An efficient abnormal cervical cell detection system based on multi-instance extreme learning machine

2017 ◽  
Author(s):  
Lili Zhao ◽  
Jianping Yin ◽  
Lihuan Yuan ◽  
Qiang Liu ◽  
Kuan Li ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Detection System ◽  
Cell Detection ◽  
Cervical Cell ◽  
Learning Machine

scholarly journals Research on Network Intrusion Detection Based on Incremental Extreme Learning Machine and Adaptive Principal Component Analysis

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1223 ◽  
Author(s):  
Jianlei Gao ◽  
Senchun Chai ◽  
Baihai Zhang ◽  
Yuanqing Xia
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Detection System ◽  
Principal Component ◽  
Detection Accuracy ◽  
Network Attack ◽  
Network Intrusion ◽  
Learning Machine ◽  
Malicious Behaviors ◽  
Adaptive Principal Component Analysis

Recently, network attacks launched by malicious attackers have seriously affected modern life and enterprise production, and these network attack samples have the characteristic of type imbalance, which undoubtedly increases the difficulty of intrusion detection. In response to this problem, it would naturally be very meaningful to design an intrusion detection system (IDS) to effectively and quickly identify and detect malicious behaviors. In our work, we have proposed a method for an IDS-combined incremental extreme learning machine (I-ELM) with an adaptive principal component (A-PCA). In this method, the relevant features of network traffic are adaptively selected, where the best detection accuracy can then be obtained by I-ELM. We have used the NSL-KDD standard dataset and UNSW-NB15 standard dataset to evaluate the performance of our proposed method. Through analysis of the experimental results, we can see that our proposed method has better computation capacity, stronger generalization ability, and higher accuracy.

scholarly journals Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yuanfa Wang ◽  
Zunchao Li ◽  
Lichen Feng ◽  
Chuang Zheng ◽  
Wenhao Zhang
Keyword(s):  
Computational Complexity ◽  
Extreme Learning Machine ◽  
Classification System ◽  
Classification Accuracy ◽  
Detection System ◽  
Automatic Detection ◽  
Seizure Detection ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Learning Machine

An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection. Three-level lifting DWT using Daubechies order 4 wavelet is introduced to decompose EEG signals into delta, theta, alpha, and beta subbands. Considering classification accuracy and computational complexity, the maximum and standard deviation values of each subband are computed to create an eight-dimensional feature vector. After comparing five multiclass SELM strategies, the one-against-one strategy with the highest accuracy is chosen for the three-class classification system. The performance of the designed three-class classification system is tested with publicly available epilepsy dataset. The results show that the system achieves high enough classification accuracy by combining the SELM and DWT and reduces training and testing time by decreasing computational complexity and feature dimension. With excellent classification performance and low computation complexity, this three-class classification system can be utilized for practical epileptic EEG detection, and it offers great potentials for portable automatic epilepsy and seizure detection system in the future hardware implementation.

scholarly journals An Application and Performance Evaluation of Twin Extreme Learning Machine Classifier for Intrusion Detection

2019 ◽  
Vol 8 (9) ◽  
pp. 219-222
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Intrusion Detection System ◽  
Detection System ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Detection Techniques ◽  
Learning Machines ◽  
And Performance ◽  
Learning Machine

Network along with Security is most significant in the digitalized environment. It is necessary to secure data from hackers and intruders. A strategy involved in protection of information from hackers will be termed as Intrusion Detection System (IDS).By taking into nature of attack or the usual conduct of user, investigation along with forecasting activities of the clients will be performed by mentioned system.Variousstrategies are utilized for the intrusion detection system. For the purpose of identification of hacking activity, utilization of machine learning based approach might be considered as novel strategy.In this paper, for identification of the hacking activity will be carried out by Twin Extreme Learning Machines (TELM).Employing the concept of Twin Support Vector Machine with the fundamental structure of Extreme Learning Machine is considered in the establishment of Twin Extreme Learning Machine (TELM).Also, its performance and accuracy are compared with the other intrusion detection techniques

scholarly journals Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Jivitesh Sharma ◽  
Charul Giri ◽  
Ole-Christoffer Granmo ◽  
Morten Goodwin
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Detection System ◽  
Binary Classification ◽  
Detection Systems ◽  
Weighted Extreme Learning Machine ◽  
Three Stages ◽  
Learning Machine ◽  
Multi Class Classification

Abstract Recent advances in intrusion detection systems based on machine learning have indeed outperformed other techniques, but struggle with detecting multiple classes of attacks with high accuracy. We propose a method that works in three stages. First, the ExtraTrees classifier is used to select relevant features for each type of attack individually for each (ELM). Then, an ensemble of ELMs is used to detect each type of attack separately. Finally, the results of all ELMs are combined using a softmax layer to refine the results and increase the accuracy further. The intuition behind our system is that multi-class classification is quite difficult compared to binary classification. So, we divide the multi-class problem into multiple binary classifications. We test our method on the UNSW and KDDcup99 datasets. The results clearly show that our proposed method is able to outperform all the other methods, with a high margin. Our system is able to achieve 98.24% and 99.76% accuracy for multi-class classification on the UNSW and KDDcup99 datasets, respectively. Additionally, we use the weighted extreme learning machine to alleviate the problem of imbalance in classification of attacks, which further boosts performance. Lastly, we implement the ensemble of ELMs in parallel using GPUs to perform intrusion detection in real time.

scholarly journals Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xingshuo An ◽  
Xianwei Zhou ◽  
Xing Lü ◽  
Fuhong Lin ◽  
Lei Yang
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Detection System ◽  
Fog Computing ◽  
Training Data ◽  
Experimental Simulation ◽  
Detection Accuracy ◽  
New Paradigm ◽  
Training Time ◽  
Learning Machine

Fog computing, as a new paradigm, has many characteristics that are different from cloud computing. Due to the resources being limited, fog nodes/MEC hosts are vulnerable to cyberattacks. Lightweight intrusion detection system (IDS) is a key technique to solve the problem. Because extreme learning machine (ELM) has the characteristics of fast training speed and good generalization ability, we present a new lightweight IDS called sample selected extreme learning machine (SS-ELM). The reason why we propose “sample selected extreme learning machine” is that fog nodes/MEC hosts do not have the ability to store extremely large amounts of training data sets. Accordingly, they are stored, computed, and sampled by the cloud servers. Then, the selected sample is given to the fog nodes/MEC hosts for training. This design can bring down the training time and increase the detection accuracy. Experimental simulation verifies that SS-ELM performs well in intrusion detection in terms of accuracy, training time, and the receiver operating characteristic (ROC) value.

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