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

LuxCDABE—Transformed Constitutively Bioluminescent Escherichia coli for Toxicity Screening: Comparison with Naturally Luminous Vibrio fischeri

We show that in vitro toxicity assay based on inhibition of the bioluminescence of recombinant Escherichia coli encoding thermostable luciferase from Photorhabdus luminescens is a versatile alternative to Vibrio fischeri Microtox™ test. Performance of two luxCDABE-transformed E. coli MC1061 constructs (pDNlux) and (pSLlux) otherwise identical, but having 100-fold different background luminescence was compared with the performance of V. fischeri. The microplate luminometer and a kinetic Flash-Assay test format was used that differently from Microtox test is also applicable for high throughput analysis. Toxic effects (30-s till 30-min EC50) of four heavy metals (Zn, Cd, Hg, Cu) and three organic chemicals (aniline, 3,5-dichloroaniline and 3,5-dichlorophenol) were studied. Both E. coli strains had comparable sensitivity and the respective 30-min EC50 values highly correlated (log-log R2 = 0.99; p < 0.01) showing that the sensitivity of the recombinant bacteria towards chemicals analyzed did not depend on the bioluminescence level of the recombinant cells. The most toxic chemical for all used bacterial strains (E. coli, V. fischeri) was mercury whereas the lowest EC50 values for Hg (0.04–0.05 mg/L) and highest EC50 values for aniline (1,300–1,700 mg/L) were observed for E. coli strains. Despite of that, toxicity results obtained with both E. coli strains (pSLlux and pDNlux) significantly correlated with V. fischeri results (log-log R2 = 0.70/0.75; p < 0.05/0.01). The use of amino acids (0.25%) and glucose (0.05%)-supplemented M9 medium instead of leucine-supplemented saline significantly (p < 0.05) reduced the apparent toxicity of heavy metals to both E. coli strains up to three orders of magnitude, but had little or no complexing effect on organic compounds. Thus, P. luminescens luxCDABE-transformed E. coli strains can be successfully used for the acute toxicity screening of various types of organic chemicals and heavy metals and can replace V. fischeri in certain cases where the thermostability of luciferase >30 °C is crucial. The kinetic Flash Assay test format of the bioluminescence inhibition assay facilitates high throughput analysis. The assay medium, especially in case of testing heavy metals should be a compromise: optimal for the viability/luminescence of the recombinant test strain and of minimum complexing potential

Date to the Copy Of Ms.w.609 From Khamsa Nizami Manuscript that saved In Walters Gallery Museum In Paltimor

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Dual Vigilance Fuzzy Adaptive Resonance Theory

Clusters retrieved by generic Adaptive Resonance Theory (ART) networks are limited to their internal categorical representation. This study extends the capabilities of ART by incorporating multiple vigilance thresholds in a single network: stricter (data compression) and looser (cluster similarity) vigilance values are used to obtain a many-to-one mapping of categories-to-clusters. It demonstrates this idea in the context of Fuzzy ART, presented as Dual Vigilance Fuzzy ART (DVFA), to improve the ability to capture clusters with arbitrary geometry. DVFA outperformed Fuzzy ART for the datasets in our experiments while yielding a statistically-comparable performance to another more complex, multi-prototype Fuzzy ART-based architecture

Dual Vigilance Hypersphere Adaptive Resonance Theory

The internal representation of the categories in Adaptive Resonance Theory (ART) neural networks can greatly affect the quality and compactness of the discovered clusters. Dual vigilance thresholds have been shown to yield a significant improvement in Fuzzy ART performance and allow it to retrieve arbitrarily shaped clusters while maintaining the important advantage of a simple architecture and implementation. In this study, we examine the use of Hypersphere ART within the same dual vigilance architecture, thereby presenting the Dual Vigilance Hypersphere ART (DVHA). We conduct an extensive comparison between 6 different ART-based approaches across a set of 30 benchmark datasets, using two different input ordering methods and 30 repeated runs. We find that DVHA ranks better than Dual Vigilance Fuzzy ART (DVFA) on average for many datasets, both in terms of performance and network compactness. Furthermore, although another multi-category-based architecture showed statistically superior results when the inputs are shuffled, we found no statistical difference in performance when the input was pre-processed using the visual assessment of cluster tendency (VAT), while generally being much simpler to implement and less computationally demanding. These findings make DVHA a viable alternative to its Fuzzy ART counterpart, and a simpler alternative to the other studied multi-category-based approaches in cases where resources are limited, such as embedded and hardware-based applications, provided that the input can be preprocessed using VAT

A study of snakebite envenomation cases admitted to egyptian national poisoning center

Introduction: Snakebite is an Egyptian health problem since ancient Egypt. Meanwhile, there is still no controlled geographical and medical studies on locally prevalent snake family intoxication. Aim and Methods: The present study aimed to investigate local snakebites presentations, management, prognosis, and the effect of the polyvalent antivenom, locally produced by the Holding Company for Biological Products and Vaccines (VACSERA) through the year 2015. Results: A total of 87 snakebites caused by venomous and nonvenomous species were recorded. Most cases were middle-aged males. Most cases presented in summer and in the evening time. Two major groups of venomous Egyptian snakes were identified, Viperidae and Elapidae species, based on history, characteristic symptoms, and laboratory findings. Most snakebites (56 cases) were reported to be nonvenomous bites (64.4%). Twenty-one cases (24.1%) of snakebites were reported to be venomous bites by Elapidae snakes and 10 cases (11.5%) were reported to be venomous bites by Viperidae snakes. Antivenom was administered before referral to 37 (42.5%) of cases, and 19 only of them were victims of venomous snakes. Thirty-six (41.3%) patients received antivenom during admission including all cases of the venomous bites and 8.9% of nonvenomous bites' cases. Conclusion: Patients who had moderate or severe symptoms were effectively treated with VACSERA's polyvalent antivenom, with doses related to the severity grading and snake species identification. Additional antivenom doses were repeated on the bases of the clinical condition. Many snakebite victims referred from primary health centers received inadequate or nonnecessary doses of antivenom. No cases of anaphylaxis were recorded. There were no mortalities with current National Center for Clinical and Environmental Toxicology's protocol of treatment

Matrix Factorization based Collaborative Filtering with Resilient Stochastic Gradient Descent

One of the leading approaches to collaborative filtering is to use matrix factorization to discover a set of latent factors that explain the pattern of preferences. In this paper, we apply a resilient stochastic gradient descent approach that uses only the sign of the gradient, similar to the R-Prop algorithm in neural network training, to matrix factorization for collaborative filtering. We evaluate the performance of our approach on the MovieLens 1M dataset, and find that test set accuracy markedly improves compared to standard gradient descent. As a follow-up experiment, we apply clustering to the learned item-factor matrix in factor space, and attempt to manually characterize each cluster of movies

Biclustering ARTMAP Collaborative Filtering Recommender System

Collaborative filtering provides recommendations based on the behavior of each user combined with behavior of users with similar interests. Recommender systems are becoming widespread, helping people choose movies, books, and things to buy. In this study, we examine the use of Biclustering ARTMAP to build a collaborative filtering recommendation system. We introduce a novel modification to how the Biclustering ARTMAP algorithm computes the item-cluster similarity, and a way to adapt it for the prediction of user ratings. We apply the algorithm to the MovieLens 100k dataset, and find that it achieves promising performance compared to other collaborative filtering techniques