Arterial Input Function (AIF) Correction Using AIF Plus Tissue Inputs with a Bi-LSTM Network

Background: The arterial input function (AIF) is vital for myocardial blood flow quantification in cardiac MRI to indicate the input time-concentration curve of a contrast agent. Inaccurate AIFs can significantly affect perfusion quantification. Purpose: When only saturated and biased AIFs are measured, this work investigates multiple ways of leveraging tissue curve information, including using AIF + tissue curves as inputs and optimizing the loss function for deep neural network training. Methods: Simulated data were generated using a 12-parameter AIF mathematical model for the AIF. Tissue curves were created from true AIFs combined with compartment-model parameters from a random distribution. Using Bloch simulations, a dictionary was constructed for a saturation-recovery 3D radial stack-of-stars sequence, accounting for deviations such as flip angle, T2* effects, and residual longitudinal magnetization after the saturation. A preliminary simulation study established the optimal tissue curve number using a bidirectional long short-term memory (Bi-LSTM) network with just AIF loss. Further optimization of the loss function involves comparing just AIF loss, AIF with compartment-model-based parameter loss, and AIF with compartment-model tissue loss. The optimized network was examined with both simulation and hybrid data, which included in vivo 3D stack-of-star datasets for testing. The AIF peak value accuracy and ktrans results were assessed. Results: Increasing the number of tissue curves can be beneficial when added tissue curves can provide extra information. Using just the AIF loss outperforms the other two proposed losses, including adding either a compartment-model-based tissue loss or a compartment-model parameter loss to the AIF loss. With the simulated data, the Bi-LSTM network reduced the AIF peak error from -23.6 ± 24.4% of the AIF using the dictionary method to 0.2 ± 7.2% (AIF input only) and 0.3 ± 2.5% (AIF + ten tissue curve inputs) of the network AIF. The corresponding ktrans error was reduced from -13.5 ± 8.8% to -0.6 ± 6.6% and 0.3 ± 2.1%. With the hybrid data (simulated data for training; in vivo data for testing), the AIF peak error was 15.0 ± 5.3% and the corresponding ktrans error was 20.7 ± 11.6% for the AIF using the dictionary method. The hybrid data revealed that using the AIF + tissue inputs reduced errors, with peak error (1.3 ± 11.1%) and ktrans error (-2.4 ± 6.7%). Conclusions: Integrating tissue curves with AIF curves into network inputs improves the precision of AI-driven AIF corrections. This result was seen both with simulated data and with applying the network trained only on simulated data to a limited in vivo test dataset.

Track Irregularity Identification Method of High-Speed Railway Based on CNN-Bi-LSTM

Track smoothness has become an important factor in the safe operation of high-speed trains. In order to ensure the safety of high-speed operations, studies on track smoothness detection methods are constantly improving. This paper presents a track irregularity identification method based on CNN-Bi-LSTM and predicts track irregularity through car body acceleration detection, which is easy to collect and can be obtained by passenger trains, so the model proposed in this paper provides an idea for the development of track irregularity identification method based on conventional vehicles. The first step is construction of the data set required for model training. The model input is the car body acceleration detection sequence, and the output is the irregularity sequence of the same length. The fluctuation trend of the irregularity data is extracted by the HP filtering (Hodrick Prescott Filter) algorithm as the prediction target. The second is a prediction model based on the CNN-Bi-LSTM network, extracting features from the car body acceleration data and realizing the point-by-point prediction of irregularities. Meanwhile, this paper proposes an exponential weighted mean square error with priority inner fitting (EIF-MSE) as the loss function, improving the accuracy of big value data prediction, and reducing the risk of false alarms. In conclusion, the model is verified based on the simulation data and the real data measured by the high-speed railway comprehensive inspection train.

MF-MNER: Multi-models Fusion for MNER in Chinese Clinical Electronic Medical Records

To address the problem of poor entity recognition performance caused by the lack of Chinese annotation in clinical electronic medical records, this paper proposes a multi-medical entity recognition method F-MNER using a fusion technique combining BART, Bi-LSTM, and CRF. First, after cleaning, encoding, and segmenting the electronic medical records, the obtained semantic representations are dynamically fused using a bidirectional autoregressive transformer (BART) model. Then, sequential information is captured using a bidirectional long short-term memory (Bi-LSTM) network. Finally, the conditional random field (CRF) is used to decode and output multi-task entity recognition. Experiments are performed on the CCKS2019 dataset, with micro avg Precision, macro avg Recall, weighted avg Precision reaching 0.880, 0.887, and 0.883, and micro avg F1-score, macro avg F1-score, weighted avg F1-score reaching 0.875, 0.876, and 0.876 respectively. Compared with existing models, our method outperforms the existing literature in three evaluation metrics (micro average, macro average, weighted average) under the same dataset conditions. In the case of weighted average, the Precision, Recall, and F1-score are 19.64%, 15.67%, and 17.58% higher than the existing BERT-BiLSTM-CRF model respectively. Experiments are performed on the actual clinical dataset with our MF-MNER, the Precision, Recall, and F1-score are 0.638, 0.825, and 0.719 under the micro-avg evaluation mechanism. The Precision, Recall, and F1-score are 0.685, 0.800, and 0.733 under the macro-avg evaluation mechanism. The Precision, Recall, and F1-score are 0.647, 0.825, and 0.722 under the weighted avg evaluation mechanism. The above results show that our method MF-MNER can integrate the advantages of BART, Bi-LSTM, and CRF layers, significantly improving the performance of downstream named entity recognition tasks with a small amount of annotation, and achieving excellent performance in terms of recall score, which has certain practical significance. Source code and datasets to reproduce the results in this paper are available at https://github.com/xfwang1969/MF-MNER .

A new interpretable streamflow prediction approach based on SWAT-BiLSTM and SHAP

Streamflow is a crucial variable for assessing the available water resources for both human and environmental use. Accurate streamflow prediction plays a significant role in water resource management and assessing the impacts of climate change. This study explores the potential of coupling conceptual hydrological models based on physical processes with machine learning algorithms to enhance the performance of streamflow simulations. Four coupled models, namely SWAT-Transformer, SWAT-LSTM, SWAT-GRU, and SWAT-BiLSTM, were constructed in this research. SWAT served as a transfer function to convert four meteorological features, including precipitation, temperature, relative humidity, and wind speed, into six hydrological features: soil water content, lateral flow, percolation, groundwater discharge, surface runoff, and evapotranspiration. Machine learning algorithms were employed to capture the underlying relationships between these ten feature variables and the target variable (streamflow) to predict daily streamflow in the Sandu-River Basin (SRB). Among the four coupled models and the calibrated SWAT model, SWAT-BiLSTM exhibited the best streamflow simulation performance. During the calibration period (training period), it achieved R2 and NSE values of 0.92 and 0.91, respectively, and maintained them at 0.90 during the validation period (testing period). Additionally, the performance of all four coupled models surpassed that of the calibrated SWAT model. Compared to the tendency of the SWAT model to underestimate streamflow, the absolute values of PBIAS for all coupled models are below 10%, which indicates that there is no significant systematic bias evident. SHapley Additive exPlanations (SHAP) were used to analyze the impact of different feature variables on streamflow prediction. The results indicated that precipitation contributed the most to streamflow prediction, with a global importance of 29.7%. Hydrological feature variable output by the SWAT model played a dominant role in the Bi-LSTM's prediction process. Coupling conceptual hydrological models with machine learning algorithms can significantly enhance the predictive performance of streamflow. The application of SHAP improves the interpretability of the coupled models and enhances researchers' confidence in the prediction results.

Sentiment analysis of COP9-related tweets: a comparative study of pre-trained models and traditional techniques

Introduction

Sentiment analysis has become a crucial area of research in natural language processing in recent years. The study aims to compare the performance of various sentiment analysis techniques, including lexicon-based, machine learning, Bi-LSTM, BERT, and GPT-3 approaches, using two commonly used datasets, IMDB reviews and Sentiment140. The objective is to identify the best-performing technique for an exemplar dataset, tweets associated with the WHO Framework Convention on Tobacco Control Ninth Conference of the Parties in 2021 (COP9).

Methods

A two-stage evaluation was conducted. In the first stage, various techniques were compared on standard sentiment analysis datasets using standard evaluation metrics such as accuracy, F1-score, and precision. In the second stage, the best-performing techniques from the first stage were applied to partially annotated COP9 conference-related tweets.

