Relevant bibliographies by topics / Convolutional LSTM Network

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Convolutional LSTM Network'

Author: Grafiati
Published: 5 September 2021
Last updated: 29 July 2025

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Journal articles on the topic "Convolutional LSTM Network"

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Cui, Ziti, Wei Wang, Wei Jiang, Jun Guo, and Yang Liu. "High-precision identification and prediction of low-voltage load characteristics in smart grids based on hybrid deep learning framework." International Journal of Low-Carbon Technologies 19 (2024): 2656–66. http://dx.doi.org/10.1093/ijlct/ctae221.

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Abstract This paper proposes a hybrid deep learning framework (HDLF) that combines improved convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and transformer models. First, feature selection and dimensionality reduction are performed using XGBoost and principal component analysis, respectively. Secondly, CNN is enhanced by multiscale convolution, residual connection, and attention mechanism. Then, the bidirectional LSTM is combined with temporal convolutional network to improve the LSTM. Then, an improved dynamic focusing mechanism of transformer is introduced. The
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Wan, Renzhuo, Shuping Mei, Jun Wang, Min Liu, and Fan Yang. "Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting." Electronics 8, no. 8 (2019): 876. http://dx.doi.org/10.3390/electronics8080876.

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Multivariable time series prediction has been widely studied in power energy, aerology, meteorology, finance, transportation, etc. Traditional modeling methods have complex patterns and are inefficient to capture long-term multivariate dependencies of data for desired forecasting accuracy. To address such concerns, various deep learning models based on Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) methods are proposed. To improve the prediction accuracy and minimize the multivariate time series data dependence for aperiodic data, in this article, Beijing PM2.5 and ISO-N
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Zhang, Jiaan, Chenyu Liu, and Leijiao Ge. "Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN." Energies 15, no. 7 (2022): 2633. http://dx.doi.org/10.3390/en15072633.

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The large fluctuations in charging loads of electric vehicles (EVs) make short-term forecasting challenging. In order to improve the short-term load forecasting performance of EV charging load, a corresponding model-based multi-channel convolutional neural network and temporal convolutional network (MCCNN-TCN) are proposed. The multi-channel convolutional neural network (MCCNN) can extract the fluctuation characteristics of EV charging load at various time scales, while the temporal convolutional network (TCN) can build a time-series dependence between the fluctuation characteristics and the f
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Yang, Wuyi, Wenlei Chang, Zhongchang Song, Fuqiang Niu, Xianyan Wang, and Yu Zhang. "Denoising odontocete echolocation clicks using a hybrid model with convolutional neural network and long short-term memory network." Journal of the Acoustical Society of America 154, no. 2 (2023): 938–47. http://dx.doi.org/10.1121/10.0020560.

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Ocean noise negatively influences the recording of odontocete echolocation clicks. In this study, a hybrid model based on the convolutional neural network (CNN) and long short-term memory (LSTM) network—called a hybrid CNN-LSTM model—was proposed to denoise echolocation clicks. To learn the model parameters, the echolocation clicks were partially corrupted by adding ocean noise, and the model was trained to recover the original echolocation clicks. It can be difficult to collect large numbers of echolocation clicks free of ambient sea noise for training networks. Data augmentation and transfer
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Mountzouris, Konstantinos, Isidoros Perikos, and Ioannis Hatzilygeroudis. "Speech Emotion Recognition Using Convolutional Neural Networks with Attention Mechanism." Electronics 12, no. 20 (2023): 4376. http://dx.doi.org/10.3390/electronics12204376.

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Speech emotion recognition (SER) is an interesting and difficult problem to handle. In this paper, we deal with it through the implementation of deep learning networks. We have designed and implemented six different deep learning networks, a deep belief network (DBN), a simple deep neural network (SDNN), an LSTM network (LSTM), an LSTM network with the addition of an attention mechanism (LSTM-ATN), a convolutional neural network (CNN), and a convolutional neural network with the addition of an attention mechanism (CNN-ATN), having in mind, apart from solving the SER problem, to test the impact
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Swapna.C. "Conv2D-LSTM-AE-GAN: Convolutional 2D LSTM Auto Encoder Generative Adversarial Network." Journal of Information Systems Engineering and Management 10, no. 14s (2025): 792–806. https://doi.org/10.52783/jisem.v10i14s.2396.

