Tagging of jets and partons in e+e− events by discriminant analysis

Mjaed, M.; Proriol, J.

doi:10.1016/0370-2693(89)90096-8

Cited by 12 publications

(6 citation statements)

References 12 publications

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“…In LDA, the discriminant function (DF) h 1 , in the simple 2-class case (C i and C j ), is computed, using the training set, as given in Eq. ( 5) [42,49].…”

Section: Features Selectionmentioning

confidence: 99%

New Denial of Service Attacks Detection Approach Using Hybridized Deep Neural Networks and Balanced Datasets

Mjahed¹,

Hadaj²,

Guarmah³

et al. 2023

Computer Systems Science and Engineering

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Denial of Service (DoS/DDoS) intrusions are damaging cyberattacks, and their identification is of great interest to the Intrusion Detection System (IDS). Existing IDS are mainly based on Machine Learning (ML) methods including Deep Neural Networks (DNN), but which are rarely hybridized with other techniques. The intrusion data used are generally imbalanced and contain multiple features. Thus, the proposed approach aims to use a DNN-based method to detect DoS/DDoS attacks using CICIDS2017, CSE-CICIDS2018 and CICDDoS 2019 datasets, according to the following key points. a) Three imbalanced CICIDS2017-2018-2019 datasets, including Benign and DoS/DDoS attack classes, are used. b) A new technique based on K-means is developed to obtain semi-balanced datasets. c) As a feature selection method, LDA (Linear Discriminant Analysis) performance measure is chosen. d) Four metaheuristic algorithms, counting Artificial Immune System (AIS), Firefly Algorithm (FA), Invasive Weeds Optimization (IWO) and Cuckoo Search (CS) are used, for the first time together, to increase the performance of the suggested DNN-based DoS attacks detection. The experimental results, based on semi-balanced training and test datasets, indicated that AIS, FA, IWO and CS-based DNNs can achieve promising results, even when cross-validated. AIS-DNN yields a tested accuracy of 99.97%, 99.98% and 99.99%, for the three considered datasets, respectively, outperforming performance established in several related works.

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“…In LDA, the discriminant function (DF) h 1 , in the simple 2-class case (C i and C j ), is computed, using the training set, as given in Eq. ( 5) [42,49].…”

Section: Features Selectionmentioning

confidence: 99%

New Denial of Service Attacks Detection Approach Using Hybridized Deep Neural Networks and Balanced Datasets

Mjahed¹,

Hadaj²,

Guarmah³

et al. 2023

Computer Systems Science and Engineering

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“…The fundamental neural data processing is carried out in hidden and output layers. The classical neuron model is described by (10).…”

Section: Back-propagation Neural Networkmentioning

confidence: 99%

“…Statistical techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) were the first to be used in this field [9][10][11]. The Random Forest Algorithms are based on the decision trees classifier; it has been considered significantly in development of fault machine diagnostics methods through a powerful method for classification and predictions [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Aircraft Air Compressor Bearing Diagnosis Using Discriminant Analysis and Cooperative Genetic Algorithm and Neural Network Approaches

Ouadine

Mjahed

Ayad³

et al. 2018

Applied Sciences

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Monitoring and diagnosis of rotating machines has become an effective and indispensable tool for the efficient and timely detection of defects, avoiding then incidents that can have serious economic and human consequences. Bearings are the most sensitive parts of these machines; this is why special attention must be paid to its monitoring. This paper presents a methodology for diagnosing an aircraft air compressor bearing using neural networks that have been optimized by genetic algorithms. We used in our study a database of vibratory signals that were recorded on a test bench from bearings with different defects. The faults features are extracted from these noisy signals using the estimate of the spectral density. The diagnostic capacity of obtained model has been demonstrated by a comparative study with two other automatic classifiers, which are discriminant analysis and neural networks whose training has been done with the Back-Propagation algorithm. This approach has the advantage of simultaneously ensuring the optimal structure of the neural network and accomplishing its learning. The importance of this study is the construction of a diagnostic tool that is characterized by efficiency, speed of decision making and ease of implementation not only on the computers on the ground, but also on the mounted calculators on aircraft.

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“…Several paths have been explored by researchers in the field of fault monitoring. First the statistical methods have been used (Mjahed & Proriol, 1989). Then, with the improved power of calculators, artificial intelligence techniques such as neural networks, genetic algorithms, fuzzy logic and other techniques have given more satisfaction (Chen et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

UAV Quadrotor Fault Detection and Isolation Using Artificial Neural Network and Hammerstein-Wiener Model

Ouadine¹,

Mjahed²,

Ayad³

et al. 2020

STUD INFORM CONTROL

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In this paper, a sensor fault diagnosis system is proposed for an aerial vehicle (UAV) quadrotor. Given the nonlinearity of this system and the inaccuracies of modeling, adapted tools have been adopted to ensure control and diagnosis. After synthesizing control laws required for quadrotor control using the Sliding Mode Control method, a Hammerstein-Wiener model has been developed. The goal is to estimate the states of the quadrotor system and build a set of residuals to detect sensor faults. The advantage of this solution is that it does not require prior knowledge of the model and can be easily generalized to other types of vehicles. Then, to ensure decision making for fault isolation, the neural network has been combined with the diagnostic system. With judicious choice of configuration, it can efficiently classify defects from residuals. Finally, typical sensor failures have been injected during simulations. The results of the diagnosis have been very satisfactory. The model has been validated by the test data.

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Tagging of jets and partons in e+e− events by discriminant analysis

Cited by 12 publications

References 12 publications

New Denial of Service Attacks Detection Approach Using Hybridized Deep Neural Networks and Balanced Datasets

New Denial of Service Attacks Detection Approach Using Hybridized Deep Neural Networks and Balanced Datasets

Aircraft Air Compressor Bearing Diagnosis Using Discriminant Analysis and Cooperative Genetic Algorithm and Neural Network Approaches

UAV Quadrotor Fault Detection and Isolation Using Artificial Neural Network and Hammerstein-Wiener Model

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