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2v. 24cm.1488

2v. 24cm.1488

2v. 24cm.1488

2v. 24cm.1488

2v. 24cm.1488

This paper presents a comparison between different expansion function for a specific structure of neural network as the functional link artificial neural network (FLANN). This technique has been employed for classification tasks of data mining. In fact, there are a few studies that used this tool for solving classification problems, and in the most case, the trigonometric expansion function is the most used. In this present research, we propose a hybrid FLANN (HFLANN) model, where the optimization process is performed using 3 known population based techniques such as genetic algorithms, particle swarm and differential evolution. This model will be empirically compared using different expansion function and the best function one will be selected. IJHMI - ASDFJournals.com

This paper presents a comparison between different expansion function for a specific structure of neural network as the functional link artificial neural network (FLANN). This technique has been employed for classification tasks of data mining. In fact, there are a few studies that used this tool for solving classification problems, and in the most case, the trigonometric expansion function is the most used. In this present research, we propose a hybrid FLANN (HFLANN) model, where the optimization process is performed using 3 known population based techniques such as genetic algorithms, particle swarm and differential evolution. This model will be empirically compared using different expansion function and the best function one will be selected. IJHMI - ASDFJournals.com

Automated techniques have been developed to automate the process of classification of objects or their analysis. The large datasets provided by upcoming spectroscopic surveys with dedicated telescopes urges scientists to use these automated techniques for analysis of such large datasets which are now available to the community. Sloan Digital Sky Survey (SDSS) is one of such surveys releasing massive datasets. We use Probabilistic Neural Network (PNN) for automatic classification of about 5000 SDSS spectra into 158 spectral type of a reference library ranging from O type to M type stars.

We describe the development of artificial neural networks (ANN) for the prediction of the properties of ceramic materials. The ceramics studied here include polycrystalline, inorganic, non-metallic materials and are investigated on the basis of their dielectric and ionic properties. Dielectric materials are of interest in telecommunication applications where they are used in tuning and filtering equipment. Ionic and mixed conductors are the subjects of a concerted effort in the search for new materials that can be incorporated into efficient, clean electrochemical devices of interest in energy production and greenhouse gas reduction applications. Multi-layer perceptron ANNs are trained using the back-propagation algorithm and utilise data obtained from the literature to learn composition-property relationships between the inputs and outputs of the system. The trained networks use compositional information to predict the relative permittivity and oxygen diffusion properties of ceramic materials. The results show that ANNs are able to produce accurate predictions of the properties of these ceramic materials which can be used to develop materials suitable for use in telecommunication and energy production applications.

Introduction In genomic selection, genetic values of individuals are predicted using genetic markers that are distributed all across the genome and are in linkage disequilibrium with quantitative trait locus. Different methods have been introduced to predict genomic breeding values. These methods take into account different assumptions. Non-parametric methods, including artificial neural networks, have fewer assumptions than parametric methods, and can apply nonlinear relationships in genomic predictions so, in theory these approaches are more robust against genetic architecture changes and are able to provide better predictions.   Materials and Methods In current study, the prediction ability of Bayesian neural networks with different architectures (1 to 5 neurons in the hidden layer) and parametric methods (GBLUP, Bayes RR, Bayes A, Bayes B, Bayes C Bayes L) in four simulated genetic architectures and four real traits of mouse (six weeks weight, growth slope, body mass index and body length) were compared using the correlation coefficient between predicted and expected values, mean square error of prediction and computation time. All simulated genetic architectures were additive and the gene effects followed a normal distribution. The number of QTLs in the first and third genetic architecture was 50 and it was 500 for second and fourth genetic architecture. The heritability of the first and second genetic architectures was 0.3 and the heritability of the third and the fourth genetic architectures was 0.7. The real data consisted of 1,296 mice which were genotyped with 9,265 SNP markers.   Results and Discussion The highest prediction accuracy of Bayesian neural networks were 0.640 (4 neuron in the hidden layer), 0.664 (4 neuron in the hidden layer), 0.800 (1 neuron in the hidden layer) and 0.810 (1 neuron in the hidden layer), and the highest prediction accuracy of parametric methods were 0.711(Bayes B), 0.685 (Bayes A), 0.903(Bayes B) and 0.836 (Bayes B) respectively for one to four simulated genetic architectures. These results showed the superiority of parametric methods to Bayesian neural networks in terms of prediction accuracy in genetic architectures with additive effects. In additive genetic architectures, the allelic effects of genetic variations are independent. In parametric models, these effects are assumed to be independent, therefore in additive genetic architectures can be expected that parametric methods are able to provide better predictions than nonparametric methods.   The maximum predictive abilities of Bayesian neural networks to predict six weeks weight, growth slope, body mass index and body length were 0.474 (1 neuron in the hidden layer), 0.349 (4 neuron in the hidden layer), 0.154 (1 neuron in the hidden layer) and 0.214 (4 neuron in the hidden layer). The predictive abilities of parametric methods to predict these traits were similar and equal to 0.477, 0.336, 0.170, and 0.221 in average. The results showed that the predictive abilities of Bayesian neural networks and parametric methods were similar on real data as the difference between the best predictive ability of Bayesian neural networks and parametric methods for Six weeks weight, growth slope and body length were less than 1%. The difference was slightly higher for the body mass index and equal to 1.8%. The mean squared error of prediction of Bayesian Neural Networks was slightly less than parametric methods in the simulated genetic architectures. The results indicate a slight superiority of Bayesian neural networks compared to parametric methods in terms of mean squared error of prediction as an indicator of overall fit. The mean square prediction error is an appropriate criterion for evaluating the prediction performance of different methods because it contains both accuracy and bias. Considering table (3) and table (5), it can be concluded that the prediction of the Bayesian neural network are less accurate but more unbiased than the parametric methods. This could be due to more applied penalty in parametric methods compared to Bayesian neural networks, which can lead to an increase in the average mean squared error of prediction. In real data, the mean squared error of prediction of the Bayesian neural networks and parametric methods were similar. The computation time of Bayesian neural networks was increased with an increase in the number of neurons in the hidden layer. The computation time of the parametric methods was the same with the exception of GBLUP. The GBLUP method took more computation time. The computation time of neural the networks with 1 to 2 neurons in the hidden layer were less than GBLUP. Genomic prediction using Bayesian Neural Networks with a greater number of neurons is really challenging, and improving their performance in terms of computational cost is necessary before applying them in genomic selection. Conclusion Although parametric methods had better predictive accuracy and predictive ability due to the additive genetic architecture of the studied traits, it can be concluded that Bayesian neural networks are powerful tools in genomic enabled prediction that can predict genomic breeding values with acceptable accuracy. The genomic prediction ability of the neural networks depends on target traits, the animal species, and neural network architecture. Before using Bayesian neural networks in genomic prediction, it is better to compare the results with parametric methods. It is also necessary to improve the computation time of the Bayesian neural networks with a greater number of neurons in hidden layer before applying them in real application of genomic selection. 

