Deep belief network-based support vector regression method for traffic flow forecasting

Xu, Haibo

doi:10.1007/s00521-019-04339-x

Cited by 51 publications

(18 citation statements)

References 14 publications

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“…Xu et al was also successful in adapting by combining DBN with Support Vector, which decreased errors and increased accuracy [22]. On the other hand, Ouyang combined DBN with the Copula Model to deliver a good performance [23].…”

Section: ) Deep Belief Network For Data Regressionmentioning

confidence: 99%

Role Comparison between Deep Belief Neural Network and NeuroEvolution of Augmenting Topologies to Detect Diabetes

Wijaya

Ikawahyuni

Gea

et al. 2021

JOIV : Int. J. Inform. Visualization

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Diabetes in Indonesia has been perceived as a grave health problem and has been a concern since the early 1980’s [2]. The prevalence of diabetes in adults in Indonesia, as stated by IDF, was 6.2% with the total case amounting to 10.681.400. Moreover, Indonesia is also in the top ten global countries with the highest diabetes case in 2013. This research will investigate the role of Deep Belief Network (DBN) and NeuroEvolution of Augmenting Topology (NEAT) in solving regression problems in detecting diabetes. DBN works by processing the data in unsupervised network architectures. The algorithm puts Restricted Boltzmann Machines (RBM) into a stacked process. The output of the first RBM will be the input for the next RBM. On the other hand, the NEAT algorithm works by investigating the neural network architecture and evaluating the architecture using a multi-layer perceptron algorithm. Collaboration with a Genetic Algorithm in NEAT is the key process in architecture development. The research results showed that DBN could be utilized as the initial weight for Backpropagation Neural Network at 22.61% on average. On the other hand, the NEAT algorithm could be used by collaborating with a multi-layer perceptron to solve this regression problem by providing 74.5% confidence. This work also reveals potential works in the future by combining the Backpropagation algorithm with NEAT as an evaluation function and by combining it with DBN algorithms to process the produced initial weight.

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Section: ) Deep Belief Network For Data Regressionmentioning

confidence: 99%

Role Comparison between Deep Belief Neural Network and NeuroEvolution of Augmenting Topologies to Detect Diabetes

Wijaya

Ikawahyuni

Gea

et al. 2021

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…Neural networks are mathematical models that simulate the structure of neuronal connections in the human brain for information processing, which can continuously adjust the relationships between internal nodes and learn changes in data based on training data and have the ability of self-learning, adaptive, and nonlinear approximation. The artificial neural network (ANN) has an active role in improving the decision-making in network operations [14]. Recurrent neural network (RNN) solves the information preservation problem due to its special network model structure, which not only learns the information of the current moment but also relies on the information of the previous sequence, allowing information persistence.…”

Section: Tssdn Tsnmentioning

confidence: 99%

TSN Switch Queue Length Prediction Based on an Improved LSTM Network

Wang

Shang

Xia

et al. 2021

Wireless Communications and Mobile Computing

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With the high-speed development of network technology, time-sensitive networks (TSNs) are experiencing a phase of significant traffic growth. At the same time, they have to ensure that highly critical time-sensitive information can be transmitted in a timely and accurate manner. In the future, TSNs will have to further improve network throughput to meet the increasing traffic demand based on the guaranteed transmission delay. Therefore, an efficient route scheduling scheme is necessary to achieve network load balance and improve network throughput. A time-sensitive software-defined network (TSSDN) can address the highly distributed industrial Internet network infrastructure, which cannot be accomplished by traditional industrial communication technologies, and it can achieve distributed intelligent dynamic route scheduling of the network through global network monitoring. The prerequisite for intelligent dynamic scheduling is that the queue length of future switches can be accurately predicted so that dynamic route planning for flow can be performed based on the prediction results. To address the queue length prediction problem, we propose a TSN switch queue length prediction model based on the TSSDN architecture. The prediction process has three steps: network topology dimension reduction, feature selection, and training prediction. The principal component analysis (PCA) algorithm is used to reduce the dimensionality of the network topology to eliminate unnecessary redundancy and overlap of relevant information. Feature selection requires comprehensive consideration of the influencing factors that affect the switch queue length, such as time and network topology. The training prediction is performed with the help of our enhanced long short-term memory (LSTM) network. The input-output structure of the network is changed based on the extracted features to improve the prediction accuracy, thus predicting the network congestion caused by bursty traffic. Finally, the results of the simulation demonstrate that our proposed TSN switch queue length prediction model based on the improved LSTM network algorithm doubles the prediction accuracy compared to the original model because it considers more influencing factors as features in the neural network for training and learning.

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“…A combination of LSTM and ARIMA model 57 was designed to predict the traffic flow. A DBN‐based support vector regression classifier 58 was developed for predicting the short‐term traffic data. The combination of LSTM and DBN 59 was used to design the model for lane changing.…”

Section: Related Workmentioning

confidence: 99%

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

Vijayalakshmi¹,

Ramar²,

Jhanjhi

et al. 2020

Int J Communication

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Summary In the development of smart cities, the intelligent transportation system (ITS) plays a major role. The dynamic and chaotic nature of the traffic information makes the accurate forecasting of traffic flow as a challengeable one in ITS. The volume of traffic data increases dramatically. We enter the epoch of big data. Hence, a 1deep architecture is necessary to process, analyze, and inference such a large volume of data. To develop a better traffic flow forecasting model, we proposed an attention‐based convolution neural network long short‐term memory (CNN‐LSTM), a multistep prediction model. The proposed scheme uses the spatial and time‐based details of the traffic data, which are extracted using CNN and LSTM networks to improve the model accuracy. The attention‐based model helps to identify the near term traffic details such as speed that is very important for predicting the future value of flow. The results show that our attention‐based CNN‐LSTM prediction model provides better accuracy in terms of prediction during weekdays and weekend days in the case of peak and nonpeak hours also. We used data from the largest traffic data set the California Department of Transportation (Caltrans) for our prediction work.

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Deep belief network-based support vector regression method for traffic flow forecasting

Cited by 51 publications

References 14 publications

Role Comparison between Deep Belief Neural Network and NeuroEvolution of Augmenting Topologies to Detect Diabetes

Role Comparison between Deep Belief Neural Network and NeuroEvolution of Augmenting Topologies to Detect Diabetes

TSN Switch Queue Length Prediction Based on an Improved LSTM Network

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

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