Non-Gaussian Hybrid Transfer Functions: Memorizing Mine Survivability Calculations

Hybrid algorithms and models have received significant interest in recent years and are increasingly used to solve real-world problems. Different from existing methods in radial basis transfer function construction, this study proposes a novel nonlinear-weight hybrid algorithm involving the non-Gaussian type radial basis transfer functions. The speed and simplicity of the non-Gaussian type with the accuracy and simplicity of radial basis function are used to produce fast and accurate on-the-fly model for survivability of emergency mine rescue operations, that is, the survivability under all conditions is precalculated and used to train the neural network. The proposed hybrid uses genetic algorithm as a learning method which performs parameter optimization within an integrated analytic framework, to improve network efficiency. Finally, the network parameters including mean iteration, standard variation, standard deviation, convergent time, and optimized error are evaluated using the mean squared error. The results demonstrate that the hybrid model is able to reduce the computation complexity, increase the robustness and optimize its parameters. This novel hybrid model shows outstanding performance and is competitive over other existing models

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154105/1/kampfner1997.pd

Augmenting Structure/Function Relationship Analysis with Deep Learning for the Classification of Psychoactive Drug Activity at Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used in the treatment of schizophrenia and other psychiatric disorders display promiscuous binding behavior linked to chronic toxicity and high-risk adverse effects. [16-18] We hypothesized that using a combination of physiochemical feature engineering with a feedforward neural network, predictive models can be trained for these specific GPCR subgroups that are more efficient and accurate than current state-of-the-art methods.. We combined normal mode analysis with deep learning to create a novel framework for the prediction of Class A GPCR/psychoactive drug interaction activities. Our deep learning classifier results in high classification accuracy (5-HT F1-score = 0.78; DRD F1-score = 0.93) and achieves a 45% reduction in model training time when structure-based feature selection is applied via guidance from an anisotropic network model (ANM). Additionally, we demonstrate the interpretability and application potential of our framework via evaluation of highly clinically relevant Class A GPCR/psychoactive drug interactions guided by our ANM results and deep learning predictions. Our model offers an increased range of applicability as compared to other methods due to accessible data compatibility requirements and low model complexity. While this model can be applied to a multitude of clinical applications, we have presented strong evidence for the impact of machine learning in the development of novel psychiatric therapeutics with improved safety and tolerability

Smart Gas Sensors: Materials, Technologies, Practical ‎Applications, and Use of Machine Learning – A Review

The electronic nose, popularly known as the E-nose, that combines gas sensor arrays (GSAs) with machine learning has gained a strong foothold in gas sensing technology. The E-nose designed to mimic the human olfactory system, is used for the detection and identification of various volatile compounds. The GSAs develop a unique signal fingerprint for each volatile compound to enable pattern recognition using machine learning algorithms. The inexpensive, portable and non-invasive characteristics of the E-nose system have rendered it indispensable within the gas-sensing arena. As a result, E-noses have been widely employed in several applications in the areas of the food industry, health management, disease diagnosis, water and air quality control, and toxic gas leakage detection. This paper reviews the various sensor fabrication technologies of GSAs and highlights the main operational framework of the E-nose system. The paper details vital signal pre-processing techniques of feature extraction, feature selection, in addition to machine learning algorithms such as SVM, kNN, ANN, and Random Forests for determining the type of gas and estimating its concentration in a competitive environment. The paper further explores the potential applications of E-noses for diagnosing diseases, monitoring air quality, assessing the quality of food samples and estimating concentrations of volatile organic compounds (VOCs) in air and in food samples. The review concludes with some challenges faced by E-nose, alternative ways to tackle them and proposes some recommendations as potential future work for further development and design enhancement of E-noses

Towards Fault Diagnosis in Reconfigurable Robot

東京電機大学201

Estudio de aplicabilidad de técnicas de inteligencia artificial en el sector agropecuario

Programa Oficial de Doutoramento en Tecnoloxías da Información e as Comunicacións. 5032V01[Resumo] O aprendizaje máquina é unha rama da intelixencia artificial (IA) que utiliza algoritmos para realizar tarefas, sen que se teña programado explícitamente. Para o seu funcionamento require un proceso de formación e validación baseado en exemplos. Nesta tese proponse estudar a aplicabilidade dalgunhas técnicas de IA na produción agrícola. A tese é apoiada por tres publicacións cun importante factor de impacto JCR. Dous deles fan referencia a unha base de datos de produción de aves de ovos e outra a unha base de datos sobre a industrialización da cana de azucre. Na produción avícola estas técnicas foron estudadas para a alerta precoz dos problemas na curva de produción. En canto á aplicación destas técnicas no proceso industrial de cana de azucre, optimizáronse os modelos de calibración dos espectros NIR para o control de calidade nunha fábrica de azucre. Usáronse máquinas de soporte vectorial e redes neuronais artificiais. A aplicación destas técnicas ten un alto potencial de uso na produción agrícola, xa que posibilita o desenvolvemento de sistemas intelixentes de apoio ás decisións produtivas.[Resumen] El aprendizaje máquina es una rama de la inteligencia artificial (IA) que utiliza algoritmos para realizar tareas, sin que hayan sido programados de manera explícita. Para su funcionamiento se requiere de un proceso de entrenamiento y validación en base a ejemplos. En esta Tesis Doctoral, se propone estudiar la aplicabilidad de algunas técnicas de IA en la producción agropecuaria. El trabajo está respaldado por tres publicaciones con un importante factor de impacto JCR. Dos de ellas se refieren a una base de datos de producción avícola de huevos y la otra, a una base de datos de la industrialización de la caña de azúcar. En la producción avícola estas técnicas fueron estudiadas para la alerta temprana de problemas en la curva de producción. En cuanto a la aplicación de estas técnicas en el proceso industrial de la caña de azúcar, se optimizó los modelos de calibración de los espectros NIR para el control de calidad en una fábrica de azúcar. Se utilizó Máquinas de Soporte Vectorial y Redes de Neuronas Artificiales. La aplicación de estas técnicas tiene un alto potencial de uso en la producción agropecuaria, ya que posibilita el desarrollo de sistemas inteligentes de apoyo a las decisiones productivas[Abstract] Machine learning is a branch of artificial intelligence that uses algorithms to perform tasks, without having been programmed explicitly. For its operation requires a process of training and validation based on examples. In this thesis the application of artificial intelligence techniques in agricultural production is studied. As main result of the thesis, three articles has been published in journals with important JCR impact factors. Two of them refer to a database of poultry production of eggs and the other to a database of the industrialization of sugar cane. In poultry production these techniques were studied for the early warning of problems in the production curve. For the application of these techniques in the industrial process of sugarcane, the calibration models of the NIR spectra for the quality control in a sugar factory were optimized. In this work were used Support Vector Machines and Artificial Neural Networks. The application of these techniques has a high potential of use in the agricultural production, since it opens up the development of intelligent systems to support productive decisions