Encog: Library of Interchangeable Machine Learning Models for Java and C#

This paper introduces the Encog library for Java and C#, a scalable, adaptable, multiplatform machine learning framework that was 1st released in 2008. Encog allows a variety of machine learning models to be applied to datasets using regression, classification, and clustering. Various supported machine learning models can be used interchangeably with minimal recoding. Encog uses efficient multithreaded code to reduce training time by exploiting modern multicore processors. The current version of Encog can be downloaded from http://www.encog.org

Applications of Deep Neural Networks

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Readers will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this book; however, familiarity with at least one programming language is assumed

Automated Feature Engineering for Deep Neural Networks with Genetic Programming

Feature engineering is a process that augments the feature vector of a machine learning model with calculated values that are designed to enhance the accuracy of a model’s predictions. Research has shown that the accuracy of models such as deep neural networks, support vector machines, and tree/forest-based algorithms sometimes benefit from feature engineering. Expressions that combine one or more of the original features usually create these engineered features. The choice of the exact structure of an engineered feature is dependent on the type of machine learning model in use. Previous research demonstrated that various model families benefit from different types of engineered feature. Random forests, gradient-boosting machines, or other tree-based models might not see the same accuracy gain that an engineered feature allowed neural networks, generalized linear models, or other dot-product based models to achieve on the same data set. This dissertation presents a genetic programming-based algorithm that automatically engineers features that increase the accuracy of deep neural networks for some data sets. For a genetic programming algorithm to be effective, it must prioritize the search space and efficiently evaluate what it finds. This dissertation algorithm faced a potential search space composed of all possible mathematical combinations of the original feature vector. Five experiments were designed to guide the search process to efficiently evolve good engineered features. The result of this dissertation is an automated feature engineering (AFE) algorithm that is computationally efficient, even though a neural network is used to evaluate each candidate feature. This approach gave the algorithm a greater opportunity to specifically target deep neural networks in its search for engineered features that improve accuracy. Finally, a sixth experiment empirically demonstrated the degree to which this algorithm improved the accuracy of neural networks on data sets augmented by the algorithm’s engineered features

Neuropsychological Assessment of HIV-Infected Populations in International Settings

Resource-limited regions of the world represent the areas most affected by the global HIV epidemic. Currently, there are insufficient data on the neurocognitive effects of HIV in these areas and neuropsychological studies that have been carried out thus far are marked by inconsistent methods, test batteries, and rating systems for levels of cognitive impairment. These differences in methods, along with genetic variability of both virus and host, differences in co-infections and other co-morbidities, differences in language and culture, and infrastructural deficiencies in many international settings create challenges to the assessment of neurocognitive functioning and interpretation of neuropsychological data. Identifying neurocognitive impairment directly attributable to HIV, exploring relationships between HIV-associated neurocognitive impairment, disease variables, and everyday functioning, evaluating differences in HIV-1 subtype associated neuropathology, and determining implications for treatment remain complicated and challenging goals. Endeavors to establish a more standardized approach to neurocognitive assessments across international studies in addition to accumulating appropriate normative data that will allow more accurate rating of neuropsychological test performance will be crucial to future efforts attempting to achieve these goals

Software engineering for enabling scientific software development

Scientific software is code written by scientists for the purpose of doing research. While the results of this software development have been widely published, there has been relatively little publication of the development of this software. There have been even fewer publications that look at the software engineering aspects of scientific software development and fewer still that have suggested software engineering techniques that will help scientists develop the software that is relied on for much of our modern knowledge. The software engineers who have studied the development processes of scientific software developers agree that scientists would be able to produce better software if they had the knowledge and familiarity to use specific software engineering practices. The primary focus of this dissertation is to provide that knowledge to scientific software developers in order to better enable them to produce quality software as efficiently as possible. In order to achieve this focus, this dissertation has three aspects. First, this dissertation provides a literature review of the claims that have been made in the software engineering and scientific software literature culminating in a list of claims about software engineering practices. Scientific software developers can use this list to find practices they are unaware of that should prove useful to their development. Additionally, software engineers can use the list to help determine what practices need support for the scientists to be able to take advantage of them. Second, this dissertation provides a series of surveys that capture the current state of software engineering knowledge in the scientific software development community. The results of these surveys show that scientific software developers are unfamiliar with many of the practices that could help them address their most challenging issues. Third, this dissertation provides examples that show, with support from software engineers, scientific software developers can take advantage of practices that have proven useful in traditional software engineering and increase the quality of their work without requiring an overwhelming amount of extra work. (Published By University of Alabama Libraries