Search

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. [ABSTRACT FROM AUTHOR]

In order to improve the precision of phase recognition and reduce the rate of misdetection, this paper applies the deep learning method to automatic phase recognition. In this paper, an automatic seismic phase recognition model based on the Bi-LSTM network is designed. To test the performance of this model, the STEAD dataset is used for training and testing, and this model is compared with the traditional STA/LTA and AIC methods. The experimental results show that, compared to STA/LTA and AIC methods, the Bi-LSTM network can reduce the misdetection rate by about 8–15%, and improve the RSEM; especially, the prediction error of S-wave is greatly reduced. [ABSTRACT FROM AUTHOR]

In the field of Chinese-named entity recognition, recent research has sparked new interest by combining lexical features with character-based methods. Although this vocabulary enhancement method provides a new perspective, it faces two main challenges: firstly, using character-by-character matching can easily lead to conflicts during the vocabulary matching process. Although existing solutions attempt to alleviate this problem by obtaining semantic information about words, they still lack sufficient temporal sequential or global information acquisition; secondly, due to the limitations of dictionaries, there may be words in a sentence that do not match the dictionary. In this situation, existing vocabulary enhancement methods cannot effectively play a role. To address these issues, this paper proposes a method based on lexical information and spatial features. This method carefully considers the neighborhood and overlap relationships of characters in vocabulary and establishes global bidirectional semantic and temporal sequential information to effectively address the impact of conflicting vocabulary and character fusion on entity segmentation. Secondly, the attention score matrix extracted by the point-by-point convolutional network captures the local spatial relationship between characters without fused vocabulary information and characters with fused vocabulary information, aiming to compensate for information loss and strengthen spatial connections. The comparison results with the baseline model show that the SISF method proposed in this paper improves the F1 metric by 0.72%, 3.12%, 1.07%, and 0.37% on the Resume, Weibo, Ontonotes 4.0, and MSRA datasets, respectively. [ABSTRACT FROM AUTHOR]

In the recent decade, the citation recommendation has emerged as an important research topic due to its need for the huge size of published scientific work. Among other citation recommendation techniques, the widely used content-based filtering (CBF) exploits research articles' textual content to produce recommendations. However, CBF techniques are prone to the well-known cold-start problem. On the other hand, deep learning has shown its effectiveness in understanding the semantics of the text. The present paper proposes a citation recommendation system using deep learning models to classify rhetorical zones of the research articles and compute similarity using rhetorical zone embeddings that overcome the cold-start problem. Rhetorical zones are the predefined linguistic categories having some common characteristics about the text. A deep learning model is trained using ART and CORE datasets with an accuracy of 76 per cent. The final ranked lists of the recommendations have an average of 0.704 normalized discounted cumulative gain (nDCG) score involving ten domain experts. The proposed system is applicable for both local and global context-aware recommendations. [ABSTRACT FROM AUTHOR]

Chinese Folk Drum music is an excellent traditional cultural resource, it has brilliant historical and cultural heritage and excellent traditional cultural connotation. However, the survey found that the social and cultural values, tourism economic values, and national self-confidence embodied in folk drum music, such as Xi'an drum music, are far from being released, and even its own inheritance and development are facing difficulties. The research focuses on the automatic generation of Xi'an drum music, with the aim of further inheriting, developing, and utilizing this exceptional traditional cultural resource. While Artificial Intelligence (AI) music generation has gained popularity in recent years, most platforms primarily focus on modern music rather than Chinese folk music. To address these issues and the unique challenges faced by Xi'an drum music, this paper proposes a Bi-LSTM network-based deep reinforcement learning model. The model incorporates the distinctive characteristics of ancient Chinese music, such as pitch, chord, and mode, and utilizes the Actor-Critic algorithm in reinforcement learning. During the simulation generation stage, an improved method of generating strategies through reward and punishment scores is introduced. Additionally, the model takes into account abstract concept constraints, such as chord progression and music theory rules, which are translated into computer language. By constructing a chord reward mechanism and a music principle reward mechanism, the model achieves harmony constraints and enables the systematic generation of drum music. Experimental results demonstrate that the proposed model, based on Bi-LSTM deep reinforcement learning, can generate Xi'an drum music with high quality and artistic aesthetics. This research contributes to the preservation, development, and utilization of Xi'an drum music, leveraging advancements in AI music generation technology. [ABSTRACT FROM AUTHOR]

