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Relevant bibliographies by topics / Parallel CNNLSTM

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Academic literature on the topic 'Parallel CNNLSTM'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Parallel CNNLSTM"

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Fu, Guanghua, Qingjuan Wei, and Yongsheng Yang. "Bearing fault diagnosis with parallel CNN and LSTM." Mathematical Biosciences and Engineering 21, no. 2 (2024): 2385–406. http://dx.doi.org/10.3934/mbe.2024105.

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<abstract> <p>Intelligent diagnosis of bearing faults is fundamental to machinery automation and their intelligent operation. Deep learning-based analysis of bearing vibration data has emerged as one research mainstream for fault diagnosis. To enhance the quality of feature extraction from bearing vibration signals and the robustness of the model, we construct a fault diagnostic model based on convolutional neural network (CNN) and long short-term memory (LSTM) parallel network to extract their temporal and spatial features from two perspectives. First, via resampling, vibration si

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Widiputra, Harya, Adele Mailangkay, and Elliana Gautama. "Multivariate CNN-LSTM Model for Multiple Parallel Financial Time-Series Prediction." Complexity 2021 (October 23, 2021): 1–14. http://dx.doi.org/10.1155/2021/9903518.

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At the macroeconomic level, the movement of the stock market index, which is determined by the moves of other stock market indices around the world or in that region, is one of the primary factors in assessing the global economic and financial situation, making it a critical topic to monitor over time. As a result, the potential to reliably forecast the future value of stock market indices by taking trade relationships into account is critical. The aim of the research is to create a time-series data forecasting model that incorporates the best features of many time-series data analysis models.

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Chung, Won Hee, Yeong Hyeon Gu, and Seong Joon Yoo. "CHP Engine Anomaly Detection Based on Parallel CNN-LSTM with Residual Blocks and Attention." Sensors 23, no. 21 (2023): 8746. http://dx.doi.org/10.3390/s23218746.

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The extreme operating environment of the combined heat and power (CHP) engine is likely to cause anomalies and defects, which can lead to engine failure; thus, detecting engine anomalies is essential. In this study, we propose a parallel convolutional neural network–long short-term memory (CNN-LSTM) residual blocks attention (PCLRA) anomaly detection model with engine sensor data. To our knowledge, this is the first time that parallel CNN-LSTM-based networks have been used in the field of CHP engine anomaly detection. In PCLRA, spatiotemporal features are extracted via CNN-LSTM in parallel and

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Nilakanta, Kshetrimayum, Robindro Singh Khumukcham, and Hoque Nazrul. "A Multi-step Short-term Load Forecasting using Hybrid DNN and GAF." Indian Journal of Science and Technology 17, no. 11 (2024): 1016–27. https://doi.org/10.17485/IJST/v17i11.3246.

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Abstract <strong>Background:</strong> Short-term Load Forecasting (STLF) is vital for grid stability, ensuring a steady power supply and resource efficiency. However, the literature review underscores imperfections in current methods, emphasizing the necessity for additional research in this domain. Objectives: This study introduces an effective framework for multi-step STLF, enhancing predictive accuracy by integrating state-of-the-art DNN models like Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) in a hybrid architecture, leveraging their complementary strengths.&n

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Zhang, Chen, Qingxu Li, and Xue Cheng. "Text Sentiment Classification Based on Feature Fusion." Revue d'Intelligence Artificielle 34, no. 4 (2020): 515–20. http://dx.doi.org/10.18280/ria.340418.

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The convolutional neural network (CNN) and long short-term memory (LSTM) network are adept at extracting local and global features, respectively. Both can achieve excellent classification effects. However, the CNN performs poorly in extracting the global contextual information of the text, while LSTM often overlooks the features hidden between words. For text sentiment classification, this paper combines the CNN with bidirectional LSTM (BiLSTM) into a parallel hybrid model called CNN_BiLSTM. Firstly, the CNN was adopted to extract the local features of the text quickly. Next, the BiLSTM was em

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Tian, Aoxiang. "Enhancing Vegetable Sales Forecasting with A CNN-LSTM-Transformer Hybrid Model." Highlights in Business, Economics and Management 25 (January 20, 2024): 112–21. http://dx.doi.org/10.54097/rr7t4d15.

