Physical Sciences and Mathematics Commons^™

Toward Automated Region Detection & Parcellation Of Rat Brain Tissue Images, Alexandro Arnal

Open Access Theses & Dissertations

People who analyze images of biological tissue often rely on segmentation of structures as a preliminary step. In particular, laboratories studying the rat brain manually delineate brain regions to position scientific findings on a brain atlas to propose hypotheses about the rat brain, and ultimately, the human brain. Our work intersects with the preliminary step of delineating regions in images of brain tissue via computational methods.

We investigate pixel-wise classification or segmentation of brain regions using ten histological images of brain tissue sections stained for Nissl substance, and two deep learning models: U-Net and Tile2Vec. Our goal is to assess …

Deep Learning For Overhead Imagery: Algorithms And Applications, Anthony Manuel Ortiz Cepeda

Open Access Theses & Dissertations

Remote sensing using overhead imagery has critical impact to the way we understand our environment and offers crucial information for scene understanding, climate change research, disaster response, urban planning, forest management, and many other applications. At present, deep learning is increasingly used in remote sensing, but mostly borrowing algorithms developed for natural images in the computer vision community. Specific challenges arise while applying deep learning to remote sensing. These challenges include issues related to the high dimensionality and limited labeled data, security and robustness to adversarial attacks, and model generalization. In this Thesis we focus on tackling these key challenges. …

Perceived Neighborhood: Preferences Versus Actualities, Saeed Moradi, Ali Nejat, Da Hu, Souparno Ghosh

Department of Statistics: Faculty Publications

Housing recovery plays a key role in the overall restoration of a community. A multitude of factors affect housing recovery, many of which are associated with interactions of residents with their perceived neighborhoods. Targeting perceived neighborhoods rather than administratively defined measures of land helps with devising recovery plans that could better address social preferences of the residents. However, such measures are commonly subject to collection of information via expensive and time-consuming surveys. The current research aims to contribute to the domain by exploring the relationship between perception of households of their neighborhood anchors (perceived anchors) and the anchors that exist …

Toward Multi-Label Sentiment Analysis: A Transfer Learning Based Approach, Jie Tao, Xing Fang

Faculty Publications - Information Technology

Sentiment analysis is recognized as one of the most important sub-areas in Natural Language Processing (NLP) research, where understanding implicit or explicit sentiments expressed in social media contents is valuable to customers, business owners, and other stakeholders. Researchers have recognized that the generic sentiments extracted from the textual contents are inadequate, thus, Aspect Based Sentiment Analysis (ABSA) was coined to capture aspect sentiments expressed toward specific review aspects. Existing ABSA methods not only treat the analytical problem as single-label classification that requires a fairly large amount of labelled data for model training purposes, but also underestimate the entity aspects …

Heterogeneous Multi-Layered Network Model For Omics Data Integration And Analysis, Bohyun Lee, Shuo Zhang, Aleksandar Poleksic, Lei Xie

Faculty Publications

Advances in next-generation sequencing and high-throughput techniques have enabled the generation of vast amounts of diverse omics data. These big data provide an unprecedented opportunity in biology, but impose great challenges in data integration, data mining, and knowledge discovery due to the complexity, heterogeneity, dynamics, uncertainty, and high-dimensionality inherited in the omics data. Network has been widely used to represent relations between entities in biological system, such as protein-protein interaction, gene regulation, and brain connectivity (i.e. network construction) as well as to infer novel relations given a reconstructed network (aka link prediction). Particularly, heterogeneous multi-layered network (HMLN) has proven successful …

Architectural Heritage Images Classification Using Deep Learning With Cnn, Mohammed Hamzah Abed, Muntasir Al-Asfoor, Zahir M. Hussain

Research outputs 2014 to 2021

© 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). Digital documentation of cultural heritage images has emerged as an important topic in data analysis. Increasing the size and number of images to be processed making the task of categorizing them a challenging task and may take an inordinate amount of time. This research paper proposes a solution to the mentioned challenges by classifying the subject of the image of the study using Convolutional Neural Network. Classification of available images leads to improve the management of the images dataset and …

Time Series Forecasting On Multivariate Solar Radiation Data Using Deep Learning (Lstm), Murat Ci̇han Sorkun, Özlem Durmaz İncel, Christophe Paoli

Turkish Journal of Electrical Engineering and Computer Sciences

Energy management is an emerging problem nowadays and utilization of renewable energy sources is an efficient solution. Solar radiation is an important source for electricity generation. For effective utilization, it is important to know precisely the amount from different sources and at different horizons: minutes, hours, and days. Depending on the horizon, two main classes of methods can be used to forecast the solar radiation: statistical time series forecasting methods for short to midterm horizons and numerical weather prediction methods for medium- to long-term horizons. Although statistical time series forecasting methods are utilized in the literature, there are a limited …

