scholarly journals A New ANN-Particle Swarm Optimization with Center of Gravity (ANN-PSOCoG) Prediction Model for the Stock Market under the Effect of COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Razan Jamous ◽  
Hosam ALRahhal ◽  
Mohamed El-Darieby
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Stock Market ◽  
Stock Markets ◽  
Prediction Accuracy ◽  
Processing Time ◽  
High Accuracy ◽  
Swarm Optimization ◽  
Proposed Model ◽  
Closing Price

Since the declaration of COVID-19 as a pandemic, the world stock markets have suffered huge losses prompting investors to limit or avoid these losses. The stock market was one of the businesses that were affected the most. At the same time, artificial neural networks (ANNs) have already been used for the prediction of the closing prices in stock markets. However, standalone ANN has several limitations, resulting in the lower accuracy of the prediction results. Such limitation is resolved using hybrid models. Therefore, a combination of artificial intelligence networks and particle swarm optimization for efficient stock market prediction was reported in the literature. This method predicted the closing prices of the shares traded on the stock market, allowing for the largest profit with the minimum risk. Nevertheless, the results were not that satisfactory. In order to achieve prediction with a high degree of accuracy in a short time, a new improved method called PSOCoG has been proposed in this paper. To design the neural network to minimize processing time and search time and maximize the accuracy of prediction, it is necessary to identify hyperparameter values with precision. PSOCoG has been employed to select the best hyperparameters in order to construct the best neural network. The created network was able to predict the closing price with high accuracy, and the proposed model ANN-PSOCoG showed that it could predict closing price values with an infinitesimal error, outperforming existing models in terms of error ratio and processing time. Using S&P 500 dataset, ANN-PSOCoG outperformed ANN-SPSO in terms of prediction accuracy by approximately 13%, SPSOCOG by approximately 17%, SPSO by approximately 20%, and ANN by approximately 25%. While using DJIA dataset, ANN-PSOCoG outperformed ANN-SPSO in terms of prediction accuracy by approximately 18%, SPSOCOG by approximately 24%, SPSO by approximately 33%, and ANN by approximately 42%. Besides, the proposed model is evaluated under the effect of COVID-19. The results proved the ability of the proposed model to predict the closing price with high accuracy where the values of MAPE, MAE, and RE were very small for S&P 500, GOLD, NASDAQ-100, and CANUSD datasets.

scholarly journals Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5609 ◽  
Author(s):  
Shahab S. Band ◽  
Saeid Janizadeh ◽  
Subodh Chandra Pal ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Gully Erosion ◽  
Slope Aspect ◽  
Swarm Optimization ◽  
Proposed Model ◽  
Deep Learning Neural Network

This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.

scholarly journals Butterfly-PSO Based Energy Efficient Data Analysis of Wireless IoT Devices

2019 ◽  
Vol 8 (3) ◽  
pp. 5779-5784
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Data Analysis ◽  
Model Comparison ◽  
Cluster Head ◽  
Back Propagation ◽  
Particle Swarm ◽  
Cluster Center ◽  
Swarm Optimization ◽  
Proposed Model

Paper collecting data from various sources for research observation, security, etc. are depend on IOT networks. As IOT device are remotely which transform information from nearby area and lifespan of this network rely on energy uses for communication. So this paper proposed a neural network and genetic algorithm combination for increasing the life span of the network. Error Back Propagation neural network was trained to identify best set of nodes for the cluster center selection. This machine learning based data selection increase the cluster selection accuracy of the BFPSO (Butterfly Particle Swarm Optimization). As combination get reduce by neural network data analysis so less number of population need to be developed for BFPSO algorithm which ultimately increase the accuracy of device selection. Various set of region size and number of nodes were developed to evaluate proposed model. Comparison of proposed model NN-BFPSO-CHS (Neural Network Butterfly Particle Swarm Optimization based Cluster Head Selection) was done with previous existing methods on different evaluation parameters and it was obtained that proposed model has improved all set of parameters

Research and Application of Wavelet Neural Networks of Particle Swarm Optimization Algorithm in the Performance Prediction of Centrifugal Compressor

2011 ◽  
Vol 187 ◽  
pp. 271-276 ◽  
Author(s):  
Sheng Zhong Huang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Centrifugal Compressor ◽  
Performance Prediction ◽  
Prediction Accuracy ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization

