Promoter region of protein-coding genes are gradually being well understood, yet no comparable studies exist for the promoter of long non-coding RNA (lncRNA) genes which has emerged as a global potential regulator in multiple cellular process and different diseases for human. To understand the difference in the transcriptional regulation pattern of these genes, previously, we proposed a machine learning based model to classify the promoter of protein-coding genes and lncRNA genes. In this study, we are presenting DeepCNPP (deep coding non-coding promoter predictor), an improved model based on deep learning (DL) framework to classify the promoter of lncRNA genes and protein-coding genes. We used convolution neural network (CNN) based deep network to classify the promoter of these two broad categories of human genes. Our computational model, built upon the sequence information only, was able to classify these two groups of promoters from human at a rate of 83.34% accuracy and outperformed the existing model. Further analysis and interpretation of the output from DeepCNPP architecture will enable us to understand the difference in transcription regulatory pattern for these two groups of genes.

Engagement with educational instruction and related materials is an important part of learning and contributes to test performance. There are various measures of engagement including self-reports, observations, pupil diameter, and posture. With the challenges associated with obtaining accurate engagement levels, such as difficulties with measuring variations in engagement, the present study used a novel approach to predict engagement from posture by using deep learning. Deep learning was used to analyze a labeled outline of the participants and extract key points that are expected to predict engagement. In the first experiment two short lectures were presented and participants were tested on a lecture to motivate engagement. The next experiment had videos that varied in interest to understand whether a more interesting presentation engages participants more, therefore helping participants achieve higher comprehension scores. In a third experiment, one video was presented to attempt to use posture to predict comprehension rather than engagement. The fourth experiment had videos that varied in level of difficulty to determine whether a challenging topic versus an easier topic affects engagement. T-tests revealed that the more interesting Ted Talk was rated as more engaging, and for the fourth study, the more difficult video was rated as more engaging. Comparing average pupil sizes did not reveal significant differences that would relate to differences in the engagement scores, and average pupil dilation did not correlate with engagement. Analyzing posture through deep learning resulted in three accurate predictive models and a way to predict comprehension. Since engagement relates to learning, researchers and educators can benefit from accurate engagement measures. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

This thesis presents a case study on the application of deep learning methods for the dense prediction of land cover types in oblique ground-based photography. While deep learning approaches are widely used in land cover classification of remote-sensing data (i.e., aerial and satellite orthoimagery) for change detection analysis, dense classification of oblique landscape imagery used in repeat photography remains undeveloped. A performance evaluation was carried out to test two state-of the-art architectures, U-net and Deeplabv3+, as well as a fully-connected conditional random fields model used to boost segmentation accuracy. The evaluation focuses on the use of a novel threshold-based data augmentation technique, and three multi-loss functions selected to mitigate class imbalance and input noise. The dataset used for this study was sampled from the Mountain Legacy Project (MLP) collection, comprised of high-resolution historic (grayscale) survey photographs of Canada’s Western mountains captured from the 1880s through the 1950s and their corresponding modern (colour) repeat images. Land cover segmentations manually created by MLP researchers were used as ground truth labels. Experimental results showed top overall F1 scores of 0.841 for historic models, and 0.909 for repeat models. Data augmentation showed modest improvements to overall accuracy (+3.0% historic / +1.0% repeat), but much larger gains for under-represented classes. / Graduate

If Pulmonary Tuberculosis (PTB) is detected early in a patient, the greater the chances of treating and curing the disease. Early detection of PTB could result in an overall lower mortality rate. Detection of PTB is achieved in many ways, for instance, by using tests like the sputum culture test. The problem is that conducting tests like these can be a lengthy process and takes up precious time. The best and quickest PTB detection method is viewing the chest X-Ray image (CXR) of the patient. To make an accurate diagnosis requires a qualified professional Radiologist. Neural Networks have been around for several years but is only now making ground-breaking advancements in speech and image processing because of the increased processing power at our disposal. Artificial intelligence, especially Deep Learning Convolutional Neural Networks (DLCNN), has the potential to diagnose and detect the disease immediately. If DLCNN can be used in conjunction with the professional medical institutions, crucial time and effort can be saved. This project aims to determine and investigate proper methods to identify and detect Pulmonary Tuberculosis in the patient chest X-Ray images using DLCNN. Detection accuracy and success form a crucial part of the research. Simulations on an input dataset of infected and healthy patients are carried out. My research consists of firstly evaluating the colour depth and image resolution of the input images. The best resolution to use is found to be 64x64. Subsequently, a colour depth of 8 bit is found to be optimal for CXR images. Secondly, building upon the optimal resolution and colour depth, various image pre-processing techniques are evaluated. In further simulations, the pre-processed images with the best outcome are used. Thirdly the techniques evaluated are transfer learning, hyperparameter adjustment and data augmentation. Of these, the best results are obtained from data augmentation. Fourthly, a proposed hybrid approach. The hybrid method is a mixture of CAD and DLCNN using only the lung ROI images as training data. Finally, a combination of the proposed hybrid method, coupled with augmented data and specific hyperparameter adjustment, is evaluated. Overall, the best result is obtained from the proposed hybrid method combined with synthetic augmented data and specific hyperparameter adjustment. / Electrical and Mining Engineering