Introduction In genomic selection, genetic values of individuals are predicted using genetic markers that are distributed all across the genome and are in linkage disequilibrium with quantitative trait locus. Different methods have been introduced to predict genomic breeding values. These methods take into account different assumptions. Non-parametric methods, including artificial neural networks, have fewer assumptions than parametric methods, and can apply nonlinear relationships in genomic predictions so, in theory these approaches are more robust against genetic architecture changes and are able to provide better predictions.   Materials and Methods In current study, the prediction ability of Bayesian neural networks with different architectures (1 to 5 neurons in the hidden layer) and parametric methods (GBLUP, Bayes RR, Bayes A, Bayes B, Bayes C Bayes L) in four simulated genetic architectures and four real traits of mouse (six weeks weight, growth slope, body mass index and body length) were compared using the correlation coefficient between predicted and expected values, mean square error of prediction and computation time. All simulated genetic architectures were additive and the gene effects followed a normal distribution. The number of QTLs in the first and third genetic architecture was 50 and it was 500 for second and fourth genetic architecture. The heritability of the first and second genetic architectures was 0.3 and the heritability of the third and the fourth genetic architectures was 0.7. The real data consisted of 1,296 mice which were genotyped with 9,265 SNP markers.   Results and Discussion The highest prediction accuracy of Bayesian neural networks were 0.640 (4 neuron in the hidden layer), 0.664 (4 neuron in the hidden layer), 0.800 (1 neuron in the hidden layer) and 0.810 (1 neuron in the hidden layer), and the highest prediction accuracy of parametric methods were 0.711(Bayes B), 0.685 (Bayes A), 0.903(Bayes B) and 0.836 (Bayes B) respectively for one to four simulated genetic architectures. These results showed the superiority of parametric methods to Bayesian neural networks in terms of prediction accuracy in genetic architectures with additive effects. In additive genetic architectures, the allelic effects of genetic variations are independent. In parametric models, these effects are assumed to be independent, therefore in additive genetic architectures can be expected that parametric methods are able to provide better predictions than nonparametric methods.   The maximum predictive abilities of Bayesian neural networks to predict six weeks weight, growth slope, body mass index and body length were 0.474 (1 neuron in the hidden layer), 0.349 (4 neuron in the hidden layer), 0.154 (1 neuron in the hidden layer) and 0.214 (4 neuron in the hidden layer). The predictive abilities of parametric methods to predict these traits were similar and equal to 0.477, 0.336, 0.170, and 0.221 in average. The results showed that the predictive abilities of Bayesian neural networks and parametric methods were similar on real data as the difference between the best predictive ability of Bayesian neural networks and parametric methods for Six weeks weight, growth slope and body length were less than 1%. The difference was slightly higher for the body mass index and equal to 1.8%. The mean squared error of prediction of Bayesian Neural Networks was slightly less than parametric methods in the simulated genetic architectures. The results indicate a slight superiority of Bayesian neural networks compared to parametric methods in terms of mean squared error of prediction as an indicator of overall fit. The mean square prediction error is an appropriate criterion for evaluating the prediction performance of different methods because it contains both accuracy and bias. Considering table (3) and table (5), it can be concluded that the prediction of the Bayesian neural network are less accurate but more unbiased than the parametric methods. This could be due to more applied penalty in parametric methods compared to Bayesian neural networks, which can lead to an increase in the average mean squared error of prediction. In real data, the mean squared error of prediction of the Bayesian neural networks and parametric methods were similar. The computation time of Bayesian neural networks was increased with an increase in the number of neurons in the hidden layer. The computation time of the parametric methods was the same with the exception of GBLUP. The GBLUP method took more computation time. The computation time of neural the networks with 1 to 2 neurons in the hidden layer were less than GBLUP. Genomic prediction using Bayesian Neural Networks with a greater number of neurons is really challenging, and improving their performance in terms of computational cost is necessary before applying them in genomic selection. Conclusion Although parametric methods had better predictive accuracy and predictive ability due to the additive genetic architecture of the studied traits, it can be concluded that Bayesian neural networks are powerful tools in genomic enabled prediction that can predict genomic breeding values with acceptable accuracy. The genomic prediction ability of the neural networks depends on target traits, the animal species, and neural network architecture. Before using Bayesian neural networks in genomic prediction, it is better to compare the results with parametric methods. It is also necessary to improve the computation time of the Bayesian neural networks with a greater number of neurons in hidden layer before applying them in real application of genomic selection. 

Introduction In genomic selection, genetic values of individuals are predicted using genetic markers that are distributed all across the genome and are in linkage disequilibrium with quantitative trait locus. Different methods have been introduced to predict genomic breeding values. These methods take into account different assumptions. Non-parametric methods, including artificial neural networks, have fewer assumptions than parametric methods, and can apply nonlinear relationships in genomic predictions so, in theory these approaches are more robust against genetic architecture changes and are able to provide better predictions.   Materials and Methods In current study, the prediction ability of Bayesian neural networks with different architectures (1 to 5 neurons in the hidden layer) and parametric methods (GBLUP, Bayes RR, Bayes A, Bayes B, Bayes C Bayes L) in four simulated genetic architectures and four real traits of mouse (six weeks weight, growth slope, body mass index and body length) were compared using the correlation coefficient between predicted and expected values, mean square error of prediction and computation time. All simulated genetic architectures were additive and the gene effects followed a normal distribution. The number of QTLs in the first and third genetic architecture was 50 and it was 500 for second and fourth genetic architecture. The heritability of the first and second genetic architectures was 0.3 and the heritability of the third and the fourth genetic architectures was 0.7. The real data consisted of 1,296 mice which were genotyped with 9,265 SNP markers.   Results and Discussion The highest prediction accuracy of Bayesian neural networks were 0.640 (4 neuron in the hidden layer), 0.664 (4 neuron in the hidden layer), 0.800 (1 neuron in the hidden layer) and 0.810 (1 neuron in the hidden layer), and the highest prediction accuracy of parametric methods were 0.711(Bayes B), 0.685 (Bayes A), 0.903(Bayes B) and 0.836 (Bayes B) respectively for one to four simulated genetic architectures. These results showed the superiority of parametric methods to Bayesian neural networks in terms of prediction accuracy in genetic architectures with additive effects. In additive genetic architectures, the allelic effects of genetic variations are independent. In parametric models, these effects are assumed to be independent, therefore in additive genetic architectures can be expected that parametric methods are able to provide better predictions than nonparametric methods.   The maximum predictive abilities of Bayesian neural networks to predict six weeks weight, growth slope, body mass index and body length were 0.474 (1 neuron in the hidden layer), 0.349 (4 neuron in the hidden layer), 0.154 (1 neuron in the hidden layer) and 0.214 (4 neuron in the hidden layer). The predictive abilities of parametric methods to predict these traits were similar and equal to 0.477, 0.336, 0.170, and 0.221 in average. The results showed that the predictive abilities of Bayesian neural networks and parametric methods were similar on real data as the difference between the best predictive ability of Bayesian neural networks and parametric methods for Six weeks weight, growth slope and body length were less than 1%. The difference was slightly higher for the body mass index and equal to 1.8%. The mean squared error of prediction of Bayesian Neural Networks was slightly less than parametric methods in the simulated genetic architectures. The results indicate a slight superiority of Bayesian neural networks compared to parametric methods in terms of mean squared error of prediction as an indicator of overall fit. The mean square prediction error is an appropriate criterion for evaluating the prediction performance of different methods because it contains both accuracy and bias. Considering table (3) and table (5), it can be concluded that the prediction of the Bayesian neural network are less accurate but more unbiased than the parametric methods. This could be due to more applied penalty in parametric methods compared to Bayesian neural networks, which can lead to an increase in the average mean squared error of prediction. In real data, the mean squared error of prediction of the Bayesian neural networks and parametric methods were similar. The computation time of Bayesian neural networks was increased with an increase in the number of neurons in the hidden layer. The computation time of the parametric methods was the same with the exception of GBLUP. The GBLUP method took more computation time. The computation time of neural the networks with 1 to 2 neurons in the hidden layer were less than GBLUP. Genomic prediction using Bayesian Neural Networks with a greater number of neurons is really challenging, and improving their performance in terms of computational cost is necessary before applying them in genomic selection. Conclusion Although parametric methods had better predictive accuracy and predictive ability due to the additive genetic architecture of the studied traits, it can be concluded that Bayesian neural networks are powerful tools in genomic enabled prediction that can predict genomic breeding values with acceptable accuracy. The genomic prediction ability of the neural networks depends on target traits, the animal species, and neural network architecture. Before using Bayesian neural networks in genomic prediction, it is better to compare the results with parametric methods. It is also necessary to improve the computation time of the Bayesian neural networks with a greater number of neurons in hidden layer before applying them in real application of genomic selection. 

