Digital Commons Network^™

Deep Learning Methods For Mining Genomic Sequence Patterns, Xin Gao

Dissertations

Nowadays, with the growing availability of large-scale genomic datasets and advanced computational techniques, more and more data-driven computational methods have been developed to analyze genomic data and help to solve incompletely understood biological problems. Among them, deep learning methods, have been proposed to automatically learn and recognize the functional activity of DNA sequences from genomics data. Techniques for efficient mining genomic sequence pattern will help to improve our understanding of gene regulation, and thus accelerate our progress toward using personal genomes in medicine.

This dissertation focuses on the development of deep learning methods for mining genomic sequences. First, we compare …

Computational Intelligence In Steganography: Adaptive Image Watermarking, Xin Zhong

Dissertations

Digital image watermarking, as an extension of traditional steganography, refers to the process of hiding certain messages into cover images. The transport image, called marked-image or stego-image, conveys the hidden messages while appears visibly similar to the cover-image. Therefore, image watermarking enables various applications such as copyright protection and covert communication. In a watermarking scheme, fidelity, capacity and robustness are considered as crucial factors, where fidelity measures the similarity between the cover- and marked-images, capacity measures the maximum amount of watermark that can be embedded, and robustness concerns the watermark extraction under attacks on the marked-image. Watermarking techniques are often …

A Microscopic Simulation Laboratory For Evaluation Of Off-Street Parking Systems, Yun Yuan

Theses and Dissertations

The parking industry produces an enormous amount of data every day that, properly analyzed, will change the way the industry operates. The collected data form patterns that, in most cases, would allow parking operators and property owners to better understand how to maximize revenue and decrease operating expenses and support the decisions such as how to set specific parking policies (e.g. electrical charging only parking space) to achieve the sustainable and eco-friendly parking.

However, there lacks an intelligent tool to assess the layout design and operational performance of parking lots to reduce the externalities and increase the revenue. To address …

Towards End-To-End Semi-Supervised Deep Learning For Drug Discovery, Xiaoyu Zhang

Computer Science and Engineering Theses

Observing the recent progress in Deep Learning, the employment of AI is surging to accelerate drug discovery and cut R&D costs in the last few years. However, the success of deep learning is attributed to large-scale clean high-quality labeled data, which is generally unavailable in drug discovery practices. In this thesis, we address this issue by proposing an end-to-end deep learning framework in a semi supervised learning fashion. That is said, the proposed deep learning approach can utilize both labeled and unlabeled data. While labeled data is of very limited availability, the amount of available unlabeled data is generally huge. …

Large-Scale Deep Learning With Application In Medical Imaging And Bio-Informatics, Zheng Xu

Computer Science and Engineering Dissertations

With the recent advancement of the deep learning technology in the artificial intelligence area, nowadays people's lives have been drastically changed. However, the success of deep learning technology mostly relies on large-scale high-quality data-sets. The complexity of deeper model and larger scale datasets have brought us significant challenges. Inspired by this trend, in this dissertation, we focus on developing efficient and effective large-scale deep learning techniques in solving real-world problems, like cell detection in hyper-resolution medical image or drug screening from millions of compound candidates. With respect to the hyper-resolution medical imaging cell detection problem, the challenges are mainly the …

Defending Neural Networks Against Adversarial Examples, Armon Barton

Computer Science and Engineering Dissertations

Deep learning is becoming a technology central to the safety of cars, the security of networks, and the correct functioning of many other types of systems. Unfortunately, attackers can create adversarial examples, small perturbations to inputs that trick deep neural networks into making a misclassification. Researchers have explored various defenses against this attack, but many of them have been broken. The most robust approaches are Adversarial Training and its extension, Adversarial Logit Pairing, but Adversarial Training requires generating and training on adversarial examples from any possible attack. This is not only expensive, but it is inherently vulnerable to novel attack …

Deepsign: A Deep-Learning Architecture For Sign Language, Jai Amrish Shah

Computer Science and Engineering Theses

Sign languages are used by deaf people for communication. In sign languages, humans use hand gestures, body, facial expressions and movements to convey meaning. Humans can easily learn and understand sign languages, but automatic sign language recognition for machines is a challenging task. Using recent advances in the field of deep-learning, we introduce a fully automated deep-learning architecture for isolated sign language recognition. Our architecture tries to address three problems: 1) Satisfactory accuracy with limited data samples 2) Reducing chances of over-fitting when the data is limited 3) Automating recognition of isolated signs. Our architecture uses deep convolutional encoder-decoder architecture …

