Open Access. Powered by Scholars. Published by Universities.®
Artificial Intelligence and Robotics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Engineering (3)
- Applied Mathematics (2)
- Applied Statistics (2)
- Electrical and Computer Engineering (2)
- Numerical Analysis and Computation (2)
-
- Numerical Analysis and Scientific Computing (2)
- Social and Behavioral Sciences (2)
- Statistics and Probability (2)
- Analytical, Diagnostic and Therapeutic Techniques and Equipment (1)
- Civil and Environmental Engineering (1)
- Controls and Control Theory (1)
- Databases and Information Systems (1)
- Diagnosis (1)
- Geography (1)
- Library and Information Science (1)
- Medicine and Health Sciences (1)
- Power and Energy (1)
- Remote Sensing (1)
- Transportation Engineering (1)
- Keyword
-
- Deep Learning (2)
- Machine Learning (2)
- Recurrent Neural Networks (2)
- Annotation efficient (1)
- Artificial intelligence (1)
-
- Artificial neural networks (1)
- Batch Normalization (1)
- Computer Vision (1)
- Computer crime (1)
- Control (1)
- Convolutional Neural Networks (1)
- Cyberattacks (1)
- Cybersecurity (1)
- Data Quality (1)
- Deep Neural Networks (1)
- EEG (1)
- Electrical transmission faults (1)
- Exploding Gradients (1)
- Feed forward neural networks (1)
- Gravity Compensation (1)
- Image-text matching (1)
- Information Extraction (1)
- Machine learning (1)
- Malware (1)
- Natural Language Processing (1)
- Network calibration (1)
- Neural Networks (1)
- Parallel transmission line (1)
- Pre-training (1)
- Protein Contact Map (1)
- Publication
- Publication Type
Articles 1 - 9 of 9
Full-Text Articles in Artificial Intelligence and Robotics
Harnessing Artificial Intelligence Capabilities To Improve Cybersecurity, Sherali Zeadally, Erwin Adi, Zubair Baig, Imran A. Khan
Harnessing Artificial Intelligence Capabilities To Improve Cybersecurity, Sherali Zeadally, Erwin Adi, Zubair Baig, Imran A. Khan
Information Science Faculty Publications
Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptographic and Artificial Intelligence (AI) techniques (in particular, machine learning and deep learning) show promise in enabling cybersecurity experts to counter the ever-evolving threat posed by adversaries. Here, we explore AI's …
Multi-Modal Medical Imaging Analysis With Modern Neural Networks, Gongbo Liang
Multi-Modal Medical Imaging Analysis With Modern Neural Networks, Gongbo Liang
Theses and Dissertations--Computer Science
Medical imaging is an important non-invasive tool for diagnostic and treatment purposes in medical practice. However, interpreting medical images is a time consuming and challenging task. Computer-aided diagnosis (CAD) tools have been used in clinical practice to assist medical practitioners in medical imaging analysis since the 1990s. Most of the current generation of CADs are built on conventional computer vision techniques, such as manually defined feature descriptors. Deep convolutional neural networks (CNNs) provide robust end-to-end methods that can automatically learn feature representations. CNNs are a promising building block of next-generation CADs. However, applying CNNs to medical imaging analysis tasks is …
Orthogonal Recurrent Neural Networks And Batch Normalization In Deep Neural Networks, Kyle Eric Helfrich
Orthogonal Recurrent Neural Networks And Batch Normalization In Deep Neural Networks, Kyle Eric Helfrich
Theses and Dissertations--Mathematics
Despite the recent success of various machine learning techniques, there are still numerous obstacles that must be overcome. One obstacle is known as the vanishing/exploding gradient problem. This problem refers to gradients that either become zero or unbounded. This is a well known problem that commonly occurs in Recurrent Neural Networks (RNNs). In this work we describe how this problem can be mitigated, establish three different architectures that are designed to avoid this issue, and derive update schemes for each architecture. Another portion of this work focuses on the often used technique of batch normalization. Although found to be successful …
Estimating Free-Flow Speed With Lidar And Overhead Imagery, Armin Hadzic
Estimating Free-Flow Speed With Lidar And Overhead Imagery, Armin Hadzic
Theses and Dissertations--Computer Science
Understanding free-flow speed is fundamental to transportation engineering in order to improve traffic flow, control, and planning. The free-flow speed of a road segment is the average speed of automobiles unaffected by traffic congestion or delay. Collecting speed data across a state is both expensive and time consuming. Some approaches have been presented to estimate speed using geometric road features for certain types of roads in limited environments. However, estimating speed at state scale for varying landscapes, environments, and road qualities has been relegated to manual engineering and expensive sensor networks. This thesis proposes an automated approach for estimating free-flow …
