Simulation of Stock Prediction System using Artificial Neural Networks

2020 ◽  
pp. 511-530
Author(s):  
Omisore Olatunji Mumini ◽  
Fayemiwo Michael Adebisi ◽  
Ofoegbu Osita Edward ◽  
Adeniyi Shukurat Abidemi
Keyword(s):  
Neural Networks ◽  
Test Data ◽  
Stock Prices ◽  
Prediction Accuracy ◽  
Stock Exchange ◽  
Training Data ◽  
Prediction System ◽  
Closing Price ◽  
Non Linear ◽  
Predicted Values

Stock trading, used to predict the direction of future stock prices, is a dynamic business primarily based on human intuition. This involves analyzing some non-linear fundamental and technical stock variables which are recorded periodically. This study presents the development of an ANN-based prediction model for forecasting closing price in the stock markets. The major steps taken are identification of technical variables used for prediction of stock prices, collection and pre-processing of stock data, and formulation of the ANN-based predictive model. Stock data of periods between 2010 and 2014 were collected from the Nigerian Stock Exchange (NSE) and stored in a database. The data collected were classified into training and test data, where the training data was used to learn non-linear patterns that exist in the dataset; and test data was used to validate the prediction accuracy of the model. Evaluation results obtained from WEKA shows that discrepancies between actual and predicted values are insignificant.

Simulation of Stock Prediction System using Artificial Neural Networks

2016 ◽  
Vol 3 (3) ◽  
pp. 25-44 ◽  
Author(s):  
Omisore Olatunji Mumini ◽  
Fayemiwo Michael Adebisi ◽  
Ofoegbu Osita Edward ◽  
Adeniyi Shukurat Abidemi
Keyword(s):  
Test Data ◽  
Stock Prices ◽  
Prediction Accuracy ◽  
Stock Exchange ◽  
Training Data ◽  
Prediction System ◽  
Stock Trading ◽  
Closing Price ◽  
Non Linear ◽  
Predicted Values

Stock trading, used to predict the direction of future stock prices, is a dynamic business primarily based on human intuition. This involves analyzing some non-linear fundamental and technical stock variables which are recorded periodically. This study presents the development of an ANN-based prediction model for forecasting closing price in the stock markets. The major steps taken are identification of technical variables used for prediction of stock prices, collection and pre-processing of stock data, and formulation of the ANN-based predictive model. Stock data of periods between 2010 and 2014 were collected from the Nigerian Stock Exchange (NSE) and stored in a database. The data collected were classified into training and test data, where the training data was used to learn non-linear patterns that exist in the dataset; and test data was used to validate the prediction accuracy of the model. Evaluation results obtained from WEKA shows that discrepancies between actual and predicted values are insignificant.

scholarly journals Estimating the Distribution of Viral Taxa in Next-generation Sequencing data using Artificial Neural Networks

2020 ◽  
Author(s):  
Moritz Kohls ◽  
Magdalena Kircher ◽  
Jessica Krepel ◽  
Pamela Liebig ◽  
Klaus Jung
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Next Generation Sequencing ◽  
Test Data ◽  
Prediction Accuracy ◽  
Confusion Matrix ◽  
Training Data ◽  
Comparative Metagenomics ◽  
Artificial Neural ◽  
Generation Sequencing

Abstract Background: Estimating the taxonomic composition of viral sequences in a biological sample processed by next-generation sequencing is an important step for comparative metagenomics. For that purpose, sequencing reads are usually classified by mapping them against a database of known viral reference genomes. This fails, however, to classify reads from novel viruses and quasispecies whose reference sequences are not yet available in public databases. Methods: In order to circumvent the problem of a mapping approach with unknown viruses, the feasibility and performance of neural networks to classify sequencing reads to taxonomic classes is studied. For that purpose, taxonomy and genome data from the NCBI database are used to sample artificial reads from known viruses with known taxonomic attribution. Based on these training data, artificial neural networks are fitted and applied to classify single viral read sequences to di erent taxa. Model building includes di erent input features derived from artificial read sequences as possible predictors which are chosen by a feature selection method. Training, validation and test data are computed from these input features. To summarise classification results, a generalised confusion matrix is proposed which lists all possible misclassification combination frequencies. Two new formulas to statistically estimate taxa frequencies are introduced for studying the overall viral composition.Results: We found that the best taxonomic level supported by the NCBI database is that of viral orders. Prediction accuracy of the fitted models is evaluated on test data and classification results are summarised in a confusion matrix, from which diagnostic measures such as sensitivity and specificity as well as positive and negative predictive values are calculated. The prediction accuracy of the artificial neural net is considerably higher than for random classification and posterior estimation of taxa frequencies is closer to the true distribution in the training data than simple classification or mapping results. Conclusions: Neural networks are helpful to classify sequencing reads into viral orders and can be used to complement the results of mapping approaches. The machine learning approach is not limited to already known viruses. In addition, statistical estimations of taxa frequencies can be used for subsequent comparative metagenomics.

