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Palavra-chave Artificial neural network (23) Mungbean (2) ถั่วเขียว (2) ผลผลิต (2) โครงข่ายประสาทเทียม (2) Absorption column (1) Accelerometers (1) Activation energy (1) Acylation (1) Animal welfare (1) Arbuscular mycorrhizal fungi spores (1) Betulinic acid (1) Bird sound classification (1) Broiler (1) Candida antarctica lipase (1) Characterization (1) Chlorophyll a (1) Circle Hough transform (1) Classification (1) Clostridium perfringens type A (1) Mais... Tipo do documento Info:eu-repo/semantics/article (17) Journal article (4) Collection (2) Ano 2022 (1) 2020 (2) 2019 (2) 2017 (1) 2016 (1) 2015 (2) 2014 (4) 2013 (3) 2011 (1) 2010 (3) 2009 (1) 2008 (1) 2006 (1) Mais... País Brazil (14) Chile (4) China (3) Thailand (2) Idioma Inglês (20) Thailandês (2) Português (1)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 23 Primeira ... 1 2 ... Última An Intelligent Procedure for the Detection and Classification of Chickens Infected by Clostridium Perfringens Based on their Vocalization Rev. Bras. Ciênc. Avic. Sadeghi,M; Banakar,A; Khazaee,M; Soleimani,MR. ABSTRACT In this study, an intelligent method was implemented for the detection and classification of chickens by infected Clostridium perfringens type A based on their vocalization. To this aim, the birds were first divided into two groups that were placed in separate cages with 15 chickens each. Chickens were inoculated with Clostridium perfringens type A on day 14. In order to ensure the absence of secondary diseases and their probable effect on bird vocalization, vaccines for common diseases were administered. During 30 days of the experiment, chicken vocalization was recorded every morning at 8 a.m. using a microphone and a data collection card under equal and controlled conditions. Sound signals were investigated in time domains, and 23 features were... Tipo: Info:eu-repo/semantics/article Palavras-chave: Poultry health; Bird sound classification; Clostridium perfringens type A; Data mining; Artificial neural network. Ano: 2015 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2015000400537 Artificial Neural Network Based Modeling of Tractor Performance at Different Field Conditions CIGR Journal Almaliki, Salim; Alimardani, Reza; Omid, Mahmoud. Application of tractors in farming is undeniable as a power supply. Therefore, performance model for evolving parameters of tractors and implements are essential for farm machinery, operators and manufacturers alike. The objective of this study was to assess the predictive capability of several configurations of ANNs for performance evaluating of tractor in parameters of drawbar power, fuel consumption, rolling resistance and tractive efficiency. A conventional tillage system which included a moldboard plow with three furrows was used for collecting data from MF285 Massey Ferguson tractor. To predict performance parameters, ANN models with back-propagation algorithm were developed using a MATLAB software with different topologies and training algorithms.... Tipo: Info:eu-repo/semantics/article Palavras-chave: Artificial neural network; Tractive efficiency; Rolling resistance; Drawbar power; Fuel consumption.. Ano: 2016 URL: http://www.cigrjournal.org/index.php/Ejounral/article/view/3880 Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester Electron. J. Biotechnol. Moghaddam,Mansour Ghaffari; Ahmad,Faujan Bin H; Basri,Mahiran; Rahman,Mohd Basyaruddin Abdul. 3β-O-phthalic ester of betulinic acid was synthesized from reaction of betulinic acid and phthalic anhydride using lipase as biocatalyst. This ester has clinical potential as an anticancer agent. In this study, artificial neural network (ANN) analysis of Candida antarctica lipase (Novozym 435) -catalyzed esterification of betulinic acid with phthalic anhydride was carried out. A multilayer feed-forward neural network trained with an error back-propagation algorithm was incorporated for developing a predictive model. The input parameters of the model are reaction time, reaction temperature, enzyme amount and substrate molar ratio while the percentage isolated yield of ester is the output. Four different training algorithms, belonging to two classes, namely... Tipo: Journal article Palavras-chave: Acylation; Artificial neural network; Betulinic acid; Candida antarctica lipase; Enzymatic synthesis; Novozym 435. Ano: 2010 URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582010000300003 Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China BJMBR Shi,L.; Wang,X.C.; Wang,Y.S.. The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The... Tipo: Info:eu-repo/semantics/article Palavras-chave: Artificial neural network; Intertrochanteric fracture; Outcome prediction; One-year mortality. Ano: 2013 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2013001100993 ARTIFICIAL NEURAL NETWORK-BASED METHOD TO IDENTIFY FIVE VARIETIES OF EGYPTIAN FABA BEAN ACCORDING TO SEED MORPHOLOGICAL FEATURES REA Aboukarima,Abdulwahed; El-Marazky,Mohamed; Elsoury,Hussien; Zayed,Moamen; Minyawi,Mamdouh. ABSTRACT One of the new crop varieties that have been adopted for high yield is the Egyptian faba bean. However, poor-quality faba bean has reduced economic value. Quality evaluation is thus important and can be performed using computational intelligence. We developed a robust method based on morphological features and artificial neural network for quality grading and classification of Egyptian faba-bean seeds, covering five varieties: Giza3, Giza461, Misr1, Nobarya1, and Sakha1. Fifteen seed morphological features were then calculated, and artificial neural networks classified faba beans into different varieties. The results indicated an overall classification accuracy of 77.5% was achieved in training phase and it was 100% when testing dataset was used.... Tipo: Info:eu-repo/semantics/article Palavras-chave: Faba bean; Quality; Classification; Artificial neural network; Features. Ano: 2020 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162020000600791 Artificial neural networks (ANN): prediction of sensory measurements from instrumental data Ciênc. Tecnol. Aliment. Carvalho,Naiara Barbosa; Minim,Valéria Paula Rodrigues; Silva,Rita de Cássia dos Santos Navarro; Della Lucia,Suzana Maria; Minim,Luis Aantonio. The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The... Tipo: Info:eu-repo/semantics/article Palavras-chave: Artificial neural network; Quantitative descriptive analysis; Texture. Ano: 2013 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612013000400018 Automated grading of left ventricular segmental wall motion by an artificial neural network using color kinesis images BJMBR Murta Jr.,L.O.; Ruiz,E.E.S.; Pazin-Filho,A.; Schmidt,A.; Almeida-Filho,O.C.; Simões,M.V.; Marin-Neto,J.A.; Maciel,B.C.. The present study describes an auxiliary tool in the diagnosis of left ventricular (LV) segmental wall motion (WM) abnormalities based on color-coded echocardiographic WM images. An artificial neural network (ANN) was developed and validated for grading LV segmental WM using data from color kinesis (CK) images, a technique developed to display the timing and magnitude of global and regional WM in real time. We evaluated 21 normal subjects and 20 patients with LVWM abnormalities revealed by two-dimensional echocardiography. CK images were obtained in two sets of viewing planes. A method was developed to analyze CK images, providing quantitation of fractional area change in each of the 16 LV segments. Two experienced observers analyzed LVWM from... Tipo: Info:eu-repo/semantics/article Palavras-chave: Artificial neural network; Color kinesis images; Left ventricular function. Ano: 2006 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2006000100001 Chlorophyll a spatial inference using artificial neural network from multispectral images and in situ measurements Anais da ABC (AABC) FERREIRA,MONIQUE S.; GALO,MARIA DE LOURDES B.T.. Considering the importance of monitoring the water quality parameters, remote sensing is a practicable alternative to limnological variables detection, which interacts with electromagnetic radiation, called optically active components (OAC). Among these, the phytoplankton pigment chlorophyll a is the most representative pigment of photosynthetic activity in all classes of algae. In this sense, this work aims to develop a method of spatial inference of chlorophyll a concentration using Artificial Neural Networks (ANN). To achieve this purpose, a multispectral image and fluorometric measurements were used as input data. The multispectral image was processed and the net training and validation dataset were carefully chosen. From this, the neural net... Tipo: Info:eu-repo/semantics/article Palavras-chave: Remote sensing of water; Fluorescence; Chlorophyll a; Spatial inference; Artificial neural network. Ano: 2013 