A method for ranking compounds based on their relative toxicity using neural networking, C. elegans, axenic liquid culture, and the COPAS parameters TOF and EXT

A method to rank order water soluble compounds according to their toxicity using Caenorhabditis elegans, a Complex Object Parametric Analyzer and Sorter, and axenic liquid media

Bank as a company that has many customers who conduct transactions every day, of course, has data that is increased continuously. In this paper, customer transaction data has been predicted to find potential customers in deposit offer. Data mining approach has been performed to classify potential customers for marketing through telemarketing. This data analysis can be used as a consideration in determining marketing strategy decisions for marketing managers. 15,713 data with 13 class attributes and 1 target class were obtained from UCI Machine Learning repository. The data was divided into 70% training data and 30% test data. Feedforward method of Artificial Neural Network was used to classify customer data. Multilayer Perceptron Neural Network and Radial Basis Function Neural Network were used to obtain optimal classification results. The result of this classification predicted potential customers to subscribe deposits. The result of this study indicated that the Radial Basis Function Neural Network method with 95.3% accuracy and 96.4% sensitivity was a better method compared to the Multilayer Perceptron Neural Network method with 88.0% accuracy and 99.4% sensitivity.

Fluvial flows and tidal flows are governing on tidal rivers while fluvial flows are governing on non-tidal rivers alone. In addition to analytical and numerical models, stochastic methods must be considered to determine water surface elevation at the tidal limit of tidal rivers because hydraulic routing of this reach is very complex. In the present research, the neural network method was considered for determination of water surface elevation at the tidal limit of tidal rivers. The artificial neural network (ANN) method was trained using results from a suitable numerical model. The results of the ANN method were compared with results for the governing regression relation for water surface elevation at the tidal limit of tidal rivers. The ANN method was applied to the Karun river in Iran and the River Severn in the UK. The ANN method was found to produce water surface elevations for different combined return periods in the Karun river and the River Severn.

Smart building management and construction is tremendous for developing smart cities in building sites and is known for its stability and durability. However, its performance can be significantly enhanced by improving material properties such as strength, fire resistance and impact protection. Conventional earthquake structural design considers only a limited number of factors, mainly elastic structural properties, to determine the critical design parameters.Yet, these parameters are often suboptimal since they do not consider the extensive plasticity expected in building structures during earthquakes. One significant challenge in concrete design is that it is difficult to predict the exact performance of a particular concrete mix without extensive testing, which is time-consuming and costly.Conventional techniques for optimizing concrete properties depend significantly on empirical testing and expert intuition, which are time-consuming and may not completely handle the complex interactions among various material components.To address the above problems, this research presents the Artificial Intelligence (AI) based Multi-Layer Perceptron Neural Network (MLPNN) method for efficient building construction that resists earthquakes.To start with the proposed work, C-Score Normalization (CSN) method is employed to normalize the collective dataset. Then, select essential features of concrete materials using the Deep Feature Elimination with Residual Network (DFE-RN)approach. Following that, the MLPNN method is used to classify the best materials for efficient building construction that resists earthquakes. The proposed framework has the potential to revolutionize the building industry by constructing concrete with improved properties, reducing the need for extensive physical testing and speeding up the innovation process.This paper demonstrates the proposed AI-based approach can effectively improve Earthquake-resistant structural design. The proposed simulation result illustrates the efficient performance regarding precision, recall, classification accuracy and F1-score with less time complexity.

Polynomials, radial basis functions and multilayer perceptron neural network methods in local geoid determination with GPS/levelling

The article describes practical application of the original method of training artificial neural network based on the poorly formalized expert knowledge. The method allows extending the range of problems to be solved in case of lack of a sufficient number of the observations due to the fact that the training vectors are formed on the basis of the expert knowledge. The expert continuously defines classes of objects that are generated by the pseudorandom number of the training vectors of input signals, and created visual images by computer for clearly describing objects by given training vectors. The method is applied to solve important practical problem for determining of the atmospheric surface layer stability. The problem is formulated as a classification problem. As being the artificial neural network was selected multilayer perceptron. This trained neural network is represented by programming model implementing as DLL-module of dynamic-link library. The research bases on the original computer program that implements the algorithm of author’s training method. The program determines and implements the steps of the author’s research using heuristic training method of the artificial neural network to solve the problems of classification on the basis of poorly formalized experts’ knowledge. Its algorithms are used to generate visual (cognitive) images of possible situations to retrieve the unconscious expert knowledge. As a result, the aim of the study was achieved. The proposed method of training artificial neural network was applied successfully to solve a practical problem and showed its efficiency on an example of the classification problem. The author’s training method is protected by Russian patent for invention; the use of computer software holds a certificate of state registration.