Deep Learning A-Z™ Hands-On Artificial Neural Networks(20. AutoEncoders Intuition)

Deep Learning A-Z™ Hands-On Artificial Neural Networks(20. AutoEncoders Intuition)

Deep Learning A-Z™ Hands-On Artificial Neural Networks(23. Regression & Classification Intuition)

In this paper we present the general problem of identifying if a nonlinear dynamic system has a chaotic behavior. If the answer is positive the system will be sensitive to small perturbations in the initial conditions which will imply that there is a chaotic attractor in its state space. A particular problem would be that of identifying a chaotic oscillator. We present an example of three well known different chaotic oscillators where we have knowledge of the equations that govern the dynamical systems and from there we can obtain the corresponding time series. In a similar example we assume that we only know the time series and, finally, in another example we have to take measurements in the Chua's circuit to obtain sample points of the time series. With the knowledge about the time series the phase plane portraits are plotted and from them, by visual inspection, it is concluded whether or not the system is chaotic. This method has the problem of uncertainty and subjectivity and for that reason a different approach is needed. A quantitative approach is the computation of the Lyapunov exponents. We describe several methods for obtaining them and apply a little known method of artificial neural networks to the different examples mentioned above. We end the paper discussing the importance of the Lyapunov exponents in the interpretation of the dynamic behavior of biological neurons and biological neural networks.

We have tested and deployed Artificial Neural Network (ANN) data mining techniques to analyze remotely sensed multi-channel imaging data from MODIS, GOES, and AVHRR. The goal is to train the ANN to learn the signatures of wildfires in remotely sensed data in order to automate the detection process. We train the ANN using the set of human-detected wildfires in the U.S., which are provided by the Hazard Mapping System (HMS) wildfire detection group at NOAA/NESDIS. The ANN is trained to mimic the behavior of fire detection algorithms and the subjective decision- making by N O M HMS Fire Analysts. We use a local extremum search in order to isolate fire pixels, and then we extract a 7x7 pixel array around that location in 3 spectral channels. The corresponding 147 pixel values are used to populate a 147-dimensional input vector that is fed into the ANN. The ANN accuracy is tested and overfitting is avoided by using a subset of the training data that is set aside as a test data set. We have achieved an automated fire detection accuracy of 80-92%, depending on a variety of ANN parameters and for different instrument channels among the 3 satellites. We believe that this system can be deployed worldwide or for any region to detect wildfires automatically in satellite imagery of those regions. These detections can ultimately be used to provide thermal inputs to climate models.

This paper reviews some of the recent applications of artificial neural networks taken from various works performed by the authors over the last four years at the NASA Glenn Research Center. This paper focuses mainly on two areas. First, artificial neural networks application in design and optimization of aircraft/engine propulsion systems to shorten the overall design cycle. Out of that specific application, a generic design tool was developed, which can be used for most design optimization process. Second, artificial neural networks application in monitoring the microgravity quality onboard the International Space Station, using on-board accelerometers for data acquisition. These two different applications are reviewed in this paper to show the broad applicability of artificial intelligence in various disciplines. The intent of this paper is not to give in-depth details of these two applications, but to show the need to combine different artificial intelligence techniques or algorithms in order to design an optimized or versatile system.

This paper reviews some of the recent applications of artificial neural networks taken from various works performed by the authors over the last four years at the NASA Glenn Research Center. This paper focuses mainly on two areas. First, artificial neural networks application in design and optimization of aircraft/engine propulsion systems to shorten the overall design cycle. Out of that specific application, a generic design tool was developed, which can be used for most design optimization process. Second, artificial neural networks application in monitoring the microgravity quality onboard the International Space Station, using on-board accelerometers for data acquisition. These two different applications are reviewed in this paper to show the broad applicability of artificial intelligence in various disciplines. The intent of this paper is not to give in-depth details of these two applications, but to show the need to combine different artificial intelligence techniques or algorithms in order to design an optimized or versatile system.

This paper reviews some of the recent applications of artificial neural networks taken from various works performed by the authors over the last four years at the NASA Glenn Research Center. This paper focuses mainly on two areas. First, artificial neural networks application in design and optimization of aircraft/engine propulsion systems to shorten the overall design cycle. Out of that specific application, a generic design tool was developed, which can be used for most design optimization process. Second, artificial neural networks application in monitoring the microgravity quality onboard the International Space Station, using on-board accelerometers for data acquisition. These two different applications are reviewed in this paper to show the broad applicability of artificial intelligence in various disciplines. The intent of this paper is not to give in-depth details of these two applications, but to show the need to combine different artificial intelligence techniques or algorithms in order to design an optimized or versatile system.

This paper reviews some of the recent applications of artificial neural networks taken from various works performed by the authors over the last four years at the NASA Glenn Research Center. This paper focuses mainly on two areas. First, artificial neural networks application in design and optimization of aircraft/engine propulsion systems to shorten the overall design cycle. Out of that specific application, a generic design tool was developed, which can be used for most design optimization process. Second, artificial neural networks application in monitoring the microgravity quality onboard the International Space Station, using on-board accelerometers for data acquisition. These two different applications are reviewed in this paper to show the broad applicability of artificial intelligence in various disciplines. The intent of this paper is not to give in-depth details of these two applications, but to show the need to combine different artificial intelligence techniques or algorithms in order to design an optimized or versatile system.

This paper reviews some of the recent applications of artificial neural networks taken from various works performed by the authors over the last four years at the NASA Glenn Research Center. This paper focuses mainly on two areas. First, artificial neural networks application in design and optimization of aircraft/engine propulsion systems to shorten the overall design cycle. Out of that specific application, a generic design tool was developed, which can be used for most design optimization process. Second, artificial neural networks application in monitoring the microgravity quality onboard the International Space Station, using on-board accelerometers for data acquisition. These two different applications are reviewed in this paper to show the broad applicability of artificial intelligence in various disciplines. The intent of this paper is not to give in-depth details of these two applications, but to show the need to combine different artificial intelligence techniques or algorithms in order to design an optimized or versatile system.

