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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Danylo Parkhomenko Oleksandr Rysovanyi Valery Burtsev Viktor Voronin Volodymyr Startsev Volodymyr Panchenko Doctoral student of the scientific and organizational department of Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Computer Engineering and Programming Department National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine Department of the faculty of anti-aircraft missile forces of Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine

the application of a distributed intelligent control system for a group of unmanned aerial vehicles is substantiated, a method for coordinating their interaction to maximize the target indicator is proposed and substa...

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Hybrid multi-criteria decision-making method with a bipolar fuzzy approach and its applications to economic condition analysis

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Engineering Applications of Artificial Intelligence 2024年 132卷

作者： Jana, Chiranjibe Simic, Vladimir Pal, Madhumangal Sarkar, Biswajit Pamucar, Dragan Tamil Nadu Chennai602105 India Faculty of Transport and Traffic Engineering University of Belgrade VojvodeStepe 305 Belgrade11010 Serbia Yuan Ze University College of Engineering Department of Industrial Engineering and Management Taoyuan City320315 Taiwan Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Midnapore721102 India Department of Industrial Engineering Yonsei University 50 Yonsei-ro Sinchon-dong Seodaemun-gu Seoul03722 Korea Republic of Center for Global Health Research Saveetha Medical College Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu 600077 India Department of Industrial Engineering and Management National Taipei University of Technology Taipei City 106344 Taiwan Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Computer Science and Mathematics Lebanese American University Byblos 1102 2801 Lebanon

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 foundation for considering a new approach to the bipolar fuzzy hybrid operators tool applied in a realistic frame that contains uncertainties. In this view, some mixed aggregation operators, namely, the hybrid bipolar fuzzy weighted averaging geometric (H-BFWAG), hybrid bipolar fuzzy order weighted averaging geometric (H-BFOWAG), hybrid interval-valued bipolar fuzzy weighted averaging geometric (H-IVBFWAG), and hybrid interval-valued bipolar fuzzy order weighted averaging geometric (H-IVBFOWAG) operators are generated in bipolar fuzzy environment. A decision making approach is developed to build the multi-criteria decision making model. A numerical example is considered to study the different effects on various Indian economic sectors in the COVID-19 environment. Managerial implications are highlighted in between many industrial sectors on which the Indian economy depends and the country's gross domestic product depends on their inter-relationship. This study investigates COVID-19's impact on society and the economy in the Indian context. A comparative investigation is introduced with some pre-existing operators and the TOPSIS (technique for order of preference by similarity to ideal solution) approach to justify the proposed method. © 2024 Elsevier Ltd

关键词： COVID-19

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Design of Intelligent Decision Support System With Ensembled Machine Learning to Predict Students' Performance

Design of Intelligent Decision Support System With Ensembled...

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Communication, computer Sciences and Engineering (IC3SE), International Conference on

作者： S Deepan Dineshkumar Rajendran Navdeep Dhaliwal K Praveena Melanie Lourens Anandhi R J School of CS & AI SR University Warangal Telangana India Computer Games Design and Programming School of Computing and Innovative Technologies British University Vietnam Vietnam Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad Deputy Dean Faculty of Management Sciences Durban University of Technology South Africa Department of Information Science Engineering New Horizon College of Engineering Bangalore

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.

关键词： Deep learning Decision support systems Analytical models Data analysis Automation Education Companies

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Using the inverse Cauchy problem of the Laplace equation for wave propagation to implement a numerical regularization homotopy method

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AIP Conference Proceedings 2023年第1期2977卷

作者： H. K. Al-Mahdawi Zainalabideen Albadran Hussein Alkattan Mostafa Abotaleb Khder Alakkari Ali J. Ramadhan Electronic Computer Centre University of Diyala Diyala Iraq Department of Computer Techniques Engineering College of Technical Engineering University of Alkafeel Najaf Iraq Department of System Programming South Ural State University South Ural State Russia Department of Statistics and Programming Faculty of Economics University of Tishreen Latakia Syria

In this paper, we introduce an approach via regularization and Homotopy way for resolving the inverse Cauchy problem of the Laplace of system partial differential equation which appears in the wave propagation for communication networks. We considered the method of Homotopy Perturbation Metheod (HPM) for solving the integral equations of the first kind named Fredholm. In order to formulate the Laplace equation into the first-kind integral equation (Fredholm) the Fourier series used. Then the discretization method used to reduce the integral equation into a linear operator equation for the first kind. It is clear that this kind of problem is callsified as an ill-posed and the direct way to solve it unacceptably. Tikhonov’s regularization method with Homotopy Perturbation algorithm used for obtaing the approximation solution for the Laplace differential equation. Finally, the numerical example is proposed.

