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"Artificial neural network"

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https://www.readbyqxmd.com/read/28523139/an-overview-of-the-use-of-artificial-neural-networks-in-lung-cancer-research
#1
EDITORIAL
Luca Bertolaccini, Piergiorgio Solli, Alessandro Pardolesi, Antonello Pasini
The artificial neural networks (ANNs) are statistical models where the mathematical structure reproduces the biological organisation of neural cells simulating the learning dynamics of the brain. Although definitions of the term ANN could vary, the term usually refers to a neural network used for non-linear statistical data modelling. The neural models applied today in various fields of medicine, such as oncology, do not aim to be biologically realistic in detail but just efficient models for nonlinear regression or classification...
April 2017: Journal of Thoracic Disease
https://www.readbyqxmd.com/read/28522333/development-of-qsars-for-parameterizing-physiology-based-toxicokinetic-models
#2
Dimosthenis Α Sarigiannis, Krystalia Papadaki, Periklis Kontoroupis, Spyros P Karakitsios
A Quantitative Structure Activity Relationship (QSAR) model was developed in order to predict physicochemical and biochemical properties of industrial chemicals of various groups. This model was based on the solvation equation, originally proposed by Abraham. In this work Abraham's solvation model got parameterized using artificial intelligence techniques such as artificial neural networks (ANNs) for the prediction of partitioning into kidney, heart, adipose, liver, muscle, brain and lung for the estimation of the bodyweight-normalized maximal metabolic velocity (Vmax) and the Michaelis - Menten constant (Km)...
May 15, 2017: Food and Chemical Toxicology
https://www.readbyqxmd.com/read/28520235/high-dimensional-neural-network-potentials-for-complex-systems
#3
Jörg Behler
Modern simulation techniques have reached a level of maturity, which allows addressing a wide range of problems in chemistry and materials science. Unfortunately, the application of first principles methods with predictive power is still limited to rather small systems, and in spite of the rapid evolution of computer hardware no fundamental change of this situation can be expected. Consequently, to reach an atomic level understanding of complex systems, the development of more efficient but equally reliable atomistic potentials has received considerable attention in recent years...
May 18, 2017: Angewandte Chemie
https://www.readbyqxmd.com/read/28510124/computer-aided-estimation-of-the-herg-mediated-cardiotoxicity-risk-of-potential-drug-components
#4
E V Radchenko, Yu A Rulev, A Ya Safanyaev, V A Palyulin, N S Zefirov
The hERG potassium channel is one of the most important anti-targets determining cardiotoxicity of potential drugs. Using fragmental descriptors and artificial neural networks, the predictive models of the relationship between the structure of organic compounds and their activity with respect to hERG were built, and the structural factors affecting it were analyzed. By their predictive ability and applicability domain, these models (N = 1000, Q (2) = 0.77, RMSE cv = 0.45 for affinity and N = 2886, Q (2) = 0...
March 2017: Doklady. Biochemistry and Biophysics
https://www.readbyqxmd.com/read/28507947/preparation-optimization-and-activity-evaluation-of-plga-streptokinase-nanoparticles-using-electrospray
#5
Nasrin Yaghoobi, Reza Faridi Majidi, Mohammad Ali Faramarzi, Hadi Baharifar, Amir Amani
Purpose: PLGA nanoparticles (NPs) have been extensively investigated as carriers of different drug molecules to enhance their therapeutic effects or preserve them from the aqueous environment. Streptokinase (SK) is an important medicine for thrombotic diseases. Methods: In this study, we used electrospray to encapsulate SK in PLGA NPs and evaluate its activity. This is the first paper which investigates activity of an electrosprayed enzyme. Effect of three input parameters, namely, voltage, internal diameter of needle (nozzle) and concentration ratio of polymer to protein on size and size distribution (SD) of NPs was evaluated using artificial neural networks (ANNs)...
April 2017: Advanced Pharmaceutical Bulletin
https://www.readbyqxmd.com/read/28505758/machine-learning-applied-to-proton-radiography-of-high-energy-density-plasmas
#6
Nicholas F Y Chen, Muhammad Firmansyah Kasim, Luke Ceurvorst, Naren Ratan, James Sadler, Matthew C Levy, Raoul Trines, Robert Bingham, Peter Norreys
Proton radiography is a technique extensively used to resolve magnetic field structures in high-energy-density plasmas, revealing a whole variety of interesting phenomena such as magnetic reconnection and collisionless shocks found in astrophysical systems. Existing methods of analyzing proton radiographs give mostly qualitative results or specific quantitative parameters, such as magnetic field strength, and recent work showed that the line-integrated transverse magnetic field can be reconstructed in specific regimes where many simplifying assumptions were needed...
