Anomaly Detection Collaborating Adaptive CEEMDAN Feature Exploitation with Intelligent Optimizing Classification for IIoT Sparse Data

IIoT (Industrial Internet of Things) has gained considerable attention and has been increasingly applied due to its ubiquitous sensing and communication. However, the sparse characteristic of sensing data in distributed IIoT networks may bring out tremendous challenges to implement the security protection measures. Based on the design of centralized data gathering and forwarding, this paper proposes a novel anomaly detection approach for IIoT sparse data, which can successfully collaborate the adaptive CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) feature exploitation with one intelligent optimizing classification. Furthermore, in the adaptive CEEMDAN feature exploitation, the CEEMDAN energy entropy based on adaptive IMF (Intrinsic Mode Function) selection is designed to extract the sensing features from IIoT sparse data; in the intelligent optimizing classification, one effective OCSVM (One-Class Support Vector Machine) classifier optimized by the IABC (Improved Artificial Bee Colony) swarm intelligence algorithm is introduced to detect various abnormal sensing features. The experimental results show that, not only does the CEEMDAN energy entropy based on adaptive IMF selection accurately describe the change of industrial production by analyzing the probability distribution and energy distribution of sparse sensing data, but also the proposed IABC-OCSVM classifier has higher detection efficiency compared with the OCSVM classifiers optimized by other swarm intelligence algorithms.

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Efficient Anomaly Detection for High-Dimensional Sensing Data with One-Class Support Vector Machine

IEEE Transactions on Knowledge and Data Engineering ◽

10.1109/tkde.2021.3077046 ◽

2021 ◽

pp. 1-1

Author(s):

Yan Qiao ◽

Kui Wu ◽

Peng Jin

Keyword(s):

Support Vector Machine ◽

Anomaly Detection ◽

High Dimensional ◽

Support Vector ◽

Sensing Data

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Research on Identification Technology of Explosive Vibration Based on EEMD Energy Entropy and Multiclassification SVM

Shock and Vibration ◽

10.1155/2020/7893925 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Huayuan Ma ◽

Xinghua Li ◽

Qiang Liu ◽

Xie Xingbo ◽

Chong Ji ◽

...

Keyword(s):

Ensemble Empirical Mode Decomposition ◽

Vibration Monitoring ◽

Support Vector ◽

Vibration Source ◽

Blast Vibration ◽

Experimental Database ◽

Building Collapse ◽

Emergency Rescue ◽

Energy Entropy ◽

Mode Decomposition

In this study, the authors introduced energy entropy as a reference feature into the field of blast vibration recognition classification and achieved good results. On the basis of the previous experimental database, 4 kinds of typical vibration signals were selected to form the sample group (building collapse vibration, surface rock blast vibration, underground tunnel blast vibration, and natural gas pipeline explosion vibration). EEMD (ensemble empirical mode decomposition) algorithm was used to calculate the energy entropy of each signal. Taking eigenvector composed of CEE (components of energy entropy) as input, multiclassification SVM algorithm was used for training and prediction. Prediction accuracy was more than 80%. Compared with BP (backpropagation) neural network algorithm, SVM (support vector machine) algorithm has higher training efficiency. The research results can be used in urban vibration monitoring, identify the nature of vibration source in time, and provide technical support for rapid response of emergency rescue.

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MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

Current Bioinformatics ◽

10.2174/1574893615999200607173829 ◽

2020 ◽

Vol 15 ◽

Author(s):

Yi Zou ◽

Hongjie Wu ◽

Xiaoyi Guo ◽

Li Peng ◽

Yijie Ding ◽

...

Keyword(s):

Dna Binding ◽

Binding Proteins ◽

Detection Efficiency ◽

Dna Binding Proteins ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Description ◽

Multiple Kernel ◽

Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

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Bridge health anomaly detection using deep support vector data description

Neurocomputing ◽

10.1016/j.neucom.2020.08.087 ◽

2021 ◽

Author(s):

JianXi Yang ◽

Fei Yang ◽

Likai Zhang ◽

Ren Li ◽

Shixin Jiang ◽

...

Keyword(s):

Anomaly Detection ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Description ◽

Deep Support

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Coal-Gangue Interface Detection Based on Ensemble Empirical Mode Decomposition Energy Entropy

IEEE Access ◽

10.1109/access.2021.3070447 ◽

2021 ◽

pp. 1-1

Author(s):

Wei Liu ◽

Peiyao Li ◽

Keyu Wang ◽

Lu Lu ◽

Manyu Zhao ◽

...

