Artificial Neural Networks Applied Research Articles

A New Empirical Correlation for Pore Pressure Prediction Based on Artificial Neural Networks Applied to a Real Case Study

Pore pressure prediction is a critical parameter in petroleum engineering and is essential for safe drilling operations and wellbore stability. However, traditional methods for pore pressure prediction, such as empirical correlations, require selecting appropriate input parameters and may not capture the complex relationships between these parameters and the pore pressure. In contrast, artificial neural networks (ANNs) can learn complex relationships between inputs and outputs from data. This paper presents a new empirical correlation for predicting pore pressure using ANNs. The proposed method uses 42 datasets of well log data, including temperature, porosity, and water saturation, to train ANNs for pore pressure prediction. The trained model, with the Bayesian regularization backpropagation function, predicts the pore pressure with an average absolute percentage error (AAPE) and correlation coefficient (R) of 4.22% and 0.875, respectively. The trained ANN is then used to develop a new empirical correlation that relates pore pressure to the input parameters considering the weights and biases of the optimized ANN model. To validate the proposed correlation, it is applied to a blind dataset, where the model successfully predicts the pore pressure with an AAPE of 5.44% and R of 0.957. The results show that the proposed correlation provides accurate and reliable predictions of pore pressure. The proposed method provides a robust and accurate approach for predicting pore pressure in petroleum engineering applications, which can be used to improve drilling safety and wellbore stability.

Artificial Neural Networks Applied in Civil Engineering

In recent years, artificial neural networks (ANN) and artificial intelligence (AI), in general, have garnered significant attention with respect to their applications in several scientific fields, varying from big data management to medical diagnosis [...]

Artificial Neural Networks Applied to Deep Foundation Engineering: Prediction of the Ultimate Axial Capacity of Drilled Shafts

Artificial Neural Networks Applied to Deep Foundation Engineering: Prediction of the Ultimate Axial Capacity of Drilled Shafts

Artificial Neural Networks Applied to Colorimetric Nanosensors: An Undergraduate Experience Tailorable from Gold Nanoparticles Synthesis to Optical Spectroscopy and Machine Learning

Nowadays, technologies involving nanoparticles, colloids, sensors, and artificial intelligence are widespread in society, media, and industry. It is thus mandatory to integrate them into the curricula of students enrolled in chemistry and materials science. To this purpose, we designed a simple assay for the detection of glutathione (GSH) using surface-clean gold nanoparticles (Au NPs). The alteration of the electric double layer of the Au NPs with increasing GSH concentration causes the particles to aggregate, producing a measurable change in color. This behavior, which is widely exploited for optical sensing, has been introduced in an undergraduate course to familiarize the students with the concepts of nanoparticles, colloids, colloidal stability, and sensor features (selectivity, sensitivity, detection range). Nonetheless, there are no analytical models to quantitatively relate the absorption of Au NP colorimetric sensors to analyte concentration, which is the ideal condition for resorting to machine learning (ML). Hence, an artificial neural network was instructed in a students’ collective data-sharing experiment about machine learning. Overall, the laboratory experience is safe and highly tailorable to students’ background, course duration, available instruments, and teacher’s didactic objectives. For instance, it can be lifted to the Master’s or Ph.D. level by improving the spectroscopic and ML contents or shifted toward the industrial ground by focusing on the nanoparticle synthesis. We propose the integration of this laboratory experience in the undergraduate and Master’s academic programs to stimulate the students with a collection of hot topics that at the same time can consolidate their preparation on arguments of great relevance for their professional life.

Artificial Neural Networks Applied in Mechanical Structural Design

Artificial Neural Networks Applied in Mechanical Structural Design

Artificial Neural Networks Applied to a Wind Energy System

In this context, we are taking a close interest in the optimization of wind energy production. It consists in designing simple to implement control strategies of a wind energy conversion system, connected to the network based on the Double Fed Induction Generator (DFIG) driven by the Converter Machine Side (CSM) in order to improve the performance of Direct Torque Control (DTC) and Direct Power Control (DPC). For this purpose, the artificial neural networks (ANNs) is used. Hysteresis comparators and voltage vector switching tables have been replaced by a comparator based on artificial neural networks. The same structure is adopted to build the two neural controllers, for the DTC – ANN and for the DPC – ANN. The simulation results show that the combination of classical and artificial neural network methods permit a double advantage: remarkable performances compared to the DTC-C and DPC-C and a significant reduction of the fluctuations of the output quantities of the DFIG and especially the improvement of the harmonics rate currents generated by the machine.

Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (FTIR) and Artificial Neural Networks Applied to Investigate Quantitative Changes of Selected Soluble Biomarkers, Correlated with H. pylori Infection in Children and Presumable Consequent Delayed Growth.

Helicobacter pylori infections causing gastroduodenal disorders are a common medical problem. The aim of this study was to determine the specific motives of infrared spectroscopy (IR) spectra of sera from H. pylori-infected and uninfected children applied to investigate quantitatively-selected soluble biomarkers correlated with H. pylori infection in children and presumable consequent delayed growth. Sera from 41 children infected with H. pylori (Hp(+)) and 43 uninfected (Hp(−)) under the care of the Polish Mother’s Hospital in Lodz, Poland, were analyzed. The H. pylori status was confirmed by gastroscopy, 13C urea breath testing, and anti-H. pylori IgG antibodies. Infrared spectra were measured using an FTIR/FT-NIR Spectrum 400 spectrometer (PerkinElmer). The IR spectrum was measured in the wavenumber range 3000–750 cm−1 and subjected to mathematical calculation of the first derivative. Based on the chi-square test, 10 wavenumbers of spectra correlating with H. pylori infection were selected for use in designing an artificial neural network. Ten parts of the IR spectra correlating with H. pylori infection were identified in the W2 and W3 windows associated mainly with proteins and the W4 window related to nucleic acids and hydrocarbons. Artificial neural networks for H. pylori infection were developed based on chemometric data. By mathematical modeling, children were classified towards H. pylori infection in conjunction with elevated levels of selected biomarkers in serum potentially related to growth retardation. The study concludes that IR spectroscopy and artificial neural networks may help to confirm H. pylori-driven growth disorders in children.

Artificial Neural Networks Applied to Prediction to Assess the Likelihood of Surgical Site Infection in Different Surgeries

Background: Based on data obtained from hospitals in the city of Belo Horizonte (population ~3,000,000), we evaluated relevant factors such as death, age, duration of surgery, potential for contamination and surgical site infection, plastic surgery, and craniotomy. The possibility of predicting surgical site infection (SSI) was then analyzed using pattern recognition algorithms based on MLP (multilayer perceptron). Methods: Data were collected by the hospital infection control committees (CCIHs) in hospitals in Belo Horizonte between 2016 and 2018. The noisy records were filtered, and the occurrences were analyzed. Finally, the predictive power of SSI of 5 types MLP was evaluated experimentally: momentum, backpropagation standard, weight decay, resilient propagation, and quick propagation. The model used 3, 5, 7, and 10 neurons in the occult layer and with resamples varied the number of records for testing (65% and 75%) and for validation (35% and 25%). Comparisons were made by measuring the AUC (area under the curve (range, 0–1). Results: From 1,096 records of craniotomy, 289 were usable for analysis. Moreover, 16% died; averaged age was 56 years (range, 40–65); mean time of surgery was 186 minutes (range, 95–250 minutes); the number of hospitalizations ranged from 1 (90.6%) to 8 (0.3%). Contamination among these cases was rated as follows: 2.7% contaminated, 23.5% potentially contaminated, 72.3% clean. The SSI rate reached 4%. The prediction process in AUCs ranged from 0.7 to 0.994. In plastic surgery, from 3,693 records, 1,099 were intact, with only 1 case of SSI and no deaths. The average age for plastic surgery was 41 years (range, 16–91); the average time of surgery was 218.5 minutes (range, 19–580 minutes); the number of hospitalizations ranged from 1 (77.4%) to 6 times (0.001%). Contamination among these cases was rated as follows: 27.90% potential contamination, 1.67% contaminated, and 0.84% infected. The prediction process ranged in AUCs from 0.2 to 0.4. Conclusions: We identified a high noise index in both surgeries due to subjectivity at the time of data collection. The profiles of each surgery in the statistical analyses were different, which was reflected in the analyzed structures. The MLP for craniotomy surgery demonstrated relevant predictive power and can guide intelligent monitoring software (available in www.sacihweb.com). However, for plastic surgeries, MLPs need more SSI samples to optimize outcomes. To optimize data collection and to enable other hospitals to use the SSI prediction tool, a mobile application was developed.Disclosures: NoneFunding: None

Artificial Neural Networks Applied as Molecular Wave Function Solvers.

