scholarly journals Anomaly Detection for Aviation Safety Based on an Improved KPCA Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaoyu Zhang ◽  
Jiusheng Chen ◽  
Quan Gan
Keyword(s):  
Anomaly Detection ◽  
Cross Validation ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Error Statistics ◽  
Validation Process ◽  
Task Execution ◽  
Detection Model ◽  
Squared Prediction Error ◽  
Fold Cross Validation

Thousands of flights datasets should be analyzed per day for a moderate sized fleet; therefore, flight datasets are very large. In this paper, an improved kernel principal component analysis (KPCA) method is proposed to search for signatures of anomalies in flight datasets through the squared prediction error statistics, in which the number of principal components and the confidence for the confidence limit are automatically determined by OpenMP-based K-fold cross-validation algorithm and the parameter in the radial basis function (RBF) is optimized by GPU-based kernel learning method. Performed on Nvidia GeForce GTX 660, the computation of the proposed GPU-based RBF parameter is 112.9 times (average 82.6 times) faster than that of sequential CPU task execution. The OpenMP-based K-fold cross-validation process for training KPCA anomaly detection model becomes 2.4 times (average 1.5 times) faster than that of sequential CPU task execution. Experiments show that the proposed approach can effectively detect the anomalies with the accuracy of 93.57% and false positive alarm rate of 1.11%.

scholarly journals Traffic Flow Anomaly Detection Based on Robust Ridge Regression with Particle Swarm Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Mingzhu Tang ◽  
Xiangwan Fu ◽  
Huawei Wu ◽  
Qi Huang ◽  
Qi Zhao
Keyword(s):  
Anomaly Detection ◽  
Traffic Flow ◽  
Ridge Regression ◽  
Cross Validation ◽  
Sliding Window ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Feature Sets ◽  
Proposed Model ◽  
Fold Cross Validation

Traffic flow anomaly detection is helpful to improve the efficiency and reliability of detecting fault behavior and the overall effectiveness of the traffic operation. The data detected by the traffic flow sensor contains a lot of noise due to equipment failure, environmental interference, and other factors. In the case of large traffic flow data noises, a traffic flow anomaly detection method based on robust ridge regression with particle swarm optimization (PSO) algorithm is proposed. Feature sets containing historical characteristics with a strong linear correlation and statistical characteristics using the optimal sliding window are constructed. Then by providing the feature sets inputs to the PSO-Huber-Ridge model and the model outputs the traffic flow. The Huber loss function is recommended to reduce noise interference in the traffic flow. The L2 regular term of the ridge regression is employed to reduce the degree of overfitting of the model training. A fitness function is constructed, which can balance the relative size between the k-fold cross-validation root mean square error and the k-fold cross-validation average absolute error with the control parameter η to improve the optimization efficiency of the optimization algorithm and the generalization ability of the proposed model. The hyperparameters of the robust ridge regression forecast model are optimized by the PSO algorithm to obtain the optimal hyperparameters. The traffic flow data set is used to train and validate the proposed model. Compared with other optimization methods, the proposed model has the lowest RMSE, MAE, and MAPE. Finally, the traffic flow that forecasted by the proposed model is used to perform anomaly detection. The abnormality of the error between the forecasted value and the actual value is detected by the abnormal traffic flow threshold based on the sliding window. The experimental results verify the validity of the proposed anomaly detection model.

scholarly journals IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rong Zhu ◽  
Yong Wang ◽  
Jin-Xing Liu ◽  
Ling-Yun Dai
Keyword(s):  
Principal Component Analysis ◽  
Random Forest ◽  
Cross Validation ◽  
Search Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Biological Data ◽  
Learning Technology ◽  
Disease Associations ◽  
Fold Cross Validation