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Due to the short shelf life of vegetable products, a significant portion of the inventory cannot be resold the following day. To facilitate more informed procurement decisions in superstores and minimize vegetable wastage, this study proposes a hybrid prediction model based on CNN-LSTM-Transformer for enhancing the accuracy of forecasting vegetable sales volumes. Firstly, an LSTM model is incorporated to account for the recurring and seasonal variations in vegetable sales. Secondly, a CNN model is introduced to address the limitations of LSTM in capturing spatial data components. The convoluti

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Reza Feizi Derakhshi, Mohammad, Elnaz Zafarani-Moattar, Hussein Ala’a Al-Kabi, and Ahmed Hashim Jawad Almarashy. "Pclf: Parallel cnn-lstm fusion model for sms spam filtering." BIO Web of Conferences 97 (2024): 00136. http://dx.doi.org/10.1051/bioconf/20249700136.

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Short Message Service (SMS) is widely used for its accessibility, simplicity, and cost-effectiveness in communication, bank notifications, and identity confirmation. The increase in spam text messages presents significant challenges, including time waste, potential financial scams, and annoyance for users and carriers. This paper proposes a novel deep learning model based on parallel structure in the feature extraction step to address this challenge, unlike the traditional models that only enhance the classifier. This parallel model fuses local and temporal features to enhance feature represen

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K, Manimekalai, and A. Kavitha Dr. "Deep Learning Methods in Classification of Myocardial Infarction by employing ECG Signals." Indian Journal of Science and Technology 13, no. 28 (2020): 2823–32. https://doi.org/10.17485/IJST/v13i28.445.

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Abstract <strong>Background/Objectives:</strong> To automatically classify and detect the Myocardial Infarction using ECG signals.<strong> Methods/Statistical analysis:</strong> Deep Learning algorithms Convolutional Neural Network(CNN), Long Short Term Memory(LSTM) and Enhanced Deep Neural Network(EDN) were implemented. The proposed model EDN, comprises the techniques CNN and LSTM. Vector operations like matrix multiplication and gradient decent were applied to large matrices of data that are executed in parallel with GPU support. Because of parallelism EDN faster the execution

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Tan, Tien-Ping, Chai Kim Lim, and Wan Rose Eliza Abdul Rahman. "Sliding Window and Parallel LSTM with Attention and CNN for Sentence Alignment on Low-Resource Languages." Pertanika Journal of Science and Technology 30, no. 1 (2021): 97–121. http://dx.doi.org/10.47836/pjst.30.1.06.

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A parallel text corpus is an important resource for building a machine translation (MT) system. Existing resources such as translated documents, bilingual dictionaries, and translated subtitles are excellent resources for constructing parallel text corpus. A sentence alignment algorithm automatically aligns source sentences and target sentences because manual sentence alignment is resource-intensive. Over the years, sentence alignment approaches have improved from sentence length heuristics to statistical lexical models to deep neural networks. Solving the alignment problem as a classification

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Arabi, Sima, Milad Asgarimehr, Martin Kada, and Jens Wickert. "Hybrid CNN-LSTM Deep Learning for Track-Wise GNSS-R Ocean Wind Speed Retrieval." Remote Sensing 15, no. 17 (2023): 4169. http://dx.doi.org/10.3390/rs15174169.

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The NASA Cyclone GNSS (CYGNSS) mission provides one Delay Doppler Map (DDM) per second along observational tracks. To account for spatiotemporal correlations within adjacent DDMs in a track, a deep hybrid CNN-LSTM model is proposed for wind speed prediction. The model combines convolutional and pooling layers to extract features from DDMs in one track, which are then processed by LSTM as a sequence of data. This method leads to a test RMSE of 1.84 m/s. The track-wise processing approach outperforms the architectures that process the DMMs individually, namely based on Long Short-Term Memory (LS

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Book chapters on the topic "Parallel CNNLSTM"

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Wang, Chaoxue, Zhenbang Wang, Fan Zhang, and Yuhang Pan. "A New PM2.5 Concentration Predication Study Based on CNN-LSTM Parallel Integration." In Intelligent Computing Theories and Application. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13870-6_21.

