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Hayoung Kim
Graduate School of Information, Yonsei University, Yonsei-ro 50, Seodaemun-gu, Seoul 03722, Korea

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Journal article
Published: 02 August 2021 in Applied Sciences
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Outbound telemarketing is an efficient direct marketing method wherein telemarketers solicit potential customers by phone to purchase or subscribe to products or services. However, those who are not interested in the information or offers provided by outbound telemarketing generally experience such interactions negatively because they perceive telemarketing as spam. In this study, therefore, we investigate the use of deep learning models to predict the success of outbound telemarketing for insurance policy loans. We propose an explainable multiple-filter convolutional neural network model called XmCNN that can alleviate overfitting and extract various high-level features using hundreds of input variables. To enable the practical application of the proposed method, we also examine ensemble models to further improve its performance. We experimentally demonstrate that the proposed XmCNN significantly outperformed conventional deep neural network models and machine learning models. Furthermore, a deep learning ensemble model constructed using the XmCNN architecture achieved the lowest false positive rate (4.92%) and the highest F1-score (87.47%). We identified important variables influencing insurance policy loan prediction through the proposed model, suggesting that these factors should be considered in practice. The proposed method may increase the efficiency of outbound telemarketing and reduce the spam problems caused by calling non-potential customers.

ACS Style

JinMo Gu; Jinhyuk Na; Jeongeun Park; Hayoung Kim. Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network. Applied Sciences 2021, 11, 7147 .

AMA Style

JinMo Gu, Jinhyuk Na, Jeongeun Park, Hayoung Kim. Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network. Applied Sciences. 2021; 11 (15):7147.

Chicago/Turabian Style

JinMo Gu; Jinhyuk Na; Jeongeun Park; Hayoung Kim. 2021. "Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network." Applied Sciences 11, no. 15: 7147.

Journal article
Published: 29 December 2020 in Information Fusion
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The remarkable performance of deep learning is based on its ability to learn high-level features by processing large amounts of data. This exceptionally superior performance has attracted the attention of researchers studying option pricing. However, option data are more expensive and less accessible than other types of data and are imbalanced because of the liquidity of options. This motivated us to propose a new option pricing and delta-hedging framework called DeepOption. This framework, which is based on deep learning, can improve the performance even when applying imbalanced real option data. In particular, the framework fuses simulated big data, known as distilled data, obtained using various traditional parametric methods. The proposed model employs the following three-stage training approach: Our model is pre-trained using big distilled data after it is fine-tuned using real option data through transfer learning. Finally, a delta branch is added to the model and trained. We experimentally evaluated the proposed method using three sets of real option data, namely S&P 500 European call options, EuroStoxx50 call options, and Hang Seng Index put options. Our experimental results on option pricing demonstrate that our proposed model outperforms parametric methods and other machine learning methods. Specifically, our model, which uses pre-training with distilled data, reduces the overall mean absolute percentage error (MAPE) by more than 50%, compared with that of a deep learning model using only real option data without pre-training.

ACS Style

Ji Hyun Jang; Jisang Yoon; Jungeun Kim; JinMo Gu; Ha Young Kim. DeepOption: A novel option pricing framework based on deep learning with fused distilled data from multiple parametric methods. Information Fusion 2020, 70, 43 -59.

AMA Style

Ji Hyun Jang, Jisang Yoon, Jungeun Kim, JinMo Gu, Ha Young Kim. DeepOption: A novel option pricing framework based on deep learning with fused distilled data from multiple parametric methods. Information Fusion. 2020; 70 ():43-59.

Chicago/Turabian Style

Ji Hyun Jang; Jisang Yoon; Jungeun Kim; JinMo Gu; Ha Young Kim. 2020. "DeepOption: A novel option pricing framework based on deep learning with fused distilled data from multiple parametric methods." Information Fusion 70, no. : 43-59.

Journal article
Published: 05 December 2020 in Materials
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There has been an increase in the deterioration of buildings and infrastructure in dense urban regions, and several defects in the structures are being exposed. To ensure the effective diagnosis of building conditions, vision-based automatic damage recognition techniques have been developed. However, conventional image processing techniques have some limitations in real-world situations owing to their manual feature extraction approach. To overcome these limitations, a convolutional neural network-based image recognition technique was adopted in this study, and a convolution-based concrete multi-damage recognition neural network (CMDnet) was developed. The image datasets consisted of 1981 types of concrete surface damages, including surface cracks, rebar exposure and delamination, as well as intact. Furthermore, it was experimentally demonstrated that the proposed model could accurately classify the damage types. The results obtained in this study reveal that the proposed model can recognize the different damage types from digital images of the surfaces of concrete structures. The trained CMDnet demonstrated a damage-detection accuracy of 98.9%. Moreover, the proposed model could be applied in automatic damage detection networks to achieve superior performance with regard to concrete surface damage detection and recognition, as well as accelerating efficient damage identification during the diagnosis of deteriorating structures used in civil engineering applications.