Results

In the first stage, BERT achieved the highest F1-scores (0.9380 for IMDB and 0.8114 for Sentiment 140), followed by GPT-3 (0.9119 and 0.7913) and Bi-LSTM (0.8971 and 0.7778). In the second stage, GPT-3 performed the best for sentiment analysis on partially annotated COP9 conference-related tweets, with an F1-score of 0.8812.

Discussion

The study demonstrates the effectiveness of pre-trained models like BERT and GPT-3 for sentiment analysis tasks, outperforming traditional techniques on standard datasets. Moreover, the better performance of GPT-3 on the partially annotated COP9 tweets highlights its ability to generalize well to domain-specific data with limited annotations. This provides researchers and practitioners with a viable option of using pre-trained models for sentiment analysis in scenarios with limited or no annotated data across different domains.

An improved Bi-LSTM method based on heterogeneous features fusion and attention mechanism for ECG recognition

Electrocardiogram (ECG) plays a critical role in early prevention and diagnosis of cardiovascular diseases. However, extracting powerful deep features from ECG signal for recognition is still a challenging problem today due to the variable abnormal rhythms and noise distribution. This work proposes a Bi-LSTM algorithm based on heterogeneous features fusion and attention mechanism (HFFAM + Bi-LSTM). Combining the empirical features and the features learned by the deep learning network, HFFAM + Bi-LSTM can comprehensively extract the temporal frequency information and spatial structure information of the ECG signal. Meanwhile, a novel attention mechanism based on improved DTW (AM-DTW) is designed to analyze and control the fusion process of features. The role of AM-DTW in HFFAM + Bi-LSTM is twofold, one is to measure the feature similarity between ECG signal sets with different labels using the improved DTW, and the other is to distinguish the features into isomorphic and heterogeneous features as well as adaptive weighting of the features. It is worth mentioning that overly similar isomorphic features are filtered out to further optimize the algorithm. Thus, HFFAM + Bi-LSTM has the advantage of strengthening the heterogeneous information in the feature subspace while accounting for the isomorphic features. The accuracy of HFFAM + Bi-LSTM reaches up to 98.1 % and 97.1 % on the simulated and real datasets, respectively. Compared to the all benchmark models, the classification accuracy of HFFAM + Bi-LSTM is 1.3 % higher than the best. The experiments also demonstrate that HFFAM + Bi-LSTM has better performance compared with existing methods, which provides a new scheme for automatic detection of ECG signal.

DeepPPThermo: A Deep Learning Framework for Predicting Protein Thermostability Combining Protein-Level and Amino Acid-Level Features

Using wet experimental methods to discover new thermophilic proteins or improve protein thermostability is time-consuming and expensive. Machine learning methods have shown powerful performance in the study of protein thermostability in recent years. However, how to make full use of multiview sequence information to predict thermostability effectively is still a challenge. In this study, we proposed a deep learning-based classifier named DeepPPThermo that fuses features of classical sequence features and deep learning representation features for classifying thermophilic and mesophilic proteins. In this model, deep neural network (DNN) and bi-long short-term memory (Bi-LSTM) are used to mine hidden features. Furthermore, local attention and global attention mechanisms give different importance to multiview features. The fused features are fed to a fully connected network classifier to distinguish thermophilic and mesophilic proteins. Our model is comprehensively compared with advanced machine learning algorithms and deep learning algorithms, proving that our model performs better. We further compare the effects of removing different features on the classification results, demonstrating the importance of each feature and the robustness of the model. Our DeepPPThermo model can be further used to explore protein diversity, identify new thermophilic proteins, and guide directed mutations of mesophilic proteins.

Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram

In this day and age, depression is still one of the biggest problems in the world. If left untreated, it can lead to suicidal thoughts and attempts. There is a need for proper diagnoses of Major Depressive Disorder (MDD) and evaluation of the early stages to stop the side effects. Early detection is critical to identify a variety of serious conditions. In order to provide safe and effective protection to MDD patients, it is crucial to automate diagnoses and make decision-making tools widely available. Although there are various classification systems for the diagnosis of MDD, no reliable, secure method that meets these requirements has been established to date. In this paper, a federated deep learning-based multimodal system for MDD classification using electroencephalography (EEG) and audio datasets is presented while meeting data privacy requirements. The performance of the federated learning (FL) model was tested on independent and identically distributed (IID) and non-IID data. The study began by extracting features from several pre-trained models and ultimately decided to use bidirectional short-term memory (Bi-LSTM) as the base model, as it had the highest validation accuracy of 91% compared to a convolutional neural network and LSTM with 85% and 89% validation accuracy on audio data, respectively. The Bi-LSTM model also achieved a validation accuracy of 98.9% for EEG data. The FL method was then used to perform experiments on IID and non-IID datasets. The FL-based multimodal model achieved an exceptional training and validation accuracy of 99.9% when trained and evaluated on both IID and non-IIID datasets. These results show that the FL multimodal system performs almost as well as the Bi-LSTM multimodal system and emphasize its suitability for processing IID and non-IIID data. Several clients were found to perform better than conventional pre-trained models in a multimodal framework for federated learning using EEG and audio datasets. The proposed framework stands out from other classification techniques for MDD due to its special features, such as multimodality and data privacy for edge machines with limited resources. Due to these additional features, the framework concept is the most suitable alternative approach for the early classification of MDD patients.

A new approach to optimizing aeration using XGB-Bi-LSTM via the online monitoring of oxygen transfer efficiency and oxygen uptake rate

In wastewater treatment plants (WWTPs), aeration is vital for microbial oxygen needs. To achieve carbon neutrality, optimizing aeration for energy and emissions reduction is imperative. Machine learning (ML) is used in wastewater treatment to reveal complex rules in large data sets has become a trend. In this vein, the present paper proposes an aeration optimization approach based on the extreme gradient boosting-bidirectional long short-term memory (XGB-Bi-LSTM) model via the online monitoring of oxygen transfer efficiency (OTE) and oxygen uptake rate (OUR), thus allowing WWTPs to conserve energy and reduce indirect carbon emissions. The approach uses gain algorithm of XGB to calculate the importance of features and identify important parameters, and then uses Bi-LSTM to predict the target with important parameters as features. Operational data from a WWTP in Suzhou, China, is employed to train and test the approach, the performance of which is compared with ML models suitable for regression prediction tasks (XGB, random forest, light gradient boosting machine, gradient boosting and LSTM). Experimental results show the approach requires only a small number of input parameters to achieve good performance and outperforms other machine-learning models. When OTE and dissolved oxygen (DO) are used as features to predict the alpha factor (αF; since diffusers were used, multiply by the pollution factor F), the R-squared (R2) is 0.9977, the root mean square error (RMSE) is 0.0043, the mean absolute percentage error (MAPE) is 0.0069 and the median absolute error (MedAE) is 0.0032. When the predicted αF and the OUR are used as features to predict the air flow rate of an aeration unit, the R2 is 0.9901, the RMSE is 3.6150, the MAPE is 0.0209 and the MedAE is 1.5472. Using our optimized aeration approach, the energy consumption can be reduced by 23%.

Contactless Heart and Respiration Rates Estimation and Classification of Driver Physiological States Using CW Radar and Temporal Neural Networks

The measurement and analysis of vital signs are a subject of significant research interest, particularly for monitoring the driver's physiological state, which is of crucial importance for road safety. Various approaches have been proposed using contact techniques to measure vital signs. However, all of these methods are invasive and cumbersome for the driver. This paper proposes using a non-contact sensor based on continuous wave (CW) radar at 24 GHz to measure vital signs. We associate these measurements with distinct temporal neural networks to analyze the signals to detect and extract heart and respiration rates as well as classify the physiological state of the driver. This approach offers robust performance in estimating the exact values of heart and respiration rates and in classifying the driver's physiological state. It is non-invasive and requires no physical contact with the driver, making it particularly practical and safe. The results presented in this paper, derived from the use of a 1D Convolutional Neural Network (1D-CNN), a Temporal Convolutional Network (TCN), a Recurrent Neural Network particularly the Bidirectional Long Short-Term Memory (Bi-LSTM), and a Convolutional Recurrent Neural Network (CRNN). Among these, the CRNN emerged as the most effective Deep Learning approach for vital signal analysis.

