Artificial Neural Network Learning Research Articles

English Grammar Detection Based on LSTM-CRF Machine Learning Model.

Deep learning and neural network have been widely used in the field of speech, vocabulary, text, pictures, and other information processing fields, which has achieved excellent research results. Neural network algorithm and prediction model were used in this paper for the study and exploration of English grammar. Aiming at the application requirements of English grammar accuracy and standardization, we proposed a machine learning model based on LSTM-CRF to detect and analyze English grammar. This paper briefly summarized the development trend of deep learning and neural network algorithm and designed the structure pattern of radial basis function neural network in grammar semantic detection and analysis on the basis of deep learning artificial neural network theory. Based on the morphological features of English grammar, a grammar database was established according to the rules of English word segmentation. In this paper, we proposed an improved conditional random field CRF (Conditional Random Field) network model based on LSTM (Long Short-Term Memory) neural network. It can improve the problem that the traditional machine learning model relies on feature point selection in English grammar detection. The machine learning model based on LSTM-CRF was used to recognize English grammar text entities. The results show that the English grammar detection system based on the LSTM-CRF model can simplify the process structure in the recognition process, reduce the unnecessary operation cycle, and improve the overall detection accuracy.

Technology Readiness and Cryptocurrency Adoption: PLS-SEM and Deep Learning Neural Network Analysis

Today’s world is increasingly dependent on technology directly or indirectly. The rapid technological advancement has impacted people to adopt the technology. As cryptocurrency recently commenced, few studies have attempted to investigate this use of technology. In this study, the technology readiness aspects- Optimism, Innovativeness, Discomfort, and Insecurity are used to understand the people’s adoption of cryptocurrency. A multi-approach of Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Deep learning Artificial Neural Network (ANN) analysis was performed. Deep learning Artificial Neural Network (ANN) analysis was performed to complement PLS-SEM findings and predict higher accuracy. This study shows that technology readiness dimensions - Optimism, Innovativeness, Discomfort, and Insecurity have meaningful relationships with cryptocurrency adoption.

Radiomics AI prediction for head and neck squamous cell carcinoma (HNSCC) prognosis and recurrence with target volume approach.

Objectives:To evaluate the performance of radiomics features extracted from planning target volume (PTV) and gross tumor volume (GTV) in the prediction of the death prognosis and cancer recurrence rate for head and neck squamous cell carcinoma (HNSCC).Methods:188 HNSCC patients’ planning CT images with radiotherapy structures sets were acquired from Cancer Imaging Archive (TCIA). The 3D slicer (v. 4.10.2) with the PyRadiomics extension (Computational Imaging and Bioinformatics Lab, Harvard medical School) was used to extract radiomics features from the radiotherapy planning images. An in-house developed deep learning artificial neural networks (DL-ANN) model was used to predict death prognosis and cancer recurrence rate based on the features extracted from GTV and PTV of the CT images.Results:The PTV radiomics features with DL-ANN model could achieve 77.7% accuracy with overall AUC equal to 0.934 and 0.932 when predicting HNSCC-related death prognosis and cancer recurrence respectively. Furthermore, the DL-ANN model can achieve an accuracy of 74.3% with AUC equal to 0.947 and 0.956 for the HNSCC-related death prognosis and cancer recurrence respectively using GTV features.Conclusion:Using both GTV and PTV radiomics features in the DL-ANN model, can aid in predicting HNSCC-related death prognosis and cancer recurrence. Clinicians may find it helpful in formulating different treatment regimens and facilitate personized medicine based on the predicted outcome when performing GTV and PTV delineation.Advances in knowledge:Radiomics features of GTV and PTV are reliable prognosis and recurrence predicting tools, which may help clinicians in GTV and PTV delineation to facilitate delivery of personalized treatment.

