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"Artificial neural network"

Vinayaka B Shet, Anusha M Palan, Shama U Rao, C Varun, Uday Aishwarya, Selvaraj Raja, Louella Concepta Goveas, C Vaman Rao, P Ujwal
In the current investigation, statistical approaches were adopted to hydrolyse non-edible seed cake (NESC) of Pongamia and optimize the hydrolysis process by response surface methodology (RSM). Through the RSM approach, the optimized conditions were found to be 1.17%v/v of HCl concentration at 54.12 min for hydrolysis. Under optimized conditions, the release of reducing sugars was found to be 53.03 g/L. The RSM data were used to train the artificial neural network (ANN) and the predictive ability of both models was compared by calculating various statistical parameters...
February 2018: 3 Biotech
Jiefen Cui, Yinping Li, Shixin Wang, Yongzhou Chi, Hueymin Hwang, Peng Wang
The sulfated polysaccharides from Enteromorpha prolifera (PE) are a potential source of anticoagulant agents. In this study, the PE was degraded by specific degradase and five hydrolysis products with different molecular weights were prepared. The product of 206 kDa is a kind of high rhamnose-containing polysaccharide with sulfate ester (34.29%). It could effectively prolong the activated partial thromboplastin time (APTT), which indicated inhibition of the intrinsic coagulation pathway. The artificial neural network (ANN) was built to realize the directional preparation of anticoagulant-active polysaccharides...
February 15, 2018: Scientific Reports
Haytham Eloqayli, Ali Al-Yousef, Raid Jaradat
AIM: Despite the high prevalence of chronic neck pain, there is limited consensus about the primary etiology, risk factors, diagnostic criteria and therapeutic outcome. Here, we aimed to determine if Ferritin and Vitamin D are modifiable risk factors with chronic neck pain using slandered statistics and artificial intelligence neural network (ANN). METHODS: Fifty-four patients with chronic neck pain treated between February 2016 and August 2016 in King Abdullah University Hospital and 54 patients age matched controls undergoing outpatient or minor procedures were enrolled...
February 15, 2018: British Journal of Neurosurgery
Guo Li, Xiaorong Zhou, Jianbing Liu, Yuanqi Chen, Hengtao Zhang, Yanyan Chen, Jianhua Liu, Hongbo Jiang, Junjing Yang, Shaofa Nie
BACKGROUND: In order to better assist medical professionals, this study aimed to develop and compare the performance of three models-a multivariate logistic regression (LR) model, an artificial neural network (ANN) model, and a decision tree (DT) model-to predict the prognosis of patients with advanced schistosomiasis residing in the Hubei province. METHODOLOGY/PRINCIPAL FINDINGS: Schistosomiasis surveillance data were collected from a previous study based on a Hubei population sample including 4136 advanced schistosomiasis cases...
February 15, 2018: PLoS Neglected Tropical Diseases
Ramin Jaberi, Zahra Siavashpour, Mahmoud Reza Aghamiri, Christian Kirisits, Reza Ghaderi
Purpose: Intra-fractional organs at risk (OARs) deformations can lead to dose variation during image-guided adaptive brachytherapy (IGABT). The aim of this study was to modify the final accepted brachytherapy treatment plan to dosimetrically compensate for these intra-fractional organs-applicators position variations and, at the same time, fulfilling the dosimetric criteria. Material and methods: Thirty patients with locally advanced cervical cancer, after external beam radiotherapy (EBRT) of 45-50 Gy over five to six weeks with concomitant weekly chemotherapy, and qualified for intracavitary high-dose-rate (HDR) brachytherapy with tandem-ovoid applicators were selected for this study...
December 2017: Journal of Contemporary Brachytherapy
Aida Catic, Lejla Gurbeta, Amina Kurtovic-Kozaric, Senad Mehmedbasic, Almir Badnjevic
BACKGROUND: The usage of Artificial Neural Networks (ANNs) for genome-enabled classifications and establishing genome-phenotype correlations have been investigated more extensively over the past few years. The reason for this is that ANNs are good approximates of complex functions, so classification can be performed without the need for explicitly defined input-output model. This engineering tool can be applied for optimization of existing methods for disease/syndrome classification. Cytogenetic and molecular analyses are the most frequent tests used in prenatal diagnostic for the early detection of Turner, Klinefelter, Patau, Edwards and Down syndrome...