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Knowledge Discovery Based on Data Analytics and Visualization Supporting Precision Medicine

Knowledge Discovery Based on Data Analytics and Visualizatio...

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International Conference on Mathematics and computers in Science and Engineering (MACISE)

作者： Veska Gancheva Department of Programming and Computer Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9781728166957

ISBN: (纸本)9781728166964

Major challenge in the analysis of clinical data and knowledge discovery is to suggest an integrated, advanced and efficient tools, methods and technologies for access and processing of progressively increasing amounts of data in multiple formats. The problem of adapting the personal treatment to a patient is extremely complex and the medical doctor should examine and analyze large amounts of various data of different type. A comprehensive system for precision medicine, which covers all phases of data discovery, data integration, data preprocessing, building models, data storage, data analysis and visualization can be very useful to scientists in support of precision medicine. The software system aims intelligent big genomic data management, analysis and visualization and allows scientists an easy, fast and flexible approach for data processing. They can choose the services they wish to be executed, use the available data sets in databases, or enter their own data to be processed.

关键词： Data visualization Bioinformatics Knowledge discovery Proteins Data analysis Medical diagnostic imaging Big Data

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A Cyberattacks Detection Technique Based on Evolutionary Algorithms

A Cyberattacks Detection Technique Based on Evolutionary Alg...

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IEEE International Conference on Dependable Systems, Services and Technologies (DESSERT)

作者： Sergii Lysenko Kira Bobrovnikova Roman Shchuka Oleg Savenko Department of Computer Engineering and System Programming Khmelnitsky National University Khmelnitsky Ukraine

ISBN: (数字)9781728199573

ISBN: (纸本)9781728199580

An article presents an approach for cyberattack detection based on genetic algorithms is presented. The method allows detecting both known and unknown cyberattacks. The method has the heuristic nature and is based on the collected data about the cyberattacks. It makes it possible to give an answer about the cyberattacks' existence in the computer networks and its hosts. Developed attack detection approach consists of training and detection stages. The mechanism of attack detection system is based on the cyberattacks' features gathering from network or hosts, extracting the subset of acquired set and generation the attacks' detection rules. Genetic algorithms are used for the minimization of the feature set, which allows effective using of the system resources for attacks detection. In order to detect the attacks, the proposed technique involves the rule generation. The attacks' features are described by the set of sub-rules. It is suggested to use the feature with the smallest domain for generating the minimal set for rules. It is possible to select the optimal feature after all selected features which were discovered while applying the genetic algorithm. The sub-rule set is used with the aim to reduce false positive rate.

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Artificial Intelligence in Medicine for Chronic Disease Classification Using Machine Learning

Artificial Intelligence in Medicine for Chronic Disease Clas...

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Mekhriddin Rakhimov Ravshanjon Akhmadjonov Shahzod Javliev Department of Artificial Intelligence Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi Tashkent Uzbekistan Department of System and Application Programming Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi Tashkent Uzbekistan Department of Computer System Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9781665451635

Artificial intelligence (AI) systems in medicine are one of the most important modern trends in global healthcare. Artificial intelligence technologies are fundamentally changing the global healthcare system, making it possible to radically rebuild the system of medical diagnostics while reducing healthcare costs. AI is actively used in research to develop methods for diagnosing coronary heart disease (CHD). There are different types of CHD. Before treating a disease, it is necessary to determine which class of diseases it belongs to. Based on the feature space of the disease, it is possible to classify the type of CHD. Machine learning algorithms can solve this problem. The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve classification problems. The dataset is the more important part of the supervised machine learning algorithm for training. Gathering data is the most important step in solving any supervised machine learning problem. But choosing more important part from the collected data is one of the tasks to be solved. The main purpose of this study is to select more useful parametric attributes from the dataset to obtain a high F1-score of CHD classification.