scholarly journals Facial Expression Recognition using Residual Convnet with Image Augmentations

2021 ◽  
Vol 14 (2) ◽  
pp. 127-135
Author(s):  
Fadhil Yusuf Rahadika ◽  
Novanto Yudistira ◽  
Yuita Arum Sari
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Test Data ◽  
Facial Expression Recognition ◽  
Optimization Method ◽  
Training Data ◽  
Batch Size ◽  
Online Video ◽  
Expression Recognition ◽  
Validation Data

During the COVID-19 pandemic, many offline activities are turned into online activities via video meetings to prevent the spread of the COVID 19 virus. In the online video meeting, some micro-interactions are missing when compared to direct social interactions. The use of machines to assist facial expression recognition in online video meetings is expected to increase understanding of the interactions among users. Many studies have shown that CNN-based neural networks are quite effective and accurate in image classification. In this study, some open facial expression datasets were used to train CNN-based neural networks with a total number of training data of 342,497 images. This study gets the best results using ResNet-50 architecture with Mish activation function and Accuracy Booster Plus block. This architecture is trained using the Ranger and Gradient Centralization optimization method for 60000 steps with a batch size of 256. The best results from the training result in accuracy of AffectNet validation data of 0.5972, FERPlus validation data of 0.8636, FERPlus test data of 0.8488, and RAF-DB test data of 0.8879. From this study, the proposed method outperformed plain ResNet in all test scenarios without transfer learning, and there is a potential for better performance with the pre-training model. The code is available at https://github.com/yusufrahadika-facial-expressions-essay.

scholarly journals Analysis of Cantilever Beam Deflection under Uniformly Distributed Load using Artificial Neural Networks

2019 ◽  
Vol 255 ◽  
pp. 06004
Author(s):  
T.M.Y.S Tuan Ya ◽  
Reza Alebrahim ◽  
Nadziim Fitri ◽  
Mahdi Alebrahim
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cantilever Beam ◽  
Training Data ◽  
Beam Deflection ◽  
Problem Solver ◽  
Distributed Load ◽  
Uniformly Distributed Load ◽  
Non Linear ◽  
Artificial Neural

In this study the deflection of a cantilever beam was simulated under the action of uniformly distributed load. The large deflection of the cantilever beam causes the non-linear behavior of beam. The prupose of this study is to predict the deflection of a cantilever beam using Artificial Neural Networks (ANN). The simulation of the deflection was carried out in MATLAB by using 2-D Finite Element Method (FEM) to collect the training data for the ANN. The predicted data was then verified again through a non linear 2-D geometry problem solver, FEM. Loads in different magnitudes were applied and the non-linear behaviour of the beam was then recorded. It was observed that, there is a close agreement between the predicted data from ANN and the results simulated in the FEM.

scholarly journals Deep Learning Neural Networks to Predict Serious Complications After Bariatric Surgery: Analysis of Scandinavian Obesity Surgery Registry Data (Preprint)

2019 ◽  
Author(s):  
Yang Cao ◽  
Scott Montgomery ◽  
Johan Ottosson ◽  
Erik Näslund ◽  
Erik Stenberg
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bariatric Surgery ◽  
Deep Learning ◽  
Postoperative Complications ◽  
Obesity Surgery ◽  
Test Data ◽  
Registry Data ◽  
Training Data ◽  
Complications After Bariatric Surgery

BACKGROUND Obesity is one of today’s most visible public health problems worldwide. Although modern bariatric surgery is ostensibly considered safe, serious complications and mortality still occur in some patients. OBJECTIVE This study aimed to explore whether serious postoperative complications of bariatric surgery recorded in a national quality registry can be predicted preoperatively using deep learning methods. METHODS Patients who were registered in the Scandinavian Obesity Surgery Registry (SOReg) between 2010 and 2015 were included in this study. The patients who underwent a bariatric procedure between 2010 and 2014 were used as training data, and those who underwent a bariatric procedure in 2015 were used as test data. Postoperative complications were graded according to the Clavien-Dindo classification, and complications requiring intervention under general anesthesia or resulting in organ failure or death were considered serious. Three supervised deep learning neural networks were applied and compared in our study: multilayer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN). The synthetic minority oversampling technique (SMOTE) was used to artificially augment the patients with serious complications. The performances of the neural networks were evaluated using accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the receiver operating characteristic curve. RESULTS In total, 37,811 and 6250 patients were used as the training data and test data, with incidence rates of serious complication of 3.2% (1220/37,811) and 3.0% (188/6250), respectively. When trained using the SMOTE data, the MLP appeared to have a desirable performance, with an area under curve (AUC) of 0.84 (95% CI 0.83-0.85). However, its performance was low for the test data, with an AUC of 0.54 (95% CI 0.53-0.55). The performance of CNN was similar to that of MLP. It generated AUCs of 0.79 (95% CI 0.78-0.80) and 0.57 (95% CI 0.59-0.61) for the SMOTE data and test data, respectively. Compared with the MLP and CNN, the RNN showed worse performance, with AUCs of 0.65 (95% CI 0.64-0.66) and 0.55 (95% CI 0.53-0.57) for the SMOTE data and test data, respectively. CONCLUSIONS MLP and CNN showed improved, but limited, ability for predicting the postoperative serious complications after bariatric surgery in the Scandinavian Obesity Surgery Registry data. However, the overfitting issue is still apparent and needs to be overcome by incorporating intra- and perioperative information. CLINICALTRIAL

