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https://www.readbyqxmd.com/read/29155609/using-neural-networks-to-predict-hfacs-unsafe-acts-from-the-pre-conditions-of-unsafe-acts
#1
Don Harris, Wen-Chin Li
HFACS (Human Factors Analysis and Classification System) is based upon Reason's (1990; 1997) organizational model of human error which suggests that there is a 'one to many' mapping of condition tokens (HFACS level 2 psychological precursors) to unsafe act tokens (HFACS level 1 error and violations). Using accident data derived from 523 military aircraft accidents, the relationship between HFACS level 2 pre-conditions and level 1 unsafe acts was modelled using an artificial Neural Network (NN). This allowed an empirical model to be developed congruent with the underlying theory of HFACS...
November 18, 2017: Ergonomics
https://www.readbyqxmd.com/read/29153957/adaptive-neuro-heuristic-hybrid-model-for-fruit-peel-defects-detection
#2
Marcin Woźniak, Dawid Połap
Fusion of machine learning methods benefits in decision support systems. A composition of approaches gives a possibility to use the most efficient features composed into one solution. In this article we would like to present an approach to the development of adaptive method based on fusion of proposed novel neural architecture and heuristic search into one co-working solution. We propose a developed neural network architecture that adapts to processed input co-working with heuristic method used to precisely detect areas of interest...
November 15, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29150800/beat-to-beat-estimation-of-stroke-volume-using-impedance-cardiography-and-artificial-neural-network
#3
S M M Naidu, Prem C Pandey, Uttam R Bagal, Suhas P Hardas
Impedance cardiography is a low-cost noninvasive technique, based on monitoring of the thoracic impedance, for estimation of stroke volume (SV). Impedance cardiogram (ICG) is the negative of the first derivative of the impedance signal. A technique for beat-to-beat SV estimation using impedance cardiography and artificial neural network (ANN) is proposed. A three-layer feed-forward ANN with error back-propagation algorithm is optimized by examining the effects of number of neurons in the hidden layer, activation function, training algorithm, and set of input parameters...
November 18, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/29145413/diffusion-based-neuromodulation-can-eliminate-catastrophic-forgetting-in-simple-neural-networks
#4
Roby Velez, Jeff Clune
A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when learning new information erases previously learned information. Catastrophic forgetting occurs in artificial neural networks (ANNs), which have fueled most recent advances in AI. A recent paper proposed that catastrophic forgetting in ANNs can be reduced by promoting modularity, which can limit forgetting by isolating task information to specific clusters of nodes and connections (functional modules)...
2017: PloS One
https://www.readbyqxmd.com/read/29144299/the-modelling-of-lead-removal-from-water-by-deep-eutectic-solvents-functionalized-cnts-artificial-neural-network-ann-approach
#5
Seef Saadi Fiyadh, Mohammed Abdulhakim AlSaadi, Mohamed Khalid AlOmar, Sabah Saadi Fayaed, Ako R Hama, Sharifah Bee, Ahmed El-Shafie
The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb(2+)...
November 2017: Water Science and Technology: a Journal of the International Association on Water Pollution Research
https://www.readbyqxmd.com/read/29143429/an-overview-of-methods-to-mitigate-artifacts-in-optical-coherence-tomography-imaging-of-the-skin
#6
Saba Adabi, Audrey Fotouhi, Qiuyun Xu, Steve Daveluy, Darius Mehregan, Adrian Podoleanu, Mohammadreza Nasiriavanaki
BACKGROUND: Optical coherence tomography (OCT) of skin delivers three-dimensional images of tissue microstructures. Although OCT imaging offers a promising high-resolution modality, OCT images suffer from some artifacts that lead to misinterpretation of tissue structures. Therefore, an overview of methods to mitigate artifacts in OCT imaging of the skin is of paramount importance. Speckle, intensity decay, and blurring are three major artifacts in OCT images. Speckle is due to the low coherent light source used in the configuration of OCT...
November 16, 2017: Skin Research and Technology
https://www.readbyqxmd.com/read/29143418/identification-of-swallowing-disorders-in-early-and-mid-stage-parkinson-s-disease-using-pattern-recognition-of-pharyngeal-high-resolution-manometry-data
#7
C A Jones, M R Hoffman, L Lin, S Abdelhalim, J J Jiang, T M McCulloch
BACKGROUND: Parkinson's disease (PD) can cause severe dysphagia, especially later in disease progression. Early identification of swallowing dysfunction may lead to earlier intervention. Pharyngeal high-resolution manometry (HRM) provides complementary information to videofluoroscopy, with advantages of being quantitative and objective. Artificial neural network (ANN) classification can examine non-linear relationships among multiple variables with relatively low bias. We evaluated if ANN techniques could differentiate between patients with PD and healthy controls...