关键词： Heart Training Machine learning algorithms Machine learning Market research Classification algorithms Information and communication technology

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Hyper-parameter tuning for the long short-term memory algorithm

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AIP Conference Proceedings 2023年第1期2977卷

作者： Tatiana Makarovskikh Mostafa Abotaleb Zainalabideen Albadran Ali J. Ramadhan System Programming Department South Ural State University Chelyabinsk Russia Department of Computer Techniques Engineering College of Technical Engineering University of Alkafeel Najaf Iraq

This research focuses on hyperparameter optimization for LSTM to forecast SARS-CoV-2 infection cases in the Russian Federation, aiming to determine the best combination of parameters for a well-fitting model. Using LSTM’s capability to analyze relationships within time series data, a bidirectional LSTM-based method is introduced for predicting daily infection cases. The study evaluates nearly 10 unique forecasting models and conducts a comprehensive analysis and comparison of their results. The Bidirectional LSTM model proves to be a reliable approach for forecasting daily SARS-CoV-2 infection cases in Russia, displaying the highest prediction accuracy among the tested models.

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Develop an unsupervised attention-based LSTM network algorithm for forecasting infectious disease

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AIP Conference Proceedings 2023年第1期2977卷

作者： Mostafa Abotaleb Tatiana Makarovskikh Zainalabideen Albadran Ali J. Ramadhan System Programming Department South Ural State University Chelyabinsk Russia Department of Computer Techniques Engineering College of Technical Engineering University of Alkafeel Najaf Iraq

time series Due to better algorithms, more accessible data, and higher computing power over the past ten years, forecasting has become more popular. It is used in a variety of industries, including as financial time series, weather forecasting, and medical diagnostics. In this study, we provide a model of the mechanism governing attention, which enables attended input to be provided to the model in place of actual input. In order for the model to produce more precise predictions, it seeks to demonstrate a fresh perspective on the data. The experiments were conducted with the (encoder-decoder) LSTM model as well to demonstrate the usefulness and superiority of the suggested strategy. The obtained results demonstrate that, when compared to the (encoder-decoder) LSTM base model, the proposed approach could reduce the mean square error (RMSE=9819.05), relative root mean square error (RRMSE=99.09), and coefficient of determination (R Square=0.96). The obtained results support the suggested approach’s efficacy, superiority, and importance in predicting SARS-CoV-2 infection cases.

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Slippery Lubricant-Infused Silica Nanoparticulate Film Processing for Anti-Biofouling Applications 73

Slippery Lubricant-Infused Silica Nanoparticulate Film Proce...

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73rd International Astronautical Congress, IAC 2022

作者： Sip, Yuen Yee Li Jacobs, Annabel Morales, Alejandra Sun, Mengdi Roberson, Luke B. Hummerick, Mary E. Roy, Herve Kik, Pieter Zhai, Lei Department of Materials Science & Engineering University of Central Florida FL United States Herbert Wertheim College of Engineering University of Florida FL United States Engineering Computer Programming and Technology Division Valencia College FL United States College of Optics and Photonics University of Central Florida FL United States Kennedy Space Center National Aeronautics and Space Administration FL United States Kennedy Space Center Amentum Services FL United States Burnett School of Biomedical Sciences University of Central Florida FL United States Department of Chemistry University of Central Florida FL United States

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 necessary replacement parts and medical resources. As a result, there is a need for coatings to be implemented onto the inner region of the pipe to minimize the adherence and growth of biofilms. Lubricant-infused surfaces has been one such interesting material for anti-biofouling applications in which their slippery property promotes repellence to many liquids and thus prevents bacterial adherence. Textured and porous films are suitable substrate candidates to infuse and contain the lubricant. However, there is little investigation in utilizing a nanoporous thin film as the substrate material for lubricant infusion. A nanoporous film has high porosity within the structure which can promote greater lubricant infusion and retention. The implementation as a thin film structure aids to reduce material consumption and cost. In our study, we utilized a well-studied nanoporous thin film fabricated via layer-by-layer assembly of polycations and colloid silica and calcination for greater stability. The film was further functionalized to promote fluorinated groups upon the surface and pores of the film, which then could interact with a fluorinated lubricant. The pristine nanoporous film was characterized to determine its morphology, thickness, wettability, and porosity. The lubricant-infused film was then tested for its lubricant layer stability upon various washing conditions and its performance against bacterial biofilm adherence as a result of its slippery and thus anti-biofouling property. Copyright © 2022 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Silica

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