For structural health monitoring (SHM), a complete dataset is crucial for further modal identification analysis and risk warning. Unfortunately, data loss can occur due to sensor failure, transmission system interruption, or hardware failure, which can lead to missing data. Therefore, this study proposes a bidirectional long short-term memory neural network (Bi-LSTM) response recovery method based on variational mode decomposition (VMD) and sparrow search algorithm (SSA) optimization that utilizes the structural response data between multiple sensors and can simultaneously consider temporal and spatial correlations. A dataset containing approximately half a month of monitoring data was collected from a certain project for training, validation, and testing. A publicly available dataset was also referenced to validate the proposed method in this paper. Using the public dataset, under 13 different data loss rates, the VMD + SSA + Bi-LSTM model reduced the RMSE of data reconstruction by an average of 65.01% and 45.35% compared to the Bi-LSTM model and the VMD + Bi-LSTM models, respectively, while the coefficient of determination increased by 62.21% and 11.19%. The data reconstruction method proposed in this paper can accurately reconstruct the variation trends of missing data without the manual optimization of hyperparameters, and the reconstruction results are close to the real data. [ABSTRACT FROM AUTHOR]

Along with the growth of Internet with its numerous potential applications and diverse fields, artificial intelligence (AI) and sentiment analysis (SA) have become significant and popular research areas. Additionally, it was a key technology that contributed to the Fourth Industrial Revolution (IR 4.0). The subset of AI known as emotion recognition systems facilitates communication between IR 4.0 and IR 5.0. Nowadays users of social media, digital marketing, and e-commerce sites are increasing day by day resulting in massive amounts of unstructured data. Medical, marketing, public safety, education, human resources, business, and other industries also use the emotion recognition system widely. Hence it provides a large amount of textual data to extract the emotions from them. The paper presents a systematic literature review of the existing literature published between 2013 to 2023 in text-based emotion detection. This review scrupulously summarized 330 research papers from different conferences, journals, workshops, and dissertations. This paper explores different approaches, methods, different deep learning models, key aspects, description of datasets, evaluation techniques, Future prospects of deep learning, challenges in existing studies and presents limitations and practical implications. [ABSTRACT FROM AUTHOR]

To enhance the software implementation process, developers frequently leverage preexisting code snippets by exploring an extensive codebase. Existing code search tools often rely on keyword- or syntactic-based methods and struggle to fully grasp the semantics and intent behind code snippets. In this paper, we propose a novel hybrid C2B model that combines CodeT5 and bidirectional long short-term memory (Bi-LSTM) for source code search and recommendation. Our proposed C2B hybrid model leverages CodeT5's domain-specific pretraining and Bi-LSTM's contextual understanding to improve code representation and capture sequential dependencies. As a proof-of-concept application, we implemented the proposed C2B hybrid model as a deep neural code search tool and empirically evaluated the model on the large-scale dataset of CodeSearchNet. The experimental findings showcase that our methodology proficiently retrieves pertinent code snippets and surpasses the performance of prior state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Effective air quality prediction models are crucial for the timely prevention and control of air pollution. However, previous models often fail to fully consider air quality's temporal and spatial distribution characteristics. In this study, Xi'an City is used as the study area. Data from 1 January 2019 to 31 October 2020 are used as the training set, while data from 1 November 2020 to 31 December 2020 are used as the test set. This paper proposes a multi-time and multi-site air quality prediction model for Xi'an, leveraging a deep learning network model based on APSO-CNN-Bi-LSTM. The CNN model extracts the spatial features of the input data, the Bi-LSTM model extracts the time series features, and the PSO algorithm with adaptive inertia weight (APSO) optimizes the model's hyperparameters. The results show that the model achieves the best results in terms of MAE and RMSE. Compared to the PSO-SVR, BPTT, CNN-LSTM, and GA-ACO-BP models, the MAE improved by 9.375%, 6.667%, 2.276%, and 4.975%, while the RMSE improved by 8.371%, 8.217%, 6.327%, and 5.293%. These significant improvements highlight the model's accuracy and its promising application prospects. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicle (USV)'s motion is represented by time-series data that exhibit highly nonlinear and non-stationary features, significantly influenced by environmental factors, such as wind speed and waves, when sailing on the sea. The accurate prediction of USV motion, particularly crucial parameters, such as the roll angle and pitch angle, is imperative for ensuring safe navigation. However, traditional and single prediction models often struggle with low accuracy and fail to capture the intricate spatial–temporal dependencies among multiple input variables. To address these limitations, this paper proposes a prediction approach integrating temporal convolutional network (TCN) and bi-directional long short-term memory network (Bi-LSTM) models, augmented with a temporal pattern attention (TPA) mechanism, termed the TCN-Bi-LSTM-TPA (TBT) USV motion predictor. This hybrid model effectively combines the strengths of TCN and Bi-LSTM architectures to extract long-term temporal features and bi-directional dependencies. The introduction of the TPA mechanism enhances the model's capability to extract spatial information, crucial for understanding the intricate interplay of various motion data. By integrating the features extracted by TCN with the output of the attention mechanism, the model incorporates additional contextual information, thereby improving prediction accuracy. To evaluate the performance of the proposed model, we conducted experiments using real USV motion data and calculated four evaluation metrics: mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R-squared (R2). The results demonstrate the superior accuracy of the TCN-Bi-LSTM-TPA hybrid model in predicting USV roll angle and pitch angle, validating its effectiveness in addressing the challenges of multivariate USV motion prediction. [ABSTRACT FROM AUTHOR]

