Computer Sciences Commons^™

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 …

Discerning Novel Splice Junctions Derived From Rna-Seq Alignment: A Deep Learning Approach, Yi Zhang, Xinan Liu, James N. Macleod, Jinze Liu

Computer Science Faculty Publications

Background: Exon splicing is a regulated cellular process in the transcription of protein-coding genes. Technological advancements and cost reductions in RNA sequencing have made quantitative and qualitative assessments of the transcriptome both possible and widely available. RNA-seq provides unprecedented resolution to identify gene structures and resolve the diversity of splicing variants. However, currently available ab initio aligners are vulnerable to spurious alignments due to random sequence matches and sample-reference genome discordance. As a consequence, a significant set of false positive exon junction predictions would be introduced, which will further confuse downstream analyses of splice variant discovery and abundance estimation.

Results: …

Using Deep Learning To Forecast Spatiotemporal Crime Patterns, Shuzhan Fan

LSU Doctoral Dissertations

The distributional patterns of crime occurrences are closely related to their spatial, temporal, and environmental contexts. It has been a hot topic for researchers and crime analysts to discover such complex relationships in order to forecast crime, both spatially and temporally. Many factors play a role in the occurrences of crimes. Conventional crime forecasting research has primarily relied on historical crime records and socioeconomic data, while ignoring the rich social media and other environmental context data. The large volume of data requires a more appropriate forecasting framework with the ability to take in massive multimodal data and possibly achieve better …

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

Computer Science and Engineering Theses - Archive

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 …

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

Computer Science and Engineering Theses - Archive

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. …

Defending Neural Networks Against Adversarial Examples, Armon Barton

Computer Science and Engineering Dissertations - Archive

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 …

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

Computer Science and Engineering Dissertations - Archive

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 …

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, …

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 …

Making A Good Thing Better: Enhancing Password/Pin-Based User Authentication With Smartwatch, Bing Chang, Yingjiu Li, Qiongxiao Wang, Wen-Tao Zhu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Wearing smartwatches becomes increasingly popular in people’s lives. This paper shows that a smartwatch can help its bearer authenticate to a login system effectively and securely even if the bearer’s password has already been revealed. This idea is motivated by our observation that a sensor-rich smartwatch is capable of tracking the wrist motions of its bearer typing a password or PIN, which can be used as an authentication factor. The major challenge in this research is that a sophisticated attacker may imitate a user’s typing behavior as shown in previous research on keystroke dynamics based user authentication. We address this …

Transfer Learning With Deep Recurrent Neural Networks For Remaining Useful Life Estimation, Ansi Zhang, Honglei Wang, Shaobo Li, Yuxin Cui, Guanci Yang, Jianjun Hu

Faculty Publications

Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results …

Learning Generalized Video Memory For Automatic Video Captioning, Poo-Hee Chang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Recent video captioning methods have made great progress by deep learning approaches with convolutional neural networks (CNN) and recurrent neural networks (RNN). While there are techniques that use memory networks for sentence decoding, few work has leveraged on the memory component to learn and generalize the temporal structure in video. In this paper, we propose a new method, namely Generalized Video Memory (GVM), utilizing a memory model for enhancing video description generation. Based on a class of self-organizing neural networks, GVM’s model is able to learn new video features incrementally. The learned generalized memory is further exploited to decode the …

Improving Knowledge Tracing Model By Integrating Problem Difficulty, Sein Minn, Feida Zhu, Michel C. Desmarais

Research Collection School Of Computing and Information Systems

Intelligent Tutoring Systems (ITS) are designed for providing personalized instructions to students with the needs of their skills. Assessment of student knowledge acquisition dynamically is nontrivial during her learning process with ITS. Knowledge tracing, a popular student modeling technique for student knowledge assessment in adaptive tutoring, which is used for tracing student's knowledge state and detecting student's knowledge acquisition by using decomposed individual skill or problems with a single skill per problem. Unfortunately, recent KT models fail to deal with practices of complex skill composition and variety of concepts included in a problem simultaneously. Our goal is to investigate a …

End-To-End Convolutional Neural Network Model For Gear Fault Diagnosis Based On Sound Signals, Yong Yao, Honglei Wang, Shaobo Li, Zhongnhao Liu, Gui Gui, Yabo Dan, Jianjun Hu

Faculty Publications

Currently gear fault diagnosis is mainly based on vibration signals with a few studies on acoustic signal analysis. However, vibration signal acquisition is limited by its contact measuring while traditional acoustic-based gear fault diagnosis relies heavily on prior knowledge of signal processing techniques and diagnostic expertise. In this paper, a novel deep learning-based gear fault diagnosis method is proposed based on sound signal analysis. By establishing an end-to-end convolutional neural network (CNN), the time and frequency domain signals can be fed into the model as raw signals without feature engineering. Moreover, multi-channel information from different microphones can also be fused …

