Search Results - (( rate detection method algorithm ) OR ( classification _ system algorithm ))
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Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition
Published 2021“…This paper proposed the fuzzy-ID3 (FID3) algorithm, a fuzzy decision tree as the classification method in breast cancer detection. …”
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Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System
Published 2020“…Feature selection and classifier parameter tuning are important factors that affect the performance of any intrusion detection system. In this paper, an improved intrusion detection algorithm for multiclass classification was presented and discussed in detail. …”
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Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi
Published 2019“…Moreover, shortage of reliable methods on a new dataset for the intrusion detection system and anomaly detection in terms of classification is an issue. …”
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Hybrid intelligent approach for network intrusion detection
Published 2015“…Hence, there must be substantial improvement in network intrusion detection techniques and systems. Due to the prevailing limitations of finding novel attacks, high false detection, and accuracy in previous intrusion detection approaches, this study has proposed a hybrid intelligent approach for network intrusion detection based on k-means clustering algorithm and support vector machine classification algorithm. …”
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Artificial immune system based on real valued negative selection algorithms for anomaly detection
Published 2015“…Experimental results illustrate that RNSA and V-Detector algorithms are suitable for the detection of anomalies, with SVM and KNN producing significant efficiency rates and increase in execution time. …”
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Features selection process can be considered a problem of global combinatorial optimization in machine learning. Genetic algorithm GA had been adopted to perform features selection method; however, this method could not deliver an acceptable detection rate, lower accuracy, and higher false alarm rates. …”
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An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons
Published 2020“…This study proposes a new binary classification model for intrusion detection, based on hybridization of Artificial Bee Colony algorithm (ABC) and Dragonfly algorithm (DA) for training an artificial neural network (ANN) in order to increase the classification accuracy rate for malicious and non-malicious traffic in networks. …”
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An efficient anomaly intrusion detection method with evolutionary neural network
Published 2020“…The third proposed method is a new Evolutionary Neural Network (ENN) algorithm with a combination of Genetic Algorithm and Multiverse Optimizer (GAMVO) as a training part of ANN to create efficient anomaly-based detection with low false alarm rate. …”
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9
Analysis of artificial neural network and viola-jones algorithm based moving object detection
Published 2014“…Analysis of moving object detection methods is presented in this paper, includes Artificial Neural Network (ANN) and Viola-Jones algorithm. …”
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Proceeding Paper -
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Quranic diacritic and character segmentation and recognition using flood fill and k-nearest neighbors algorithm
Published 2019“…The diacritic detections are performed using a region-based algorithm with 89% accuracy and 95% improved by using flood fill segmentations method. 2DMED feature extraction accuracy was 90% for diacritics and 96% improved by applied CNN. …”
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Cloud Worm Detection and Response Technique By Integrating The Enhanced Genetic Algorithm An Threat Level
Published 2024thesis::doctoral thesis -
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Non-invasive pathological voice classifications using linear and non-linear classifiers
Published 2010“…For the MEEI voice disorders database, the success rate of the classifiers is above 98% for the classification of normal and pathological voices and for the detection of specific disorders the best classification accuracy of 100% is achieved. …”
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A hybrid method using haar-like and skin-color algorithm for hand posture detection, recognition and tracking
Published 2010“…The system combines two algorithms to achieve better detection rate for hand. …”
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Proceeding Paper -
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Anomaly-based intrusion detection through K-means clustering and naives Bayes classification
Published 2013“…Anomaly-based intrusion detection methods, which employ machine learning algorithms, are able to identify unforeseen attacks. …”
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Anomaly-based intrusion detection through K-Means clustering and Naives Bayes classification
Published 2013“…Intrusion detection systems (IDSs) effectively balance extra security appliance by identifying intrusive activities on a computer system, and their enhancement is emerging at an unexpected rate.Anomaly-based intrusion detection methods, which employ machine learning algorithms, are able to identify unforeseen attacks. …”
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A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
Published 2016“…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. …”
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A new intelligent multilayer framework for insider threat detection
Published 2021“…The selection procedure has been developed based on the integration of the entropy-VIKOR methods. For the second layer, a hybrid insider threat detection method has been proposed, where the Misuse Insider Threat Detection (MITD) model has been created using the random forest algorithm. …”
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Compressing and improving fuzzy rules using genetic algorithm and its application to fault detection
Published 2023Subjects:Conference Paper -
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Multi-sensor fusion based on multiple classifier systems for human activity identification
Published 2019“…To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. …”
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