Semantics is the study of meaning and here we explore it through three major representations: brain, image and text. Researchers in the past have performed various studies to understand the similarities between semantic features across all the three representations. Distributional Semantic (DS) models or word vectors that are trained on text corpora have been widely used to study the convergence of semantic information in the human brain. Moreover, they have been incorporated into various NLP applications such as document categorization, speech to text and machine translation. Due to their widespread adoption by researchers and industry alike, it becomes imperative to test and evaluate the performance of di erent word vectors models. In this thesis, we publish the second iteration of BrainBench: a system designed to evaluate and benchmark word vectors using brain data by incorporating two new Italian brain datasets collected using fMRI and EEG technology. In the second half of the thesis, we explore semantics in Convolutional Neural Network (CNN). CNN is a computational model that is the state of the art technology for object recognition from images. However, these networks are currently considered a black-box and there is an apparent lack of understanding on why various CNN architectures perform better than the other. In this thesis, we also propose a novel method to understand CNNs by studying the semantic representation through its hierarchical layers. The convergence of semantic information in these networks is studied with the help of DS models following similar methodologies used to study semantics in the human brain. Our results provide substantial evidence that Convolutional Neural Networks do learn semantics from the images, and the features learned by the CNNs correlate to the semantics of the object in the image. Our methodology and results could potentially pave the way for improved design and debugging of CNNs. / Graduate

Gliomas are an aggressive class of brain tumors that are associated with a better prognosis at a lower grade level. Effective differentiation and classification are imperative for early treatment. MRI scans are a popular medical imaging modality to detect and diagnosis brain tumors due to its capability to non-invasively highlight the tumor region. With the rise of deep learning, researchers have used convolution neural networks for classification purposes in this domain, specifically pre-trained networks to reduce computational costs. However, with various MRI modalities, MRI machines, and poor image scan quality cause different network structures to have different performance metrics. Each pre-trained network is designed with a different structure that allows robust results given specific problem conditions. This thesis aims to cover the gap in the literature to compare the performance of popular pre-trained networks on a controlled dataset that is different than the network trained domain. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

Graph Convolutional Networks (GCNs) draws significant attention due to its power of representation learning on graphs. Recent works developed frameworks to train deep GCNs. Such works show impressive results in tasks like point cloud classification and segmentation, and protein interaction prediction. While for large-scale graphs, doing full-batch training by GCNs is still challenging especially when GCNs go deeper. By fully analyzing a clustering-based mini-batch training algorithm ClusterGCN, we propose random partition which is a more efficient and effective method to implement mini-batch training. Besides, selecting different permutation invariance function (such as max, mean or add) for neighbors’ information aggregation will result in every different results. Therefore, we propose to alleviate it by introducing a novel Generalized Aggregation Function. In this thesis, I analyze the drawbacks caused by ClusterGCN and discuss about its limits. I further compare the performance of ClusterGCN with random partition and the final experimental results show that simple random partition outperforms ClusterGCN with very obvious advantageous for node property prediction task. For the techniques which are commonly used to make GCNs go deeper, I demonstrate a better way of applying residual connections (pre-activation) to stack more layers for GCNs. Last, I show the complete work of training deeper GCNs with generalized aggregators and display the promising results over several datasets from the Open Graph Benchmark (OGB).

Multiphase flow provides a solution to the high heat flux and precision required by modern-day gadgets and heat transfer devices as phase change processes make high heat transfer rates achievable at moderate temperature differences. An application of multiphase flow commonly used in industry is the condensation of refrigerants in inclined tubes. The identification of two-phase flow patterns, or flow regimes, is fundamental to the successful design and subsequent optimisation given that the heat transfer efficiency and pressure gradient are dependent on the flow structure of the working fluid. This study showed that with visualisation data and artificial neural networks (ANN), a machine could learn, and subsequently classify the separate flow patterns of condensation of R-134a refrigerant in inclined smooth tubes with more than 98% accuracy. The study considered 10 classes of flow pattern images acquired from previous experimental works that cover a wide range of flow conditions and the full range of tube inclination angles. Two types of classifiers were considered, namely multilayer perceptron (MLP) and convolutional neural networks (CNN). Although not the focus of this study, the use of a principal component analysis (PCA) allowed feature dimensionality reduction, dataset visualisation, and decreased associated computational cost when used together with multilayer perceptron neural networks. The superior two-dimensional spatial learning capability of convolutional neural networks allowed improved image classification and generalisation performance across all 10 flow pattern classes. In both cases, the classification was done sufficiently fast to enable real-time implementation in two-phase flow systems. The analysis sequence led to the development of a predictive tool for the classification of multiphase flow patterns in inclined tubes, with the goal that the features learnt through visualisation would apply to a broad range of flow conditions, fluids, tube geometries and orientations, and would even generalise well to identify adiabatic and boiling two-phase flow patterns. The method was validated by the prediction of flow pattern images found in the existing literature. / Dissertation (MEng)--University of Pretoria, 2021. / NRF / Mechanical and Aeronautical Engineering / MEng / Restricted

The aim of this thesis is to study problems of deep convolutional neural networks and the connected classification of images and to experiment with the architecture of particular network with the aim to get the most accurate results on the selected dataset. The thesis is divided into two parts, the first part theoretically outlines the properties and structure of neural networks and briefly introduces selected networks. The second part deals with experiments with this network, such as the impact of data augmentation, batch size and the impact of dropout layers on the accuracy of the network. Subsequently, all results are compared and discussed with the best result achieved an accuracy of 86, 44% on test data.

