CNN LSTM

CNN-LSTM models combine the spatial feature extraction capabilities of Convolutional Neural Networks (CNNs) with the temporal processing strengths of Long Short-Term Memory networks (LSTMs) to analyze spatiotemporal data. Current research focuses on applying these hybrid models to diverse prediction tasks, including weather forecasting, malware detection, and time series analysis in various domains like finance and healthcare, often incorporating enhancements like attention mechanisms or social pooling to improve accuracy. The effectiveness of CNN-LSTMs in handling complex, high-dimensional data makes them a valuable tool across numerous scientific fields and practical applications, demonstrating superior performance compared to using CNNs or LSTMs alone in many cases.

Papers

September 17, 2024

Chess Rating Estimation from Moves and Clock Times Using a CNN-LSTM
Michael Omori, Prasad Tadepalli
Player Performance CNN LSTM Rating Prediction Effective Non Local Move Analog Clock

September 14, 2024

Weather Prediction Using CNN-LSTM for Time Series Analysis: A Case Study on Delhi Temperature Data
Bangyu Li, Yang Qian
Case Study Long Short Term Memory LSTM Network Time Series Analysis Weather Forecasting CNN LSTM Temperature Prediction

August 11, 2024

A Comparative Study of Convolutional and Recurrent Neural Networks for Storm Surge Prediction in Tampa Bay
Mandana Farhang Ghahfarokhi, Seyed Hossein Sonbolestan, Mahta Zamanizadeh
Recurrent Neural Network Comparative Study Deep Convolutional Neural Network LSTM Network Neural Network Architecture LSTM Model CNN LSTM Storm Surge

May 13, 2024

Fighter flight trajectory prediction based on spatio-temporal graphcial attention network
Yao Sun, Tengyu Jing, Jiapeng Wang, Wei Wang
Graph Attention CNN LSTM Aircraft Trajectory Air Combat Flight Trajectory Prediction

May 4, 2024

CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls
Ahmed Bensaoud, Jugal Kalita
Convolutional Neural Network Transfer Learning Long Short Term Memory Deep Learning Architecture Malware Classification CNN LSTM Malware Classifier

April 30, 2024

Flight Trajectory Prediction Using an Enhanced CNN-LSTM Network
Qinzhi Hao, Jiali Zhang, Tengyu Jing, Wei Wang
LSTM Network CNN LSTM Flight Trajectory Prediction

March 7, 2024

Efficient CNN-LSTM based Parameter Estimation of Levy Driven Stochastic Differential Equations
Shuaiyu Li, Yang Ruan, Changzhou Long, Yuzhong Cheng
LSTM Network Parameter Estimation Diffusion SDE CNN LSTM

January 16, 2024

Enhancing Wind Speed and Wind Power Forecasting Using Shape-Wise Feature Engineering: A Novel Approach for Improved Accuracy and Robustness
Mulomba Mukendi Christian, Yun Seon Kim, Hyebong Choi, Jaeyoung Lee, SongHee You
Deep Learning Native Robustness Long Horizon Accuracy Improvement Wind Speed CNN LSTM Shape Feature Wind Power Forecasting Wind Energy

December 29, 2023

Malware Detection in IOT Systems Using Machine Learning Techniques
Ali Mehrban, Pegah Ahadian
Machine Learning Internet of Thing Malware Detection CNN LSTM IoT System

December 15, 2023

Beyond Empirical Windowing: An Attention-Based Approach for Trust Prediction in Autonomous Vehicles
Minxue Niu, Zhaobo Zheng, Kumar Akash, Teruhisa Misu
Autonomous Vehicle Attention Based CNN LSTM Trust Prediction Human State Estimation

September 22, 2023

A Hybrid Deep Learning-based Approach for Optimal Genotype by Environment Selection
Zahra Khalilzadeh, Motahareh Kashanian, Saeed Khaki, Lizhi Wang
Learning Based Approach Hybrid Deep Learning Weather Data CNN LSTM Crop Yield Prediction General Environment

July 15, 2023

Political Sentiment Analysis of Persian Tweets Using CNN-LSTM Model
Mohammad Dehghani, Zahra Yazdanparast
Sentiment Analysis LSTM Network CNN LSTM Twitter Sentiment News Sentiment Persian Tweet

