Relevant bibliographies by topics / Temporal Deep Belief Network (TDBN)

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

Academic literature on the topic 'Temporal Deep Belief Network (TDBN)'

Author: Grafiati
Published: 2 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "Temporal Deep Belief Network (TDBN)"

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Obaid, Ahmed J., and Hassanain K. Alrammahi. "An Intelligent Facial Expression Recognition System Using a Hybrid Deep Convolutional Neural Network for Multimedia Applications." Applied Sciences 13, no. 21 (2023): 12049. http://dx.doi.org/10.3390/app132112049.

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Recognizing facial expressions plays a crucial role in various multimedia applications, such as human–computer interactions and the functioning of autonomous vehicles. This paper introduces a hybrid feature extraction network model to bolster the discriminative capacity of emotional features for multimedia applications. The proposed model comprises a convolutional neural network (CNN) and deep belief network (DBN) series. First, a spatial CNN network processed static facial images, followed by a temporal CNN network. The CNNs were fine-tuned based on facial expression recognition (FER) dataset
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Wang, Shuqin, Gang Hua, Guosheng Hao, and Chunli Xie. "A Cycle Deep Belief Network Model for Multivariate Time Series Classification." Mathematical Problems in Engineering 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/9549323.

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Multivariate time series (MTS) data is an important class of temporal data objects and it can be easily obtained. However, the MTS classification is a very difficult process because of the complexity of the data type. In this paper, we proposed a Cycle Deep Belief Network model to classify MTS and compared its performance with DBN and KNN. This model utilizes the presentation learning ability of DBN and the correlation between the time series data. The experimental results showed that this model outperforms other four algorithms: DBN, KNN_ED, KNN_DTW, and RNN.
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Ashok Kumar, L., M. R. Ebenezar Jebarani, and V. Gokula Krishnan. "Optimized Deep Belief Neural Network for Semantic Change Detection in Multi-Temporal Image." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 2 (2023): 86–93. http://dx.doi.org/10.17762/ijritcc.v11i2.6132.

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Nowadays, a massive quantity of remote sensing images is utilized from tremendous earth observation platforms. For processing a wide range of remote sensing data to be transferred based on knowledge and information of them. Therefore, the necessity for providing the automated technologies to deal with multi-spectral image is done in terms of change detection. Multi-spectral images are associated with plenty of corrupted data like noise and illumination. In order to deal with such issues several techniques are utilized but they are not effective for sensitive noise and feature correlation may b
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Wang, Hong, Hongbin Wang, Guoqian Jiang, Yueling Wang, and Shuang Ren. "A Multiscale Spatio-Temporal Convolutional Deep Belief Network for Sensor Fault Detection of Wind Turbine." Sensors 20, no. 12 (2020): 3580. http://dx.doi.org/10.3390/s20123580.

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Sensor fault detection of wind turbines plays an important role in improving the reliability and stable operation of turbines. The supervisory control and data acquisition (SCADA) system of a wind turbine provides promising insights into sensor fault detection due to the accessibility of the data and the abundance of sensor information. However, SCADA data are essentially multivariate time series with inherent spatio-temporal correlation characteristics, which has not been well considered in the existing wind turbine fault detection research. This paper proposes a novel classification-based fa
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Peng, Feitong, and Tangzhi Liu. "Method for Fault Diagnosis of Track Circuits Based on a Time–Frequency Intelligent Network." Electronics 13, no. 5 (2024): 859. http://dx.doi.org/10.3390/electronics13050859.

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In response to the limitations posed by noise interference in complex environments and the narrow focus of existing diagnosis methods for jointless track circuit faults, an innovative approach is put forward in this study. It involves the application of the continuous wavelet transform (CWT) for signal preprocessing, along with the integration of a deep belief network (DBN) and a genetic algorithm (GA) to improve the least-squares support vector machine (LSSVM) model for intelligent time–frequency fault diagnosis. Initially, the raw induced voltage signals are transformed using continuous wave
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Narejo, Sanam, Muhammad Moazzam Jawaid, Shahnawaz Talpur, Rizwan Baloch, and Eros Gian Alessandro Pasero. "Multi-step rainfall forecasting using deep learning approach." PeerJ Computer Science 7 (May 4, 2021): e514. http://dx.doi.org/10.7717/peerj-cs.514.

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Rainfall prediction is immensely crucial in daily life routine as well as for water resource management, stochastic hydrology, rain run-off modeling and flood risk mitigation. Quantitative prediction of rainfall time series is extremely challenging as compared to other meteorological parameters due to its variability in local features that involves temporal and spatial scales. Consequently, this requires a highly complex system having an advance model to accurately capture the highly non linear processes occurring in the climate. The focus of this work is direct prediction of multistep forecas
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Rehn, Erik M., and Davide Maltoni. "Incremental Learning by Message Passing in Hierarchical Temporal Memory." Neural Computation 26, no. 8 (2014): 1763–809. http://dx.doi.org/10.1162/neco_a_00617.