CAN EXCHANGE RATES BE PREDICTABLE? EXCHANGE RATE PREDICTION MODEL WITH ANN FOR TURKEY

Ponte Academic JournalMar 2021, Volume 77, Issue 3 CAN EXCHANGE RATES BE PREDICTABLE? EXCHANGE RATE PREDICTION MODEL WITH ANN FOR TURKEYAuthor(s): Fuat Sekmen ,Haşmet Gokırmak, Murat Kürkcü, Şükrü Apaydın, Hasan MemişJ. Ponte - Mar 2021 - Volume 77 - Issue 3 doi: 10.21506/j.ponte.2021.3.9 Abstract:The forecasting concept can be expressed as predicting a variable’s future values using various methods under certain assumptions. In the estimations made using the time series, the value of a variable in the current period and the past period values are used. In the time series analysis, past time values of the variable to be estimated constitute the model’s independent variables. Learning from data, generalizing, working with very large samples, etc. are essential Artificial Neural Networks (ANN) methodology features. ANN methodology, which provides significant advantages thanks to these features, has vast usage possibilities in prediction modeling as in other fields. ANN has become one of the time series estimations’ methods since the late 1980s. This study handles an artificial neural network learning paradigm to estimate the exchange rate for economic targets. This paradigm has not prior conditions such as normal distribution or stationary. Therefore it has been frequently preferred in time-series analyses. The present study predicts the Turkish Lira’s value against the U.S. Dollar by using an ANN methodology. Our study is built upon purchasing power parity and the different researches dealing with exchange rate predictions. Our primary purpose is to minimize the error between the expected output of the network and the output it produces. This study uses an Artificial Neural Network model that builds upon the MLP to estimate the exchange rates. We first define the MLP with one hidden layer for this model, using monthly data from 2000 to 2019. In contrast, the independent variables are interest rates, Gross Domestic Product, and Consumer Price Index data for Turkey and the United States of America. We prefer batch training to train the ANN in SPSS Neural Networks 17.0 software. Sixty percent of the data were used in training, 25 percent in the testing, and 10 percent in the holdout, of the artificial neural networks, respectively. According to research results, the performance of ANN is relatively higher. While the relative error in the training set is 1 percent, the test set’s relative error is below 1 percent (0,007). Download full text:Check if you have access through your login credentials or your institution Username Password

YAPAY SİNİR AĞLARI VE DERİN ÖĞRENME KULLANARAK TÜRKİYE''DEKİ COVID-19 VAKALARI İÇİN TAHMİN MODELLERİ

Governments face a dilemma between public health and the economy while making strategic decisions on health during a pandemic outbreak. It is of great importance to forecast the number of cases in terms of strategic decisions to be taken by governments especially in outbreak periods and to manage the dilemma mentioned. One of the important issues today is the Covid-19 outbreak for almost all countries. Unfortunately, no effective vaccine or treatment has been found for Covid-19 yet. At the time of this study, however, it was reported that the total number of reported cases by the World Health Organization worldwide was more than thirteen million. Various quarantine measures have been necessary to deal with such a large epidemic. Quarantine measures taken by governments bring countries to face to face with the economic crisis. This creates economic uncertainties and puts governments under tremendous pressure to make accurate and least harmful strategic decisions. For these reasons, governments prefer to make strategic decisions for Covid-19 step by step observing the situation rather than making a sudden decision. If the number of pandemic cases could be predicted before a predetermined time, it would be used as an important guide for governments to manage public health and economic dilemma more accurately. Therefore, this study provides artificial neural network (ANN) and deep learning models (long-short term memory, LSTM networks) to forecast Covid-19 cases before 7-day. The proposed models were tested on real data for Turkey. The results showed that LSTM models performed better than ANN models in both cumulative cases and new cases on the training data set. Comparing the performance of the proposed models over the whole data set, it was observed that the ANN and LSTM algorithms gave competitive results. In addition, the cumulative case forecast performances of both ANN and LSTM models were observed to be better than the new case forecast.

