scholarly journals Application of Artificial Intelligence to the Prediction of the Antimicrobial Activity of Essential Oils

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Mathieu Daynac ◽  
Alvaro Cortes-Cabrera ◽  
Jose M. Prieto
Keyword(s):  
Neural Networks ◽  
Antimicrobial Activity ◽  
Artificial Neural Networks ◽  
Essential Oils ◽  
Maximum Error ◽  
Antimicrobial Activities ◽  
Output Data ◽  
Chemical Variability ◽  
Artificial Neural ◽  
Disk Susceptibility

Essential oils (EOs) are vastly used as natural antibiotics in Complementary and Alternative Medicine (CAM). Their intrinsic chemical variability and synergisms/antagonisms between its components make difficult to ensure consistent effects through different batches. Our aim is to evaluate the use of artificial neural networks (ANNs) for the prediction of their antimicrobial activity.Methods.The chemical composition and antimicrobial activity of 49 EOs, extracts, and/or fractions was extracted from NCCLS compliant works. The fast artificial neural networks (FANN) software was used and the output data reflected the antimicrobial activity of these EOs against four common pathogens:Staphylococcus aureus, Escherichia coli, Candida albicans, andClostridium perfringensas measured by standardised disk diffusion assays.Results.ANNs were able to predict >70% of the antimicrobial activities within a 10 mm maximum error range. Similarly, ANNs were able to predict 2 or 3 different bioactivities at the same time. The accuracy of the prediction was only limited by the inherent errors of the popular antimicrobial disk susceptibility test and the nature of the pathogens.Conclusions.ANNs can be reliable, fast, and cheap tools for the prediction of the antimicrobial activity of EOs thus improving their use in CAM.

scholarly journals Artificial Neural Networks to Predict the Power Output of a PV Panel

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Valerio Lo Brano ◽  
Giuseppina Ciulla ◽  
Mariavittoria Di Falco
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Monitoring System ◽  
Power Output ◽  
Department Of Energy ◽  
Electricity Production ◽  
Energy Output ◽  
Weather Data ◽  
Output Data ◽  
Artificial Neural

The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs) for the power energy output forecasting of photovoltaic (PV) modules. The analysis of the PV module’s power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy) has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP), a recursive neural network (RNN), and a gamma memory (GM) trained with the back propagation. In order to investigate the influence of climate variability on the electricity production, the ANNs were trained using weather data (air temperature, solar irradiance, and wind speed) along with historical power output data available for the two test modules. The model validation was performed by comparing model predictions with power output data that were not used for the network's training. The results obtained bear out the suitability of the adopted methodology for the short-term power output forecasting problem and identified the best topology.

scholarly journals Predicting Tooth Surface Loss Using Genetic Algorithms-Optimized Artificial Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ali Al Haidan ◽  
Osama Abu-Hammad ◽  
Najla Dar-Odeh
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Tooth Surface ◽  
Output Data ◽  
Surface Loss ◽  
Artificial Neural ◽  
High Degree

Our aim was to predict tooth surface loss in individuals without the need to conduct clinical examinations. Artificial neural networks (ANNs) were used to construct a mathematical model. Input data consisted of age, smoker status, type of tooth brush, brushing, and consumption of pickled food, fizzy drinks, orange, apple, lemon, and dried seeds. Output data were the sum of tooth surface loss scores for selected teeth. The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer. The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model. Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%. In conclusion, this study shows that modeling tooth surface loss using ANNs is possible and can be achieved with a high degree of accuracy.

Prediction of Closing Prices on the Stock Exchange with the Use of Artificial Neural Networks

2012 ◽  
Vol 17 (4) ◽  
pp. 275-282 ◽  
Author(s):  
Michał Paluch ◽  
Lidia Jackowska-Strumiłło
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stock Market ◽  
Stock Exchange ◽  
Maximum Error ◽  
Moving Window ◽  
Multi Layer Perceptron ◽  
Value Prediction ◽  
Window Method ◽  
Artificial Neural

Abstract Article describes, the use of Artificial Neural Networks (ANN) for predicting values of Stock Exchange shares. Rules of Stock Exchange functioning, principles of technical analysis and the most important stock market indices are described, which support investors, who plan to make transactions. ANN of Multi-Layer Perceptron (MLP) type, and a moving window method are applied. A hybrid method is also proposed, in which time series of CLOSE values as a function of the following trading days are used to stock market indices calculation, such as moving averages and oscillators, which are applied to ANN inputs. Research was conducted for 80 companies, selected from the 1218 companies functioning on Stock Exchange. The achieved maximum error in one day ahead CLOSE value prediction is 1,31%.

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