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Hierarchical temporal memory (HTM) is a biologically inspired framework that can be used to learn invariant representations of patterns in a wide range of applications. Classical HTM learning is mainly unsupervised, and once training is completed, the network structure is frozen, thus making further training (i.e., incremental learning) quite critical. In this letter, we develop a novel technique for HTM (incremental) supervised learning based on gradient descent error minimization. We prove that error backpropagation can be naturally and elegantly implemented through native HTM message passin
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Wang, Li, Yuxin Xie, Jiping Xu, et al. "Prediction method of cyanobacterial blooms spatial-temporal sequence based on deep belief network and fuzzy expert system." Journal of Intelligent & Fuzzy Systems 38, no. 2 (2020): 1487–98. http://dx.doi.org/10.3233/jifs-179512.

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Alsufyani, Ahlam, Bashayer Alotaibi, and Samah Alajmani. "Hybrid Deep Learning Approach for Enhanced Detection and Mitigation of DDOS Attack in SDN Networks." International Journal of Network Security & Its Applications 16, no. 6 (2024): 77–93. https://doi.org/10.5121/ijnsa.2024.16605.

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The pervasiveness of (DDoS) Distributed Denial of Service attacks has intensified the demand for effective and dependable detection methods in Software-Defined Networks (SDNs). This proposed study introduces a hybrid Deep Learning framework designed to identify and address DDoS attacks in Software-Defined Networking (SDN) contexts. Due to the centralization of SDN control planes, these networks are especially susceptible to DDoS attacks, which can saturate system resources and disrupt critical services. Utilizing the CICDDoS2019 dataset, this research integrates Recurrent Neural Networks (RNN)
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Lu, Tianliang, Yanhui Du, Li Ouyang, Qiuyu Chen, and Xirui Wang. "Android Malware Detection Based on a Hybrid Deep Learning Model." Security and Communication Networks 2020 (August 28, 2020): 1–11. http://dx.doi.org/10.1155/2020/8863617.

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In recent years, the number of malware on the Android platform has been increasing, and with the widespread use of code obfuscation technology, the accuracy of antivirus software and traditional detection algorithms is low. Current state-of-the-art research shows that researchers started applying deep learning methods for malware detection. We proposed an Android malware detection algorithm based on a hybrid deep learning model which combines deep belief network (DBN) and gate recurrent unit (GRU). First of all, analyze the Android malware; in addition to extracting static features, dynamic be
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Dissertations / Theses on the topic "Temporal Deep Belief Network (TDBN)"

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Wheng, Ko-Cheng, and 翁恪誠. "Multi-Task Learning based Deep Belief Network for Speech Emotion Recognition using Spectro-Temporal Modulations." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/dbfe9d.

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碩士<br>國立交通大學<br>電信工程研究所<br>103<br>Speech emotion recognition is a popular research topic from the last decade. Meanwhile, since the revival of deep learning in 2007, it has been adopted in various research fields. In this thesis, we use a deep belief network (DBN) as the classifier and examine its performance in detecting emotion states of noisy speech signals using rate-scale features (RS features) extracted from an auditory model. The noisy speech is derived by adding white and babble noises to clean utterances from the Berlin Emotional Speech database under various SNR levels. Afterward, th
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Book chapters on the topic "Temporal Deep Belief Network (TDBN)"

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Weng, Ching-Hua, Ying-Hsiu Lai, and Shang-Hong Lai. "Driver Drowsiness Detection via a Hierarchical Temporal Deep Belief Network." In Computer Vision – ACCV 2016 Workshops. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54526-4_9.

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M, Gunavathi, Sudha S, Oviyaajanani S, and Girija V. "Detecting the Seizure Conditions of Humans with EEG Dataset using Deep Belief Network Algorithm." In Applied Intelligence and Computing. Soft Computing Research Society, 2024. https://doi.org/10.56155/978-81-955020-9-7-23.

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Seizure detection is a critical aspect of epilepsy management, as timely intervention can significantly improve patient outcomes. This study presents a comprehensive investigation into the application of Deep Belief Network (DBN) and Recurrent Neural Network (RNN) algorithms for the automatic detection of seizure conditions in humans using EEG (Electroencephalogram) datasets. EEG data, known for its high temporal resolution, is particularly well-suited for capturing the dynamic patterns associated with seizures. In this research, we first describe the preprocessing steps applied to the EEG dataset, including noise removal, filtering, and feature extraction, aimed at enhancing the quality of the input data. To enhance the robustness of our approach, we explore ensemble techniques to combine the outputs of the two algorithms. Rigorous experiments are conducted, employing standard evaluation metrics such as sensitivity, specificity, and F1-score, with cross-validation to assess the model’s performance. Our results demonstrate the promise of the combined DBN and RNN approach in detecting seizure conditions with a high degree of accuracy. We provide comprehensive analyses of the experimental outcomes, including visualizations such as confusion matrices and ROC curves, and discuss the clinical implications of our findings. This research contributes to the growing body of knowledge in EEG-based seizure detection, offering insights into the potential for leveraging deep learning algorithms to improve the early detection and management of seizures in clinical settings.
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Chen, Xiaoxu, Lin Mei, Yunguo Xie, and Tao Yan. "Intelligent Pipeline Corrosion Monitoring System Based on Deep Belief Network and Modified Particle Swarm Optimization." In Advances in Transdisciplinary Engineering. IOS Press, 2025. https://doi.org/10.3233/atde250225.