Keyword(s):

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Coal Gangue ◽

Energy Entropy ◽

Mode Decomposition

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Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

Security and Communication Networks ◽

10.1155/2020/5874935 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Randa Aljably ◽

Yuan Tian ◽

Mznah Al-Rodhaan

Keyword(s):

Machine Learning ◽

Social Networks ◽

Access Control ◽

Anomaly Detection ◽

Privacy Preservation ◽

Support Vector ◽

Detection Techniques ◽

Access Control Models ◽

Control Models ◽

Multimedia Social Networks

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

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Electronic Devices for Stress Detection in Academic Contexts during Confinement Because of the COVID-19 Pandemic

Electronics ◽

10.3390/electronics10030301 ◽

2021 ◽

Vol 10 (3) ◽

pp. 301

Author(s):

Cristhian Manuel Durán-Acevedo ◽

Jeniffer Katerine Carrillo-Gómez ◽

Camilo Andrés Albarracín-Rojas

Keyword(s):

Electrical Response ◽

Electronic Devices ◽

Data Gathering ◽

Trapezius Muscle ◽

Support Vector ◽

Linear Discriminant ◽

Upper Trapezius ◽

Detection And Identification ◽

Skin Response ◽

Engineering Department

This article studies the development and implementation of different electronic devices for measuring signals during stress situations, specifically in academic contexts in a student group of the Engineering Department at the University of Pamplona (Colombia). For the research’s development, devices for measuring physiological signals were used through a Galvanic Skin Response (GSR), the electrical response of the heart by using an electrocardiogram (ECG), the electrical activity produced by the upper trapezius muscle (EMG), and the development of an electronic nose system (E-nose) as a pilot study for the detection and identification of the Volatile Organic Compounds profiles emitted by the skin. The data gathering was taken during an online test (during the COVID-19 Pandemic), in which the aim was to measure the student’s stress state and then during the relaxation state after the exam period. Two algorithms were used for the data process, such as Linear Discriminant Analysis and Support Vector Machine through the Python software for the classification and differentiation of the assessment, achieving 100% of classification through GSR, 90% with the E-nose system proposed, 90% with the EMG system, and 88% success by using ECG, respectively.

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A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity

Minerals ◽

10.3390/min11020159 ◽

2021 ◽

Vol 11 (2) ◽

pp. 159

Author(s):

Nan Lin ◽

Yongliang Chen ◽

Haiqi Liu ◽

Hanlin Liu

Keyword(s):

Machine Learning ◽

Swarm Intelligence ◽

Optimization Algorithm ◽

Geochemical Data ◽

Support Vector ◽

Learning Models ◽

Hyperparameter Optimization ◽

Mineral Prospectivity ◽

Swarm Intelligence Optimization ◽

Machine Learning Models

Selecting internal hyperparameters, which can be set by the automatic search algorithm, is important to improve the generalization performance of machine learning models. In this study, the geological, remote sensing and geochemical data of the Lalingzaohuo area in Qinghai province were researched. A multi-source metallogenic information spatial data set was constructed by calculating the Youden index for selecting potential evidence layers. The model for mapping mineral prospectivity of the study area was established by combining two swarm intelligence optimization algorithms, namely the bat algorithm (BA) and the firefly algorithm (FA), with different machine learning models. The receiver operating characteristic (ROC) and prediction-area (P-A) curves were used for performance evaluation and showed that the two algorithms had an obvious optimization effect. The BA and FA differentiated in improving multilayer perceptron (MLP), AdaBoost and one-class support vector machine (OCSVM) models; thus, there was no optimization algorithm that was consistently superior to the other. However, the accuracy of the machine learning models was significantly enhanced after optimizing the hyperparameters. The area under curve (AUC) values of the ROC curve of the optimized machine learning models were all higher than 0.8, indicating that the hyperparameter optimization calculation was effective. In terms of individual model improvement, the accuracy of the FA-AdaBoost model was improved the most significantly, with the AUC value increasing from 0.8173 to 0.9597 and the prediction/area (P/A) value increasing from 3.156 to 10.765, where the mineral targets predicted by the model occupied 8.63% of the study area and contained 92.86% of the known mineral deposits. The targets predicted by the improved machine learning models are consistent with the metallogenic geological characteristics, indicating that the swarm intelligence optimization algorithm combined with the machine learning model is an efficient method for mineral prospectivity mapping.

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