February 13, 2018: BMC Medical Genomics
Nasibeh Talebi, Ali Motie Nasrabadi, Iman Mohammad-Rezazadeh
Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN's ability to generate appropriate input-output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output...
February 2018: Cognitive Neurodynamics
Mitra Moghaddari, Fakhri Yousefi, Mehrorang Ghaedi, Kheibar Dashtian
In this study, the artificial neural network (ANN) and response surface methodology (RSM) based on central composite design (CCD) were applied for modeling and optimization of the simultaneous ultrasound-assisted removal of quinoline yellow (QY) and eosin B (EB). The MWCNT-NH2 and its composites were prepared by sonochemistry method and characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) and energy dispersive spectroscopy (EDS) analysis's. Initial dyes concentrations, adsorbent mass, sonication time and pH contribution on QY and EB removal percentage were investigated by CCD and replication of experiments at conditions suggested by model has results which statistically are close to experimented data...
April 2018: Ultrasonics Sonochemistry
E Zare, A Beucher, J Huang, A Boman, S Mattbäck, M H Greve, J Triantafilis
One of the major environmental issues in Finland is the presence of large tracts of acid sulfate soil (ASS) landscapes along the coast. Accurately identifying the distribution of ASS sediments, and in particular soil pH, is essential for developing targeted management strategies. One approach is the use of digital soil mapping (DSM) with various ancillary information. Although electromagnetic (EM) induction data has shown potential in mapping ASS, few studies have been conducted to map the spatial distribution of pH at different depths...
February 8, 2018: Journal of Environmental Management
Zhenglong Sun, Luc Maréchal, Shaohui Foong
BACKGROUND AND OBJECTIVE: Motion tracking and navigation systems are paramount for both safety and efficacy in a variety of surgical insertions, interventions and procedures. Among the state-of-art tracking technology, passive magnetic tracking using permanent magnets or passive magnetic sources for localization is an effective technology to provide untethered medical instrument tracking without cumbersome wires needed for signal or power transmission. Motivated by practical needs in two medical insertion procedures: Nasogastric intubation and Ventriculostomy, we propose a unified method based on passive magnetic-field localization, for enhanced efficacy and safety...
March 2018: Computer Methods and Programs in Biomedicine
Ömer Faruk Ertuğrul
Determining optimal activation function in artificial neural networks is an important issue because it is directly linked with obtained success rates. But, unfortunately, there is not any way to determine them analytically, optimal activation function is generally determined by trials or tuning. This paper addresses, a simpler and a more effective approach to determine optimal activation function. In this approach, which can be called as trained activation function, an activation function was trained for each particular neuron by linear regression...
January 31, 2018: Neural Networks: the Official Journal of the International Neural Network Society
Xianming Dou, Yongguo Yang
With the recent availability of large amounts of data from the global flux towers across different terrestrial ecosystems based on the eddy covariance technique, the use of data-driven techniques has been viable. In this study, two advanced techniques, namely adaptive neuro-fuzzy inference system (ANFIS) and extreme learning machine (ELM), were developed and investigated for their viability in estimating daily carbon fluxes at the ecosystem level. All the data used in this study were based upon the long-term chronosequence observations derived from the flux towers in eight forest ecosystems...
January 27, 2018: Science of the Total Environment
K W DeGregory, P Kuiper, T DeSilvio, J D Pleuss, R Miller, J W Roginski, C B Fisher, D Harness, S Viswanath, S B Heymsfield, I Dungan, D M Thomas
Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis...
February 9, 2018: Obesity Reviews: An Official Journal of the International Association for the Study of Obesity
Rens van de Goor, Michel van Hooren, Anne-Marie Dingemans, Bernd Kremer, Kenneth Kross
INTRODUCTION: Profiling volatile organic compounds in exhaled breath enables the diagnosis of several types of cancer. In this study we investigated if a portable point-of-care version of an electronic nose (Aeonose™) is able to discriminate between lung-cancer patients and healthy controls, based on their volatile organic compound pattern. METHODS: In this study, we used five e-nose devices to collect breath samples from lung-cancer patients and healthy controls...
