The education world has moved from analogue mediums to digital. This offers teachers the opportunity to take advantage of tools and features that can decrease the load. By using new technologies, we increase the avail...
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Intrusion detection system (IDS) can identify abnormal network traffic and attacks, which is an important means of network security defense. However, some intrusion data are often disguised as normal data for transmis...
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Intrusion detection system (IDS) can identify abnormal network traffic and attacks, which is an important means of network security defense. However, some intrusion data are often disguised as normal data for transmission, which increases the difficulty of intrusion data classification. In addition, the existing packet-based or flow-based data feature extraction methods result in low feature dimensions, causing the problem of class overlapping between different categories with the same features. To clarify, overlapping samples are those that overlap between erroneous samples and correct samples. Nonoverlapping samples are those in the test set that do not match the characteristics of the already identified overlapping samples and are therefore considered nonoverlapping samples. Therefore, the detection effect of some attacks with high concealment is poor. In order to solve the above problems, this paper proposes a multistage intrusion detection method: an existing intrusion detection model with higher classification performance (OBLR) is used to predict the data in the first stage. In the second stage, for the overlapping data in the confusing data, the method learns the distribution of each feature group according to the randomly divided "intermediary set," and realizes the prediction of overlapping samples through the prior distribution knowledge, and achieves efficient classification of overlapping samples without increasing the computational burden of the model. For nonoverlapping data in the confusing data, KPCA (kernel principal component analysis) dimension elevation is used in the third stage to capture more detailed difference information between samples, and GMM (Gaussian mixed model) is combined with the "representative samples" proposed in this paper to assist classifier classification. At the same time, all the base classifiers are integrated through LTR (learning to rank) to improve the classification effect of the model for nonoverlapping data in the
This paper discusses various applications of fractals in neurosciences and presents a methodology for their investigation and modeling with appropriate software. It is presented how to use multifractal analysis for ch...
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Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution *** desulphurization control system,however,is subjected to complex reaction mechanisms and s...
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Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution *** desulphurization control system,however,is subjected to complex reaction mechanisms and severe disturbances,which make for it difficult to achieve certain practically relevant control goals including emission and economic performances as well as system *** address these challenges,a new robust control scheme based on uncertainty and disturbance estimator(UDE)and model predictive control(MPC)is proposed in this *** UDE is used to estimate and dynamically compensate acting disturbances,whereas MPC is deployed for optimal feedback regulation of the resultant *** viewing the system nonlinearities and unknown dynamics as disturbances,the proposed control framework allows to locally treat the considered nonlinear plant as a linear *** obtained simulation results confirm that the utilization of UDE makes the tracking error negligibly small,even in the presence of unmodeled *** the conducted comparison study,the introduced control scheme outperforms both the standard MPC and PID(proportional-integral-derivative)control strategies in terms of transient performance and ***,the results reveal that a lowpass-filter time constant has a significant effect on the robustness and the convergence range of the tracking error.
The present paper introduces a mathematical model for the cross-talking between microRNA and Protein. Studying the qualitative properties of the proposed model, we infer that the microRNA is an inhibitor for the Prote...
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Pneumonia is one of the top causes of death in Romania and early detection of this disease improves the recovery chances and shortens the length of hospitalization. In this work, we develop a solution for automatic pn...
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The proliferation of Wireless Sensor Networks (WSN) in various applications has necessitated the exploration of network architectures that can ensure efficient, scalable, and reliable communication. This study present...
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As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around...
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As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSAEnsemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. Authors
In this paper, we introduce new discourse quality metrics and an evaluation method, and furthermore, we provide a pilot implementation and evaluate it in a specific use case – that of public speaking. Voice analysis ...
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