June 25, 2023

Visual Question Answering in Remote Sensing with Cross-Attention and Multimodal Information Bottleneck
Jayesh Songara, Shivam Pande, Shabnam Choudhury, Biplab Banerjee, Rajbabu Velmurugan
Visual Question Answering Remote Sensing Cross Attention Information Bottleneck Visual Question CNN LSTM Representation Bottleneck

May 21, 2023

Predicting Stock Market Time-Series Data using CNN-LSTM Neural Network Model
Aadhitya A, Rajapriya R, Vineetha R S, Anurag M Bagde
LSTM Network CNN LSTM Stock Market Data

April 13, 2023

Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks
Chien-Pin Liu, Ju-Hsuan Li, En-Ping Chu, Chia-Yeh Hsieh, Kai-Chun Liu, Chia-Tai Chan, Yu Tsao
Ensemble Model Coarse to Fine Fall Detection CNN LSTM Winter Related Fall Injury GRU Net

March 9, 2023

Depression Diagnosis and Drug Response Prediction via Recurrent Neural Networks and Transformers Utilizing EEG Signals
Abdolkarim Saeedi, Arash Maghsoudi, Fereidoun Nowshiravan Rahatabad
Recurrent Neural Network LSTM Network EEG Signal Analysis CNN LSTM Drug Response Prediction Major Depressive Disorder

January 6, 2023

An Image captioning algorithm based on the Hybrid Deep Learning Technique (CNN+GRU)
Rana Adnan Ahmad, Muhammad Azhar, Hina Sattar
Long Short Term Memory CNN Network Hybrid Deep Learning CNN LSTM Semantic Reconstruction

October 28, 2022

Forecasting Local Behavior of Self-organizing Many-agent System without Reconstruction
Beomseok Kang, Minah Lee, Harshit Kumar, Saibal Mukhopadhyay
Full State Reconstruction Self Organization CNN LSTM

August 4, 2022

Surgical Skill Assessment via Video Semantic Aggregation
Zhenqiang Li, Lin Gu, Weimin Wang, Ryosuke Nakamura, Yoichi Sato
CNN LSTM Surgical Skill Assessment Video Based Semantic Aggregation

June 27, 2022

Improving Clinical Efficiency and Reducing Medical Errors through NLP-enabled diagnosis of Health Conditions from Transcription Reports
Krish Maniar, Shafin Haque, Kabir Ramzan
LSTM Network Medical Diagnosis Adverse Condition CNN LSTM Unstructured Information Medical Error

CNN LSTM

Papers

Chess Rating Estimation from Moves and Clock Times Using a CNN-LSTM

Weather Prediction Using CNN-LSTM for Time Series Analysis: A Case Study on Delhi Temperature Data

A Comparative Study of Convolutional and Recurrent Neural Networks for Storm Surge Prediction in Tampa Bay

Fighter flight trajectory prediction based on spatio-temporal graphcial attention network

CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls

Flight Trajectory Prediction Using an Enhanced CNN-LSTM Network

Efficient CNN-LSTM based Parameter Estimation of Levy Driven Stochastic Differential Equations

Enhancing Wind Speed and Wind Power Forecasting Using Shape-Wise Feature Engineering: A Novel Approach for Improved Accuracy and Robustness

Malware Detection in IOT Systems Using Machine Learning Techniques

Beyond Empirical Windowing: An Attention-Based Approach for Trust Prediction in Autonomous Vehicles

A Hybrid Deep Learning-based Approach for Optimal Genotype by Environment Selection

Political Sentiment Analysis of Persian Tweets Using CNN-LSTM Model

Visual Question Answering in Remote Sensing with Cross-Attention and Multimodal Information Bottleneck

Predicting Stock Market Time-Series Data using CNN-LSTM Neural Network Model

Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

Depression Diagnosis and Drug Response Prediction via Recurrent Neural Networks and Transformers Utilizing EEG Signals

An Image captioning algorithm based on the Hybrid Deep Learning Technique (CNN+GRU)

Forecasting Local Behavior of Self-organizing Many-agent System without Reconstruction

Surgical Skill Assessment via Video Semantic Aggregation

Improving Clinical Efficiency and Reducing Medical Errors through NLP-enabled diagnosis of Health Conditions from Transcription Reports