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Artificial neural networks

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https://www.readbyqxmd.com/read/29777650/modeling-dengue-vector-population-using-remotely-sensed-data-and-machine-learning
#1
Juan M Scavuzzo, Francisco Trucco, Manuel Espinosa, Carolina B Tauro, Marcelo Abril, Carlos M Scavuzzo, Alejandro C Frery
Mosquitoes are vectors of many human diseases. In particular, Aedes ægypti (Linnaeus) is the main vector for Chikungunya, Dengue, and Zika viruses in Latin America and it represents a global threat. Public health policies that aim at combating this vector require dependable and timely information, which is usually expensive to obtain with field campaigns. For this reason, several efforts have been done to use remote sensing due to its reduced cost. The present work includes the temporal modeling of the oviposition activity (measured weekly on 50 ovitraps in a north Argentinean city) of Aedes ægypti (Linnaeus), based on time series of data extracted from operational earth observation satellite images...
May 16, 2018: Acta Tropica
https://www.readbyqxmd.com/read/29777175/artificial-intelligence-in-radiology
#2
REVIEW
Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H Schwartz, Hugo J W L Aerts
Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics...
May 17, 2018: Nature Reviews. Cancer
https://www.readbyqxmd.com/read/29775850/electrical-resistivity-imaging-inversion-an-isfla-trained-kernel-principal-component-wavelet-neural-network-approach
#3
Feibo Jiang, Li Dong, Qianwei Dai
The traditional artificial neural network (ANN) inversion of electrical resistivity imaging (ERI) based on gradient descent algorithm is known to be inept for its low computation efficiency and does not ensure global convergence. In order to solve above problems, a kernel principal component wavelet neural network (KPCWNN) trained by an improved shuffled frog leaping algorithm (ISFLA) method is proposed in this study. An additional kernel principal component (KPC) layer is applied to reduce the dimensionality of apparent resistivity data and increase the computational efficiency of wavelet neural network (WNN)...
April 24, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29775394/degradation-of-ticarcillin-by-subcritial-water-oxidation-method-application-of-response-surface-methodology-and-artificial-neural-network-modeling
#4
Erdal Yabalak
This study was performed to investigate the mineralization of ticarcillin in the artificially prepared aqueous solution presenting ticarcillin contaminated waters, which constitute a serious problem for human health. 81.99% of total organic carbon removal, 79.65% of chemical oxygen demand removal, and 94.35% of ticarcillin removal were achieved by using eco-friendly, time-saving, powerful and easy-applying, subcritical water oxidation method in the presence of a safe-to-use oxidizing agent, hydrogen peroxide...
May 18, 2018: Journal of Environmental Science and Health. Part A, Toxic/hazardous Substances & Environmental Engineering
https://www.readbyqxmd.com/read/29775077/evaluating-the-influence-of-road-lighting-on-traffic-safety-at-accesses-using-an-artificial-neural-network
#5
Yueru Xu, Zhirui Ye, Yuan Wang, Chao Wang, Cuicui Sun
OBJECTIVES: This paper focuses on the effect of road lighting on road safety at accesses and tries to quantitatively analyze the relationship between road lighting and road safety. METHODS: An Artificial Neural Network (ANN) was applied in this study. This method is one of the most popular machine-learning methods in recent years and does not require any pre-defined assumptions. This method was applied using field data collected from ten road segments in Nanjing, Jiangsu Province, China...
May 18, 2018: Traffic Injury Prevention
https://www.readbyqxmd.com/read/29772820/effective-crack-detection-in-railway-axles-using-vibration-signals-and-wpt-energy
#6
María Jesús Gómez, Eduardo Corral, Cristina Castejón, Juan Carlos García-Prada
Crack detection for railway axles is key to avoiding catastrophic accidents. Currently, non-destructive testing is used for that purpose. The present work applies vibration signal analysis to diagnose cracks in real railway axles installed on a real Y21 bogie working on a rig. Vibration signals were obtained from two wheelsets with cracks at the middle section of the axle with depths from 5.7 to 15 mm, at several conditions of load and speed. Vibration signals were processed by means of wavelet packet transform energy...