Convolutional Auto Encoders For Sentence Representation Generation, Ali̇ Mert Ceylan, Vecdi̇ Aytaç

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, we have proposed an alternative approach for sentence modeling problem. The difficulty of the choice of answer, the semantically related questions and the lack of syntactic closeness of the answers give rise to the difficulty of selecting the answer. The deep learning field has recently achieved a pivotal success in semantic analysis, machine translation, and text summaries. The essence of this work, inspired by the human orthographic processing mechanism and using multiple convolution filters with pre-rendered 2-Dimension (2D) representations of sentences, input or output size is to learn the basic features of the language without concerns. For …

Gated Recurrent Unit Based Demand Response For Preventing Voltage Collapse In A Distribution System, Venkateswarlu Gundu, Sishaj Pulikottil Simon, Kinattingal Sundareswaran, Srinivasa Rao Nayak Panugothu

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents the application of deep learning algorithms towards demand response management. Demand limit violation and voltage stability are the major problems associated with a secondary distribution system. These problems are solved using demand response models by day ahead scheduling loads at every 15 min interval through linear integer programming and based on short term forecasting of load (kW). A new architecture for short term load forecasting is presented namely gated recurrent unit in which statistical analysis is carried out to get the optimal architecture of the neural network model. Reliability indices such as loss of load probability (LOLP) …

Improving M-Learners' Performance Through Deep Learning Techniques By Leveraging Features Weights, Muhammad Adnan, Asad Habib, Jawad Ashraf, Babar Shah, Gohar Ali

All Works

© 2013 IEEE. Mobile learning (M-learning) has gained tremendous attention in the educational environment in the past decade. For effective M-learning, it is important to create an efficient M-learning model that can identify the exact requirements of mobile learners (M-learners). M-learning model is composed of features that are generated during M-learners' interaction with mobile devices. For an adaptive M-learning model, not only learning features are required, but it is also important to determine how they differ for various M-learners, their weights, and interrelationship. This study proposes a robust and adaptive M-learning model that is based on machine learning and deep …

Deep Neural Networks For Sentiment Analysis In Tweets With Emoticons, Mutharasu Narayanaperumal

CCE Theses and Dissertations

Businesses glean meaningful feedback in regard to products and services from social media posts in order to improve the quality of products and services, as well as to meet customer expectations. Sentiment analysis is increasingly being used to help businesses by assigning positive or negative polarity to such posts. Although methods currently exist to determine the polarity of sentiments, such methods are unreliable when posts contain terms that are not typically part of a standard dictionary used for sentiment analysis, such as slang and informal language. This dissertation has aimed to empirically investigate alternative methods to improve the classification accuracy …

Ai Techniques For Covid-19, Adedoyin Ahmed Hussain, Ouns Bouachir, Fadi Al-Turjman, Moayad Aloqaily

All Works

© 2013 IEEE. Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the …

Transformer Neural Networks For Automated Story Generation, Kemal Araz

Dissertations

Towards the last two-decade Artificial Intelligence (AI) proved its use on tasks such as image recognition, natural language processing, automated driving. As discussed in the Moore’s law the computational power increased rapidly over the few decades (Moore, 1965) and made it possible to use the techniques which were computationally expensive. These techniques include Deep Learning (DL) changed the field of AI and outperformed other models in a lot of fields some of which mentioned above. However, in natural language generation especially for creative tasks that needs the artificial intelligent models to have not only a precise understanding of the given …

Experiments On The Neural Network Approach To The Handwritten Digit Classification Problem, William Meissner

Electronic Theses and Dissertations

When the MNIST dataset was introduced in 1998, training a network was a multiple week problem in order to receive results far less accurate than an average CPU can produce within a couple of hours today. While this indicates that training a network on such a dataset is not the complicated problem it may have been twenty years ago, the MNIST dataset makes a good tool for study and testing with beginner and medium complexity neural networks. This paper follows along with the work presented in the online textbook “Neural Networks and Deep Learning” by Michael Nielson and an updated …

Deep Learning For Digitized Histology Image Analysis, Sudhir Sornapudi

Doctoral Dissertations

“Cervical cancer is the fourth most frequent cancer that affects women worldwide. Assessment of cervical intraepithelial neoplasia (CIN) through histopathology remains as the standard for absolute determination of cancer. The examination of tissue samples under a microscope requires considerable time and effort from expert pathologists. There is a need to design an automated tool to assist pathologists for digitized histology slide analysis. Pre-cervical cancer is generally determined by examining the CIN which is the growth of atypical cells from the basement membrane (bottom) to the top of the epithelium. It has four grades, including: Normal, CIN1, CIN2, and CIN3. In …

Wind Power Forecasting Methods Based On Deep Learning: A Survey, Xing Deng, Haijian Shao, Chunlong Hu, Dengbiao Jiang, Yingtao Jiang