Pyrolysis is one of the thermochemical methods used to produce value-added products from biomass. Thermogravimetric analysis (TGA) is frequently used to examine the energy potential and thermal behavior of biomass, coal, and their blends. The investigation of the TGA data using Artificial Neural Networks (ANN) is one of the most important research areas in recent years. While there are different research papers on the use of Machine Learning (ML) in this field, there is a lack of systematic application of deep learning (DL) algorithms. As such, we applied DL algorithms together with ML algorithms to evaluate the predictive performance of thermal behaviors of proposed bioenergy sources. Thermal behavior of tomato, pepper, eggplant, squash, and cucumber harvest wastes, the equal mass (20%) mixture of them, and the blends of the mixture with coal in the ratios of 20, 33, and 50% under nitrogen atmosphere were investigated by the TGA and ML models. Based on the pyrolysis thermal behavior of the harvest wastes, the eggplant, pepper, tomato, and 5-biomass mixture had the highest conversion potential. According to the thermal behavior of co-pyrolysis of coal and harvest waste mixtures, it had positive effects on pyrolysis conversion degrees and temperature range compared to the coal, and therefore, they can be used as alternative sources for energy production. The MSE and R 2 scores of Bi-directional LSTM demonstrate that an improved performance can be obtained with DL based solutions. Promising results were obtained when the Bi-directional LSTM is applied for modeling the pyrolysis. [ABSTRACT FROM AUTHOR]

Parkinson's Disease (PD) is a brain‐related disease that eventually causes disability and disrupts a person's normal life. Most physicians and researchers try to diagnose it quickly and treat it on time. In recent years, computer science and the field of artificial intelligence (machine learning) have helped researchers find a way to detect the disease early. This article proposes a method that diagnoses Parkinson's disease by analyzing the hand drawing shaped by the individuals using Bi‐Directional Long Short‐Term Memory (Bi‐LSTM) neural network. In addition, this paper proposes a Fuzzy Inferential Classifier for the Dense layer, which classifies the output of LSTM Blocks to the associated classes by modifying the Soft‐max function. Our decision to propose this classifier was because, in some cases, hand‐drawing data related to people with Parkinson's disease have no significant difference with the healthy subjects for the distinguishing and are often very similar to each other. A standard dataset has been used in this paper, which includes spirals drawn test (including static spiral, dynamic spiral, and stability specific point) by a group of healthy people and people with Parkinson's disease. The proposed method has reached 97%, 98.5%, and 100% accuracy rates for the three mentioned spiral tests with a smoother training loss and accuracy plots. In addition, the results outperform state‐of‐the‐art research conducted on this dataset and show at least more than 2.5% improvement in the accuracy rate in comparison. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Automatic essay scoring (AES) is an essential educational application in natural language processing. This automated process will alleviate the burden by increasing the reliability and consistency of the assessment. With the advances in text embedding libraries and neural network models, AES systems achieved good results in terms of accuracy. However, the actual goals still need to be attained, like embedding essays into vectors with cohesion and coherence, and providing student feedback is still challenging. In this paper, we proposed coherence‐based embedding of an essay into vectors using sentence‐Bidirectional Encoder Representation for Transformers. We trained these vectors on Long short‐term memory and bidirectional long short‐term memory to capture sentence connectivity with other sentences' semantics. We used two datasets: standard ASAP Kaggle and a domain‐specific dataset with almost 2500 responses from 650 students. Our model performed well on both datasets, with an average quadratic weighted kappa score of 0.76. Furthermore, we achieved good results compared to other prescribed models, and we also tested our model on adversarial responses of both datasets and observed decent outcomes. [ABSTRACT FROM AUTHOR]