The traditional method of centrifugal compressor performance prediction is usually the BP neural network, however, the problems are that prediction accuracy is not high enough, convergence is slow and it is apt to fall into local optimal solution. In order to predict the performance of centrifugal compressors more accurately and identify the implicit problems in advance, now we combine the particle swarm optimization, wavelet theory and neural networks, to establish performance prediction model of centrifugal compressor based on wavelet neural network of PSO. First, set the various parameters of wavelet neural network as the particle position vector X and the energy function of mean square error as the optimized objective function. By particle swarm optimization algorithm to iterate the basic formula to obtain the corresponding WNN coefficient and then use back-propagation algorithm to train WNN to approach any nonlinear function. Simulation results show that application of the prediction model can achieve the accurate prediction of performance and monitoring of centrifugal compressor. The prediction model has the advantages of simple algorithm, stable structure, fast calculation of convergence speed and strong generalization ability with a prediction accuracy of 99%, 13% higher than prediction accuracy of traditional methods, which has a certain theoretical research value and practical value.

Improved Dynamic Process Neural Network and its Application

2010 ◽  
Vol 458 ◽  
pp. 143-148
Author(s):  
Pei Yin Zhang ◽  
G.B. Yu ◽  
B. Dai ◽  
Ying Jie Ao
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Particle Swarm Optimization ◽  
Dynamic Process ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Test Results ◽  
Tourism Demand ◽  
Swarm Optimization ◽  
Proposed Model

The tourism demand is essential in terms of national economy and the improvement of people’ income. But it is difficult for traditional methods to predict the tendency of the tourism demand. In this paper, a time series prediction method based on dynamic process neural network (DPNN) is proposed to solve this problem. An improved particle swarm optimization (IPSO) is developed. By tuning the structure and improving the connection weights of PNN simultaneously, a partially connected DPNN can be obtained. The effectiveness of the proposed DPNN is proved by Henon system. Finally, the proposed DPNN is utilized to predict the tourism demand, and the test results indicate that the proposed model seems to perform well and appears suitable for using as a predictive maintenance tool.

scholarly journals Short-Term Wind Speed Forecast Based on B-Spline Neural Network Optimized by PSO

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Zhongqiang Wu ◽  
Wenjing Jia ◽  
Liru Zhao ◽  
Changhan Wu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Wind Speed ◽  
Prediction Accuracy ◽  
Particle Swarm ◽  
Short Term ◽  
Swarm Optimization ◽  
B Spline ◽  
Input Space ◽  
Definition Of

Considering the randomness and volatility of wind, a method based on B-spline neural network optimized by particle swarm optimization is proposed to predict the short-term wind speed. The B-spline neural network can change the division of input space and the definition of basis function flexibly. For any input, only a few outputs of hidden layers are nonzero, the outputs are simple, and the convergence speed is fast, but it is easy to fall into local minimum. The traditional method to divide the input space is thoughtless and it will influence the final prediction accuracy. Particle swarm optimization is adopted to solve the problem by optimizing the nodes. Simulated results show that it has higher prediction accuracy than traditional B-spline neural network and BP neural network.

scholarly journals A Tourist Flow Prediction Model for Scenic Areas Based on Particle Swarm Optimization of Neural Network

2020 ◽  
Vol 34 (4) ◽  
pp. 395-402
Author(s):  
Nan Chen ◽  
Yi Liang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Short Term Memory ◽  
Service Industry ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Flow Prediction ◽  
Proposed Model ◽  
Tourist Flow

In recent years, China has been expanding domestic demand and promoting the service industry. This is a mixed blessing for the further development of tourism. To make accurate prediction of tourist flow, this paper proposes a tourist flow prediction model for scenic areas based on the particle swarm optimization (PSO) of neural network (NN). Firstly, a system of influencing factors was constructed for the tourist flow in scenic areas, and the factors with low relevance were eliminated through grey correlation analysis (GCA). Next, the long short-term memory (LSTM) NN was optimized with adaptive PSO, and used to establish the tourist flow prediction model for scenic areas. After that, the workflow of the proposed model was introduced in details. Experimental results show that the proposed model can effectively predict the tourist flow in scenic areas, and provide a desirable prediction tool for other fields.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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