We use artificial neural networks (ANNs) based on the Boltzmann machine (BM) architectures as an encoder of ab initio molecular many-electron wave functions represented with the complete active space configuration interaction (CAS-CI) model. As first introduced by the work of Carleo and Troyer for physical systems, the coefficients of the electronic configurations in the CI expansion are parametrized with the BMs as a function of their occupancies that act as descriptors. This ANN-based wave function ansatz is referred to as the neural-network quantum state (NQS). The machine learning is used for training the BMs in terms of finding a variationally optimal form of the ground-state wave function on the basis of the energy minimization. It is relevant to reinforcement learning and does not use any reference data nor prior knowledge of the wave function, while the Hamiltonian is given based on a user-specified chemical structure in the first-principles manner. Carleo and Troyer used the restricted Boltzmann machine (RBM), which has hidden units, for the neural network architecture of NQS, while, in this study, we further introduce its replacement with the BM that has only visible units but with different orders of connectivity. For this hidden-node free BM, the second- and third-order BMs based on quadratic and cubic energy functions, respectively, were implemented. We denote these second- and third-order BMs as BM2 and BM3, respectively. The pilot implementation of the NQS solver into an exact diagonalization module of the quantum chemistry program was made to assess the capability of variants of the BM-based NQS. The test calculations were performed by determining the CAS-CI wave functions of illustrative molecular systems, indocyanine green, and dinitrogen dissociation. The simulated energies have been shown to converge to CAS-CI energy in most cases by improving RBM with an increasing number of hidden nodes. BM3 systematically yields lower energies than BM2, reproducing the CAS-CI energies of dinitrogen across potential energy curves within an error of 50 μEh.

Experience with Artificial Neural Networks Applied in Multi-object Adaptive Optics

The use of artificial Intelligence techniques has become widespread in many fields of science, due to their ability to learn from real data and adjust to complex models with ease. These techniques have landed in the field of adaptive optics, and are being used to correct distortions caused by atmospheric turbulence in astronomical images obtained by ground-based telescopes. Advances for multi-object adaptive optics are considered here, focusing particularly on artificial neural networks, which have shown great performance and robustness when compared with other artificial intelligence techniques. The use of artificial neural networks has evolved to the extent of the creation of a reconstruction technique that is capable of estimating the wavefront of light after being deformed by the atmosphere. Based on this idea, different solutions have been proposed in recent years, including the use of new types of artificial neural networks. The results of techniques based on artificial neural networks have led to further applications in the field of adaptive optics, which are included in here, such as the development of new techniques for solar observation or their application in novel types of sensors.

Artificial Neural Networks Applied to Image Steganography

This paper presents a technique for transmitting information efficiently and securely, hiding confidential messages on seemingly innocent messages using steganography. The insertion technique in the least significant bit is used to insert images into digital pictures or other secret watermark. Artificial Neural Networks are used in the process of withdrawal of encrypted information acting as keys that determine the existence of hidden information.

Artificial Neural Networks Applied to Flow Prediction: A Use Case for the Tomebamba River

The main aim of this research is to create a model based on Artificial Neural Networks (ANN) that allows predicting the flow in Tomebamba river, at real time and in a specific day of a year. As inputs, this research is using information of rainfall and flow of the stations along of the river. This information is organized in scenarios and each scenario is prepared to a specific area. For this article, we have selected two scenarios. The information is acquired from the hydrological stations placed in the watershed using an electronic system developed at real time and it supports any kind or brands of this type of sensors. The prediction works very good three days in advance. This research includes two ANN models: Backpropagation and a hybrid model between back propagation and OWO-HWO (output weight optimization–hidden weight optimization) to select the initial weights of the connection. These last two models have been tested in a preliminary research. To validate the results we are using some error indicators such as MSE, RMSE, EF, CD and BIAS. The results of this research reached high levels of reliability and the level of error is minimal. These predictions are useful to avoid floods in the city of Cuenca in Ecuador.

