RETRACTED ARTICLE: Deep learning based energy efficient novel scheduling algorithms for body-fog-cloud in smart hospital

Amudha, S.; Murali, M.

doi:10.1007/s12652-020-02421-0

Cited by 13 publications

(7 citation statements)

References 20 publications

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“…LSTM has been extensively used in many fields such as in generating music, captioning images, recognition of speech, and machine translation for improving the hidden layer cell on the basis of the recurrent neural network (RNN) [55]. The network consists of a cell to commemorate the values aloft the time intervals from LSTM memories and the gates [57,58]. The LSTM network is the RNN that consorts with LSTM units which is paraded in Figure 2.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…The composed method must be enforced and tested in an online form of a forecast system of traffic Wireless Communications and Mobile Computing three gate states in the network. LSTM cells G t and h t are calculated by evaluating the three gate states and cell input state and can be transmitted to the next neural network [57]. The input gate is given in equation ( 4) [57].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…LSTM cells G t and h t are calculated by evaluating the three gate states and cell input state and can be transmitted to the next neural network [57]. The input gate is given in equation ( 4) [57].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…Then, ensemble all the outputs HðxÞ = HðxÞ = sign ð∑α k T k Þ/α k 12. The calculated output state of a cell is given in equation ( 8) [57].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

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A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Kothai¹,

Poovammal²,

Dhiman³

et al. 2021

Wireless Communications and Mobile Computing

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The vehicular adhoc network (VANET) is an emerging research topic in the intelligent transportation system that furnishes essential information to the vehicles in the network. Nearly 150 thousand people are affected by the road accidents that must be minimized, and improving safety is required in VANET. The prediction of traffic congestions plays a momentous role in minimizing accidents in roads and improving traffic management for people. However, the dynamic behavior of the vehicles in the network degrades the rendition of deep learning models in predicting the traffic congestion on roads. To overcome the congestion problem, this paper proposes a new hybrid boosted long short-term memory ensemble (BLSTME) and convolutional neural network (CNN) model that ensemble the powerful features of CNN with BLSTME to negotiate the dynamic behavior of the vehicle and to predict the congestion in traffic effectively on roads. The CNN extracts the features from traffic images, and the proposed BLSTME trains and strengthens the weak classifiers for the prediction of congestion. The proposed model is developed using Tensor flow python libraries and are tested in real traffic scenario simulated using SUMO and OMNeT++. The extensive experimentations are carried out, and the model is measured with the performance metrics likely prediction accuracy, precision, and recall. Thus, the experimental result shows 98% of accuracy, 96% of precision, and 94% of recall. The results complies that the proposed model clobbers the other existing algorithms by furnishing 10% higher than deep learning models in terms of stability and performance.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…Then, ensemble all the outputs HðxÞ = HðxÞ = sign ð∑α k T k Þ/α k 12. The calculated output state of a cell is given in equation ( 8) [57].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

See 2 more Smart Citations

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Kothai¹,

Poovammal²,

Dhiman³

et al. 2021

Wireless Communications and Mobile Computing

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“…Amudha et al. [47] propose an improved WORN‐DEAR algorithm for the BAN–IoT network. The final result is compared with the WORN–DEAR algorithm and other machine learning algorithms, such as logistic regression, naive bias, supporting vector machines, and K‐nearest neighbors.…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based scheduling: a bibliometric perspective

Cao

et al. 2021

IET Collab Intel Manufact

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In recent years, the rapid development of artificial intelligence and data science has given rise to the study of data driven algorithms in highly volatile systems. The scheduling of complex shop floor resources falls into such a category, which is often non‐linear in nature, time varying, multi‐objective, and subject to interruptions. Ergo, the machine learning‐based scheduling, has become a research hotspot and attracted the attention of many scholars. In the literature, the research methods employed in solving scheduling problems are based on various perspectives, such as mathematical programming, combinatorial optimization, and heuristic rules. However, due to the inherent complexity of the problem, many issues remain to be addressed. In particular, with the availability of production data, the progress of computing power, and the breakthrough in intelligent algorithms, a novel branch of data driven algorithms present great potential, for example, the deep learning and reinforcement learning‐based algorithms. To reveal the value of machine learning‐based scheduling methods, bibliometric analysis was conducted to analyse the relevant articles and documents from the year 1980 to 2019. Finally, the future research trend in the domain of machine learning‐based scheduling is considered and tips are provided for researchers as well as practitioners to find leading scientists for collaborations.

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