This paper describes a new approach on optimization of constraint satisfaction problems (CSPs) by means of substituting sub-CSPs with locally consistent regular membership constraints. The purpose of this approach is ...
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The principles of solving the multicriteria problem of retail marketing for the rational choice of the location of trade enterprises (alternatives) based on the classical method of analysis of Saaty hierarchies, using...
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作者：
Mucahit SoyluResul DasInonu University
Department of Organized Industrial Zone Vocational School Computer Programming Malatya Turkiye Firat University
Faculty of Technology Department of Software Engineering 23119 Elazig Turkiye
This study proposes a hybrid approach for visualizing cyberattacks by combining the deep learning-based GAT model with JavaScript-based graph visualization tools. The model processes large, heterogeneous data from the...
This study proposes a hybrid approach for visualizing cyberattacks by combining the deep learning-based GAT model with JavaScript-based graph visualization tools. The model processes large, heterogeneous data from the UNSW-NB15 dataset to generate dynamic and meaningful graphs. In the data cleaning phase, missing and erroneous data were removed, unnecessary columns were discarded, and the data was transformed into a format suitable for modeling. Then, the data was converted into homogeneous graphs, and heterogeneous structures were created for analysis using the GAT model. GAT prioritizes relationships between nodes in the graph with an attention mechanism, effectively detecting attack patterns. The analyzed data was then converted into interactive graphs using tools like SigmaJS, with attacks between the same nodes grouped to reduce graph complexity. Users can explore these dynamic graphs in detail, examine attack types, and track events over time. This approach significantly benefits cybersecurity professionals, allowing them to better understand, track, and develop defense strategies against cyberattacks.
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 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.
The bipolar fuzzy set and interval-valued bipolar fuzzy set efficiently analyse real-world problems where for each input of an object, there has counter information. This study's main objective is to lay a foundat...
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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.
Microbial biofilm build-up in water distribution systems can pose a risk to human health and pipe material integrity. The impact is more devastating in space stations and to astronauts due to the isolation from necess...
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This study investigated the effects of maltodextrin-based nanoemulsions as fat substitutes in cookies, focusing on the oxidative stability and physical properties. Full-fat cookies (control, C) and 50% fat-reduced coo...
This study investigated the effects of maltodextrin-based nanoemulsions as fat substitutes in cookies, focusing on the oxidative stability and physical properties. Full-fat cookies (control, C) and 50% fat-reduced cookies with nanoemulsions (FC) were produced. The addition of nanoemulsions increased the cookie diameter from 46.3 mm (control) to 56.1 mm and reduced the thickness, resulting in a desirable texture. Initial hardness values (30.3 and 45.8 N) were lower in nanoemulsion samples and remained reduced over a 90 day storage period. Black cumin oil-loaded nanoemulsions provided the lowest peroxide values (1.7, 2.7, and 2.4 mequiv O2/kg), maintaining oxidative stability during storage. Final free fatty acid (FFA) values ranged from 0.23% to 0.44% after storage. Thiobarbituric acid (TBA) values indicated slower lipid oxidation, with values ranging from 1.47 to 2.51 mg MDA/kg on day 0 and increasing to a maximum of 4.13 mg MDA/kg by day 90 in fat-reduced cookies. Among the tested formulations, nanoemulsions enriched with black cumin oil demonstrated the highest effectiveness, yielding enhanced oxidative stability and improved quality characteristics. This study presents an innovative strategy by utilizing maltodextrin-based nanoemulsions containing naturally antioxidant-rich oils as fat replacers, offering a clean-label alternative to improve the oxidative resilience and physical quality of cookies.
This article presents a comprehensive methodology for enhancing the capabilities of conversational AI systems, focusing on ChatGPT, through the integration of ontology-driven structured prompts and meta-learning techn...
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