Discovery of New Ti-Based Alloys Aimed at Avoiding/Minimizing Formation of α” and ω-Phase Using CALPHAD and Artificial Intelligence

In this work, we studied a Ti-Nb-Zr-Sn system for exploring novel composition and temperatures that will be helpful in maximizing the stability of β phase while minimizing the formation of α” and ω-phase. The Ti-Nb-Zr-Sn system is free of toxic elements. This system was studied under the framework of CALculation of PHAse Diagram (CALPHAD) approach for determining the stability of various phases. These data were analyzed through artificial intelligence (AI) algorithms. Deep learning artificial neural network (DLANN) models were developed for various phases as a function of alloy composition and temperature. Software was written in Python programming language and DLANN models were developed utilizing TensorFlow/Keras libraries. DLANN models were used to predict various phases for new compositions and temperatures and provided a more complete dataset. This dataset was further analyzed through the concept of self-organizing maps (SOM) for determining correlations between phase stability of various phases, chemical composition, and temperature. Through this study, we determined candidate alloy compositions and temperatures that will be helpful in avoiding/minimizing formation of α” and ω-phase in a Ti-Zr-Nb-Sn system. This approach can be utilized in other systems such as ω-free shape memory alloys. DLANN models can even be used on a common Android mobile phone.

Box Office Success Prediction Through Artificial Neural Network and Machine Learning Algorithm

In this study, a comparison is made between Artificial Neural Network Algorithm and Machine Learning Algorithm of Support Vector Machine by considering pre-release and pre plus post release features of a movie for predicting a movie to a particular success class. These success classes includes Flop/Disaster, Average, Hit, Super-Hit and Block-Buster of a movie. Our best performing model from Artificial Neural Network and Support Vector Machine is able to give better performance in order to predict any particular movie to specific success class of a movie for both the cases of prerelease as well as pre plus post release features of a movie.

Comparison of River Basin Water Level Forecasting Methods: Sequential Neural Networks and Multiple-Input Functional Neural Networks

To precisely forecast downstream water levels in catchment areas during typhoons, the deep learning artificial neural networks were employed to establish two water level forecasting models using sequential neural networks (SNNs) and multiple-input functional neural networks (MIFNNs). SNNs, which have a typical neural network structure, are network models constructed using sequential methods. To develop a network model capable of flexibly consolidating data, MIFNNs are employed for processing data from multiple sources or with multiple dimensions. Specifically, when images (e.g., radar reflectivity images) are used as input attributes, feature extraction is required to provide effective feature maps for model training. Therefore, convolutional layers and pooling layers were adopted to extract features. Long short-term memory (LSTM) layers adopted during model training enabled memory cell units to automatically determine the memory length, providing more useful information. The Hsintien River basin in northern Taiwan was selected as the research area and collected relevant data from 2011 to 2019. The input attributes comprised one-dimensional data (e.g., water levels at river stations, rain rates at rain gauges, and reservoir release) and two-dimensional data (i.e., radar reflectivity mosaics). Typhoons Saola, Soudelor, Dujuan, and Megi were selected, and the water levels 1 to 6 h after the typhoons struck were forecasted. The results indicated that compared with linear regressions (REG), SNN using dense layers (SNN-Dense), and SNN using LSTM layers (SNN-LSTM) models, superior forecasting results were achieved for the MIFNN model. Thus, the MIFNN model, as the optimal model for water level forecasting, was identified.

