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 65 for “"Convolutional Neural Networks (CNN)"”.
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Deep Convolutional Neural Networks for Classification of Fused Hyperspectral and LiDAR data
Convolutional neural networks (CNN) have demonstrated excellent performance on
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Muon/Pion separation using Convolutional Neural Networks for the MicroBooNE Charged Current Inclusive Cross Section Measurement.
<p>The purpose of this thesis was to use Convolutional Neural Networks (CNN) to separate muons and pions for use in increasing the acceptance rate of muons below the implemented 75cm track length cut in the Charged Current Inclusive (CC-Inclusive) event selection for the CC-Inclusive Cross-Section …
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Bearing Fault Detection and Classification Using Artificial Neural Networks
… applied to fault detection. In this thesis, convolutional neural networks (CNN) are employed for bearing fault detection and classification. Computer simulations results demonstrate that the CNN based approach is advantageous over the conventional regression model, with an overall accuracy of …
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Multi-scale target detection based on morphological shared-weight neural network
Convolutional Neural Networks (CNN) are a popular neural network structure for image based applications. This thesis discusses an alternative network, the morphological shared-weight neural network (MSNN) for object detection. In this thesis, three combined network structures are developed for …
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Using deep learning to classify community network traffic
… to the ever-changing dynamics of modern computer networks and the traffic they generate. Numerous studies on traffic classification make use of the Machine Learning (ML) and single Deep Learning (DL) models. ML classification models are effective to a certain degree. However, studies have shown …
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Strawberry Detection Under Various Harvestation Stages
… Gradients (HOG), Local Binary Patterns (LBP) and Convolutional Neural Networks (CNN) were implemented on a limited custom-built dataset. The methodologies were compared in terms of accuracy and computational efficiency. Computational efficiency is defined in terms of image resolution as testing on …
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Region-based Convolutional Neural Network and Implementation of the Network Through Zedboard Zynq
… vehicles and many other new technologies, the neural network and computer vision has become extremely popular and influential. In particular, for classifying objects, convolutional neural networks (CNN) is very efficient and accurate. One version is the Region-based CNN (RCNN). This is our …
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Deep CNN and MLP-based vision systems for algae detection in automatic inspection of underwater pipelines
Artificial neural networks, such as the multilayer perceptron (MLP), have been increasingly employed in various applications. Recently, deep neural networks, specially convolutional neural networks (CNN), have received considerable attention due to their ability to extract and represent high-level …
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Apply Machine Learning on Cattle Behavior Classification Using Accelerometer Data
… approach, we designed an end-to-end trainable Convolutional Neural Networks (CNN) to predict activities for given segments, applied distillation, and quantization to reduce model size. In addition to the fixed window size approach, we used CNN to predict dense labels that each data point has an …
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Joint spatial and layer attention for convolutional networks
… that learns to sequentially attend to different Convolutional Neural Networks (CNN) layers (i.e., “what” feature abstraction to attend to) and different spatial locations of the selected feature map (i.e., “where”) to perform the task at hand. Specifically, at each Recurrent Neural Network (RNN) …
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AudioCNN: Audio Event Classification With Deep Learning Based Multi-Channel Fusion Networks
… sound classification. We propose the AudioCNN model based on a fusion network consisting of multiple Convolutional Neural Networks (CNN) with aggregation methods for various spectral image spectrogram features and audio-specific data augmentation techniques. We have conducted our extensive …
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Predicting the Likelihood and Scale of Wildfires in California using Meteorological and Vegetation Data
… such as Support Vector Machines (SVM), Basic Neural Networks (BNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Convolutional Neural Networks (CNN) have been highly used in wildfire prediction. The goal of this research is to discover the best combination of …
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Learning Efficient Deep Feature Extraction For Mobile Ocular Biometrics
… better performances have been obtained using Convolutional Neural Networks(CNN) for feature extraction and person recognition. Most of the early works proposed using large CNNs for ocular recognition in subject-dependent evaluation, where the subjects overlap between the training and testing …
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Multi-scale local explanation approach for image analysis using model-agnostic explainable artificial intelligence (XAI)
The recent success of deep neural networks has generated a remarkable growth in Artificial Intelligence (AI) research, and it received much interest over the past few years. However, one of the main challenges for the broad adoption of deep learning based models such as Convolutional Neural …
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Secure analog-to-digital conversion against power side-channel attack
… Firstly, this thesis proposes two neural-network-based SAR ADC PSA methods based on multi-layer perceptron net-works (MLP-PSA) and convolutional neural networks (CNN-PSA). When applied to a SAR ADC without PSA protection, the proposed attack methods decode the power supply current …
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Reliable and efficient wireless communication channel management using optimization and artificial intelligence
… superior predictive accuracy of Feed- Forward Neural Networks (FFNN) in these conditions. Finally, we present AIR-CAV, an AI-assisted reliable channel selection framework for Connected and Autonomous Vehicles (CAVs), which integrates real-world and simulated data to dynamically predict signal …
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Clinical event prediction and understanding with deep neural networks
… along with both long short-term memory networks (LSTM) and convolutional neural networks (CNN) for prediction of five intervention tasks: invasive ventilation, non-invasive ventilation, vasopressors, colloid boluses, and crystalloid boluses. Our predictions are done in a forward-facing …
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Spam Review Detection Using Self-Organizing Maps and Convolutional Neural Networks
… self-organizing maps (SOM) in conjunction with a convolutional neural networks (CNN) are employed to perform classification of the reviews. We transform the reviews into images by arranging semantically-similar words around a pixel of the image or equivalently a SOM grid cell. The resulting review …
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Advanced neural networking and classification techniques for human brain tissues diagnoses: segmenting healthy, cancer affected and edema brain tissues
… the Region Proposal Network (RPN) by Faster R-CNN algorithm. Here, the concept of transfer learning is used during training. The proposed system helps to predict the correct type of tumor with better accuracy about 99%. and classifying by using Convolutional Neural Networks (CNN). The deeper …
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