A DBN-Based Deep Neural Network Model with Multitask Learning for Online Air Quality Prediction

Li, Jiangeng; Shao, Xingyang; Sun, Ruizhi

doi:10.1155/2019/5304535

Cited by 25 publications

(7 citation statements)

References 33 publications

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“…(l) , h (l−1) ) ∝ exp (b (l) T h (l) + b (l−1) T h (l−1) + h (l−1) T W (l) h (l) ) (4) Where h (1) & h (2) signifies the state vector of hidden layer, v denotes state vector of visible layer, W (1) and W (2) signifies the matrix of the symmetrical weight, b (1) and b (2) implies bias vector of hidden layers, and b (0) indicates the bias vector of visible layers. P(ν i = 1|h (1) ) = σ (b (0) i + W (1) T i h (1) )∀i (6) If the real valued visible units are occurred, employed v using β diagonal for controllability [19].…”

Section: Design Of Coa-dbn Techniquementioning

confidence: 99%

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

Madhu¹,

Bharadwaj²,

Boddeda³

et al. 2022

Computers, Materials &Amp; Continua

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In recent days, advancements in the Internet of Things (IoT) and cloud computing (CC) technologies have emerged in different application areas, particularly healthcare. The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server. Therefore, energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices. At the same time, the advent of deep learning (DL) models helps to analyze the healthcare data in the cloud server for decision making. With this motivation, this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems, called IDDM-EAC technique. The proposed IDDM-EAC technique involves a 3-stage process namely data acquisition, clustering, and disease diagnosis. In addition, the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering (CSOEAC) technique to group the IoT devices into clusters and select cluster heads (CHs). Moreover, a new coyote optimization algorithm (COA) with deep belief network (DBN), called COA-DBN technique is employed for the disease diagnostic process. The COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN model. In order to inspect the betterment of the IDDM-EAC technique, a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI repository. The experimental results demonstrate the promising performance with the minimal total energy consumption of 63% whereas the EEPSOC, ABC, GWO, and ACO algorithms have showcased a higher total energy consumption of 69%, 78%, 83%, and 84% correspondingly.

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Section: Design Of Coa-dbn Techniquementioning

confidence: 99%

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

Madhu¹,

Bharadwaj²,

Boddeda³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…When the real valued visible layers have existed, then apply v with β diagonal for manageability [21].…”

Section: Load Prediction Using Dbn Modelmentioning

confidence: 99%

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

Shoab¹,

Alotaibi²

2022

Computers, Materials &Amp; Continua

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With the incorporation of distributed energy systems in the electric grid, transactive energy market (TEM) has become popular in balancing the demand as well as supply adaptively over the grid. The classical grid can be updated to the smart grid by the integration of Information and Communication Technology (ICT) over the grids. The TEM allows the Peerto-Peer (P2P) energy trading in the grid that effectually connects the consumer and prosumer to trade energy among them. At the same time, there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning (DML) models. Though some of the short term load prediction techniques have existed in the literature, there is still essential to consider the intrinsic features, parameter optimization, etc. into account. In this aspect, this study devises new deep learning enabled short term load forecasting model for P2P energy trading (DLSTLF-P2P) in TEM. The proposed model involves the design of oppositional coyote optimization algorithm (OCOA) based feature selection technique in which the OCOA is derived by the integration of oppositional based learning (OBL) concept with COA for improved convergence rate. Moreover, deep belief networks (DBN) are employed for the prediction of load in the P2P energy trading systems. In order to additional improve the predictive performance of the DBN model, a hyperparameter optimizer is introduced using chicken swarm optimization (CSO) algorithm is applied for the optimal choice of DBN parameters to improve the predictive outcome. The simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training, testing, and validation accuracy of 90.17%, 87.39%, and 87.86%.

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“…After a trained RBM layer, the preceding unseen layer input is given to the next unseen layer. The pictorial representation of DBN is described in Figure 2 [11].…”

Section: Danmentioning

confidence: 99%

Performance Evaluation Of Machine Learning Algorithms In Traffic Flow Prediction

Ramchandra¹,

Rajabhushanam²

2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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The main and foremost reason for traffic congestion is overpopulation and the poor condition of the roads. This mostly happens in the urban cities where all the people in the urban areas go for some work or certain purposes. Due to the current growth of the communication technology various computing techniques are used to predict the outcome based on a given dataset. This research work uses four kinds of machine learning techniques line Deep AutoEncoder (DAN), Deep Belief Network (DBN), Random Forest (RF), and Long Short Term Memory (LSTM) to predict the traffic flow. This proposed system is implemented using Python programming. Lastly, the outcome describes that the proposed model using the LSTM technique produces 94.3% accuracy and less error value.

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A DBN-Based Deep Neural Network Model with Multitask Learning for Online Air Quality Prediction

Cited by 25 publications

References 33 publications

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

Performance Evaluation Of Machine Learning Algorithms In Traffic Flow Prediction

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