Evaluating student performance is important for universities and institutions in the current student education landscape because it helps them create models that work better for students. The automation of various fea...
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ISBN:
(数字)9798350366846
ISBN:
(纸本)9798350366853
Evaluating student performance is important for universities and institutions in the current student education landscape because it helps them create models that work better for students. The automation of various features related to fundamental student traits and behaviours that manage massive amounts of data efficiently processes these. To handle student records that included information about students' behaviour and how it related to their academic performance, the companies employed models of classification with mining concepts. Additionally, the quality of result classification can be substantially improved by using learning analytics and Educational Data Mining (EDM). The educational establishments are making an effort to lower the low student performance. To address this issue, numerous methods for assessing student performance have been devised, allowing the relevant faculties to intervene and enhance the final product. Three classes—Low Performance Student, Average Student, and Smart Student—were created using the K-Mean Clustering methodology for classifying student records. Features including grade point, number of deficits, student attendance, medium of education, and board of education are taken into account when classifying the data. In this case, the WEKA tool is also utilized for implementing the model and outcome assessments.
The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition ...
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The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simulta...
The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy.
The covid-19 pandemic and Economic Policy Uncertainty resulting from the shutdown of production, withdrawal of investments, enforcement of lockdowns and quarantines globally, have been directly affecting stock markets...
The covid-19 pandemic and Economic Policy Uncertainty resulting from the shutdown of production, withdrawal of investments, enforcement of lockdowns and quarantines globally, have been directly affecting stock markets worldwide. This study is thus an attempt to analyse the impact of the COVID-19 pandemic on stock market behaviour in major affected economies. Moreover, the time frame was extended by using current data which investigate the impact of the virus during the boom and the blast phase in the country's most hit by the pandemic crisis such as China, Italy, UK and US. The frequency of the data is daily, and it dates from 3 January 2020 up to 10 February 2021. The considered time framework will give a deep insight into how stock markets behave in the case of an exogenous shock. The Dickey-Fuller Augmented Unit Root Test indicates that all the variables are stationary at first difference, which is one of the main conditions to have robust result, and the ARCH-LM test for the heteroskedasticity of the residuals, which show that all the probability values are significant, rejecting the null hypothesis of no ARCH effect. Based on the results of GARCH (1,1), we conclude that the change in stock markets volatility is positive and significant in China, Italy, UK and USA. This suggests that the impacts of COVID-19 outbreak and economic policy uncertainty on Stock Markets are a significant and Homogeneous across the studied countries, and the shock on FTSE Italia All Share has the longest time to vanish which makes it the riskiest to invest during this period, while the shock on SP500 USA has the shortest time to disappear meaning that it is safest Stock Market in this study. These findings have significant implications for policymakers, institutional and individual investors and Financial Markets analysts.
Pedestrian detection from a drone-based images has many potential applications such as searching for missing persons, surveillance of illegal immigrants, and monitoring of critical infrastructure. However, it is consi...
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On large-scale clusters, tens to hundreds of applications can simultaneously access a parallel le system, leading to contention and, in its wake, to degraded application performance. In this article, we analyze the in...
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Sleep disorders have a significant impact on physical health, cognitive ability, as well as quality of life. However, they are still underdiagnosed due to the practical limitations of traditional diagnostic methods, s...
Sleep disorders have a significant impact on physical health, cognitive ability, as well as quality of life. However, they are still underdiagnosed due to the practical limitations of traditional diagnostic methods, such as polysomnography. Polysomnography has been accepted as the gold standard with a good accurate diagnostic value. However, it is laborious, time-consuming and frequently unavailable, particularly in the setting with limited resources. To address these challenges, this study aims to improve sleep disorder classification using machine learning (ML) techniques with metaheuristic optimization strategies. The study utilizes the Sleep Health and Lifestyle Dataset, which includes demographic data, sleep parameters, lifestyle factors, and cardiovascular indicators. To improve model efficiency and predictive accuracy, the binary Al-Biruni Earth Radius (bBER) optimizer is applied for feature selection, effectively reducing data dimensionality by eliminating irrelevant and redundant features. Among the evaluated models, the multilayer perceptron (MLP) achieved the strongest baseline performance, reaching an accuracy of 89.92 % and a sensitivity of 91 % after feature selection. Further optimization through the BER algorithm led to the development of the BER-MLP model, which delivered superior classification results, achieving an accuracy of 95.41 % and a sensitivity of 92.45 %. These findings reinforce the effectiveness of integrating metaheuristic optimization with machine learning approaches to enhance diagnostic accuracy and reliability in sleep disorder detection.
In response to the centralized single-architecture abnormal traffic detection method in Software Defined Network (SDN), which consumes massive computational and network resources, and may lead to the decline of servic...
In response to the centralized single-architecture abnormal traffic detection method in Software Defined Network (SDN), which consumes massive computational and network resources, and may lead to the decline of service quality of SDN network, this paper proposes a large-scale abnormal traffic detection method of SDN network based on Distributed Convolutional Neural Networks and Gate Recurrent Unit (DCNN-GRU) architecture. This method utilizes lightweight detection agents based on CNN deployed on each controller to extract traffic features preliminarily. Then it inputs the feature data into the GRU-based deep detection model hosted in the cloud for collaborative training and completes the final abnormal detection task. Since the feature extraction tasks are distributed across multiple controllers, the cloud server only needs to relearn and classify the extracted feature data, which is less costly than directly extracting feature information from the original traffic data and occupies less bandwidth resources than transmitting complete data packets. The experiment shows that the method achieves an abnormal detection accuracy of 0.9939, a recall rate of 0.9831, and a false alarm rate of only 0.0244, obtaining a higher precision and lower false alarm rate than traditional detection methods.
In this paper we study an elastic-plastic isotropic body weakened by a rectilinear crack, directed along the abscissa axis, under the action of stresses symmetrical about its plane. An approximate (analytical and nume...
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ISBN:
(数字)9781728171807
ISBN:
(纸本)9781728171814
In this paper we study an elastic-plastic isotropic body weakened by a rectilinear crack, directed along the abscissa axis, under the action of stresses symmetrical about its plane. An approximate (analytical and numerical) solution of this problem is constructed under the condition that the hydrostatic deformation of the split expansion is approximated by a parabola. The concentration of hydrogen near the top of the crack is calculated. Note that when using the above calculation formulas, at each node we get several approximations to the exact solution. In the proposed methods, only three appeals to the right-hand side of the differential equation are necessary to obtain both the third order accuracy method and the two approximations (upper and lower) of the second order of accuracy.
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