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

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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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Du, Jinghan, Haiyan Chen, and Weining Zhang. "A deep learning method for data recovery in sensor networks using effective spatio-temporal correlation data." Sensor Review 39, no. 2 (2019): 208–17. http://dx.doi.org/10.1108/sr-02-2018-0039.

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Purpose In large-scale monitoring systems, sensors in different locations are deployed to collect massive useful time-series data, which can help in real-time data analytics and its related applications. However, affected by hardware device itself, sensor nodes often fail to work, resulting in a common phenomenon that the collected data are incomplete. The purpose of this study is to predict and recover the missing data in sensor networks. Design/methodology/approach Considering the spatio-temporal correlation of large-scale sensor data, this paper proposes a data recover model in sensor netwo

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Abdulhai, Marwa, Dong-Ki Kim, Matthew Riemer, Miao Liu, Gerald Tesauro, and Jonathan P. How. "Context-Specific Representation Abstraction for Deep Option Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 5959–67. http://dx.doi.org/10.1609/aaai.v36i6.20541.

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Hierarchical reinforcement learning has focused on discovering temporally extended actions, such as options, that can provide benefits in problems requiring extensive exploration. One promising approach that learns these options end-to-end is the option-critic (OC) framework. We examine and show in this paper that OC does not decompose a problem into simpler sub-problems, but instead increases the size of the search over policy space with each option considering the entire state space during learning. This issue can result in practical limitations of this method, including sample inefficient l

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Wang, Hai, and Yingfeng Cai. "A Multistep Framework for Vision Based Vehicle Detection." Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/876451.

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Vision based vehicle detection is a critical technology that plays an important role in not only vehicle active safety but also road video surveillance application. In this work, a multistep framework for vision based vehicle detection is proposed. In the first step, for vehicle candidate generation, a novel geometrical and coarse depth information based method is proposed. In the second step, for candidate verification, a deep architecture of deep belief network (DBN) for vehicle classification is trained. In the last step, a temporal analysis method based on the complexity and spatial inform

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Zheng, Lili, Shiyu Cao, Tongqiang Ding, Jian Tian, and Jinghang Sun. "Research on Active Safety Situation of Road Passenger Transportation Enterprises: Evaluation, Prediction, and Analysis." Entropy 26, no. 6 (2024): 434. http://dx.doi.org/10.3390/e26060434.

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The road passenger transportation enterprise is a complex system, requiring a clear understanding of their active safety situation (ASS), trends, and influencing factors. This facilitates transportation authorities to promptly receive signals and take effective measures. Through exploratory factor analysis and confirmatory factor analysis, we delved into potential factors for evaluating ASS and extracted an ASS index. To predict obtaining a higher ASS information rate, we compared multiple time series models, including GRU (gated recurrent unit), LSTM (long short-term memory), ARIMA, Prophet,

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Almulhim, Fatimah, Ben Ishak, Azhari Elhag, Hassan Aljohani, and Sayed Abdel-Khalek. "Short term wind speed prediction using search and rescue optimization with deep belief network on scatterometry data." Thermal Science 28, no. 6 Part B (2024): 5097–111. https://doi.org/10.2298/tsci2406097a.

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Scatterometry is a technique used to transmit radio or microwaves to examine different geophysical properties, wind speed, and direction. Precise and rapid weather predictions become essential in several applications in assisting planning and management in response to weather conditions. At the same time, timely wind speed prediction gains considerable attention in several economical, business, and management areas. With the consideration of wind speed as an arbitrary variable, precise wind speed prediction using machine learning and deep learning models can be established. With this motivatio

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Han, Pang Ying, Liew Yee Ping, Goh Fan Ling, Ooi Shih Yin, and Khoh Wee How. "Stacked deep analytic model for human activity recognition on a UCI HAR database." F1000Research 10 (October 15, 2021): 1046. http://dx.doi.org/10.12688/f1000research.73174.1.

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Background Owing to low cost and ubiquity, human activity recognition using smartphones is emerging as a trendy mobile application in diverse appliances such as assisted living, healthcare monitoring, etc. Analysing this one-dimensional time-series signal is rather challenging due to its spatial and temporal variances. Numerous deep neural networks (DNNs) are conducted to unveil deep features of complex real-world data. However, the drawback of DNNs is the un-interpretation of the network's internal logic to achieve the output. Furthermore, a huge training sample size (i.e. millions of samples

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Pang, Ying Han, Liew Yee Ping, Goh Fan Ling, Ooi Shih Yin, and Khoh Wee How. "Stacked deep analytic model for human activity recognition on a UCI HAR database." F1000Research 10 (April 1, 2022): 1046. http://dx.doi.org/10.12688/f1000research.73174.3.

