Search Results - RNN-LSTM autoencoder

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  1. 1

    Data-feature-driven nonlinear process monitoring based on joint deep learning models with dual-scale by Yu, Jianbo, Yan, Xuefeng

    ISSN: 0020-0255, 1872-6291
    Published: Elsevier Inc 01.04.2022
    Published in Information sciences (01.04.2022)
    “…The interactions among the gauged data in most exiting real-life cases are correlative inevitably given the complicated behavior of process systems, that is…”
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    Journal Article
  2. 2

    Variational Autoencoders for Anomaly Detection and Transfer Knowledge in Electricity and District Heating Consumption by Shahid, Zahraa Khais, Saguna, Saguna, Ahlund, Christer

    ISSN: 0093-9994, 1939-9367, 1939-9367
    Published: New York IEEE 01.09.2024
    Published in IEEE transactions on industry applications (01.09.2024)
    “…Real-time anomaly detection in energy consumption supports identifying issues related to technical and user behaviour that result in significant energy waste…”
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    Journal Article
  3. 3

    Autoencoders for Anomaly Detection in Electricity and District Heating Consumption: A Case Study in School Buildings in Sweden by Shahid, Zahraa Khais, Saguna, Saguna, Ahlund, Christer

    ISBN: 9798350347449, 9798350347432
    Published: IEEE 06.06.2023
    “… We evaluated the performance of three proposed models, stacked RNN-LSTM autoencoder, CNN-LSTM autoencoder, and LSTM Variational Autoencoder (VAE…”
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    Conference Proceeding
  4. 4

    Short-Term Fault Prediction of Wind Turbines Based on Integrated RNN-LSTM by Rama, V Siva Brahmaiah, Hur, Sung-Ho, Yang, Jung-Min

    ISSN: 2169-3536, 2169-3536
    Published: Piscataway IEEE 2024
    Published in IEEE access (2024)
    “…This paper presents a data-driven approach to short-term wind turbine fault prediction and condition monitoring based on a hybrid architecture of recurrent…”
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    Journal Article
  5. 5

    RNN-based Method for Fault Diagnosis of Grinding System by Xing-yu, Qu, Peng, Zeng, Chengcheng, Xu, Dong-dong, Fu

    Published: IEEE 01.07.2017
    “… Aiming at the above problems, a RNN-LSTM based deep learning method is proposed in the paper, which realizes the intelligent fault diagnosis of grinding system…”
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    Conference Proceeding
  6. 6

    A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator by Alrifaey, Moath, Lim, Wei Hong, Ang, Chun Kit

    ISSN: 2169-3536, 2169-3536
    Published: Piscataway IEEE 2021
    Published in IEEE access (2021)
    “…The electrical generator is the key part of the electrical generation system for the oil and gas industry, and it is easy to fail, which disturbs the…”
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    Journal Article
  7. 7

    A Multi-Step Comparative Framework for Anomaly Detection in IoT Data Streams by Al-Qudah, Mohammed, AlMahamid, Fadi

    Published: IEEE 16.04.2025
    “…: RNN-LSTM, autoencoder neural networks (ANN), and Gradient Boosting (GBoosting). Experiments on the IoTID20 dataset shows that GBoosting consistently delivers…”
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    Conference Proceeding
  8. 8

    Towards Efficient Predictive Maintenance: Evaluating LSTM Autoencoders, CNNs, and RNNs for Industrial Machinery Anomaly Detection by Hossian, Md. Ismail, Anonto, Hasanur Zaman, Roy, Sudipto, Arif, Nur Uddin Mahmud, Ashraf, Md Sazid, Rezwan, Nasimur, Shufian, Abu

    Published: IEEE 11.05.2025
    “…This work investigates a variety of anomaly detection models in the scope of machinery faults including the 1D CNN, LSTM Autoencoders, RNN architectures…”
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    Conference Proceeding
  9. 9

    Comparative analysis of speaker identification performance using deep learning, machine learning, and novel subspace classifiers with multiple feature extraction techniques by Keser, Serkan, Gezer, Esra

    ISSN: 1051-2004
    Published: Elsevier Inc 01.01.2025
    Published in Digital signal processing (01.01.2025)
    “…•For the first time in the literature, speaker identification was achieved using HCF.•Many hybrid algorithms were tested in the study.•Six different feature…”
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    Journal Article
  10. 10

    Zero-Day Malware Detection Using Autoencoder and Hybrid Deep Learning Model by Sai Kiran, Chintha Reddy, S, Hariharasitaraman, Ahmed, Sajjad, Phulre, Ajay Kumar

    Published: IEEE 16.05.2025
    “… Known malware is classified using an RNN-LSTM model, while an Autoencoder detects unfamiliar threats by learning typical benign behavior and flagging anomalies…”
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    Conference Proceeding
  11. 11

    Generative machine learning for de novo drug discovery: A systematic review by Martinelli, Dominic D.