Electrical & Computer Engineering Faculty Research

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of …

Pretraining Deep Learning Models For Natural Language Understanding, Han Shao

Honors Papers

Since the first bidirectional deep learn- ing model for natural language understanding, BERT, emerged in 2018, researchers have started to study and use pretrained bidirectional autoencoding or autoregressive models to solve language problems. In this project, I conducted research to fully understand BERT and XLNet and applied their pretrained models to two language tasks: reading comprehension (RACE) and part-of-speech tagging (The Penn Treebank). After experimenting with those released models, I implemented my own version of ELECTRA, a pretrained text encoder as a discriminator instead of a generator to improve compute-efficiency, with BERT as its underlying architecture. To reduce the number …

Recent Advances In Deep Learning For Object Detection, Xiongwei Wu, Doyen Sahoo, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades. Visual object detection aims to find objects of certain target classes with precise localization in a given image and assign each object instance a corresponding class label. Due to the tremendous successes of deep learning based image classification, object detection techniques using deep learning have been actively studied in recent years. In this paper, we give a comprehensive survey of recent advances in visual object detection with deep learning. By reviewing a large body of recent related work in literature, …

Applying Deep Learning Models To Structural Mri For Stage Prediction Of Alzheimer's Disease, Altuğ Yi̇ği̇t, Zerri̇n Işik

Turkish Journal of Electrical Engineering and Computer Sciences

Alzheimer's disease is a brain disease that causes impaired cognitive abilities in memory, concentration, planning, and speaking. Alzheimer's disease is defined as the most common cause of dementia and changes different parts of the brain. Neuroimaging, cerebrospinal fluid, and some protein abnormalities are commonly used as clinical diagnostic biomarkers. In this study, neuroimaging biomarkers were applied for the diagnosis of Alzheimer's disease and dementia as a noninvasive method. Structural magnetic resonance (MR) brain images were used as input of the predictive model. T1 weighted volumetric MR images were reduced to two-dimensional space by several preprocessing methods for three different projections. …

Geographic Variation And Ethnicity In Diabetic Retinopathy Detection Via Deeplearning, Ali Serener, Sertan Serte

Turkish Journal of Electrical Engineering and Computer Sciences

The prevalence of diabetes is on the rise steadily around the globe. Diabetic retinopathy (DR) is a result of damage to the blood vessels in the retina due to diabetes and its fast treatment is crucial for preventing possible blindness. The diagnosis of DR is done mostly using a comprehensive eye exam, where the eye is dilated for better inspection. Analysis by an ophthalmologist is prone to human error and thus automatic and highly accurate detection of DR is preferred for an earlier and better diagnosis. It is important, however, that automatic detection be accurate for all data collected from …

Deepmag+ : Sniffing Mobile Apps In Magnetic Field Through Deep Learning, Rui Ning, Cong Wang, Chunsheng Xin, Jiang Li, Hongyi Wu

Electrical & Computer Engineering Faculty Publications

This paper reports a new side-channel attack to smartphones using the unrestricted magnetic sensor data. We demonstrate that attackers can effectively infer the Apps being used on a smartphone with an accuracy of over 80%, through training a deep Convolutional Neural Networks (CNN). Various signal processing strategies have been studied for feature extractions, including a tempogram based scheme. Moreover, by further exploiting the unrestricted motion sensor to cluster magnetometer data, the sniffing accuracy can increase to as high as 98%. To mitigate such attacks, we propose a noise injection scheme that can effectively reduce the App sniffing accuracy to only …

Quantifying Seagrass Distribution In Coastal Water With Deep Learning Models, Daniel Perez, Kazi Islam, Victoria Hill, Richard Zimmerman, Blake Schaeffer, Yuzhong Shen, Jiang Li

OES Faculty Publications

Coastal ecosystems are critically affected by seagrass, both economically and ecologically. However, reliable seagrass distribution information is lacking in nearly all parts of the world because of the excessive costs associated with its assessment. In this paper, we develop two deep learning models for automatic seagrass distribution quantification based on 8-band satellite imagery. Specifically, we implemented a deep capsule network (DCN) and a deep convolutional neural network (CNN) to assess seagrass distribution through regression. The DCN model first determines whether seagrass is presented in the image through classification. Second, if seagrass is presented in the image, it quantifies the seagrass …

Chronic Obstructive Pulmonary Disease Severity Analysis Using Deep Learning Onmulti-Channel Lung Sounds, Gökhan Altan, Yakup Kutlu, Ahmet Gökçen