November 16, 2017: Neurogastroenterology and Motility: the Official Journal of the European Gastrointestinal Motility Society
https://www.readbyqxmd.com/read/29140274/an-efficient-approach-for-lipase-catalyzed-synthesis-of-retinyl-laurate-nutraceutical-by-combining-ultrasound-assistance-and-artificial-neural-network-optimization
#8
Shang-Ming Huang, Hsin-Ju Li, Yung-Chuan Liu, Chia-Hung Kuo, Chwen-Jen Shieh
Although retinol is an important nutrient, retinol is highly sensitive to oxidation. At present, some ester forms of retinol are generally used in nutritional supplements because of its stability and bioavailability. However, such esters are commonly synthesized by chemical procedures which are harmful to the environment. Thus, this study utilized a green method using lipase as a catalyst with sonication assistance to produce a retinol derivative named retinyl laurate. Moreover, the process was optimized by an artificial neural network (ANN)...
November 15, 2017: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
https://www.readbyqxmd.com/read/29137394/measuring-plasma-levels-of-three-micrornas-can-improve-the-accuracy-for-identification-of-malignant-breast-lesions-in-women-with-bi-rads-4-mammography
#9
Julia Alejandra Pezuk, Thiago Luiz Araujo Miller, José Luiz Barbosa Bevilacqua, Alfredo Carlos Simões Dornellas de Barros, Felipe Eduardo Martins de Andrade, Luiza Freire de Andrade E Macedo, Vera Aguilar, Amanda Natasha Menardo Claro, Anamaria Aranha Camargo, Pedro Alexandre Favoretto Galante, Luiz F L Reis
A BI-RADS category of 4 from a mammogram indicates suspicious breast lesions, which require core biopsies for diagnosis and have an approximately one third chance of being malignant. Human plasma contains many circulating microRNAs, and variations in their circulating levels have been associated with pathologies, including cancer. Here, we present a novel methodology to identify malignant breast lesions in women with BI-RADS 4 mammography. First, we used the miRNome array and qRT-PCR to define circulating microRNAs that were differentially represented in blood samples from women with breast tumor (BI-RADS 5 or 6) in comparison to controls (BI-RADS 1 or 2)...
October 13, 2017: Oncotarget
https://www.readbyqxmd.com/read/29136004/predictability-of-machine-learning-techniques-to-forecast-the-trends-of-market-index-prices-hypothesis-testing-for-the-korean-stock-markets
#10
Sujin Pyo, Jaewook Lee, Mincheol Cha, Huisu Jang
The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels...
2017: PloS One
https://www.readbyqxmd.com/read/29134430/modern-drug-design-the-implication-of-using-artificial-neuronal-networks-and-multiple-molecular-dynamic-simulations
#11
Oleksandr Yakovenko, Steven J M Jones
We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource ( https://drugdesigndata.org/ ). The challenge was focused on the ligands of the farnesoid X receptor (FXR), a highly flexible nuclear receptor of the cholesterol derivative chenodeoxycholic acid. FXR is considered an important therapeutic target for metabolic, inflammatory, bowel and obesity related diseases (Expert Opin Drug Metab Toxicol 4:523-532, 2015), but in the context of this competition it is also interesting due to the significant ligand-induced conformational changes displayed by the protein...
November 13, 2017: Journal of Computer-aided Molecular Design
https://www.readbyqxmd.com/read/29131760/deep-learning-a-primer-for-radiologists
#12
Gabriel Chartrand, Phillip M Cheng, Eugene Vorontsov, Michal Drozdzal, Simon Turcotte, Christopher J Pal, Samuel Kadoury, An Tang
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance...
November 2017: Radiographics: a Review Publication of the Radiological Society of North America, Inc
https://www.readbyqxmd.com/read/29127801/the-potential-of-chironomid-larvae-based-metrics-in-the-bioassessment-of-non-wadeable-rivers
#13
Djuradj Milošević, Dejan Mančev, Dubravka Čerba, Milica Stojković Piperac, Nataša Popović, Ana Atanacković, Jelena Đuknić, Vladica Simić, Momir Paunović
The chironomid community in non-wadeable lotic systems was tested as a source of information in the construction of biological metrics which could be used into the bioassessment protocols of large rivers. In order to achieve this, we simultaneously patterned the chironomid community structure and environmental factors along the catchment of the Danube and Sava River. The Self organizing map (SOM) recognized and visualized three different structural types of chironomid community for different environmental properties, described by means of 7 significant abiotic factors (a multi-stressor gradient)...
November 9, 2017: Science of the Total Environment
https://www.readbyqxmd.com/read/29127485/oct-based-deep-learning-algorithm-for-the-evaluation-of-treatment-indication-with-anti-vascular-endothelial-growth-factor-medications
#14
Philipp Prahs, Viola Radeck, Christian Mayer, Yordan Cvetkov, Nadezhda Cvetkova, Horst Helbig, David Märker
PURPOSE: Intravitreal injections with anti-vascular endothelial growth factor (anti-VEGF) medications have become the standard of care for their respective indications. Optical coherence tomography (OCT) scans of the central retina provide detailed anatomical data and are widely used by clinicians in the decision-making process of anti-VEGF indication. In recent years, significant progress has been made in artificial intelligence and computer vision research. We trained a deep convolutional artificial neural network to predict treatment indication based on central retinal OCT scans without human intervention...
