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artificial neural network

Li Wang, Qingrui Chang, Jing Yang, Xiaohua Zhang, Fenling Li
The performance of three machine learning methods (support vector regression, random forests and artificial neural network) for estimating the LAI of paddy rice was evaluated in this study. Traditional univariate regression models involving narrowband NDVI with optimized band combinations as well as linear multivariate calibration partial least squares regression models were also evaluated for comparison. A four year field-collected dataset was used to test the robustness of LAI estimation models against temporal variation...
2018: PloS One
Malena Correa, Mirko Zimic, Franklin Barrientos, Ronald Barrientos, Avid Román-Gonzalez, Mónica J Pajuelo, Cynthia Anticona, Holger Mayta, Alicia Alva, Leonardo Solis-Vasquez, Dante Anibal Figueroa, Miguel A Chavez, Roberto Lavarello, Benjamín Castañeda, Valerie A Paz-Soldán, William Checkley, Robert H Gilman, Richard Oberhelman
Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ultrasound has proved to be a useful tool to detect lung consolidation as evidence of pneumonia. However, diagnosis of pneumonia by ultrasound has limitations: it is operator-dependent, and it needs to be carried out and interpreted by trained personnel. Pattern recognition and image analysis is a potential tool to enable automatic diagnosis of pneumonia consolidation without requiring an expert analyst...
2018: PloS One
S Abdovic, M Cuk, N Cekada, M Milosevic, A Geljic, S Fusic, M Bastic, Z Bahtijarevic
PURPOSE: To assess the prediction model for late-presenting posterior urethral valve (PUV) in boys with lower urinary tract symptoms (LUTS) using artificial neural network (ANN). MATERIALS AND METHODS: 408 boys aged 3-17 years (median 7.2 years) with LUTS were examined and had bladder diary, ultrasound, uroflowmetry, urine, and urine culture. Cystoscopy was recommended when peak flow rate (Qmax ) was persistently ≤ 5th percentile in patients who were unresponsive to urotherapy and pharmacological treatment (oxybutynin)...
December 4, 2018: World Journal of Urology
Dipendra Jha, Logan Ward, Arindam Paul, Wei-Keng Liao, Alok Choudhary, Chris Wolverton, Ankit Agrawal
Conventional machine learning approaches for predicting material properties from elemental compositions have emphasized the importance of leveraging domain knowledge when designing model inputs. Here, we demonstrate that by using a deep learning approach, we can bypass such manual feature engineering requiring domain knowledge and achieve much better results, even with only a few thousand training samples. We present the design and implementation of a deep neural network model referred to as ElemNet; it automatically captures the physical and chemical interactions and similarities between different elements using artificial intelligence which allows it to predict the materials properties with better accuracy and speed...
December 4, 2018: Scientific Reports
Yang Tao, Hongbin Han, Wangxin Liu, Lu Peng, Yue Wang, Shekhar Kadam, Pau Loke Show, Xiaosong Ye
In this work, sonication (20-kHz) was conducted to assist the biosorption of phenolics from blueberry pomace extracts by brewery waste yeast biomass. The adsorption capacity of yeast increased markedly under ultrasonic fields. After sonication at 394.2 W/L and 40 °C for 120 min, the adsorption capacity was increased by 62.7% compared with that under reciprocating shaking. An artificial neural network was used to model and visualize the effects of different parameters on yeast biosorption capacity. Both biosorption time and acoustic energy density had positive influences on yeast biosorption capacity, whereas no clear influence of temperature on biosorption process was observed...
November 24, 2018: Ultrasonics Sonochemistry
C P Devatha, N Pavithra
Triclosan (TCS) is a well-known emerging contaminant got wide use in daily use products of domestic purpose, which provides the way to enter the ecological cycle, and is preferably detected in sewage treatment plants. In this study, TCS degrading bacteria (TDB) was isolated and identified from a wastewater treatment plant at the National Institute of Technology-Karnataka, Surathkal (NITK), India. The isolate was reported as Pseudomonas strain by performing 16S RNA Sequencing using BLAST analysis. Bacterial growth depends upon several environmental factors...
December 1, 2018: Journal of Environmental Management
Taher Abunama, Faridah Othman, Mozafar Ansari, Ahmed El-Shafie
Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environment. In developing countries, the accurate evaluation of leachate generation rates has often considered a challenge due to the lack of reliable data and high measurement costs. Leachate generation is related to several factors, including meteorological data, waste generation rates, and landfill design conditions...
