This paper presents a new method of eye state recognition. Firstly, it uses NTU as the input eigenvalue, which is picked up from texture character of eye images. RBF neural network is used as classifier. In order to i...
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This paper presents a new method of eye state recognition. Firstly, it uses NTU as the input eigenvalue, which is picked up from texture character of eye images. RBF neural network is used as classifier. In order to improve the precision of the RBF neural network models, bagging algorithm is used to build an integration neural network model for eye state recognition. Some experiments to make sure that the method works effective are performed.
This paper proposes an automated visual classification framework in which a novel analysis method (LSTMS-B) of EEG signals guides the selection of multiple networks that leads to the improvement of classification perf...
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This paper proposes an automated visual classification framework in which a novel analysis method (LSTMS-B) of EEG signals guides the selection of multiple networks that leads to the improvement of classification performance. The method, called LSTMS-B, combines deep learning and ensemble learning to extract the category-dependent representations of EEG signals. Specifically, it introduces Swish activation function into traditional LSTM which reduces the effect of vanishing gradient and optimize the training process. Besides, the bagging theory is applied to increase the generalization. The LSTMS-B method reaches the average precision of 97.13% for learning EEG visual presentations, which greatly outperforms traditional LSTM network and other contrast models. Then, to verify its application value, a ResNet-based regression is trained using original images and relevant EEG representations learned before. We use the output of the regression as the features to classify the images, and finally obtain the average classification accuracy of 90.16%. (C) 2019 Elsevier Ltd. All rights reserved.
The automatic classification of power quality disturbances (PQD) is of great significance for solving power quality problems. In this study, we propose an ensemble deep learning framework to realize intelligent classi...
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The automatic classification of power quality disturbances (PQD) is of great significance for solving power quality problems. In this study, we propose an ensemble deep learning framework to realize intelligent classi-fication of PQ disturbances. Specifically, based on the characteristics of the sequence of disturbance signals, the Long Short Term Memory (LSTM) network is used to classify the signals. In addition, the bagging theory is used to integrate the training results of multiple LSTM networks to improve the generalization of the network. Our contribution lies in the combination of deep learning and ensemble learning to extract the classification repre-sentation of PQD signals. In view of the large number of unlabeled power quality disturbance samples in the power grid, the active learning strategy is adopted to select the most representative samples from the data set, which can enhance the model performance with less labeled data. Finally, experiments were conducted in different noise environments. Compared with the existing multi-label learning models, this method achieves better classification performance with good calculation speed. Furthermore, the proposed active learning strategy is able to train the classification model with fewer labeled samples, reducing the manual labeling costs.
Sensitive data including identity information, passwords, financial and business processes belonging to the user are kept in the databases. These data can be obtained by attackers with malicious code added to SQL quer...
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Sensitive data including identity information, passwords, financial and business processes belonging to the user are kept in the databases. These data can be obtained by attackers with malicious code added to SQL queries. The malicious and clean SQL queries are taken from OWASP dataset to ensure that the proposed approach effective and practical. The middleware application which is developed in this study analyzes these SQL queries instantly to prevent attackers from accessing sensitive data in databases. In order to provide protection, an ensemble classification algorithm is trained with 22 features which are obtained from queries containing malicious codes. The trained ensemble algorithm classifies queries as clean and malicious. For the first time in this study, malicious SQL injections are detected as simple, unified or lateral to determine the level of the cyber-attack. If the query is clean, the request is provided in the flow forwarding scheme, otherwise the query is blocked. If SQL injection is detected as simple, the SQL request is blocked. In other cases source IP address is blocked at different time intervals. The accuracy of the model maintains over 98% to detect SQL injections, and 92% to classify as simple, unified or lateral these attacks. This result demonstrates that the developed middleware application has an active role against simple, unified and lateral SQL Injection attacks which are so hard to detect and provides flexible decisions against the attacks.
Industry 4.0 integrates cyber systems, physical devices, and digital networks to automate the industrial process. Many sectors aim to adopt the best practices outlined in Industry 4.0. This indicates well for the futu...
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Industry 4.0 integrates cyber systems, physical devices, and digital networks to automate the industrial process. Many sectors aim to adopt the best practices outlined in Industry 4.0. This indicates well for the future networking of an increasing number of devices. As crucial as intelligent automation is, it is essential that it be protected. The proliferation of Internet-enabled gadgets could raise vulnerability to a variety of threats, malware among them. Intruders see a synthesis of factors as a chance to carry out their malicious plan. Keeping sensitive data and information protected from malicious software is a high responsibility for all industries. It is critical to have both a trustworthy approach and a large dataset to work with when constructing a malware traffic classifier. Malware's capacity to elude detection by antivirus programs improves with the day. Because this malware has the potential to compromise the entire network, establishing a malware traffic classifier requires a strong approach. As the number of data increases, the classifier has a harder time distinguishing between benign and malicious network entries. As a result, weighing too many factors is a time-consuming process. To assist with these types of real-world challenges, we construct an effective hybrid selection component, which is subsequently followed by a neural network classifier in this research. The Malware traffic classifier provided here selects the principal feature using filter and wrapper techniques. The feature columns provided by the feature selection program are used to construct a neural network-based binary malware classifier. The given malware traffic classification framework was tested using the MTA-KDD'19 dataset. We set up an experiment in this investigation to examine the way different feature counts perform using a neural-based classifier. The suggested framework achieves 96.8% accuracy while just considering the bare minimum of five features, which is a substanti
Raman spectroscopy has gained popularity in analyzing blood glucose levels due to its non-invasive identification and minimal interference from water. However, the challenge lies in how to accurately predict blood glu...
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Raman spectroscopy has gained popularity in analyzing blood glucose levels due to its non-invasive identification and minimal interference from water. However, the challenge lies in how to accurately predict blood glucose concentrations in human blood using Raman spectroscopy. This paper researches a novel integrated machine learning algorithm called bagging-ABC-ELM. The optimal input weights and biases of extreme learning machine (ELM) model are obtained by artificial bee colony (ABC) algorithm. The bagging algorithm is used to obtain a better the stability of the model and higher performance than ELM algorithm. The results show that the mean value of coefficient of determination is 0.9928, and root mean square error is 0.1928. Compared to other regression models, the bagging-ABC-ELM model exhibited superior prediction accuracy, robustness, and generalization capability. The bagging-ABC-ELM model presents a promising alternative for analyzing blood glucose levels in clinical and research settings.
A neural network (NN) ensemble is a very successful technique where the outputs of a set of separately trained NNs are combined to form one unified prediction. An effective ensemble should consist of a set of networks...
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A neural network (NN) ensemble is a very successful technique where the outputs of a set of separately trained NNs are combined to form one unified prediction. An effective ensemble should consist of a set of networks that are not only highly correct, but ones that make their errors on different parts of the input space as well;however, most existing techniques only indirectly address the problem of creating such a set. We present an algorithm called ADDEMUP that uses genetic algorithms to search explicitly for a highly diverse set of accurate trained networks. ADDEMUP works by first creating an initial population, then uses genetic operators to create new networks continually, keeping the set of networks that are highly accurate while disagreeing with each other as much as possible. Experiments on four real-world domains show that ADDEMUP is able to generate a set of trained networks that is more accurate than several existing ensemble approaches. Experiments also show ADDEMUP is able to incorporate prior knowledge effectively, if available, to improve the quality of its ensemble.
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