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Background Owing to low cost and ubiquity, human activity recognition using smartphones is emerging as a trendy mobile application in diverse appliances such as assisted living, healthcare monitoring, etc. Analysing this one-dimensional time-series signal is rather challenging due to its spatial and temporal variances. Numerous deep neural networks (DNNs) are conducted to unveil deep features of complex real-world data. However, the drawback of DNNs is the un-interpretation of the network's internal logic to achieve the output. Furthermore, a huge training sample size (i.e. millions of samples

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Pang, Ying Han, Liew Yee Ping, Goh Fan Ling, Ooi Shih Yin, and Khoh Wee How. "Stacked deep analytic model for human activity recognition on a UCI HAR database." F1000Research 10 (February 18, 2022): 1046. http://dx.doi.org/10.12688/f1000research.73174.2.

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Background Owing to low cost and ubiquity, human activity recognition using smartphones is emerging as a trendy mobile application in diverse appliances such as assisted living, healthcare monitoring, etc. Analysing this one-dimensional time-series signal is rather challenging due to its spatial and temporal variances. Numerous deep neural networks (DNNs) are conducted to unveil deep features of complex real-world data. However, the drawback of DNNs is the un-interpretation of the network's internal logic to achieve the output. Furthermore, a huge training sample size (i.e. millions of samples

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Al-Tameemi, M. M. A., A. A. H. Alzaghir, and M. A. M. Alsweity. "Comprehensive Review of Deep Learning in Intrusion Detection Systems." Proceedings of Telecommunication Universities 11, no. 3 (2025): 72–86. https://doi.org/10.31854/1813-324x-2025-11-3-72-86.

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Deep learning methods play a crucial role in enhancing the effectiveness of intrusion detection systems. This study presents a comparative analysis of seven deep learning models, including autoencoders, restricted Boltzmann machines, deep belief networks, convolutional and recurrent neural networks, generative adversarial networks, and deep neural networks. The primary focus is on accuracy, precision, and recall metrics, evaluated using the NSL-KDD dataset. The analysis demonstrated the high effectiveness of recurrent neural networks, which achieved an accuracy of 99.79 %, precision of 99.67 %

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Rui, Yikang, Wenqi Lu, Ziwei Yi, Renfei Wu, and Bin Ran. "A Novel Hybrid Model for Predicting Traffic Flow via Improved Ensemble Learning Combined with Deep Belief Networks." Mathematical Problems in Engineering 2021 (October 22, 2021): 1–16. http://dx.doi.org/10.1155/2021/7328056.

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The intelligent transportation system (ITS) plays an irreplaceable role in alleviating urban traffic congestion and realizing sustainable urban development. Accurate and efficient short-term traffic state forecasting is a significant issue in ITS. This study proposes a novel hybrid model (ELM-IBF) to predict the traffic state on urban expressways by taking advantage of both deep learning models and ensemble learning framework. First, a developed bagging framework is introduced to combine several deep belief networks (DBNs) that are utilized to capture the complicated temporal characteristic of

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Liu, Huan. "Research on Performance Prediction of Technological Innovation Enterprises Based on Deep Learning." Wireless Communications and Mobile Computing 2021 (September 23, 2021): 1–12. http://dx.doi.org/10.1155/2021/1682163.

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High-tech enterprises are the leaders in promoting economic development. The study of the relationship between their scientific and technological innovation capabilities and corporate performance is of far-reaching practical significance for guiding companies to formulate independent innovation strategies scientifically, improving their independent innovation capabilities, and promoting further transformation into an innovative country. In view of the large-scale technological innovation enterprise network, the traditional technological innovation enterprise performance prediction method canno

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Li, Rui, Tailai Huang, Yu Song, Shuzhe Huang, and Xiang Zhang. "Generating 1 km Spatially Seamless and Temporally Continuous Air Temperature Based on Deep Learning over Yangtze River Basin, China." Remote Sensing 13, no. 19 (2021): 3904. http://dx.doi.org/10.3390/rs13193904.