DLC-ac4C: A Prediction Model for N4-acetylcytidine Sites in Human mRNA Based on DenseNet and Bidirectional LSTM Methods

Introduction

N4 acetylcytidine (ac4C) is a highly conserved nucleoside modification that is essential for the regulation of immune functions in organisms. Currently, the identification of ac4C is primarily achieved using biological methods, which can be time-consuming and labor-intensive. In contrast, accurate identification of ac4C by computational methods has become a more effective method for classification and prediction.

Aim

To the best of our knowledge, although there are several computational methods for ac4C locus prediction, the performance of the models they constructed is poor, and the network structure they used is relatively simple and suffers from the disadvantage of network degradation. This study aims to improve these limitations by proposing a predictive model based on integrated deep learning to better help identify ac4C sites.

Methods

In this study, we propose a new integrated deep learning prediction framework, DLC-ac4C. First, we encode RNA sequences based on three feature encoding schemes, namely C2 encoding, nucleotide chemical property (NCP) encoding, and nucleotide density (ND) encoding. Second, one-dimensional convolutional layers and densely connected convolutional networks (DenseNet) are used to learn local features, and bi-directional long short-term memory networks (Bi-LSTM) are used to learn global features. Third, a channel attention mechanism is introduced to determine the importance of sequence characteristics. Finally, a homomorphic integration strategy is used to limit the generalization error of the model, which further improves the performance of the model.

Results

The DLC-ac4C model performed well in terms of sensitivity (Sn), specificity (Sp), accuracy (Acc), Mathews correlation coefficient (MCC), and area under the curve (AUC) for the independent test data with 86.23%, 79.71%, 82.97%, 66.08%, and 90.42%, respectively, which was significantly better than the prediction accuracy of the existing methods.

Conclusion

Our model not only combines DenseNet and Bi-LSTM, but also uses the channel attention mechanism to better capture hidden information features from a sequence perspective, and can identify ac4C sites more effectively.

Reconstruction of central arterial pressure waveform based on CBi-SAN network from radial pressure waveform

The central arterial pressure (CAP) is an important physiological indicator of the human cardiovascular system which represents one of the greatest threats to human health. Accurate non-invasive detection and reconstruction of CAP waveforms are crucial for the reliable treatment of cardiovascular system diseases. However, the traditional methods are reconstructed with relatively low accuracy, and some deep learning neural network models also have difficulty in extracting features, as a result, these methods have potential for further advancement. In this study, we proposed a novel model (CBi-SAN) to implement an end-to-end relationship from radial artery pressure (RAP) waveform to CAP waveform, which consisted of the convolutional neural network (CNN), the bidirectional long-short-time memory network (BiLSTM), and the self-attention mechanism to improve the performance of CAP reconstruction. The data on invasive measurements of CAP and RAP waveform were used in 62 patients before and after medication to develop and validate the performance of CBi-SAN model for reconstructing CAP waveform. We compared it with traditional methods and deep learning models in mean absolute error (MAE), root mean square error (RMSE), and Spearman correlation coefficient (SCC). Study results indicated the CBi-SAN model performed great performance on CAP waveform reconstruction (MAE: 2.23 ± 0.11 mmHg, RMSE: 2.21 ± 0.07 mmHg), concurrently, the best reconstruction effect was obtained in the central artery systolic pressure (CASP) and the central artery diastolic pressure(CADP) (RMSECASP: 2.94 ± 0.48 mmHg, RMSECADP: 1.96 ± 0.06 mmHg). These results implied the performance of the CAP reconstruction based on CBi-SAN model was superior to the existing methods, hopped to be effectively applied to clinical practice in the future.

Patient-specific warning of epileptic seizure upon shapelets features

Epilepsy is an intractable chronic neurological disease attached to extensive attention. Due to the fact that unpredictable seizure attacks result in serious physical injuries, early warning before seizure occurrence can help patients to get timely treatment and intervention. This paper presents a novel patient-specific method to predict epileptic seizures by learning shapelets of scalp electroencephalogram (EEG) signals recorded from different channels. In the proposed method, EEG signals are preprocessed to raise the Signal to Noise Rate (SNR). Multichannel shapelets space is constructed by the learning-near-to-optimal shapelets method. EEG signals are converted to distance matrices by projecting them on the shapelets' space. Bi-LSTM, SVM, CNN, and an ensemble of them are used to classify the feature set. Based on the prediction results then raise alarms. The proposed methodology is applied to the CHB-MIT scalp EEG dataset of 10 cases. The proposed method achieves a sensitivity of 91.33% and a false prediction rate of 0.16 h-1.

Drivers' Comprehensive Emotion Recognition Based on HAM

Negative emotions of drivers may lead to some dangerous driving behaviors, which in turn lead to serious traffic accidents. However, most of the current studies on driver emotions use a single modality, such as EEG, eye trackers, and driving data. In complex situations, a single modality may not be able to fully consider a driver's complete emotional characteristics and provides poor robustness. In recent years, some studies have used multimodal thinking to monitor single emotions such as driver fatigue and anger, but in actual driving environments, negative emotions such as sadness, anger, fear, and fatigue all have a significant impact on driving safety. However, there are very few research cases using multimodal data to accurately predict drivers' comprehensive emotions. Therefore, based on the multi-modal idea, this paper aims to improve drivers' comprehensive emotion recognition. By combining the three modalities of a driver's voice, facial image, and video sequence, the six classification tasks of drivers' emotions are performed as follows: sadness, anger, fear, fatigue, happiness, and emotional neutrality. In order to accurately identify drivers' negative emotions to improve driving safety, this paper proposes a multi-modal fusion framework based on the CNN + Bi-LSTM + HAM to identify driver emotions. The framework fuses feature vectors of driver audio, facial expressions, and video sequences for comprehensive driver emotion recognition. Experiments have proved the effectiveness of the multi-modal data proposed in this paper for driver emotion recognition, and its recognition accuracy has reached 85.52%. At the same time, the validity of this method is verified by comparing experiments and evaluation indicators such as accuracy and F1 score.

Spectral Efficiency Improvement Using Bi-Deep Learning Model for IRS-Assisted MU-MISO Communication System

The intelligent reflecting surface (IRS) is a two-dimensional (2D) surface with a programmable structure and is composed of many arrays. The arrays are used to supervise electromagnetic wave propagation by altering the electric and magnetic properties of the 2D surface. IRS can influentially convert wireless channels to very effectively enhance spectral efficiency (SE) and communication performance in wireless systems. However, proper channel information is necessary to realize the IRS anticipated gains. The conventional technique has been taken into consideration in recent attempts to fix this issue, which is straightforward but not ideal. A deep learning model which is called the long short-term memory (Bi-LSTM) model can tackle this issue due to its good learning capability and it plays a vital role in enhancing SE. Bi-LSTM can collect data from both forward and backward directions simultaneously to provide improved prediction accuracy. Because of the tremendous benefits of the Bi-LSTM model, in this paper, an IRS-assisted Bi-LSTM model-based multi-user multiple input single output downlink system is proposed for SE improvement. A Wiener filter is used to determine the optimal phase of each IRS element. In the simulation results, the proposed system is compared with other DL models and methods for the SE performance evaluation. The model exhibits satisfactory SE performance with a different signal-to-noise ratio compared to other schemes in the online phase.

CNN-Bi-LSTM: A Complex Environment-Oriented Cattle Behavior Classification Network Based on the Fusion of CNN and Bi-LSTM

Cattle behavior classification technology holds a crucial position within the realm of smart cattle farming. Addressing the requisites of cattle behavior classification in the agricultural sector, this paper presents a novel cattle behavior classification network tailored for intricate environments. This network amalgamates the capabilities of CNN and Bi-LSTM. Initially, a data collection method is devised within an authentic farm setting, followed by the delineation of eight fundamental cattle behaviors. The foundational step involves utilizing VGG16 as the cornerstone of the CNN network, thereby extracting spatial feature vectors from each video data sequence. Subsequently, these features are channeled into a Bi-LSTM classification model, adept at unearthing semantic insights from temporal data in both directions. This process ensures precise recognition and categorization of cattle behaviors. To validate the model's efficacy, ablation experiments, generalization effect assessments, and comparative analyses under consistent experimental conditions are performed. These investigations, involving module replacements within the classification model and comprehensive analysis of ablation experiments, affirm the model's effectiveness. The self-constructed dataset about cattle is subjected to evaluation using cross-entropy loss, assessing the model's generalization efficacy across diverse subjects and viewing perspectives. Classification performance accuracy is quantified through the application of a confusion matrix. Furthermore, a set of comparison experiments is conducted, involving three pertinent deep learning models: MASK-RCNN, CNN-LSTM, and EfficientNet-LSTM. The outcomes of these experiments unequivocally substantiate the superiority of the proposed model. Empirical results underscore the CNN-Bi-LSTM model's commendable performance metrics: achieving 94.3% accuracy, 94.2% precision, and 93.4% recall while navigating challenges such as varying light conditions, occlusions, and environmental influences. The objective of this study is to employ a fusion of CNN and Bi-LSTM to autonomously extract features from multimodal data, thereby addressing the challenge of classifying cattle behaviors within intricate scenes. By surpassing the constraints imposed by conventional methodologies and the analysis of single-sensor data, this approach seeks to enhance the precision and generalizability of cattle behavior classification. The consequential practical, economic, and societal implications for the agricultural sector are of considerable significance.

Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory

Accurately forecasting electrical signals from three-phase Direct Torque Control (DTC) induction motors is crucial for achieving optimal motor performance and effective condition monitoring. However, the intricate nature of multiple DTC induction motors and the variability in operational conditions present significant challenges for conventional prediction methodologies. To address these obstacles, we propose an innovative solution that leverages the Fast Fourier Transform (FFT) to preprocess simulation data from electrical motors. A Bidirectional Long Short-Term Memory (Bi-LSTM) network then uses this altered data to forecast processed motor signals. Our proposed approach is thoroughly examined using a comparative examination of cutting-edge forecasting models such as the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). This rigorous comparison underscores the remarkable efficacy of our approach in elevating the precision and reliability of forecasts for induction motor signals. The results unequivocally establish the superiority of our method across stator and rotor current testing data, as evidenced by Mean Absolute Error (MAE) average results of 92.6864 and 93.8802 for stator and rotor current data, respectively. Additionally, compared to alternative forecasting models, the Root Mean Square Error (RMSE) average results of 105.0636 and 85.7820 underscore reduced prediction loss.

Sentiment analysis in multilingual context: Comparative analysis of machine learning and hybrid deep learning models

This research paper investigates the efficacy of various machine learning models, including deep learning and hybrid models, for text classification in the English and Bangla languages. The study focuses on sentiment analysis of comments from a popular Bengali e-commerce site, "DARAZ," which comprises both Bangla and translated English reviews. The primary objective of this study is to conduct a comparative analysis of various models, evaluating their efficacy in the domain of sentiment analysis. The research methodology includes implementing seven machine learning models and deep learning models, such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional 1D (Conv1D), and a combined Conv1D-LSTM. Preprocessing techniques are applied to a modified text set to enhance model accuracy. The major conclusion of the study is that Support Vector Machine (SVM) models exhibit superior performance compared to other models, achieving an accuracy of 82.56% for English text sentiment analysis and 86.43% for Bangla text sentiment analysis using the porter stemming algorithm. Additionally, the Bi-LSTM Based Model demonstrates the best performance among the deep learning models, achieving an accuracy of 78.10% for English text and 83.72% for Bangla text using porter stemming. This study signifies significant progress in natural language processing research, particularly for Bangla, by enhancing improved text classification models and methodologies. The results of this research make a significant contribution to the field of sentiment analysis and offer valuable insights for future research and practical applications.

BiCaps-DBP: Predicting DNA-binding proteins from protein sequences using Bi-LSTM and a 1D-capsule network

Predicting DNA-binding proteins (DBPs) based solely on primary sequences is one of the most challenging problems in genome annotation. DBPs play a crucial role in various biological processes, including DNA replication, transcription, repair, and splicing. Some DBPs are essential in pharmaceutical research on various human cancers and autoimmune diseases. Existing experimental methods for identifying DBPs are time-consuming and costly. Therefore, developing a rapid and accurate computational technique is necessary to address the issue. This study introduces BiCaps-DBP, a deep learning-based method that improves DBP prediction performance by combining bidirectional long short-term memory with a 1D-capsule network. This study uses three training and independent datasets to evaluate the proposed model's generalizability and robustness. Based on three independent datasets, BiCaps-DBP achieved 1.05%, 5.79% and 0.40% higher accuracies than an existing predictor for PDB2272, PDB186 and PDB20000, respectively. These outcomes indicate that the proposed method is a promising DBP predictor.

Pilot study of contactless sleep apnea detection based on snore signals with hardware implementation

Objective.Sleep apnea has a high incidence and is a potentially dangerous disease, and its early detection and diagnosis are challenging. Polysomnography (PSG) is considered the best approach for sleep apnea detection, but it requires cumbersome and complicated operations. Thus, it cannot satisfy the family healthcare needs.Approach.To facilitate the initial detection of sleep apnea in the home environment, we developed a sleep apnea classification model based on snoring and hybrid neural network, and implemented the well trained model in an embedded hardware platform. We used snore signals from 32 patients at Shenzhen People's Hospital. The Mel-Fbank features were extracted from snore signals to build a sleep apnea classification model based on Bi-LSTM with attention mechanism.Main results.The proposed model classified snore signals into four types: hypopnea, normal condition, obstructive sleep apnea, and central sleep apnea, with 83.52% and 62.31% accuracies, corresponding to the subject-dependence and subject-independence validation, respectively. After pruning and model quantization, at the cost of 0.81% and 0.95% accuracy loss of the subject dependence and subject independence classification, respectively, the number of model parameters and model storage space were reduced by 32.12% and 60.37%, respectively. The model exhibited accuracies of 82.71% and 61.36% based on the subject dependence and subject independence validations, respectively. When the well trained model was successfully porting and running on an STM32 ARM-embedded platform, the model accuracy was 58.85% for the four classifications based on leave-one-subject-out validation.Significance.The proposed sleep apnea detection model can be used in home healthcare for the initial detection of sleep apnea.

Design of a corporate financial crisis prediction model based on improved ABC-RNN+Bi-LSTM algorithm in the context of sustainable development

In the context of sustainable economic development, while economic globalization brings new vitality to the company, it also makes the company face an increasingly severe external environment. The managers have to shift their focus to capital market investment. The excessive pursuit of investment benefits can easily lead to decision-making errors, resulting in a financial crisis for the company, and even may be forced to delist in severe cases. This article proposes a financial crisis prediction model based on Artificial Bee Colony-recurrent neural network (ABC-RNN) and bidirectional long short-term memory (Bi-LSTM) company with a characteristic attention mechanism. We combined ABC-RNN with Bi-LSTM to extract more temporal feature vectors from financial data. Then we introduced a feature attention mechanism to extract better depth features from financial data; the ABC algorithm is introduced to optimize the weight and bias of RNN to improve the reasoning speed and accuracy. The experiment shows that the prediction accuracy and recall of the model on the test set have reached 88.94% and 88.23%, respectively, which has good prediction ability. The outcome of this research helps the company to prevent and deal with the financial crisis in time and promote the sustainable development of the market economy.

Omicron virus emotions understanding system based on deep learning architecture

Emotions understanding has acquired a significant interest in the last few years because it has introduced remarkable services in many aspects regarding public opinion mining and recognition in the field of marketing, seeking product reviews, reviews of movies, and healthcare issues based on sentiment understanding. This conducted research has utilized the issue of Omicron virus as a case study to implement a emotions analysis framework to explore the global attitude and sentiment toward Omicron variant as an expression of Positive feeling, Neutral, and Negative feeling. Because since December 2021. Omicron variant has gained obvious attention and wide discussions on social media platforms that revealed lots of fears and anxiety feeling, due to its rapid spreading and infection ability between humans that could exceed the Delta variant infection. Therefore, this paper proposes to develop a framework utilizes techniques of natural languages processing (NLP) in deep learning methods using neural network model of Bidirectional-Long-Short-Term-Memory (Bi-LSTM) and deep neural network (DNN) to achieve accurate results. This study utilizes textual data collected and pulled from the Twitter platform (users' tweets) for the time interval from 11-Dec.-2021 to 18-Dec.-2021. Consequently, the overall achieved accuracy for the developed model is 0.946%. The produced results from carrying out the proposed framework for sentiment understanding have recorded Negative sentiment at 42.3%, Positive sentiment at 35.8%, and Neutral sentiment at 21.9% of overall extracted tweets. The acquired accuracy using data of validation for the deployed model  is 0.946%.

