Detection of Web site visitors based on fuzzy rough sets

Hamidzadeh, Javad; Zabihimayvan, Mahdieh

doi:10.1007/s00500-016-2476-4

Cited by 34 publications

(20 citation statements)

References 36 publications

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“…The study also can be extended to develop a framework using sensors, laboratory data, and information cached from intensivists and nurses’ reports using knowledge graph [38] and text mining [39]. Another direction is to explore the effect of computing features from vital signals with different length of windows and using dynamic feature selections [40] [41]. Finally, we plan on creating a real-time mortality prediction system based on the variability of physiological signals [42] that can predict patient outcomes for early intervention.…”

Section: Discussionmentioning

confidence: 99%

Early hospital mortality prediction using vital signals

2018

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Early hospital mortality prediction is critical as intensivists strive to make efficient medical decisions about the severely ill patients staying in intensive care units (ICUs). As a result, various methods have been developed to address this problem based on clinical records. However, some of the laboratory test results are time-consuming and need to be processed. In this paper, we propose a novel method to predict mortality using features extracted from the heart signals of patients within the first hour of ICU admission. In order to predict the risk, quantitative features have been computed based on the heart rate signals of ICU patients suffering cardiovascular diseases. Each signal is described in terms of 12 statistical and signal-based features. The extracted features are fed into eight classifiers: decision tree, linear discriminant, logistic regression, support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and K-nearest neighborhood (K-NN). To derive insight into the performance of the proposed method, several experiments have been conducted using the well-known clinical dataset named Medical Information Mart for Intensive Care III (MIMIC-III). The experimental results demonstrate the capability of the proposed method in terms of precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The decision tree classifier satisfies both accuracy and interpretability better than the other classifiers, producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It indicates that heart rate signals can be used for predicting mortality in patients in the care units especially coronary care units (CCUs), achieving a comparable performance with existing predictions that rely on high dimensional features from clinical records which need to be processed and may contain missing information.

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Section: Discussionmentioning

confidence: 99%

Early hospital mortality prediction using vital signals

2018

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“…It indicates that although the universal feature set has lower number of features, its F-measure value is comparable with the result of the larger set of features selected by FRS. Since no domain-based feature belongs to the universal feature set, this finding can not only reduce the detection time and make it robust towards zero-day attacks, it can solve the curse of dimensionality for classification techniques [26] [27].…”

Section: Methodsmentioning

confidence: 99%

Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection

Zabihimayvan

Doran

2019

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Phishing as one of the most well-known cybercrime activities is a deception of online users to steal their personal or confidential information by impersonating a legitimate website. Several machine learning-based strategies have been proposed to detect phishing websites. These techniques are dependent on the features extracted from the website samples. However, few studies have actually considered efficient feature selection for detecting phishing attacks. In this work, we investigate an agreement on the definitive features which should be used in phishing detection. We apply Fuzzy Rough Set (FRS) theory as a tool to select most effective features from three benchmarked data sets. The selected features are fed into three often used classifiers for phishing detection. To evaluate the FRS feature selection in developing a generalizable phishing detection, the classifiers are trained by a separate out-of-sample data set of 14,000 website samples. The maximum F-measure gained by FRS feature selection is 95% using Random Forest classification. Also, there are 9 universal features selected by FRS over all the three data sets. The F-measure value using this universal feature set is approximately 93% which is a comparable result in contrast to the FRS performance. Since the universal feature set contains no features from third-part services, this finding implies that with no inquiry from external sources, we can gain a faster phishing detection which is also robust toward zero-day attacks.

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“…Therefore, the selected decision attribute can avoid subjectivity. This is advantageous from the viewpoint of data pre-treatment, uncertainty description, knowledge rule acquisition, and for the objective handling of data [ 30 , 31 ].…”

Section: Key Fault Diagnosis Technologiesmentioning

confidence: 99%

Fault Diagnosis Method for a Mine Hoist in the Internet of Things Environment

Xie

Zhao-jian

et al. 2018

Sensors

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To reduce the difficulty of acquiring and transmitting data in mining hoist fault diagnosis systems and to mitigate the low efficiency and unreasonable reasoning process problems, a fault diagnosis method for mine hoisting equipment based on the Internet of Things (IoT) is proposed in this study. The IoT requires three basic architectural layers: a perception layer, network layer, and application layer. In the perception layer, we designed a collaborative acquisition system based on the ZigBee short distance wireless communication technology for key components of the mine hoisting equipment. Real-time data acquisition was achieved, and a network layer was created by using long-distance wireless General Packet Radio Service (GPRS) transmission. The transmission and reception platforms for remote data transmission were able to transmit data in real time. A fault diagnosis reasoning method is proposed based on the improved Dezert-Smarandache Theory (DSmT) evidence theory, and fault diagnosis reasoning is performed. Based on interactive technology, a humanized and visualized fault diagnosis platform is created in the application layer. The method is then verified. A fault diagnosis test of the mine hoisting mechanism shows that the proposed diagnosis method obtains complete diagnostic data, and the diagnosis results have high accuracy and reliability.

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Detection of Web site visitors based on fuzzy rough sets

Cited by 34 publications

References 36 publications

Early hospital mortality prediction using vital signals

Early hospital mortality prediction using vital signals

Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection

Fault Diagnosis Method for a Mine Hoist in the Internet of Things Environment

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