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Air temperature is one of the most essential variables in understanding global warming as well as variations of climate, hydrology, and eco-systems. However, current products and assimilation approaches alone can provide temperature data with high resolution, high spatio-temporal continuity, and high accuracy simultaneously (refer to 3H data). To explore this kind of potential, we proposed an integrated temperature downscaling framework by fusing multiple remotely sent, model-based, and in-situ datasets, which was inspired by point-surface data fusion and deep learning. First, all of the predi

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Ma, Ao, Jundong Zhang, Haosheng Shen, Yang Cao, Hongbo Xu, and Jiale Liu. "Research on Fault Diagnosis of Marine Diesel Engines Based on CNN-TCN–ATTENTION." Applied Sciences 15, no. 3 (2025): 1651. https://doi.org/10.3390/app15031651.

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In response to the typical fault issues encountered during the operation of marine diesel engines, a fault diagnosis method based on a convolutional neural network (CNN), a temporal convolutional network (TCN), and the attention mechanism (ATTENTION) is proposed, referred to as CNN-TCN–ATTENTION. This method successfully addresses the issue of insufficient feature extraction in previous fault diagnosis algorithms. The CNN is employed to capture the local features of diesel engine faults; the TCN is employed to explore the correlations and temporal dependencies in sequential data, further obtai

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Alzahab, Nibras Abo, Luca Apollonio, Angelo Di Iorio, et al. "Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review." Brain Sciences 11, no. 1 (2021): 75. http://dx.doi.org/10.3390/brainsci11010075.

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Background: Brain-Computer Interface (BCI) is becoming more reliable, thanks to the advantages of Artificial Intelligence (AI). Recently, hybrid Deep Learning (hDL), which combines different DL algorithms, has gained momentum over the past five years. In this work, we proposed a review on hDL-based BCI starting from the seminal studies in 2015. Objectives: We have reviewed 47 papers that apply hDL to the BCI system published between 2015 and 2020 extracting trends and highlighting relevant aspects to the topic. Methods: We have queried four scientific search engines (Google Scholar, PubMed, IE

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Alzahab, Nibras Abo, Luca Apollonio, Angelo Di Iorio, et al. "Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review." Brain Sciences 11, no. 1 (2021): 75. http://dx.doi.org/10.3390/brainsci11010075.

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Background: Brain-Computer Interface (BCI) is becoming more reliable, thanks to the advantages of Artificial Intelligence (AI). Recently, hybrid Deep Learning (hDL), which combines different DL algorithms, has gained momentum over the past five years. In this work, we proposed a review on hDL-based BCI starting from the seminal studies in 2015. Objectives: We have reviewed 47 papers that apply hDL to the BCI system published between 2015 and 2020 extracting trends and highlighting relevant aspects to the topic. Methods: We have queried four scientific search engines (Google Scholar, PubMed, IE

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Jia, Meng, and Zhiqiang Zhao. "Change Detection in Synthetic Aperture Radar Images Based on a Generalized Gamma Deep Belief Networks." Sensors 21, no. 24 (2021): 8290. http://dx.doi.org/10.3390/s21248290.

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Change detection from synthetic aperture radar (SAR) images is of great significance for natural environmental protection and human societal activity, which can be regarded as the process of assigning a class label (changed or unchanged) to each of the image pixels. This paper presents a novel classification technique to address the SAR change-detection task that employs a generalized Gamma deep belief network (gΓ-DBN) to learn features from difference images. We aim to develop a robust change detection method that can adapt to different types of scenarios for bitemporal co-registered Yellow R

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Zhang, Xiang, Tailai Huang, Aminjon Gulakhmadov, et al. "Deep Learning-Based 500 m Spatio-Temporally Continuous Air Temperature Generation by Fusing Multi-Source Data." Remote Sensing 14, no. 15 (2022): 3536. http://dx.doi.org/10.3390/rs14153536.

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The all-weather high-resolution air temperature data is crucial for understanding the urban thermal conditions with their spatio-temporal characteristics, driving factors, socio-economic and environmental consequences. In this study, we developed a novel 5-layer Deep Belief Network (DBN) deep learning model to fuse multi-source data and then generated air temperature data with 3H characteristics: High resolution, High spatio-temporal continuity (spatially seamless and temporally continuous), and High accuracy simultaneously. The DBN model was developed and applied for two different urban regio

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Yang, Qinmeng, Ningming Nie, Yangang Wang, et al. "Spatial–Temporal Correlation Considering Environmental Factor Fusion for Estimating Gross Primary Productivity in Tibetan Grasslands." Applied Sciences 13, no. 10 (2023): 6290. http://dx.doi.org/10.3390/app13106290.

