scholarly journals A comparative study on detection of osteoporosis using deep learning methods: A review

2021 ◽  
Vol 7 (3) ◽  
pp. 108-115
Author(s):  
Kavita Avinash Patil ◽  
KV Mahendra Prashanth ◽  
Dr. A Ramalingaiah
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Learning Methods

scholarly journals Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study

2020 ◽  
Vol 140 ◽  
pp. 110121 ◽  
Author(s):  
Abdelhafid Zeroual ◽  
Fouzi Harrou ◽  
Abdelkader Dairi ◽  
Ying Sun
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Comparative Study ◽  
Time Series Data ◽  
Series Data ◽  
Learning Methods

scholarly journals Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2692 ◽  
Author(s):  
Juncheng Zhu ◽  
Zhile Yang ◽  
Monjur Mourshed ◽  
Yuanjun Guo ◽  
Yimin Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Load Forecasting ◽  
Learning Approaches ◽  
Short Term ◽  
Learning Methods ◽  
Electric Vehicle Charging ◽  
Vehicle Charging

Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.

scholarly journals A Comparative Study of Unsupervised Deep Learning Methods for MRI Reconstruction

2020 ◽  
Vol 24 (4) ◽  
pp. 179
Author(s):  
Zhuonan He ◽  
Cong Quan ◽  
Siyuan Wang ◽  
Yuanzheng Zhu ◽  
Minghui Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Learning Methods ◽  
Unsupervised Deep Learning ◽  
Mri Reconstruction

scholarly journals Comparative Study of Deep Learning Methods in Detection Face Mask Utilization

2020 ◽  
Author(s):  
Ivan Muhammad Siegfried
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Face Mask ◽  
Face Image ◽  
Learning Methods ◽  
Accuracy And Precision ◽  
The World ◽  
Image Dataset ◽  
Emerging Virus

In 2020, the world is facing new and emerging virus called COVID-19 where the transmission could be halted using a face mask. A method and model needed to anticipate the spread of such virus. We study some transfer and deep learning methods: MobileNetV2, ResNet50V2, and Xception. The result is that the usage of ResNet50V2 and Xception for face image dataset using mask has better accuracy and precision than that of MobileNetV2 method.

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