ACS Style

Hyun Kyu Shin; Yong Han Ahn; Sang Hyo Lee; Ha Young Kim. Automatic Concrete Damage Recognition Using Multi-Level Attention Convolutional Neural Network. Materials 2020, 13, 5549 .

AMA Style

Hyun Kyu Shin, Yong Han Ahn, Sang Hyo Lee, Ha Young Kim. Automatic Concrete Damage Recognition Using Multi-Level Attention Convolutional Neural Network. Materials. 2020; 13 (23):5549.

Chicago/Turabian Style

Hyun Kyu Shin; Yong Han Ahn; Sang Hyo Lee; Ha Young Kim. 2020. "Automatic Concrete Damage Recognition Using Multi-Level Attention Convolutional Neural Network." Materials 13, no. 23: 5549.

Journal article
Published: 23 November 2020 in Sustainability
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Defects in residential building façades affect the structural integrity of buildings and degrade external appearances. Defects in a building façade are typically managed using manpower during maintenance. This approach is time-consuming, yields subjective results, and can lead to accidents or casualties. To address this, we propose a building façade monitoring system that utilizes an object detection method based on deep learning to efficiently manage defects by minimizing the involvement of manpower. The dataset used for training a deep-learning-based network contains actual residential building façade images. Various building designs in these raw images make it difficult to detect defects because of their various types and complex backgrounds. We employed the faster regions with convolutional neural network (Faster R-CNN) structure for more accurate defect detection in such environments, achieving an average precision (intersection over union (IoU) = 0.5) of 62.7% for all types of trained defects. As it is difficult to detect defects in a training environment, it is necessary to improve the performance of the network. However, the object detection network employed in this study yields an excellent performance in complex real-world images, indicating the possibility of developing a system that would detect defects in more types of building façades.

ACS Style

Kisu Lee; Goopyo Hong; Lee Sael; Sanghyo Lee; Ha Kim. MultiDefectNet: Multi-Class Defect Detection of Building Façade Based on Deep Convolutional Neural Network. Sustainability 2020, 12, 9785 .

AMA Style

Kisu Lee, Goopyo Hong, Lee Sael, Sanghyo Lee, Ha Kim. MultiDefectNet: Multi-Class Defect Detection of Building Façade Based on Deep Convolutional Neural Network. Sustainability. 2020; 12 (22):9785.

Chicago/Turabian Style

Kisu Lee; Goopyo Hong; Lee Sael; Sanghyo Lee; Ha Kim. 2020. "MultiDefectNet: Multi-Class Defect Detection of Building Façade Based on Deep Convolutional Neural Network." Sustainability 12, no. 22: 9785.

Research article
Published: 27 July 2020 in PLOS ONE
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Despite active research on trading systems based on reinforcement learning, the development and performance of research methods require improvements. This study proposes a new action-specialized expert ensemble method consisting of action-specialized expert models designed specifically for each reinforcement learning action: buy, hold, and sell. Models are constructed by examining and defining different reward values that correlate with each action under specific conditions, and investment behavior is reflected with each expert model. To verify the performance of this technique, profits of the proposed system are compared to those of single trading and common ensemble systems. To verify robustness and account for the extension of discrete action space, we compared and analyzed changes in profits of the three actions to our model’s results. Furthermore, we checked for sensitivity with three different reward functions: profit, Sharpe ratio, and Sortino ratio. All experiments were conducted with S&P500, Hang Seng Index, and Eurostoxx50 data. The model was 39.1% and 21.6% more efficient than single and common ensemble models, respectively. Considering the extended discrete action space, the 3-action space was extended to 11- and 21-action spaces, and the cumulative returns increased by 427.2% and 856.7%, respectively. Results on reward functions indicated that our models are well trained; results of the Sharpe and Sortino ratios were better than the implementation of profit only, as in the single-model cases. The Sortino ratio was slightly better than the Sharpe ratio.

ACS Style

Joonbum Leem; Ha Young Kim. Action-specialized expert ensemble trading system with extended discrete action space using deep reinforcement learning. PLOS ONE 2020, 15, e0236178 .

AMA Style

Joonbum Leem, Ha Young Kim. Action-specialized expert ensemble trading system with extended discrete action space using deep reinforcement learning. PLOS ONE. 2020; 15 (7):e0236178.

Chicago/Turabian Style

Joonbum Leem; Ha Young Kim. 2020. "Action-specialized expert ensemble trading system with extended discrete action space using deep reinforcement learning." PLOS ONE 15, no. 7: e0236178.

