Detecting DDoS Attacks in Cloud Computing Using Extreme Learning Machine and Adaptive Differential Evolution

Author(s):  
Gopal Singh Kushwah ◽  
Virender Ranga
2019 ◽  
Vol 27 (1(133)) ◽  
pp. 67-77 ◽  
Author(s):  
Zhiyu Zhou ◽  
Chao Wang ◽  
Xu Gao ◽  
Zefei Zhu ◽  
Xudong Hu ◽  
...  

To develop an automatic detection and classifier model for fabric defects, a novel detection and classifier technique based on multi-scale dictionary learning and the adaptive differential evolution algorithm optimised regularisation extreme learning machine (ADE-RELM) is proposed. Firstly in order to speed up dictionary updating under the condition of guaranteeing dictionary sparseness, k-means singular value decomposition (KSVD) dictionary learning is used. Then multi-scale KSVD dictionary learning is presented to extract texture features of textile images more accurately. Finally a unique ADE-RELM is designed to build a defect classifier model. In the training ADE-RELM classifier stage, a self-adaptive mutation operator is used to solve the parameter setting problem of the original differential evolution algorithm, then the adaptive differential evolution algorithm is utilised to calculate the optimal input weights and hidden bias of RELM. The method proposed is committed to detecting common defects like broken warp, broken weft, oil, and the declining warp of grey-level and pure colour fabrics. Experimental results show that compared with the traditional Gabor filter method, morphological operation and local binary pattern, the method proposed in this paper can locate defects precisely and achieve high detection efficiency.


2014 ◽  
Vol 11 (6) ◽  
pp. 1066-1070 ◽  
Author(s):  
Yakoub Bazi ◽  
Naif Alajlan ◽  
Farid Melgani ◽  
Haikel AlHichri ◽  
Salim Malek ◽  
...  

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 4033
Author(s):  
Jonas Bielskus ◽  
Violeta Motuzienė ◽  
Tatjana Vilutienė ◽  
Audrius Indriulionis

Despite increasing energy efficiency requirements, the full potential of energy efficiency is still unlocked; many buildings in the EU tend to consume more energy than predicted. Gathering data and developing models to predict occupants’ behaviour is seen as the next frontier in sustainable design. Measurements in the analysed open-space office showed accordingly 3.5 and 2.7 times lower occupancy compared to the ones given by DesignBuilder’s and EN 16798-1. This proves that proposed occupancy patterns are only suitable for typical open-space offices. The results of the previous studies and proposed occupancy prediction models have limited applications and limited accuracies. In this paper, the hybrid differential evolution online sequential extreme learning machine (DE-OSELM) model was applied for building occupants’ presence prediction in open-space office. The model was not previously applied in this area of research. It was found that prediction using experimentally gained indoor and outdoor parameters for the whole analysed period resulted in a correlation coefficient R2 = 0.72. The best correlation was found with indoor CO2 concentration—R2 = 0.71 for the analysed period. It was concluded that a 4 week measurement period was sufficient for the prediction of the building’s occupancy and that DE-OSELM is a fast and reliable model suitable for this purpose.


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