Table 5.
CNN in economics
| Article | Aim of study | Specific approach | Benchmark methods for comparison | Superiority of the proposed method |
|---|---|---|---|---|
| (Yasir et al., 2019) | Forecast foreign exchange rate | CNN | Linear regression, SVR | Perform better than other methods in prediction accuracy |
| (Chen et al., 2019) | Forecast interaction of exchange rates |
CNN, fixed-length binary Strings, a binary component |
A random selection rule method, a trend rule method | Higher prediction performance |
| (Galeshchuk & Demazeau, 2017) | Predict exchange rates | CNN | RW, ARIMA, Shallow neural networks | Outperform the baseline methods in prediction |
| (Yu et al., 2020a) | Estimate economy | CNN | Luminosity product | Improve the estimation accuracy |
| (Guo, 2020) | Encode image features and select the image features of commodities | CNN, attention mechanism | Analyze different impact of different situations with the assistance of CNN | Successfully extract the most important image feature corresponding to the decoding time |
| (Ullah et al., 2019) | Detect cyber security threats | CNN, DNN | GIST-SVM, LBP-SVM, CLGM-SVM | Outperform when measuring the cybersecurity threats |
| (Wang & Zeng, 2020) | Select typical economic indicators | CNN | Deep confidence network, Multilayer trestle automatic coder | Improve the classification accuracy and adaptability |
| (Adebowale et al., 2020) | Detect intelligent phishing | CNN, LSTM | Single CNN, single LSTM | Higher classifier prediction performance and less training time |
| (Liu et al., 2020) | Forecast stock price | CNN, GBoost | WSAEs-LSTM | More accurate prediction |
| (Liu et al., 2018) | Predict stock price movement from financial news | TransE Model, CNN, LSTM | T-SVM, J-SVM, C-SVM, C-LSTM, J-LSTM | Predict better |
| (Conte et al., 2019) | Estimate catfish density | CNN, Aerial images ainalysis | - | - |
| (Gadekallu et al., 2020) | Classify tomato plant diseases | CNN, Whale optimization algorithm | DNN without Dimensionality Reduction, DNN with Dimensionality Reduction using PCA | Higher accuracy and low rate, lesser time for training and testing of the data |
| (Ajami et al., 2019) | Predict data-driven index of multiple deprivation | CNN | Principal component regression model combining hand-crafted and GIS features, ensemble model | Outperform than others in terms of R2, RMSE, BIAS |
| (Yao et al., 2018) | Map fine-scale urban housing prices | UMCNN and RF | CNN (HSR), PCA-CNN (HSR), SD, CNN (HSR & SD), PCA-CNN (HSR & SD), CNN (HSR) & SD, PCA-CNN (HSR) & SD, CNN (SD), CNN (HSR) & CNN (SD) | The highest housing price simulation accuracy |
| (Lan et al., 2018) | Extract features of trademark images | CNN, Constraint theory | LBP, SIFT, HOG, CNN-original, CNN-LBP, CNN-Siamese | Best comprehensive retrieval ability |
| (Yeh et al., 2020) | Predict asset wealth | Deep CNN | Simpler KNN, scalar NL | Meets or exceeds published performance |
Note: RW (Random walk without a drift), GIST (Generalized Search Tree),T-SVM (Tf-idf algorithm feature extraction and SVM prediction model), J-SVM (Joint learning feature extraction and SVM prediction model), C-SVM (CNN feature extraction and SVM prediction model), C-LSTM (CNN feature extraction and LSTM prediction model), J-LSTM (Joint learning and LSTM prediction model), PCA (Principle Component Analysis), GIS (Geographic Information System), UMCNN (Convolutional Neural Network for United Mining), HSR (High Spatial Resolution), PCA (Principal Component Analysis), SD (Spatial Data), LBP (Local Binary Pattern), SIFT (Scale Invariant Feature Transform), HOG (Histogram of Oriented Gradients), KNN (K-Nearest Neighbor), NL (Nighlights)