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"Artificial neural network"

Z Ghaemi, A Alimohammadi, M Farnaghi
Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction...
April 20, 2018: Environmental Monitoring and Assessment
Md Mohaimenul Islam, Chieh-Chen Wu, Tahmina Nasrin Poly, Hsuan-Chia Yang, Yu-Chuan Jack Li
: Fatty liver disease (FLD) is considered the most prevalent form of chronic liver disease worldwide. The prediction of fatty liver disease is an important factor for effective treatment and reduce serious health consequences. We, therefore construct a prediction model based on machine learning algorithms. A dataset was developed with ten attributes that included 994 liver patients in which 533 patients were females and others were male. Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Logistic Regression (RF) data mining technique with 10-fold cross-validation was used in the proposed model for the prediction of fatty liver disease...
2018: Studies in Health Technology and Informatics
Hong-Seok Jang, Ju-Hee Kim, Xing Shuli, Seung-Young So
Colored concrete uses pigments and white Portland cement (WPC) to perform decorative functions together with structural function. Pigments are used in permanent coloring of concrete with colors different from the natural color of the cement or the aggregates with mixing WPC. In this study, an artificial neural networks study was carried out to predict the color evaluation of black mortar using pigment and carbon black. A data set of a laboratory work, in which a total of 9 mortars were produced, was utilized in the Artificial Neural Networks (ANNs) study...
September 1, 2018: Journal of Nanoscience and Nanotechnology
Fabrizia Caiazzo, Alessandra Caggiano
Laser welding of titanium alloys is attracting increasing interest as an alternative to traditional joining techniques for industrial applications, with particular reference to the aerospace sector, where welded assemblies allow for the reduction of the buy-to-fly ratio, compared to other traditional mechanical joining techniques. In this research work, an investigation on laser welding of Ti⁻6Al⁻4V alloy plates is carried out through an experimental testing campaign, under different process conditions, in order to perform a characterization of the produced weld bead geometry, with the final aim of developing a cognitive methodology able to support decision-making about the selection of the suitable laser welding process parameters...
April 20, 2018: Materials
David P Stonko, Bradley M Dennis, Richard D Betzold, Allan B Peetz, Oliver L Gunter, Oscar D Guillamondegui
INTRODUCTION: The goal of this study was to integrate temporal and weather data in order to create an artificial neural network (ANN) to predict trauma volume, the number of emergent operative cases, and average daily acuity at a level 1 trauma center. METHODS: Trauma admission data from TRACS and weather data from the National Oceanic and Atmospheric Administration (NOAA) was collected for all adult trauma patients from July 2013-June 2016. The ANN was constructed using temporal (time, day of week), and weather factors (daily high, active precipitation) to predict four points of daily trauma activity: number of traumas, number of penetrating traumas, average ISS, and number of immediate OR cases per day...
April 19, 2018: Journal of Trauma and Acute Care Surgery
Sheng-Yang Tsui, Chiao-Yi Wang, Tsan-Hsueh Huang, Kung-Bin Sung
A robust modelling method was proposed to extract chromophore information in multi-layered skin tissue with spatially-resolved diffuse reflectance spectroscopy. Artificial neural network models trained with a pre-simulated database were first built to map geometric and optical parameters into diffuse reflectance spectra. Nine fitting parameters including chromophore concentrations and oxygen saturation were then determined by solving the inverse problem of fitting spectral measurements from three different parts of the skin...
April 1, 2018: Biomedical Optics Express
Shruti R Kulkarni, Bipin Rajendran
We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy...
April 6, 2018: Neural Networks: the Official Journal of the International Neural Network Society
Daniel Gebler, Gerhard Wiegleb, Krzysztof Szoszkiewicz
The aim of the study was to develop predictive models of the ecological status of rivers by using artificial neural networks. The relationships between five macrophyte indices and the combined impact of water pollution as well as hydromorphological degradation were examined. The dataset consisted of hydromorphologically modified rivers representing a wide water quality gradient. Three ecological status indices, namely the Macrophyte Index for Rivers (MIR), the Macrophyte Biological Index for Rivers (IBMR) and the River Macrophyte Nutrient Index (RMNI), were tested...