May 17, 2018: Sensors
https://www.readbyqxmd.com/read/29772670/ann-surface-roughness-optimization-of-az61-magnesium-alloy-finish-turning-minimum-machining-times-at-prime-machining-costs
#7
Adel Taha Abbas, Danil Yurievich Pimenov, Ivan Nikolaevich Erdakov, Mohamed Adel Taha, Mahmoud Sayed Soliman, Magdy Mostafa El Rayes
Magnesium alloys are widely used in aerospace vehicles and modern cars, due to their rapid machinability at high cutting speeds. A novel Edgeworth⁻Pareto optimization of an artificial neural network (ANN) is presented in this paper for surface roughness ( Ra ) prediction of one component in computer numerical control (CNC) turning over minimal machining time ( Tm ) and at prime machining costs ( C ). An ANN is built in the Matlab programming environment, based on a 4-12-3 multi-layer perceptron (MLP), to predict Ra , Tm , and C , in relation to cutting speed, vc , depth of cut, ap , and feed per revolution, fr ...
May 16, 2018: Materials
https://www.readbyqxmd.com/read/29772004/correction-predicting-all-cause-risk-of-30-day-hospital-readmission-using-artificial-neural-networks
#8
Mehdi Jamei, Aleksandr Nisnevich, Everett Wetchler, Sylvia Sudat, Eric Liu, Kirtan Upadhyaya
[This corrects the article DOI: 10.1371/journal.pone.0181173.].
2018: PloS One
https://www.readbyqxmd.com/read/29770940/effluent-composition-prediction-of-a-two-stage-anaerobic-digestion-process-machine-learning-and-stoichiometry-techniques
#9
Luz Alejo, John Atkinson, Víctor Guzmán-Fierro, Marlene Roeckel
Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. The analytical method considers the protein as the only source of ammonia production in AD after degradation. Total ammonia nitrogen (TAN), total solids (TS), chemical oxygen demand (COD), and total volatile solids (TVS) were measured in the influent and effluent of the process...
May 16, 2018: Environmental Science and Pollution Research International
https://www.readbyqxmd.com/read/29770244/predicting-length-of-stay-in-intensive-care-units-after-cardiac-surgery-comparison-of-artificial-neural-networks-and-adaptive-neuro-fuzzy-system
#10
Hamidreza Maharlou, Sharareh R Niakan Kalhori, Shahrbanoo Shahbazi, Ramin Ravangard
Objectives: Accurate prediction of patients' length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients' length of stay in intensive care units (ICU) after cardiac surgery. Methods: A cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016...
April 2018: Healthcare Informatics Research
https://www.readbyqxmd.com/read/29769565/adaptation-to-criticality-through-organizational-invariance-in-embodied-agents
#11
Miguel Aguilera, Manuel G Bedia
Many biological and cognitive systems do not operate deep within one or other regime of activity. Instead, they are poised at critical points located at phase transitions in their parameter space. The pervasiveness of criticality suggests that there may be general principles inducing this behaviour, yet there is no well-founded theory for understanding how criticality is generated at a wide span of levels and contexts. In order to explore how criticality might emerge from general adaptive mechanisms, we propose a simple learning rule that maintains an internal organizational structure from a specific family of systems at criticality...
May 16, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29769044/visualizing-histopathologic-deep-learning-classification-and-anomaly-detection-using-nonlinear-feature-space-dimensionality-reduction
#12
Kevin Faust, Quin Xie, Dominick Han, Kartikay Goyle, Zoya Volynskaya, Ugljesa Djuric, Phedias Diamandis
BACKGROUND: There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce...
May 16, 2018: BMC Bioinformatics
https://www.readbyqxmd.com/read/29761957/-the-research-of-near-infrared-blood-glucose-measurement-using-particle-swarm-optimization-and-artificial-neural-network
#13
Juan Dai, Zhong Ji, Yubao Du
Existing near-infrared non-invasive blood glucose detection modelings mostly detect multi-spectral signals with different wavelength, which is not conducive to the popularization of non-invasive glucose meter at home and does not consider the physiological glucose dynamics of individuals. In order to solve these problems, this study presented a non-invasive blood glucose detection model combining particle swarm optimization (PSO) and artificial neural network (ANN) by using the 1 550 nm near-infrared absorbance as the independent variable and the concentration of blood glucose as the dependent variable, named as PSO-2ANN...
August 1, 2017: Sheng Wu Yi Xue Gong Cheng Xue za Zhi, Journal of Biomedical Engineering, Shengwu Yixue Gongchengxue Zazhi
https://www.readbyqxmd.com/read/29758628/extreme-learning-machine-for-reduced-order-modeling-of-turbulent-geophysical-flows
#14
Omer San, Romit Maulik
We investigate the application of artificial neural networks to stabilize proper orthogonal decomposition-based reduced order models for quasistationary geophysical turbulent flows. An extreme learning machine concept is introduced for computing an eddy-viscosity closure dynamically to incorporate the effects of the truncated modes. We consider a four-gyre wind-driven ocean circulation problem as our prototype setting to assess the performance of the proposed data-driven approach. Our framework provides a significant reduction in computational time and effectively retains the dynamics of the full-order model during the forward simulation period beyond the training data set...