Deep Neural Architectures For End-To-End Relation Extraction, Tung Tran
Deep Neural Architectures For End-To-End Relation Extraction, Tung Tran
Theses and Dissertations--Computer Science
The rapid pace of scientific and technological advancements has led to a meteoric growth in knowledge, as evidenced by a sharp increase in the number of scholarly publications in recent years. PubMed, for example, archives more than 30 million biomedical articles across various domains and covers a wide range of topics including medicine, pharmacy, biology, and healthcare. Social media and digital journalism have similarly experienced their own accelerated growth in the age of big data. Hence, there is a compelling need for ways to organize and distill the vast, fragmented body of information (often unstructured in the form of natural …
Fault Identification On Electrical Transmission Lines Using Artificial Neural Networks, Christopher W. Asbery
Fault Identification On Electrical Transmission Lines Using Artificial Neural Networks, Christopher W. Asbery
Theses and Dissertations--Electrical and Computer Engineering
Transmission lines are designed to transport large amounts of electrical power from the point of generation to the point of consumption. Since transmission lines are built to span over long distances, they are frequently exposed to many different situations that can cause abnormal conditions known as electrical faults. Electrical faults, when isolated, can cripple the transmission system as power flows are directed around these faults therefore leading to other numerous potential issues such as thermal and voltage violations, customer interruptions, or cascading events. When faults occur, protection systems installed near the faulted transmission lines will isolate these faults from the …
Temporal Data Extraction And Query System For Epilepsy Signal Analysis, Yan Huang
Temporal Data Extraction And Query System For Epilepsy Signal Analysis, Yan Huang
Theses and Dissertations--Computer Science
The 2016 Epilepsy Innovation Institute (Ei2) community survey reported that unpredictability is the most challenging aspect of seizure management. Effective and precise detection, prediction, and localization of epileptic seizures is a fundamental computational challenge. Utilizing epilepsy data from multiple epilepsy monitoring units can enhance the quantity and diversity of datasets, which can lead to more robust epilepsy data analysis tools. The contributions of this dissertation are two-fold. One is the implementation of a temporal query for epilepsy data; the other is the machine learning approach for seizure detection, seizure prediction, and seizure localization. The three key components of our temporal …
A Comparative Analysis Of Reinforcement Learning Applied To Task-Space Reaching With A Robotic Manipulator With And Without Gravity Compensation, Jonathan Fugal
A Comparative Analysis Of Reinforcement Learning Applied To Task-Space Reaching With A Robotic Manipulator With And Without Gravity Compensation, Jonathan Fugal
Theses and Dissertations--Electrical and Computer Engineering
Advances in computing power in recent years have facilitated developments in autonomous robotic systems. These robotic systems can be used in prosthetic limbs, wearhouse packaging and sorting, assembly line production, as well as many other applications. Designing these autonomous systems typically requires robotic system and world models (for classical control based strategies) or time consuming and computationally expensive training (for learning based strategies). Often these requirements are difficult to fulfill. There are ways to combine classical control and learning based strategies that can mitigate both requirements. One of these ways is to use a gravity compensated torque control with reinforcement …
Unitary And Symmetric Structure In Deep Neural Networks, Kehelwala Dewage Gayan Maduranga
Unitary And Symmetric Structure In Deep Neural Networks, Kehelwala Dewage Gayan Maduranga
Theses and Dissertations--Mathematics
Recurrent neural networks (RNNs) have been successfully used on a wide range of sequential data problems. A well-known difficulty in using RNNs is the vanishing or exploding gradient problem. Recently, there have been several different RNN architectures that try to mitigate this issue by maintaining an orthogonal or unitary recurrent weight matrix. One such architecture is the scaled Cayley orthogonal recurrent neural network (scoRNN), which parameterizes the orthogonal recurrent weight matrix through a scaled Cayley transform. This parametrization contains a diagonal scaling matrix consisting of positive or negative one entries that can not be optimized by gradient descent. Thus the …