Sensor-Based Human Activity Recognition Using Bidirectional Lstm For Closely Related Activities, Arumugam Thendramil Pavai

Electronic Theses, Projects, and Dissertations

Recognizing human activities using deep learning methods has significance in many fields such as sports, motion tracking, surveillance, healthcare and robotics. Inertial sensors comprising of accelerometers and gyroscopes are commonly used for sensor based HAR. In this study, a Bidirectional Long Short-Term Memory (BLSTM) approach is explored for human activity recognition and classification for closely related activities on a body worn inertial sensor data that is provided by the UTD-MHAD dataset. The BLSTM model of this study could achieve an overall accuracy of 98.05% for 15 different activities and 90.87% for 27 different activities performed by 8 persons with 4 …

Efficacy Of Deep Learning In Support Of Smart Services, Basheer Mohammed Basheer Qolomany

Dissertations

The massive amount of streaming data generated and captured by smart service appliances, sensors and devices needs to be analyzed by algorithms, transformed into information, and minted to extract knowledge to facilitate timely actions and better decision making. This can lead to new products and services that can dramatically transform our lives. Machine learning and data analytics will undoubtedly play a critical role in enabling the delivery of smart services. Within the machine-learning domain, Deep Learning (DL) is emerging as a superior new approach that is much more effective than any rule or formula used by traditional machine learning. Furthermore, …

Personality Recognition For Deception Detection, Guozhen An

Dissertations, Theses, and Capstone Projects

Personality aims at capturing stable individual characteristics, typically measurable in quantitative terms, that explain and predict observable behavioral differences. Personality has been proved to be very useful in many life outcomes, and there has been huge interests on predicting personality automatically. Previously, there are tremendous amount of approaches successfully predicting personality. However, most previous research on personality detection has used personality scores assigned by annotators based solely on the text or audio clip, and found that predicting self-reported personality is a much more difficult task than predicting observer-report personality. In our study, we will demonstrate how to accurately detect self-reported …

Artificial Intelligence For Cognitive Behavior Assessment In Children, Srujana Gattupalli

Computer Science and Engineering Dissertations

Cognitive impairments in early childhood can lead to poor academic performance and require proper remedial intervention at the appropriate time. ADHD a?ects about 6-7% of children and is a psychiatric neurodevelopmental disorder that is very hard to diagnose or tell apart from other disorders. Cognitive insu?ciencies hinder the development of working memory and can a?ect school success and even have long term e?ects that can result in low self-esteem and self-acceptance. The main aim of this research is to investigate development of an automated and non-intrusive system for assessing physical exercises related to the treatment and diagnosis of Attention De?cit …

Human Activity Recognition Based On Transfer Learning, Jinyong Pang

USF Tampa Graduate Theses and Dissertations

Human activity recognition (HAR) based on time series data is the problem of classifying various patterns. Its widely applications in health care owns huge commercial benefit. With the increasing spread of smart devices, people have strong desires of customizing services or product adaptive to their features. Deep learning models could handle HAR tasks with a satisfied result. However, training a deep learning model has to consume lots of time and computation resource. Consequently, developing a HAR system effectively becomes a challenging task. In this study, we develop a solid HAR system using Convolutional Neural Network based on transfer learning, which …

Deep Learning For Segmentation Of 3d Cryo-Em Images, Devin Reid Haslam

Computer Science Theses & Dissertations

Cryo-electron microscopy (cryo-EM) is an emerging biophysical technique for structural determination of protein complexes. However, accurate detection of secondary structures is still challenging when cryo-EM density maps are at medium resolutions (5-10 Å). Most existing methods are image processing methods that do not fully utilize available images in the cryo-EM database. In this paper, we present a deep learning approach to segment secondary structure elements as helices and β-sheets from medium- resolution density maps. The proposed 3D convolutional neural network is shown to detect secondary structure locations with an F1 score between 0.79 and 0.88 for six simulated test cases. …

Exploring The Role Of Semi-Supervised Deep Reinforcement Learning And Ensemble Methods In Support Of The Internet Of Things, Mehdi Mohammadi

Dissertations

Smart services are an important element of the Internet of Things (IoT) ecosystem where insights are drawn from raw data through the use of machine learning techniques. However, the pathway to develop IoT smart services is complicated as IoT data presents several challenges for machine learning, including handling big data, shortage of labeled data, and the need to benefit from the spatio-temporal relations hidden in the training data.

