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.
Results
Showing 1 to 20 of 25 for “"Convolution Neural Network"”.
-
Pruning Convolution Neural Network (SqueezeNet) for Efficient Hardware Deployment
… focuses on reducing the model size of the Convolution Neural Network (CNN) by various compression techniques like Architectural compression, Pruning, Quantization, and Encoding (e.g., Huffman encoding). Network pruning is one of the promising technique to solve these problems. This thesis …
-
Multi-class segmentation of brain tumor using Convolution Neural Network
In this report a fully Convolution Neural Network (CNN) architecture is used to segment multi-modal Brain Tumors from Magnetic Resonance (MR) images. Due to the challenges in manual segmentation, computerized brain tumor segmentation is one of the most important challenges in medical imaging. The …
-
Understanding Adversarial Training: Improve Image Recognition Accuracy of Convolution Neural Network
… digits. Recently many researchers work on Convolution Neural Network for image recognition, and get results as good as human being. Additionally, Image recognition task is getting more popular and high demand to apply to other fields, but also there are still many problems to utilize in …
-
Fast and Accurate Image Feature Detection for On-The-Go Field Monitoring Through Precision Agriculture. Computer Predictive Modelling for Farm Image Detection and Classification with Convolution Neural Network (CNN)
… cycle. Then, an ensemble of classifiers with Convolution Neural Networks (CNN) was used as off the shelf feature extractor to train images to develop an end-to-end feature detection and multiclass classification system for plant overall health’s conditions. Whereby previous works have …
-
Visualization for Solving Non-image Problems and Saliency Mapping
… with the CPC-R algorithm for different Convolution Neural Network classifiers, and the methods to optimize the several versions of CPC-R images for the same n-point. These results show that the combined CPC-R and deep learning Convolution Neural Network algorithms are able to solve …
-
Online, low-latency decision making for Fetal Magnetic Resonance Imaging with machine learning
… fetal landmarks from 3D EPI data using a deep convolution neural network. We further demonstrate its capability by applying this model to fetal motion analysis. In an attempt to improve the current fetal MRI protocol, we develop a machine learning based online decision making system for fetal …
-
Development of an Emotion Recognition Classifier from Body Language Using Deep Learning for the Children with Autism to Help Identifying Human Emotions
… tested to classify emotions from body language: Convolution Neural Network (CNN), the combination of CNN and Recurrent Neural Network (CNN+RNN), and the combination of CNN and Long Short-Term Memory (CNN+LSTM). For this research, though the CNN model provides better accuracy than CNN+RNN and …
-
Machine Learning for Radio Frequency Interference Flagging
… Classifier, Random Forest Classifier, the U-Net convolution neural network and the Multilayer Perceptron. These algorithms are trained on real data, in which the ground truth includes inherent false positives, and simulated data where the ground truth positions of RFI are absolute. This is done …
-
Real-Time Computed Tomography-based Medical Diagnosis Using Deep Learning
… of low-dose CT images. Our algorithm uses a convolution neural network called DenseNet and Deconvolution network (DDnet) to remove noise and artifacts from the input image. To evaluate its advantages in medical diagnosis, we use DDnet to enhance chest CT scans of COVID-19 patients. We show …
-
Strategies for the Characterization and Virtual Testing of SLM 316L Stainless Steel
… analysis. A custom trained mask region-based convolution neural network (Mask R-CNN) model is used to segment cell features from scanning electron microscopy (SEM) images with an instance segmentation accuracy nearly identical to that of a human researcher, but about four orders of magnitude …
-
Chest X-Ray Image Classification with Deep Learning
… global and local cues into an attention guided convolution neural network (AG-CNN) to identify thorax diseases. AG-CNN consists of three branches (global, local, and fusion branches). The global branch learns the global features for classification. The local branch localizes the discriminative …
-
Essays on Volatility Forecasting
… specifically for volatility. We then apply convolution neural network models on the transformed volatility images and find the forecasting performance is significantly better than both the econometrics and machine learning benchmark models in classification and regression. Moreover, we also …
-
Highly Accurate Fragment Library for Protein Fold Recognition
… the first stage employs a multimodal Deep Belief Network (DBN) to predict the potential structural fragments given a sequence, represented as a fragment vector, and then the second stage uses a deep convolution neural network (CNN) to classify the fragment vectors into the corresponding folds. Our …
-
Emotion recognition in video using deep learning method with subtract pre-processing
… picture and video information;Basic knowledge of convolution neural network; The basic principle of recurrent neural network; Background technology of face features and traditional classification methods; Application of depth learning method in video; Related technologies of object detection and …
-
A Structure-based Machine Learning Approach for Spatiotemporal Fluctuations in Nuclear Thermal Fluids
… In the second level, a parameterized convolution neural network is trained to predict the bias introduced by reference structures approximation. The two-level design leverages vortex identification and local bias correction techniques, which largely increase the data efficiency for …
-
Satellite change detection in the albany thicket biome
… identifying clearings of Thickets using Temporal Convolution Neural Networks and comparing it against the Continuous Change Detection and Classification (CCDC) algorithm. Finally chapter three sets out to develop a Domain adaptive Temporal Convolution Neural Network for continuous change detection …
-
Study on speech emotion recognition based on deep learning
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01
-
Acceleration of deep learning applications using Intel distribution of OpenVINO toolkit
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01
-
Deep Learning Models for Traffic Prediction in Urban Transport Networks.
… component of this vision is the urban transport network which is continuously challenged with increasing number of vehicles on roads. This results in issues including long travel time, increasingly persistent traffic congestion, accidents and road safety concerns. The research work in this thesis …
-
Nano-particle count estimation in light microscopy images
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01
Page 1 of 2