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Surveillance video refers to video footage captured by cameras for the purpose of monitoring and recording activities in specific environments. These videos are commonly used for security purposes in places such as airports, shopping malls, streets, industrial facilities, hospitals, and other public or private spaces. The primary objective of surveillance video systems is to maintain safety, detect suspicious activities, and collect evidence for investigation. Anomaly detection in Surveillance video is an important and evolving field with applications across various industries. It involves ana
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Guan, Wenhui, and Binbin Li. "Research on diagnosis method of motor vibration signal based on MSCNN-LSTM." Journal of Physics: Conference Series 2816, no. 1 (2024): 012035. http://dx.doi.org/10.1088/1742-6596/2816/1/012035.

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Abstract Vibration signal is often considered an important basis for diagnosing motor faults. However, the original vibration signal features a single time series that needs to be shorter. This paper introduces a fault diagnosis approach, MSCNN-LSTM, which integrates a multi-scale one-dimensional convolutional neural network with a long short-term memory network, reflecting the ongoing advancements in deep learning for fault diagnosis. Convolution kernels of varying sizes are accustomed to realizing information integration of various scales and broadening the dimensions of vibration signals. I
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Zhang, Feizhou, Ke Shang, Lei Yan, Haijing Nan, and Zicong Miao. "Prediction of Parking Space Availability Using Improved MAT-LSTM Network." ISPRS International Journal of Geo-Information 13, no. 5 (2024): 151. http://dx.doi.org/10.3390/ijgi13050151.

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The prediction of parking space availability plays a crucial role in information systems providing parking guidance. However, controversy persists regarding the efficiency and accuracy of mainstream time series prediction methods, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In this study, a comparison was made between a temporal convolutional network (TCN) based on CNNs and a long short-term memory (LSTM) network based on RNNs to determine an appropriate baseline for predicting parking space availability. Subsequently, a multi-head attention (MAT) mechani
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Waczyńska, Joanna, Edoardo Martelli, Sofia Vallecorsa, Edward Karavakis, and Tony Cass. "Convolutional LSTM models to estimate network traffic." EPJ Web of Conferences 251 (2021): 02050. http://dx.doi.org/10.1051/epjconf/202125102050.

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Network utilisation efficiency can, at least in principle, often be improved by dynamically re-configuring routing policies to better distribute ongoing large data transfers. Unfortunately, the information necessary to decide on an appropriate reconfiguration—details of on-going and upcoming data transfers such as their source and destination and, most importantly, their volume and duration—is usually lacking. Fortunately, the increased use of scheduled transfer services, such as FTS, makes it possible to collect the necessary information. However, the mere detection and characterisation of la
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Wang, Kejun, Xiaoxia Qi, and Hongda Liu. "Photovoltaic power forecasting based LSTM-Convolutional Network." Energy 189 (December 2019): 116225. http://dx.doi.org/10.1016/j.energy.2019.116225.

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Dissertations / Theses on the topic "Convolutional LSTM Network"

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Holm, Noah, and Emil Plynning. "Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks." Thesis, KTH, Geoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229952.

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The low amount solved residential burglary crimes calls for new and innovative methods in the prevention and investigation of the cases. There were 22 600 reported residential burglaries in Sweden 2017 but only four to five percent of these will ever be solved. There are many initiatives in both Sweden and abroad for decreasing the amount of occurring residential burglaries and one of the areas that are being tested is the use of prediction methods for more efficient preventive actions. This thesis is an investigation of a potential method of prediction by using neural networks to identify are
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Silfver, Anton. "Short-Term Forecasting of Taxi Demand using a two Channelled Convolutional LSTM network." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165743.

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In this thesis a model capable of predicting taxidemand with high accuracy across five different real world single company datasets is presented. The model uses historical drop off and arrival information to make accurate shortterm predictions about future taxi demand. The model is compared to and outperforms both LSTM and statistical baselines. This thesis uniquely uses a different tessellation strategy which makes the results directly applicable to smaller taxi companies. This paper shows that accurate short term predictions of taxi demand can be made using real world data available to taxi
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Shu, Xingliang. "Electrocardiograph Signal Classification By Using Neural Network." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592395089900722.

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Imramovská, Klára. "Detekce komorových extrasystol v EKG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442489.

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The thesis deals with problems of automatic detection of premature ventricular contractions in ECG records. One detection method which uses a convolutional neural network and LSTM units is implemented in the Python language. Cardiac cycles extracted from one-lead ECG were used for detection. F1 score for binary classification (PVC and normal beat) on the test dataset reached 96,41 % and 81,76 % for three-class classification (PVC, normal beat and other arrhythmias). Lastly, the accuracy of the classification is evaluated and discussed, the achieved results for binary classification are compara
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Chowdhury, Muhammad Iqbal Hasan. "Question-answering on image/video content." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/205096/1/Muhammad%20Iqbal%20Hasan_Chowdhury_Thesis.pdf.

