Convolutional Bidirectional Long Short-term Memory Research Articles

A C-BiLSTM Approach to Classify Construction Accident Reports

The construction sector is widely recognized as having the most hazardous working environment among the various business sectors, and many research studies have focused on injury prevention strategies for use on construction sites. The risk-based theory emphasizes the analysis of accident causes extracted from accident reports to understand, predict, and prevent the occurrence of construction accidents. The first step in the analysis is to classify the incidents from a massive number of reports into different cause categories, a task which is usually performed on a manual basis by domain experts. The research described in this paper proposes a convolutional bidirectional long short-term memory (C-BiLSTM)-based method to automatically classify construction accident reports. The proposed approach was applied on a dataset of construction accident narratives obtained from the Occupational Safety and Health Administration website, and the results indicate that this model performs better than some of the classic machine learning models commonly used in classification tasks, including support vector machine (SVM), naïve Bayes (NB), and logistic regression (LR). The results of this study can help safety managers to develop risk management strategies.

Extracting Membrane Borders in IVUS Images Using a Multi-Scale Feature Aggregated U-Net.

Automatic extraction of the lumen-intima border (LIB) and the media-adventitia border (MAB) in intravascular ultrasound (IVUS) images is of high clinical interest. Despite the superior performance achieved by deep neural networks (DNNs) on various medical image segmentation tasks, there are few applications to IVUS images. The complicated pathological presentation and the lack of enough annotation in IVUS datasets make the learning process challenging. Several existing networks designed for IVUS segmentation train two groups of weights to detect the MAB and LIB separately. In this paper, we propose a multi-scale feature aggregated U-Net (MFAU-Net) to extract two membrane borders simultaneously. The MFAU-Net integrates multi-scale inputs, the deep supervision, and a bi-directional convolutional long short-term memory (BConvLSTM) unit. It is designed to sufficiently learn features from complicated IVUS images through a small number of training samples. Trained and tested on the publicly available IVUS datasets, the MFAU-Net achieves both 0.90 Jaccard measure (JM) for the MAB and LIB detection on 20 MHz dataset. The corresponding metrics on 40 MHz dataset are 0.85 and 0.84 JM respectively. Comparative evaluations with state-of-the-art published results demonstrate the competitiveness of the proposed MFAU-Net.

Volumetric Choroidal Segmentation Using Sequential Deep Learning Approach in High Myopia Subjects.

Many ocular diseases are associated with choroidal changes. Therefore, it is crucial to be able to segment the choroid to study its properties. Previous methods for choroidal segmentation have focused on single cross-sectional scans. Volumetric choroidal segmentation has yet to be widely reported. In this paper, we propose a sequential segmentation approach using a variation of U-Net with a bidirectional C-LSTM(Convolutional Long Short Term Memory) module in the bottleneck region. The model is evaluated on volumetric scans from 40 high myopia subjects, obtained using SS-OCT(Swept Source Optical Coherence Tomography). A comparison with other U-Net-based variants is also presented. The results demonstrate that volumetric segmentation of the choroid can be achieved with an accuracy of IoU(Intersection over Union) 0.92.Clinical relevance- This deep learning approach can automatically segment the choroidal volume, which can enable better evaluation and monitoring at ocular diseases.

Spatio-temporal visual attention modelling of standard biometry plane-finding navigation.

We present a novel multi-task neural network called Temporal SonoEyeNet (TSEN) with a primary task to describe the visual navigation process of sonographers by learning to generate visual attention maps of ultrasound images around standard biometry planes of the fetal abdomen, head (trans-ventricular plane) and femur. TSEN has three components: a feature extractor, a temporal attention module (TAM), and an auxiliary video classification module (VCM). A soft dynamic time warping (sDTW) loss function is used to improve visual attention modelling. Variants of the model are trained on a dataset of 280 video clips, each containing one of the three biometry planes and lasting 3-7 seconds, with corresponding real-time recorded gaze tracking data of an experienced sonographer. We report the performances of the different variants of TSEN for visual attention prediction at standard biometry plane detection. The best model performance is achieved using bi-directional convolutional long-short term memory (biCLSTM) in both TAM and VCM, and it outperforms a previous spatial model on all static and dynamic saliency metrics. As an auxiliary task to validate the clinical relevance of the visual attention modelling, the predicted visual attention maps were used to guide standard biometry plane detection in consecutive US video frames. All spatio-temporal TSEN models achieve higher scores compared to a spatial-only baseline; the best performing TSEN model achieves F1 scores on these standard biometry planes of 83.7%, 89.9% and 81.1%, respectively.

Looseness detection in cup-lock scaffolds using percussion-based method

Across multiple construction processes, the cup-lock scaffolding systems have been widely applied as temporary facilities, while several catastrophes caused by scaffolding collapse have been reported. Therefore, in this paper, we conduct an exploratory study to attempt to research one issue that can affect the stability of scaffolding systems, namely the looseness of the cup-lock joint. In this paper, to detect looseness of cup-lock scaffold, we develop a new percussion-based method to avoid current structural health monitoring (SHM) methods that depend on constant contact between structures and sensors. Particularly, inspired by the rapid development of automatic speech recognition (ASR), we propose a convolutional bi-directional long short-term memory (CBLSTM) model to classify the Mel frequency cepstral coefficient (MFCC) features extracted from percussion-induced sound signals. To the best of our knowledge, this is the first application of ASR technique in looseness detection of cup-lock scaffold. The working mechanism of CBLSTM is given as follows: a convolutional neural network (CNN) is used to craft characteristics from MFCC features, and a bi-directional long short-term memory architecture (BLSTM) can improve classification accuracy by assimilating the learned CNN features. Finally, a laboratory experiment is conducted to verify the effectiveness of the proposed method, and we demonstrate that CBLSTM outperforms the CNN and BLSTM in classifying the MFCC features. Overall, the percussion-based method proposed in this paper can provide a new direction for the investigation, particularly health monitoring, on the cup-lock scaffolding system.