The models of the computing for the perform the pattern recognition methods by the performance and the structure of the biological neural network. A network consists of computing units which can display the features of the biological network. In this paper, the features of the neural network that motivate the study of the neural computing are discussed and the differences in processing by the brain and a computer presented, historical development of neural network principle, artificial neural network (ANN) terminology, neuron models and topology are discussed. Rajesh CVS | M. Padmanabham"Basics and Features of Artificial Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9578.pdf  http://www.ijtsrd.com/engineering/mechanical-engineering/9578/basics-and-features-of-artificial-neural-networks/rajesh-cvs

The models of the computing for the perform the pattern recognition methods by the performance and the structure of the biological neural network. A network consists of computing units which can display the features of the biological network. In this paper, the features of the neural network that motivate the study of the neural computing are discussed and the differences in processing by the brain and a computer presented, historical development of neural network principle, artificial neural network (ANN) terminology, neuron models and topology are discussed. Rajesh CVS | M. Padmanabham"Basics and Features of Artificial Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9578.pdf Article URL: http://www.ijtsrd.com/engineering/mechanical-engineering/9578/basics-and-features-of-artificial-neural-networks/rajesh-cvs

Introduction One of the most important sources of the sugar production is sugarcane.Sugar is one of the eight human food sources (wheat, rice, corn, sugar, cattle, sorghum, millet and cassava). Also sugarcane is mainly used for livestock feed, electricity generation, fiber and fertilizer and in many countries sugarcane is a renewable source for the biofuel. The efficient use of inputs in agriculture lead to the sustainable production and help to reduce the fossil fuel consumption and greenhouse gases emission and save financial resources. Furthermore, detecting relationship between the energy consumption and the yield is necessary to approach the sustainable agriculture. It is generally accepted that many countries try to reduce their dependence to agricultural crop productions of other countries. The being Independent on agricultural productions lead to take more attention to modern methods and the objective of all these methods is increasing the performance with the efficient use of inputs or optimizing energy consumptions in agricultural systems. Energy modeling is a modern method for farm management that this model can predict yield with using the different amount of inputs. The objective of this study was to predict sugarcane production yield and (greenhouse gas) GHG emissions on the basis of energy inputs. Materials and Methods This study was carried out in Khouzestan province of Iran. Data were collected from 55 plant farms in Debel khazai Agro-Industry using face to face questionnaire method. In this study, the energy used in the sugarcane production has considered for the energy analysis without taking into account the environmental sources of the energy such as radiation, wind, rain, etc. Energy consumption in sugarcane production was calculated based on direct and indirect energy sources including human, diesel fuel, chemical fertilizers, pesticides, machinery, irrigation water, electricity and sugarcane stalk. Energy values were calculated by multiplying inputs and outputs per hectare by their coefficients of energy equivalents. Input energy in agricultural systems includes both direct and indirect energy and renewable and non-renewable forms. Direct energies include human labor, diesel fuel, water for irrigation and electricity and indirect energies consisted of machinery, seed (cultivation of sugarcane has been done with cutting of sugarcane instead of seed), chemical fertilizer. Renewable energies include machinery, sugarcane stalk, chemical fertilizer while non-renewable energy consisted of machinery, chemical fertilizer, electricity and diesel fuel. Energy values were calculated by multiplying inputs and outputs per hectare by their coefficients of energy equivalents. The amounts of GHG emissions from inputs in sugarcane production per hectare were calculated by CO 2 emissions coefficient of agricultural inputs. Energy modeling is an attractive subject for engineers and scientists who are concerned about the energy management. In the energy area, many different of models have been applied for modeling future energy. An artificial neural network (ANN) is an artificial intelligence that it can applied as a predictive tool for nonlinear multi parametric. Artificial neural network has been applied successfully in structural engineering modeling ANNs are inspired by biological neural networks. Results and Discussion The total energy used in the farm operations during the sugarcane production and the energy output was 1742883.769 and 111000 MJha_1, respectively. Electricity (52%) and chemical fertilizers (16%) were the most influential factors in the energy consumption. The electricity contribution was the highest due to the low efficiency of energy conversion in electric motors which were used for irrigation in the study area. In some areas, inefficient surface irrigation wastes a lot of water and energy (in forms of electricity). Another reason is that electricity energy equivalent for Iranian electricity production is higher than developed countries because Iran’s electricity grid is highly dependent on fossil fuels, so that 95% of the electrical energy in Iran is generated in thermal power plants using fossil fuels sources. In addition, the electricity transmission system is too old. GHG emissions data analysis indicated that the total GHG emissions was 415337.62 kg ha -1 (CO 2 eq) kgCO 2 eq ha -1 in which burning trash with the share of 62% had the highest GHG emission and followed by electricity (32%), respectively. The ANN model with 7-5-15-1 and 5-5-1 structure were the best model for predicting the sugarcane yield and GHG emissions, respectively. The coefficients of determination (R 2 ) of the best topology were 0.98 and 0.99 for the sugarcane yield and GHG emissions, respectively. The values of RMSE for sugarcane production and GHG emission were found to be 0.0037 and 4.52×10-6, respectively. Conclusion The statistical parameters of R2 and RMSE demonstrated that the proposed artificial neural networks results have best accuracy and can predict the yield and GHG emission. It is generally showed that artificial neural networks have good potential to predict the yield of the sugarcane production.

Introduction One of the most important sources of the sugar production is sugarcane.Sugar is one of the eight human food sources (wheat, rice, corn, sugar, cattle, sorghum, millet and cassava). Also sugarcane is mainly used for livestock feed, electricity generation, fiber and fertilizer and in many countries sugarcane is a renewable source for the biofuel. The efficient use of inputs in agriculture lead to the sustainable production and help to reduce the fossil fuel consumption and greenhouse gases emission and save financial resources. Furthermore, detecting relationship between the energy consumption and the yield is necessary to approach the sustainable agriculture. It is generally accepted that many countries try to reduce their dependence to agricultural crop productions of other countries. The being Independent on agricultural productions lead to take more attention to modern methods and the objective of all these methods is increasing the performance with the efficient use of inputs or optimizing energy consumptions in agricultural systems. Energy modeling is a modern method for farm management that this model can predict yield with using the different amount of inputs. The objective of this study was to predict sugarcane production yield and (greenhouse gas) GHG emissions on the basis of energy inputs. Materials and Methods This study was carried out in Khouzestan province of Iran. Data were collected from 55 plant farms in Debel khazai Agro-Industry using face to face questionnaire method. In this study, the energy used in the sugarcane production has considered for the energy analysis without taking into account the environmental sources of the energy such as radiation, wind, rain, etc. Energy consumption in sugarcane production was calculated based on direct and indirect energy sources including human, diesel fuel, chemical fertilizers, pesticides, machinery, irrigation water, electricity and sugarcane stalk. Energy values were calculated by multiplying inputs and outputs per hectare by their coefficients of energy equivalents. Input energy in agricultural systems includes both direct and indirect energy and renewable and non-renewable forms. Direct energies include human labor, diesel fuel, water for irrigation and electricity and indirect energies consisted of machinery, seed (cultivation of sugarcane has been done with cutting of sugarcane instead of seed), chemical fertilizer. Renewable energies include machinery, sugarcane stalk, chemical fertilizer while non-renewable energy consisted of machinery, chemical fertilizer, electricity and diesel fuel. Energy values were calculated by multiplying inputs and outputs per hectare by their coefficients of energy equivalents. The amounts of GHG emissions from inputs in sugarcane production per hectare were calculated by CO 2 emissions coefficient of agricultural inputs. Energy modeling is an attractive subject for engineers and scientists who are concerned about the energy management. In the energy area, many different of models have been applied for modeling future energy. An artificial neural network (ANN) is an artificial intelligence that it can applied as a predictive tool for nonlinear multi parametric. Artificial neural network has been applied successfully in structural engineering modeling ANNs are inspired by biological neural networks. Results and Discussion The total energy used in the farm operations during the sugarcane production and the energy output was 1742883.769 and 111000 MJha_1, respectively. Electricity (52%) and chemical fertilizers (16%) were the most influential factors in the energy consumption. The electricity contribution was the highest due to the low efficiency of energy conversion in electric motors which were used for irrigation in the study area. In some areas, inefficient surface irrigation wastes a lot of water and energy (in forms of electricity). Another reason is that electricity energy equivalent for Iranian electricity production is higher than developed countries because Iran’s electricity grid is highly dependent on fossil fuels, so that 95% of the electrical energy in Iran is generated in thermal power plants using fossil fuels sources. In addition, the electricity transmission system is too old. GHG emissions data analysis indicated that the total GHG emissions was 415337.62 kg ha -1 (CO 2 eq) kgCO 2 eq ha -1 in which burning trash with the share of 62% had the highest GHG emission and followed by electricity (32%), respectively. The ANN model with 7-5-15-1 and 5-5-1 structure were the best model for predicting the sugarcane yield and GHG emissions, respectively. The coefficients of determination (R 2 ) of the best topology were 0.98 and 0.99 for the sugarcane yield and GHG emissions, respectively. The values of RMSE for sugarcane production and GHG emission were found to be 0.0037 and 4.52×10-6, respectively. Conclusion The statistical parameters of R2 and RMSE demonstrated that the proposed artificial neural networks results have best accuracy and can predict the yield and GHG emission. It is generally showed that artificial neural networks have good potential to predict the yield of the sugarcane production.