Coupling SWAT and Bi-LSTM for improving daily-scale hydro-climatic simulation and climate change impact assessment in a tropical river basin

Global climate change has led to an increase in both the frequency and magnitude of extreme events around the world, the risk of which is especially imminent in tropical regions. Developing hydrological models with better capabilities to simulate streamflow, especially peak flow, is urgently needed to facilitate water resource planning and management as well as climate change mitigation efforts in the tropics. In view of the need, this paper explores the feasibility of improving streamflow simulation performance in the tropical Kelantan River Basin (KRB) of Peninsular Malaysia through coupling a conceptual process-based hydrological model - Soil and Water Assessment Tool (SWAT) with a deep learning model - Bidirectional Long Short-Term Memory (Bi-LSTM) in two ways. All SWAT parameters were set as their default values in one hybrid model (SWAT-D-LSTM), whereas three most sensitive SWAT parameters were calibrated in the other hybrid model (SWAT-T-LSTM). Comparison of daily streamflow simulation results have shown that SWAT-T-LSTM consistently performs better than SWAT-D-LSTM as well as the stand-alone SWAT and Bi-LSTM model throughout the simulation period. Particularly, SWAT-T-LSTM performs considerably better than the other three models in simulating daily peak flow. Based on the latest projection results of five GCMs from the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) under three emission scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), the best-performed SWAT-T-LSTM was run to assess the potential impacts of climate change on streamflow in the KRB. Ensemble assessment results have concluded that both average and extreme streamflow is much likely to increase considerably in the already wet northeast monsoon season from November to January, which has surely raised the alarm for more frequent flood occurrence in the KRB.

Vision-Based HAR in UAV Videos Using Histograms and Deep Learning Techniques

Activity recognition in unmanned aerial vehicle (UAV) surveillance is addressed in various computer vision applications such as image retrieval, pose estimation, object detection, object detection in videos, object detection in still images, object detection in video frames, face recognition, and video action recognition. In the UAV-based surveillance technology, video segments captured from aerial vehicles make it challenging to recognize and distinguish human behavior. In this research, to recognize a single and multi-human activity using aerial data, a hybrid model of histogram of oriented gradient (HOG), mask-regional convolutional neural network (Mask-RCNN), and bidirectional long short-term memory (Bi-LSTM) is employed. The HOG algorithm extracts patterns, Mask-RCNN extracts feature maps from the raw aerial image data, and the Bi-LSTM network exploits the temporal relationship between the frames for the underlying action in the scene. This Bi-LSTM network reduces the error rate to the greatest extent due to its bidirectional process. This novel architecture generates enhanced segmentation by utilizing the histogram gradient-based instance segmentation and improves the accuracy of classifying human activities using the Bi-LSTM approach. Experimental outcomes demonstrate that the proposed model outperforms the other state-of-the-art models and has achieved 99.25% accuracy on the YouTube-Aerial dataset.

Deep-EEG: An Optimized and Robust Framework and Method for EEG-Based Diagnosis of Epileptic Seizure

Detecting brain disorders using deep learning methods has received much hype during the last few years. Increased depth leads to more computational efficiency, accuracy, and optimization and less loss. Epilepsy is one of the most common chronic neurological disorders characterized by repeated seizures. We have developed a deep learning model using Deep convolutional Autoencoder-Bidirectional Long Short Memory for Epileptic Seizure Detection (DCAE-ESD-Bi-LSTM) for automatic detection of seizures using EEG data. The significant feature of our model is that it has contributed to the accurate and optimized diagnosis of epilepsy in ideal and real-life situations. The results on the benchmark (CHB-MIT) dataset and the dataset collected by the authors show the relevance of the proposed approach over the baseline deep learning techniques by achieving an accuracy of 99.8%, classification accuracy of 99.7%, sensitivity of 99.8%, specificity and precision of 99.9% and F1 score of 99.6%. Our approach can contribute to the accurate and optimized detection of seizures while scaling the design rules and increasing performance without changing the network's depth.

Social media-based COVID-19 sentiment classification model using Bi-LSTM

Internet public social media and forums provide a convenient channel for people concerned about public health issues, such as COVID-19, to share and discuss information/misinformation with each other. In this paper, we propose a natural language processing (NLP) method based on Bidirectional Long Short-Term Memory (Bi-LSTM) technique to perform sentiment classification and uncover various issues related to COVID-19 public opinions. Bi-LSTM is an improved version of conventional LSTMs for generating the output from both left and right contexts at each time step. We experimented with real datasets extracted from Twitter and Reddit social media platforms, and our experimental results showed improved metrics compared with the conventional LSTM model as well as recent studies available in the literature. The proposed model can be used by official institutions to mitigate the effects of negative messages and to understand peoples' concerns during the pandemic. Furthermore, our findings shed light on the importance of using NLP techniques to analyze public opinion and to combat the spreading of misinformation and to guide health decision-making.

Siamese-Based Architecture for Cross-Lingual Plagiarism Detection in English-Hindi Language Pairs

The cross-lingual plagiarism detection (CLPD) is a challenging problem in natural language processing. Cross-lingual plagiarism is when a text is translated from any other language and used as it is without proper acknowledgment. Most of the existing methods provide good results for monolingual plagiarism detection, whereas the performances of existing methods for the CLPD are very limited. The reason for this is that it is difficult to represent the text from two different languages in a common semantic space. In this article, a novel Siamese architecture-based model is proposed to detect the cross-lingual plagiarism in English-Hindi language pairs. The proposed model combines the convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) network to learn the semantic similarity among the cross-lingual sentences for the English-Hindi language pairs. In the proposed model, the CNN model learns the local context of words, whereas the Bi-LSTM model learns the global context of sentences in forward and backward directions. The performances of the proposed models are evaluated on the benchmark data set, that is, Microsoft paraphrase corpus, which is converted in the English-Hindi language pairs. The proposed model outperforms other models giving 67%, 72%, and 67% weighted average precision, recall, and F1-measure scores. The experimental results show the effectiveness of the proposed models over the baseline models because the proposed model is very efficient in representing the cross-lingual text very efficiently.

A Method of Noise Reduction for Radio Communication Signal Based on RaGAN

Radio signals are polluted by noise in the process of channel transmission, which will lead to signal distortion. Noise reduction of radio signals is an effective means to eliminate the impact of noise. Using deep learning (DL) to denoise signals can reduce the dependence on artificial domain knowledge, while traditional signal-processing-based denoising methods often require knowledge of the artificial domain. Aiming at the problem of noise reduction of radio communication signals, a radio communication signal denoising method based on the relativistic average generative adversarial networks (RaGAN) is proposed in this paper. This method combines the bidirectional long short-term memory (Bi-LSTM) model, which is good at processing time-series data with RaGAN, and uses the weighted loss function to construct a noise reduction model suitable for radio communication signals, which realizes the end-to-end denoising of radio signals. The experimental results show that, compared with the existing methods, the proposed algorithm has significantly improved the noise reduction effect. In the case of a low signal-to-noise ratio (SNR), the signal modulation recognition accuracy is improved by about 10% after noise reduction.

Research on air quality prediction based on improved long short-term memory network algorithm

Air quality is changing due to the influence of industry, agriculture, people's living activities and other factors. Traditional machine learning methods generally do not consider the time series of the data itself and cannot handle long-range dependencies, thus ignoring information relevant to the predicted items and affecting the accuracy of air quality predictions. Therefore, an attention mechanism is introduced based on the long short term memory network model (LSTM), which attenuates unimportant information by controlling the proportion of the weight distribution. Finally, an integrated lightGBM+LSTM-attention model was constructed based on the light gradient boosting machine (lightGBM), and the prediction results were compared with those of 11 models. The experimental results show that the integrated model constructed in this article performs better, with the coefficient of determination (R2) of prediction accuracy reaching 0.969 and the root mean square error (RMSE) improving by 5.09, 4.94, 4.85 and 4.0 respectively compared to other models, verifying the superiority of the model.

