The work carried out in this paper consists of the classification of the physiological signal generated by eye movement called Electrooculography (EOG). The human eye performs simultaneous movements, when focusing on ...
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The work carried out in this paper consists of the classification of the physiological signal generated by eye movement called Electrooculography (EOG). The human eye performs simultaneous movements, when focusing on an object, generating a potential change in origin between the retinal epithelium and the cornea and modeling the eyeball as a dipole with a positive and negative hemisphere. Supervised learning algorithms were implemented to classify five eye movements;left, right, down, up and blink. Wavelet Transform was used to obtain information in the frequency domain characterizing the EOG signal with a bandwidth of 0.5 to 50 Hz;training results were obtained with the implementation of K-Nearest Neighbor (KNN) 69.4%, a Support Vector Machine (SVM) of 76.9% and Decision Tree (DT) 60.5%, checking the accuracy through the Jaccard index and other metrics such as the confusion matrix and ROC (Receiver Operating Characteristic) curve. As a result, the best classifier for this application was the SVM with Jaccard Index.
Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vul...
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Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users' security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF cl
In our previous research work, we proposed a methodology that uses magnetic-field and multivariate methods to estimate user location in an indoor environment. In this paper, we propose the use of this methodology to e...
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In our previous research work, we proposed a methodology that uses magnetic-field and multivariate methods to estimate user location in an indoor environment. In this paper, we propose the use of this methodology to evaluate the performance of four different classification algorithms: Random Forest, Nearest Centroid, K Nearest Neighbors and Artificial Neural Networks; each classifier will be considered as a cost function of a genetic algorithm (GA) used in the feature selection process task of the methodology. The motivation to evaluate the algorithms of classification was that several ILSs use a classification algorithm in order to estimate the location of the user, but the classifiers performance vary from application to application. In order to evaluate the performance of each classification algorithm, the following issues were considered: (1) the time of the training phase to obtain the final classification algorithm; (2) the number of features needed for getting the model; (3) the type of the features from the final model; and (4) the sensitivity and specificity of the model. Our results indicate that Nearest centroid is the classfier algorithm that is best suited to be implemented in an end-user application given the obtained results on the evaluated criteria for the indoor location system (ILS).
Tweets classification became interest topics in recent years, especially for the Arabic language. In this paper, the Arabic tweets are classified automatically into one of some predetermined categories mainly: sport, ...
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Tweets classification became interest topics in recent years, especially for the Arabic language. In this paper, the Arabic tweets are classified automatically into one of some predetermined categories mainly: sport, culture, politics, technology and general, based on their linguistic characteristics and their contents, also the classification accuracy is improved for Arabic tweets, by using ensemble methods mainly: bagging, boosting and stacking on the same dataset that we used it before in the classification, to verify of the results, and identify the best classifier gives high accuracy. The experimental results showed that using ensemble methods are better than using individual classifier, to improve the accuracy of classification. Increased accuracy of classifier Naïve Bayes (NB) to 1.6%, classifier Sequential Minimal Optimization (SMO) to 2.2% and finally Decision Tree (J48) classifier reached up to 3.2%, comparing to using the J48, NB, or SMO as a single classifier.
Vehicles become an inevitable factor in everyone's life. Sometimes it becomes a threat to human lives and society. For any real-time-based applications, everyone should focus on predicting failure-prone components...
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Vehicles become an inevitable factor in everyone's life. Sometimes it becomes a threat to human lives and society. For any real-time-based applications, everyone should focus on predicting failure-prone components. A vehicle's air pressure system (APS) is one of its most important parts. If any system failure happens against APS it leads to core-financial losses, which in turn sometimes leads to loss of human lives. Prediction of APS negligence in a real-time application requires a deep diagnosis and diligent solution. In this study, we developed a machine learning model to predict system failure against APS. A real-time dataset that includes the 170 features and the presence of high-class imbalance data and missing values has been taken and experimentally validated with existing linear and nonlinear classifiers. The performance metrics results show that the Random Forest classifier exceeds other algorithms for training and testing data with an accuracy and F1 score of 99.5 and 99.5 percent respectively.
The presence of internal voids in watermelons has an impact on the costs of producers and on consumer confidence. Various studies have shown that the vibrational parameters of the fruit are related to maturity, qualit...
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The presence of internal voids in watermelons has an impact on the costs of producers and on consumer confidence. Various studies have shown that the vibrational parameters of the fruit are related to maturity, quality and the existence of internal defects. A method for the detection of internal voids in seedless watermelons based on vibrational parameters obtained in impact hammer tests and machine learning is presented. After a statistical study of the test results, the frequency of the first peak of the vibrational response and the density of the watermelon are selected as predictors to be used in the classification algorithms. The accuracy of detecting hollow watermelons increases if firmness estimator is introduced as a predictor. Probabilities of success above 89% in the detection of internal voids have been achieved using different classification algorithm.
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