Outdoor air pollution causes a lot of health problems for humans. Particulate Matter 2.5 (PM2.5), due to its small size, can enter the human respiratory system with ease and cause significant health effects on humans. This makes PM2.5 significant among the various air pollutants. Hence, it is important to measure the value of PM2.5 accurately for better management of air quality. Algorithms for deep learning and machine learning can be used to forecast air quality data. A model that minimizes the prediction error of the PM2.5 forecast is needed. In this paper, a PM2.5 concentration estimation model using Bi-LSTM (Bidirectional Long Short-Term Memory) with meteorological data as predictor variables is proposed. For a better estimation of PM2.5 values, the hyperparameters of the Bi-LSTM model used are tuned using the Osprey Optimization Algorithm (OOA), a recent meta-heuristic algorithm. The model that works with the optimal values of hyperparameters identified by OOA performed better than the other models when they are compared based on evaluation metrics like Mean-Squared Error and R². [ABSTRACT FROM AUTHOR]

Deep learning models have overcome traditional machine learning techniques for text classification domains in the field of natural language processing (NLP). Since, NLP is a branch of machine learning, used for interpreting language, classifying text of interest, and the same can be applied to analyse the medical clinical electronic health records. Medical text consists of lot of rich data which can altogether provide a good insight, by determining patterns from the clinical text data. In this paper, bidirectional‐long short‐term memory (Bi‐LSTM), bi‐LSTM attention and bidirectional encoder representations from transformers (BERT) base models are used to classify the text which are of privacy concern to a person and which should be extracted and can be tagged as sensitive. This text data which we might think not of privacy concern would majorly reveal a lot about the patient's integrity and personal life. Clinical data not only have patient demographic data but lot of hidden data which might go unseen and thus could arise privacy issues. Bi‐LSTM with attention layer is also added on top to realize the importance of critical words which will be of great importance in terms of classification, we are able to achieve accuracy of about 92%. About 206,926 sentences are used out of which 80% are used for training and rest for testing we get accuracy of 90% approx. with Bi‐LSTM alone. The same set of datasets is used for BERT model with accuracy of 93% approx. [ABSTRACT FROM AUTHOR]

This paper introduces a novel diagnostic approach for bearing ball failures: a synergistic implementation of a bidirectional Long Short-Term Memory (LSTM) network, empowered by Gradient Minimum Mean Square. This method leverages deep analysis of operational data from bearings, enabling the precise identification of incipient bearing ball failures at early stages, thus markedly improving prediction accuracy. Our empirical results underscore the superior performance of this composite methodology in accurately detecting a spectrum of five mechanical bearing ball failure types, achieving a substantial enhancement in diagnostic precision. [ABSTRACT FROM AUTHOR]

The creeping of Armoured Face Conveyor (AFC) is an engineering problem that needs to be avoided in coal mining production process. In this paper, a method for predicting the creeping accident of AFC based on fuzzy reasoning and Bi-directional Long Short-Term Memory (Bi-LSTM) fusion iteration is put forward. Firstly, through the force analysis of the AFC and the fuzzy correlation analysis method in the actual operation process, the reasons for the creeping of AFC are analyzed; Secondly, according to the propulsion characteristics of the AFC in the time sequence development, the method of the AFC running track based on Bi-LSTM neural network is proposed; Then, on the basis of the virtual transformation of the prediction results, a judgment mechanism for the extent of the creeping of the AFC based on fuzzy evidence reasoning based on fuzzy comprehensive evaluation method and Dempster-Shafer evidence theory (D-S evidence theory) is established; Finally, the analysis on the creeping of 9711 full-mechanized mining face in Kaiyuan Mine under virtual environment after 6 cycles of continuous advancement shows that the extent of creeping of AFC is relatively high and coal mining accidents are likely to occur. [ABSTRACT FROM AUTHOR]