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Li, Xinyu, Yi Zuo, Tieshan Li, and C. L. Philip Chen. "A Novel Machine Learning Model Using CNN-LSTM Parallel Networks for Predicting Ship Fuel Consumption." In Neural Information Processing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8082-6_9.

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Jadouli, Ayoub, and Chaker El Amrani. "Hybrid Parallel Architecture Integrating FFN, 1D CNN, and LSTM for Predicting Wildfire Occurrences in Morocco." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88653-9_16.

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Rana, Naveen, A. Ramkumar, D. Sai Chaitanya Kishore, Narayan Pandurang Sapkal, K. Gowdhami, and D. Satheesh Kumar. "Advancing vehicle energy systems: Optimisation of fuel-cell hybrid electric vehicles using parallel LSTM and CNN." In Hybrid and Advanced Technologies. CRC Press, 2025. https://doi.org/10.1201/9781003559115-28.

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Shetty, Amrithkala M., and D. H. Manjaiah. "Enhancing Sentiment Analysis of Amazon Reviews with Deep Hybrid Parallel Fusion of CNN and LSTM Using Pretrained Word Embeddings." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1188-1_22.

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Peeperkorn, Jari, Seppe vanden Broucke, and Jochen De Weerdt. "Can Deep Neural Networks Learn Process Model Structure? An Assessment Framework and Analysis." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_10.

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AbstractPredictive process monitoring concerns itself with the prediction of ongoing cases in (business) processes. Prediction tasks typically focus on remaining time, outcome, next event or full case suffix prediction. Various methods using machine and deep learning have been proposed for these tasks in recent years. Especially recurrent neural networks (RNNs) such as long short-term memory nets (LSTMs) have gained in popularity. However, no research focuses on whether such neural network-based models can truly learn the structure of underlying process models. For instance, can such neural ne

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Gharehbaghi, Arash, Elaheh Partovi, and Ankica Babic. "Recurrent vs Non-Recurrent Convolutional Neural Networks for Heart Sound Classification." In Studies in Health Technology and Informatics. IOS Press, 2023. http://dx.doi.org/10.3233/shti230525.

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Convolutional Neural Network (CNN) has been widely proposed for different tasks of heart sound analysis. This paper presents the results of a novel study on the performance of a conventional CNN in comparison to the different architectures of recurrent neural networks combined with CNN for the classification task of abnormal-normal heart sounds. The study considers various combinations of parallel and cascaded integration of CNN with Gated Recurrent Network (GRN) as well as Long- Short Term Memory (LSTM) and explores the accuracy and sensitivity of each integration independently, using the Phy

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Gharehbaghi, Arash, Elaheh Partovi, and Ankica Babic. "Parralel Recurrent Convolutional Neural Network for Abnormal Heart Sound Classification." In Caring is Sharing – Exploiting the Value in Data for Health and Innovation. IOS Press, 2023. http://dx.doi.org/10.3233/shti230198.

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This paper presents the results of a study performed on Parallel Convolutional Neural Network (PCNN) toward detecting heart abnormalities from the heart sound signals. The PCNN preserves dynamic contents of the signal in a parallel combination of the recurrent neural network and a Convolutional Neural Network (CNN). The performance of the PCNN is evaluated and compared to the one obtained from a Serial form of the Convolutional Neural Network (SCNN) as well as two other baseline studies: a Long- and Short-Term Memory (LSTM) neural network and a Conventional CNN (CCNN). We employed a well-known

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Chauhan, Rahul, and Jyoti Rawat. "Emotion-Infused Multimodal Biometric Fusion for Elevating User Authentication and Interaction." In Applications of Parallel Data Processing for Biomedical Imaging. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2426-4.ch010.

