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25 papers · ranked by Valyu relevance

01
PPubMed3 Sep 2018
A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series
Huihui Qiao, Taiyong Wang, Peng Wang, Shibin Qiao + 1 more
Data-driven methods with multi-sensor time series data are the most promising approaches for monitoring machine health. Extracting fault-sensitive features from multi-sensor time series is a daunting task for both traditional data-driven methods and current deep learning models. A novel hybrid end-to-end deep learning…
92relevance
02
AarXiv9 May 2019
Feature Extraction and Classification Based on Spatial-Spectral ConvLSTM Neural Network for Hyperspectral Images.
Wen-Shuai Hu, Heng-Chao Li, Lei Pan, Wei Li + 2 more
—In recent years, deep learning has presented a great advance in hyperspectral image (HSI) classification. Particularly, Long Short-Term Memory (LSTM), as a special deep learning structure, has shown great ability in modeling long-term dependencies in the time dimension of video or the spectral dimension of HSIs.…
91relevance
03
AarXiv2019
Learning from Videos with Deep Convolutional LSTM Networks
Logan Courtney, R.S. Sreenivas
This paper explores the use of convolution LSTMs to simultaneously learn spatial- and temporalinformation in videos. A deep network of convolutional LSTMs allows the model to access the entire range of temporal information at all spatial scales of the data. We describe our experiments involving convolution LSTMs for…
88relevance
04
PPubMed9 Apr 2024
The Prediction of the Remaining Useful Life of Rotating Machinery Based on an Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution and a Convolutional LSTM Autoencoder
Yangde Gao, Zahoor Ahmad, Jong-Myon Kim, Yi Qin
The prediction of the remaining useful life (RUL) is important for the conditions of rotating machinery to maintain reliability and decrease losses. This study proposes an efficient approach based on an adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD) and a convolutional LSTM autoencoder to…
88relevance
05
AarXiv2016
Long Short-Term Memory based Convolutional Recurrent Neural Networks for Large Vocabulary Speech Recognition
Xiangang Li, Xihong Wu
Long short-term memory (LSTM) recurrent neural networks (RNNs) have been shown to give state-of-the-art performance on many speech recognition tasks, as they are able to provide the learned dynamically changing contextual window of all sequence history. On the other hand, the convolutional neural networks (CNNs) have…
87relevance
06
AarXiv30 Jul 2019
Deep Multi-Kernel Convolutional LSTM Networks and an Attention-Based Mechanism for Videos
Sebastian Agethen, Winston H. Hsu
—Action recognition greatly benefits motion understanding in video analysis. Recurrent networks such as long short-term memory (LSTM) networks are a popular choice for motion-aware sequence learning tasks. Recently, a convolutional extension of LSTM was proposed, in which input-to-hidden and hidden-to-hidden…
86relevance
07
BbioRxiv12 Dec 2024
Convolutional-LSTM Approach for Temporal Catch Hotspots (CATCH): An AI-Driven Model for Spatiotemporal Forecasting of Fisheries Catch Probability Densities
Altair Agmata, Svanur Guðmundsson
Efficient fisheries management is crucial for sustaining both marine ecosystems and the economies that heavily depend on them, such as Iceland, where the fishing industry contributes significantly to national export revenues. Current fishing practices involve decisions informed by a combination of personal experience…
85relevance
08
BbioRxiv13 Sep 2022
HiC4D: Forecasting spatiotemporal Hi-C data with residual ConvLSTM
Tong Liu, Zheng Wang
The Hi-C experiments have been extensively used for the studies of mammalian genomic structures. In the last few years, spatiotemporal Hi-C has enormously contributed to the investigation of genome dynamic reorganization. However, computationally modeling and forecasting spatiotemporal Hi-C data still have not been…
85relevance
09
PPubMed1 Sep 2021
Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
Sujan Ghimire, Zaher Mundher Yaseen, Aitazaz A. Farooque, Ravinesh C. Deo + 2 more
'Ravinesh C. Deo' 'Ji Zhang' 'Xiaohui Tao'] Streamflow (Q*flow) prediction is one of the essential steps for the reliable and robust water resources planning and management. It is highly vital for hydropower operation, agricultural planning, and flood control. In this study, the convolution neural network (CNN) and…
83relevance
10
BbioRxiv20 Sep 2020
Development of LSTM&CNN Based Hybrid Deep Learning Model to Classify Motor Imagery Tasks
Caglar Uyulan
Recent studies underline the contribution of brain-computer interface (BCI) applications to the enhancement process of the life quality of physically impaired subjects. In this context, to design an effective stroke rehabilitation or assistance system, the classification of motor imagery (MI) tasks are performed…
81relevance
11
AarXiv2016
Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data
Gil Keren, Björn W. Schuller
—Traditional convolutional layers extract features from patches of data by applying a non-linearity on an affine function of the input. We propose a model that enhances this feature extraction process for the case of sequential data, by feeding patches of the data into a recurrent neural network and using the outputs…
78relevance
12
CchemRxiv5 May 2025
Obstacle detection during Navigation using Convolutional Neural Networks with LSTM
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
75relevance
13
PPubMed26 Jun 2023