In fine-grained visual categorization (FGVC), there is a near-singular focus in pursuit of attaining state-of-the-art (SOTA) accuracy. This work carefully analyzes the performance of recent SOTA methods, quantitatively, but more importantly, qualitatively. We show that these models universally struggle with certain "hard" images, while also making complementary mistakes. We underscore the importance of such analysis, and demonstrate that combining complementary models can improve accuracy on the popular CUB-200 dataset by over 5%. In addition to detailed analysis and characterization of the errors made by these SOTA methods, we provide a clear set of recommended directions for future FGVC researchers.

We propose an e cient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHQ dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of the embedding algorithm provides valuable insights into the structure of the StyleGAN latent space. We propose a set of experiments to test what class of images can be embedded, how they are embedded, what latent space is suitable for embedding, and if the embedding is semantically meaningful.

Cracks are common defects on surfaces of man-made structures such as pavements, bridges, walls of nuclear power plants, ceilings of tunnels, etc. Timely discovering and repairing of the cracks are of great significance and importance for keeping healthy infrastructures and preventing further damages. Traditionally, the cracking inspection was conducted manually which was labor-intensive, time-consuming and costly. For example, statistics from the Central Intelligence Agency show that the world’s road network length has reached 64,285,009 km, of which the United States has 6,586,610 km. It is a huge cost to maintain and upgrade such an immense road network. Thus, fully automatic crack detection has received increasing attention. With the development of artificial intelligence (AI), the deep learning technique has achieved great success and has been viewed as the most promising way for crack detection. Based on deep learning, this research has solved four important issues existing in crack-like object detection. First, the noise problem caused by the textured background is solved by using a deep classification network to remove the non-crack region before conducting crack detection. Second, the computational efficiency is highly improved. Third, the crack localization accuracy is improved. Fourth, the proposed model is very stable and can be used to deal with a wide range of crack detection tasks. In addition, this research performs a preliminary study about the future AI system, which provides a concept that has potential to realize fully automatic crack detection without human’s intervention.