A watershed water quality prediction model based on attention mechanism and Bi-LSTM

Accurate prediction of water quality contributes to the intelligent management and control of watershed ecology. Water Quality data has time series characteristics, but the existing models only focus on the forward time series when LSTM is introduced and do not consider the effect of the reverse time series on the model. Also did not take into account the different contributions of water quality sequences to the model at different moments. In order to solve this problem, this paper proposes a watershed water quality prediction model called AT-BILSTM. The model mainly contains a Bi-LSTM layer and a temporal attention layer and introduces an attention mechanism after bidirectional feature extraction of water quality time series data to highlight the data series that have a critical impact on the prediction results. The effectiveness of the method was verified with actual datasets from four monitoring stations in Lanzhou section of the Yellow River basin in China. After comparing with the reference model, the results show that the proposed model combines the bidirectional nonlinear mapping capability of Bi-LSTM and the feature weighting feature of the attention mechanism. Taking Fuhe Bridge as an example, compared with the original LSTM model, the RMSE and MAE of the model are reduced to 0.101 and 0.059, respectively, and the R2 is improved to 0.970, which has the best prediction performance among the four cross-sections and can provide a decision basis for the comprehensive water quality management and pollutant control in the basin.

Dual attention-based sequential auto-encoder for Covid-19 outbreak forecasting: A case study in Vietnam

For preventing the outbreaks of Covid-19 infection in different countries, many organizations and governments have extensively studied and applied different kinds of quarantine isolation policies, medical treatments as well as organized massive/fast vaccination strategy for over-18 citizens. There are several valuable lessons have been achieved in different countries this Covid-19 battle. These studies have presented the usefulness of prompt actions in testing, isolating confirmed infectious cases from community as well as social resource planning/optimization through data-driven anticipation. In recent times, many studies have demonstrated the effectiveness of short/long-term forecasting in number of new Covid-19 cases in forms of time-series data. These predictions have directly supported to effectively optimize the available healthcare resources as well as imposing suitable policies for slowing down the Covid-19 spreads, especially in high-populated cities/regions/nations. There are several progresses of deep neural architectures, such as recurrent neural network (RNN) have demonstrated significant improvements in analyzing and learning the time-series datasets for conducting better predictions. However, most of recent RNN-based techniques are considered as unable to handle chaotic/non-smooth sequential datasets. The consecutive disturbances and lagged observations from chaotic time-series dataset like as routine Covid-19 confirmed cases have led to the low performance in temporal feature learning process through recent RNN-based models. To meet this challenge, in this paper, we proposed a novel dual attention-based sequential auto-encoding architecture, called as: DAttAE. Our proposed model supports to effectively learn and predict the new Covid-19 cases in forms of chaotic and non-smooth time series dataset. Specifically, the integration between dual self-attention mechanism in a given Bi-LSTM based auto-encoder in our proposed model supports to directly focus the model on a specific time-range sequence in order to achieve better prediction. We evaluated the performance of our proposed DAttAE model by comparing with multiple traditional and state-of-the-art deep learning-based techniques for time-series prediction task upon different real-world datasets. Experimental outputs demonstrated the effectiveness of our proposed attention-based deep neural approach in comparing with state-of-the-art RNN-based architectures for time series based Covid-19 outbreak prediction task.

Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System

Non-orthogonal multiple access (NOMA) has great potential to implement the fifth-generation (5G) requirements of wireless communication. For a NOMA traditional detection method, successive interference cancellation (SIC) plays a vital role at the receiver side for both uplink and downlink transmission. Due to the complex multipath channel environment and prorogation of error problems, the traditional SIC method has a limited performance. To overcome the limitation of traditional detection methods, the deep-learning method has an advantage for the highly efficient tool. In this paper, a deep neural network which has bi-directional long short-term memory (Bi-LSTM) for multiuser uplink channel estimation (CE) and signal detection of the originally transmitted signal is proposed. Unlike the traditional CE schemes, the proposed Bi-LSTM model can directly recover multiuser transmission signals suffering from channel distortion. In the offline training stage, the Bi-LTSM model is trained using simulation data based on channel statistics. Then, the trained model is used to recover the transmitted symbols in the online deployment stage. In the simulation results, the performance of the proposed model is compared with the convolutional neural network model and traditional CE schemes such as MMSE and LS. It is shown that the proposed method provides feasible improvements in performance in terms of symbol-error rate and signal-to-noise ratio, making it suitable for 5G wireless communication and beyond.

LTPConstraint: a transfer learning based end-to-end method for RNA secondary structure prediction

Background

RNA secondary structure is very important for deciphering cell’s activity and disease occurrence. The first method which was used by the academics to predict this structure is biological experiment, But this method is too expensive, causing the promotion to be affected. Then, computing methods emerged, which has good efficiency and low cost. However, the accuracy of computing methods are not satisfactory. Many machine learning methods have also been applied to this area, but the accuracy has not improved significantly. Deep learning has matured and achieves great success in many areas such as computer vision and natural language processing. It uses neural network which is a kind of structure that has good functionality and versatility, but its effect is highly correlated with the quantity and quality of the data. At present, there is no model with high accuracy, low data dependence and high convenience in predicting RNA secondary structure.

Results

This paper designs a neural network called LTPConstraint to predict RNA secondary structure. The network is based on many network structure such as Bidirectional LSTM, Transformer and generator. It also uses transfer learning to train modelso that the data dependence can be reduced.

Conclusions

LTPConstraint has achieved high accuracy in RNA secondary structure prediction. Compared with the previous methods, the accuracy improves obviously both in predicting the structure with pseudoknot and the structure without pseudoknot. At the same time, LTPConstraint is easy to operate and can achieve result very quickly.

Novel double-layer bidirectional LSTM network with improved attention mechanism for predicting energy consumption

For power generation management and power system dispatching, it is of big significance to predict the consumption of electric energy accurately. For the sake of improving the prediction accuracy of power consumption, taking the complex features of time series data into consideration, a novel neural network sandwich structure with an improved attention mechanism is inserted into the double-layer bidirectional long short-term memory network shortened as A-DBLSTM is put forward in this article. In A-DBLSTM, compared with traditional attention mechanism, the presented attention mechanism focuses on different features in each time unit and the A-DBLLSTM network extracts time information in sequence. The parameter optimization of A-DBLSTM is based on the method of particle swarm optimization (PSO). For confirming the effectiveness and feasibility of A-DBLSTM, case studies using two datasets of the hourly temperature values and power loads between 2012 and 2014 and the electric energy consumption are carried out. The experimental results indicate that the presented A-DBLSTM with the novel sandwich network structure achieves superior performance in the aspects of the mean square error, root mean square, the average absolute error and the mean absolute percentage error to other advanced methods. What is more, the factors that have the greatest impact on the prediction performance can be found through analyzing the heatmap of the attention layer.

Emotion Recognition Based on Dynamic Energy Features Using a Bi-LSTM Network

Among electroencephalogram (EEG) signal emotion recognition methods based on deep learning, most methods have difficulty in using a high-quality model due to the low resolution and the small sample size of EEG images. To solve this problem, this study proposes a deep network model based on dynamic energy features. In this method, first, to reduce the noise superposition caused by feature analysis and extraction, the concept of an energy sequence is proposed. Second, to obtain the feature set reflecting the time persistence and multicomponent complexity of EEG signals, the construction method of the dynamic energy feature set is given. Finally, to make the network model suitable for small datasets, we used fully connected layers and bidirectional long short-term memory (Bi-LSTM) networks. To verify the effectiveness of the proposed method, we used leave one subject out (LOSO) and 10-fold cross validation (CV) strategies to carry out experiments on the SEED and DEAP datasets. The experimental results show that the accuracy of the proposed method can reach 89.42% (SEED) and 77.34% (DEAP).

Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation

Water is a prime necessity for the survival and sustenance of all living beings. Over the past few years, the water quality of rivers is adversely affected due to harmful wastes and pollutants. This ever-increasing water pollution is a big matter of concern as it deteriorating the water quality, making it unfit for any type of use. Recently, water quality modelling using machine learning techniques has generated a lot of interest and can be very beneficial in ecological and water resources management. However, they suffer many times from high computational complexity and high prediction error. The good performance of a deep neural network like long short-term memory network (LSTM) has been exploited for the time-series data. In this paper, a deep learning-based Bi-LSTM model (DLBL-WQA) is introduced to forecast the water quality factors of Yamuna River, India. The existing schemes do not perform missing value imputation and focus only on the learning process without including a loss function pertaining to training error. The proposed model shows a novel scheme which includes missing value imputation in the first phase, the second phase generates the feature maps from the given input data, the third phase includes a Bi-LSTM architecture to improve the learning process, and finally, an optimized loss function is applied to reduce the training error. Thus, the proposed model improves forecasting accuracy. Data comprising monthly samples of different water quality factors were collected for 6 years (2013-2019) at several locations in the Delhi region. Experimental results reveal that predicted values of the model and the actual values were in a close agreement and could reveal a future trend. The performance of our model was compared with various state of the art techniques like SVR, random forest, artificial neural network, LSTM, and CNN-LSTM. To check the accuracy, metrics like root mean square errors (RMSE), the mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) have been used. Experimental analysis is carried out by measuring the COD and BOD levels. COD analysis reveals the MSE, RMSE, MAE, and MAPE values as 0.015, 0.117, 0.115, and 20.32, respectively, for the Palla region. Similarly, BOD analysis indicates the MSE, RMSE, MAE, and MAPE values as 0.107, 0.108, 0.124, and 18.22, respectively. A comparative analysis reveals that the proposed model outperforms all other models in terms of the best forecasting accuracy and lowest error rates.

