Optimized extreme learning machine for detecting DDoS attacks in cloud computing

Abstract Energy consumption forecasting is a kind of fundamental work of the energy management in equipment-manufacturing enterprises, and an important way to reduce energy consumption. Therefore, this paper proposes an intellectualized, short-term distributed energy consumption forecasting model for equipment-manufacturing enterprises based on cloud computing and extreme learning machine considering the practical enterprise situation of massive and high-dimension data. The analysis of the real energy consumption data provided by LB Enterprise was undertaken and corresponding calculating experiments were completed using a 32-node cloud computing cluster. The experimental results show that the energy consumption forecasting accuracy of the proposed model is higher than the traditional support vector regression and the generalized neural network algorithm. Furthermore, the proposed forecasting algorithm possesses excellent parallel performance, overcomes the shortcoming of a single computer’s insufficient computing power when facing massive and high-dimensional data without increasing the cost.

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Distributed denial of service attacks detection in cloud computing using extreme learning machine

International Journal of Communication Networks and Distributed Systems ◽

10.1504/ijcnds.2019.101915 ◽

2019 ◽

Vol 23 (3) ◽

pp. 328

Author(s):

Gopal Singh Kushwah ◽

Syed Taqi Ali

Keyword(s):

Cloud Computing ◽

Extreme Learning Machine ◽

Denial Of Service ◽

Denial Of Service Attacks ◽

Distributed Denial Of Service ◽

Learning Machine

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Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

Diagnostics ◽

10.3390/diagnostics11020241 ◽

2021 ◽

Vol 11 (2) ◽

pp. 241

Author(s):

Vivek Lahoura ◽

Harpreet Singh ◽

Ashutosh Aggarwal ◽

Bhisham Sharma ◽

Mazin Abed Mohammed ◽

...

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Cloud Computing ◽

Extreme Learning Machine ◽

Feature Selection Method ◽

Breast Cancer Diagnosis ◽

Disease Diagnosis ◽

Classification Problems ◽

Risk Of Death ◽

Learning Machine

Globally, breast cancer is one of the most significant causes of death among women. Early detection accompanied by prompt treatment can reduce the risk of death due to breast cancer. Currently, machine learning in cloud computing plays a pivotal role in disease diagnosis, but predominantly among the people living in remote areas where medical facilities are scarce. Diagnosis systems based on machine learning act as secondary readers and assist radiologists in the proper diagnosis of diseases, whereas cloud-based systems can support telehealth services and remote diagnostics. Techniques based on artificial neural networks (ANN) have attracted many researchers to explore their capability for disease diagnosis. Extreme learning machine (ELM) is one of the variants of ANN that has a huge potential for solving various classification problems. The framework proposed in this paper amalgamates three research domains: Firstly, ELM is applied for the diagnosis of breast cancer. Secondly, to eliminate insignificant features, the gain ratio feature selection method is employed. Lastly, a cloud computing-based system for remote diagnosis of breast cancer using ELM is proposed. The performance of the cloud-based ELM is compared with some state-of-the-art technologies for disease diagnosis. The results achieved on the Wisconsin Diagnostic Breast Cancer (WBCD) dataset indicate that the cloud-based ELM technique outperforms other results. The best performance results of ELM were found for both the standalone and cloud environments, which were compared. The important findings of the experimental results indicate that the accuracy achieved is 0.9868, the recall is 0.9130, the precision is 0.9054, and the F1-score is 0.8129.

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A Cloud Load Forecasting Model with Nonlinear Changes using Whale Optimization Algorithm Hybrid Strategy - extreme Learning Machine

10.21203/rs.3.rs-412160/v1 ◽

2021 ◽

Author(s):

Hua Peng ◽

Wu-Shao Wen ◽

Ming-Lang Tseng ◽

Ling-Ling Li

Keyword(s):

Cloud Computing ◽

Prediction Model ◽

Extreme Learning Machine ◽

Load Forecasting ◽

Forecasting Model ◽

Random Parameters ◽

Load Prediction ◽

Hybrid Strategy ◽

Whale Optimization ◽

Learning Machine

Abstract This study proposes a novel cloud load prediction model and combines hybrid whale optimizer (HWOA) and extreme learning machine (ELM) together for strong nonlinear mapping ability. Accurate cloud load prediction improves the cloud service efficiency and serves as the foundation for network scheme due to traditional linear forecasting models are unable to predict cloud computing resources with nonlinear changes on massive multiplication and cloud computing data complexity, effectively. The proposed cloud load forecasting model is to employ HWOA optimizer to optimize the ELM model random parameters. The contributions of this study are as follows. (1) the HWOA optimizer is to solve the whale optimizer local extremum problem; (2) the proposed HWOA optimizer reduces the ELM random parameters on cloud load forecasting; (3) the convergence performance verifies the benchmark testing functions; and (4) three simulation experiments are conducted to test the cloud load forecast effect. The result indicated that the convergence analysis reveals the HWOA optimizer outperforms the prior optimizers. The proposed cloud load prediction model obtains better forecasting results. The mean absolute percentage error and root mean square error of the proposed model are less than 14% and 11, respectively. Accurate cloud load forecasting lays a foundation for effective deployment of cloud computing resources and maximization of economic benefits.

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