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Gross primary productivity (GPP) is an important indicator in research on carbon cycling in terrestrial ecosystems. High-accuracy GPP prediction is crucial for ecosystem health and climate change assessments. We developed a site-level GPP prediction method based on the GeoMAN model, which was able to extract spatiotemporal features and fuse external environmental factors to predict GPP on the Tibetan Plateau. We evaluated four models’ behavior—Random Forest (RF), Support Vector Machine (SVM), Deep Belief Network (DBN), and GeoMAN—in predicting GPP at nine flux observation sites on the Tibetan

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B, Priya. "CROSS-LAYER DESIGN AND OPTIMIZATION IN NETWORKING LEVERAGING AI AND DEEP LEARNING ALGORITHMS FOR ENHANCED PERFORMANCE." ICTACT Journal on Communication Technology 15, no. 4 (2024): 3380–85. https://doi.org/10.21917/ijct.2024.0501.

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The growing demand for efficient and reliable sensor networks in applications such as environmental monitoring, healthcare, and smart cities has highlighted the need for optimizing communication protocols, power consumption, and data management. Traditional methods often focus on optimizing individual layers of the network without considering the interactions across layers. This approach limits the overall performance, especially as networks scale in size and complexity. The lack of effective cross-layer optimization strategies hinders the performance of sensor networks, leading to suboptimal

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Akhmetshin, Elvir, Alexander Nemtsev, Rustem Shichiyakh, Denis Shakhov, and Inna Dedkova. "Evolutionary Algorithm with Deep Learning based Fall Detection on Internet of Things Environment." Fusion: Practice and Applications 14, no. 2 (2024): 132–45. http://dx.doi.org/10.54216/fpa.140211.

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Falling is among the most threatening event proficient by the ageing population. There is a necessity for the development of the fall detection (FD) system with the increasing ageing population. FD in an Internet of Things (IoT) platform has developed as a vital application with the rapidly increasing population of aging population and the essential for continuous health monitoring. Falls among the ageing can performance in serious injuries, decreased independence, and longer recovery periods. The FD approach can constructed on deep learning (DL) approaches, especially, Recurrent Neural Networ

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Sanyal, Manas K., Indranil Ghosh, and R. K. Jana. "Characterization and Predictive Analysis of Volatile Financial Markets Using Detrended Fluctuation Analysis, Wavelet Decomposition, and Machine Learning." International Journal of Data Analytics 2, no. 1 (2021): 1–31. http://dx.doi.org/10.4018/ijda.2021010101.

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This paper proposes a granular framework for examining the dynamics of stock indexes that exhibit nonparametric and highly volatile behavior, and subsequently carries out the predictive analytics task by integrating detrended fluctuation analysis (DFA), maximal overlap discrete wavelet transformation (MODWT), and machine learning algorithms. DFA test ascertains the key temporal characteristics of the daily closing prices. MODWT decomposes the time series into granular components. Four pattern recognition algorithms—adaptive neuro fuzzy inference system (ANFIS), dynamic evolving neural-fuzzy in

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Qiao, Weizheng, Xiaojun Bi, Lu Han, and Yulin Zhang. "Epilepsy Prediction and Detection Using Attention-CssCDBN with Dual-Task Learning." Sensors 25, no. 1 (2024): 51. https://doi.org/10.3390/s25010051.

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Epilepsy is a group of neurological disorders characterized by epileptic seizures, and it affects tens of millions of people worldwide. Currently, the most effective diagnostic method employs the monitoring of brain activity through electroencephalogram (EEG). However, it is critical to predict epileptic seizures in patients prior to their onset, allowing for the administration of preventive medications before the seizure occurs. As a pivotal application of artificial intelligence in medical treatment, learning the features of EEGs for epilepsy prediction and detection remains a challenging pr

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Luo, Xianglong, Danyang Li, Yu Yang, and Shengrui Zhang. "Spatiotemporal Traffic Flow Prediction with KNN and LSTM." Journal of Advanced Transportation 2019 (February 27, 2019): 1–10. http://dx.doi.org/10.1155/2019/4145353.

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The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic

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Gangappa, Malige. "Feature level fusion for land cover classification with landsat images: A hybrid classification model." Multiagent and Grid Systems 19, no. 2 (2023): 149–68. http://dx.doi.org/10.3233/mgs-230034.

