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RCLSTMNet: A Residual-convolutional-LSTM Neural Network for Forecasting Cutterhead Torque in Shield Machine
During tunneling process, it is of critical importance to dynamically adjust operation parameters of shield machine due to changes of geological...
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Short-Term Load Forecasting for Commercial Building Using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network with Similar Day Selection Model
Load forecasting is essential in power systems for reliable and efficient energy planning and operation. Commercial buildings usually account for 20%...
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High-speed railway seismic response prediction using CNN-LSTM hybrid neural network
In addressing the challenges of analyzing seismic response data for high-speed railroads, this research introduces a hybrid prediction model...
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An intelligent of fault diagnosis and predicting remaining useful life of rolling bearings based on convolutional neural network with bidirectional LSTM
The importance of the quality of life of rotating machinery increases the Bearing fault diagnosis. Deep learning models (DL)-based databases become...
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An ensemble deep learning classifier of entropy convolutional neural network and divergence weight bidirectional LSTM for efficient disease prediction
According to the World Health Organization (WHO), worldwide rise in death rates amongst women is mostly attributable to HD (Heart Disease) and BC...
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Speech emotion recognition based on Graph-LSTM neural network
Currently, Graph Neural Networks have been extended to the field of speech signal processing. It is the more compact and flexible way to represent...
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An optimized convolutional neural network for speech enhancement
Speech enhancement is an important property in today’s world because most applications use voice recognition as an important feature for performing...
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Prediction of wing buffet pressure loads using a convolutional and recurrent neural network framework
In the present study, a hybrid deep learning reduced-order model (ROM) is applied for the prediction of wing buffet pressure distributions on a civil...
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ABCNN-IDS: Attention-Based Convolutional Neural Network for Intrusion Detection in IoT Networks
This paper proposes an attention-based convolutional neural network (ABCNN) for intrusion detection in the Internet of Things (IoT). The proposed...
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Residual Convolutional Neural Network-Based Dysarthric Speech Recognition
People with dysarthric speech face problems communicating with others and voice-based smart devices. This paper presents the development of a spatial...
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Multi-channel Convolutional Neural Network with Sentiment Information for Sentiment Classification
The sentence-level sentiment classification is a classic topic of natural language processing, which aims to decide the sentiment tendency toward a...
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Automated identification of steel weld defects, a convolutional neural network improved machine learning approach
This paper proposes a machine-learning-based methodology to automatically classify different types of steel weld defects, including lack of the...
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Wearable Sensors-Based Human Activity Recognition with Deep Convolutional Neural Network and Fuzzy Classification
The elderly and disabled often live without direct supervision and rely on remote monitoring for their care. However, current methods for information...
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Speech Emotion Recognition Using Generative Adversarial Network and Deep Convolutional Neural Network
Speech emotion recognition (SER) has recently increased because of vast innovations in human–computer interaction and affective computing. In recent...
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Using cutting temperature and chip characteristics with neural network BP and LSTM method to predicting tool life
The die steel NAK80 is used in specular optical molds, deep drawing forming dies, and cold extrusion dies in large quantities; high strength and...
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A novel one-dimensional convolutional neural network with parallel attention for fault diagnosis of rigid guides
Rigid guides are key components of the vertical shaft hoisting system as they guide and stabilize the conveyance during its operation. Excessive...
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A deep learning approach for diagnosis of schizophrenia disorder via data augmentation based on convolutional neural network and long short-term memory
Schizophrenia (SZ) is a severe, chronic mental disorder without specific treatment. Due to the increasing prevalence of SZ in societies and the...
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Structural Damage Identification Based on Convolutional Neural Network Group Considering the Sensor Fault
This article proposes a structural damage identification method based on one-dimensional convolutional neural network group considering sensor...
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Novel Multi-flow Multi-scale Convolutional Neural Network Developed for Quality Prediction of Batch Processes to Fuse Data With Different Sampling Frequencies
Quality prediction is a challenging task due to the nonlinearity and complexity of batch processes. In real batch processes, the presence of...
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A novel fully convolutional neural network approach for detection and classification of attacks on industrial IoT devices in smart manufacturing systems
Recently, Internet of things (IoT) devices have been widely implemented and technologically advanced in manufacturing settings to monitor, collect,...
