Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Clustering (2)
- Machine Learning (2)
- <p>Pattern recognition systems<br />Machine learning<br />Swarm intelligence<br />Time-series analysis</p> (1)
- Adaptive Resonance Theory (1)
- Biomedical Data Analysis (1)
-
- Cervical cancer (1)
- Classification (1)
- Convolutional neural networks (1)
- Deep learning (1)
- Demand-Side Management (1)
- Digital pathology (1)
- Feature Selection (1)
- Histopathology (1)
- Industrial Robot (1)
- Iterative Learning Control (1)
- Kalman Filtering (1)
- Kinematic Error Compensation (1)
- Laser Tracker (1)
- Machine Tools (1)
- Medicine (1)
- Neurology (1)
- Particle Swarm Optimization (1)
- Reinforcement Learning (1)
- Smart Grid (1)
- Switched System (1)
- Time Series Prediction (1)
- Whole slide image (1)
- Publication
Articles 1 - 6 of 6
Full-Text Articles in Computer Engineering
Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii
Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii
Masters Theses
“As the medical world becomes increasingly intertwined with the tech sphere, machine learning on medical datasets and mathematical models becomes an attractive application. This research looks at the predictive capabilities of neural networks and other machine learning algorithms, and assesses the validity of several feature selection strategies to reduce the negative effects of high dataset dimensionality. Our results indicate that several feature selection methods can maintain high validation and test accuracy on classification tasks, with neural networks performing best, for both single class and multi-class classification applications. This research also evaluates a proof-of-concept application of a deep-Q-learning network (DQN) to …
Deep Learning For Digitized Histology Image Analysis, Sudhir Sornapudi
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 …
Volumetric Error Compensation For Industrial Robots And Machine Tools, Le Ma
Volumetric Error Compensation For Industrial Robots And Machine Tools, Le Ma
Doctoral Dissertations
“A more efficient and increasingly popular volumetric error compensation method for machine tools is to compute compensation tables in axis space with tool tip volumetric measurements. However, machine tools have high-order geometric errors and some workspace is not reachable by measurement devices, the compensation method suffers a curve-fitting challenge, overfitting measurements in measured space and losing accuracy around and out of the measured space. Paper I presents a novel method that aims to uniformly interpolate and extrapolate the compensation tables throughout the entire workspace. By using a uniform constraint to bound the tool tip error slopes, an optimal model with …
Controlled Switching In Kalman Filtering And Iterative Learning Controls, He Li
Controlled Switching In Kalman Filtering And Iterative Learning Controls, He Li
Masters Theses
“Switching is not an uncommon phenomenon in practical systems and processes, for examples, power switches opening and closing, transmissions lifting from low gear to high gear, and air planes crossing different layers in air. Switching can be a disaster to a system since frequent switching between two asymptotically stable subsystems may result in unstable dynamics. On the contrary, switching can be a benefit to a system since controlled switching is sometimes imposed by the designers to achieve desired performance. This encourages the study of system dynamics and performance when undesired switching occurs or controlled switching is imposed. In this research, …
Machine Learning Techniques Implementation In Power Optimization, Data Processing, And Bio-Medical Applications, Khalid Khairullah Mezied Al-Jabery
Machine Learning Techniques Implementation In Power Optimization, Data Processing, And Bio-Medical Applications, Khalid Khairullah Mezied Al-Jabery
Doctoral Dissertations
"The rapid progress and development in machine-learning algorithms becomes a key factor in determining the future of humanity. These algorithms and techniques were utilized to solve a wide spectrum of problems extended from data mining and knowledge discovery to unsupervised learning and optimization. This dissertation consists of two study areas. The first area investigates the use of reinforcement learning and adaptive critic design algorithms in the field of power grid control. The second area in this dissertation, consisting of three papers, focuses on developing and applying clustering algorithms on biomedical data. The first paper presents a novel modelling approach for …
Fuzzy Adaptive Resonance Theory: Applications And Extensions, Clayton Parker Smith
Fuzzy Adaptive Resonance Theory: Applications And Extensions, Clayton Parker Smith
Masters Theses
"Adaptive Resonance Theory, ART, is a powerful clustering tool for learning arbitrary patterns in a self-organizing manner. In this research, two papers are presented that examine the extensibility and applications of ART. The first paper examines a means to boost ART performance by assigning each cluster a vigilance value, instead of a single value for the whole ART module. A Particle Swarm Optimization technique is used to search for desirable vigilance values. In the second paper, it is shown how ART, and clustering in general, can be a useful tool in preprocessing time series data. Clustering quantization attempts to meaningfully …