    ISSN: 0010-4825, 1879-0534, 1879-0534
    Published: United States Elsevier Ltd 01.06.2022
    Published in Computers in biology and medicine (01.06.2022)
    “…Recent research on artificial intelligence indicates that machine learning algorithms can auto-generate novel drug-like molecules. Generative models have…”
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    Journal Article
  12. 12

    Predicting the Risk of Depression Based on ECG Using RNN by Noor, Sumaiya Tarannum, Asad, Syeda Tasmiah, Khan, Mohammad Monirujjaman, Gaba, Gurjot Singh, Al-Amri, Jehad F., Masud, Mehedi

    ISSN: 1687-5265, 1687-5273, 1687-5273
    Published: United States Hindawi 2021
    “…). This proposed model uses a Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) autoencoder to predict normal, abnormal, and PVC heartbeats…”
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    Journal Article
  13. 13

    A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation by Koutroumpa, Nikoletta-Maria, Papavasileiou, Konstantinos D., Papadiamantis, Anastasios G., Melagraki, Georgia, Afantitis, Antreas

    ISSN: 1422-0067, 1661-6596, 1422-0067
    Published: Switzerland MDPI AG 31.03.2023
    “…The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on…”
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    Journal Article
  14. 14

    Comparative study of deep learning models for Parkinson’s disease detection by Salihu Aliero, Abdulaziz, Malhotra, Neha

    ISSN: 2772-4859, 2772-4859
    Published: Elsevier B.V 01.06.2025
    “…•Models evaluated include: MLP, RNN-LSTM, GRU,GAN and Autoencoder using voice data.•Performance assessed using standard metrics such as accuracy, precision, recall, and F1-score…”
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    Journal Article
  15. 15

    Deep Learning Paradigms for Multi-Dimensional Big Data Analytics: A Critical Assessment by Praveena Mandapati

    ISSN: 2468-4376, 2468-4376
    Published: 18.04.2025
    “…), and autoencoders in transaction fraud detection over the Kaggle “Credit Card Fraud Detection” dataset that contains more than 284000 transactions…”
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    Journal Article
  16. 16
  17. 17

    An improved long short-term memory with denoising autoencoder for solving text classification problems by Alija, Amani Saleh, Yahaya, Nor Adnan

    ISSN: 0254-7821
    Published: Mehran University of Engineering and Technology 01.07.2024
    “…, weak, and chaotic components. This paper proposes a new model referred to as DAE-LSTM for reducing data dimension through the use of a denoising autoencoder…”
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    Journal Article
  18. 18

    Intrusion Detection System in Wireless Sensor Networks using Modified Recurrent Neural Network with Long Short-Term Memory by Ramkumar, K., Alzubaidi, Laith H., Malathy, V, Venkatesh, T., C G, Kruthika

    Published: IEEE 23.02.2024
    “… This paper introduces a novel approach: a modified Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM…”
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    Conference Proceeding
  19. 19

    Deep Learning-Based Autonomous Anomaly Detection for Security in SDN-IoT Networks by Lakshan Yasarathna, Tharindu, Liyanage, Madhusanka, Le-Khac, Nhien-An

    ISSN: 2644-125X, 2644-125X
    Published: New York IEEE 2025
    “…Converging Software-Defined Networking (SDN) and the Internet of Things (IoT) has directed innovative network architectures and applications. However, this…”
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    Journal Article
  20. 20

    A Predictive Approach For Assessing Medication Responsiveness in Breast Cancer Through Analysis of Gene Data by Gupta, Kimmi, Singh, Jagendra, Singh, Harender Pratap, Singh, Manoj Kumar, Gupta, Nitin, Roohani, Basudeo Singh

    Published: IEEE 13.02.2025
    “…Breast cancer remains one of the leading causes of cancer-related deaths worldwide, with treatment responses varying wildly among patients. The inability to…”
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    Conference Proceeding