Turkish Journal of Electrical Engineering and Computer Sciences

Chronic obstructive pulmonary disease (COPD) is one of the deadliest diseases which cannot be treated but can be kept under control in certain stages. COPD has five severities, including at-risk, mild, moderate, severe, and very severe stages. Diagnosis of COPD at early stages needs additional clinical tests for even experienced specialists. The study aims at detecting the severity of the COPD to start treatment for preventing the progression of the disease to the next levels. We analyzed 12-channel lung sounds with different COPD severities from RespiratoryDatabase@TR. The lung sounds were recorded from the clinical auscultation points from 41 patients on …

Image And Video-Based Autism Spectrum Disorder Detection Via Deep Learning, Mindi Ruan

Graduate Theses, Dissertations, and Problem Reports

People with Autism Spectrum Disorder (ASD) show atypical attention to social stimuli and aberrant gaze when viewing images of the physical world. However, it is unknown how they perceive the world from a first-person perspective. In this study, we used machine learning to classify photos taken in three different categories (people, indoors, and outdoors) as either having been taken by individuals with ASD or by peers without ASD. Our classifier effectively discriminated photos from all three categories but was particularly successful at classifying photos of people with >80% accuracy. Importantly, the visualization of our model revealed critical features that led …

Text Mining Methods For Analyzing Online Health Information And Communication, Sifei Han

Theses and Dissertations--Computer Science

The Internet provides an alternative way to share health information. Specifically, social network systems such as Twitter, Facebook, Reddit, and disease specific online support forums are increasingly being used to share information on health related topics. This could be in the form of personal health information disclosure to seek suggestions or answering other patients' questions based on their history. This social media uptake gives a new angle to improve the current health communication landscape with consumer generated content from social platforms. With these online modes of communication, health providers can offer more immediate support to the people seeking advice. Non-profit …

Early Detection Of Fake News On Social Media, Yang Liu

Dissertations

The ever-increasing popularity and convenience of social media enable the rapid widespread of fake news, which can cause a series of negative impacts both on individuals and society. Early detection of fake news is essential to minimize its social harm. Existing machine learning approaches are incapable of detecting a fake news story soon after it starts to spread, because they require certain amounts of data to reach decent effectiveness which take time to accumulate. To solve this problem, this research first analyzes and finds that, on social media, the user characteristics of fake news spreaders distribute significantly differently from those …

Graph Deep Learning: Methods And Applications, Muhan Zhang

McKelvey School of Engineering Theses & Dissertations

The past few years have seen the growing prevalence of deep neural networks on various application domains including image processing, computer vision, speech recognition, machine translation, self-driving cars, game playing, social networks, bioinformatics, and healthcare etc. Due to the broad applications and strong performance, deep learning, a subfield of machine learning and artificial intelligence, is changing everyone's life.Graph learning has been another hot field among the machine learning and data mining communities, which learns knowledge from graph-structured data. Examples of graph learning range from social network analysis such as community detection and link prediction, to relational machine learning such as …

Towards Interpretable Machine Learning With Applications To Clinical Decision Support, Zhicheng Cui

McKelvey School of Engineering Theses & Dissertations

Machine learning models have achieved impressive predictive performance in various applications such as image classification and object recognition. However, understanding how machine learning models make decisions is essential when deploying those models in critical areas such as clinical prediction and market analysis, where prediction accuracy is not the only concern. For example, in the clinical prediction of ICU transfers, in addition to accurate predictions, doctors need to know the contributing factors that triggered the alert, which factors can be quickly altered to prevent the ICU transfer. While interpretable machine learning has been extensively studied for years, challenges remain as among …

Computational Screening Of New Perovskite Materials Using Transfer Learning And Deep Learning, Xiang Li, Yabo Dan, Rongzhi Dong, Zhuo Cao, Chengcheng Niu, Yuqi Song, Shaobo Li, Jianjun Hu

Faculty Publications

As one of the most studied materials, perovskites exhibit a wealth of superior properties that lead to diverse applications. Computational prediction of novel stable perovskite structures has big potential in the discovery of new materials for solar panels, superconductors, thermal electric, and catalytic materials, etc. By addressing one of the key obstacles of machine learning based materials discovery, the lack of sufficient training data, this paper proposes a transfer learning based approach that exploits the high accuracy of the machine learning model trained with physics-informed structural and elemental descriptors. This gradient boosting regressor model (the transfer learning model) allows us …

Research And Implementation Of Driving Concern Area Detection Based On Deep Learning, Jihua Ye, Shuxia Shi, Hanxi Li, Shimin Wang, Siyu Yang

Journal of System Simulation

Abstract: As a key technology of intelligent driving, driving concern area detection method has an important impact on the performance of intelligent driving or intelligent early warning system. In view of the shortcomings of the existing methods, this paper proposes an effective method for driving concern area detection based on the deep learning. We obtain the camera internal and external parameters by using camera self-calibration method based on camera model, use the Canny edge detection and Bisecting K-means clustering to realize the vanishing point estimation, and establish the road detection model based on the obtained estimates. We obtain the depth …