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 14 of 14 for “"Hybrid neural network."”.
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Active learning based on a hybrid neural network modeller
… training data for the purpose of training neural networks. A new method called MIQR (Maximum Inter-Quartile Range) is proposed for effectively selecting a concise set of training data. In addition, the ensemble concept is introduced in this new method. Data selection is not unduly …
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A Hybrid Neural Network Architecture for Texture Analysis in Digital Image Processing Applications
A new hybrid neural network model capable of texture analysis in a digital image processing environment is presented in this thesis. This model is constructed from two different types of neural network, self-organisation and back-propagation. Along with a brief resume of digital image processing …
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A hybrid neural network and genetic programming approach to the automatic construction of computer vision systems
Both genetic programming and neural networks are machine learning techniques that have had a wide range of success in the world of computer vision. Recently, neural networks have been able to achieve excellent results on problems that even just ten years ago would have been considered intractable, …
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Adapting an LCD for weight generation in an electro-optic neural processor
… an LCD as a weight image display for use in the Hybrid Electro-optical Neural Network (HENN). The HENN project is a proof of concept prototype hybrid neural network that will be used to gather information for a more advanced project in the future. After thoroughly explaining the HENN, this thesis …
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A comparative analysis of statistical and machine learning models with application in AI-powered stroke risk prediction
… and high costs. This study introduces a hybrid neural network (HNN) that integrates classical statistical learning with neural networks to combine interpretability and structure with flexibility and regularization. The model was validated through simulations using NIHSS scores, …
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Application of computational intelligence to explore and analyze system architecture and design alternatives
… and complex sensor-enabled, remote, and highly networked cyber-technical systems. These complex modern systems present several challenges for systems engineers including: increased complexity associated with integration and emergent behavior, multiple and competing design metrics, and an …
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Facial image restoration and retrieval through orthogonality
… in experiments on longer bits. In social networks, heterogeneous multimedia data correlates to each other, such as videos and their corresponding tags in YouTube and image-text pairs in Facebook. Nearest neighbor retrieval across multiple modalities on large data sets becomes a hot yet …
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Autonomous Cricothyroid Membrane Detection and Manipulation using Neural Networks and Robot Arm for First-Aid Airway Management
… the detected position on a medical manikin. A hybrid neural network (HNNet) that can balance both speed and accuracy is proposed. HNNet is an ensemble-based network architecture that consists of two ensembles: the region proposal ensemble and the keypoint detection ensemble. This architecture …
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Moving Toward Intelligence: A Hybrid Neural Computing Architecture for Machine Intelligence Applications
… are often deployed to train a large-scale neural network, resulting in a colossal amount of resources in use while themselves exposing other significant security issues. Among potential approaches, the neuromorphic architecture, which is not only amenable to low-cost implementation, but can …
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Applications of machine learning in nuclear imaging and radiation detection
… are primarily based on a variant of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN), which is one of the most popular forms of 'deep learning' technique.</p> <p>The first problem is about interpreting and analyzing 3D medical radiation images automatically. A method is …
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Mining heterogeneous enterprise data
… level is heterogeneous. The cost-sensitive hybrid neural network (Cs-HNN) proposed leverages parallel network architectures and an algorithm specifically designed for minority classification to generate a robust model for learning heterogeneous objects. Events trace an object’s behaviours or …
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Estimation Of Hybrid Models For Real-time Crash Risk Assessment On Freeways
… and NRBF (normalized radial basis function) neural network architecture were explored to identify regime 2 rear-end crashes. The performance of individual neural network models was improved by hybridizing their outputs. Individual and hybrid PNN (probabilistic neural network) models were also …
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Influence of Architecture Design on the Performance and Fuel Efficiency of Hydraulic Hybrid Transmissions
Hydraulic hybrids are a proven and effective alternative to electric hybrids for increasing the fuel efficiency of on-road vehicles. To further the state-of-the-art this work investigates how architecture design influences the performance, fuel efficiency, and controllability of hydraulic hybrid …