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652013000200519 Evaluation of Energy Consumption Pattern in Rice Processing Using Taguchi and Artificial Neural Network Approaches CIGR Journal SANUSI, Mayowa Saheed; Akinoso, Rahman. This study was designed to evaluate and model the impacts of processing parameters (steaming time, soaking time, paddy moisture content and soaking temperature) on the energy consumption of five rice varieties (NERICA 8, FARO 52, FARO 61, FARO 60 and FARO 44). Energy consumption in the cleaning, soaking, steaming, drying, dehusking, polishing and grading operations were estimated by fitting data on labour, fuel and electricity consumption, time and machine efficiency into standard equations to determine total energy consumption. The energy consumptions were separately modelled using Taguchi and Artificial Neural Network (ANN) techniques for each rice variety. The accuracy of models was determined using the coefficient of determination (R2) and Mean Square... Tipo: Info:eu-repo/semantics/article Palavras-chave: Artificial neural network; Energy Consumption; Modelling; Rice varieties; Taguchi. Ano: 2022 URL: http://www.cigrjournal.org/index.php/Ejounral/article/view/7235 How soaring agricultural prices will impact the way we do feed business R. Bras. Zootec. Pathumnakul,Supachai; Piewthongngam,Kullapapruk. The rising price of agricultural products leads to frequent change of feed recipe, which can cause a high number of reprocessing batches, elevating the overall cost of production. In this study, we proposed an artificial neural network to predict production rate. The conversion of production rate to production cost, the tips for data collection as well as tips for implementation of new feed cost estimation are also discussed. Being able to estimate production rate enables feed mills to improve their operations. In this study, we elaborate its application to feed scheduling (although the applications can be extended to other aspects such as productivity improvement, which goes beyond the scope of this particular study). Tipo: Info:eu-repo/semantics/article Palavras-chave: Artificial neural network; Feed cost estimation; Feed scheduling. Ano: 2010 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982010001300053 Lipase production and growth modeling of a novel thermophilic bacterium: Aneurinibacillus thermoaerophilus strain AFNA Electron. J. Biotechnol. Ebrahimpour,Afshin; Rahman,Raja Noor Zaliha Raja Abd; Kamarudin,Nor Hafizah Ahmad; Basri,Mahiran; Salleh,Abu Bakar. Aneurinibacillus thermoaerophilus strain AFNA as a novel isolated extracellular thermostable organic solvent tolerant lipase producing bacterium was employed in the present study. The lipase production of strain AFNA and its correlation with bacterial growth was studied via a modeling assessment by response surface methodology (RSM) and artificial neural network (ANN) techniques. The best achieved models were multilayer full feed forward incremental back propagation network and modified cubic response surface model (mRSM) using backward elimination. The highest lipase specific activity (13.1 Umg-1) and bacterial growth (OD600 = 3.0) were obtained at technically similar: growth temperature (53 and 53ºC), inoculum size (2.6 and 3.0%), agitation rate (118 and... Tipo: Journal article Palavras-chave: Artificial neural network; Modified response surface methodology; Thermostable; Organic solvent tolerant. Ano: 2011 URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582011000400006 MODELING OF DRAFT AND ENERGY REQUIREMENTS OF A MOLDBOARD PLOW USING ARTIFICIAL NEURAL NETWORKS BASED ON TWO NOVEL VARIABLES REA Al-Janobi,Abdulrahman; Al-Hamed,Saad; Aboukarima,Abdulwahed; Almajhadi,Yousef. ABSTRACT Draft and energy requirements are the most important factors in the activities of farm machinery management owing to their role in matching the tractor with implements for different tillage operations. This study's aim was to model the draft and energy requirements of a moldboard plow based on two novel variables. The first was the soil texture index (STI), which was formed from the clay, sand, and silt contents with a range of 0.03–0.84. The second variable was the field working index (FWI), formed by combining the plow width, plowing speed, soil bulk density, soil moisture content, plowing depth, and tractor power into one dimensionless variable, which had a range of 7.17–82.45. The coefficient of determination (R2) values obtained using a... Tipo: Info:eu-repo/semantics/article Palavras-chave: Soil texture index; Field working index; Artificial neural network; Prediction; Tillage. Ano: 2020 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162020000300363 Modeling some drying characteristics of sour cherry (Prunus cerasus L.) under infrared radiation using mathematical models and artificial neural networks CIGR Journal Amiri Chayjan, Reza; Kaveh, Mohammad; Khayati, Sasan. The effect of air temperature, air velocity and infrared (IR) radiation on the drying kinetics of sour cherry was investigated using a laboratory infrared dryer.  