Toward Audio Beehive Monitoring: Deep Learning Vs. Standard Machine Learning In Classifying Beehive Audio Samples, Vladmir Kulyukin, Sarbajit Mukherjee, Prakhar Amlathe

Computer Science Faculty and Staff Publications

Electronic beehive monitoring extracts critical information on colony behavior and phenology without invasive beehive inspections and transportation costs. As an integral component of electronic beehive monitoring, audio beehive monitoring has the potential to automate the identification of various stressors for honeybee colonies from beehive audio samples. In this investigation, we designed several convolutional neural networks and compared their performance with four standard machine learning methods (logistic regression, k-nearest neighbors, support vector machines, and random forests) in classifying audio samples from microphones deployed above landing pads of Langstroth beehives. On a dataset of 10,260 audio samples where the training and testing …

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 …

Product Innovation Design Based On Deep Learning And Kansei Engineering, Huafeng Quan, Shaobo Li, Jianjun Hu

Faculty Publications

Creative product design is becoming critical to the success of many enterprises. However, the conventional product innovation process is hindered by two major challenges: the difficulty to capture users’ preferences and the lack of intuitive approaches to visually inspire the designer, which is especially true in fashion design and form design of many other types of products. In this paper, we propose to combine Kansei engineering and the deep learning for product innovation (KENPI) framework, which can transfer color, pattern, etc. of a style image in real time to a product’s shape automatically. To capture user preferences, we combine Kansei …

A Distributed Semi-Supervised Platform For Dnase-Seq Data Analytics Using Deep Generative Convolutional Networks, Shayan Shams, Richard Platania, Joohyun Kim, Jian Zhang, Kisung Lee, Seungwon Yang, Seung Jong Park

Computer Science Faculty Research & Creative Works

A deep learning approach for analyzing DNase-seq datasets is presented, which has promising potentials for unraveling biological underpinnings on transcription regulation mechanisms. Further understanding of these mechanisms can lead to important advances in life sciences in general and drug, biomarker discovery, and cancer research in particular. Motivated by recent remarkable advances in the field of deep learning, we developed a platform, Deep Semi-Supervised DNase-seq Analytics (DSSDA). Primarily empowered by deep generative Convolutional Networks (ConvNets), the most notable aspect is the capability of semi-supervised learning, which is highly beneficial for common biological settings often plagued with a less sufficient number of …

Artificial Intelligence For Cognitive Behavior Assessment In Children, Srujana Gattupalli

Computer Science and Engineering Dissertations - Archive

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 …

Deep Learning For Practical Image Recognition: Case Study On Kaggle Competitions, Xulei Yang, Zeng Zeng, Sin G. Teo, Li Wang, Vijay Chandrasekar, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

In past years, deep convolutional neural networks (DCNN) have achieved big successes in image classification and object detection, as demonstrated on ImageNet in academic field. However, There are some unique practical challenges remain for real-world image recognition applications, e.g., small size of the objects, imbalanced data distributions, limited labeled data samples, etc. In this work, we are making efforts to deal with these challenges through a computational framework by incorporating latest developments in deep learning. In terms of two-stage detection scheme, pseudo labeling, data augmentation, cross-validation and ensemble learning, the proposed framework aims to achieve better performances for practical image …

Towards Distributed Cyberinfrastructure For Smart Cities Using Big Data And Deep Learning Technologies, Shayan Shams, Sayan Goswami, Kisung Lee, Seungwon Yang, Seung Jong Park

Computer Science Faculty Research & Creative Works

Recent advances in big data and deep learning technologies have enabled researchers across many disciplines to gain new insight into large and complex data. For example, deep neural networks are being widely used to analyze various types of data including images, videos, texts, and time-series data. In another example, various disciplines such as sociology, social work, and criminology are analyzing crowd-sourced and online social network data using big data technologies to gain new insight from a plethora of data. Even though many different types of data are being generated and analyzed in various domains, the development of distributed city-level cyberinfrastructure …

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. …

Deep Learning For Link Prediction In Dynamic Networks Using Weak Estimators, Carter Chiu, Justin Zhan

Computer Science Faculty Research

Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques have shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization …

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 …

Ai-Human Collaboration Via Eeg, Adam Noack

All College Thesis Program, 2016-2019

As AI becomes ever more competent and integrated into our lives, the issue of AI-human goal misalignment looms larger. This is partially because there is often a rift between what humans explicitly command and what they actually mean. Most contemporary AI systems cannot bridge this gap. In this study we attempted to reconcile the goals of human and machine by using EEG signals from a human to help a simulated agent complete a task.

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

Computer Science and Engineering Theses - Archive

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 …

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

Computer Science and Engineering Theses - Archive

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 - Archive

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 …

Hierarchical Representation Learning With Connectionist Models, De Wang

Computer Science and Engineering Dissertations - Archive

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 …