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artificial neural network

Mahesh R Gadekar, M Mansoor Ahammed
In this study, response surface methodology (RSM)-artificial neural network (ANN) approach was used to optimise/model disperse dye removal by adsorption using water treatment residuals (WTR). RSM was first applied to evaluate the process using three controllable operating parameters, namely WTR dose, initial pH (pHinitial ) and dye concentration, and optimal conditions for colour removal were determined. In the second step, the experimental results of the design data of RSM were used to train the neural network along with a non-controllable parameter, the final pH (pHfinal )...
October 18, 2018: Journal of Environmental Management
Parviz Rashidi Khazaee, Jamshid Bagherzadeh, Zahra Niazkhani, Habibollah Pirnejad
BACKGROUND: Predicting the function of transplanted kidneys would help clinicians in individualized medical interventions. We aimed to develop and validate a predictive tool for a future value of estimated glomerular filtration rate (eGFR) at upcoming visits. METHODS: We used static and time-dependent covariates as inputs of artificial neural network based prediction models for predicting an eGFR value for an upcoming visit. We included 675 kidney recipients, who received transplant in the Urmia kidney transplant center in 2001-2013 and were longitudinally cared for in 2001-2017...
November 2018: International Journal of Medical Informatics
Davor Antanasijević, Viktor Pocajt, Aleksandra Perić-Grujić, Mirjana Ristić
Urban population exposure to tropospheric ozone is a serious health concern in Europe countries. Although there are insufficient evidence to derive a level below which ozone has no effect on mortality WHO (World Health Organization) uses SOMO35 (sum of means over 35 ppb) in their health impact assessments. Is this paper, the artificial neural network (ANN) approach was used to forecast SOMO35 at the national level for a set of 24 European countries, mostly EU members. Available ozone precursors' emissions, population and climate data for the period 2003-2013 were used as inputs...
October 13, 2018: Environmental Pollution
Shupeng Chen, An Qin, Dingyi Zhou, Di Yan
PURPOSE: Clinical implementation of magnetic resonance imaging (MRI)-only radiotherapy requires a method to derive synthetic CT image (S-CT) for dose calculation. This study investigated the feasibility of building a deep convolutional neural network for MRI based S-CT generation and evaluate the dosimetric accuracy on prostate IMRT planning. METHODS: A Paired CT and T2-weighted MR images were acquired from each of 51 prostate cancer patients. Fifteen pairs were randomly chosen as tested set and the remaining 36 pairs as training set...
October 20, 2018: Medical Physics
A D Tranter, H J Slatyer, M R Hush, A C Leung, J L Everett, K V Paul, P Vernaz-Gris, P K Lam, B C Buchler, G T Campbell
Machine learning based on artificial neural networks has emerged as an efficient means to develop empirical models of complex systems. Cold atomic ensembles have become commonplace in laboratories around the world, however, many-body interactions give rise to complex dynamics that preclude precise analytic optimisation of the cooling and trapping process. Here, we implement a deep artificial neural network to optimise the magneto-optic cooling and trapping of neutral atomic ensembles. The solution identified by machine learning is radically different to the smoothly varying adiabatic solutions currently used...
October 19, 2018: Nature Communications
Fei-Fei Fu, Jian Li
A method for gas⁻solid two-phase flow pattern identification in horizontal pneumatic conveying pipelines is proposed based on an electrostatic sensor array (ESA) and artificial neural network (ANN). The ESA contains eight identical arc shaped electrodes. Numerical simulation is conducted to discuss the contributions of the electrostatic signals to the flow patterns according to the error recognition rate, and the results show that the amplitudes of the output signals from each electrode of the ESA can give important information on the particle distribution and further infer the flow patterns...
October 18, 2018: Sensors
Lívia Vieira Carlini Charamba, Rayany Magali da Rocha Santana, Graziele Elisandra do Nascimento, Bruno Vieira Carlini Charamba, Maiara Celine de Moura, Luana Cassandra Breitenbach Barroso Coelho, Julierme Gomes Correia de Oliveira, Marta Maria Menezes Bezerra Duarte, Daniella Carla Napoleão
The study evaluated the advanced oxidative processes concerning the degradation of green leaf and purple açaí dyes, as well as the prediction of data through artificial neural networks (ANNs). It was verified that percentage of degradation on the wavelengths (λ) of 215, 248, 523 and 627 nm was 5.95, 49.99, 98.17 and 95.99%, respectively, when UV/H2 O2 action and UV-C radiation was applied. A non-linear kinetic model proposed by Chan and Chu presented a good fit to the experimental data, reaching an R2 value between 0...