In this dissertation, after reviewing the state-of-the-art deep learning (DL) and deep reinforcement learning (DRL) techniques and their use in support of IoT applications, this study proposes to extend DRL to semi-supervised …

Design And Implementation Of A Domain Specific Language For Deep Learning, Xiao Bing Huang

Theses and Dissertations

\textit {Deep Learning} (DL) has found great success in well-diversified areas such as machine vision, speech recognition, big data analysis, and multimedia understanding recently. However, the existing state-of-the-art DL frameworks, e.g. Caffe2, Theano, TensorFlow, MxNet, Torch7, and CNTK, are programming libraries with fixed user interfaces, internal representations, and execution environments. Modifying the code of DL layers or data structure is very challenging without in-depth understanding of the underlying implementation. The optimization of the code and execution in these tools is often limited and relies on the specific DL computation graph manipulation and scheduling that lack systematic and universal strategies. Furthermore, …

Parameterizing And Aggregating Activation Functions In Deep Neural Networks, Luke Benjamin Godfrey

Graduate Theses and Dissertations

The nonlinear activation functions applied by each neuron in a neural network are essential for making neural networks powerful representational models. If these are omitted, even deep neural networks reduce to simple linear regression due to the fact that a linear combination of linear combinations is still a linear combination. In much of the existing literature on neural networks, just one or two activation functions are selected for the entire network, even though the use of heterogenous activation functions has been shown to produce superior results in some cases. Even less often employed are activation functions that can adapt their …

Face Detection And Recognition Using Moving Window Accumulator With Various Deep Learning Architecture, Anil Kumar Nayak

Computer Science and Engineering Theses

Recent advancement in the field of Computer Vision and Deep Learning is making object detection and recognition easier. Hence, growing research activities in the field of deep learning are enabling researchers to find new ideas in the area of face detection and recognition. Implementation of such systems has a number of challenges when it comes to the current approaches. In this paper, we have presented a system of Face Detection and Recognition with newly designed deep learning classification models like CNN, Inception and various state of art models like SVM and we also compared the result with FaceNet. Multiple approaches …

Hierarchical Representation Learning With Connectionist Models, De Wang

Computer Science and Engineering Dissertations

To unleash the power of big data, efficient algorithms which are scalable to millions of data are desired. Deep learning is one area that benefits from big data enormously. Deep learning uses neural networks to mimic human brains, this approach is termed connectionist in AI community. In this dissertation, we propose several novel learning strategies to improve the performance of connectionist models. Evaluation of a large neural network during inference phase requires a lot of GPU memory and computation, which will degrade user experience due to response latency. Model distillation is one way to distill the knowledge contained in one …

From Text Classification To Image Clustering, Problems Less Optimized, Amirhossein Herandi

Computer Science and Engineering Theses

Machine Learning is thriving. Every industry is using its techniques in some way to improve their efficiency and revenue. However, the focus on research is not divided equally between all of the different areas and problems that this field can tackle and analyze. Currently, Computer Vision is the one area that is being focused very extensively by researchers and companies alike, and as a result has seen an amazing boost in the recent years. This ranges from the well-known problems of classification that use discriminative models all the way to more novel problems that use generative models such as style …

Deep Learning For Recognition Of Objects, Activities, Faces, And Spatio-Temporal Patterns, Amir Ghaderi

Computer Science and Engineering Dissertations

A popular method in machine learning is Convolutional Neural Network (CNN). CNN had was of high interest to the research community in the 1990s, but after that its popularity receded compared to the Support Vector Machine Support Vector Machine (SVM)[1]. One of the reasons was the relatively lower computational demands of SVM. Training CNNs requires significantly more computational power, time, and data than training SVM. One of the important issues in showing the power of the CNN is the availability of the huge amount of data and introducing big datasets. With increased availability of powerful GPU processing, using several improvements …

Object Localization, Segmentation, And Classification In 3d Images, Allan Zelener

Dissertations, Theses, and Capstone Projects

We address the problem of identifying objects of interest in 3D images as a set of related tasks involving localization of objects within a scene, segmentation of observed object instances from other scene elements, classifying detected objects into semantic categories, and estimating the 3D pose of detected objects within the scene. The increasing availability of 3D sensors motivates us to leverage large amounts of 3D data to train machine learning models to address these tasks in 3D images. Leveraging recent advances in deep learning has allowed us to develop models capable of addressing these tasks and optimizing these tasks jointly …

Representation Learning With Convolutional Neural Networks, Haotian Xu

Wayne State University Dissertations

Deep learning methods have achieved great success in the areas of Computer Vision and Natural Language Processing. Recently, the rapidly developing field of deep learning is concerned with questions surrounding how we can learn meaningful and effective representations of data. This is because the performance of machine learning approaches is heavily dependent on the choice and quality of data representation, and different kinds of representation entangle and hide the different explanatory factors of variation behind the data.