With the increasing level of grid intelligence and the related demand response database expanding, it is important to study a compound problem data governance method for demand response, while the traditional data governance methods have problems such as not considering data temporality and ignoring the impact of noise and duplicate data on data repair. As a result, this project will develop an anomaly data extraction and repair model based on two-way long and short memory networks, and repair the anomaly data by respective noise smoothing, missing data filling, and duplicate data cleaning. The paper also provides an adaptive moment estimation approach for optimisation to raise the model's accuracy. The outcomes demonstrated that the study model's precision for anomalous data extraction was 100% and its recall rate was 80%, which was a significant improvement over the previous state. In terms of anomalous data repair, the research model had the root mean square error value and lowest mean absolute percentage error value when compared with related models, at 0.0049 MPa and 1.375% respectively. Both the abnormal data extraction and repair performance of the research model are greatly improved over the related models, and have important value in the abnormal data governance of demand response databases. [ABSTRACT FROM AUTHOR]

Natural Language processing (NLP) includes the task of classifying texts. The proposed paper uses the ability and power of machine learning and deep learning techniques for sentiment analysis on restaurant reviews. With the increasing use of social media, classification of reviews has significant role in the development of public opinion. This work has extensive applications from client opinion study, market research and social media monitoring. The machine learning algorithms and NLP techniques are used classifying reviews and the results are rigorously analysed. It was noted that results obtained from logistic regression and multinomial naïve bayes algorithms are better than other machine learning models. Hence these two algorithms are selected to optimize further. After tuning the hyperparameters by grid search technique, the best accuracy obtained by logistic regression and multinomial naive bayes on validation dataset was 89.9% and 89.6% respectively. Deep learning architectures were also utilized for reviews classification. The accuracy obtained by Convolutional Neural Net (CNN) and Bi-Directional Long-short-term-memory (Bi-LSTM) was 89% and 90% respectively. [ABSTRACT FROM AUTHOR]

The spread of false information in the digital age has led to serious threats about the veracity and dependability of information traded online. Fake news can exacerbate social and political divisions, as it often targets specific groups or promotes divisive narratives. This paper sheds light on the pioneering venture into the field of fake news detection in Assamese, filling a significant research gap that had previously remained unaddressed. A comprehensive dataset of 6,000 news articles was meticulously collected to aid with the proposed research. The dataset contains 3,000 true news items and an equal number of fraudulent news stories. A fake news detection system for the Assamese language was created in this work using Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) models. The accuracy of the Bi-LSTM model was 94.17%, compared to the LSTM model's 93.50%. This research establishes a foundational framework for fake news detection in Assamese. The findings demonstrate the potential of LSTM and Bi-LSTM models for effectively preventing the propagation of fake news in the Assamese language and improving the accuracy and reliability of information in the digital sphere. [ABSTRACT FROM AUTHOR]

Information Management Systems are vital tools for businesses and organizations to manage their data effectively, make informed decisions, ensure data security and compliance, and enhance operational efficiency. This paper presents a robust app roach to information management through the utilization of the Optimized Probabilistic Bidirectional Long Short-Term Memory (OP_Bi-LSTM) model. The study developed into the architecture and application of Simulated Annealing (SA) for hyperparameter optimization, emphasizing the impact of fine-tuning on model performance. OP_Bi-LSTM, rooted in Bidirectional Long Short-Term Memory, exhibits superior sequential data processing capabilities, making it well-suited for a variety of information management tasks. The proposed OP_BILSTM hierarchical architecture further enhances its pattern recognition capabilities in the information management system. Results from extensive experimentation demonstrate the model's versatility and adaptability in various applications, such as sentiment analysis, fraud detection, and time-series forecasting. The proposed OP_BI-LSTM model performance analysis is evaluated for the consideration of the different customers and product data. The propsoed OP_Bi-LSTM model achieves the classification accuracy of 0.98. It is stated that OP_Bi-LSTM emerges as a powerful tool for information management, offering exceptional performance and adaptability. As the field of deep learning and information management continues to evolve, this model holds great promise for addressing complex data chal lenges and facilitating data-driven decision-making in a variety of industries. [ABSTRACT FROM AUTHOR]

In order to improve the precision of phase recognition and reduce the rate of misdetection, this paper applies the deep learning method to automatic phase recognition. In this paper, an automatic seismic phase recognition model based on the Bi-LSTM network is designed. To test the performance of this model, the STEAD dataset is used for training and testing, and this model is compared with the traditional STA/LTA and AIC methods. The experimental results show that, compared to STA/LTA and AIC methods, the Bi-LSTM network can reduce the misdetection rate by about 8–15%, and improve the RSEM; especially, the prediction error of S-wave is greatly reduced.

