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Extraction of hyper-elastic material parameters using BLSTM neural network from instrumented indentation
Instrumented indentation is a versatile method of extracting hyper-elastic material parameters, particularly useful for applications where...
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Ship Track Prediction Based on Sliding Window BLSTM Network
The trajectory prediction of maritime targets is an urgent problem to be solved in order to effectively manage and control the situation in the sea...
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A Robust Speaking Rate Estimator Using a CNN-BLSTM Network
Direct acoustic feature-based speaking rate estimation is useful in applications including pronunciation assessment, dysarthria detection and...
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Source Code Plagiarism Detection Using Siamese BLSTM Network and Embedding Models
Source code plagiarism is a severe ongoing problem that threatens academic integrity and intellectual rights. Students from computing disciplines...
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Research on digital media animation control technology based on recurrent neural network using speech technology
A vivid and lifelike virtual speaker can attract the user's attention, and the construction of a lifelike virtual speaker not only requires a...
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Data-driven prognostics method for turbofan engine degradation using hybrid deep neural network
Powerful sequence modeling capability for massive multi-sensor data enables deep-learning-based methods to obtain accurate remaining useful life...
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Deep Learning-Based Modified Bidirectional LSTM Network for Classification of ADHD Disorder
Attention deficit hyperactivity disorder (ADHD) is a neurological disorder that affects an individual’s behavior. The rising cases of ADHD among...
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A novel hybrid model integrating residual structure and bi-directional long short-term memory network for tool wear monitoring
Tool wear monitoring in machine process is essential for quality assurance, efficiency improvement, and cost reduction. Data-driven methods based on...
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Towards Protein Tertiary Structure Prediction Using LSTM/BLSTM
Antony, Jisna Penikalapati, Akhil Reddy, J. Vinod Kumar Pournami, P. N. Jayaraj, P. B.Determining the native structure of a protein, given its...
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BLSTM-API: Bi-LSTM Recurrent Neural Network-Based Approach for Arabic Paraphrase Identification
Advances in communication technologies have enabled peoples to deliver more. Due to this phenomenon, an increasing amount of data are easily...
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A data-driven approach to RUL prediction of tools
An effective and reliable prediction of the remaining useful life (RUL) of a tool is important to a metal forming process because it can...
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End-to-End Acoustic Model Using 1D CNN and BLSTM Networks with Focal CTC Loss
With the advancement of modern technologies, the human-machine interaction conveyed towards a more natural means of communication. The frequent...
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Predicting cortical oscillations with bidirectional LSTM network: a simulation study
It has been stated that up-down-state (UDS) cortical oscillation levels between excitatory and inhibitory neurons play a fundamental role in brain...
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Design of English teaching speech recognition system based on LSTM network and feature extraction
The development of educational informatization has had a profound impact on college English education. In order to better meet the needs of students,...
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A novel dual-modal emotion recognition algorithm with fusing hybrid features of audio signal and speech context
With regard to human–machine interaction, accurate emotion recognition is a challenging problem. In this paper, efforts were taken to explore the...
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An accurate detection of tool wear type in drilling process by applying PCA and one-hot encoding to SSA-BLSTM model
Tool condition monitoring (TCM) is significant in advanced manufacturing systems for achieving high productivity in the manufacturing industries. The...
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Social network malicious insider detection using time-based trust evaluation
In recent years, malicious insider attacks have become a common fraudulent activity in which an attacker is often perceived as a trusted entity in...
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Cost analysis using hybrid gazelle and seagull optimization for home energy management system
Conventional electricity is more dependable, cost-effective, and robust, but it cannot meet the demands of the modern world. As a result, numerous...
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Vertical Wind Profile Estimation Using Hybrid Convolutional Neural Networks and Bidirectional Long Short-Term Memory
Determining the potential for develo** a wind farm requires accurate knowledge of the vertical profile of wind speed (WS) at present and in the...
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A hybrid network capturing multisource feature correlations for tool remaining useful life prediction
Multisource signal fusion provides rich feature information for tool predictive maintenance. However, complex signal combinations of homologous or...