October 2018: Water Science and Technology: a Journal of the International Association on Water Pollution Research
Atae Akhrif, Marcel Romanos, Katharina Domschke, Angelika Schmitt-Boehrer, Susanne Neufang
Fractal phenomena can be found in numerous scientific areas including neuroscience. Fractals are structures, in which the whole has the same shape as its parts. A specific structure known as pink noise (also called fractal or 1/f noise) is one key fractal manifestation, exhibits both stability and adaptability, and can be addressed via the Hurst exponent ( H ). FMRI studies using H on regional fMRI time courses used fractality as an important characteristic to unravel neural networks from artificial noise. In this fMRI-study, we examined 103 healthy male students at rest and while performing the 5-choice serial reaction time task...
2018: Frontiers in Physiology
Levi Madden, James Archer, Enbang Li, Dean Wilkinson, Anatoly Rosenfeld
The irradiation of scintillator-fiber optic dosimeters by clinical LINACs results in the measurement of scintillation and Cerenkov radiation. In scintillator-fiber optic dosimetry, the scintillation and Cerenkov radiation responses are separated to determine the dose deposited in the scintillator volume. Artificial neural networks (ANNs) were trained and applied in a novel single probe method for the temporal separation of scintillation and Cerenkov radiation. Six dose profiles were measured using the ANN, with the dose profiles compared to those measured using background subtraction and an ionisation chamber...
October 2018: Physica Medica: PM
Mahmoud Ismail, Mina Attari, Saeid Habibi, Samir Ziada
Deep-Learning has become a leading strategy for artificial intelligence and is being applied in many fields due to its excellent performance that has surpassed human cognitive abilities in a number of classification and control problems (Ciregan, Meier, & Schmidhuber, 2012; Mnih et al., 2015). However, the training process of Deep-Learning is usually slow and requires high-performance computing, capable of handling large datasets. The optimization of the training method can improve the learning rate of the Deep-Learning networks and result in a higher performance while using the same number of training epochs (cycles)...
October 3, 2018: Neural Networks: the Official Journal of the International Neural Network Society
Matthew E Dilsizian, Eliot L Siegel
PURPOSE OF REVIEW: An understanding of the basics concepts of deep learning can be helpful in not only understanding the potential applications of this technique but also in critically reviewing literature in which neural networks are utilized for analysis and modeling. RECENT FINDINGS: The term "deep learning" has been applied to a subset of machine learning that utilizes a "neural network" and is often used interchangeably with "artificial intelligence...
October 18, 2018: Current Cardiology Reports
Binbin Wang, Li Xiao, Yang Liu, Jing Wang, Beihong Liu, Tengyan Li, Xu Ma, Yi Zhao
There is a disparity between the increasing application of digital retinal imaging to neonatal ocular screening and slowly growing number of pediatric ophthalmologists. Assistant tools that can automatically detect ocular disorders may be needed. In present study, we develop a deep convolutional neural network (DCNN) for automated classification and grading of retinal hemorrhage. We used 48,996 digital fundus images from 3770 newborns with retinal hemorrhage of different severity (grade 1, 2, and 3) and normal controls from a large cross-sectional investigation in China...
October 17, 2018: Bioscience Reports
Takumi Takeuchi, Mami Hattori-Kato, Yumiko Okuno, Satoshi Iwai, Koji Mikami
INTRODUCTION: To predict the rate of prostate cancer detection on prostate biopsy more accurately, the performance of deep learning using a multilayer artificial neural network was investigated. METHODS: A total of 334 patients who underwent multiparametric magnetic resonance imaging before ultrasonography-guided transrectal 12-core prostate biopsy were enrolled in the analysis. Twenty-two non-selected variables, as well as selected ones by least absolute shrinkage and selection operator (Lasso) regression analysis and by stepwise logistic regression analysis were input into the constructed multilayer artificial neural network (ANN) programs; 232 patients were used as training cases of ANN programs, and the remaining 102 patients were for the test to output the probability of prostate cancer existence, accuracy of prostate cancer prediction, and area under the receiver operating characteristic (ROC) curve with the learned model...