November 10, 2017: Graefe's Archive for Clinical and Experimental Ophthalmology
https://www.readbyqxmd.com/read/29126697/use-of-automated-learning-techniques-for-predicting-mandibular-morphology-in-skeletal-class-i-ii-and-iii
#15
Tania Camila Niño-Sandoval, Sonia V Guevara Pérez, Fabio A González, Robinson Andrés Jaque, Clementina Infante-Contreras
BACKGROUND: The prediction of the mandibular bone morphology in facial reconstruction for forensic purposes is usually performed considering a straight profile corresponding to skeletal class I, with application of linear and parametric analysis which limit the search for relationships between mandibular and craniomaxillary variables. OBJECTIVE: To predict the mandibular morphology through craniomaxillary variables on lateral radiographs in patients with skeletal class I, II and III, using automated learning techniques, such as Artificial Neural Networks and Support Vector Regression...
October 12, 2017: Forensic Science International
https://www.readbyqxmd.com/read/29126439/artificial-neural-networks-to-predict-future-bone-mineral-density-and-bone-loss-rate-in-japanese-postmenopausal-women
#16
Mitsunori Shioji, Takehisa Yamamoto, Takeshi Ibata, Takayuki Tsuda, Kazushige Adachi, Noriko Yoshimura
OBJECTIVE: Predictions of the future bone mineral density and bone loss rate are important to tailor medicine for women with osteoporosis, because of the possible presence of personal risk factors affecting the severity of osteoporosis in the future. We investigated whether it was possible to predict bone mineral density and bone loss rate in the future using artificial neural networks. RESULTS: A total of 135 women over 50 years old residing in T town of Wakayama Prefecture, Japan were analyzed to establish a statistical model...
November 10, 2017: BMC Research Notes
https://www.readbyqxmd.com/read/29126007/quantitative-monitoring-of-sucrose-reducing-sugar-and-total-sugar-dynamics-for-phenotyping-of-water-deficit-stress-tolerance-in-rice-through-spectroscopy-and-chemometrics
#17
Bappa Das, Rabi N Sahoo, Sourabh Pargal, Gopal Krishna, Rakesh Verma, Viswanathan Chinnusamy, Vinay K Sehgal, Vinod K Gupta, Sushanta K Dash, Padmini Swain
In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes...
October 31, 2017: Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
https://www.readbyqxmd.com/read/29121946/improving-precision-of-glomerular-filtration-rate-estimating-model-by-ensemble-learning
#18
Xun Liu, Ningshan Li, Linsheng Lv, Yongmei Fu, Cailian Cheng, Caixia Wang, Yuqiu Ye, Shaomin Li, Tanqi Lou
BACKGROUND: Accurate assessment of kidney function is clinically important, but estimates of glomerular filtration rate (GFR) by regression are imprecise. METHODS: We hypothesized that ensemble learning could improve precision. A total of 1419 participants were enrolled, with 1002 in the development dataset and 417 in the external validation dataset. GFR was independently estimated from age, sex and serum creatinine using an artificial neural network (ANN), support vector machine (SVM), regression, and ensemble learning...
November 9, 2017: Journal of Translational Medicine
https://www.readbyqxmd.com/read/29119695/hierarchical-classification-of-microorganisms-based-on-high-dimensional-phenotypic-data
#19
Valeria Tafintseva, Evelyne Vigneau, Volha Shapaval, Véronique Cariou, El Mostafa Qannari, Achim Kohler
The classification of microorganisms by high-dimensional phenotyping methods such as FTIR spectroscopy is often a complicated process due to the complexity of microbial phylogenetic taxonomy. A hierarchical structure developed for such data can often facilitate the classification analysis. The hierarchical tree structure can either be imposed to a given set of phenotypic data by integrating the phylogenetic taxonomic structure or set up by revealing the inherent clusters in the phenotypic data. In this study, we wanted to compare different approaches to hierarchical classification of microorganisms based on high-dimensional phenotypic data...
November 9, 2017: Journal of Biophotonics
https://www.readbyqxmd.com/read/29118752/role-of-the-human-breast-milk-associated-microbiota-on-the-newborns-immune-system-a-mini-review
#20
REVIEW
Marco Toscano, Roberta De Grandi, Enzo Grossi, Lorenzo Drago
The human milk is fundamental for a correct development of newborns, as it is a source not only of vitamins and nutrients, but also of commensal bacteria. The microbiota associated to the human breast milk contributes to create the "initial" intestinal microbiota of infants, having also a pivotal role in modulating and influencing the newborns' immune system. Indeed, the transient gut microbiota is responsible for the initial change from an intrauterine Th2 prevailing response to a Th1/Th2 balanced one. Bacteria located in both colostrum and mature milk can stimulate the anti-inflammatory response, by stimulating the production of specific cytokines, reducing the risk of developing a broad range of inflammatory diseases and preventing the expression of immune-mediated pathologies, such as asthma and atopic dermatitis...
2017: Frontiers in Microbiology
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