Journal article
Published: 08 July 2020 in Expert Systems with Applications
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Forecasting stock market indexes is an important issue for market participants, because even a small improvement in forecast accuracy may lead to better trading decisions than those of other participants. Rising interest in deep learning has led to its application in stock market forecasting. However, it is still challenging to use market-size time-series data to predict composite index prices. In this study, we propose a new stock market forecasting framework, NuNet, which can successfully learn high-level features from super-high dimensional time-series data. NuNet is an end-to-end integrated neural network framework consisting of two feature extractor modules, a super-high dimensional market information feature extractor and a target index feature extractor. In addition, we propose a mini-batch sampling technique, trend sampling, which probabilistically samples more recent data when training. Furthermore, we propose a novel regularization method, called column-wise random shuffling, which is a data augmentation technique that can be applied to convolutional neural networks. The experiments are comprehensively carried out in three aspects for three indexes, namely S&P500, KOSPI200, and FTSE100. The results demonstrate that the proposed model outperforms all baseline models. Specifically, for the S&P500, KOSPI200, and FTSE100, the overall mean squared error of our proposed model NuNet(DA, T) is 60.79%, 51.29%, and 43.36% lower than that of the baseline model SingleNet(R), respectively. Moreover, we employ trading simulations with realistic transaction costs. Our proposed model outperforms the buy-and-hold strategy being an average of 2.57 times more profitable in three indexes.

ACS Style

Si Woon Lee; Ha Young Kim. Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation. Expert Systems with Applications 2020, 161, 113704 .

AMA Style

Si Woon Lee, Ha Young Kim. Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation. Expert Systems with Applications. 2020; 161 ():113704.

Chicago/Turabian Style

Si Woon Lee; Ha Young Kim. 2020. "Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation." Expert Systems with Applications 161, no. : 113704.

Journal article
Published: 01 January 2020 in Computers, Materials & Continua
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In this study, we examined the efficacy of a deep convolutional neural network (DCNN) in recognizing concrete surface images and predicting the compressive strength of concrete. A digital single-lens reflex (DSLR) camera and microscope were simultaneously used to obtain concrete surface images used as the input data for the DCNN. Thereafter, training, validation, and testing of the DCNNs were performed based on the DSLR camera and microscope image data. Results of the analysis indicated that the DCNN employing DSLR image data achieved a relatively higher accuracy. The accuracy of the DSLR-derived image data was attributed to the relatively wider range of the DSLR camera, which was beneficial for extracting a larger number of features. Moreover, the DSLR camera procured more realistic images than the microscope. Thus, when the compressive strength of concrete was evaluated using the DCNN employing a DSLR camera, time and cost were reduced, whereas the usefulness increased. Furthermore, an indirect comparison of the accuracy of the DCNN with that of existing non-destructive methods for evaluating the strength of concrete proved the reliability of DCNN-derived concrete strength predictions. In addition, it was determined that the DCNN used for concrete strength evaluations in this study can be further expanded to detect and evaluate various deteriorative factors that affect the durability of structures, such as salt damage, carbonation, sulfation, corrosion, and freezing-thawing.

ACS Style

Sanghyo Lee; Yonghan Ahn; Ha Young Kim. Predicting Concrete Compressive Strength using Deep Convolutional Neural Network based on Image Characteristics. Computers, Materials & Continua 2020, 65, 1 -17.

AMA Style

Sanghyo Lee, Yonghan Ahn, Ha Young Kim. Predicting Concrete Compressive Strength using Deep Convolutional Neural Network based on Image Characteristics. Computers, Materials & Continua. 2020; 65 (1):1-17.

Chicago/Turabian Style

Sanghyo Lee; Yonghan Ahn; Ha Young Kim. 2020. "Predicting Concrete Compressive Strength using Deep Convolutional Neural Network based on Image Characteristics." Computers, Materials & Continua 65, no. 1: 1-17.

Research article
Published: 12 November 2019 in Complexity
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Many researchers have tried to optimize pairs trading as the numbers of opportunities for arbitrage profit have gradually decreased. Pairs trading is a market-neutral strategy; it profits if the given condition is satisfied within a given trading window, and if not, there is a risk of loss. In this study, we propose an optimized pairs-trading strategy using deep reinforcement learning—particularly with the deep Q-network—utilizing various trading and stop-loss boundaries. More specifically, if spreads hit trading thresholds and reverse to the mean, the agent receives a positive reward. However, if spreads hit stop-loss thresholds or fail to reverse to the mean after hitting the trading thresholds, the agent receives a negative reward. The agent is trained to select the optimum level of discretized trading and stop-loss boundaries given a spread to maximize the expected sum of discounted future profits. Pairs are selected from stocks on the S&P 500 Index using a cointegration test. We compared our proposed method with traditional pairs-trading strategies which use constant trading and stop-loss boundaries. We find that our proposed model is trained well and outperforms traditional pairs-trading strategies.

ACS Style

Taewook Kim; Ha Young Kim. Optimizing the Pairs-Trading Strategy Using Deep Reinforcement Learning with Trading and Stop-Loss Boundaries. Complexity 2019, 2019, 1 -20.

AMA Style

Taewook Kim, Ha Young Kim. Optimizing the Pairs-Trading Strategy Using Deep Reinforcement Learning with Trading and Stop-Loss Boundaries. Complexity. 2019; 2019 ():1-20.

Chicago/Turabian Style

Taewook Kim; Ha Young Kim. 2019. "Optimizing the Pairs-Trading Strategy Using Deep Reinforcement Learning with Trading and Stop-Loss Boundaries." Complexity 2019, no. : 1-20.