This study aims to predict the compressive strength of existing concrete without using destructive tests which can damage the surface of the concrete. Destructive testing has the disadvantage of damaging the surface of the concrete, requires a long time and need expensive cost, while the Non Destructive Test (NDT) has the advantage of not damaging the surface of the concrete and faster when combined with the Artificial Neural Network (ANN) method . In this research, the Non Destructive Test (NDT) result such as hammer test and UPV were combined with concrete mix design properties and used to predict the compressive strength of concrete at three and 28 days. The Artificial Neural Network (ANN) method is used to make correlation of mix design properties data and NDT. In this study experimental tests were performed using variation of design parameters such as water per cement ratio and weight ratio of fly ash. The water per cement ratio used in this research was in range 0.45 until 0.55. Furthermore, the weight ratio of fly ash was in range 0% until 25%. Based on the modeling result using ANN method, it found that that the neural network method successfully predicts the compressive strength of concrete at three and 28 days with the mean square error (MSE) value and regression of concrete at three days are5.83 and 0.89 respectively. While at 28 days the MSE and regression value are 4.7 and 0.87 respectively.

In addition to its nutritional properties, raisins are also a beneficial food in terms of health due to its vitamins, minerals, antioxidants and phenolic compounds. Turkey ranks first in global raisin production with a production capacity of 24%. Many problems are encountered in the classification of raisins according to their type and quality by traditional methods. In order to overcome these problems, artificial intelligence systems, whose usage area is increasing day by day, are utilized. In this study, raisin grains were classified using 3 different Artificial Neural Network (ANN) methods using the ‘Raisin’ dataset from the UCI Machine Learning Repository. Performance measurements of Competitive Layer Neural Network (CLNN), Pattern Recognition Artificial Neural Network (PRNN) and Self-Organizing Map (SOM) methods used in classification were performed. In the obtained performance measurements, PRNN has the highest success, while SOM is weaker compared to the other two methods. CLNN, on the other hand, remains at similar levels to PRNN and offers a good alternative to PRNN.

This study performed modeling of the scattering (S) and noise (N) parameters of the ATF53189 using the General Regression Neural Network (GRNN) and Multi Layer Perceptron Neural Network (MLPNN) methods based on Artificial Neural Network (ANN). For modeling the linear behavior of the transistor, the optimum design parameters of the GRNN and the MLPNN methods were determined using four different optimization algorithms. These are whale optimization algorithm (WOA), artificial bee colony (ABC), particle swarm optimization (PSO) and ant lion optimizer (ALO) algorithms. With the help of these algorithms, the sigma parameter of the GRNN and the number of hidden layers, numbers of neurons in the hidden layers and the activation functions of the hidden layers of the MLPNN were optimized. This way, the best models required for prediction of theSandNparameters of the ATF53189 were obtained. Different models that provided each of the angles and magnitudes of theS11,S21,S12,S22parameters and theFmin, Γmin, Γoptmagnitude, Γoptangle andRnnoise parameters as the output were created. The experimental results showed that the GRNN method should be used in linear behavior modeling ofSparameters of the ATF53189 and the MLPNN method should be used in linear behavior modeling ofNparameters of the ATF53189. It is understood that the best algorithm for optimizing the design parameters of the GRNN and the MLPNN methods is the PSO. As a result, the modeling of theSandNparameters of the ATF53189 transistor was successfully carried out with the methods used in this study.

In this study, the calculability of slope stability using the artificial neural networks (ANN) method was examined. Initially, 100 synthetic slope models were created to be used in calculations and the factors of safety of these slopes were calculated by a conventional stability calculation method using slope parameters. Then, factors of safety were calculated by through ANN method. 80 of the datasets from the generated data were used for training while 20 were used for testing in these calculations with the ANN method. In both conventional calculation of stability and the ANN method, input parameters included slope height, height of the water level, slope angle, unit weight, cohesion and angle of internal friction, while the output parameter was factor of safety (SF). A good level of consistency was obtained between the SFs calculated through the conventional method and the ANN method. Furthermore, SFs were calculated separately via the ANN method by assigning range values to unit weight, cohesion and angle of internal friction from amongst the parameters that affect SF for Giresun landslides (Eastern Turkey). The data obtained in this scope revealed that cohesion was the parameter with the highest level of effect on SF. Consequently, it was established that the factors of safety of slopes could be calculated by means of the ANN method in a rapid and convenient manner, the effects of slope parameters on the factors of safety in landslide were examined and the factors of safety for Giresun landslides were calculated through the ANN method.