December 3, 2018: Environmental Science and Pollution Research International
Tao Li, Runtao Tian, Xinlan Yu, Lei Sun, Yi He, Peishan Xie, Shuangcheng Ma
Background: The use of HPTLC fingerprinting for the analysis of traditional Chinese medicines (TCMs) usually involves several image-processing steps. However, these image-processing steps are time consuming Objective: We describe a new approach that applies artificial neural networks (ANN) directly to raw high-performance thin-layer chromatography HPTLC images. Methods: This approach combines image processing and chemometric modeling and was used to classify TCMs [dried tangerine eel (Chen Pi), green tangerine peel (Qing Pi), immature bitter orange fruit, and bitter orange fruit (Zhi Qiao)]...
December 3, 2018: Journal of AOAC International
Yanye Lu, Shuqing Chen, Shiyang Hu, Joachim Hornegger, Andreas Maier, Markus Kowarschik, Rebecca Fahrig, Xiaolin Huang, Yan Xia, Jang-Hwan Choi, Qiushi Ren
PURPOSE: Benefiting from multi-energy X-ray imaging technology, material decomposition facilitates the characterization of different materials in X-ray imaging. However, the performance of material decomposition is limited by the accuracy of the decomposition model. Due to the presenceof non-ideal effects in X-ray imaging systems, it is difficult to explicitly build the imaging system models for material decomposition. As an alternative, this paper explores the feasibility of using machine learning approaches for material decomposition tasks...
December 3, 2018: Medical Physics
Sandeep Reddy, John Fox, Maulik P Purohit
In recent years, there has been massive progress in artificial intelligence (AI) with the development of deep neural networks, natural language processing, computer vision and robotics. These techniques are now actively being applied in healthcare with many of the health service activities currently being delivered by clinicians and administrators predicted to be taken over by AI in the coming years. However, there has also been exceptional hype about the abilities of AI with a mistaken notion that AI will replace human clinicians altogether...
December 3, 2018: Journal of the Royal Society of Medicine
Sattar Dorafshan, Robert J Thomas, Marc Maguire
SDNET2018 is an annotated image dataset for training, validation, and benchmarking of artificial intelligence based crack detection algorithms for concrete. SDNET2018 contains over 56,000 images of cracked and non-cracked concrete bridge decks, walls, and pavements. The dataset includes cracks as narrow as 0.06 mm and as wide as 25 mm. The dataset also includes images with a variety of obstructions, including shadows, surface roughness, scaling, edges, holes, and background debris. SDNET2018 will be useful for the continued development of concrete crack detection algorithms based on deep convolutional neural networks (DCNNs), which are a subject of continued research in the field of structural health monitoring...
December 2018: Data in Brief
Renjith Paulose, Kalirajan Jegatheesan, Gopal Samy Balakrishnan
CONTEXT: Chemical toxicity prediction at early stage drug discovery phase has been researched for years, and newest methods are always investigated. Research data comprising chemical physicochemical properties, toxicity, assay, and activity details create massive data which are becoming difficult to manage. Identifying the desired featured chemical with the desired biological activity from millions of chemicals is a challenging task. AIMS: In this study, we investigate and explore big data technologies and machine learning approaches to do an efficient chemical data mining for endocrine receptor disruption prediction and virtual compound screening...
July 2018: Indian Journal of Pharmacology
Seunghwan Seo, Seo-Hyeon Jo, Sungho Kim, Jaewoo Shim, Seyong Oh, Jeong-Hoon Kim, Keun Heo, Jae-Woong Choi, Changhwan Choi, Saeroonter Oh, Duygu Kuzum, H-S Philip Wong, Jin-Hong Park
The priority of synaptic device researches has been given to prove the device potential for the emulation of synaptic dynamics and not to functionalize further synaptic devices for more complex learning. Here, we demonstrate an optic-neural synaptic device by implementing synaptic and optical-sensing functions together on h-BN/WSe2 heterostructure. This device mimics the colored and color-mixed pattern recognition capabilities of the human vision system when arranged in an optic-neural network. Our synaptic device demonstrates a close to linear weight update trajectory while providing a large number of stable conduction states with less than 1% variation per state...
November 30, 2018: Nature Communications
Gajanan V Sherbet, Wai Lok Woo, Satnam Dlay
Artificial intelligence was recognised many years ago as a potential and powerful tool to predict disease outcome in many clinical situations. The conventional approaches using statistical methods have provided much information, but are subject to limitations imposed by the complexity of medical data. The structures of the important variants of the machine learning system artificial neural networks (ANN) are discussed and emphasis is given to the powerful analytical support that could be provided by ANN for the prediction of cancer progression and prognosis...