April 2017: Physical Review. E
https://www.readbyqxmd.com/read/28504360/oxygen-extraction-fraction-mapping-at-3-tesla-using-an-artificial-neural-network-a-feasibility-study
#7
Sebastian Domsch, Bettina Mürle, Sebastian Weingärtner, Jascha Zapp, Frederik Wenz, Lothar R Schad
PURPOSE: The oxygen extraction fraction (OEF) is an important biomarker for tissue-viability. MRI enables noninvasive estimation of the OEF based on the blood-oxygenation-level-dependent (BOLD) effect. Quantitative OEF-mapping is commonly applied using least-squares regression (LSR) to an analytical tissue model. However, the LSR method has not yet become clinically established due to the necessity for long acquisition times. Artificial neural networks (ANNs) recently have received increasing interest for robust curve-fitting and might pose an alternative to the conventional LSR method for reduced acquisition times...
May 14, 2017: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
https://www.readbyqxmd.com/read/28500895/a-universal-multilingual-weightless-neural-network-tagger-via-quantitative-linguistics
#8
Hugo C C Carneiro, Carlos E Pedreira, Felipe M G França, Priscila M V Lima
In the last decade, given the availability of corpora in several distinct languages, research on multilingual part-of-speech tagging started to grow. Amongst the novelties there is mWANN-Tagger (multilingual weightless artificial neural network tagger), a weightless neural part-of-speech tagger capable of being used for mostly-suffix-oriented languages. The tagger was subjected to corpora in eight languages of quite distinct natures and had a remarkable accuracy with very low sample deviation in every one of them, indicating the robustness of weightless neural systems for part-of-speech tagging tasks...
April 26, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28499818/biosurfactant-biopolymer-driven-microbial-enhanced-oil-recovery-meor-and-its-optimization-by-an-ann-ga-hybrid-technique
#9
Gunaseelan Dhanarajan, Vivek Rangarajan, Chandrakanth Bandi, Abhivyakti Dixit, Susmita Das, Kranthikiran Ale, Ramkrishna Sen
A lipopeptide biosurfactant produced by marine Bacillus megaterium and a biopolymer produced by thermophilic Bacillus licheniformis were tested for their application potential in the enhanced oil recovery. The crude biosurfactant obtained after acid precipitation effectively reduced the surface tension of deionized water from 70.5 to 28.25mN/m and the interfacial tension between lube oil and water from 18.6 to 1.5mN/m at a concentration of 250mgL(-1). The biosurfactant exhibited a maximum emulsification activity (E24) of 81...
May 10, 2017: Journal of Biotechnology
https://www.readbyqxmd.com/read/28499334/recent-advances-on-neuromorphic-systems-using-phase-change-materials
#10
REVIEW
Lei Wang, Shu-Ren Lu, Jing Wen
Realization of brain-like computer has always been human's ultimate dream. Today, the possibility of having this dream come true has been significantly boosted due to the advent of several emerging non-volatile memory devices. Within these innovative technologies, phase-change memory device has been commonly regarded as the most promising candidate to imitate the biological brain, owing to its excellent scalability, fast switching speed, and low energy consumption. In this context, a detailed review concerning the physical principles of the neuromorphic circuit using phase-change materials as well as a comprehensive introduction of the currently available phase-change neuromorphic prototypes becomes imperative for scientists to continuously progress the technology of artificial neural networks...
December 2017: Nanoscale Research Letters
https://www.readbyqxmd.com/read/28498299/machine-learning-for-predicting-outcomes-in-trauma
#11
Nehemiah T Liu, Jose Salinas
To date, there are no reviews on machine learning (ML) for predicting outcomes in trauma. Consequently, it remains unclear as to how ML-based prediction models compare in the triage and assessment of trauma patients. The objective of this review was to survey and identify studies involving ML for predicting outcomes in trauma, with the hypothesis that models predicting similar outcomes may share common features but the performance of ML in these studies will differ greatly. MEDLINE and other databases were searched for studies involving trauma and ML...
May 11, 2017: Shock
https://www.readbyqxmd.com/read/28497769/automated-eeg-artifact-elimination-by-applying-machine-learning-algorithms-to-ica-based-features
#12
Thea Radüntz, Jon Scouten, Olaf Hochmuth, Beate Meffert
OBJECTIVE: Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent component analysis (ICA) is a widely used method for eliminating various artifacts from recordings. However, evaluating and classifying the calculated independent components (IC) as artifact or EEG is not fully automated at present. APPROACH: In this study, we propose a new approach for automated artifact elimination, which applies machine learning algorithms to ICA-based features...
May 12, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28496470/qsar-studying-of-oxidation-behavior-of-benzoxazines-as-an-important-pharmaceutical-property
#13
Elham Baher, Naser Darzi
In this work the electrooxidation half-wave potentials of some Benzoxazines were predicted from their structural molecular descriptors by using quantitative structure-property relationship (QSAR) approaches. The dataset consist the half-wave potential of 40 benzoxazine derivatives which were obtained by DC-polarography. Descriptors which were selected by stepwise multiple selection procedure are: HOMO energy, partial positive surface area, maximum valency of carbon atom, relative number of hydrogen atoms and maximum electrophilic reaction index for nitrogen atom...