[Display omitted] In today's world, a large number of people suffer from motor impairment-related challenges. Rehabilitation is the main method used to overcome these difficulties. The goal of the paper is to develop a deep learning-based electroencephalogram (EEG) sensor-controlled assistive device for the rehabilitation of elbow and finger movements. We have introduced an innovative finger and elbow movement rehabilitation method using an EEG sensor. The EEG sensor's recorded EEG signals, attention values, and meditation values have been used for this purpose. This rehabilitation technique helps a person perform basic finger movement rehabilitation motions, such as finger extension and flexion. Also, basic elbow movement rehabilitation exercises, i.e., elbow extension and elbow flexion, can be performed by using this rehabilitation technique. In this research, an EEG sensor records the prefrontal lobe's EEG signals, attention value, and meditation value of a person while the person performs motor imagery. A deep learning-based CNN-TLSTM (Convolution Neural Network-tanh Long Short-Term Memory) model with attention mechanism has been designed for decoding the EEG sensor recorded data. The trained deep learning model decides the course of action of the rehabilitation device. The designed model achieves an accuracy of 99.6%. A working prototype model of the rehabilitation device has been developed, and the overall success rate of the model is found to be 98.66%. The novelty of the paper lies in i) designing an attention-based CNN-TLSTM model for motor imagery classification and ii) developing a low-cost EEG sensor-driven rehabilitation device for finger and elbow movement rehabilitation. [ABSTRACT FROM AUTHOR]

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.

In the field of Chinese-named entity recognition, recent research has sparked new interest by combining lexical features with character-based methods. Although this vocabulary enhancement method provides a new perspective, it faces two main challenges: firstly, using character-by-character matching can easily lead to conflicts during the vocabulary matching process. Although existing solutions attempt to alleviate this problem by obtaining semantic information about words, they still lack sufficient temporal sequential or global information acquisition; secondly, due to the limitations of dictionaries, there may be words in a sentence that do not match the dictionary. In this situation, existing vocabulary enhancement methods cannot effectively play a role. To address these issues, this paper proposes a method based on lexical information and spatial features. This method carefully considers the neighborhood and overlap relationships of characters in vocabulary and establishes global bidirectional semantic and temporal sequential information to effectively address the impact of conflicting vocabulary and character fusion on entity segmentation. Secondly, the attention score matrix extracted by the point-by-point convolutional network captures the local spatial relationship between characters without fused vocabulary information and characters with fused vocabulary information, aiming to compensate for information loss and strengthen spatial connections. The comparison results with the baseline model show that the SISF method proposed in this paper improves the F1 metric by 0.72%, 3.12%, 1.07%, and 0.37% on the Resume, Weibo, Ontonotes 4.0, and MSRA datasets, respectively.

For structural health monitoring (SHM), a complete dataset is crucial for further modal identification analysis and risk warning. Unfortunately, data loss can occur due to sensor failure, transmission system interruption, or hardware failure, which can lead to missing data. Therefore, this study proposes a bidirectional long short-term memory neural network (Bi-LSTM) response recovery method based on variational mode decomposition (VMD) and sparrow search algorithm (SSA) optimization that utilizes the structural response data between multiple sensors and can simultaneously consider temporal and spatial correlations. A dataset containing approximately half a month of monitoring data was collected from a certain project for training, validation, and testing. A publicly available dataset was also referenced to validate the proposed method in this paper. Using the public dataset, under 13 different data loss rates, the VMD + SSA + Bi-LSTM model reduced the RMSE of data reconstruction by an average of 65.01% and 45.35% compared to the Bi-LSTM model and the VMD + Bi-LSTM models, respectively, while the coefficient of determination increased by 62.21% and 11.19%. The data reconstruction method proposed in this paper can accurately reconstruct the variation trends of missing data without the manual optimization of hyperparameters, and the reconstruction results are close to the real data.