The challenge posed by the many-body problem in quantum physics originates from the difficulty of describing the non-trivial correlations encoded in the exponential complexity of the many-body wave function. Here we demonstrate that systematic machine learning of the wave function can reduce this complexity to a tractable computational form, for some notable cases of physical interest. We introduce a variational representation of quantum states based on artificial neural networks with variable number of hidden neurons. A reinforcement-learning scheme is then demonstrated, capable of either finding the ground-state or describing the unitary time evolution of complex interacting quantum systems. We show that this approach achieves very high accuracy in the description of equilibrium and dynamical properties of prototypical interacting spins models in both one and two dimensions, thus offering a new powerful tool to solve the quantum many-body problem.

Artificial neural networks are algorithms which have been developed to tackle a range of computational problems. These range from modelling brain function to making predictions of time-dependent phenomena to solving hard (NP-complete) problems. In this introduction we describe a single, yet very important, type of network known as a feedforward network. This network is a mathematical model which can be trained to learn an arbitrarily complex relationship between a data and a parameter domain, so can be used to solve interpolation and classification problems. We discuss the structure, training and interpretation of these networks, and their implementation, taking the classification of stellar spectra as an example.

This paper presents a new model based on the backpropagation multilayered perception network to find accurately the bandwidth of both electrically thin and thick rectangular microstrip antennas. This proposed neural model does not require complicated Green's function methods and integral transformation techniques. The method can be used for a wide range of substrate thickness and permittivities and is useful for the computer-aided design of microstrip antennas. The results obtained by using this new method are in conformity with those reported elsewhere. This method may find wide applications in high-frequency printed antennas, especially at the millimeter-wave frequency range. Gauri Shankar, Manish Kumar and Bijendra Mohan 2020. Study of resonant microstrip antennas on artificial neural networks. International Journal on Integrated Education. 3, 9 (Sep. 2020), 218-223. DOI:https://doi.org/10.31149/ijie.v3i9.623. Pdf Url : https://journals.researchparks.org/index.php/IJIE/article/view/623/595 Paper Url : https://journals.researchparks.org/index.php/IJIE/article/view/623

Introduction: The diversity and abundance in quality properties of agricultural products are leading factors to develop non-destructive methods. Machine vision and artificial intelligence are powerful techniques in detection of many physical, mechanical and chemical properties of agricultural products. Prior to exporting, fruits are sorted in terms of their shapes, volumes or weights. Non-destructively taste-based sorting can be of importance in terms of markability and application. Artificial Neural Network (ANN) has been introduced as a new method to predict quality parameters such as firmness, total sugar content (TSC) and pH of agricultural products and to grade the products accordingly.   Material and Methods: In this research, the quality properties of Mango (Kelke- Sorkh var) were predicted using the combination of image processing and artificial intellect techniques. The mango samples were harvested from the orchard in Minab, Hormozgan province in Iran. The samples were transferred to computer vision lab, room temperature of 24 ° C and 22% RH. The samples were divided into three groups for temperature treatment. They were kept at three temperature levels of 5 ° C, 15 ° C and 24 ° C (control group) for 48 hours. The sample were then placed in room temperature and were imaged every second day for 14 day period. After imaging, each sample was undergon destructive tests for determining their quality attributes including sugar content, firmness and pH. The images were taken by a digital camera in visible spectrum (Nickon Coolpix p510, Nikon Inc, Japan). The taken images were, then, transferred to Matlab software environment (Mathworks Inc, US) for analysis and processing. The color factors from regions of intrest were extracted from the images in L*a*b* color space. The segmentation of images was performed by thresholding (threshhold value of 0.3) the image of difference between red and blue channels of taken RGB images. The conversion of RGB color space to L*a*b* was done by converting RGB image to XYZ basic color space first and before converting X, Y, and Z basic color components to L*, a*, b* color factors. The L* represent the lightness in the image from black (0) to white (100). In this project, a multilayer perceptron neural network with a hidden layer was used. The optimum number of neurons in the hidden layer was found to be 25. The maximum iterations was set as 1000 and the learning rate was set as 0.001.   Results and discussions: The input variables to the network were temperature treatment at three levels (control, 5°C and 15°C), the color factors (L*, a* and b*) and the variations of three color factors across the regions of interest (standard deviations of L*, a* and b*). The output variables were sugar content, pH and texture firmness. The results showed that the accuracy of the ANN model on the prediction of pH, sugar content and firmness were 45%, 85 and 88%, respectively. Although the accuracy of ANN model for predicting pH from color factors was rather low, this model was able to predict firmness and sugar content with highly accurately. The histogram of errors among three ANN models also showed the ANN model for predicting firmness and sugar content performed better than that for predicting pH. The MAPE prediction error were 9.53, 22.74 and 6.14, respectively, for predicting firmness, pH and sugar content. Comparing the results from the network in training and testing stages showed that ANN can be considered as a reliable method for estimating quality factors of firmness and sugar content with high accuracy and estimating pH with rather non-applicable accuracy .

The use of Adaptive Optics in Extremely Large Telescopes brings new challenges, one of which is the treatment of Shack-Hartmann Wavefront sensors images. When using this type of sensors in conjunction with laser guide stars for sampling the pupil of telescopes with 30+ m in diameter, it is necessary to compute the centroid of elongated spots, whose elongation angle and aspect ratio are changing across the telescope pupil. Existing techniques such as Matched Filter have been considered as the best technique to compute the centroid of elongated spots, however they are not good at coping with the effect of a variation in the Sodium profile. In this work we propose a new technique using artificial neural networks, which take advantage of the neural network's ability to cope with changing conditions, outperforming existing techniques in this context. We have developed comprehensive simulations to explore this technique and compare it with existing algorithms.