In this dissertation, we focus on representation learning with deep neural networks for different data formats including text, 3D polygon shapes, and brain …

Quantitative Behavior Tracking Of Xenopus Laevis Tadpoles For Neurobiology Research, Alexander Hansen Hamme

Senior Projects Fall 2018

Xenopus laevis tadpoles are a useful animal model for neurobiology research because they provide a means to study the development of the brain in a species that is both physiologically well-understood and logistically easy to maintain in the laboratory. For behavioral studies, however, their individual and social swimming patterns represent a largely untapped trove of data, due to the lack of a computational tool that can accurately track multiple tadpoles at once in video feeds. This paper presents a system that was developed to accomplish this task, which can reliably track up to six tadpoles in a controlled environment, thereby …

Deep Recurrent Learning For Efficient Image Recognition Using Small Data, Mahbubul Alam

Electrical & Computer Engineering Theses & Dissertations

Recognition is fundamental yet open and challenging problem in computer vision. Recognition involves the detection and interpretation of complex shapes of objects or persons from previous encounters or knowledge. Biological systems are considered as the most powerful, robust and generalized recognition models. The recent success of learning based mathematical models known as artificial neural networks, especially deep neural networks, have propelled researchers to utilize such architectures for developing bio-inspired computational recognition models. However, the computational complexity of these models increases proportionally to the challenges posed by the recognition problem, and more importantly, these models require a large amount of data …

Deep Learning Methods For Visual Object Recognition, Zeyad Hailat

Wayne State University Dissertations

Convolutional neural networks (CNNs) attain state-of-the-art performance on various classification tasks assuming a sufficiently large number of labeled training examples. Unfortunately, curating sufficiently large labeled training dataset requires human involvement, which is expensive, time-consuming, and susceptible to noisy labels. Semi-supervised learning methods can alleviate the aforementioned problems by employing one of two techniques. First, utilizing a limited number of labeled data in conjunction with sufficiently large unlabeled data to construct a classification model. Second, exploiting sufficiently large noisy label training data to learn a classification model. In this dissertation, we proposed a few new methods to mitigate the aforementioned problems. …

Recurrent Neural Networks And Their Applications To Rna Secondary Structure Inference, Devin Willmott

Theses and Dissertations--Mathematics

Recurrent neural networks (RNNs) are state of the art sequential machine learning tools, but have difficulty learning sequences with long-range dependencies due to the exponential growth or decay of gradients backpropagated through the RNN. Some methods overcome this problem by modifying the standard RNN architecure to force the recurrent weight matrix W to remain orthogonal throughout training. The first half of this thesis presents a novel orthogonal RNN architecture that enforces orthogonality of W by parametrizing with a skew-symmetric matrix via the Cayley transform. We present rules for backpropagation through the Cayley transform, show how to deal with the Cayley …

Automated Tree-Level Forest Quantification Using Airborne Lidar, Hamid Hamraz

Theses and Dissertations--Computer Science

Traditional forest management relies on a small field sample and interpretation of aerial photography that not only are costly to execute but also yield inaccurate estimates of the entire forest in question. Airborne light detection and ranging (LiDAR) is a remote sensing technology that records point clouds representing the 3D structure of a forest canopy and the terrain underneath. We present a method for segmenting individual trees from the LiDAR point clouds without making prior assumptions about tree crown shapes and sizes. We then present a method that vertically stratifies the point cloud to an overstory and multiple understory tree …

Smart Augmented Reality Instructional System For Mechanical Assembly, Ze-Hao Lai

Masters Theses

"Quality and efficiency are pivotal indicators of a manufacturing company. Many companies are suffering from shortage of experienced workers across the production line to perform complex assembly tasks such as assembly of an aircraft engine. This could lead to a significant financial loss. In order to further reduce time and error in an assembly, a smart system consisting of multi-modal Augmented Reality (AR) instructions with the support of a deep learning network for tool detection is introduced. The multi-modal smart AR is designed to provide on-site information including various visual renderings with a fine-tuned Region-based Convolutional Neural Network, which is …