The following master's thesis paper equipped with a short description of CT scans and MR images and the main differences between them, explanation of the structure of convolutional neural networks and how they implemented into biomedical image analysis, besides it was taken a popular modification of U-Net and tested on two loss-functions. As far as segmentation quality plays a highly important role for doctors, in experiment part it was paid significant attention to training quality and prediction results of the model. The experiment has shown the effectiveness of the provided algorithm and performed 100 training cases with the following analysis through the similarity. The proposed outcome gives us certain ideas for future improving the quality of image segmentation via deep learning techniques.

We trained a combined deep convolutional neural network to predict seals’ age (3 categories) and gender (2 categories). The entire dataset contains 110 seals with around 489 thousand location records. Most records are continuous and measured in a certain step. We created five convolutional layers for feature representation and established two fully connected structure as age’s and gender’s classifier, respectively. Each classifier consists of three fully connected layers. Treating seals’ latitude and longitude as input, entire deep learning network, which includes 780,000 neurons and 2,097,000 parameters, can reach to 70.72% accuracy rate for predicting seals’ age and simultaneously achieve 79.95% for gender estimation.

With the rapid development of deep learning techniques, the application of Convolutional Neural Network (CNN) has benefited the task of target object recognition. Several state-of-the-art object detectors have achieved excellent performance on the precision for object recognition. When it comes to applying the detection results for the real world application of collaborative robots, the reliability and robustness of the target object detection stage is essential to support efficient task allocation. In this work, collaborative robots task allocation is based on the assumption that each individual robotic agent possesses specialized capabilities to be matched with detected targets representing tasks to be performed in the surrounding environment which impose specific requirements. The goal is to reach a specialized labor distribution among the individual robots based on best matching their specialized capabilities with the corresponding requirements imposed by the tasks. In order to further improve task recognition with convolutional neural networks in the context of robotic task allocation, this thesis proposes an innovative approach for progressively refining the target detection process by taking advantage of the fact that additional images can be collected by mobile cameras installed on robotic vehicles. The proposed methodology combines a CNN-based object detection module with a refinement module. For the detection module, a two-stage object detector, Mask RCNN, for which some adaptations on region proposal generation are introduced, and a one-stage object detector, YOLO, are experimentally investigated in the context considered. The generated recognition scores serve as input for the refinement module. In the latter, the current detection result is considered as the a priori evidence to enhance the next detection for the same target with the goal to iteratively improve the target recognition scores. Both the Bayesian method and the Dempster-Shafer theory are experimentally investigated to achieve the data fusion process involved in the refinement process. The experimental validation is conducted on indoor search-and-rescue (SAR) scenarios and the results presented in this work demonstrate the feasibility and reliability of the proposed progressive refinement framework, especially when the combination of adapted Mask RCNN and D-S theory data fusion is exploited.

<div>Sorghum, which is grown internationally as a cereal crop that is robust to heat, drought, and disease, has numerous applications for food, forage, and biofuels. When monitoring the growth stages of sorghum, or phenotyping specific traits for plant breeding, it is important to identify and monitor the panicles in the field due to their impact relative to grain production. Several studies have focused on detecting panicles based on data acquired by RGB and multispectral remote sensing technologies. However, few experiments have included hyperspectral data because of its high dimensionality and computational requirements, even though the data provide abundant spectral information. Relative to analysis approaches, machine learning, and specifically deep learning models have the potential of accommodating the complexity of these data. In order to detect panicles in the field with different physical characteristics, such as colors and shapes, very high spectral and spatial resolution hyperspectral data were collected with a wheeled-based platform, processed, and analyzed with multiple extensions of the VGG-16 Fully Convolutional Network (FCN) semantic segmentation model.</div><div><br></div><div>In order to have correct positioning, orthorectification experiments were also conducted in the study to obtain the proper positioning of the image data acquired by the pushbroom hyperspectral camera at near range. The scale of the DSM derived from LiDAR that was used for orthorectification of the hyperspectral data was determined to be a critical issue, and the application of the Savitzky-Golay filter to the original DSM data was shown to contribute to the improved quality of the orthorectified imagery.</div><div><br></div><div>Three tuned versions of the VGG-16 FCN Deep Learning architecture were modified to accommodate the hyperspectral data: PCA&FCN, 2D-FCN, and 3D-FCN. It was concluded that all the three models can detect the late season panicles included in this study, but the end-to-end models performed better in terms of precision, recall, and the F-score metrics . Future work should focus on improving annotation strategies and the model architecture to detect different panicle varieties and to separate overlapping panicles based on an adequate quantities of training data acquired during the flowering stage.</div>

As the world is moving towards a more digital world with the majority of people having tablets, smartphones and smart objects, solving real-world computational problems with handheld devices seems more common. Detection or tracking of objects using a camera is starting to be used in all kinds of fields, from self-driving cars, sorting items to x-rays, referenced in Introduction. Object detection is very calculation heavy which is why a good computer is necessary for it to work relatively fast. Object detection using lightweight models is not as accurate as a heavyweight model because the model trades accuracy for inference to work relatively fast on such devices. As handheld devices get more powerful and people have better access to object detection models that can work on limited-computing devices, the ability to build their own small object detection machines at home or at work increases substantially. Knowing what kind of factors that have a big impact on object detection can help the user to design or choose the correct model. This study aims to explore what kind of impact distance, angle and light have on Inceptionv2 SSD, MobileNetv3 Large SSD and MobileNetv3 Small SSD on the COCO dataset. The results indicate that distance is the most dominant factor on the Inceptionv2 SSD model using the COCO dataset. The data for the MobileNetv3 SSD models indicate that the angle might have the biggest impact on these models but the data is too inconclusive to say that with certainty. With the knowledge of knowing what kind of factors that affect a certain model’s performance the most, the user can make a more informed choice to their field of use.