April 7, 2018: Water Research
M S Zarchi, S M M Fatemi Bushehri, M Dehghanizadeh
Self-care problems diagnosis and classification is an important challenge in exceptional children health care systems. Since, self-care problems classification is a time-consuming process and requires expert occupational therapists, using an expert system in classifying these problems can decrease cost and time, efficiently. Expert systems refer to the systems that are based on artificial intelligence methods, which have the ability to learn, infer, and predict. In order to configure and train an expert system, a standard dataset is critical for the learning phase...
June 2018: International Journal of Medical Informatics
Rita Flores-Asis, Juan M Méndez-Contreras, Ulises Juárez-Martínez, Alejandro Alvarado-Lassman, Daniel Villanueva-Vásquez, Alberto A Aguilar-Lasserre
This article focuses on the analysis of the behavior patterns of the variables involved in the anaerobic digestion process. The objective is to predict the impact factor and the behavior pattern of the variables, i.e., temperature, pH, volatile solids (VS), total solids, volumetric load, and hydraulic residence time, considering that these are the control variables for the conservation of the different groups of anaerobic microorganisms. To conduct the research, samples of physicochemical sludge were taken from a water treatment plant in a poultry processing factory, and, then, the substrate was characterized, and a thermal pretreatment was used to accelerate the hydrolysis process...
April 19, 2018: Journal of Environmental Science and Health. Part A, Toxic/hazardous Substances & Environmental Engineering
H Gong, R Pishgar, J H Tay
Aerobic granulation is a recent technology with high level of complexity and sensitivity to environmental and operational conditions. Artificial neural networks (ANN), computational tools capable of describing complex nonlinear systems, are the best fit to simulate aerobic granular bioreactors. In this study, two feedforward backpropagation ANN models were developed to predict chemical oxygen demand (COD) (Model I) and total nitrogen (TN) removal efficiencies (Model II) of aerobic granulation technology under steady-state condition...
April 19, 2018: Environmental Technology
Abhinav Parihar, Matthew Jerry, Suman Datta, Arijit Raychowdhury
Artificial neural networks can harness stochasticity in multiple ways to enable a vast class of computationally powerful models. Boltzmann machines and other stochastic neural networks have been shown to outperform their deterministic counterparts by allowing dynamical systems to escape local energy minima. Electronic implementation of such stochastic networks is currently limited to addition of algorithmic noise to digital machines which is inherently inefficient; albeit recent efforts to harness physical noise in devices for stochasticity have shown promise...
2018: Frontiers in Neuroscience
Sherif Sakr, Radwa Elshawi, Amjad Ahmed, Waqas T Qureshi, Clinton Brawner, Steven Keteyian, Michael J Blaha, Mouaz H Al-Mallah
This study evaluates and compares the performance of different machine learning techniques on predicting the individuals at risk of developing hypertension, and who are likely to benefit most from interventions, using the cardiorespiratory fitness data. The dataset of this study contains information of 23,095 patients who underwent clinician- referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 10-year follow-up. The variables of the dataset include information on vital signs, diagnosis and clinical laboratory measurements...
2018: PloS One
Katharine Brigham, Samir Gupta, John C Brigham
Infants born prematurely are particularly susceptible to respiratory illness due to underdeveloped lungs, which can often result in fatality. Preterm infants in acute stages of respiratory illness typically require mechanical ventilation assistance, and the efficacy of the type of mechanical ventilation and its delivery has been the subject of a number clinical studies. With recent advances in machine learning approaches, particularly deep learning, it may be possible to estimate future responses to mechanical ventilation in real-time, based on ventilation monitoring up to the point of analysis...