2017: Iranian Journal of Pharmaceutical Research: IJPR
https://www.readbyqxmd.com/read/28496225/altmetric-analysis-of-2015-dental-literature-a-cross-sectional-survey
#14
J Kolahi, P Iranmanesh, S Khazaei
Introduction To report and analyse Altmetric data of all dental articles and journals in 2015.Methods To identify all 2015 dental articles, PubMed was searched via Altmetric platform using the following query: ("2015/1/1"[PDAT]: "2015/12/31"[PDAT]) AND jsubsetd[text] NOT 2016[PDAT] on November 12, 2016. Altmetric data of all 2015 dental articles and journals were extracted and analysed by Microsoft Office Excel 2016 using descriptive statistics, graphs and trend-line analysis. To find the most important and influential Altmetric factors, multi-layered perceptron artificial neural network was employed using SPSS 22...
May 12, 2017: British Dental Journal
https://www.readbyqxmd.com/read/28489045/discontinuity-detection-in-the-shield-metal-arc-welding-process
#15
José Alberto Naves Cocota, Gabriel Carvalho Garcia, Adilson Rodrigues da Costa, Milton Sérgio Fernandes de Lima, Filipe Augusto Santos Rocha, Gustavo Medeiros Freitas
This work proposes a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal Arc Welding (SMAW) processes. The detection system is based on two sensors-a microphone and piezoelectric-that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with a high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities...
May 10, 2017: Sensors
https://www.readbyqxmd.com/read/28484720/eeg-based-computer-aided-diagnosis-of-autism-spectrum-disorder-using-wavelet-entropy-and-ann
#16
Ridha Djemal, Khalil AlSharabi, Sutrisno Ibrahim, Abdullah Alsuwailem
Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD) of autism ‎based on electroencephalography (EEG) signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT), entropy (En), and artificial neural network (ANN). DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28481568/key-finding-by-artificial-neural-networks-that-learn-about-key-profiles
#17
Michael R W Dawson, Jasen A Z Zielinski
We explore the ability of a very simple artificial neural network, a perceptron, to assert the musical key of novel stimuli. First, perceptrons are trained to associate standardized key profiles (taken from 1 of 3 different sources) to different musical keys. After training, we measured perceptron accuracy in asserting musical keys for 296 novel stimuli. Depending upon which key profiles were used during training, perceptrons can perform as well as established key-finding algorithms on this task. Further analyses indicate that perceptrons generate higher activity in a unit representing a selected key and much lower activities in the units representing the competing keys that are not selected than does a traditional algorithm...
May 8, 2017: Canadian Journal of Experimental Psychology, Revue Canadienne de Psychologie Expérimentale
https://www.readbyqxmd.com/read/28473865/artificial-neural-networks-in-image-processing-for-early-detection-of-breast-cancer
#18
REVIEW
M M Mehdy, P Y Ng, E F Shair, N I Md Saleh, C Gomes
Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. The drawback of applying these techniques is the large time consumption in the manual diagnosis of each image pattern by a professional radiologist. Automated classifiers could substantially upgrade the diagnosis process, in terms of both accuracy and time requirement by distinguishing benign and malignant patterns automatically. Neural network (NN) plays an important role in this respect, especially in the application of breast cancer detection...
2017: Computational and Mathematical Methods in Medicine
https://www.readbyqxmd.com/read/28473053/applications-of-artificial-neural-networks-for-adsorption-removal-of-dyes-from-aqueous-solution-a-review
#19
REVIEW
Abdol Mohammad Ghaedi, Azam Vafaei
Artificial neural networks (ANNs) have been widely applied for the prediction of dye adsorption during the last decade. In this paper, the applications of ANN methods, namely multilayer feedforward neural networks (MLFNN), support vector machine (SVM), and adaptive neuro fuzzy inference system (ANFIS) for adsorption of dyes are reviewed. The reported researches on adsorption of dyes are classified into four major categories, such as (i) MLFNN, (ii) ANFIS, (iii) SVM and (iv) hybrid with genetic algorithm (GA) and particle swarm optimization (PSO)...
April 26, 2017: Advances in Colloid and Interface Science
https://www.readbyqxmd.com/read/28472991/resolving-the-effect-of-wrist-position-on-myoelectric-pattern-recognition-control
#20
Adenike A Adewuyi, Levi J Hargrove, Todd A Kuiken
BACKGROUND: The use of pattern recognition-based methods to control myoelectric upper-limb prostheses has been well studied in individuals with high-level amputations but few studies have demonstrated that it is suitable for partial-hand amputees, who often possess a functional wrist. This study's objective was to evaluate strategies that allow partial-hand amputees to control a prosthetic hand while allowing retain wrist function. METHODS: EMG data was recorded from the extrinsic and intrinsic hand muscles of six non-amputees and two partial-hand amputees while they performed 4 hand motions in 13 different wrist positions...
May 4, 2017: Journal of Neuroengineering and Rehabilitation
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