Identification of phantom movements with an ensemble learning approach

Phantom limb pain after amputation is a debilitating condition that negatively affects activities of daily life and the quality of life of amputees. Most amputees are able to control the movement of the missing limb, which is called the phantom limb movement. Recognition of these movements is crucial for both technology-based amputee rehabilitation and prosthetic control. The aim of the current study is to classify and recognize the phantom movements in four different amputation levels of the upper and lower extremities. In the current study, we utilized ensemble learning algorithms for the recognition and classification of phantom movements of the different amputation levels of the upper and lower extremity. In this context, sEMG signals obtained from 38 amputees and 25 healthy individuals were collected and the dataset was created. Studies of processing sEMG signals in amputees are rather limited, and studies are generally on the classification of upper extremity and hand movements. Our study demonstrated that the ensemble learning-based models resulted in higher accuracy in the detection of phantom movements. The ensemble learning-based approaches outperformed the SVM, Decision tree, and kNN methods. The accuracy of the movement pattern recognition in healthy people was up to 96.33%, this was at most 79.16% in amputees. 2022 The Author(s)This study is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under grant no. EEEAG-117E579. The data that support the findings of this study are available on request from the principle investigator of the project EEEAG-117E579, Akhan Akbulut, PhD. The data are not publicly available due to the confidential information that could compromise the privacy of research participants. Open Access funding provided by the Qatar National Library.Scopus2-s2.0-8513934593

WDM Network and Multicasting Protocol Strategies

Optical technology gains extensive attention and ever increasing improvement because of the huge amount of network traffic caused by the growing number of internet users and their rising demands. However, with wavelength division multiplexing (WDM), it is easier to take the advantage of optical networks and optical burst switching (OBS) and to construct WDM networks with low delay rates and better data transparency these technologies are the best choices. Furthermore, multicasting in WDM is an urgent solution for bandwidth-intensive applications. In the paper, a new multicasting protocol with OBS is proposed. The protocol depends on a leaf initiated structure. The network is composed of source, ingress switches, intermediate switches, edge switches, and client nodes. The performance of the protocol is examined with Just Enough Time (JET) and Just In Time (JIT) reservation protocols. Also, the paper involves most of the recent advances about WDM multicasting in optical networks. WDM multicasting in optical networks is given as three common subtitles: Broadcast and-select networks, wavelength-routed networks, and OBS networks. Also, in the paper, multicast routing protocols are briefly summarized and optical burst switched WDM networks are investigated with the proposed multicast schemes

WDM Network and Multicasting Protocol Strategies

Optical technology gains extensive attention and ever increasing improvement because of the huge amount of network traffic caused by the growing number of internet users and their rising demands. However, with wavelength division multiplexing (WDM), it is easier to take the advantage of optical networks and optical burst switching (OBS) and to construct WDM networks with low delay rates and better data transparency these technologies are the best choices. Furthermore, multicasting in WDM is an urgent solution for bandwidth-intensive applications. In the paper, a new multicasting protocol with OBS is proposed. The protocol depends on a leaf initiated structure. The network is composed of source, ingress switches, intermediate switches, edge switches, and client nodes. The performance of the protocol is examined with Just Enough Time (JET) and Just In Time (JIT) reservation protocols. Also, the paper involves most of the recent advances about WDM multicasting in optical networks. WDM multicasting in optical networks is given as three common subtitles: Broadcast and-select networks, wavelength-routed networks, and OBS networks. Also, in the paper, multicast routing protocols are briefly summarized and optical burst switched WDM networks are investigated with the proposed multicast schemes

Deep Learning Approaches for Predictive Masquerade Detection

In computer security, masquerade detection is a special type of intrusion detection problem. Effective and early intrusion detection is a crucial factor for computer security. Although considerable work has been focused on masquerade detection for more than a decade, achieving a high level of accuracy and a comparatively low false alarm rate is still a big challenge. In this paper, we present a comprehensive empirical study in the area of anomaly-based masquerade detection using three deep learning models, namely, Deep Neural Networks (DNN), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Convolutional Neural Networks (CNN). In order to surpass previous studies on this subject, we used three UNIX command line-based datasets, with six variant data configurations implemented from them. Furthermore, static and dynamic masquerade detection approaches were utilized in this study. In a static approach, DNN and LSTM-RNN models are used along with a Particle Swarm Optimization-based algorithm for their hyperparameters selection. On the other hand, a CNN model is employed in a dynamic approach. Moreover, twelve well-known evaluation metrics are used to assess model performance in each of the data configurations. Finally, intensive quantitative and ROC curves analyses of results are provided at the end of this paper. The results not only show that deep learning models outperform all traditional machine learning methods in the literature but also prove their ability to enhance masquerade detection on the used datasets significantly

An Efficient Big Data Anonymization Algorithm Based on Chaos and Perturbation Techniques

The topic of big data has attracted increasing interest in recent years. The emergence of big data leads to new difficulties in terms of protection models used for data privacy, which is of necessity for sharing and processing data. Protecting individuals’ sensitive information while maintaining the usability of the data set published is the most important challenge in privacy preserving. In this regard, data anonymization methods are utilized in order to protect data against identity disclosure and linking attacks. In this study, a novel data anonymization algorithm based on chaos and perturbation has been proposed for privacy and utility preserving in big data. The performance of the proposed algorithm is evaluated in terms of Kullback–Leibler divergence, probabilistic anonymity, classification accuracy, F-measure and execution time. The experimental results have shown that the proposed algorithm is efficient and performs better in terms of Kullback–Leibler divergence, classification accuracy and F-measure compared to most of the existing algorithms using the same data set. Resulting from applying chaos to perturb data, such successful algorithm is promising to be used in privacy preserving data mining and data publishing