Performance of a language identification system using hybrid features and ANN learning algorithms

This paper discusses the use of hybrid features and artificial neural network (ANN) for spoken language identification (LID). At the feature extraction stage, we have used RASTA-PLP features and later hybrid features by combining the state-of-the-art MFCC features with RASTA-PLP features. The classifier used at the back-end of the LID system is Feed-forward Back Propagation Neural Network (FFBPNN). Performance comparison is done for all the thirteen learning algorithms available in FFBPNN. Results indicate better performance with MFCC + RASTA-PLP features as compared to RASTA-PLP features when used individually. The classification accuracy of 94.6 percent is obtained with ‘trainlm’ network training function and a test error rate of 0.10 with MFCC + RASTA-PLP hybrid features. On the other hand, RASTA-PLP features provide the best classification accuracy of 89.6%. Also, in this paper two new training functions are proposed. Experimental results show that for the MFCC + RASTA-PLP features, the proposed training function 1 offers 95.1 percent accuracy and 0.0993 mean square error. Similarly, the proposed training function 2 offers 95.3 per cent classification accuracy and 0.093 minimum mean square error for MFCC + RASTA-PLP features. The feasibility of the proposed solution is estimated by simulating multiple experiments on a language database of four languages i.e. Hindi, Tamil, Malayalam and English in the working Platform of MATLAB.

Development of an application for creation and learning of neural networks to utilize in environmental sciences

Machine learning methods originated from artificial intelligence and today are applied in several fields concerning environmental sciences. Thanks to their powerful nonlinear modelling capability, machine learning methods today are utilized in satellite data processing, general circulation models(GCM), weather and climate prediction, air quality forecasting, analysis and modelling of environmental data, oceanographic and hydrological forecasting, ecological modelling, and monitoring of snow, ice and forests. Currently, the popularity of neural networks is growing; their areas of application are constantly expanding. In these conditions, the task of choosing a convenient tool for utilizing in environmental science with neural networks becomes urgent. There are many tools for working with neural networks, but each of them has its own drawbacks. So most of the existing tools require users to have programming knowledge; there are no tools to help quickly select the optimal network structure for the problem being solved. The purpose of the research is to simplify the process of choosing the optimal structure of an artificial neural network by developing an application with a graphical user interface with a visual representation of the stages of creating and learning neural networks in environmental sciences. The object of research is artificial feed-forward neural networks. Research work on the study, comparison and analysis of existing tools for the creation, learning and use of artificial neural networks has been carried out. Based on the research results, an application with a graphical interface aimed at solving the assigned tasks has been developed. An application developed to achieve this goal works correctly, without failures, and allows creating and learning feed-forward neural networks without programming knowledge.

Use of Deep Learning Networks and Statistical Modeling to Predict Changes in Mechanical Parameters of Contaminated Bone Cements.

The purpose of the study was to test the usefulness of deep learning artificial neural networks and statistical modeling in predicting the strength of bone cements with defects. The defects are related to the introduction of admixtures, such as blood or saline, as contaminants into the cement at the preparation stage. Due to the wide range of applications of deep learning, among others in speech recognition, bioinformation processing, and medication design, the extent was checked to which it is possible to obtain information related to the prediction of the compressive strength of bone cements. Development and improvement of deep learning network (DLN) algorithms and statistical modeling in the analysis of changes in the mechanical parameters of the tested materials will enable determining an acceptable margin of error during surgery or cement preparation in relation to the expected strength of the material used to fill bone cavities. The use of the abovementioned computer methods may, therefore, play a significant role in the initial qualitative assessment of the effects of procedures and, thus, mitigation of errors resulting in failure to maintain the required mechanical parameters and patient dissatisfaction.

Data Normalisation-Based Solar Irradiance Forecasting Using Artificial Neural Networks

Due to continual day-to-day increase in electricity demand, and hazardous and critical threats of fossil fuels to the environment, researchers are scrutinizing over substitute energy sources. Solar radiation intensity prediction is essential for conducting various research work in the emerging field of Renewable Energy Sources (RESs). This paper has presented development of monthly averaged solar radiation intensity prediction model by employing Artificial Neural Network (ANN) algorithm. Various meteorological parameters have been considered over period of 2 years to execute forecasting for Chandigarh, India. Different normalisation techniques such as min-max, decimal and z-score have been utilised to normalise database. Structure and parameter learning of ANNs has been carried out. Comparative analysis has been done to select optimal architecture based on different performance evaluation measures such as mean square error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and correlation coefficient (R-value) and training time. The network topology with least forecasting errors, higher R-value has been found to be optimum and further simulated for predicting monthly averaged solar radiation intensity for Chandigarh region.

