-scale training and …
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Pattern recognition and the nondeterminable affine parameter problem
… systems using classification models such as artificial neural networks (ANNs) and algorithms whose theoretical foundations come from statistics. The issues involved in implementing several classification models and pre-processing operators - that are applied to patterns before classification …
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Fuzzy neural networks
… other. Interconnected neurons are modeled with artificial neural networks (ANNs or NNs). Neural networks, mathematically speaking, are a system of linked parallel equations that are solved simultaneously and iteratively. Initial research can be found in papers by McCulloch-Pitts (1943), Hebb …
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An Integrated Machine Learning Approach to Optimize the Estimation of Preterm Birth
… a new methodology used in conjunction with artificial intelligence tools to create multiple models for prediction of preterm birth in obstetrical environments. The data mining approach integrates: Decision Trees (DTs), Artificial Neural Networks (ANNs) - specifically a Feed Forward Back …
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Forecasting Chlorine Residual for Water Safety Using Artificial Neural Networks Ensembles in Humanitarian Water Systems
… This thesis investigated the use of ensembles of artificial neural networks (ANNs) to probabilistically forecast the point-of-consumption free residual chlorine (FRC) concentration using water quality data from six refugee and IDP settlements. These models were then used to generate …
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Artificial Neural Network and Dynamic Probabilistic Risk Assessment for passive safety systems
This research emphasizes the application of Artificial Neural Networks (ANNs) and Dynamic Probabilistic Risk Assessment (DPRA) as advanced methodologies for the safety assessment of passive safety systems, particularly in mitigating Loss of Coolant Accidents (LOCAs). While using the BWRX-300 SMR, …
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Implementation of a New Sigmoid Function in Backpropagation Neural Networks.
… sigmoid activation function in backpropagation artificial neural networks (ANNs). ANNs using conventional activation functions may generalize poorly when trained on a set which includes quirky, mislabeled, unbalanced, or otherwise complicated data. This new activation function is an attempt to …
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Development and Demonstration of Surrogate Models to Predict Energy-Related Building Features from Heating and Cooling Load Signature
… features, and 2) supervised learning using artificial neural networks (ANNs) to predict these features. Both methods utilize simulated data and three-parameter change point models (3P CPMs) for efficient characterization of energy performance. The results show reasonable accuracy in …
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Analysis and forecast of the capesize bulk carriers shipping market using Artificial Neural Networks
… it is attempted to uncover the benefits of using Artificial Neural Networks (ANNs) in forecasting the Capesize Ore Voyage Rates from Tubarao to Rotterdam with a 145,000 dwt Bulk carrier. Initially, market analysis allows the assessment of the relation of some parameters of the dry bulk market with …
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OFDM Channel Estimation with Artificial Neural Networks
… This thesis investigates the application of artificial neural networks (ANNs) as a means of improving existing channel estimation techniques. Multi-layer feed forward neural networks (FNNs) and convolutional neural networks (CNNs) are tested on a variety of random fading channels with …
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AI/Machine learning approach to identifying potential statistical arbitrage opportunities with FX and Bitcoin Markets
… system combining an evolutionary algorithm with artificial neural networks (ANNs) is designed to make weekly directional change forecasts on the USD by inferring a prediction using closing spot rates of three currency pairs: EUR/USD, GBP/USD and CHF/USD. The forecasts made by the genetically …
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Towards more biologically plausible deep learning and visual processing
… we have witnessed tremendous successes of Artificial Neural Networks (ANNs) on solving a wide range of Al tasks. However, there is considerably less development in understanding the biological neural networks in primate cortex. In this thesis, I try to bridge the gap between artificial and …
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Information extraction with neural networks
… the first de-identification system based on artificial neural networks (ANNs), which achieves state-of-the-art results without any human-engineered features. The ANN architecture is extended to incorporate features, further improving the de-identification performance. Under practical …
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Improving user modeling via the integration of learner characteristics and learner behaviors
… educational psychology, cognitive science and artificial intelligence, were critically surveyed to identify useful variables for learner modeling in order to identify the subset of variables that proved to be useful in modeling individual learners as they interacted with a computer-based …
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An investigation of neural computing applied to the ambulatory monitoring of the electrocardiogram
… An alternative classification technique based on artificial neural networks (ANNs) was selected for further research. It is shown that whilst techniques based on artificial neural networks are capable of performing the required pattern recognition task, results are presented to demonstrate that …
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