Neural network supervised and reinforcement learning for neurological, diagnostic, and modeling problems

“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 model the impact of altered pH on respiratory rate, based on the Henderson-Hasselbalch equation. The model behaves as expected and is a preliminary example of how reinforcement learning can be utilized for medical modelling. Its sophistication will be improved in future works”--Abstract, page iv

Application of The Method of Elastic Maps In Analysis of Genetic Texts

Abstract - Method of elastic maps ( http://cogprints.ecs.soton.ac.uk/archive/00003088/ and http://cogprints.ecs.soton.ac.uk/archive/00003919/ ) allows us to construct efficiently 1D, 2D and 3D non-linear approximations to the principal manifolds with different topology (piece of plane, sphere, torus etc.) and to project data onto it. We describe the idea of the method and demonstrate its applications in analysis of genetic sequences. The animated 3D-scatters are available on our web-site: http://www.ihes.fr/~zinovyev/7clusters/ We found the universal cluster structure of genetic sequences, and demonstrated the thin structure of these clusters for coding regions. This thin structure is related to different translational efficiency

A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers

FuzzyART: An R Package for ART-based Clustering

Adaptive Resonance Theory (ART) was introduced by Steven Grossberg as a theory of human cognitive information processing (Grossberg 1976, 1980). Extending the capabilities of the ART 1 model, which can learn to categorize patterns in binary data, fuzzy ART as described in (Carpenter, Grossberg, and Rosen 1991) has become one of the most commenly used Adaptive Resonance Theory models (Brito da Silva, Elnabarawy, and Wunsch 2019). By incorporating fuzzy set theroy operators, fuzzy ART is capable of learning from binaray and bounded real valued data. Its advantage over other unsupervised learning algorithms lies in the flexibility of the learning rule. If a given input feature does not resemble a known category satisfactorily, as determined by the vigilance test, a new category is initialized. Hence, the total number of categories (or clusters) is not determined a-priori, like k-means, but chosen in accordance with the data and the context of already learnt representations. This vignette explores the use of the fuzzy ART implementation as provided by the FuzzyART R package

Fuzzy Logic in Collective Robotic Search

One important application of mobile robots is searching a geographical region to locate the origin of a specific sensible phenomenon. We first propose a fuzzy logic approach using a decision table. A novel fuzzy rule based was designed. And then a fuzzy search strategy is adopted by utilizing the three tier centers of mass coordination. Experimental results show that fuzzy logic algorithm is an efficient approach for the collective robots to locate the target source. In addition, noise and the position of the target affect the searching result

Engine Data Classification with Simultaneous Recurrent Network using a Hybrid PSO-EA Algorithm

We applied an architecture which automates the design of simultaneous recurrent network (SRN) using a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of particle swarm optimization (PSO) and evolutionary algorithm (EA). By combining the searching abilities of these two global optimization methods, the evolution of individuals is no longer restricted to be in the same generation, and better performed individuals may produce offspring to replace those with poor performance. The novel algorithm is then applied to the simultaneous recurrent network for the engine data classification. The experimental results show that our approach gives solid performance in categorizing the nonlinear car engine data

A Comparison of Dual Heuristic Programming (DHP) and Neural Network Based Stochastic Optimization Approach on Collective Robotic Search Problem

An important application of mobile robots is searching a region to locate the origin of a specific phenomenon. A variety of optimization algorithms can be employed to locate the target source, which has the maximum intensity of the distribution of some detected function. We propose two neural network algorithms: stochastic optimization algorithm and dual heuristic programming (DHP) to solve the collective robotic search problem. Experiments were carried out to investigate the effect of noise and the number of robots on the task performance, as well as the expenses. The experimental results showed that the performance of the dual heuristic programming (DHP) is better than the stochastic optimization method