Le diagnostic assisté par ordinateur du cancer du sein suscite de plus en plus un réel engouement en raison de la quantité sans cesse croissante d'images mammographiques issues des campagnes de dépistage. L'accent est mis sur les opacités mammaires en raison du risque élevé de cancer qui leur est associé. En effet, la variabilité des formes rencontrées et la difficulté à discerner les masses surtout quand ces dernières sont embarquées dans des densités importantes exigent une nouvelle stratégie plutôt adaptée aux cas les plus complexes à savoir les masses appartenant aux classes BI-RADS IV et V, c-à-d. respectivement les masses malignes spiculées et les distorsions architecturales. Dans ce travail, un système de diagnostic assisté par ordinateur entièrement automatique et conçu pour la segmentation et la classification des opacités dans les catégories bénigne/maligne ou graisseuse/dense, spécifiquement pour celles de type BI-RADS IV et V est abordé. Dans un premier temps, nous avons développé une approche de pré-traitement des images fondée sur l'apprentissage d'un dictionnaire parcimonieux sur les bases d'images, combiné à une réduction de dimension afin de supprimer de façon efficace et rapide le bruit de numérisation des images mammographiques présentes dans les bases utilisées pour concevoir notre système de diagnostic en comparaison des approches déjà existantes. Une fois les images pré-traitées, nous avons mis en place une procédure de segmentation non-supervisée des masses basée sur les champs de Markov et qui a l'avantage d'être à la fois plus rapide, plus efficace et plus robuste que les meilleures techniques de segmentation disponibles dans l'état-de-l'art. De plus, la méthode proposée s'affranchit de la variabilité des masses et ce quelque soit la densité de l'image. Dans l'idée de décrire convenablement les lésions malignes spiculées, nous avons conçu une méthode de segmentation des spicules qui présente la particularité de ne pas recourir à l'utilisation de descripteurs extraits manuellement dont les performances peuvent varier en fonction de leur qualité. L'approche proposée repose sur des hypothèses que nous avons formulées concernant l'aspect des spicules. Celles-ci nous ont conduits à développer un modèle Markovien combiné à une transformée de Radon locale pour extraire les structures curvilignes de l'image. Ensuite, nous servant d'un modèle a contrario, nous avons pu extraire les spicules de l'ensemble des structures détectées. Cette phase, vient clore la première partie de la conception de notre système, qui est en mesure d'extraire soit des masses spiculées, soit des distorsions architecturales. Afin de finaliser sa conception, nous avons procédé à la création d'un modèle d'aide à la décision qui, à l'inverse de ce qui s'est toujours fait dans l'état-de-l'art pour la discrimination des masses, procède à une extraction non-supervisée des descripteurs à l'aide d'une méthode issue du Deep learning, à savoir les réseaux de neurones à convolution. Les descripteurs extraits, sont ensuite utilisés dans un classifieur SVM pour apprendre un modèle. Ce modèle servira par la suite à la reconnaissance du cancer du sein. Les résultats obtenus pour chacune des étapes du système de diagnostic sont très intéressants et viennent combler un vide important dans la classification des masses en général et dans la distinction des masses malignes entre elles en particulier en se fondant sur trois niveaux de décision que sont la forme, la densité et les spicules. / Computer-aided diagnosis of breast cancer is raising increasingly a genuine enthusiasm because of the ever-increasing quantity of mammographic images from breast cancer screening campaigns. The focus is on breast masses due to the high risk of cancer associated with them. Indeed, the variability of shape encountered and the difficulty to discern the masses especially when theyare embedded in a high density require a new approach especially suited for the most complex cases namely the masses which belong to classes BI-RADS IV and V, i.e. spiculated breast mass and architectural distortion. In this work, a fully automatic computer-aided diagnosis system is designed for the segmentation and classification of breast mass especially for malignant masses of classes BI-RADS IV and BI-RADS V. Initially, we developped a pre-processing method combined with the reduction of the dictionary size in order to remove effectively and quickly the digitization noise of the mammographic images that make up the database used to design our computer-aided diagnosis system in comparison with the existing approaches. After the image pre-processing, we haveproposed an unsupervised segmentation method based on a Markov random field which has the advantage of being faster, more efficient and more robust than the state-of-art segmentation methods. Furthermore, the proposed method overcomes the variability of the breast masses whatever the image density. In purpose to describe correctly the spiculated malignant lesions, we proposed anapproach which avoid the computation and extraction of local features, and to rely on general-purpose classification procedures whose performance and computational efficiency can greatly vary depending on design and image characteristics. The proposed method is based on several assumptions on the structure of spicules as they appear in mammograms which have been reported in the literature. In order to make use of the above assumptions, the proposed method proceeds the following steps: first the mammogram is separated into patches onto which the curvilinear structures are discretized into segments due to Radon transform. Then, Markov modeling and contextual information are used to refine the segment positions and associate segments into curvilinear structures. Finally, spicules are detected based on a contrario model. This stage conclude the first part of the design of our computer-aided diagnosis system, that is able to extract both spiculated masses and architectural distortion. In order to complete the design of the diagnosis system, we carried out the creation of a decision support model which, contrary to what has always been done in the state-of-art for discrimination of the masses, conducts an unsupervised extraction of features through Deep learning approach - namely convolutional artificial neural networks -, combined with an SVM-type classifier. The obtained model is then stored and used as a classifier for breast cancer recognition tasks during the generalization phase. The results obtained for each step of the design of our system are very interesting and come to fill an important gap in the distinction of different type of malignant masses.