Experiments were conducted at air temperatures of 35, 50 and 65°C, air velocities of 0.5, 1.1 and 1.7 m/s and IR radiations of 500, 1,000 and 1,500 W.  Five empirical drying models for describing time dependence of the moisture ratio change were fitted to experimental data.  Artificial neural network (ANN) method was used to predict the effective moisture diffusivity and specific energy consumption of the samples.  Among the applied models, Midilli et al. model was the best to predict the thin layer drying behavior of sour cherry.  Effective moisture diffusivity of sour cherry varied between... Tipo: Info:eu-repo/semantics/article Palavras-chave: Sour cherry; Drying; Effective moisture diffusivity; Activation energy; Artificial neural network. Ano: 2014 URL: http://www.cigrjournal.org/index.php/Ejounral/article/view/2552 Neural network based control of an absorption column in the process of bioethanol production BABT Eyng,Eduardo; Silva,Flávio Vasconcelos da; Palú,Fernando; Fileti,Ana Maria Frattini. Gaseous ethanol may be recovered from the effluent gas mixture of the sugar cane fermentation process using a staged absorption column. In the present work, the development of a nonlinear controller, based on a neural network inverse model (ANN controller), was proposed and tested to manipulate the absorbent flow rate in order to control the residual ethanol concentration in the effluent gas phase. Simulation studies were carried out, in which a noise was applied to the ethanol concentration signals from the rigorous model. The ANN controller outperformed the dynamic matrix control (DMC) when step disturbances were imposed to the gas mixture composition. A security device, based on a conventional feedback algorithm, and a digital filter were added to the... Tipo: Info:eu-repo/semantics/article Palavras-chave: Absorption column; Artificial neural network; Feedforward control. Ano: 2009 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132009000400020 Predicting chick body mass by artificial intelligence-based models PAB Ferraz,Patricia Ferreira Ponciano; Yanagi Junior,Tadayuki; Hernández Julio,Yamid Fabián; Castro,Jaqueline de Oliveira; Gates,Richard Stephen; Reis,Gregory Murad; Campos,Alessandro Torres. The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks) - with the variables dry-bulb air temperature, duration of thermal stress (days), chick age (days), and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs) and neuro-fuzzy networks (NFNs). The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age... Tipo: Info:eu-repo/semantics/article Palavras-chave: Animal welfare; Artificial neural network; Broiler; Modeling; Neuro-fuzzy network; Thermal comfort. Ano: 2014 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2014000700559 Production of recombinant human epidermal growth factor in Pichia pastoris BJM Eissazadeh,Samira; Moeini,Hassan; Dezfouli,Maryam Ghandizadeh; Heidary,Somayyeh; Nelofer,Rubina; Abdullah,Mohd Puad. Abstract This study was carried out to express human epidermal growth factor (hEGF) in Pichia pastoris GS115. For this aim, the hEGF gene was cloned into the pPIC9K expression vector, and then integrated into P. pastoris by electroporation. ELISA-based assay showed that the amount of hEGF secreted into the medium can be affected by the fermentation conditions especially by culture medium, pH and temperature. The best medium for the optimal hEGF production was BMMY buffered at a pH range of 6.0 and 7.0. The highest amount of hEGF with an average yield of 2.27 µg/mL was obtained through an induction of the culture with 0.5% (v/v) methanol for 60 h. The artificial neural network (ANN) analysis revealed that changes in both pH and temperature significantly... Tipo: Info:eu-repo/semantics/article