Models of Artificial Neural Networks Applied to Demand Forecasting in Nonconsolidated Tourist Destinations

This article focuses on a new proposed artificial neural network (ANN) model for tourism demand forecasting using time-series which, unlike previous models, uses different seasons of arrivals and values of months with similar behavior as input variables and achieves a forecast up to a year in advance. We demonstrate the validity and greater precision of the proposed model in forecasting a nonconsolidated destination with marked seasonality, which has been scarcely dealt with in other research. We achieve a comparatively greater quality of results and a longer period in advance than previously used auto-regressive integrated moving average (ARIMA) and ANN models. Highly accurate results were also obtained in destinations such as Portugal, which also proves its validity for mature destinations.

SU‐E‐T‐131: Artificial Neural Networks Applied to Overall Survival Prediction for Patients with Periampullary Carcinoma

Purpose:Artificial neural networks (ANN) can be used to discover complex relations within datasets to help with medical decision making. This study aimed to develop an ANN method to predict two‐year overall survival of patients with peri‐ampullary cancer (PAC) following resection.Methods:Data were collected from 334 patients with PAC following resection treated in our institutional pancreatic tumor registry between 2006 and 2012. The dataset contains 14 variables including age, gender, T‐stage, tumor differentiation, positive‐lymph‐node ratio, positive resection margins, chemotherapy, radiation therapy, and tumor histology.After censoring for two‐year survival analysis, 309 patients were left, of which 44 patients (∼15%) were randomly selected to form testing set. The remaining 265 cases were randomly divided into training set (211 cases, ∼80% of 265) and validation set (54 cases, ∼20% of 265) for 20 times to build 20 ANN models. Each ANN has one hidden layer with 5 units. The 20 ANN models were ranked according to their concordance index (c‐index) of prediction on validation sets. To further improve prediction, the top 10% of ANN models were selected, and their outputs averaged for prediction on testing set.Results:By random division, 44 cases in testing set and the remaining 265 cases have approximately equal two‐year survival rates, 36.4% and 35.5% respectively. The 20 ANN models, which were trained and validated on the 265 cases, yielded mean c‐indexes as 0.59 and 0.63 on validation sets and the testing set, respectively. C‐index was 0.72 when the two best ANN models (top 10%) were used in prediction on testing set. The c‐index of Cox regression analysis was 0.63.Conclusion:ANN improved survival prediction for patients with PAC. More patient data and further analysis of additional factors may be needed for a more robust model, which will help guide physicians in providing optimal post‐operative care.This project was supported by PA CURE Grant.

Artificial Neural Networks Applied to Bandsawing Process Control

In the search for productivity increase, industry has invested on the development of intelligent, flexible and self-adjusting method, capable of controlling processes through the assistance of autonomous systems, independently whether they are hardware or software. Notwithstanding, simulating conventional computational techniques is rather challenging, regarding the complexity and non-linearity of the production systems. Compared to traditional models, the approach with Artificial Neural Networks (ANN) performs well as noise suppression and treatment of non-linear data. Therefore, the challenges in the wood industry justify the use of ANN as a tool for process improvement and, consequently, add value to the final product. Furthermore, Artificial Intelligence techniques such as Neuro-Fuzzy Networks (NFNs) have proven effective, since NFNs combine the ability to learn from previous examples and generalize the acquired information from the ANNs with the capacity of Fuzzy Logic to transform linguistic variables in rules.

Combination of Wavelet Analysis and Artificial Neural Networks Applied to Forecast of Daily Cosmic Ray Impulses

Artificial neural network modelling has proven incredibly effective in an impressively wide range of scientific disciplines. The combination of these various methods with wavelet decomposition signal processing has similarly proven to be a powerful development for statistical forecasting of a number of environmental processes. Space weather modelling and prediction has often been applied to forecasting of solar activity and that of the planetary magnetic field. However, prediction of cosmic ray impulses has seen little development in the context of neural network modelling. In the present study, a combination of wavelet neural networks was adapted from previous research in order to predict daily average values of cosmic ray impulses 30 days in advance. Additional comparison of both neural network and linear regression modelling with and without wavelet decomposition was conducted for further demonstration of increased accuracy with wavelet neural networks in a simple input-output fitting model