April 2018: Physical Review. E
https://www.readbyqxmd.com/read/29751584/a-finite-state-machine-approach-to-algorithmic-lateral-inhibition-for-real-time-motion-detection-%C3%A2
#15
María T López, Aurelio Bermúdez, Francisco Montero, José L Sánchez, Antonio Fernández-Caballero
Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best-characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The neurally-inspired lateral inhibition method, and its application to motion detection tasks, have been successfully implemented in recent years. In this paper, control knowledge of the algorithmic lateral inhibition (ALI) method is described and applied by means of finite state machines, in which the state space is constituted from the set of distinguishable cases of accumulated charge in a local memory...
May 3, 2018: Sensors
https://www.readbyqxmd.com/read/29751419/mapping-flood-susceptibility-in-mountainous-areas-on-a-national-scale-in-china
#16
Gang Zhao, Bo Pang, Zongxue Xu, Jiajia Yue, Tongbi Tu
Mountainous terrain covers nearly half of China and is susceptible to floods, which can lead to substantial losses of human life and property. Historical flooding records from government bulletins and newspapers, the only available information regarding floods that have occurred in some mountainous areas, are valuable for understanding flood disaster mechanisms in these regions. In this study, the flood susceptibility in mountainous regions in China was mapped based on historical flooding records from 1949 to 2000...
February 15, 2018: Science of the Total Environment
https://www.readbyqxmd.com/read/29751328/space-time-pm-2-5-mapping-in-the-severe-haze-region-of-jing-jin-ji-china-using-a-synthetic-approach
#17
Junyu He, George Christakos
Long- and short-term exposure to PM2.5 is of great concern in China due to its adverse population health effects. Characteristic of the severity of the situation in China is that in the Jing-Jin-Ji region considered in this work a total of 2725 excess deaths have been attributed to short-term PM2.5 exposure during the period January 10-31, 2013. Technically, the processing of large space-time PM2.5 datasets and the mapping of the space-time distribution of PM2.5 concentrations often constitute high-cost projects...
May 7, 2018: Environmental Pollution
https://www.readbyqxmd.com/read/29750116/classification-of-osteoporosis-by-artificial-neural-network-based-on-monarch-butterfly-optimisation-algorithm
#18
D Devikanniga, R Joshua Samuel Raj
Osteoporosis is a life threatening disease which commonly affects women mostly after their menopause. It primarily causes mild bone fractures, which on advanced stage leads to the death of an individual. The diagnosis of osteoporosis is done based on bone mineral density (BMD) values obtained through various clinical methods experimented from various skeletal regions. The main objective of the authors' work is to develop a hybrid classifier model that discriminates the osteoporotic patient from healthy person, based on BMD values...
April 2018: Healthcare Technology Letters
https://www.readbyqxmd.com/read/29747693/blood-hyperviscosity-identification-with-reflective-spectroscopy-of-tongue-tip-based-on-principal-component-analysis-combining-artificial-neural-network
#19
Ming Liu, Jing Zhao, XiaoZuo Lu, Gang Li, Taixia Wu, LiFu Zhang
BACKGROUND: With spectral methods, noninvasive determination of blood hyperviscosity in vivo is very potential and meaningful in clinical diagnosis. In this study, 67 male subjects (41 health, and 26 hyperviscosity according to blood sample analysis results) participate. METHODS: Reflectance spectra of subjects' tongue tips is measured, and a classification method bases on principal component analysis combined with artificial neural network model is built to identify hyperviscosity...
May 10, 2018: Biomedical Engineering Online
https://www.readbyqxmd.com/read/29747692/low-abundant-bacteria-drive-compositional-changes-in-the-gut-microbiota-after-dietary-alteration
#20
Jacquelynn Benjamino, Stephen Lincoln, Ranjan Srivastava, Joerg Graf
BACKGROUND: As the importance of beneficial bacteria is better recognized, understanding the dynamics of symbioses becomes increasingly crucial. In many gut symbioses, it is essential to understand whether changes in host diet play a role in the persistence of the bacterial gut community. In this study, termites were fed six dietary sources and the microbial community was monitored over a 49-day period using 16S rRNA gene sequencing. A deep backpropagation artificial neural network (ANN) was used to learn how the six different lignocellulose food sources affected the temporal composition of the hindgut microbiota of the termite as well as taxon-taxon and taxon-substrate interactions...
May 10, 2018: Microbiome
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