Gender identification for Egyptian Arabic dialect in twitter using deep learning models

Although the number of Arabic language writers in social media is increasing, the research work targeting Author Profiling (AP) is at the initial development phase. This paper investigates Gender Identification (GI) (male or female) of authors posting Egyptian dialect tweets using Neural Networks (NN) models. Various architectures of NN are explored with extensive parameters’ selection such as simple Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long–Short Term Memory (LSTM), Convolutional Bidirectional Long-Short Term Memory (C-Bi-LSTM) and Convolutional Bidirectional Gated Recurrent Units (C-Bi-GRU) NN which is tuned for the GI problem at hand. The best acquired GI accuracy using C-Bi-GRU multichannel model is 91.37%. It is worth noting that the presence of the bidirectional layer as well as the convolutional layer in the NN models has significantly enhanced the GI accuracy.

DevsNet: Deep Video Saliency Network using Short-term and Long-term Cues

Recently, there have been various saliency detection methods proposed for still images based on deep learning techniques. However, the research on saliency detection for video sequences is still limited. In this study, we introduce a novel deep learning framework of saliency detection for video sequences, namely Deep Video Saliency Network (DevsNet). DevsNet mainly consists of two components: 3D Convolutional Network (3D-ConvNet) and Bidirectional Convolutional Long-Short Term Memory Network (B-ConvLSTM). 3D-ConvNet is constructed to learn short-term spatiotemporal information and the long-term spatiotemporal features are learned by B-ConvLSTM. The designed B-ConvLSTM can extract the temporal information not just from the previous video frames but also from the next frames, which demonstrates that the proposed model considers both the forward and backward temporal information. By combining the short-term and long-term spatiotemporal cues, the proposed DevsNet can extract saliency information for video sequences effectively and efficiently. Extensive experiments have been conducted to show that the proposed model can obtain better performance in spatiotemporal saliency prediction than the state-of-the-art models.

Residual Convolutional Neural Network for Cardiac Image Segmentation and Heart Disease Diagnosis

Deep learning (DL) has been widely used in biomedical image segmentation and automatic disease diagnosis, leading to state-of-the-art performance. However, automated cardiac disease diagnosis heavily relies on cardiac segmentation maps from cardiac magnetic resonance (CMR), most current DL segmentation methods, such as 2D convolution on planes, 3D convolution, are not fully applicable to CMR due to loss of spatial structure information or large gap between slices. To make better exploit spatial aspects of the CMR data to improve cardiac segmentation accuracy, we propose a new DL segmentation structure, which consists of a residual convolution neural network for compressing the intra-slice information, and a bidirectional-convolutional long short term memory (Bi-CLSTM) for leveraging the inter-slice contexts. Moreover, automatic disease diagnosis has been conducted using the segmentation maps. Experimental results of the automatic cardiac diagnosis challenge (ACDC) show that our cardiac segmentation structure and disease diagnosis methods have achieved promising results and it can be widely extended to computer-aided diagnosis.

CSI Feedback Based on Deep Learning for Massive MIMO Systems

Aiming at the problem of high complexity and low feedback accuracy of existing channel state information (CSI) feedback algorithms for frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, this paper proposes a CSI compression feedback algorithm based on deep learning (DL), which is suitable for single-user and multi-user scenarios in massive MIMO systems. This algorithm considers the spatial correlation of massive MIMO channel and uses bidirectional long short-term memory (Bi-LSTM) and bidirectional convolutional long short-term memory (Bi-ConvLSTM) network to decompress and recover the CSI for single-user and multi-user, respectively. The proposed DL-based CSI feedback network is trained offline by massive MIMO channel data and could learn the structural characteristics of the massive MIMO channel by fully exploiting the channel information in the training samples. The simulation results show that compared with several classical CSI compression feedback algorithms, the proposed CSI compression feedback algorithm has lower computational complexity, higher feedback accuracy, and better system performance in massive MIMO systems.

Spatio-temporal Prediction of Air Quality using Distance Based Interpolation and Deep Learning Techniques

The harmful impact of air pollution has drawn raising concerns from ordinary citizens, researchers, policy makers, and smart city users. It is of great importance to identify air pollution levels at the spatial resolution on time so that its negative impact on human health and environment can be minimized. This paper proposed the CNN-BILSTM-IDW model, which aims to predict and spatially analyze the pollutant levelin the study area in advance using past observations. The neural network-based Convolutional Bidirectional Long short-term memory (CNN-BILSTM) network is employed to perform time series prediction over the next four weeks. Inverse Distance Weighting (IDW) is utilized to perform spatial prediction. The proposed CNN-BILSTM-IDW model provides almost 16% better prediction performance than the ordinary IDW method, which fails to predict spatial prediction at a high temporal period. The results of the presented comparative analysis signify the efficiency of the proposed model.

Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial features from hyperspectral images (HSIs). In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN), a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. In addition, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a Softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with six state-of-the-art methods, including the popular 3D-CNN model, on three widely used HSIs (i.e., Indian Pines, Pavia University, and Kennedy Space Center). The obtained results show that Bi-CLSTM can improve the classification performance by almost 1.5 % as compared to 3D-CNN.