Combination of Wavelet Analysis and Artificial Neural Networks Applied to Forecast of Daily Cosmic Ray Impulses

Artificial neural network modelling has proven incredibly effective in an impressively wide range of scientific disciplines. The combination of these various methods with wavelet decomposition signal processing has similarly proven to be a powerful development for statistical forecasting of a number of environmental processes. Space weather modelling and prediction has often been applied to forecasting of solar activity and that of the planetary magnetic field. However, prediction of cosmic ray impulses has seen little development in the context of neural network modelling. In the present study, a combination of wavelet neural networks was adapted from previous research in order to predict daily average values of cosmic ray impulses 30 days in advance. Additional comparison of both neural network and linear regression modelling with and without wavelet decomposition was conducted for further demonstration of increased accuracy with wavelet neural networks in a simple input-output fitting model

Artificial Neural Networks Applied for Simultaneous Analysis of Mixtures of Nitrophenols by Conductometric Acid–Base Titration

In this study, the simultaneous conductometric titration method for determination of mixtures of 4-nitrophenol, 2,4-dinitrophenol, and 2,4,6- trinitrophenol based on principal component artificial neural network (ANN) calibration model was proposed. The three-layered feed-forward ANN trained by back-propagation learning was used to model the complex nonlinear relationship between the concentration of 4-nitrophenol, 2,4-dinitrophenol, and 2,4,6-trinitrophenol in their ternary mixtures and the conductance of the solutions at different volumes of titrant. The principal components of the conductance matrix were used as the input of the network. The network architecture and parameters were optimized to give low prediction error. The optimized networks predicted the concentrations of nitrophenols in synthetic mixtures. The results showed that the used ANN can proceed the titration data with low relative prediction errors (5.53%, 4.03%, and 4.71% for 4-nitrophenol, 2,4-dinitrophenol, and 2,4,6-trinitrophenol, re...

Artificial Neural Networks Applied to the Classification of Emission Lines in Inductively Coupled Plasma–Atomic Emission Spectroscopy

Spectral-analytical lines used in inductively coupled plasma–atomic emission spectroscopy were classified in two different groups, identified as soft and hard lines, using artificial neural networks. The neural strategies of back-propagation of errors and self-organizing maps were applied. The spectral lines were characterized by means of their theoretical spectral data, namely wavelength, the excitation energies and the lower level of the electronic transition, statistical weights of the upper and lower level and the transition probability. The analytical sensitivity of the lines was also considered. The prediction results with the back-propagation model were obtained with an error lower than 0.1% in the test set. The self-organizing map allowed the localization of unknown lines in two different regions of a Kohonen map, which were identified as soft, and hard lines. The results were obtained with a similarity of 97% with respect to the back-propagation method.

Artificial Neural Networks Applied to Survival Prediction in Breast Cancer

In this study, we evaluated the accuracy of a neural network in predicting 5-, 10- and 15-year breast-cancer-specific survival. A series of 951 breast cancer patients was divided into a training set of 651 and a validation set of 300 patients. Eight variables were entered as input to the network: tumor size, axillary nodal status, histological type, mitotic count, nuclear pleomorphism, tubule formation, tumor necrosis and age. The area under the ROC curve (AUC) was used as a measure of accuracy of the prediction models in generating survival estimates for the patients in the independent validation set. The AUC values of the neural network models for 5-, 10- and 15-year breast-cancer-specific survival were 0.909, 0.886 and 0.883, respectively.The corresponding AUC values for logistic regression were 0.897, 0.862 and 0.858. Axillary lymph node status (N0 vs. N+) predicted 5-year survival with a specificity of 71% and a sensitivity of 77%. The sensitivity of the neural network model was 91% at this specificity level. The rate of false predictions at 5 years was 82/300 for nodal status and 40/300 for the neural network. When nodal status was excluded from the neural network model, the rate of false predictions increased only to 49/300 (AUC 0.877). An artificial neural network is very accurate in the 5-, 10- and 15-year breast-cancer-specific survival prediction. The consistently high accuracy over time and the good predictive performance of a network trained without information on nodal status demonstrate that neural networks can be important tools for cancer survival prediction.