A Deep Learning Approach for Predicting Bus Passenger Demand Based on Weather Conditions

Abstract This work apply a deep learning artificial neural network model – the Multilayer Perceptron – as a regression model to estimate the demand of bus passengers. Transit bus ridership and weather conditions were collected over a year from a medium-size European metropolitan area and linked under the assumption: individuals choose the travel mode based on the weather conditions that are observed during (a) the departure hour, (b) the hour before or (c) two hours prior to the travel start. The transit ridership data were also labelled according to the hour of the day, day of the week, month, and whether there was a strike and/or holiday or not. The results show that the prediction error of the model decrease by ~9% when the weather conditions observed two hours before travel start is taken into account. The model sensitivity analyses reveals that the worst performance is obtained for a strike day of a weekday in spring (typically Wednesdays or Thursdays).

(Invited) Density Functional Theory Modeling - Machine Learning Approach for Redox Potential of Organic Electrode Materials

In this talk, we present the first-principles density functional theory modeling of redox properties of various carbon materials, including quinone derivatives, dopamine, and DNA. First, we discuss the contribution of electron affinity of molecules and solvation to redox potential from our density functional theory (DFT) calculations. Second, we discuss the relationship of redox potential with the design of molecular structure. It is found that aromaticity and electron-withdrawing functional groups can be used to tune the redox potential. Especially, the variation of redox potential as a function of Li association is discussed. The conclusion is that we can tune up the redox potential by designing the detailed molecular structure. Next, all these results are used to build up machine learning. We discuss our artificial neural network and other machine learning algorithms regarding its validation and predictability. Through this study, the redox characteristics of carbon materials are systematically investigated using the first-principles computational modeling method, and then utilized via machine learning methods for high-throughput screening process.

A Novel Assisted Artificial Neural Network Modeling Approach for Improved Accuracy Using Small Datasets: Application in Residual Strength Evaluation of Panels with Multiple Site Damage Cracks

An artificial neural network (ANN) extracts knowledge from a training dataset and uses this acquired knowledge to forecast outputs for any new set of inputs. When the input/output relations are complex and highly non-linear, the ANN needs a relatively large training dataset (hundreds of data points) to capture these relations adequately. This paper introduces a novel assisted-ANN modeling approach that enables the development of ANNs using small datasets, while maintaining high prediction accuracy. This approach uses parameters that are obtained using the known input/output relations (partial or full relations). These so called assistance parameters are included as ANN inputs in addition to the traditional direct independent inputs. The proposed assisted approach is applied for predicting the residual strength of panels with multiple site damage (MSD) cracks. Different assistance levels (four levels) and different training dataset sizes (from 75 down to 22 data points) are investigated, and the results are compared to the traditional approach. The results show that the assisted approach helps in achieving high predictions’ accuracy (<3% average error). The relative accuracy improvement is higher (up to 46%) for ANN learning algorithms that give lower prediction accuracy. Also, the relative accuracy improvement becomes more significant (up to 38%) for smaller dataset sizes.

Learning Of Artificial Neural Networks by Genetic Algorithms (Dept.E)

Learning Of Artificial Neural Networks by Genetic Algorithms (Dept.E)

Machine learning approaches for sex estimation using cranial measurements.