Palavras-chave: Pichia pastoris; Human epidermal growth factor; Artificial neural network. Ano: 2017 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1517-83822017000200286 Proposal of automated computational method to support Virginia tobacco classification AGRIAMBI Tedesco,Leonel P. C.; Freitas,Adriano da C. de; Molz,Rolf F.; Schreiber,Jacques N. C.. ABSTRACT This article proposes an automatic method for classification of cured tobacco leaves. Typically this process is performed manually, allowing the occurrence of human errors. In addition, the existence of an automated comparative procedure, helping to perform the classification, can make this process faster and more transparent. In order to implement the method, non-invasive to the agricultural product, 250 samples of Virginia tobacco digital images in the RGB and HSV color models were analyzed. The validation of the method was carried out using partial least squares (PLS) and artificial neural network (ANN), presenting a qualitative and quantitative analysis of both tools. It has been verified that the PLS can be applied to this method, as it has a... Tipo: Info:eu-repo/semantics/article Palavras-chave: Image processing; Partial least square; Artificial neural network. Ano: 2019 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662019001000782 Semi-automated counting model for arbuscular mycorrhizal fungi spores using the Circle Hough Transform and an artificial neural network Anais da ABC (AABC) MELO,CLÊNIA A.O. DE; LOPES,JULIANE G.; ANDRADE,ALEXSANDRA O.; TRINDADE,ROQUE M.P.; MAGALHÃES,ROBSON S.. Abstract: Arbuscular Mycorrhizae (AM) are mutualistic associations between Arbuscular Mycorrhizal Fungi (AMF) and the roots of many plant species. AMF spores give rise to filaments that develop in the root system of plants and contribute to the absorption of water and some nutrients. This article introduces a semi-automated counting model of AMF spores in slide images based on Artificial Neural Network (ANN). The semi-automated counting of AMF spores facilitates and accelerates the tasks of researchers, who still do the AMF spore counting manually. We built a representative database of spore images, processing images through the Circle Hough Transform (CHT) method and training an ANN to classify patterns automatically. The classification analysis and the... Tipo: Info:eu-repo/semantics/article Palavras-chave: Arbuscular mycorrhizal fungi spores; Artificial neural network; Circle Hough transform; Image preprocessing; Pattern classification; Semi-automated counting. Ano: 2019 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000700901 Synergistic control of forearm based on accelerometer data and artificial neural networks BJMBR Mijovic,B.; Popovic,M.B.; Popovic,D.B.. In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over... Tipo: Info:eu-repo/semantics/article Palavras-chave: Synergy; Accelerometers; Artificial neural network; Control; Neural prosthesis; Functional electrical stimulation. Ano: 2008 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2008000500007 Thermostable lipase from a newly isolated Staphylococcus xylosus strain; process optimization and characterization using RSM and ANN Electron. J. Biotechnol. Khoramnia,Anahita; Lai,Oi Ming; Ebrahimpour,Afshin; Tanduba,Carynn Josue; Voon,Tan Siow; Mukhlis,Suriati. Normal feed forward back-propagation artificial neural network (ANN) and cubic backward elimination response surface methodology (RSM) were used to build a predictive model of the combined effects and optimization of culture parameters for the lipase production of a newly isolated Staphylococcus xylosus. The results demonstrated a high predictive accuracy of artificial neural network compared to response surface methodology. The optimum operating condition obtained from the ANN model was found to be at 30ºC incubation temperature, pH 7.5, 60 hrs incubation period, 1.8% inoculum size and 60 rpm agitation. The lipase production increased 3.5 fold for optimal medium. The produced enzyme was characterized biochemically and this is the first report about a... Tipo: Journal article Palavras-chave: Artificial neural network; Characterization; Lipase; Optimization; Response surface methodology; Staphylococcus xylosus. Ano: 2010 URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582010000500015 Registros recuperados: 23 Primeira ... 1 2 ... Última

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