Residual CNN + Bi-LSTM model to analyze GPR B scan images

Özkaya, Umut; Öztürk, Şaban; Melgani, Farid; Seyfi, Levent

doi:10.1016/j.autcon.2020.103525

Cited by 36 publications

(12 citation statements)

References 12 publications

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“…In 2018, the ''Turing Award'' was awarded to well-known deep learning scholars, which pushed machine learning/deep learning to the peak in one fell swoop and left an extremely important milestone in history [32]. Deep learning technologies have been widely used in different fields in recent years, such as autonomous driving systems [33], voice recognition systems [34,35], AIoT [36,37], face recognition [38], smart home and mart city [39][40][41][42], smart campus [43,44], machine translation [45][46][47], image processing and retrieval [48][49][50], natural language processing [51,52], etc.…”

Section: Machine Learning and Deep Learningmentioning

confidence: 99%

Applying attention-based BiLSTM and technical indicators in the design and performance analysis of stock trading strategies

Lee

Chang

Yeh

et al. 2022

Neural Comput & Applic

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With the development of the Internet, information on the stock market has gradually become transparent, and stock information is easy to obtain. For investors, investment performance depends on the amount of capital and effective trading strategies. The analysis tool commonly used by investors and securities analysts is technical analysis (TA). Technical analysis is the study of past and current financial market information, and a large amount of statistical data is used to predict price trends and determine trading strategies. Technical indicators (TIs) are a type of technical analysis that summarizes possible future trends of stock prices based on historical statistical data to assist investors in making decisions. The stock price trend is a typical time series data with special characteristics such as trend, seasonality, and periodicity. In recent years, time series deep neural networks (DNNs) have demonstrated their powerful performance in machine translation, speech processing, and natural language processing fields. This research proposes the concept of attentionbased BiLSTM (AttBiLSTM) applied to trading strategy design and verified the effectiveness of a variety of TIs, including stochastic oscillator, RSI, BIAS, W%R, and MACD. This research also proposes two trading strategies that suitable for DNN, combining with TIs and verifying their effectiveness. The main contributions of this research are as follows: (1) As our best knowledge, this is the first research to propose the concept of applying TIs to the LSTM-attention time series model for stock price prediction. (2) This study introduces five well-known TIs, which reached a maximum of 68.83% in the accuracy of stock trend prediction. (3) This research introduces the concept of exporting the probability of the deep model to the trading strategy. On the backtest of TPE0050, the experimental results reached the highest return on investment of 42.74%. (4) This research concludes from an empirical point of view that technical analysis combined with time series deep neural network has significant effects in stock price prediction and return on investment.

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Section: Machine Learning and Deep Learningmentioning

confidence: 99%

Applying attention-based BiLSTM and technical indicators in the design and performance analysis of stock trading strategies

Lee

Chang

Yeh

et al. 2022

Neural Comput & Applic

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“…Residual blocks are helpful for improving accuracy (Özkaya et al, 2021). The traditional residual block is used in YOLOv5s.…”

Section: Improved Yolov5s Model For Detecting Silicon On Wheat Straw ...mentioning

confidence: 99%

Using an improved YOLOv5s network for the automatic detection of silicon on wheat straw epidermis of micrographs

Zhang

Wang

Song

et al. 2022

Journal of Field Robotics

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The silicon on wheat straw epidermis is an obstacle to its resource utilization, and pretreated methods should be applied to remove this structure. Due to the difficulties in detecting the silicon on wheat straw epidermis, judging the efficiency of pretreatment is still a challenging task. In this study, an automatic detection method based on you only look once (YOLO) v5s was proposed to detect the silicon on wheat straw epidermis of micrographs. To improve the efficiency of the network, the Input was modified, the inverted residual module, the pointwise convolution, and the attention mechanism were added, while the focus module was cut off. A total of 4690 micrographs of wheat straw epidermis were collected for training and testing. The training results showed that the proposed model can efficiently detect silicon on wheat straw epidermis of micrographs, and had the highest mean Average Precision of 98.88% among five state‐of‐the‐art comparison models, including RetinaNet, Single Shot MultiBox Detector, YOLOv4tiny, YOLOv4, and YOLOv5s. The weight of the proposed model was 11.7 M, indicating that it can be transplanted to mobile devices. The proposed model showed good robustness under different imaging conditions. All the results indicated that the proposed model could detect the silicon on wheat straw epidermis of micrographs accurately and efficiently.

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“…Moreover, in Mughees's work [20], the bidirectional LSTM network model is proven to be not only capable of learning long-term dependencies between the time steps of sequence data, but also can effectively use past and future information for prediction. Consequently, thanks to the ability to process time series data, the Bi-LSTM model has been successfully applied in many time series prediction tasks [21][22][23][24][25][26][27]. Inspired by this idea, this paper aims to exploit the learning method based on Bi-LSTM model to deal with event trend prediction with a sequence to label model.…”

Section: Related Workmentioning

confidence: 99%

Trend Prediction of Event Popularity from Microblogs

Zhao

2021

Future Internet

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Owing to rapid development of the Internet and the rise of the big data era, microblog has become the main means for people to spread and obtain information. If people can accurately predict the development trend of a microblog event, it will be of great significance for the government to carry out public relations activities on network event supervision and guide the development of microblog event reasonably for network crisis. This paper presents effective solutions to deal with trend prediction of microblog events’ popularity. Firstly, by selecting the influence factors and quantifying the weight of each factor with an information entropy algorithm, the microblog event popularity is modeled. Secondly, the singular spectrum analysis is carried out to decompose and reconstruct the time series of the popularity of microblog event. Then, the box chart method is used to divide the popularity of microblog event into various trend spaces. In addition, this paper exploits the Bi-LSTM model to deal with trend prediction with a sequence to label model. Finally, the comparative experimental analysis is carried out on two real data sets crawled from Sina Weibo platform. Compared to three comparative methods, the experimental results show that our proposal improves F1-score by up to 39%.

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Residual CNN + Bi-LSTM model to analyze GPR B scan images

Cited by 36 publications

References 12 publications

Applying attention-based BiLSTM and technical indicators in the design and performance analysis of stock trading strategies

Applying attention-based BiLSTM and technical indicators in the design and performance analysis of stock trading strategies

Using an improved YOLOv5s network for the automatic detection of silicon on wheat straw epidermis of micrographs

Trend Prediction of Event Popularity from Microblogs

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