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This study combines many biometric techniques with emotions and provides a viable method for user engagement and authentication. The study uses recurrent neural networks, which contain LSTM (Long-Short-Term Memory) cells, while convolutional neural networks (CNN) are used for feature extraction. This work provides a standard extraction strategy that combines multiple biometric data sources, such as voice, fingerprints, irises, and faces. This allows the system to more easily\understand the user's emotional state temporal and geographical dimensions. After emotional expressions have changed ove

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Mishra, Rahul. "An Algorithm for Textual Classification of News Utilizing Artificial Intelligence Technology." In Demystifying Emerging Trends in Machine Learning. BENTHAM SCIENCE PUBLISHERS, 2025. https://doi.org/10.2174/9789815305395125020006.

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The rate at which technology is improving is increasing all throughout the world. Every day, a tremendous amount of textual data is produced as a result of the Internet, websites, business data, medical information, and the media. Extraction of interesting patterns from text data with varied lengths such as views, summaries, and facts is a challenging issue. This work provides a deep learning (DL) algorithm-based approach to news text classification to address the issues of large amounts of text data and cumbersome features obtaining value in news. Although the relationship among words as well

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Conference papers on the topic "Parallel CNNLSTM"

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Hu, Boyu. "Maritime Spectrum Sensing Using a Parallel CNN-LSTM Network." In 2025 5th International Conference on Consumer Electronics and Computer Engineering (ICCECE). IEEE, 2025. https://doi.org/10.1109/iccece65250.2025.10985381.

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Zhang, Man, and Dongning Liu. "CNN-LSTM based Multimodal Models for Music Generation." In 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2024. https://doi.org/10.1109/ispa63168.2024.00117.

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Hasan, Nowshad, Md Maskat Sharif, Mohammad Woli Ullah, Md Murad Kabir Nipun, Md Imtiaj Uddin, and Raju Chandra Nath. "Deep Parallel CNN and LSTM for Enhanced Breast Cancer Classification." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11013897.

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Lella, VeeraKotlu, Bathula Raju, Yasmeena, Vigya Saxena, Somesh Vinayak Tewari, and Tarkeshwar Mahto. "Advanced Wind Power Forecasting Using Parallel Convolutional Networks and Attention-Driven CNN-LSTM." In 2025 IEEE 1st International Conference on Smart and Sustainable Developments in Electrical Engineering (SSDEE). IEEE, 2025. https://doi.org/10.1109/ssdee64538.2025.10968613.

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Huang, Yuzhi, Haibin Liu, Jun Li, et al. "Research on Fault Warning Algorithm for Coal Mills Based on CNN-LSTM-Attention." In 2024 IEEE 4th International Conference on Digital Twins and Parallel Intelligence (DTPI). IEEE, 2024. https://doi.org/10.1109/dtpi61353.2024.10778879.

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Gong, Li, Jing Fu, Jinting Sun, Hongjuan Tang, Shuaixuan Gu, and Fengqiao Wang. "Electricity Price Prediction Based on QPSO-CNN-LSTM in the Spot Market Environment." In 2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS). IEEE, 2024. http://dx.doi.org/10.1109/ispds62779.2024.10667613.

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Wen, Hao, Haochen Ma, Yu Du, Jinchao Zhang, and Li He. "Prediction of irrigation water requirement based on parallel CNN-LSTM model and Mann-Kendall test." In 2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS). IEEE, 2024. http://dx.doi.org/10.1109/icps59941.2024.10640026.

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Zhang, Yibo, Dewei Li, Yuanqiang Zhou, and Furong Gao. "Quality Prediction of Injection Molding Process Using CNN and Parallel-Cascade LSTMs." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662413.

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Xu, Mingdong, Zhendong Yin, Mingyang Wu, Zhilu Wu, Yanlong Zhao, and Zhenlei Gao. "Spectrum Sensing Based on Parallel CNN-LSTM Network." In 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). IEEE, 2020. http://dx.doi.org/10.1109/vtc2020-spring48590.2020.9129229.

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Fan, Sijiang, Jiawei Fei, Xiao-Wei Guo, Canqun Yang, and Alistair Revell. "CNN+LSTM Accelerated Turbulent Flow Simulation with Link-Wise Artificial Compressibility Method." In ICPP 2021: 50th International Conference on Parallel Processing. ACM, 2021. http://dx.doi.org/10.1145/3472456.3472525.

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