An Improved Harris’s Hawks Optimization for SAR Target Recognition and Stock Market Index Prediction

Hu, Hongping; Ao, Yan; Bai, Yanping; Cheng, Rong; Xu, Ting

doi:10.1109/access.2020.2985596

Cited by 33 publications

(11 citation statements)

References 46 publications

(68 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because rough matching has been fulfilled, the simple Harris operator is chosen to detect feature points [23] and the popular SIFT operator is utilized to describe point features [24,25]. Corner detection is executed both on the reference image and on the transformed image according to the rough registration model [26].…”

Section: Extraction and Representation Of Point Featuresmentioning

confidence: 99%

Multi-Angle Optical Image Automatic Registration by Combining Point and Line Features

Meng

Hou

et al. 2022

Sensors

View full text Add to dashboard Cite

Image registration is an important basis of image processing, which is of great significance in image mosaicking, target recognition, and change detection. Aiming at the automatic registration problem of multi-angle optical images for ground scenes, a registration method combining point features and line features to register images is proposed. Firstly, the LSD (Line Segment Detector) algorithm is used to extract line features of images. The obtained line segments whose length are less than a given threshold are eliminated by a visual significant algorithm. Then, an affine transform model obtained by estimating a Gaussian mixture model (GMM) is applied to the image to be matched. Lastly, Harris point features are utilized in fine matching to overcome shortages of methods based on line features. In experiments, the proposed algorithm is compared with popular feature-based registration algorithms. The results indicate that the proposed algorithm in this work has obvious advantages in terms of registration accuracy and reliability for optical images acquired at different angles.

show abstract

Section: Extraction and Representation Of Point Featuresmentioning

confidence: 99%

Multi-Angle Optical Image Automatic Registration by Combining Point and Line Features

Meng

Hou

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Yuan et al [27] compared the performance of the stock index prediction models such as a support vector machine (SVM) [28], random forest [29], and an artificial neural network. Hu et al [30] improved the performance of stock index prediction by improving Harris hawks optimization.…”

Section: Stock Index Predictionmentioning

confidence: 99%

Genetic Feature Selection Applied to KOSPI and Cryptocurrency Price Prediction

2021

View full text Add to dashboard Cite

Feature selection reduces the dimension of input variables by eliminating irrelevant features. We propose feature selection techniques based on a genetic algorithm, which is a metaheuristic inspired by a natural selection process. We compare two types of feature selection for predicting a stock market index and cryptocurrency price. The first method is a newly devised genetic filter involving a fitness function designed to increase the relevance between the target and the selected features and decrease the redundancy between the selected features. The second method is a genetic wrapper, whereby we can find the better feature subsets related to KOPSI by exploring the solution space more thoroughly. Both genetic feature selection methods improved the predictive performance of various regression functions. Our best model was applied to predict the KOSPI, cryptocurrency price, and their respective trends after COVID-19.

show abstract

“…Elsken et al (2018c) proposed a Lamarckian evolution technique for automatic searching the multi-objective neural network architecture while dealing with the problem of computational resource constraints. Hu et al (2020) recently developed a hybrid model by combining the novel nature inspired paradigm namely Improved Harris's hawks optimization (IHHO) algorithm with extreme learning machine (ELM) in order to predict two major stock indices of S&P 500 and DJIA. In this work, IHHO is utilized to obtain the optimize value of connectionist weights and bias of ELM.…”

Section: Related Workmentioning

confidence: 99%

Swarm Intelligence Based Hybrid Neural Network Approach for Stock Price Forecasting

2021

View full text Add to dashboard Cite

In this paper, a two-stage swarm intelligence based hybrid feed-forward neural network approach is designed for optimal feature selection and joint optimization of trainable parameters of neural networks in order to forecast the close price of Nifty 50, Sensex, S&P 500, DAX and SSE Composite Index for multiple-horizon (1-day ahead, 5-days-ahead and 10-days ahead) forecasting. Although the neural network can deal with complex non-linear and uncertain data but defining its architecture in terms of number of input features in the input layer, the number of neurons in the hidden layer and optimizing the weights is a challenging problem. The backpropagation algorithm is frequently used in the neural network and has a drawback to getting stuck in local minima and overfitting the data. Motivated by this, we introduce a swarm intelligence based hybrid neural network model for automatic search of features and other hlearnable neural networks' parameters. The proposed model is a combination of discrete particle swarm optimization (DPSO), particle swarm optimization (PSO) and Levenberg-Marquardt algorithm (LM) for training the feedforward neural networks. The DPSO attempts to search automatically the optimum number of features and the optimum number of neurons in the hidden layer of FFNN whereas PSO, simultaneously tune the weights and bias in different layers of FFNN. This paper also compares the forecasting efficiency of proposed model with another hybrid model obtained by integrating binary coded genetic algorithm and real coded genetic algorithm with FFNN. Simulation results indicate that the proposed model is effective for obtaining the optimized feature subset and network structure and also shows superior forecasting accuracy.

show abstract

An Improved Harris’s Hawks Optimization for SAR Target Recognition and Stock Market Index Prediction

Cited by 33 publications

References 46 publications

Multi-Angle Optical Image Automatic Registration by Combining Point and Line Features

Multi-Angle Optical Image Automatic Registration by Combining Point and Line Features

Genetic Feature Selection Applied to KOSPI and Cryptocurrency Price Prediction

Swarm Intelligence Based Hybrid Neural Network Approach for Stock Price Forecasting

Contact Info

Product

Resources

About