Stance classification is used to understand the relationship between sentences so that the model can recognize the attitude of a response to a topic, where the attitudes are classified into three, namely supporting (for), neutral (observing), and opposing (against). Furthermore, stance classification could aid the automatic fake news detection. This research is specially made for Indonesian news titles. The proposed model used to recognize these news attitudes is Bidirectional Long Short-Term Memory (Bi-LSTM). Thus, to obtain the word representation vector, the pre-trained Bidirectional Encoder Representations from Transformers (BERT) embedding model from indoBERT is used to process words in Indonesian. In Bi-LSTM, each word representation will be processed twice in a forward and backward direction sequentially, so to get a vector representation of the sentence from the input, the output is taken from the results of the representation process of the last word in the forward direction process and the representation process results of the first word in the backward direction. Then the results of the two outputs are combined to serve as a sentence representation. Based on the test results on the Indonesian news dataset, the model for stance classification task was able to achieve an F1 score with an average of 78.30%, with an F1 score label for (supportive) of 73.10%, label observing (neutral) of 89.57%, and label against (against) by 72.23%. The performance is on par with the results of experiments with several Large Language Models currently available. [ABSTRACT FROM AUTHOR]

Efforts to enhance the accuracy of protein sequence classification are of utmost importance in driving forward biological analyses and facilitating significant medical advancements. This study presents a cutting-edge model called ProtICNN-BiLSTM, which combines attention-based Improved Convolutional Neural Networks (ICNN) and Bidirectional Long Short-Term Memory (BiLSTM) units seamlessly. Our main goal is to improve the accuracy of protein sequence classification by carefully optimizing performance through Bayesian Optimisation. ProtICNN-BiLSTM combines the power of CNN and BiLSTM architectures to effectively capture local and global protein sequence dependencies. In the proposed model, the ICNN component uses convolutional operations to identify local patterns. Captures long-range associations by analyzing sequence data forward and backwards. In advanced biological studies, Bayesian Optimisation optimizes model hyperparameters for efficiency and robustness. The model was extensively confirmed with PDB-14,189 and other protein data. We found that ProtICNN-BiLSTM outperforms traditional categorization models. Bayesian Optimization's fine-tuning and seamless integration of local and global sequence information make it effective. The precision of ProtICNN-BiLSTM improves comparative protein sequence categorization. The study improves computational bioinformatics for complex biological analysis. Good results from the ProtICNN-BiLSTM model improve protein sequence categorization. This powerful tool could improve medical and biological research. The breakthrough protein sequence classification model is ProtICNN-BiLSTM. Bayesian optimization, ICNN, and BiLSTM analyze biological data accurately. [ABSTRACT FROM AUTHOR]

Biological bone materials, complex and anisotropic, require precise machining in surgeries. Bone drilling, a key technique, is susceptible to increased friction from tool wear, leading to excessive forces and high temperatures that can damage bone and surrounding tissues, affecting recovery. This study develops a monitoring platform to assess tool wear during bone drilling, employing an experimental setup that gathers triaxial force and vibration data. A recognition model using a bidirectional long short-term memory network (BI-LSTM) with a multi-head attention mechanism identified wear levels. This model, termed ABI-LSTM, was optimized and benchmarked against SVR, RNN, and CNN models. The results from implementing the ABI-LSTM-based monitoring system demonstrated its efficacy in detecting tool wear, thereby potentially reducing surgical risks such as osteonecrosis and drill breakage, and enhancing surgical outcomes. [ABSTRACT FROM AUTHOR]

This paper proposes a new model for creating fuzzy time series and conducting future forecasts using the Bi-directional Long Short-term Memory model (Bi-LSTM). This study contributes by using both classical machine learning and deep learning techniques. Firstly, a self-updating clustering algorithm is used to determine the optimal number of clusters for time series. Secondly, the study describes a method for determining the fuzzy relationship between elements of time series and the identified clusters. Thirdly, the developed model creates the fuzzy time series based on the established rules. Finally, forecasting for the future value using the Bi-LSTM model with some improvement in optimizing the parameters and increasing the accuracy of forecasting result. The steps of the proposed model are clearly illustrated by a numerical example. Moreover, this model also tested on various datasets, including M3, M4, and M5 datasets. By evaluating its performance using parameters such as sMAPE and RMSSE, the proposed model has shown significant enhancements when compared to existing models. Additionally, the model's effectiveness is further demonstrated through its successful application in forecasting the VN index stock in Vietnam. [ABSTRACT FROM AUTHOR]