Early detection of emergency events from social media: a new text clustering approach

Emergency events require early detection, quick response, and accurate recovery. In the era of big data, social media users can be seen as social sensors to monitor real-time emergency events. This paper proposed an integrated approach to detect all four kinds of emergency events early, including natural disasters, man-made accidents, public health events, and social security events. First, the BERT-Att-BiLSTM model is used to detect emergency-related posts from massive and irrelevant data. Then, the 3 W attribute information (what, where, and when) of the emergency event is extracted. With the 3 W attribute information, we create an unsupervised dynamical event clustering algorithm based on text similarity and combine it with the supervised logistical regression model to cluster posts into different events. Experiments on Sina Weibo data demonstrate the superiority of the proposed framework. Case studies on some real emergency events show that the proposed framework has good performance and high timeliness. Practical applications of the framework are also discussed, followed by future directions for improvement.

Emotion Recognition Using Electroencephalography Signals of Older People for Reminiscence Therapy

Objective: Numerous communication support systems based on reminiscence therapy have been developed. However, when using communication support systems, the emotional assessment of older people is generally conducted using verbal feedback or questionnaires. The purpose of this study is to investigate the feasibility of using Electroencephalography (EEG) signals for automatic emotion recognition during RT for older people. Participants: Eleven older people (mean 71.25, SD 4.66) and seven young people (mean 22.4, SD 1.51) participated in the experiment. Methods: Old public photographs were used as material for reminiscence therapy. The EEG signals of the older people were collected while the older people and young people were talking about the contents of the photos. Since emotions change slowly and responses are characterized by delayed effects in EEG, the depth models LSTM and Bi-LSTM were selected to extract complex emotional features from EEG signals for automatic recognition of emotions. Results: The EEG data of 8 channels were inputted into the LSTM and Bi-LSTM models to classify positive and negative emotions. The recognition highest accuracy rate of the two models were 90.8% and 95.8% respectively. The four-channel EEG data based Bi-LSTM also reached 94.4%. Conclusion: Since the Bi-LSTM model could tap into the influence of "past" and "future" emotional states on the current emotional state in the EEG signal, we found that it can help improve the ability to recognize positive and negative emotions in older people. In particular, it is feasible to use EEG signals without the necessity of multimodal physiological signals for emotion recognition in the communication support systems for reminiscence therapy when using this model.

A hybrid approach of Weighted Fine-Tuned BERT extraction with deep Siamese Bi - LSTM model for semantic text similarity identification

The conventional semantic text-similarity methods requires high amount of trained labeled data and also human interventions. Generally, it neglects the contextual-information and word-orders information resulted in data sparseness problem and latitudinal-explosion issue. Recently, deep-learning methods are used for determining text-similarity. Hence, this study investigates NLP application tasks usage in detecting text-similarity of question pairs or documents and explores the similarity score predictions. A new hybridized approach using Weighted Fine-Tuned BERT Feature extraction with Siamese Bi-LSTM model is implemented. The technique is employed for determining question pair sets using Semantic-text-similarity from Quora dataset. The text features are extracted using BERT process, followed by words embedding with weights. The features along with weight values, are represented as embedded vectors, are subjected to various layers of Siamese Networks. The embedded vectors of input text features were trained by using Deep Siamese Bi-LSTM model, in various layers. Finally, similarity scores are determined for each sentence, and the semantic text-similarity is learned. The performance evaluation of proposed-framework is established with respect to accuracy rate, precision value, F1 score data and Recall values parameters compared with other existing text-similarity detection methods. The proposed-framework exhibited higher efficiency rate with 91% in accuracy level in determining semantic-text-similarity compared with other existing algorithms.

Deep learning-based exchange rate prediction during the COVID-19 pandemic

This study proposes an ensemble deep learning approach that integrates Bagging Ridge (BR) regression with Bi-directional Long Short-Term Memory (Bi-LSTM) neural networks used as base regressors to become a Bi-LSTM BR approach. Bi-LSTM BR was used to predict the exchange rates of 21 currencies against the USD during the pre-COVID-19 and COVID-19 periods. To demonstrate the effectiveness of our proposed model, we compared the prediction performance with several more traditional machine learning algorithms, such as the regression tree, support vector regression, and random forest regression, and deep learning-based algorithms such as LSTM and Bi-LSTM. Our proposed ensemble deep learning approach outperformed the compared models in forecasting exchange rates in terms of prediction error. However, the performance of the model significantly varied during non-COVID-19 and COVID-19 periods across currencies, indicating the essential role of prediction models in periods of highly volatile foreign currency markets. By providing an improved prediction performance and identifying the most seriously affected currencies, this study is beneficial for foreign exchange traders and other stakeholders in that it offers opportunities for potential trading profitability and for reducing the impact of increased currency risk during the pandemic.

A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries

Accurate prediction models have become the first goal for aiding pandemic-related decisions. Modeling and predicting the number of new active cases and deaths are important steps for anticipating and controlling COVID-19 outbreaks. The aim of this research was to develop an accurate prediction system for the COVID-19 pandemic that can predict the numbers of active cases and deaths in the Gulf countries of Saudi Arabia, Oman, the United Arab Emirates (UAE), Kuwait, Bahrain, and Qatar. The novelty of the proposed approach is that it uses an advanced prediction model—the bidirectional long short-term memory (Bi-LSTM) network deep learning model. The datasets were collected from an available repository containing updated registered cases of COVID-19 and showing the global numbers of active COVID-19 cases and deaths. Statistical analyses (e.g., mean square error, root mean square error, mean absolute error, and Spearman’s correlation coefficient) were employed to evaluate the results of the adopted Bi-LSTM model. The Bi-LSTM results based on the correlation metric gave predicted confirmed COVID-19 cases of 99.67%, 99.34%, 99.94%, 99.64%, 98.95%, and 99.91% for Saudi Arabia, Oman, the UAE, Kuwait, Bahrain, and Qatar, respectively, while testing the Bi-LSTM model for predicting COVID-19 mortality gave accuracies of 99.87%, 97.09%, 99.53%, 98.71%, 95.62%, and 99%, respectively. The Bi-LSTM model showed significant results using the correlation metric. Overall, the Bi-LSTM model demonstrated significant success in predicting COVID-19. The Bi-LSTM-based deep learning network achieves optimal prediction results and is effective and robust for predicting the numbers of active cases and deaths from COVID-19 in the studied Gulf countries.

Predicting COVID-19 cases using bidirectional LSTM on multivariate time series

To assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. We validate the effectiveness of the proposed approach by studying the disease outbreak in Qatar and the proposed model prediction from December 1st until December 31st, 2020. The quantitative evaluation shows that the proposed technique outperforms state-of-art forecasting approaches.

Research on Migrant Works' Concern Recognition and Emotion Analysis Based on Web Text Data

Based on the characteristics of convenience, autonomy, and equality, online self-media has become an important way for contemporary migrant workers to observe the world, understand society, examine themselves and express their demands. On the basis of the analysis of the domestic migrant works' concerns and their emotion analysis, we crawl data on Weibo about migrant works' topics as the basic corpus of migrant works' concerns, and then uses a combination of TF-IDF and Word2Vec methods to construct a recognition model of migrant workers' concerns. We found that wages, children's education, medical care and returning home are the main concerns of migrant workers. Meanwhile, further emotion analysis of the migrant works' concerns of using a deep learning model fused with Bi-LSTM and CNN was conducted. The results show that the proportion of negative emotion such as worries, complaints and impetuosity was significantly higher than that of other positive and neutral emotion like encourage and comfort. And the time when the negative emotion are concentrated is significantly related to the social events that occur in the corresponding time period. On the one hand, it shows that the concerns and emotion of migrant workers can be effectively observed and predicted through web text data. On the other hand, it also shows that the core well-being issues of migrant workers in the process of urban integration have not been effectively solved, and the government and relevant departments need to take targeted measures and give priority attention.

Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images

Coronavirus disease, which comes up in China at the end of 2019 and showed different symptoms in people infected, affected millions of people. Computer-aided expert systems are needed due to the inadequacy of the reverse transcription-polymerase chain reaction kit, which is widely used in the diagnosis of this disease. Undoubtedly, expert systems that provide effective solutions to many problems will be very useful in the detection of Covid-19 disease, especially when unskilled personnel and financial deficiencies in underdeveloped countries are taken into consideration. In the literature, there are numerous machine learning approaches built with different classifiers in the detection of this disease. This paper proposes an approach based on deep learning which detects Covid-19 and no-finding cases using chest X-ray images. Here, the classification performance of the Bi-LSTM network on the deep features was compared with the Deep Neural Network within the frame of the fivefold cross-validation technique. Accuracy, sensitivity, specificity and precision metrics were used to evaluate the classification performance of the trained models. Bi-LSTM network presented better performance compare to DNN with 97.6% value of high accuracy despite the few numbers of Covid-19 images in the dataset. In addition, it is understood that concatenated deep features more meaningful than deep features obtained with pre-trained networks by one by, as well. Consequently, it is thought that the proposed study based on the Bi-LSTM network and concatenated deep features will be noteworthy in the design of highly sensitive automated Covid-19 monitoring systems.

Word embeddings and deep learning for location prediction: tracking Coronavirus from British and American tweets

With the propagation of the Coronavirus pandemic, current trends on determining its individual and societal impacts become increasingly important. Recent researches grant special attention to the Coronavirus social networks infodemic to study such impacts. For this aim, we think that applying a geolocation process is crucial before proceeding to the infodemic management. In fact, the spread of reported events and actualities on social networks makes the identification of infected areas or locations of the information owners more challenging especially at a state level. In this paper, we focus on linguistic features to encode regional variations from short and noisy texts such as tweets to track this disease. We pay particular attention to contextual information for a better encoding of these features. We refer to some neural network-based models to capture relationships between words according to their contexts. Being examples of these models, we evaluate some word embedding ones to determine the most effective features’ combination that has more spatial evidence. Then, we ensure a sequential modeling of words for a better understanding of contextual information using recurrent neural networks. Without defining restricted sets of local words in relation to the Coronavirus disease, our framework called DeepGeoloc demonstrates its ability to geolocate both tweets and twitterers. It also makes it possible to capture geosemantics of nonlocal words and to delimit the sparse use of local ones particularly in retweets and reported events. Compared to some baselines, DeepGeoloc achieved competitive results. It also proves its scalability to handle large amounts of data and to geolocate new tweets even those describing new topics in relation to this disease.

Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory

Due to lockdown measures taken by the UK government during the Coronavirus disease 2019 pandemic, the national electricity demand profile presented a notably different performance. The Coronavirus disease 2019 crisis has provided a unique opportunity to investigate how such a landscape-scale lockdown can influence the national electricity system. However, the impacts of social and economic restrictions on daily electricity demands are still poorly understood. This paper investigated how the UK-wide electricity demand was influenced during the Coronavirus disease 2019 crisis based on multivariate time series forecasting with Bidirectional Long Short Term Memory, to comprehend its correlations with containment measures, weather conditions, and renewable energy supplies. A deep-learning-based predictive model was established for daily electricity demand time series forecasting, which was trained by multiple features, including the number of coronavirus tests (smoothed), wind speed, ambient temperature, biomass, solar & wind power supplies, and historical electricity demand. Besides, the effects of Coronavirus disease 2019 pandemic on the Net-Zero target of 2050 were also studied through an interlinked approach.

Automated detection of Covid-19 disease using deep fused features from chest radiography images

The health systems of many countries are desperate in the face of Covid-19, which has become a pandemic worldwide and caused the death of hundreds of thousands of people. In order to keep Covid-19, which has a very high propagation rate, under control, it is necessary to develop faster, low-cost and highly accurate methods, rather than a costly Polymerase Chain Reaction test that can yield results in a few hours. In this study, a deep learning-based approach that can detect Covid-19 quickly and with high accuracy on X-ray images, which are common in every hospital and can be obtained at low cost, was proposed. Deep features were extracted from X-Ray images in RGB, CIE Lab and RGB CIE color spaces using DenseNet121 and EfficientNet B0 pre-trained deep learning architectures and then obtained features were fed into a two-stage classifier approach. Each of the classifiers in the proposed approach performed binary classification. In the first stage, healthy and infected samples were separated, and in the second stage, infected samples were detected as Covid-19 or pneumonia. In the experiments, Bi-LSTM network and well-known ensemble approaches such as Gradient Boosting, Random Forest and Extreme Gradient Boosting were used as the classifier model and it was seen that the Bi-LSTM network had a superior performance than other classifiers with 92.489% accuracy.

Traffic accident detection and condition analysis based on social networking data

Accurate detection of traffic accidents as well as condition analysis are essential to effectively restoring traffic flow and reducing serious injuries and fatalities. This goal can be obtained using an advanced data classification model with a rich source of traffic information. Several systems based on sensors and social networking platforms have been presented recently to detect traffic events and monitor traffic conditions. However, sensor-based systems provide limited information, and may fail owing to the long detection times and high false-alarm rates. In addition, social networking data are unstructured, unpredictable, and contain idioms, jargon, and dynamic topics. The machine learning algorithms utilized for traffic event detection might not extract valuable information from social networking data. In this paper, a social network-based, real-time monitoring framework is proposed for traffic accident detection and condition analysis using ontology and latent Dirichlet allocation (OLDA) and bidirectional long short-term memory (Bi-LSTM). First, the query-based search engine effectively collects traffic information from social networks, and the data preprocessing module transforms it into structured form. Second, the proposed OLDA-based topic modeling method automatically labels each sentence (e.g., traffic or non-traffic) to identify the exact traffic information. In addition, the ontology-based event recognition approach detects traffic events from traffic-related data. Next, the sentiment analysis technique identifies the polarity of traffic events employing user's opinions, which helps determine accurate conditions of traffic events. Finally, the FastText model and Bi-LSTM with softmax regression are trained for traffic event detection and condition analysis. The proposed framework is evaluated using traffic-related data, comparing OLDA and Bi-LSTM with existing topic modeling methods and traditional classifiers using word embedding models, respectively. Our system outperforms state-of-the-art methods and achieves accuracy of 97 %. This finding demonstrates that the proposed system is more efficient for traffic event detection and condition analysis, in comparison to other existing systems.

Attention-based multi-scale features fusion for unobtrusive atrial fibrillation detection using ballistocardiogram signal

Background

Atrial fibrillation (AF) represents the most common arrhythmia worldwide, related to increased risk of ischemic stroke or systemic embolism. It is critical to screen and diagnose AF for the benefits of better cardiovascular health in lifetime. The ECG-based AF detection, the gold standard in clinical care, has been restricted by the need to attach electrodes on the body surface. Recently, ballistocardiogram (BCG) has been investigated for AF diagnosis, which is an unobstructive and convenient technique to monitor heart activity in daily life. However, here is a lack of high-dimension representation and deep learning analysis of BCG.

Method

Therefore, this paper proposes an attention-based multi-scale features fusion method by using BCG signal. The 1-D morphology feature extracted from Bi-LSTM network and 2-D rhythm feature extracted from reconstructed phase space are integrated by means of CNN network to improve the robustness of AF detection. To the best of our knowledge, this is the first study where the phase space trajectory of BCG is conducted.

Results

2000 segments (AF and NAF) of BCG signals were collected from 59 volunteers suffering from paroxysmal AF in this survey. Compared to the classical time and frequency features and the state-of-the-art energy features with the popular machine learning classifiers, AF detection performance of the proposed method is superior, which has 0.947 accuracy, 0.935 specificity, 0.959 sensitivity, and 0.937 precision, for the same BCG dataset. The experimental results show that combined feature could excavate more potential characteristics, and the attention mechanism could enhance the pertinence for AF recognition.

Conclusions

The proposed method can provide an innovative solution to capture the diverse scale descriptions of BCG and explore ways to involve the deep learning method to accurately screen AF in routine life.