December 2018: Anticancer Research
David M G Williams, Wolfgang Eisfeld
A new diabatization method based on artificial neural networks (ANNs) is presented, which is capable of reproducing high-quality ab initio data with excellent accuracy for use in quantum dynamics studies. The diabatic potential matrix is expanded in terms of a set of basic coupling matrices and the expansion coefficients are made geometry-dependent by the output neurons of the ANN. The ANN is trained with respect to ab initio data using a modified Marquardt-Levenberg back-propagation algorithm. Due to its setup, this approach combines the stability and straightforwardness of a standard low-order vibronic coupling model with the accuracy by the ANN, making it particularly advantageous for problems with a complicated electronic structure...
November 28, 2018: Journal of Chemical Physics
Janis Timoshenko, Cody J Wrasman, Mathilde Luneau, Tanya Shirman, Matteo Cargnello, Simon Russell Bare, Joanna Aizenberg, Cynthia M Friend, Anatoly I Frenkel
Properties of mono- and bimetallic metal nanoparticles (NPs) may depend strongly on their compositional, structural (or geometrical) attributes and their atomic dynamics, all of which can be efficiently described by a partial radial distribution function (PRDF) of metal atoms. For NPs that are several nanometers in size, finite size effects may play a role in determining crystalline order, interatomic distances and particle shape. Bimetallic NPs may also have different compositional distributions than bulk materials...
December 3, 2018: Nano Letters
Shane Nanayakkara, Sam Fogarty, Michael Tremeer, Kelvin Ross, Brent Richards, Christoph Bergmeir, Sheng Xu, Dion Stub, Karen Smith, Mark Tacey, Danny Liew, David Pilcher, David M Kaye
BACKGROUND: Resuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing illness severity scores is limited. Using in-hospital data available within the first 24 hours of admission, we aimed to develop more accurate models of risk prediction using both logistic regression (LR) and machine learning (ML) techniques, with a combination of demographic, physiologic, and biochemical information. METHODS AND FINDINGS: Patient-level data were extracted from the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database for patients who had experienced a cardiac arrest within 24 hours prior to admission to an intensive care unit (ICU) during the period January 2006 to December 2016...
November 2018: PLoS Medicine
Ulises A Aregueta-Robles, Penny J Martens, Laura A Poole-Warren, Rylie A Green
Promoting nerve regeneration requires engineering cellular carriers to physically and biochemically support neuronal growth into a long lasting functional tissue. This study systematically evaluated the capacity of a biosynthetic poly(vinyl alcohol) (PVA) hydrogel to support growth and differentiation of co-encapsulated neurons and glia. A significant challenge is to understand the role of the dynamic degradable hydrogel mechanical properties on expression of relevant cellular morphologies and function. It was hypothesised that a carrier with mechanical properties akin to neural tissue will provide glia with conditions to thrive, and that glia in turn will support neuronal survival and development...
November 27, 2018: Acta Biomaterialia
Yishan Guo, Zhewei Xu, Chenghang Zheng, Jian Shu, Hong Dong, Yongxin Zhang, Weiguo Weng, Xiang Gao
Sulfur dioxide (SO2 ) is one of the main air pollutants from many industries. Most coal-fired power plants in China use wet flue gas desulfurization (WFGD) as main method for SO2 removal. Presently, the operating of WFGD lacks accurate modeling method to predict outlet concentration, let alone optimization method. As a result, operating parameters and running status of WFGD are adjusted based on the experience of the experts, which brings about the possibility of material waste and excessive emissions. In this paper, a novel WFGD model combining a mathematical model and an artificial neural network was developed to forecast SO2 emissions...
November 30, 2018: Journal of the Air & Waste Management Association
Juan Alberto Castillo-Garit, Naivi Flores-Balmaseda, Orlando Álvarez, Hai Pham-The, Virginia Pérez-Doñate, Francisco Torrens, Facundo Pérez-Giménez
Leishmaniasis is poverty-related disease endemic in 98 countries worldwide, with morbidity and mortality increasing daily. All currently used first-line and second-line drugs for the treatment of leishmaniasis exhibit several drawbacks including toxicity, high costs and route of administration. Consequently, the development of new treatments for leishmaniasis is a priority in the field of neglected tropical diseases. The aim of this work is to develop computational models those allow the identification of new chemical compounds with potential anti-leishmanial activity...
November 30, 2018: Current Topics in Medicinal Chemistry
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