February 6, 2018: Journal of Thoracic Oncology
Jon Paul Janet, Lydia Chan, Heather J Kulik
Machine learning (ML) has emerged as a powerful complement to simulation for materials discovery by reducing time for evaluation of energies and properties at accuracy competitive with first-principles methods. We use genetic algorithm (GA) optimization to discover unconventional spin-crossover complexes in combination with efficient scoring from an artificial neural network (ANN) that predicts spin-state splitting of inorganic complexes. We explore a compound space of over 5,600 candidate materials derived from eight metal/oxidation state combinations and a 32-ligand pool...
February 9, 2018: Journal of Physical Chemistry Letters
Rajat Pandey, Nitin Kumar, Ashish A Prabhu, Venkata Dasu Veeranki
The present study is focused upon improving biomass of Kluyveromyces lactis (K. lactis) cells expressing recombinant human interferon gamma (hIFN-γ), with the aim of augmenting hIFN-γ concentration by using statistical and artificial intelligence approach. Optimization of medium components viz., lactose, yeast extract and trace elements was carried out with Box-behnken design (BBD) & artificial neural network linked genetic algorithm (ANN-GA) for maximizing biomass of recombinant K. lactis (objective function)...
February 9, 2018: Preparative Biochemistry & Biotechnology
Manoj Mannil, Jochen von Spiczak, Robert Manka, Hatem Alkadhi
OBJECTIVES: The aim of this study was to test whether texture analysis and machine learning enable the detection of myocardial infarction (MI) on non-contrast-enhanced low radiation dose cardiac computed tomography (CCT) images. MATERIALS AND METHODS: In this institutional review board-approved retrospective study, we included non-contrast-enhanced electrocardiography-gated low radiation dose CCT image data (effective dose, 0.5 mSv) acquired for the purpose of calcium scoring of 27 patients with acute MI (9 female patients; mean age, 60 ± 12 years), 30 patients with chronic MI (8 female patients; mean age, 68 ± 13 years), and in 30 subjects (9 female patients; mean age, 44 ± 6 years) without cardiac abnormality, hereafter termed controls...
February 8, 2018: Investigative Radiology
Bartosz Szulczyński, Krzysztof Armiński, Jacek Namieśnik, Jacek Gębicki
This paper presents application of an electronic nose prototype comprised of eight sensors, five TGS-type sensors, two electrochemical sensors and one PID-type sensor, to identify odour interaction phenomenon in two-, three-, four- and five-component odorous mixtures. Typical chemical compounds, such as toluene, acetone, triethylamine, α-pinene and n-butanol, present near municipal landfills and sewage treatment plants were subjected to investigation. Evaluation of predicted odour intensity and hedonic tone was performed with selected artificial neural network structures with the activation functions tanh and Leaky rectified linear units (Leaky ReLUs) with the parameter a = 0...
February 8, 2018: Sensors
Milad Pero, Gholamreza Askari, Torstein Skåra, Dagbjørn Skipnes, Hossein Kiani
BACKGROUND: Vacuum packed broccoli stems and florets were subjected to heat treatment (60 - 99 °C) for various time intervals. The activity of peroxidase was measured after processing. Thermally processed samples were then stored at 4 °C for 35 days and the color of the samples were measured every 7 days. Effects of parameters (heating temperature and duration, storage time) on the color of broccoli were modeled and simulated by artificial neural network. RESULTS: Simulations confirmed that stems were predicted to be more prone to changes than florets...
February 8, 2018: Journal of the Science of Food and Agriculture
Magdalena Sadyś, Joanna Kaczmarek, Agnieszka Grinn-Gofron, Victoria Rodinkova, Alex Prikhodko, Elena Bilous, Agnieszka Strzelczak, Robert J Herbert, Malgorzata Jedryczka
The genus Leptosphaeria contains numerous fungi that cause the symptoms of asthma and also parasitize wild and crop plants. In search of a robust and universal forecast model, the ascospore concentration in air was measured and weather data recorded from 1 March to 31 October between 2006 and 2012. The experiment was conducted in three European countries of the temperate climate, i.e., Ukraine, Poland, and the UK. Out of over 150 forecast models produced using artificial neural networks (ANNs) and multivariate regression trees (MRTs), we selected the best model for each site, as well as for joint two-site combinations...
January 27, 2018: International Journal of Biometeorology
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