Notre perception est par nature multimodale, i.e. fait appel à plusieurs de nos sens. Pour résoudre certaines tâches, il est donc pertinent d’utiliser différentes modalités, telles que le son ou l’image.Cette thèse s’intéresse à cette notion dans le cadre de l’apprentissage neuronal profond. Pour cela, elle cherche à répondre à une problématique en particulier : comment fusionner les différentes modalités au sein d’un réseau de neurones ?Nous proposons tout d’abord d’étudier un problème d’application concret : la reconnaissance automatique des émotions dans des contenus audio-visuels.Cela nous conduit à différentes considérations concernant la modélisation des émotions et plus particulièrement des expressions faciales. Nous proposons ainsi une analyse des représentations de l’expression faciale apprises par un réseau de neurones profonds.De plus, cela permet d’observer que chaque problème multimodal semble nécessiter l’utilisation d’une stratégie de fusion différente.C’est pourquoi nous proposons et validons ensuite deux méthodes pour obtenir automatiquement une architecture neuronale de fusion efficace pour un problème multimodal donné, la première se basant sur un modèle central de fusion et ayant pour visée de conserver une certaine interprétation de la stratégie de fusion adoptée, tandis que la seconde adapte une méthode de recherche d'architecture neuronale au cas de la fusion, explorant un plus grand nombre de stratégies et atteignant ainsi de meilleures performances.Enfin, nous nous intéressons à une vision multimodale du transfert de connaissances. En effet, nous détaillons une méthode non traditionnelle pour effectuer un transfert de connaissances à partir de plusieurs sources, i.e. plusieurs modèles pré-entraînés. Pour cela, une représentation neuronale plus générale est obtenue à partir d’un modèle unique, qui rassemble la connaissance contenue dans les modèles pré-entraînés et conduit à des performances à l'état de l'art sur une variété de tâches d'analyse de visages. / Our perception is by nature multimodal, i.e. it appeals to many of our senses. To solve certain tasks, it is therefore relevant to use different modalities, such as sound or image.This thesis focuses on this notion in the context of deep learning. For this, it seeks to answer a particular problem: how to merge the different modalities within a deep neural network?We first propose to study a problem of concrete application: the automatic recognition of emotion in audio-visual contents.This leads us to different considerations concerning the modeling of emotions and more particularly of facial expressions. We thus propose an analysis of representations of facial expression learned by a deep neural network.In addition, we observe that each multimodal problem appears to require the use of a different merge strategy.This is why we propose and validate two methods to automatically obtain an efficient fusion neural architecture for a given multimodal problem, the first one being based on a central fusion network and aimed at preserving an easy interpretation of the adopted fusion strategy. While the second adapts a method of neural architecture search in the case of multimodal fusion, exploring a greater number of strategies and therefore achieving better performance.Finally, we are interested in a multimodal view of knowledge transfer. Indeed, we detail a non-traditional method to transfer knowledge from several sources, i.e. from several pre-trained models. For that, a more general neural representation is obtained from a single model, which brings together the knowledge contained in the pre-trained models and leads to state-of-the-art performances on a variety of facial analysis tasks.

The field of medicine is always evolving and one step in this evolution is the use of decision support systems like artificial intelligence. These systems open the possibility to minimize human error in diagnostics as practitioners can use objective measurements and analysis to assist with the diagnosis. In this study the focus has been to explore the possibility of using deep learning models to classify stroke, vestibular neuritis and control groups based on datafrom a video head impulse test (vHIT). This was done by pre-processing data from vHIT into features that could be used as input to an artificial neural network. Three different modelswere designed, where the first two used mean motion data describing the motion of the head and eyes and their standard deviations, and the last model used extracted parameters. The models were trained from vHIT-data from 76 control cases, 37 vestibular neuritis cases and 46 stroke cases. To get a better grasp of the differences between the groups, a comparison was made between the parameters and the mean curves. The resulting models performed to a varying degree with the first model correctly classified 77.8 % of the control cases, 55.6 % of the stroke cases and 80 % of the vestibular neuritis cases. The second model correctly classified 100 % of the control cases, 11.1 % of the stroke cases and 80.0 % of thevestibular neuritis cases. Lastly the third model correctly classified 77.8 % of the control cases, 22.2 % of the stroke cases and 100 % of the vestibular neuritis cases. The results are still insufficient when it comes to clinical use, as the stroke classification requires a higher sensitivity. This means that the cases are correctly classified and gets the urgent care they need. However, with more data and research, these methods could improve further and then provide a valuable service as decision support systems.