Computer Engineering Commons^™

Extractive Text Summarization On Single Documents Using Deep Learning, Shehab Mostafa Abdel-Salam Mohamed

Theses and Dissertations

The task of summarization can be categorized into two methods, extractive and abstractive summarization. Extractive approach selects highly meaningful sentences to form a summary while the abstractive approach interprets the original document and generates the summary in its own words. The task of generating a summary, whether extractive or abstractive, has been studied with different approaches such as statistical-based, graph-based, and deep-learning based approaches. Deep learning has achieved promising performance in comparison with the classical approaches and with the evolution of neural networks such as the attention network or commonly known as the Transformer architecture, there are potential areas for …

A Neural Network Based Proportional Hazard Model For Iot Signal Fusion And Failure Prediction, Yuxin Wen, Xingxin Guo, Junbo Son, Jianguo Wu

Engineering Faculty Articles and Research

Accurate prediction of remaining useful life (RUL) plays a critical role in optimizing condition-based maintenance decisions. In this paper, a novel joint prognostic modeling framework that simultaneously combines both time-to-event data and multi-sensor degradation signals is proposed. With the increasing use of IoT devices, unprecedented amounts of diverse signals associated with the underlying health condition of in-situ units have become easily accessible. To take full advantage of the modern IoT-enabled engineering systems, we propose a specialized framework for RUL prediction at the level of individual units. Specifically, a Bayesian linear regression model is developed for the multi-sensor degradation signals and …

Discrepancies Among Pre-Trained Deep Neural Networks: A New Threat To Model Zoo Reliability, Diego Montes, Pongpatapee Peerapatanapokin, Jeff Schultz, Chengjun Guo, Wenxin Jiang, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Training deep neural networks (DNNs) takes significant time and resources. A practice for expedited deployment is to use pre-trained deep neural networks (PTNNs), often from model zoos.collections of PTNNs; yet, the reliability of model zoos remains unexamined. In the absence of an industry standard for the implementation and performance of PTNNs, engineers cannot confidently incorporate them into production systems. As a first step, discovering potential discrepancies between PTNNs across model zoos would reveal a threat to model zoo reliability. Prior works indicated existing variances in deep learning systems in terms of accuracy. However, broader measures of reliability for PTNNs from …

Artificial Intelligence And The Ethics Behind It, Isaac Johnston

Senior Honors Theses

Artificial intelligence (AI) has been a widely used buzzword for the past couple of years. If there is a technology that works without human interaction, it is labeled as AI. But what is AI, and should individuals be concerned? The following research aims to define what artificial intelligence is, specifically machine learning (ML) and neural networks. It is important to understand how AI is used today in cars, image recognition, ad marketing, and other areas. Although AI has many benefits, there are areas of ethical concerns such as autonomous cars, military applications, social media marketing, and others. This paper helps …

Machine Learning-Based Recognition On Crowdsourced Food Images, Aditya Kulkarni

Honors Scholar Theses

With nearly a third of the world’s population suffering from food-induced chronic diseases such as obesity, the role of food in community health is required now more than ever. While current research underscores food proximity and density, there is a dearth in regard to its nutrition and quality. However, recent research in geospatial data collection and analysis as well as intelligent deep learning will help us study this further.

Employing the efficiency and interconnection of computer vision and geospatial technology, we want to study whether healthy food in the community is attainable. Specifically, with the help of deep learning in …

Analysis Of Information Security Methods In Biosystems And Application Of Intelligent Tools In Information Security Systems, Sherzod Sayfullaev

Chemical Technology, Control and Management

In this paper, the methods of information protection in bio systems are studied. The paper considers the use of intelligent tools in information security systems and the use of adaptive information security systems. Several articles on the field of information protection in bio systems are analyzed. Disadvantages and advantages of neural network technologies in modern information security systems are described. The characteristics of bio systems and the specificity of DNA, the main features of the DNA code that provide information security and functional stability of bio systems data protection structure. Application of intelligent tools to create a comprehensive adaptive protection …

Predicting Imports In Java Code With Graph Neural Networks, Aleksandr Fedchin

Senior Projects Spring 2020

Programmers tend to split their code into multiple files or sub-modules. When a program is executed, these sub-modules interact to produce the desired effect. One can, therefore, represent programs with graphs, where each node corresponds to some file and each edge corresponds to some relationship between files, such as two files being located in the same package or one file importing the content of another. This project trains Graph Neural Networks on such graphs to learn to predict future imports in Java programs and shows that Graph Neural Networks outperform various baseline methods by a wide margin.

Multiple Face Detection And Recognition System Design Applying Deep Learning In Web Browsers Using Javascript, Cristhian Gabriel Espinosa Sandoval

Computer Science and Computer Engineering Undergraduate Honors Theses

Deep learning has advanced progressively in the last years and now demonstrates state-of-the-art performance in various fields. In the era of big data, transformation of data into valuable knowledge has become one of the most important challenges in computing. Therefore, we will review multiple algorithms for face recognition that have been researched for a long time and are maturely developed, and analyze deep learning, presenting examples of current research.

To provide a useful and comprehensive perspective, in this paper we categorize research by deep learning architecture, including neural networks, convolutional neural networks, depthwise Separable Convolutions, densely connected convolutional networks, and …

Improved Study Of Side-Channel Attacks Using Recurrent Neural Networks, Muhammad Abu Naser Rony Chowdhury

Boise State University Theses and Dissertations

Differential power analysis attacks are special kinds of side-channel attacks where power traces are considered as the side-channel information to launch the attack. These attacks are threatening and significant security issues for modern cryptographic devices such as smart cards, and Point of Sale (POS) machine; because after careful analysis of the power traces, the attacker can break any secured encryption algorithm and can steal sensitive information.

In our work, we study differential power analysis attack using two popular neural networks: Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). Our work seeks to answer three research questions(RQs):

RQ1: Is it …

Investigating Semantic Properties Of Images Generated From Natural Language Using Neural Networks, Samuel Ward Schrader

Boise State University Theses and Dissertations

This work explores the attributes, properties, and potential uses of generative neural networks within the realm of encoding semantics. It works toward answering the questions of: If one uses generative neural networks to create a picture based on natural language, does the resultant picture encode the text's semantics in a way a computer system can process? Could such a system be more precise than current solutions at detecting, measuring, or comparing semantic properties of generated images, and thus their source text, or their source semantics?

This work is undertaken in the hope that detecting previously unknown properties, or better understanding …

Depressiongnn: Depression Prediction Using Graph Neural Network On Smartphone And Wearable Sensors, Param Bidja

Honors Scholar Theses

Depression prediction is a complicated classification problem because depression diagnosis involves many different social, physical, and mental signals. Traditional classification algorithms can only reach an accuracy of no more than 70% given the complexities of depression. However, a novel approach using Graph Neural Networks (GNN) can be used to reach over 80% accuracy, if a graph can represent the depression data set to capture differentiating features. Building such a graph requires 1) the definition of node features, which must be highly correlated with depression, and 2) the definition for edge metrics, which must also be highly correlated with depression. In …

Hyper-Parameter Optimization Of A Convolutional Neural Network, Steven H. Chon

Theses and Dissertations

In the world of machine learning, neural networks have become a powerful pattern recognition technique that gives a user the ability to interpret high-dimensional data whereas conventional methods, such as logistic regression, would fail. There exists many different types of neural networks, each containing its own set of hyper-parameters that are dependent on the type of analysis required, but the focus of this paper will be on the hyper-parameters of convolutional neural networks. Convolutional neural networks are commonly used for classifications of visual imagery. For example, if you were to build a network for the purpose of predicting a specific …