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This paper presents an innovative deep learning-based approach for automatic corrosion pattern recognition in industrial pipeline monitoring systems. The proposed methodology integrates a deep belief network (DBN) architecture with optimized multi-scale feature extraction and hierarchical classification strategies. To address the challenges of parameter optimization in complex industrial environments, we introduce an enhanced particle swarm optimization algorithm with adaptive inertia weight mechanisms. The system implements a novel feature extraction framework that effectively captures both spatial and temporal corrosion characteristics, enabling comprehensive pattern analysis across multiple scales. A comprehensive experimental validation conducted across three chemical processing facilities demonstrates the system’s superior performance, achieving 96.5% classification accuracy with a 1.2% false alarm rate. The system maintains robust performance under varying environmental conditions, including temperature variations from 15°C to 85°C and pressure fluctuations between 1-15 bar, while enabling real-time monitoring with 23.5 ms processing latency. Implementation in a petrochemical facility validated practical effectiveness, providing 48-hour early warning capabilities and reducing maintenance costs by 65%. The results establish a significant advancement in automated corrosion monitoring technology, offering improved reliability and efficiency for industrial applications while demonstrating exceptional stability and adaptability across diverse operational scenarios.
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Martin Sagayam K., Vedha Viyas T., Ho Chiung Ching, and Henesey Lawrence E. "Virtual Robotic Arm Control with Hand Gesture Recognition and Deep Learning Strategies." In Advances in Parallel Computing. IOS Press, 2017. https://doi.org/10.3233/978-1-61499-822-8-50.

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Hand gestures and Deep Learning Strategies can be used to control a virtual robotic arm for real-time applications. A robotic arm which is portable to carry various places and which can be easily programmed to do any work of a hand and is controlled by using deep learning techniques. Deep hand is a combination of both virtual reality and deep learning techniques. It estimated the active spatio-temporal feature and the corresponding pose parameter for various hand movements, to determine the unknown pose parameter of hand gestures by using various deep learning algorithms. A novel framework for hand gestures has been made to estimate by using a deep convolution neural network (CNN) and a deep belief network (DBN). A comparison in terms of accuracy and recognition rate has been drawn. This helps in analyzing the movement of a hand and its fingers which can be made to control a robotic arm with high recognition rate and less error rate.
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Conference papers on the topic "Temporal Deep Belief Network (TDBN)"

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Guo, Feng, Deshun Yang, and Xiaoou Chen. "Using Deep Belief Network to Capture Temporal Information for Audio Event Classification." In 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2015. http://dx.doi.org/10.1109/iih-msp.2015.46.

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Ichimura, Takumi, and Shin Kamada. "Adaptive learning method of recurrent temporal deep belief network to analyze time series data." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966140.

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Darmana, Igm Surya A., and Erdefi Rakun. "Generating of Sign System for Bahasa Indonesia (SIBI) Root Word Gestures Using Deep Temporal Sigmoid Belief Network." In the 2019 5th International Conference. ACM Press, 2019. http://dx.doi.org/10.1145/3330482.3330494.

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Ren, Yudan, Zeyang Tao, Wei Zhang, and Tianming Liu. "Modeling Hierarchical Spatial and Temporal Patterns of Naturalistic fMRI Volume via Volumetric Deep Belief Network with Neural Architecture Search." In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021. http://dx.doi.org/10.1109/isbi48211.2021.9433811.

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Zan, Fangqing, Linwei Yue, Zheng Zhou, and Xiuguo Liu. "The reconstruction of lake water levels with a deep belief network based method considering the spatial and temporal heterogeneity in contributions of the driving factors." In 2021 7th International Conference on Hydraulic and Civil Engineering & Smart Water Conservancy and Intelligent Disaster Reduction Forum (ICHCE & SWIDR). IEEE, 2021. http://dx.doi.org/10.1109/ichceswidr54323.2021.9656461.

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Li, Yaqiong, Xuhui Fan, Ling Chen, Bin Li, Zheng Yu, and Scott A. Sisson. "Recurrent Dirichlet Belief Networks for interpretable Dynamic Relational Data Modelling." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/342.

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The Dirichlet Belief Network~(DirBN) has been recently proposed as a promising approach in learning interpretable deep latent representations for objects. In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network~(Recurrent-DBN) -- to study interpretable hidden structures from dynamic relational data. The proposed Recurrent-DBN has the following merits: (1) it infers interpretable and organised hierarchical latent structures for objects within and across time steps; (2) it enables recurrent lo
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