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17th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2023

作者： Varga, Henrik Kiss, Attila Kolarovszki, Zoltan Elte Eötvös Loránd University Department of Algorithms and Their Applications Budapest Hungary Elte Eötvös Loránd University Department of Information Systems Budapest Hungary J. Selye University Department of Informatics Komárno Slovakia Department of Programming Languages and Compilers Elte Eotvos Lorand University Budapest Hungary Wigner Research Centre for Physics Department of Computational Sciences Budapest Hungary

ISBN: (纸本)9798350321104

In the past few years, quantum computing has gotten more attention, and the need for efficient simulations is getting increasingly important as well. A significant branch of quantum computing is photonic quantum computing. For simulating photonic quantum circuits, Strawberry Fields is the most popular framework. In this paper, we compared it with another framework currently under development called Piquasso regarding gradient calculation time, which is an essential part of continuous-variable quantum neural networks. We present the apparent scalability of Piquasso over Strawberry Fields by storing fewer data, but leading to possible accuracy differences as a trade-off, which could motivate future work. © 2023 IEEE.

关键词： Quantum optics

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Input Data Time Range Impact on Forecast Quality Using Automated Procedures

Input Data Time Range Impact on Forecast Quality Using Autom...

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Computer Science (COMSCI), International Scientific Conference on

作者： Kiril Andreev Koparanov Elena Ventseslavova Antonova Ognyan Nakov Nakov Daniela Veleva Minkovska Krasin Krasimirov Georgiev Lyudmila Yordanova Stoyanova Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia 8 Kliment Ohridski blvd Sofia Bulgaria Department of Computer Systems Faculty of Computer Systems and Technologies Technical University of Sofia 8 Kliment Ohridski blvd Sofia Bulgaria Department of Air Transport Faculty of Transport Technical University of Sofia 8 Kliment Ohridski blvd Sofia Bulgaria

Easy access to time series forecasting provides a strong competitive advantage for any company or organization. As automated forecasting environments become more prevalent in the market, it is crucial to explore their capabilities and features to utilize them competently. This study demonstrates the influence of data coverage on prediction accuracy and validates the initial assumption. Based on the findings, recommendations have been presented to enhance the quality of predictive processes.

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Application of IoT in the study of biogas release parameters

Application of IoT in the study of biogas release parameters

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International Scientific Conference Electronics (ET)

作者： Neven K. Nikolov Anelia I. Tzanova Kiril A. Koparanov Elena V. Antonova Daniela V. Minkovska Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia 8 Kliment Ohridski blvd. Sofia Bulgaria Department of Chemistry Faculty of Electronic Engineering and Technologies Technical University of Sofia 8 Kliment Ohridski blvd. Sofia Bulgaria

This paper describes the development of an IoT device prototype for measuring biogas release parameters. Through the device, measurements are made, and statistics are accumulated based on which calculations are perfor... 详细信息

ISBN: (数字)9798350376449

ISBN: (纸本)9798350376456

关键词： Performance evaluation Heating systems Prototypes Production Sensor phenomena and characterization Biogas Sensor systems Internet of Things Information technology Monitoring

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Optimizing Machine Learning Algorithms to Enhance Blood Donation for Liver Patients: A Case Study 7

Optimizing Machine Learning Algorithms to Enhance Blood Dona...

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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of Engineering & Technology 2023.

关键词： Support vector machines

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Experiences with an internal dsl in the iot domain 4

Experiences with an internal dsl in the iot domain

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4th Workshop on Model-Driven Engineering for the Internet-of-Things, 1st International Workshop on Modeling Smart Cities, and 5th International Workshop on Open and Original Problems in Software Language Engineering, STAF-WS 2020

作者： Tichy, Matthias Pietron, Jakob Mdinger, David Juhnke, Katharina Hauck, Franz J. Institute of Software Engineering and Programming Languages Ulm University Germany Institute of Distributed Systems Ulm University Germany

Modeling the architecture and behavior of embedded systems has long been a success story in the engineering of embedded systems due to the positive effects on quality and productivity, e.g., by declarative specifications, by enabling formal analyses, and by the generation of optimized code. These benefits, however, can only be reaped with extensive investments in specialized languages and tools which typically come with a closed and highly restrictive ecosystem. In this paper, we report our experiences while building an internal domain-specific language for IoT systems. We present our modeling language realized in Type-Script and integrated into the TypeScript/JavaScript ecosystem. The modeling language supports the declarative specification and execution of components, connectors, and state machines. We also provide a simple state space exploration to enable quality assurance techniques like test case generation and model checking. The language is illustrated by a running example with IoT devices. We believe that our solution lies at a sweet spot of providing a declarative modeling experience while reaping benefits from modern programming languages and their ecosystem to boost productivity. © 2020 CEUR-WS. All rights reserved.

关键词： Productivity

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Storage of key Features of Images of Sulfur Imprints in the Mathematical Structure Form

Storage of key Features of Images of Sulfur Imprints in the ...