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Classification of land cover using satellite images was a major area for the past few years. A raise in the quantity of data obtained by satellite image systems insists on the requirement for an automated tool for classification. Satellite images demonstrate temporal or/and spatial dependencies, where the traditional artificial intelligence approaches do not succeed to execute well. Hence, the suggested approach utilizes a brand-new framework for classifying land cover Histogram Linearisation is first carried out throughout pre-processing. The features are then retrieved, including spectral an

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Sakthivel, S., and V. Prabhu. "Optimal Deep Learning-Based Vocal Fold Disorder Detection and Classification Model on High-Speed Video Endoscopy." Journal of Healthcare Engineering 2022 (October 17, 2022): 1–12. http://dx.doi.org/10.1155/2022/4248938.

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The use of high-speed video-endoscopy (HSV) in the study of phonatory processes linked to speech needs the precise identification of vocal fold boundaries at the time of vibration. The HSV is a unique laryngeal imaging technology that captures intracycle vocal fold vibrations at a higher frame rate without the need for auditory inputs. The HSV is also effective in identifying the vibrational characteristics of the vocal folds with an increased temporal resolution during retained phonation and flowing speech. Clinically significant vocal fold vibratory characteristics in running speech can be r

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Li, Xiaomin, Yi Ma, and Jie Zhang. "Coral Shoals Detection from Optical Satellite Imagery Using Deep Belief Network Algorithm: A Case Study for the Xisha Islands, South China Sea." Journal of Marine Science and Engineering 12, no. 6 (2024): 922. http://dx.doi.org/10.3390/jmse12060922.

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Coral islands and reefs are formed by the cementation of the remains of shallow water reef-building coral polyps and other reef dwelling organisms in tropical oceans. They can be divided into coral islands, coral sandbanks, coral reefs, and coral shoals, of which, Coral shoals are located below the depth datum and are not exposed even at low tide, and sometimes are distributed at water depths exceeding 30 m. Satellite images with wide spatial–temporal coverage have played a crucial role in coral island and reef monitoring, and remote sensing data with multiple platforms, sensors, and spatial a

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B, Manikandan, Rama P, and Chakaravarthi S. "Sentiment and Fuzzy Aware Product Recommendation System Using HOA and FT-DBN in E- Commerce." Indian Journal of Science and Technology 16, no. 27 (2023): 2058–67. https://doi.org/10.17485/IJST/v16i27.430.

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Abstract <strong>Objectives:</strong> To identify and select the customers’ liked products by introducing a new product recommendation system. <strong>Methods:</strong> This work proposes a new product recommendation system that incorporates a new feature optimization method called Sentiment weighted Horse herd Optimization Algorithm (SHOA) to identify the most suitable words that help perform effective prediction. This work’s prediction process is carried out by applying a newly proposed Deep Belief Network incorporating fuzzy temporal features. This work uses two d

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Alo, Uzoma Rita, Henry Friday Nweke, Ying Wah Teh, and Ghulam Murtaza. "Smartphone Motion Sensor-Based Complex Human Activity Identification Using Deep Stacked Autoencoder Algorithm for Enhanced Smart Healthcare System." Sensors 20, no. 21 (2020): 6300. http://dx.doi.org/10.3390/s20216300.

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Human motion analysis using a smartphone-embedded accelerometer sensor provided important context for the identification of static, dynamic, and complex sequence of activities. Research in smartphone-based motion analysis are implemented for tasks, such as health status monitoring, fall detection and prevention, energy expenditure estimation, and emotion detection. However, current methods, in this regard, assume that the device is tightly attached to a pre-determined position and orientation, which might cause performance degradation in accelerometer data due to changing orientation. Therefor

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Li, Xia, Guanming Lu, Jingjie Yan, and Zhengyan Zhang. "A Multi-Scale Multi-Task Learning Model for Continuous Dimensional Emotion Recognition from Audio." Electronics 11, no. 3 (2022): 417. http://dx.doi.org/10.3390/electronics11030417.