The use of Adaptive Optics in Extremely Large Telescopes brings new challenges, one of which is the treatment of Shack-Hartmann Wavefront sensors images. When using this type of sensors in conjunction with laser guide stars for sampling the pupil of telescopes with 30+ m in diameter, it is necessary to compute the centroid of elongated spots, whose elongation angle and aspect ratio are changing across the telescope pupil. Existing techniques such as Matched Filter have been considered as the best technique to compute the centroid of elongated spots, however they are not good at coping with the effect of a variation in the Sodium profile. In this work we propose a new technique using artificial neural networks, which take advantage of the neural network's ability to cope with changing conditions, outperforming existing techniques in this context. We have developed comprehensive simulations to explore this technique and compare it with existing algorithms.

Artificial neural networks were used successfully to sequence operations in a small, recently modernized, supersonic wind tunnel at NASA-Lewis Research Center. The neural nets generated correct estimates of shadowgraph patterns, pressure sensor readings and mach numbers for conditions occurring shortly after startup and extending to fully developed flow. Artificial neural networks were trained and tested for estimating: sensor readings from shadowgraph patterns, shadowgraph patterns from shadowgraph patterns and sensor readings from sensor readings. The 3.81 by 10 in. (0.0968 by 0.254 m) tunnel was operated with its mach 2.0 nozzle, and shadowgraph was recorded near the nozzle exit. These results support the thesis that artificial neural networks can be combined with current workstation technology to automate wind tunnel operations.

Artificial neural networks were used successfully to sequence operations in a small, recently modernized, supersonic wind tunnel at NASA-Lewis Research Center. The neural nets generated correct estimates of shadowgraph patterns, pressure sensor readings and mach numbers for conditions occurring shortly after startup and extending to fully developed flow. Artificial neural networks were trained and tested for estimating: sensor readings from shadowgraph patterns, shadowgraph patterns from shadowgraph patterns and sensor readings from sensor readings. The 3.81 by 10 in. (0.0968 by 0.254 m) tunnel was operated with its mach 2.0 nozzle, and shadowgraph was recorded near the nozzle exit. These results support the thesis that artificial neural networks can be combined with current workstation technology to automate wind tunnel operations.

This article is from Nanoscale Research Letters , volume 7 . Abstract The use of electrostatic force microscopy (EFM) to characterize and manipulate surfaces at the nanoscale usually faces the problem of dealing with systems where several parameters are not known. Artificial neural networks (ANNs) have demonstrated to be a very useful tool to tackle this type of problems. Here, we show that the use of ANNs allows us to quantitatively estimate magnitudes such as the dielectric constant of thin films. To improve thin film dielectric constant estimations in EFM, we first increase the accuracy of numerical simulations by replacing the standard minimization technique by a method based on ANN learning algorithms. Second, we use the improved numerical results to build a complete training set for a new ANN. The results obtained by the ANN suggest that accurate values for the thin film dielectric constant can only be estimated if the thin film thickness and sample dielectric constant are known.PACS: 07.79.Lh; 07.05.Mh; 61.46.Fg.

This article is from Nanoscale Research Letters , volume 7 . Abstract The use of electrostatic force microscopy (EFM) to characterize and manipulate surfaces at the nanoscale usually faces the problem of dealing with systems where several parameters are not known. Artificial neural networks (ANNs) have demonstrated to be a very useful tool to tackle this type of problems. Here, we show that the use of ANNs allows us to quantitatively estimate magnitudes such as the dielectric constant of thin films. To improve thin film dielectric constant estimations in EFM, we first increase the accuracy of numerical simulations by replacing the standard minimization technique by a method based on ANN learning algorithms. Second, we use the improved numerical results to build a complete training set for a new ANN. The results obtained by the ANN suggest that accurate values for the thin film dielectric constant can only be estimated if the thin film thickness and sample dielectric constant are known.PACS: 07.79.Lh; 07.05.Mh; 61.46.Fg.

This study aims to demonstrate the optimal way to determine the cut-off score to be used to interpret the total scores obtained from an achievement test or scale using the Artificial Neural Networks method. To this end, the multiple-choice item responses in the Booklet-11 Mathematics subtest at the 8th grade level in the TIMSS 2015 Turkey sample dataset were used to determine the cut-off score for the achievement test. The item responses in the “Students Like Learning Mathematics Scale” in the TIMSS 2015 8th grade Mathematics Student Questionnaire were used to determine the cut-off score for the scale. The data were accessed from the TIMSS international database and the data were analyzed in MATLAB R2017b software. As a result of the study, the most appropriate cut-off score to be used for the evaluation of the total scores obtained from the TIMSS 2015 8th grade level Booklet-11 Mathematics subtest was determined as 45.5 out of 0-100 points with the Artificial Neural Network analysis method. The overall level of agreement between the cut-off score and the pass/fail classification based on 400 points, which is the lowest level of the TIMSS International Benchmark, was determined as 81%. The most appropriate cut-off score to be used for the evaluation of the scores obtained from the Students Like Learning Mathematics Scale (SLLSS) in the TIMSS 2015 8th grade student survey was determined as 19.6 out of 9-36 points. The overall level of agreement between the cut-off score and the classification of students who like/don’t like learning mathematics using the criterion based on the expression given in the original scale description was found to be 83%. The results concluded that the validity of the standard-setting studies conducted with the artificial neural network method was high. As a result, researchers are recommended to use the Artificial Neural Networks method to determine the cut-off score to be used in the interpretation of the total scores obtained from the achievement test or the total scale scores obtained from the scales.

Artificial Neural Networks are easy to build and take good care of large amounts of noisy data. They are especially suitable for the solution of nonlinear problems. They work well for problems where domain experts arent available or there are no known rules. Artificial Neural Networks can also be adapted to civil engineering structures and suffer from dynamic effects. Structures around the world were badly damaged by the earthquake. Thus, loss of life and property was experienced. This particularly affected countries on active fault lines. Pre and post earthquake precautions have been developed in the world. For these reasons, it is necessary to determine the dynamic performance of structures in the world. There are several methods to determine dynamic performance. System identification is one of these methods. The mathematical model of the structural system is obtained by system identification method. Artificial Neural Networks ANN is a system identification method. ANN can adapt to their environment, work with incomplete information, make decisions under uncertainties and tolerate errors. Steel Model Bridge was used in this study. The system identification of the steel model bridge with the ANN method of 0.90 was made successfully. As a result of this study, ANN approach can provide a very useful and accurate tool to solve the problem in modal identification studies. Hakan Aydin ""Obtaining Modal Parameters in Steel Model Bridge by System Identification using Artificial Neural Networks"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30013.pdf Paper Url : https://www.ijtsrd.com/engineering/civil-engineering/30013/obtaining-modal-parameters-in-steel-model-bridge-by-system-identification-using-artificial-neural-networks/hakan-aydin