• The hybrid model handles temporal and static factors without conflicts. • Errors are effectively reduced by incorporating temporal and static factors. • Time delayed prediction issue for LSTM-based model is successfully addressed. • Interpreted model predictive mechanism accords with the physical mechanics. This paper presents a novel hybrid model designed for predicting mountain tunnel deformation during construction, incorporating both temporal and static factors. Utilizing a boosting ensemble technique, the model effectively integrates a bidirectional Long Short-Term Memory (Bi-LSTM) network—acclaimed for its proficiency with time-series data—with the Light Gradient Boosting Machine (Light GBM) model—recognized for its adept handling of tabular data. In the prediction procedure, the Bi-LSTM and Light GBM modules are engaged to process time-dependent and static factors, respectively. The model's performance was evaluated against seven other established machine learning models using data from the Liangwangshan Tunnel, whose outcomes demonstrated the superiority of our model in terms of its local and overall reduction in prediction errors. By introducing static factors associated with each monitoring section via the Light GBM, our model offers a robust solution to the issue of time delayed prediction—a challenge inadequately addressed in time series prediction. Finally, we used the SHAP (SHapley Additive exPlanations) method to interpret the hybrid model's decision-making mechanisms. The findings reveal a significant correlation between the prediction rules employed by our model and the fundamental principles of tunnel engineering and physical mechanics, thereby underlining its reliability and potential applicability in practical scenarios. [ABSTRACT FROM AUTHOR]

Hexavalent chromium [Cr(VI)] is a high-priority environmental pollutant because of its toxicity and potential to contaminate water sources. Biosorption, using low-cost biomaterials, is an emerging technology for removing pollutants from water. In this study, Long Short-Term Memory (LSTM) and bidirectional LSTM (Bi-LSTM) neural networks were used to model and predict the kinetics of the removal capacity of Cr(VI) and total chromium [Cr(T)] using Cupressus lusitanica bark (CLB) particles. The models were developed using 34 experimental kinetics datasets under various temperature, pH, particle size, and initial Cr(VI) concentration conditions. Data preprocessing via interpolation was implemented to augment the sparse time-series data. Early stopping regularization prevented overfitting, and dropout techniques enhanced model robustness. The Bi-LSTM models demonstrated a superior performance compared to the LSTM models. The inherent complexities of the process and data limitations resulted in a heavy-tailed and left-skewed residual distribution, indicating occasional deviations in the predictions of capacities obtained under extreme conditions. K-fold cross-validation demonstrated the stability of Bi-LSTM models 38 and 43, while response surfaces and validation with unseen datasets assessed their predictive accuracy and generalization capabilities. Shapley additive explanations analysis (SHAP) identified the initial Cr(VI) concentration and time as the most influential input features for the models. This study highlights the capabilities of deep recurrent neural networks in comprehending and predicting complex pollutant removal kinetic phenomena for environmental applications. [ABSTRACT FROM AUTHOR]

Recently, with the growing popularity of mobile devices as well as video sharing platforms (e.g., YouTube, Facebook, TikTok, and Twitch), User-Generated Content (UGC) videos have become increasingly common and now account for a large portion of multimedia traffic on the internet. Unlike professionally generated videos produced by filmmakers and videographers, typically, UGC videos contain multiple authentic distortions, generally introduced during capture and processing by naive users. Quality prediction of UGC videos is of paramount importance to optimize and monitor their processing in hosting platforms, such as their coding, transcoding, and streaming. However, blind quality prediction of UGC is quite challenging, because the degradations of UGC videos are unknown and very diverse, in addition to the unavailability of pristine reference. Therefore, in this article, we propose an accurate and efficient Blind Video Quality Assessment (BVQA) model for UGC videos, which we name 2BiVQA for double Bi-LSTM Video Quality Assessment. 2BiVQA metric consists of three main blocks, including a pre-trained Convolutional Neural Network to extract discriminative features from image patches, which are then fed into two Recurrent Neural Networks for spatial and temporal pooling. Specifically, we use two Bi-directional Long Short-term Memory networks, the first is used to capture short-range dependencies between image patches, while the second allows capturing long-range dependencies between frames to account for the temporal memory effect. Experimental results on recent large-scale UGC VQA datasets show that 2BiVQA achieves high performance at lower computational cost than most state-of-the-art VQA models. The source code of our 2BiVQA metric is made publicly available at https://github.com/atelili/2BiVQA. [ABSTRACT FROM AUTHOR]