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Due to the advantages of many aspects of the dimensional emotion model, continuous dimensional emotion recognition from audio has attracted increasing attention in recent years. Features and dimensional emotion labels on different time scales have different characteristics and contain different information. To make full use of the advantages of features and emotion representations from multiple time scales, a novel multi-scale multi-task (MSMT) learning model is proposed in this paper. The MSMT model is constructed by a deep belief network (DBN) with only one hidden layer. The same hidden laye

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Wang, Mingchang, Haiming Zhang, Weiwei Sun, Sheng Li, Fengyan Wang, and Guodong Yang. "A Coarse-to-Fine Deep Learning Based Land Use Change Detection Method for High-Resolution Remote Sensing Images." Remote Sensing 12, no. 12 (2020): 1933. http://dx.doi.org/10.3390/rs12121933.

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In recent decades, high-resolution (HR) remote sensing images have shown considerable potential for providing detailed information for change detection. The traditional change detection methods based on HR remote sensing images mostly only detect a single land type or only the change range, and cannot simultaneously detect the change of all object types and pixel-level range changes in the area. To overcome this difficulty, we propose a new coarse-to-fine deep learning-based land-use change detection method. We independently created a new scene classification dataset called NS-55, and innovati

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Zhang, Xining, and Hao Dai. "Significant Wave Height Prediction with the CRBM-DBN Model." Journal of Atmospheric and Oceanic Technology 36, no. 3 (2019): 333–51. http://dx.doi.org/10.1175/jtech-d-18-0141.1.

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AbstractIn recent years, deep learning technology has been gradually used for time series data prediction in various fields. In this paper, the restricted Boltzmann machine (RBM) in the classical deep belief network (DBN) is substituted with the conditional restricted Boltzmann machine (CRBM) containing temporal information, and the CRBM-DBN model is constructed. Key model parameters, which are determined by the particle swarm optimization (PSO) algorithm, are used to predict the significant wave height. Observed data in 2016, which are from nearshore and offshore buoys (i.e., 42020 and 42001)

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Srinivasan, Vidhusha, N. Udayakumar, and Kavitha Anandan. "Influence of Primary Auditory Cortex in the Characterization of Autism Spectrum in Young Adults using Brain Connectivity Parameters and Deep Belief Networks: An fMRI Study." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 9 (2020): 1059–73. http://dx.doi.org/10.2174/1573405615666191111142039.

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Background: The spectrum of autism encompasses High Functioning Autism (HFA) and Low Functioning Autism (LFA). Brain mapping studies have revealed that autism individuals have overlaps in brain behavioural characteristics. Generally, high functioning individuals are known to exhibit higher intelligence and better language processing abilities. However, specific mechanisms associated with their functional capabilities are still under research. Objective: This work addresses the overlapping phenomenon present in autism spectrum through functional connectivity patterns along with brain connectivi

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Salehiyan, Ahmad, Pardis Sadatian Moghaddam, and Masoud Kaveh. "An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets." Future Internet 17, no. 7 (2025): 279. https://doi.org/10.3390/fi17070279.

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The rapid expansion of Edge and Industrial Internet of Things (IIoT) systems has intensified the risk and complexity of cyberattacks. Detecting advanced intrusions in these heterogeneous and high-dimensional environments remains challenging. As the IIoT becomes integral to critical infrastructure, ensuring security is crucial to prevent disruptions and data breaches. Traditional IDS approaches often fall short against evolving threats, highlighting the need for intelligent and adaptive solutions. While deep learning (DL) offers strong capabilities for pattern recognition, single-model architec

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Yang, Hui, Qine Liu, Kang Xiao, Long Guo, Lucheng Yang, and Hongbo Zou. "Scenario-Driven Optimization Strategy for Energy Storage Configuration in High-Proportion Renewable Energy Power Systems." Processes 12, no. 8 (2024): 1721. http://dx.doi.org/10.3390/pr12081721.

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The output of renewable energy sources is characterized by random fluctuations, and considering scenarios with a stochastic renewable energy output is of great significance for energy storage planning. Existing scenario generation methods based on random sampling fail to account for the volatility and temporal characteristics of renewable energy output. To enhance photovoltaic (PV) absorption capacity and reduce the cost of planning distributed PV and energy storage systems, a scenario-driven optimization configuration strategy for energy storage in high-proportion renewable energy power syste

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Qian, Chunhua, Hequn Qiang, Feng Wang, and Mingyang Li. "Estimation of Forest Aboveground Biomass in Karst Areas Using Multi-Source Remote Sensing Data and the K-DBN Algorithm." Remote Sensing 13, no. 24 (2021): 5030. http://dx.doi.org/10.3390/rs13245030.