A Novel Gait Phase Recognition Method Based on DPF-LSTM-CNN Using Wearable Inertial Sensors
Kun Liu, Yong Liu, Shuo Ji, Chi Gao + 3 more
'Albert Vette'] Gait phase recognition is of great importance in the development of rehabilitation devices. The advantages of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are combined (LSTM-CNN) in this paper, then a gait phase recognition method based on LSTM-CNN neural network model is…
75relevance
14
BbioRxiv19 Nov 2021
The Flip-flop neuron – A memory efficient alternative for solving challenging sequence processing and decision making problems
Sweta Kumari, C Vigneswaran, V. Srinivasa Chakravarthy
Sequential decision making tasks that require information integration over extended durations of time are challenging for several reasons including the problem of vanishing gradients, long training times and significant memory requirements. To this end we propose a neuron model fashioned after the JK flip-flops in…
73relevance
15
PPubMed28 Apr 2019
A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks
Hüseyin Kutlu, Engin Avcı
Rapid classification of tumors that are detected in the medical images is of great importance in the early diagnosis of the disease. In this paper, a new liver and brain tumor classification method is proposed by using the power of convolutional neural network (CNN) in feature extraction, the power of discrete wavelet…
73relevance
16
PPubMed28 Feb 2020
An Introductory Review of Deep Learning for Prediction Models With Big Data
Frank Emmert-Streib, Zhen Yang, Han Feng, Shailesh Tripathi + 1 more
'Matthias Dehmer'] Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine learning. Recent breakthrough results in image analysis and speech recognition have generated a massive interest in this field because also applications in many other domains providing big data seem…
73relevance
17
PPubMed9 Feb 2018
Feature Representations for Neuromorphic Audio Spike Streams
Jithendar Anumula, Daniel Neil, Tobi Delbruck, Shih-Chii Liu
Event-driven neuromorphic spiking sensors such as the silicon retina and the silicon cochlea encode the external sensory stimuli as asynchronous streams of spikes across different channels or pixels. Combining state-of-art deep neural networks with the asynchronous outputs of these sensors has produced encouraging…
70relevance
18
AarXiv30 Jul 2019
Structural Health Monitoring of Cantilever Beam, a Case Study—Using Bayesian Neural Network and Deep Learning
Rahul Vashisht, H. Viji, T. Sundararajan, Dharshini Mohankumar + 1 more
'S. Sumitra'] The advancement of machine learning algorithms has opened a wide scope for vibration based SHM (Structural Health Monitoring). Vibration based SHM is based on the fact that damage will alter the dynamic properties viz., strucural response, frequencies, mode shapes, etc of the structure. The responses…
68relevance
19
CchemRxiv27 Sep 2018
Evaluation of Cross-Validation Strategies in Sequence-Based Binding Prediction Using Deep Learning
Angela Lopez-del Rio, Alfons Nonell-Canals, David Vidal, Alexandre Perera-Lluna
Binding prediction between targets and drug-like compounds through Deep Neural Networks have generated promising results in recent years, outperforming traditional machine learning-based methods. However, the generalization capability of these classification models is still an issue to be addressed. In this work, we…
65relevance
20
PPubMed12 Jun 2020
Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)
Savita Ahlawat, Amit Choudhary, Anand Nayyar, Saurabh Singh + 1 more
'Byungun Yoon'] Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research in the handwriting recognition field is focused around deep…
65relevance
21
BbioRxiv11 Dec 2025
Corticofugal gated recurrency captures auditory cortical responses
Lorenzo Mazzaschi, Andrew J King, Ben DB Willmore, Nicol S Harper
Memory is essential for the neural processing of natural sounds. It has been proposed that cortical memory is subserved by gated recurrency, a powerful machine learning method that enables memory of sequential dependencies. However, standard forms lack biological realism. We built a computational model using a…
64relevance
22
CchemRxiv2 Jun 2021
Neural Language Modeling for Molecule Generation
Sanjar Adilov
Generative neural networks have shown promising results in de novo drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this…
59relevance
23
CchemRxiv7 Jul 2023
Designing electrodes and electrolytes for batteries by leveraging deep learning
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
59relevance
24
CchemRxiv22 Feb 2024
CNSMolGen: a bidirectional recurrent neural networks based generative model for de novo central nervous system drug design
Rongpei Gou, Jingyi Yang, Menghan Guo, Yingjun Chen + 1 more
Central nervous system (CNS) drugs have had a significant impact on human health, e.g., treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models, particularly those for designing drugs from scratch, have shown great potential for accelerating drug…
57relevance
25
CchemRxiv16 Apr 2020
Critical Assessment of Artificial Intelligence Methods for Prediction of hERG Channel Inhibition in the ‘Big Data’ Era
Vishal Babu Siramshetty, Dac-Trung Nguyen, Natalia J. Martinez, Anton Simeonov + 2 more
The rise of novel artificial intelligence methods necessitates a comparison of this wave of new approaches with classical machine learning for a typical drug discovery project. Inhibition of the potassium ion channel, whose alpha subunit is encoded by human Ether-à-go-go-Related Gene (hERG), leads to prolonged QT…
53relevance