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This thesis explores a computer's ability to understand multimodal data where the correspondence between image/video content and natural language text are utilised to answer open-ended natural language questions through question-answering tasks. Static image data consisting of both indoor and outdoor scenes, where complex textual questions are arbitrarily posed to a machine to generate correct answers, was examined. Dynamic videos consisting of both single-camera and multi-camera settings for the exploration of more challenging and unconstrained question-answering tasks were also considered. I
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Hamerník, Pavel. "Využití hlubokého učení pro rozpoznání textu v obrazu grafického uživatelského rozhraní." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403823.

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Optical character recognition (OCR) has been a topic of interest for many years. It is defined as the process of digitizing a document image into a sequence of characters. Despite decades of intense research, OCR systems with capabilities to that of human still remains an open challenge. In this work there is presented a design and implementation of such system, which is capable of detecting texts in graphical user interfaces.
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Ujihara, Rintaro. "Multi-objective optimization for model selection in music classification." Thesis, KTH, Optimeringslära och systemteori, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298370.

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With the breakthrough of machine learning techniques, the research concerning music emotion classification has been getting notable progress combining various audio features and state-of-the-art machine learning models. Still, it is known that the way to preprocess music samples and to choose which machine classification algorithm to use depends on data sets and the objective of each project work. The collaborating company of this thesis, Ichigoichie AB, is currently developing a system to categorize music data into positive/negative classes. To enhance the accuracy of the existing system, thi
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Sansano, Sansano Emilio. "Machine learning-based techniques for indoor localization and human activity recognition through wearable devices." Doctoral thesis, Universitat Jaume I, 2020. http://dx.doi.org/10.6035/14101.2020.180044.

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This thesis approaches the study of several machine learning techniques to improve the performance of indoor positioning systems, with a special focus on wearable and low-cost devices. It also presents some tools designed to facilitate the research in this field through the development of a software framework for indoor positioning-related research, and the creation of a web platform committed to becoming a collaborative repository of data. The framework has been developed as an open-source package for the R language platform. This allows other users to collaborate in the development of future
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Kramář, Denis. "Analýza zvukových nahrávek pomocí hlubokého učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442571.

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This master thesis deals with the problem of audio-classification of the chainsaw logging sound in natural environment using mainly convolutional neural networks. First, a theory of grafical representation of audio signal is discussed. Following part is devoted to the machine learning area. In third chapter, some of present works dealing with this problematics are given. Within the practical part, used dataset and tested neural networks are presented. Final resultes are compared by achieved accuracy and by ROC curves. The robustness of the presented solutions was tested by proposed detection p
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Coen, Paul Dixon. "Human Activity Recognition and Prediction using RGBD Data." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/theses/2562.

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Being able to predict and recognize human activities is an essential element for us to effectively communicate with other humans during our day to day activities. A system that is able to do this has a number of appealing applications, from assistive robotics to health care and preventative medicine. Previous work in supervised video-based human activity prediction and detection fails to capture the richness of spatiotemporal data that these activities generate. Convolutional Long short-term memory (Convolutional LSTM) networks are a useful tool in analyzing this type of data, showing good res
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Books on the topic "Convolutional LSTM Network"

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Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

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Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip it
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Book chapters on the topic "Convolutional LSTM Network"

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Yeh, Chia-Hung, Yao-Pao Huang, Chih-Yang Lin, and Min-Hui Lin. "Learning Depth from Monocular Sequence with Convolutional LSTM Network." In Advances in Networked-based Information Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29029-0_48.

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Zhao, Yu, Yuan Liu, Yansheng Kan, et al. "Spatial-Frequency Non-local Convolutional LSTM Network for pRCC Classification." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32226-7_3.

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Guo, Canyang, Wenzhong Guo, Chi-Hua Chen, Xin Wang, and Genggeng Liu. "The Air Quality Prediction Based on a Convolutional LSTM Network." In Web Information Systems and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30952-7_12.

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Ye, Xiaoyu, Dong Wang, Chenlu Yu, Zhuo Yang, and Along Zhang. "Deep Learning-Based Multi-Model Coupled Flood Season Daily Runoff Prediction Model." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-9184-2_10.

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AbstractAccurate runoff forecasting is of great significance for flood control, drought prevention, reservoir scheduling, and ecological protection. To explore the applicability of deep learning networks combined with signal processing techniques in runoff forecasting, an ICEEMDAN-VMD-CNN-LSTM daily runoff forecasting model for the flood season was developed. First, the original runoff series was decomposed using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). Then, the complex series was further decomposed using Variational Mode Decomposition (VMD)
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Saravana Velu, D., and U. Arul. "Network DDOS Attacks Using Convolutional Neural Networks with CNN + LSTM for Improving Accuracy." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-5223-5_45.