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2020 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2020

作者： Bagaev, I.I. Logunova, O.S. Institute of Energy and Automated Systems Nosov Magnitogorsk State Technical University Department of Computer Engineering and Programming Magnitogorsk Russia

ISBN: (纸本)9781728169514

The aim of the study is to develop a method for storing images of sulfur imprints. In the framework of the study, a more effective method of representing images of sulfur imprints by using knowledge of the specificity of these images was developed compared to universal methods. In particular, a method for storing images of sulfur imprints has been developed, which stores information about objects that are significant for technological analysis in the image. The developed method was tested for images of sulfur imprints of continuously cast billets for the conditions of a large metallurgical enterprise in Russia. To conduct a computational experiment, a software product was designed and developed that allows you to convert the image of a sulfur imprint into a mathematical representation, as well as the process of restoring the main image. Using the array of retrospective information of 2011-2018 and the developed software product, the calculation of the effectiveness of the presentation of graphic information in the form of a structural unit of information without directly saving the image was performed. The average share of stored information from the original volume is 0.53 percent © 2020 IEEE.

关键词： Sulfur

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Model-based Generation of Attack-Fault Trees

arXiv

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arXiv 2023年

作者： Groner, Raffaela Witte, Thomas Raschke, Alexander Hirn, Sophie Pekaric, Irdin Frick, Markus Tichy, Matthias Felderer, Michael Institute of Software Engineering and Programming Languages Ulm University Germany Department of Computer Science University of Innsbruck Austria Department of Information Systems and Computer Science University of Liechtenstein Liechtenstein Germany Department of Mathematics and Computer Science University of Cologne Germany

Joint safety and security analysis of cyber-physical systems is a necessary step to correctly capture inter-dependencies between these properties. Attack-Fault Trees represent a combination of dynamic Fault Trees and Attack Trees and can be used to model and model-check a holistic view on both safety and security. Manually creating a complete AFT for the whole system is, however, a daunting task. It needs to span multiple abstraction layers, e.g., abstract application architecture and data flow as well as system and library dependencies that are affected by various vulnerabilities. We present an AFT generation tool-chain that facilitates this task using partial Fault and Attack Trees that are either manually created or mined from vulnerability databases. We semi-automatically create two system models that provide the necessary information to automatically combine these partial Fault and Attack Trees into complete AFTs using graph transformation rules. © 2023, CC BY-NC-ND.

关键词： Trees (mathematics)

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Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

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Computer systems Science & Engineering 2023年第7期46卷 883-896页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Tatiana Makarovskikh Amal H.Alharbi Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of System Programming South Ural State University454080ChelyabinskRussia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,*** world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi *** prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus ***:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)***:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),*** addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed *** achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base *** the other hand,a statistical analysis is performed to measure the significance of the proposed ***:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.

关键词： COVID-19 prediction meta-heuristic optimization LSTM Al-Biruni earth radius algorithm

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Word-Based Turkish Lip Reading System with Two Stream 3dcnn and Bilstm

SSRN

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SSRN 2023年

作者： Erbey, Ali Barışçı, Necaattin Department of Computer Programming Distance Education Vocational School Usak University Usak64200 Turkey Department of Information Systems Informatics Institute Gazi University Ankara Turkey Department of Computer Engineering Faculty of Technology Gazi University Ankara06500 Turkey

Lip reading is a technique that aims to understand spoken words by analyzing people's lip movements. Deep learning algorithms are used as a powerful tool for detecting and recognizing lip movements more accurately. In this study, we present a lip-reading architecture for the Turkish language. The architecture is trained to recognize Turkish words using 3DCNN (3D Convolutional Neural Network) architecture and two-stream deep learning methods. The model was trained on video data representing lip movements and learned to classify words accurately. The Turkish lip reading system has been trained and tested on a large Turkish lip reading dataset. In the experiments, an accuracy of 87.12% was achieved with the proposed model, indicating successful results in the Turkish language. The proposed model demonstrates high accuracy rates in recognizing Turkish words, showcasing its effectiveness. This paper introduces a novel architecture, a lip-reading system that employs deep learning techniques, providing a robust tool for successful Turkish lipreading applications. © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Customs Fraud Detection Method Based on Artificial Intelligence and Data Analytics

Customs Fraud Detection Method Based on Artificial Intellige...

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Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： Veska Gancheva George Popov Ognyan Nakov Kamelia Raynova Antoaneta Popova dept. of Programming and Computer Technologies Technical University of Sofia Sofia Bulgaria dept. of Cybersecurity Technical University of Sofia Sofia Bulgaria dept. of Computer Systems Technical University of Sofia Sofia Bulgaria dept. of Information Technologies in Industry Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

Because of the increased market opening and massive data collection brought about by globalization, the need of maintaining control over customs procedures has increased. However, the integration and processing of customs data are complicated by its extreme imbalance. Finding autonomous, computationally sophisticated solutions for customs handling is therefore crucial. The study described in this paper aims to propose an artificial intelligence-based method for customs fraud detection through data analytics. A machine learning-based approach for detecting customs fraud is proposed. The research methodology includes data preprocessing, data analytics, combining results, and deriving precise solutions from results obtained. The aim is to extract meaningful information from a dataset. For the purpose of classifying customs fraud, seven algorithms for machine-learning-kNN, SGD, Constant, Logistic Regression, Random Forest, Neural Network, and Naive Bayes-are chosen and empirically assessed.

关键词： Training Analytical models Logistic regression Data analysis Neural networks Data models Fraud Bayes methods Data mining Random forests

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