The aim of Artificial Intelligence is to develop the machines to perform the tasks in a better way than the humans. Another aim of Artificial Intelligence is to understand the actions whether it occurs in humans, machines or animals. As a result, Artificial Intelligence is gaining Importance in science and engineering fields. The use of Artificial Intelligence in medical diagnosis too is becoming increasingly common and has been used widely in the diagnosis of cancers, tumors, hepatitis, lung diseases, etc... The main aim of this paper is to build an Artificial Intelligent System that after analysis of certain parameters can predict that whether a person is diabetic or not. Diabetes is the name used to describe a metabolic condition of having higher than normal blood sugar levels. Diabetes is becoming increasingly more common throughout the world, due to increased obesity - which can lead to metabolic syndrome or pre-diabetes leading to higher incidences of type 2 diabetes. Authors have identified 10 parameters that play an important role in diabetes and prepared a rich database of training data which served as the backbone of the prediction algorithm. Keeping in view this training data authors developed a system that uses the artificial neural networks algorithm to serve the purpose. These are capable of predicting new observations (on specific variables) from previous observations (on the same or other variables) after executing a process of so-called learning from existing training data (Haykin1998).The results indicate that the ANN is the best predictor with the accuracy of about 96%. This system can be used to assist medical programs especially in geographically remote areas where expert human diagnosis not possible with an advantage of minimal expenses and faster results. Abid Sarwar"Diagnosis of hyperglycemia using Artificial Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7045.pdf Article URL: http://www.ijtsrd.com/computer-science/artificial-intelligence/7045/diagnosis-of-hyperglycemia-using--artificial-neural-networks/abid-sarwar

The aim of Artificial Intelligence is to develop the machines to perform the tasks in a better way than the humans. Another aim of Artificial Intelligence is to understand the actions whether it occurs in humans, machines or animals. As a result, Artificial Intelligence is gaining Importance in science and engineering fields. The use of Artificial Intelligence in medical diagnosis too is becoming increasingly common and has been used widely in the diagnosis of cancers, tumors, hepatitis, lung diseases, etc... The main aim of this paper is to build an Artificial Intelligent System that after analysis of certain parameters can predict that whether a person is diabetic or not. Diabetes is the name used to describe a metabolic condition of having higher than normal blood sugar levels. Diabetes is becoming increasingly more common throughout the world, due to increased obesity - which can lead to metabolic syndrome or pre-diabetes leading to higher incidences of type 2 diabetes. Authors have identified 10 parameters that play an important role in diabetes and prepared a rich database of training data which served as the backbone of the prediction algorithm. Keeping in view this training data authors developed a system that uses the artificial neural networks algorithm to serve the purpose. These are capable of predicting new observations (on specific variables) from previous observations (on the same or other variables) after executing a process of so-called learning from existing training data (Haykin1998).The results indicate that the ANN is the best predictor with the accuracy of about 96%. This system can be used to assist medical programs especially in geographically remote areas where expert human diagnosis not possible with an advantage of minimal expenses and faster results. Abid Sarwar"Diagnosis of hyperglycemia using Artificial Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7045.pdf Article URL: http://www.ijtsrd.com/computer-science/artificial-intelligence/7045/diagnosis-of-hyperglycemia-using--artificial-neural-networks/abid-sarwar

The aim of Artificial Intelligence is to develop the machines to perform the tasks in a better way than the humans. Another aim of Artificial Intelligence is to understand the actions whether it occurs in humans, machines or animals. As a result, Artificial Intelligence is gaining Importance in science and engineering fields. The use of Artificial Intelligence in medical diagnosis too is becoming increasingly common and has been used widely in the diagnosis of cancers, tumors, hepatitis, lung diseases, etc... The main aim of this paper is to build an Artificial Intelligent System that after analysis of certain parameters can predict that whether a person is diabetic or not. Diabetes is the name used to describe a metabolic condition of having higher than normal blood sugar levels. Diabetes is becoming increasingly more common throughout the world, due to increased obesity - which can lead to metabolic syndrome or pre-diabetes leading to higher incidences of type 2 diabetes. Authors have identified 10 parameters that play an important role in diabetes and prepared a rich database of training data which served as the backbone of the prediction algorithm. Keeping in view this training data authors developed a system that uses the artificial neural networks algorithm to serve the purpose. These are capable of predicting new observations (on specific variables) from previous observations (on the same or other variables) after executing a process of so-called learning from existing training data (Haykin1998).The results indicate that the ANN is the best predictor with the accuracy of about 96%. This system can be used to assist medical programs especially in geographically remote areas where expert human diagnosis not possible with an advantage of minimal expenses and faster results. Abid Sarwar"Diagnosis of hyperglycemia using Artificial Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7045.pdf Article URL: http://www.ijtsrd.com/computer-science/artificial-intelligence/7045/diagnosis-of-hyperglycemia-using--artificial-neural-networks/abid-sarwar

The aim of Artificial Intelligence is to develop the machines to perform the tasks in a better way than the humans. Another aim of Artificial Intelligence is to understand the actions whether it occurs in humans, machines or animals. As a result, Artificial Intelligence is gaining Importance in science and engineering fields. The use of Artificial Intelligence in medical diagnosis too is becoming increasingly common and has been used widely in the diagnosis of cancers, tumors, hepatitis, lung diseases, etc... The main aim of this paper is to build an Artificial Intelligent System that after analysis of certain parameters can predict that whether a person is diabetic or not. Diabetes is the name used to describe a metabolic condition of having higher than normal blood sugar levels. Diabetes is becoming increasingly more common throughout the world, due to increased obesity - which can lead to metabolic syndrome or pre-diabetes leading to higher incidences of type 2 diabetes. Authors have identified 10 parameters that play an important role in diabetes and prepared a rich database of training data which served as the backbone of the prediction algorithm. Keeping in view this training data authors developed a system that uses the artificial neural networks algorithm to serve the purpose. These are capable of predicting new observations (on specific variables) from previous observations (on the same or other variables) after executing a process of so-called learning from existing training data (Haykin1998).The results indicate that the ANN is the best predictor with the accuracy of about 96%. This system can be used to assist medical programs especially in geographically remote areas where expert human diagnosis not possible with an advantage of minimal expenses and faster results. Abid Sarwar"Diagnosis of hyperglycemia using Artificial Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7045.pdf Article URL: http://www.ijtsrd.com/computer-science/artificial-intelligence/7045/diagnosis-of-hyperglycemia-using--artificial-neural-networks/abid-sarwar

The aim of Artificial Intelligence is to develop the machines to perform the tasks in a better way than the humans. Another aim of Artificial Intelligence is to understand the actions whether it occurs in humans, machines or animals. As a result, Artificial Intelligence is gaining Importance in science and engineering fields. The use of Artificial Intelligence in medical diagnosis too is becoming increasingly common and has been used widely in the diagnosis of cancers, tumors, hepatitis, lung diseases, etc... The main aim of this paper is to build an Artificial Intelligent System that after analysis of certain parameters can predict that whether a person is diabetic or not. Diabetes is the name used to describe a metabolic condition of having higher than normal blood sugar levels. Diabetes is becoming increasingly more common throughout the world, due to increased obesity - which can lead to metabolic syndrome or pre-diabetes leading to higher incidences of type 2 diabetes. Authors have identified 10 parameters that play an important role in diabetes and prepared a rich database of training data which served as the backbone of the prediction algorithm. Keeping in view this training data authors developed a system that uses the artificial neural networks algorithm to serve the purpose. These are capable of predicting new observations (on specific variables) from previous observations (on the same or other variables) after executing a process of so-called learning from existing training data (Haykin1998).The results indicate that the ANN is the best predictor with the accuracy of about 96%. This system can be used to assist medical programs especially in geographically remote areas where expert human diagnosis not possible with an advantage of minimal expenses and faster results. Abid Sarwar"Diagnosis of hyperglycemia using Artificial Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7045.pdf Article URL: http://www.ijtsrd.com/computer-science/artificial-intelligence/7045/diagnosis-of-hyperglycemia-using--artificial-neural-networks/abid-sarwar