April 17, 2018: International Journal for Numerical Methods in Biomedical Engineering
Ahmad Firdaus B Lajis
For decades, microbial lipases are notably used as biocatalysts and efficiently catalyze various processes in many important industries. Biocatalysts are less corrosive to industrial equipment and due to their substrate specificity and regioselectivity they produced less harmful waste which promotes environmental sustainability. At present, thermostable and alkaline tolerant lipases have gained enormous interest as biocatalyst due to their stability and robustness under high temperature and alkaline environment operation...
2018: Journal of Lipids
Federico Da Rold
The embodied and situated view of cognition stresses the importance of real-time and nonlinear bodily interaction with the environment for developing concepts and structuring knowledge. In this article, populations of robots controlled by an artificial neural network learn a wall-following task through artificial evolution. At the end of the evolutionary process, time series are recorded from perceptual and motor neurons of selected robots. Information-theoretic measures are estimated on pairings of variables to unveil nonlinear interactions that structure the agent-environment system...
March 27, 2018: Neural Networks: the Official Journal of the International Neural Network Society
Jana Striova, Chiara Ruberto, Marco Barucci, Jan Blažek, Diane Kuzman, Alice Dal Fovo, Enrico Pampaloni, Raffaella Fontana
A concise insight into the outputs provided by the latest prototype of visible-near infrared (VIS-NIR) multispectral scanner (National Research Council-National Institute of Optics, CNR-INO, Italy) is presented. The analytical data acquired on an oil painting Madonna of the Rabbit by É. Manet are described. In this work, the VIS-NIR data were complemented with X-ray fluorescence (XRF) mapping for the chemical and spatial characterization of several pigments. The spatially registered VIS-NIR data facilitated their processing with spectral correlation mapping (SCM) and artificial neural network (ANN) algorithm respectively for pigment mapping and improved visibility of pentimenti and of underdrawing style...
April 17, 2018: Angewandte Chemie
Wilson Lee, Ariana Gonzalez, Paolo Arguelles, Ricardo Guevara, Maria Jose Gonzalez-Guerrero, Frank A Gomez
This paper describes the fabrication of and data collection from two microfluidic devices: a microfluidic thread/paper based analytical device (μTPAD) and 3D microfluidic paper-based analytical device (μPAD). Flowing solutions of glucose oxidase (GOx), horseradish peroxidase (HRP), and potassium iodide (KI), through each device, on contact with glucose, generated a calibration curve for each platform. The resultant yellow-brown color from the reaction indicates oxidation of iodide to iodine. The devices were dried, scanned, and analyzed yielding a correlation between yellow intensity and glucose concentration...
April 16, 2018: Electrophoresis
Wei Ouyang, Andrey Aristov, Mickaël Lelek, Xian Hao, Christophe Zimmer
The speed of super-resolution microscopy methods based on single-molecule localization, for example, PALM and STORM, is limited by the need to record many thousands of frames with a small number of observed molecules in each. Here, we present ANNA-PALM, a computational strategy that uses artificial neural networks to reconstruct super-resolution views from sparse, rapidly acquired localization images and/or widefield images. Simulations and experimental imaging of microtubules, nuclear pores, and mitochondria show that high-quality, super-resolution images can be reconstructed from up to two orders of magnitude fewer frames than usually needed, without compromising spatial resolution...
April 16, 2018: Nature Biotechnology
Stefano A Bini
This article was presented at the 2017 annual meeting of the American Association of Hip and Knee Surgeons to introduce the members gathered as the audience to the concepts behind artificial intelligence (AI) and the applications that AI can have in the world of health care today. We discuss the origin of AI, progress to machine learning, and then discuss how the limits of machine learning lead data scientists to develop artificial neural networks and deep learning algorithms through biomimicry. We will place all these technologies in the context of practical clinical examples and show how AI can act as a tool to support and amplify human cognitive functions for physicians delivering care to increasingly complex patients...
February 27, 2018: Journal of Arthroplasty
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