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https://www.readbyqxmd.com/read/29346451/an-approach-for-predicting-the-compressive-strength-of-cement-based-materials-exposed-to-sulfate-attack
#1
Huaicheng Chen, Chunxiang Qian, Chengyao Liang, Wence Kang
In this paper, a support vector machine (SVM) model which can be used to predict the compressive strength of mortars exposed to sulfate attack was established. An accelerated corrosion test was applied to collect compressive strength data. For predicting the compressive strength of mortars, a total of 638 data samples obtained from experiment was chosen as a dataset to establish a SVM model. The values of the coefficient of determination, the mean absolute error, the mean absolute percentage error and the root mean square error were used for evaluating the predictive accuracy...
2018: PloS One
https://www.readbyqxmd.com/read/29346107/brain-mr-image-restoration-using-an-automatic-trilateral-filter-with-gpu-based-acceleration
#2
Herng-Hua Chang, Cheng-Yuan Li, Audrey Haihong Gallogly
OBJECTIVE: Noise reduction in brain magnetic resonance (MR) images has been a challenging and demanding task. This study develops a new trilateral filter that aims to achieve robust and efficient image restoration. METHODS: Extended from the bilateral filter, the proposed algorithm contains one additional intensity similarity funct-ion, which compensates for the unique characteristics of noise in brain MR images. An entropy function adaptive to intensity variations is introduced to regulate the contributions of the weighting components...
February 2018: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/29342848/a-voltammetric-electronic-tongue-for-the-resolution-of-ternary-nitrophenol-mixtures
#3
Andreu González-Calabuig, Xavier Cetó, Manel Del Valle
This work reports the applicability of a voltammetric sensor array able to quantify the content of 2,4-dinitrophenol, 4-nitrophenol, and picric acid in artificial samples using the electronic tongue (ET) principles. The ET is based on cyclic voltammetry signals, obtained from an array of metal disk electrodes and a graphite epoxy composite electrode, compressed using discrete wavelet transform with chemometric tools such as artificial neural networks (ANNs). ANNs were employed to build the quantitative prediction model...
January 13, 2018: Sensors
https://www.readbyqxmd.com/read/29341917/a-novel-method-for-the-production-of-core-shell-microparticles-by-inverse-gelation-optimized-with-artificial-intelligent-tools
#4
Rosalia Rodríguez-Dorado, Mariana Landín, Ayça Altai, Paola Russo, Rita P Aquino, Pasquale Del Gaudio
Numerous studies have been focused on hydrophobic compounds encapsulation as oils. In fact, oils can provide numerous health benefits as synergic ingredient combined with other hydrophobic active ingredients. However, stable microparticles for pharmaceutical purposes are difficult to achieve when commonly techniques are used. In this work, sunflower oil was encapsulated in calcium-alginate capsules by prilling technique in co-axial configuration. Core-shell beads were produced by inverse gelation directly at the nozzle using w/o emulsion containing aqueous calcium chloride solution in sunflower oil pumped through the inner nozzle while an aqueous alginate solution, coming out from the annular nozzle, produced the bead shell...
January 13, 2018: International Journal of Pharmaceutics
https://www.readbyqxmd.com/read/29339998/planning-training-loads-for-the-400-m-hurdles-in-three-month-mesocycles-using-artificial-neural-networks
#5
Krzysztof Przednowek, Janusz Iskra, Krzysztof Wiktorowicz, Tomasz Krzeszowski, Adam Maszczyk
This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes' training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler...
December 2017: Journal of Human Kinetics
https://www.readbyqxmd.com/read/29335825/application-of-artificial-intelligence-using-a-convolutional-neural-network-for-detecting-gastric-cancer-in-endoscopic-images
#6
Toshiaki Hirasawa, Kazuharu Aoyama, Tetsuya Tanimoto, Soichiro Ishihara, Satoki Shichijo, Tsuyoshi Ozawa, Tatsuya Ohnishi, Mitsuhiro Fujishiro, Keigo Matsuo, Junko Fujisaki, Tomohiro Tada
BACKGROUND: Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images. METHODS: A CNN-based diagnostic system was constructed based on Single Shot MultiBox Detector architecture and trained using 13,584 endoscopic images of gastric cancer...
January 15, 2018: Gastric Cancer
https://www.readbyqxmd.com/read/29328958/stdp-based-spiking-deep-convolutional-neural-networks-for-object-recognition
#7
Saeed Reza Kheradpisheh, Mohammad Ganjtabesh, Simon J Thorpe, Timothée Masquelier
Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively shallow architectures, and only one layer was trainable. Another line of research has demonstrated - using rate-based neural networks trained with back-propagation - that having many layers increases the recognition robustness, an approach known as deep learning. We thus designed a deep SNN, comprising several convolutional (trainable with STDP) and pooling layers...
