APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTING pH IN SEAWATER ALONG GAZA BEACH

Zaqoot, Hossam Adel; Baloch, A.; Ansari, Abdul Khalique; Unar, Mukhtiar Ali

doi:10.1080/08839514.2010.499499

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…Predictive computing techniques, such as artificial neural networks (ANNs), can interpret underlying trends in complex data sets by learning from case examples. There are several types of ANN, but the feed-forward multilayer type is commonly the most used across a range of scientific applications such as analytical chemistry, environmental science, and molecular biology. − By interconnecting a set of input data with a series of hidden layer neurons, statistical weights and biases between them are systematically optimized toward producing a minimized error overall output. In the training phase, the ANN requires a known true value as a comparator and once an acceptable number of training cycles (or epochs) is determined, the optimized ANN can be used to predict the same output where experimentally derived data is unavailable (i.e., a blind test).…”

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confidence: 99%

Prediction of Chromatographic Retention Time in High-Resolution Anti-Doping Screening Data Using Artificial Neural Networks

et al. 2013

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The computational generation of gradient retention time data for retrospective detection of suspected sports doping species in postanalysis human urine sample data is presented herein. Retention data for a selection of 86 compounds included in the London 2012 Olympic and Paralympic Games drug testing schedule were used to train, verify, and test a range of computational models for this purpose. Spiked urine samples were analyzed using solid phase extraction followed by ultrahigh-pressure gradient liquid chromatography coupled to electrospray ionization high-resolution mass spectrometry. Most analyte retention times varied ≤0.2 min over the relatively short runtime of 10 min. Predicted retention times were within 0.5 min of experimental values for 12 out of 15 blind test compounds (largest error: 0.97 min). Minimizing the variance in predictive ability across replicate networks of identical architecture is presented for the first time along with a quantitative discussion of the contribution of each selected molecular descriptor toward the overall predicted value. The performance of neural computing predictions for isobaric compound retention time is also discussed. This work presents the application of neural networks to the prediction of gradient retention time in archived high-resolution urine analysis sample data for the first time in the field of anti-doping.

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confidence: 99%

Prediction of Chromatographic Retention Time in High-Resolution Anti-Doping Screening Data Using Artificial Neural Networks

et al. 2013

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“…ANNs have also been found to be useful in construction (Argiriou, Bellas-Velidis, and Balaras 2000), in demand analysis in the form of forecasting (Efendigil, Önüt, and Kahraman 2009), in controlling drug delivery systems (Rafienia et al 2010), and in predicting ph in seawater along a Gaza beach (Adel Zaqoot et al 2010), among many other applications. The multilayer perceptron (MLP) is the most commonly used architecture for developing an ANN (Fausett 1994;Barreto 2002;Von Zuben 2003) and contains input, hidden, and output layers.…”

Section: Artificial Neural Networkmentioning

confidence: 98%

Models for Prediction of Physiological Responses of Holstein Dairy Cows

Hernández-Julio

Yanagi

Pires

et al. 2014

Applied Artificial Intelligence

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The goal of the present study was to evaluate techniques for modeling the physiological responses, rectal temperature, and respiratory rate of black and white Holstein dairy cows. Data from the literature (792 data points) and obtained experimentally (5884 data points) were used to fit and validate the models. Each datum included dry bulb air temperature, relative humidity, rectal temperature, and respiratory rate. Two models based on artificial intelligence-artificial neural networks and neurofuzzy networks-and one based on regression were evaluated for each response variable. The adjusted models predict rectal temperature and respiratory rate as a function of dry-bulb air temperature and relative humidity. These models were compared using statistical indices. The model based on artificial neural networks showed the best performance, followed by the models based on neurofuzzy networks and regression; the last two performed similarly.

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“…Artificial neural network (ANN) adopting non-linear regression analysis tools can be thought of as being related to artificial intelligence [11,12], machine learning [13], , statistics [14], et al It has been used in material science [15], biology [16] and medicine [17], economics and society, and so on. An ANN learns from experimental data and recognizes patterns in a series of input and output data sets without any prior assumptions about their interrelations.…”

Section: Introductionmentioning

confidence: 99%

Rapid Depressurization Burning Behavior Prediction of Base Bleed Propellant Based on BP Neutral Network

Zhang

Zhou

2011

2011 3rd International Workshop on Intelligent Systems and Applications

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Base bleed propellant is an important component of the increasing rang projectile using base bleed technology. Unsteady strongly combustion leads to extinguish, reignition or critical state which produce an effect on rang dispersion. The burning behavior is determined by the initial pressure of combustion chamber and the maximum pressure decay rate, which was investigated by simulation experimental device in this study. A BP neural network prediction model of combustion state related to rapid depressurization was constructed. This prediction model is very easy to give the burning behavior according to the two pressure changing parameters. It is convenient to predict the base bleed unit combustion state at the moment of the projectile flying out of gun muzzle using this model in engineering practice.

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APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTING pH IN SEAWATER ALONG GAZA BEACH

Cited by 6 publications

References 13 publications

Prediction of Chromatographic Retention Time in High-Resolution Anti-Doping Screening Data Using Artificial Neural Networks

Prediction of Chromatographic Retention Time in High-Resolution Anti-Doping Screening Data Using Artificial Neural Networks

Models for Prediction of Physiological Responses of Holstein Dairy Cows

Rapid Depressurization Burning Behavior Prediction of Base Bleed Propellant Based on BP Neutral Network

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