scholarly journals Deep Learning Neural Networks to Predict Serious Complications After Bariatric Surgery: Analysis of Scandinavian Obesity Surgery Registry Data

10.2196/15992 ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. e15992 ◽  
Author(s):  
Yang Cao ◽  
Scott Montgomery ◽  
Johan Ottosson ◽  
Erik Näslund ◽  
Erik Stenberg
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bariatric Surgery ◽  
Deep Learning ◽  
Postoperative Complications ◽  
Obesity Surgery ◽  
Test Data ◽  
Registry Data ◽  
Training Data ◽  
Complications After Bariatric Surgery

Background Obesity is one of today’s most visible public health problems worldwide. Although modern bariatric surgery is ostensibly considered safe, serious complications and mortality still occur in some patients. Objective This study aimed to explore whether serious postoperative complications of bariatric surgery recorded in a national quality registry can be predicted preoperatively using deep learning methods. Methods Patients who were registered in the Scandinavian Obesity Surgery Registry (SOReg) between 2010 and 2015 were included in this study. The patients who underwent a bariatric procedure between 2010 and 2014 were used as training data, and those who underwent a bariatric procedure in 2015 were used as test data. Postoperative complications were graded according to the Clavien-Dindo classification, and complications requiring intervention under general anesthesia or resulting in organ failure or death were considered serious. Three supervised deep learning neural networks were applied and compared in our study: multilayer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN). The synthetic minority oversampling technique (SMOTE) was used to artificially augment the patients with serious complications. The performances of the neural networks were evaluated using accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the receiver operating characteristic curve. Results In total, 37,811 and 6250 patients were used as the training data and test data, with incidence rates of serious complication of 3.2% (1220/37,811) and 3.0% (188/6250), respectively. When trained using the SMOTE data, the MLP appeared to have a desirable performance, with an area under curve (AUC) of 0.84 (95% CI 0.83-0.85). However, its performance was low for the test data, with an AUC of 0.54 (95% CI 0.53-0.55). The performance of CNN was similar to that of MLP. It generated AUCs of 0.79 (95% CI 0.78-0.80) and 0.57 (95% CI 0.59-0.61) for the SMOTE data and test data, respectively. Compared with the MLP and CNN, the RNN showed worse performance, with AUCs of 0.65 (95% CI 0.64-0.66) and 0.55 (95% CI 0.53-0.57) for the SMOTE data and test data, respectively. Conclusions MLP and CNN showed improved, but limited, ability for predicting the postoperative serious complications after bariatric surgery in the Scandinavian Obesity Surgery Registry data. However, the overfitting issue is still apparent and needs to be overcome by incorporating intra- and perioperative information.

Elimination of the Collision States of the Effectors of Industrial Robots by Application of Neural Networks

2015 ◽  
Vol 798 ◽  
pp. 276-281
Author(s):  
Erik Prada ◽  
Lenka Baločková ◽  
Michael Valášek
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Test Data ◽  
Learning Algorithms ◽  
Robotic System ◽  
Industrial Robots ◽  
Training Data ◽  
Methods Of Learning ◽  
In The Beginning

The article deals with the usage of methods of learning algorithms of neural networks for solving of collision states problem within multi-robotic cooperation. Nowadays, multi-robotic cooperation is a highly used method of work of two or more industrial robots. The requirements for elimination of collision states are getting more difficult when the multi-robotic system is more complicated. Methods of neural networks provide suitable tools for solving of complex cooperating problems. In the beginning of the article, we discuss the term “collision state” and the possibilities of its solving. In the following chapter, we discuss the theory of neural networks and learning algorithms, which we applied in solving of the collision states. In the final chapter, we implemented the practical verification of the model neural network in JSNN programme. It consisted of creating and learning of the training data and subsequent verification of the test data.

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