Micrometeoroid and orbital debris (MMOD) can cause serious impact damage to long-term on-orbit spacecraft. MMOD impact detection and location are essential to improve the safety and reliability of the on-orbit spacecraft. In this article, a combined artificial neural network (ANN) and Mahalanobis distance method for MMOD impact detection and location based on fiber Bragg grating (FBG) sensor network is proposed. The inputs of the proposed ANN are the Mahalanobis distances determined by the wavelength changes of the FBG sensors at impact points and reference points. A finite element model is developed to explore the relationship between the Mahalanobis distance and the actual distance, which proved to be nonlinear based on the finite element simulation results of MMOD impacts. Four FBG sensors were installed on the back of the test board and sampled at a frequency of 100 Hz using a four-channel FBG demodulator. A total of 360 impact experiments were carried out on this experiment system. The average location error of the proposed method is 0.89 cm, which is 39% and 31% lower compared with the Mahalanobis distance discriminant analysis method and the ANN method, respectively. The combined ANN and Mahalanobis distance method reduces the location errors and has higher accuracy of position prediction.

Neural network method for solving nonlocal two-temperature nanoscale heat conduction in gold films exposed to ultrashort-pulsed lasers

Artificial neural network methods for the estimation of zeolite molar compositions that form from different reaction mixtures

In this paper the Support Vector Machines (SVM) method is used to correlate the transitional forced and mixed convection experimental data of Ghajar and Tam (1994) that were obtained along a stainless steel horizontal circular tube fitted with re-entrant, square-edged, and bell-mouth inlets under uniform wall heat flux boundary condition. The SVM method has been chosen to further improve the accuracy of the correlations that were developed by Ghajar and his co-workers using the traditional least-squares method (Ghajar and Tam, 1994) and more recently the artificial neural networks (ANN) method (Ghajar et al., 2004). Using the ANN method improved the accuracy of their correlation. However, there are drawbacks associated with ANN method. One of the major problems with the ANN method is that it does not provide a unique correlation due to different coefficient matrices. The SVM method used in this study eliminated the drawbacks associated with the ANN method and provided a unique correlation with comparable accuracy as the ANN method. For the experimental data used, majority of the data points were predicted within 5% deviation. Comparisons were made regarding the accuracy of the developed correlation and its characteristic using SVM and ANN methods. The results showed that SVM is a good method to correlate complex heat transfer data.

Background: The use of statistical methods to analyze data, regardless of their theoretical assumptions, leads to misinterpretation of the results. Objectives: Effective attributes in colorectal cancer relapse were investigated through survival analysis in the present study. Comparison between the results of artificial neural network (ANN) method and Cox proportional hazards (Cox PH) model was the main purpose of this research. Patients and Methods: A total of 184 patients with locoregional colorectal cancer, referred to Shahid Faghihi Hospital (Shiraz, Iran) for surgery, were followed in a five-year period for possible relapse during 2003-2011. Disease-free survival was then modeled based on the patients’ attributes, using Cox PH regression and ANN methods. All the attributes effective on disease relapse were investigated by these two methods. Results: A total of 114 (62%) males and 70 (38%) females with a median age of 54 (range: 23-84) years old participated in the study. Among them, there were 95 (51.6%) patients with colon cancer and 89 (48.4%) with rectum cancer. In addition, 53 patients relapsed and 131 patients did not present any relapse or missed the follow up (censored data). The results showed that the accuracy rate in prediction was higher for the ANN method than the Cox PH model (78.2% versus 72.7%). In addition, the area under the receiver operating curve (ROC) was also more for the ANN method (0.86 versus 0.74). Five attributes of the patients, including neoadjuvant treatment, perforation and/or obstruction, perineural invasion, stage, and tumor grade, were significant through the Cox HP model. The first five attributes by the ANN method were surgeon, primary tumor site, perforation and/or obstruction, age, and adjuvant treatments. In this study, the order of attributes determined by the ANN method was rather confirmed by the physicians. Conclusions: The results showed superiority of the ANN method over the Cox PH model with respect to the area under the ROC and the accuracy rate in prediction. However, this method requires a large data set to learn the relations and cannot distinguish the confounding attributes.