October 15, 2018: Canadian Urological Association Journal, Journal de L'Association des Urologues du Canada
Kamal Gholipour, Mohammad Asghari-Jafarabadi, Shabnam Iezadi, Ali Jannati, Sina Keshavarz
Background: Type 2 diabetes mellitus (T2DM) is a metabolic disease with complex causes, manifestations, complications and management. Understanding the wide range of risk factors for T2DM can facilitate diagnosis, proper classification and cost-effective management of the disease. Aims: To compare the power of an artificial neural network (ANN) and logistic regression in identifying T2DM risk factors. Methods: This descriptive and analytical study was conducted in 2013...
October 10, 2018: Eastern Mediterranean Health Journal, la Revue de Santé de la Méditerranée Orientale
Changhui Jiang, Shuai Chen, Yuwei Chen, Boya Zhang, Ziyi Feng, Hui Zhou, Yuming Bo
Microelectromechanical Systems (MEMS) Inertial Measurement Unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in position and navigation, due to gradually improved accuracy and its small size and low cost. However, the errors of a MEMS IMU based standalone Inertial Navigation System (INS) will diverge over time dramatically, since there are various and nonlinear errors contained in the MEMS IMU measurements. Therefore, MEMS INS is usually integrated with a Global Positioning System (GPS) for providing reliable navigation solutions...
October 15, 2018: Sensors
Felix Hammann, Verena Schöning, Jürgen Drewe
Drug-induced liver injury (DILI) is the most common cause of acute liver failure and often responsible for drug withdrawals from the market. Clinical manifestations vary, and toxicity may or may not appear dose-dependent. We present several machine-learning models (decision tree induction, k-nearest neighbor, support vector machines, artificial neural networks) for the prediction of clinically relevant DILI based solely on drug structure, with data taken from published DILI cases. Our models achieved corrected classification rates of up to 89%...
October 16, 2018: Journal of Applied Toxicology: JAT
Fuk Hay Tang, Jasmine L C Chan, Bill K L Chan
This study proposes an accurate method in assessing chronological age of the adolescents using a machine learning approach using MRI images. We also examined the value of MRI with Tanner-Whitehouse 3 (TW3) method in assessing skeletal maturity. Seventy-nine 12-17-year-old healthy Hong Kong Chinese adolescents were recruited. The left hand and wrist region were scanned by a dedicated skeletal MRI scanner. T1-weighted three-dimensional coronal view images for the left hand and wrist region were acquired. Independent maturity indicators such as subject body height, body weight, bone marrow composition intensity quantified by MRI, and TW3 skeletal age were included for artificial neural network (ANN) analysis...
October 15, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Timo Flesch, Jan Balaguer, Ronald Dekker, Hamed Nili, Christopher Summerfield
Humans can learn to perform multiple tasks in succession over the lifespan ("continual" learning), whereas current machine learning systems fail. Here, we investigated the cognitive mechanisms that permit successful continual learning in humans and harnessed our behavioral findings for neural network design. Humans categorized naturalistic images of trees according to one of two orthogonal task rules that were learned by trial and error. Training regimes that focused on individual rules for prolonged periods (blocked training) improved human performance on a later test involving randomly interleaved rules, compared with control regimes that trained in an interleaved fashion...
October 15, 2018: Proceedings of the National Academy of Sciences of the United States of America
Abdol Mohammad Ghaedi, Shahnaz Karamipour, Azam Vafaei, Mohammad Mehdi Baneshi, Vahid Kiarostami
The present study examines simultaneous adsorption of ternary dyes such as rose bengal (RB), safranin O (SO) and malachite green (MG) from aqueous media on copper oxide nanoparticles immobilized on activated carbon (CuO-NPs-AC) in a batch system. To forecast and optimize the adsorption, artificial neural network (ANN) and response surface methodology (RSM) were utilized. The effect of various factors, e.g. dye concentration, sonication time, adsorbent dosage and pH on the adsorption process were evaluated through five level six factor central composite design (CCD) using RSM...
October 6, 2018: Ultrasonics Sonochemistry
Edgar Guevara, Juan Carlos Torres-Galván, Miguel G Ramírez-Elías, Claudia Luevano-Contreras, Francisco Javier González
Type 2 diabetes mellitus (DM2) is one of the most widely prevalent diseases worldwide and is currently screened by invasive techniques based on enzymatic assays that measure plasma glucose concentration in a laboratory setting. A promising plan of action for screening DM2 is to identify molecular signatures in a non-invasive fashion. This work describes the application of portable Raman spectroscopy coupled with several supervised machine-learning techniques, to discern between diabetic patients and healthy controls (Ctrl), with a high degree of accuracy...
October 1, 2018: Biomedical Optics Express
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