The aim of the present study is to apply support vector machines (SVM) and artificial neural network (ANN) as sex classifiers and to generate useful classification models for sex estimation based on cranial measurements. Besides, the performance of the generated sub-symbolic machine learning models is compared with models developed through logistic regression (LR). The study was carried out on computed tomography images of 393 Bulgarian adults (169 males and 224 females). The three-dimensional coordinates of 47 landmarks were acquired and used for calculation of the cranial measurements. A total of 64 measurements (linear distances, angles, triangle areas and heights) and 22 indices were calculated. Two datasets were assembled including the linear measurements only and all measurements and index, respectively. An additional third dataset comprising all possible interlandmark distances between the landmarks was constructed. Two machine learning algorithms-SVM and ANN and a traditional statistical analysis LR-were applied to generate models for sex estimation. In addition, two advanced attribute selection techniques (Weka BestFirst and Weka GeneticSearch) were used. The classification accuracy of the models was evaluated by means of 10 × 10-fold cross-validation procedure. All three methods achieved accuracy results higher than 95%. The best accuracy (96.1 ± 0.5%) was obtained by SVM and it was statistically significantly higher than the best results achieved by ANN and LR. SVM and ANN reached higher accuracy by training on the full datasets than the selection datasets, except for the sample described by the interlandmark distances, where the reduction of attributes by the GeneticSearch algorithm improved the accuracy.

Turkish handwriting recognition system using multi-layer perceptron

Recently, handwriting recognition has found many application areas along with technological advances. Handwriting recognition systems can greatly simplify human life by reading tax returns, forwarding mail, reading bank checks, and so on. On the other hand, these systems can reduce the need for human interaction. Therefore, academic and commercial studies of handwriting characters have recently become an important research topic in pattern recognition. In this study, Turkish handwritten letter recognition system from A to Z was developed in C++ environment by using Artificial Neural Networks (ANNs). After the feature data were extracted, handwriting images were presented to the network, the training process of ANN was completed, and different handwriting images were classified with trained ANN. In this study, MLP (Multi-Layered Perceptron: MLP) type ANN and back-propagation learning algorithm were used. The ANN used has 35 inputs and 23 outputs. In the hidden layer, ANNs with different numbers of artificial neural cells (neurons) were evaluated and the most appropriate neural number ANN was selected. As a result, ANN with 24 neurons was selected in the hidden layer and handwriting images was classified with an accuracy rate of 94.90 %.

Possibilities of an artificial neural network use to control oxygen consumption in a converter shop

Converter steelmaking process uses high-performance processing plants and a high level of process automation. Complex multi-stage processes typically use many factors that have effect both on individual stages of the processes and on how a combination of the processes runs as a whole. This makes it necessary to select and/or develop control methods for the converter process having no full current data on process parameters and effects of random disturbances. Use of artificial neural networks is the most effective method available now for analyzing and predicting parameters of different processes (including converter process). This article deals with an algorithm for oxygen measurement based on chemical analysis of steel and process history through some period, using an artificial neural network and machine learning. Initial data were reviewed using correlation and regression analysis, the artificial neural network architecture chosen is reasoned as the most appropriate for the objective on hand and the results are analyzed. The element measurement accuracy achieved is satisfactory, which makes the resulting artificial neural network usable in an automation system for testing purposes. Network performance can be improved with the help of experts in the subject area, whose expertise can be used to correct the model.

Neural Networks Multiobjective Learning With Spherical Representation of Weights.

This article presents a novel representation of artificial neural networks (ANNs) that is based on a projection of weights into a new spherical space defined by a radius r and a vector of angles Θ . This spherical representation of ANNs further simplifies the multiobjective learning problem, which is usually treated as a constrained optimization problem that requires great computational effort to maintain the constraints. With the proposed spherical representation, the constrained optimization problem becomes unconstrained, which simplifies the formulation and computational effort required. In addition, it also allows the use of any nonlinear optimization method for the multiobjective learning of ANNs. Results presented in this article show that the proposed spherical representation of weights yields more accurate estimates of the Pareto set than the classical multiobjective approach. Regarding the final solution selected from the Pareto set, our approach was effective and outperformed some state-of-the-art methods on several data sets.