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

Preface to Computational Intelligence Applications in Medicine and Biology

This special edition of the European Science Journal is devoted to applying computational intelligence methods to solving complex problems in medicine and biology

Promoting Search Diversity in Ant Colony Optimization with Stubborn Ants

AbstractIn ant colony optimization (ACO) methods, including Ant System and MAX-M IN Ant System, each ant stochastically generates its candidate solution, in a given iteration, based on the same pheromone T and heuristic η information as every other ant. Stubborn ants is an ACO variation in which if an ant generates a particular candidate solution in a given iteration, then the components of that solution will have a higher probability of being selected in the candidate solution generated by that ant in the next iteration. In previous work, we evaluated this variation with the M M AS Ant System model and the Traveling Salesman Problem (TSP), and found that it can both improve solution quality and reduce execution-time. In this paper, we evaluate stubborn ants with Ranked Ant System, and find that performance also improves in terms of solution quality and execution time

Enhanced neurologic concept recognition using a named entity recognition model based on transformers

Although deep learning has been applied to the recognition of diseases and drugs in electronic health records and the biomedical literature, relatively little study has been devoted to the utility of deep learning for the recognition of signs and symptoms. The recognition of signs and symptoms is critical to the success of deep phenotyping and precision medicine. We have developed a named entity recognition model that uses deep learning to identify text spans containing neurological signs and symptoms and then maps these text spans to the clinical concepts of a neuro-ontology. We compared a model based on convolutional neural networks to one based on bidirectional encoder representation from transformers. Models were evaluated for accuracy of text span identification on three text corpora: physician notes from an electronic health record, case histories from neurologic textbooks, and clinical synopses from an online database of genetic diseases. Both models performed best on the professionally-written clinical synopses and worst on the physician-written clinical notes. Both models performed better when signs and symptoms were represented as shorter text spans. Consistent with prior studies that examined the recognition of diseases and drugs, the model based on bidirectional encoder representations from transformers outperformed the model based on convolutional neural networks for recognizing signs and symptoms. Recall for signs and symptoms ranged from 59.5% to 82.0% and precision ranged from 61.7% to 80.4%. With further advances in NLP, fully automated recognition of signs and symptoms in electronic health records and the medical literature should be feasible

High Throughput Neurological Phenotyping with MetaMap

The phenotyping of neurological patients involves the conversion of signs and symptoms into machine readable codes selected from an appropriate ontology. The phenotyping of neurological patients is manual and laborious. MetaMap is used for high throughput mapping of the medical literature to concepts in the Unified Medical Language System Metathesaurus (UMLS). MetaMap was evaluated as a tool for the high throughput phenotyping of neurological patients. Based on 15 patient histories from electronic health records, 30 patient histories from neurology textbooks, and 20 clinical summaries from the Online Mendelian Inheritance in Man repository, MetaMap showed a recall of 61-89%, a precision of 84-93%, and an accuracy of 56-84% for the identification of phenotype concepts. The most common cause of false negatives (failure to recognize a phenotype concept) was an inability of MetaMap to find concepts that were represented as a description or a definition of the concept. The most common cause of false positives (incorrect identification of a concept in the text) was a failure to recognize that a concept was negated. MetaMap shows potential for high throughput phenotyping of neurological patients if the problems of false negatives and false positives can be solved

A Robot Advisor to Improve Computerized Game Play

This paper explores using a trained machine learning agent as a robot advisor for StarCraft II. A targeted visual representation of the robot advisor decision vector advised players of superior decisions in real-time. The robot advisor provided players with the best decisions given the game state and time remaining. Study subjects had to generalize a game strategy from the robot advisor recommendations to a later game round. We sought to determine whether different advice representations (1) improved performance when an advisor is available, (2) improved subsequent performance when an advisor was not available (i.e., did carry over learning occur?), and (3) whether subjects reported that the robot advice was a positive learning experience. The research design involved a pre-test condition (play without an advisor to gauge initial performance), a test condition (subjects were randomized to receive no robot advice, single-recommendation robot advice, or multiplerecommendation robot advice), and a post-test condition (play without an advisor to gauge performance gains). Some high-performing subjects had a ceiling effect and did not improve over the three experiment rounds. After excluding subjects with a ceiling effect, subjects assigned to the singlerecommendation robot advisor showed more learning across the rounds than the subjects in the control group (no robot advisor) or those assigned to the multiple-recommendation robot advisor. In the randomized test round, the single-recommendation robot advisor group outperformed no advisor group or the multiple-recommendation robot advisor group. Our project offers a research framework for evaluating the potential of robot advisors in other training scenarios