December 23, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29328623/mimicking-biological-synaptic-functionality-with-an-indium-phosphide-synaptic-device-on-silicon-for-scalable-neuromorphic-computing
#8
Debarghya Sarkar, Jun Tao, Wei Wang, Qingfeng Lin, Matthew Yeung, Chenhao Ren, Rehan Kapadia
Neuromorphic or "brain-like" computation is a leading candidate for efficient, fault-tolerant processing of large-scale data, as well as real-time sensing and transduction of complex multivariate systems and networks such as self-driving vehicles or Internet of Things applications. In biology, the synapse serves as an active memory unit in the neural system, and is the component responsible for learning and memory. Electronically emulating this element via a compact, scalable technology which can be integrated in a 3-D architecture is critical for future implementations of neuromorphic processors...
January 12, 2018: ACS Nano
https://www.readbyqxmd.com/read/29324935/right-putamen-and-age-are-the-most-discriminant-features-to-diagnose-parkinson-s-disease-by-using-123i-fp-cit-brain-spet-data-by-using-an-artificial-neural-network-classifier-a-classification-tree-clt
#9
S Cascianelli, C Tranfaglia, M L Fravolini, F Bianconi, M Minestrini, S Nuvoli, N Tambasco, M E Dottorini, B Palumbo
OBJECTIVE: The differential diagnosis of Parkinson's disease (PD) and other conditions, such as essential tremor and drug-induced parkinsonian syndrome or normal aging brain, represents a diagnostic challenge. 123I-FP-CIT brain SPET is able to contribute to the differential diagnosis. Semiquantitative analysis of radiopharmaceutical uptake in basal ganglia (caudate nuclei and putamina) is very useful to support the diagnostic process. An artificial neural network classifier using 123I-FP-CIT brain SPET data, a classification tree (CIT), was applied...
September 2017: Hellenic Journal of Nuclear Medicine
https://www.readbyqxmd.com/read/29323207/discovery-of-hepatotoxic-equivalent-combinatorial-markers-from-dioscorea-bulbifera-tuber-by-fingerprint-toxicity-relationship-modeling
#10
Wei Shi, Cai Zhang, Dongsheng Zhao, Lingli Wang, Ping Li, Huijun Li
Due to extremely chemical complexity, identification of potential toxicity-related constituents from an herbal medicine (HM) still remains challenging. Traditional toxicity-guided separation procedure suffers from time- and labor-consumption and neglects the additive effect of multi-components. In this study, we proposed a screening strategy called "hepatotoxic equivalent combinatorial markers (HECMs)" for a hepatotoxic HM, Dioscorea bulbifera tuber (DBT). Firstly, the chemical constituents in DBT extract were globally characterized...
January 11, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29323152/unravelling-the-effects-of-mechanical-physiological-conditioning-on-cardiac-adipose-tissue-derived-progenitor-cells-in-vitro-and-in-silico
#11
Aida Llucià-Valldeperas, Ramon Bragós, Carolina Soler-Botija, Santiago Roura, Carolina Gálvez-Montón, Cristina Prat-Vidal, Isaac Perea-Gil, Antoni Bayes-Genis
Mechanical conditioning is incompletely characterized for stimulating therapeutic cells within the physiological range. We sought to unravel the mechanism of action underlying mechanical conditioning of adipose tissue-derived progenitor cells (ATDPCs), both in vitro and in silico. Cardiac ATDPCs, grown on 3 different patterned surfaces, were mechanically stretched for 7 days at 1 Hz. A custom-designed, magnet-based, mechanical stimulator device was developed to apply ~10% mechanical stretching to monolayer cell cultures...
January 11, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29319225/de-novo-design-of-bioactive-small-molecules-by-artificial-intelligence
#12
Daniel Merk, Lukas Friedrich, Francesca Grisoni, Gisbert Schneider
Generative artificial intelligence offers a fresh view on molecular design. We present the first-time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine-tuned on recognizing retinoid X and peroxisome proliferator-activated receptor agonists. We synthesized five top-ranking compounds designed by the generative model...
January 10, 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29315219/proposal-of-a-method-to-determine-the-correlation-between-total-suspended-solids-and-dissolved-organic-matter-in-water-bodies-from-spectral-imaging-and-artificial-neural-networks
#13
Maurício R Veronez, Lucas S Kupssinskü, Tainá T Guimarães, Emilie C Koste, Juarez M da Silva, Laís V de Souza, William F M Oliverio, Rogélio S Jardim, Ismael É Koch, Jonas G de Souza, Luiz Gonzaga, Frederico F Mauad, Leonardo C Inocencio, Fabiane Bordin
Water quality monitoring through remote sensing with UAVs is best conducted using multispectral sensors; however, these sensors are expensive. We aimed to predict multispectral bands from a low-cost sensor (R, G, B bands) using artificial neural networks (ANN). We studied a lake located on the campus of Unisinos University, Brazil, using a low-cost sensor mounted on a UAV. Simultaneously, we collected water samples during the UAV flight to determine total suspended solids (TSS) and dissolved organic matter (DOM)...