Electronic Nose gas sensors are used to detect various chemical vapors present in Environment. Each sensor has its own kind of response from sensory arrays. The various pattern recognition methods are used to process the signals from a sensor array employed in E-nose and predict the toxic 

Electronic Nose gas sensors are used to detect various chemical vapors present in Environment. Each sensor has its own kind of response from sensory arrays. The various pattern recognition methods are used to process the signals from a sensor array employed in E-nose and predict the toxic 

In this study, it was aimed to predict elementary education teacher candidates' achievements in "Science and Technology Education I and II" courses by using artificial neural networks. It was also aimed to show the independent variables importance in the prediction. In the data set used in this study, variables of gender, type of education, field of study in high school and transcript information of 14 courses including end-of-term letter grades were collected. The fact that the artificial neural network performance in this study was R = 0.84 for the Science and Technology Education I course, and R = 0.84 for the Science and Technology Education II course shows that the network performance overlaps with the findings obtained from the related studies.

In this study, it was aimed to predict elementary education teacher candidates' achievements in "Science and Technology Education I and II" courses by using artificial neural networks. It was also aimed to show the independent variables importance in the prediction. In the data set used in this study, variables of gender, type of education, field of study in high school and transcript information of 14 courses including end-of-term letter grades were collected. The fact that the artificial neural network performance in this study was R = 0.84 for the Science and Technology Education I course, and R = 0.84 for the Science and Technology Education II course shows that the network performance overlaps with the findings obtained from the related studies.

This study proposes Artificial Neural Network ANN based field strength prediction models for the rural areas of Abuja, the federal capital territory of Nigeria. The ANN based models were created on bases of the Generalized Regression Neural network GRNN and the Multi Layer Perceptron Neural Network MLP NN . These networks were created, trained and tested for field strength prediction using received power data recorded at 900MHz from multiple Base Transceiver Stations BTSs distributed across the rural areas. Results indicate that the GRNN and MLP NN based models with Root Mean Squared Error RMSE values of 4.78dBm and 5.56dBm respectively, offer significant improvement over the empirical Hata Okumura counterpart, which overestimates the signal strength by an RMSE value of 20.17dBm. Deme C. Abraham ""Mobile Network Coverage Determination at 900MHz for Abuja Rural Areas using Artificial Neural Networks"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30228.pdf Paper Url : https://www.ijtsrd.com/computer-science/artificial-intelligence/30228/mobile-network-coverage-determination-at-900mhz-for-abuja-rural-areas-using-artificial-neural-networks/deme-c-abraham

This article is from BMC Medical Research Methodology , volume 12 . Abstract Background: Projection pursuit regression, multilayer feed-forward networks, multivariate adaptive regression splines and trees (including survival trees) have challenged classic multivariable models such as the multiple logistic function, the proportional hazards life table Cox model (Cox), the Poisson’s model, and the Weibull’s life table model to perform multivariable predictions. However, only artificial neural networks (NN) have become popular in medical applications. Results: We compared several Cox versus NN models in predicting 45-year all-cause mortality (45-ACM) by 18 risk factors selected a priori: age; father life status; mother life status; family history of cardiovascular diseases; job-related physical activity; cigarette smoking; body mass index (linear and quadratic terms); arm circumference; mean blood pressure; heart rate; forced expiratory volume; serum cholesterol; corneal arcus; diagnoses of cardiovascular diseases, cancer and diabetes; minor ECG abnormalities at rest. Two Italian rural cohorts of the Seven Countries Study, made up of men aged 40 to 59 years, enrolled and first examined in 1960 in Italy. Cox models were estimated by: a) forcing all factors; b) a forward-; and c) a backward-stepwise procedure. Observed cases of deaths and of survivors were computed in decile classes of estimated risk. Forced and stepwise NN were run and compared by C-statistics (ROC analysis) with the Cox models. Out of 1591 men, 1447 died. Model global accuracies were extremely high by all methods (ROCs > 0.810) but there was no clear-cut superiority of any model to predict 45-ACM. The highest ROCs (> 0.838) were observed by NN. There were inter-model variations to select predictive covariates: whereas all models concurred to define the role of 10 covariates (mainly cardiovascular risk factors), family history, heart rate and minor ECG abnormalities were not contributors by Cox models but were so by forced NN. Forced expiratory volume and arm circumference (two protectors), were not selected by stepwise NN but were so by the Cox models. Conclusions: There were similar global accuracies of NN versus Cox models to predict 45-ACM. NN detected specific predictive covariates having a common thread with physical fitness as related to job physical activity such as arm circumference and forced expiratory volume. Future attention should be concentrated on why NN versus Cox models detect different predictors.

This article is from BMC Medical Research Methodology , volume 12 . Abstract Background: Projection pursuit regression, multilayer feed-forward networks, multivariate adaptive regression splines and trees (including survival trees) have challenged classic multivariable models such as the multiple logistic function, the proportional hazards life table Cox model (Cox), the Poisson’s model, and the Weibull’s life table model to perform multivariable predictions. However, only artificial neural networks (NN) have become popular in medical applications. Results: We compared several Cox versus NN models in predicting 45-year all-cause mortality (45-ACM) by 18 risk factors selected a priori: age; father life status; mother life status; family history of cardiovascular diseases; job-related physical activity; cigarette smoking; body mass index (linear and quadratic terms); arm circumference; mean blood pressure; heart rate; forced expiratory volume; serum cholesterol; corneal arcus; diagnoses of cardiovascular diseases, cancer and diabetes; minor ECG abnormalities at rest. Two Italian rural cohorts of the Seven Countries Study, made up of men aged 40 to 59 years, enrolled and first examined in 1960 in Italy. Cox models were estimated by: a) forcing all factors; b) a forward-; and c) a backward-stepwise procedure. Observed cases of deaths and of survivors were computed in decile classes of estimated risk. Forced and stepwise NN were run and compared by C-statistics (ROC analysis) with the Cox models. Out of 1591 men, 1447 died. Model global accuracies were extremely high by all methods (ROCs > 0.810) but there was no clear-cut superiority of any model to predict 45-ACM. The highest ROCs (> 0.838) were observed by NN. There were inter-model variations to select predictive covariates: whereas all models concurred to define the role of 10 covariates (mainly cardiovascular risk factors), family history, heart rate and minor ECG abnormalities were not contributors by Cox models but were so by forced NN. Forced expiratory volume and arm circumference (two protectors), were not selected by stepwise NN but were so by the Cox models. Conclusions: There were similar global accuracies of NN versus Cox models to predict 45-ACM. NN detected specific predictive covariates having a common thread with physical fitness as related to job physical activity such as arm circumference and forced expiratory volume. Future attention should be concentrated on why NN versus Cox models detect different predictors.