Results for Convolutional LSTM

A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series

Feature Extraction and Classification Based on Spatial-Spectral ConvLSTM Neural Network for Hyperspectral Images.

Learning from Videos with Deep Convolutional LSTM Networks

The Prediction of the Remaining Useful Life of Rotating Machinery Based on an Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution and a Convolutional LSTM Autoencoder

Long Short-Term Memory based Convolutional Recurrent Neural Networks for Large Vocabulary Speech Recognition

Deep Multi-Kernel Convolutional LSTM Networks and an Attention-Based Mechanism for Videos

Convolutional-LSTM Approach for Temporal Catch Hotspots (CATCH): An AI-Driven Model for Spatiotemporal Forecasting of Fisheries Catch Probability Densities

HiC4D: Forecasting spatiotemporal Hi-C data with residual ConvLSTM

Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks

Development of LSTM&CNN Based Hybrid Deep Learning Model to Classify Motor Imagery Tasks

Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data

Obstacle detection during Navigation using Convolutional Neural Networks with LSTM

A Novel Gait Phase Recognition Method Based on DPF-LSTM-CNN Using Wearable Inertial Sensors

The Flip-flop neuron – A memory efficient alternative for solving challenging sequence processing and decision making problems

A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks

An Introductory Review of Deep Learning for Prediction Models With Big Data

Feature Representations for Neuromorphic Audio Spike Streams

Structural Health Monitoring of Cantilever Beam, a Case Study—Using Bayesian Neural Network and Deep Learning

Evaluation of Cross-Validation Strategies in Sequence-Based Binding Prediction Using Deep Learning

Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)

Corticofugal gated recurrency captures auditory cortical responses

Neural Language Modeling for Molecule Generation

Designing electrodes and electrolytes for batteries by leveraging deep learning

CNSMolGen: a bidirectional recurrent neural networks based generative model for de novo central nervous system drug design

Critical Assessment of Artificial Intelligence Methods for Prediction of hERG Channel Inhibition in the ‘Big Data’ Era

A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series

Feature Extraction and Classification Based on Spatial-Spectral ConvLSTM Neural Network for Hyperspectral Images.

Learning from Videos with Deep Convolutional LSTM Networks

The Prediction of the Remaining Useful Life of Rotating Machinery Based on an Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution and a Convolutional LSTM Autoencoder

Long Short-Term Memory based Convolutional Recurrent Neural Networks for Large Vocabulary Speech Recognition

Deep Multi-Kernel Convolutional LSTM Networks and an Attention-Based Mechanism for Videos

Convolutional-LSTM Approach for Temporal Catch Hotspots (CATCH): An AI-Driven Model for Spatiotemporal Forecasting of Fisheries Catch Probability Densities

HiC4D: Forecasting spatiotemporal Hi-C data with residual ConvLSTM

Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks

Development of LSTM&CNN Based Hybrid Deep Learning Model to Classify Motor Imagery Tasks

Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data

Obstacle detection during Navigation using Convolutional Neural Networks with LSTM

A Novel Gait Phase Recognition Method Based on DPF-LSTM-CNN Using Wearable Inertial Sensors

The Flip-flop neuron – A memory efficient alternative for solving challenging sequence processing and decision making problems

A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks

An Introductory Review of Deep Learning for Prediction Models With Big Data

Feature Representations for Neuromorphic Audio Spike Streams

Structural Health Monitoring of Cantilever Beam, a Case Study—Using Bayesian Neural Network and Deep Learning

Evaluation of Cross-Validation Strategies in Sequence-Based Binding Prediction Using Deep Learning

Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)

Corticofugal gated recurrency captures auditory cortical responses

Neural Language Modeling for Molecule Generation

Designing electrodes and electrolytes for batteries by leveraging deep learning

CNSMolGen: a bidirectional recurrent neural networks based generative model for de novo central nervous system drug design

Critical Assessment of Artificial Intelligence Methods for Prediction of hERG Channel Inhibition in the ‘Big Data’ Era