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Lv, Hengxing, Xuemei Ren, and Yongfeng Lv. "EEG Recognition with Adaptive Noise Reduction Based on Convolutional LSTM Network." In Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019). Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0474-7_22.

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Yu, Baek-Woon, Ji-Hoon Jeong, Dae-Hyeok Lee, and Seong-Whan Lee. "Detection of Pilot’s Drowsiness Based on Multimodal Convolutional Bidirectional LSTM Network." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41299-9_41.

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Song, Mengjiao, Xingyu Zhao, Yong Liu, and Zhihong Zhao. "Text Sentiment Analysis Based on Convolutional Neural Network and Bidirectional LSTM Model." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2206-8_6.

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Olulana, Kolawole, Pius Owolawi, Chunling Tu, and Bolanle Abe. "Nodule Generation of Lung CT Images Using a 3D Convolutional LSTM Network." In Advances in Visual Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64559-5_60.

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Li, Xilian, Wei Chen, Tengjiao Wang, and Weijing Huang. "Target-Specific Convolutional Bi-directional LSTM Neural Network for Political Ideology Analysis." In Web and Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63564-4_5.

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Conference papers on the topic "Convolutional LSTM Network"

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Maity, Monalisa, Ishita Gupta, and Dinesh Kumar Vishwakarma. "Stuttering Detection Using LSTM and LSTM-Attention Based Convolutional Neural Network." In 2025 10th International Conference on Signal Processing and Communication (ICSC). IEEE, 2025. https://doi.org/10.1109/icsc64553.2025.10968952.

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Liang, Jiesong, and Yuzhu Ran. "Time-Series Data Prediction Using Convolutional-LSTM Network." In 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2024. https://doi.org/10.1109/icpics62053.2024.10796195.

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He, Yanlin, Xiuyun Teng, Yuan Xu, and Qunxiong Zhu. "Multi-Scale Convolutional LSTM Network for Sewage Flow Prediction." In 2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2024. http://dx.doi.org/10.1109/ddcls61622.2024.10606563.

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Ma, Zhengzheng, Jie Han, Liang Chen, et al. "Weather Radar Echo Extrapolation Based on Convolutional LSTM Prediction Neural Network." In 2024 14th International Symposium on Antennas, Propagation and EM Theory (ISAPE). IEEE, 2024. https://doi.org/10.1109/isape62431.2024.10841211.

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Alsalami, Zaid, E. G. Satish, M. PonniBala, J. Rajalakshmi, and C. Sushama. "A Denoising Convolutional Auto Encoder with Bi-LSTM for Corrupted ECG Signals Reduction." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691263.

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Jungum, Nevin Vunka. "An Adaptive Learning Elderly Fall Detection System using LSTM and Convolutional Neural Network." In 2024 International Conference on Circuit, Systems and Communication (ICCSC). IEEE, 2024. http://dx.doi.org/10.1109/iccsc62074.2024.10616802.

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Lizo, Leander, and Jheanel Estrada. "Lightweight Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) for Dynamic Hand Gesture Recognition." In 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA). IEEE, 2024. http://dx.doi.org/10.1109/icsintesa62455.2024.10748189.

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Alfianda, Shelly, Yennifer Wilanata, Alfi Yusrotis Zakiyyah, and Kelvin Asclepius Minor. "Comparative Model Study: Gold Price Prediction Using Long Short-Term Memory (LSTM) Network and Graph Convolutional Network (GCN)." In 2025 International Conference on Smart Computing, IoT and Machine Learning (SIML). IEEE, 2025. https://doi.org/10.1109/siml65326.2025.11080763.

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Tashk, A., and M. A. Alavianmehr. "Enhanced Pedestrian Detection and Tracking Using Multi-Person Pose Extraction and Deep Convolutional LSTM Network." In 2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics. IEEE, 2024. http://dx.doi.org/10.1109/ithings-greencom-cpscom-smartdata-cybermatics62450.2024.00079.

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Mekruksavanich, Sakorn, Datchakorn Tancharoen, and Anuchit Jitpattanakul. "Gym Exercise Recognition Using Deep Convolutional and LSTM Neural Network Based on IMU Sensor Data." In 2024 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC). IEEE, 2024. http://dx.doi.org/10.1109/itc-cscc62988.2024.10628426.

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