Abstract Background Identifying lncRNA-disease associations not only helps to better comprehend the underlying mechanisms of various human diseases at the lncRNA level but also speeds up the identification of potential biomarkers for disease diagnoses, treatments, prognoses, and drug response predictions. However, as the amount of archived biological data continues to grow, it has become increasingly difficult to detect potential human lncRNA-disease associations from these enormous biological datasets using traditional biological experimental methods. Consequently, developing new and effective computational methods to predict potential human lncRNA diseases is essential. Results Using a combination of incremental principal component analysis (IPCA) and random forest (RF) algorithms and by integrating multiple similarity matrices, we propose a new algorithm (IPCARF) based on integrated machine learning technology for predicting lncRNA-disease associations. First, we used two different models to compute a semantic similarity matrix of diseases from a directed acyclic graph of diseases. Second, a characteristic vector for each lncRNA-disease pair is obtained by integrating disease similarity, lncRNA similarity, and Gaussian nuclear similarity. Then, the best feature subspace is obtained by applying IPCA to decrease the dimension of the original feature set. Finally, we train an RF model to predict potential lncRNA-disease associations. The experimental results show that the IPCARF algorithm effectively improves the AUC metric when predicting potential lncRNA-disease associations. Before the parameter optimization procedure, the AUC value predicted by the IPCARF algorithm under 10-fold cross-validation reached 0.8529; after selecting the optimal parameters using the grid search algorithm, the predicted AUC of the IPCARF algorithm reached 0.8611. Conclusions We compared IPCARF with the existing LRLSLDA, LRLSLDA-LNCSIM, TPGLDA, NPCMF, and ncPred prediction methods, which have shown excellent performance in predicting lncRNA-disease associations. The compared results of 10-fold cross-validation procedures show that the predictions of the IPCARF method are better than those of the other compared methods.

scholarly journals Recognition of Protein Pupylation Sites by Adopting Resampling Approach

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3097
Author(s):  
Tao Li ◽  
Yan Chen ◽  
Taoying Li ◽  
Cangzhi Jia
Keyword(s):  
Amino Acid ◽  
Correlation Coefficient ◽  
Posttranslational Modification ◽  
Cross Validation ◽  
Pearson Correlation ◽  
Principal Component ◽  
Pearson Correlation Coefficient ◽  
Protein Ubiquitination ◽  
Independent Test Dataset ◽  
Fold Cross Validation

With the in-depth study of posttranslational modification sites, protein ubiquitination has become the key problem to study the molecular mechanism of posttranslational modification. Pupylation is a widely used process in which a prokaryotic ubiquitin-like protein (Pup) is attached to a substrate through a series of biochemical reactions. However, the experimental methods of identifying pupylation sites is often time-consuming and laborious. This study aims to propose an improved approach for predicting pupylation sites. Firstly, the Pearson correlation coefficient was used to reflect the correlation among different amino acid pairs calculated by the frequency of each amino acid. Then according to a descending ranked order, the multiple types of features were filtered separately by values of Pearson correlation coefficient. Thirdly, to get a qualified balanced dataset, the K-means principal component analysis (KPCA) oversampling technique was employed to synthesize new positive samples and Fuzzy undersampling method was employed to reduce the number of negative samples. Finally, the performance of our method was verified by means of jackknife and a 10-fold cross-validation test. The average results of 10-fold cross-validation showed that the sensitivity (Sn) was 90.53%, specificity (Sp) was 99.8%, accuracy (Acc) was 95.09%, and Matthews Correlation Coefficient (MCC) was 0.91. Moreover, an independent test dataset was used to further measure its performance, and the prediction results achieved the Acc of 83.75%, MCC of 0.49, which was superior to previous predictors. The better performance and stability of our proposed method showed it is an effective way to predict pupylation sites.

scholarly journals A Hybrid Network Anomaly Detection system using Glowworm Swarm Optimization with Principal Component Analysis

2021 ◽  
Author(s):  
Rashmita Khilar ◽  
K. Mariyappan ◽  
Mary Subaja Christo ◽  
J Amutharaj ◽  
Anitha T ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Detection Rate ◽  
Detection System ◽  
Principal Component ◽  
Swarm Optimization ◽  
Detection Model ◽  
Glowworm Swarm Optimization ◽  
Proposed Model

Abstract The security of the network is a significant issue in any distributed system. For that intrusion detection system (IDS), have been proposed for securing the network from malicious activities. This research is proposed to design and develop an anomaly detection model for detecting attacks and unusual activities in IoT networks. The primary objective of this research is to design efficient IDS for IoT network. The intrusion detection plays an essential role in detecting different attacks on IoT and enhances the performance of the IoT. In this research, anomaly detection in IoT networks using glowworm swarm optimization (GSO) algorithm with principal component analysis (PCA) is proposed. However, the proposed model is metaheuristic algorithm-based anomaly detection model to identify attacks by using the NSL-KDD dataset. The GSO algorithm based on PCA is implemented to perform the anomaly detection. For feature extraction, the PCA is used, and for classification, the GSO algorithm is used. For performance analysis, various parameters like accuracy, precision, recall, detection rate and FAR are evaluated. For normal class the proposed model achieved 94.14% accuracy, for DoS 95.52%, for R2L 93.15%, for probe 93.50% and for U2R 88.62% accuracy. Overall the detection rate was 94.08% and FAR was 3.41%.

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