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Accurate estimation of forest biomass is the basis for monitoring forest productivity and carbon sink function, which is of great significance for the formulation of forest carbon neutralization strategy and forest quality improvement measures. Taking Guizhou, a typical karst region in China, as the research area, this study used Landsat 8 OLI, Sentinel-1A, and China national forest resources continuous inventory data (NFCI) in 2015 to build a deep belief network (DBN) model for aboveground biomass (AGB) estimation. Based on the introduction of forest canopy density (FCD), we improved the DBN

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Cui, Qian, Feng Zhang, Shaoyun Fu, Xiaoli Wei, Yue Ma, and Kun Wu. "High Spatiotemporal Resolution PM2.5 Concentration Estimation with Machine Learning Algorithm: A Case Study for Wildfire in California." Remote Sensing 14, no. 7 (2022): 1635. http://dx.doi.org/10.3390/rs14071635.

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As an aggregate of suspended particulate matter in the air, atmospheric aerosols can affect the regional climate. With the help of satellite remote sensing technology to retrieve AOD (aerosol optical depth) on a global or regional scale, accurate estimation of PM2.5 concentration has become an important task to quantify the spatiotemporal distribution of AOD and PM2.5. However, due to the limitations of satellite platforms, sensors, and inversion algorithms, the spatiotemporal resolution of current major AOD products is still relatively low. Meanwhile, for the impact of cloud, the AOD products

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Zhou, Zeyu, Wei Tang, Mingyang Li, Wen Cao, and Zhijie Yuan. "A Novel Hybrid Intelligent SOPDEL Model with Comprehensive Data Preprocessing for Long-Time-Series Climate Prediction." Remote Sensing 15, no. 7 (2023): 1951. http://dx.doi.org/10.3390/rs15071951.

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Long-time-series climate prediction is of great significance for mitigating disasters; promoting ecological civilization; identifying climate change patterns and preventing floods, drought and typhoons. However, the general public often struggles with the complexity and extensive temporal range of meteorological data when attempting to accurately forecast climate extremes. Sequence disorder, weak robustness, low characteristics and weak interpretability are four prevalent shortcomings in predicting long-time-series data. In order to resolve these deficiencies, our study gives a novel hybrid sp

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Shoumi, Milyun Ni'ma, and Mohamad Ivan Fanany. "A SPARSE ENCODING SYMMETRIC MACHINES PRE-TRAINING FOR TEMPORAL DEEP BELIEF NETWORKS FOR MOTION ANALYSIS AND SYNTHESIS." February 10, 2015. https://doi.org/10.5281/zenodo.34149.

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We present a modified Temporal Deep Belief Networks (TDBN) for human motion analysis and synthesis by incorporating Sparse Encoding Symmetric Machines (SESM) improvement on its pre-training. SESM consisted of two important terms: regularization and sparsity. In this paper, we measure the effect of these two terms on the smoothness of synthesized (or generated) motion. The smoothness is measured as the standard deviation of five bones movements with three motion transitions. We also address how these two terms influence the free energy and reconstruction error profiles during pre-training of th

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"Learning Effective Video Features for Facial Expression Recognition via Hybrid Deep Learning." International Journal of Recent Technology and Engineering 8, no. 5 (2020): 5602–4. http://dx.doi.org/10.35940/ijrte.e6767.018520.

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Facial Expression Recognition is one of the recent trends to detect human expression in streaming video sequences. To identify emotions of video like sad, happy or angry. In this paper, the proposed method employs two individual deep convolution neural networks (CNNs), including a permanent CNN processing of static facial images and a temporary CN network processing of optical flow images, to separately learn high-level spatial and temporal characteristics on the separated video segments. Such two CNNs are fine tuned from a pre-trained CNN model to target video facial expression datasets. The

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Liu, Hongyun. "Dance Emotion Characteristic Parameters Based on Deep Learning Model." Applied Mathematics and Nonlinear Sciences, June 21, 2023. http://dx.doi.org/10.2478/amns.2023.1.00440.

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Abstract In this paper, the emotions of dancers are identified in combination with the integrated deep-learning model. Firstly, four initial value features with important emotional states are extracted from the time, frequency, and time-frequency domains, respectively. It was isolated using a deep belief network enhanced by neuro colloidal chains. Finally, the finite Boltzmann criterion integrates the features of higher abstractions and predicts the emotional states. The results of DEAP data show that the correlation between EEG channels can be discovered and applied by glial chains. The fused

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