January 9, 2018: Sensors
https://www.readbyqxmd.com/read/29310296/voltammetric-electronic-tongue-to-identify-brett-character-in-wines-on-site-quantification-of-its-ethylphenol-metabolites
#14
Andreu González-Calabuig, Manel Del Valle
This work reports the applicability of a voltammetric sensor array able to evaluate the content of the metabolites of the Brett defect: 4-ethylphenol, 4-ethylguaiacol and 4-ethylcatechol in spiked wine samples using the electronic tongue (ET) principles. The ET used cyclic voltammetry signals, obtained from an array of six graphite epoxy modified composite electrodes; these were compressed using Discrete Wavelet transform while chemometric tools, among these artificial neural networks (ANNs), were employed to build the quantitative prediction model...
March 1, 2018: Talanta
https://www.readbyqxmd.com/read/29309734/methodologic-guide-for-evaluating-clinical-performance-and-effect-of-artificial-intelligence-technology-for-medical-diagnosis-and-prediction
#15
Seong Ho Park, Kyunghwa Han
The use of artificial intelligence in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysis of medical images. Adoption of an artificial intelligence tool in clinical practice requires careful confirmation of its clinical utility. Herein, the authors explain key methodology points involved in a clinical evaluation of artificial intelligence technology for use in medicine, especially high-dimensional or overparameterized diagnostic or predictive models in which artificial deep neural networks are used, mainly from the standpoints of clinical epidemiology and biostatistics...
January 8, 2018: Radiology
https://www.readbyqxmd.com/read/29306807/assessing-the-impact-of-pm2-5-on-respiratory-disease-using-artificial-neural-networks
#16
Gabriela Polezer, Yara S Tadano, Hugo V Siqueira, Ana F L Godoi, Carlos I Yamamoto, Paulo A de André, Theotonio Pauliquevis, Maria de Fatima Andrade, Andrea Oliveira, Paulo H N Saldiva, Philip E Taylor, Ricardo H M Godoi
Understanding the impact on human health during peak episodes in air pollution is invaluable for policymakers. Particles less than PM2.5 can penetrate the respiratory system, causing cardiopulmonary and other systemic diseases. Statistical regression models are usually used to assess air pollution impacts on human health. However, when there are databases missing, linear statistical regression may not process well and alternative data processing should be considered. Nonlinear Artificial Neural Networks (ANN) are not employed to research environmental health pollution even though another advantage in using ANN is that the output data can be expressed as the number of hospital admissions...
January 4, 2018: Environmental Pollution
https://www.readbyqxmd.com/read/29306756/a-loop-based-neural-architecture-for-structured-behavior-encoding-and-decoding
#17
Thomas Gisiger, Mounir Boukadoum
We present a new type of artificial neural network that generalizes on anatomical and dynamical aspects of the mammal brain. Its main novelty lies in its topological structure which is built as an array of interacting elementary motifs shaped like loops. These loops come in various types and can implement functions such as gating, inhibitory or executive control, or encoding of task elements to name a few. Each loop features two sets of neurons and a control region, linked together by non-recurrent projections...
December 8, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29305341/a-pilot-study-of-biomedical-text-comprehension-using-an-attention-based-deep-neural-reader-design-and-experimental-analysis
#18
Seongsoon Kim, Donghyeon Park, Yonghwa Choi, Kyubum Lee, Byounggun Kim, Minji Jeon, Jihye Kim, Aik Choon Tan, Jaewoo Kang
BACKGROUND: With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain...
January 5, 2018: JMIR Medical Informatics
https://www.readbyqxmd.com/read/29301294/magnetic-flux-leakage-sensing-and-artificial-neural-network-pattern-recognition-based-automated-damage-detection-and-quantification-for-wire-rope-non-destructive-evaluation
#19
Ju-Won Kim, Seunghee Park
In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes...
January 2, 2018: Sensors
https://www.readbyqxmd.com/read/29300432/falls-risk-prediction-for-older-inpatients-in-acute-care-medical-wards-is-there-an-interest-to-combine-an-early-nurse-assessment-and-the-artificial-neural-network-analysis
#20
O Beauchet, F Noublanche, R Simon, H Sekhon, J Chabot, E J Levinoff, A Kabeshova, C P Launay
BACKGROUND: Identification of the risk of falls is important among older inpatients. This study aims to examine performance criteria (i.e.; sensitivity, specificity, positive predictive value, negative predictive value and accuracy) for fall prediction resulting from a nurse assessment and an artificial neural networks (ANNs) analysis in older inpatients hospitalized in acute care medical wards. METHODS: A total of 848 older inpatients (mean age, 83.0±7.2 years; 41...
2018: Journal of Nutrition, Health & Aging
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