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Prediction of the final size for COVID-19 epidemic using machine learning:A case study of Egypt

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Infectious Disease Modelling 2020年第1期5卷 622-634页

作者： Lamiaa A.Amar Ashraf A.Taha Marwa Y.Mohamed Networks and Distributed Systems Department Informatics Research InstituteCity of Scientific Research and Technological ApplicationsSRTA-CITYEgypt Multimedia and Computer Graphics Department Informatics Research InstituteCity of Scientific Research and Technological ApplicationsSRTA-CITYEgypt Department of Mathematics and Computer Science Faculty of ScienceAlexandria UniversityEgypt

COVID-19 is spreading within the sort of an enormous epidemic for the *** epidemic infects a lot of individuals in *** World Health Organization states that COVID-19 could be spread from one person to another at a very fast speed through contact and respiratory *** these days,Egypt and all countries worldwide should rise to an effective step to investigate this disease and eliminate the effects of this *** this paper displayed,the real database of COVID-19 for Egypt has been analysed from February 15,2020,to June 15,2020,and predicted with the number of patients that will be infected with COVID-19,and estimated the epidemic final *** regression analysis models have been applied for data analysis of COVID-19 of *** this study,we’ve been applied seven regression analysis-based models that are exponential polynomial,quadratic,thirddegree,fourth-degree,fifth-degree,sixth-degree,and logit growth respectively for the COVID-19 ***,the exponential,fourth-degree,fifth-degree,and sixth-degree polynomial regression models are excellent models specially fourth-degree model that will help the government preparing their procedures for one *** addition,we have applied the well-known logit growth regression model and we obtained the following epidemiological insights:Firstly,the epidemic peak could possibly reach at 22-June 2020 and final time of epidemic at 8-September ***,the final total size for cases 1.6676Et05 *** action from government of interevent over a relatively long interval is necessary to minimize the final epidemic size.

关键词： COVID-19 Regression analysis model Epidemic model

ID-Booth: Identity-consistent Face Generation with Diffusion Models

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arXiv 2025年

作者： Tomašević, Darian Boutros, Fadi Lin, Chenhao Damer, Naser Štruc, Vitomir Peer, Peter University of Ljubljana Faculty of Computer and Information Science Ljubljana Slovenia Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an China University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia

Recent advances in generative modeling have enabled the generation of high-quality synthetic data that is applicable in a variety of domains, including face recognition. Here, state-of-the-art generative models typically rely on conditioning and fine-tuning of powerful pretrained diffusion models to facilitate the synthesis of realistic images of a desired identity. Yet, these models often do not consider the identity of subjects during training, leading to poor consistency between generated and intended identities. In contrast, methods that employ identity-based training objectives tend to overfit on various aspects of the identity, and in turn, lower the diversity of images that can be generated. To address these issues, we present in this paper a novel generative diffusion-based framework, called ID-Booth. ID-Booth consists of a denoising network responsible for data generation, a variational auto-encoder for mapping images to and from a lower-dimensional latent space and a text encoder that allows for prompt-based control over the generation procedure. The framework utilizes a novel triplet identity training objective and enables identity-consistent image generation while retaining the synthesis capabilities of pretrained diffusion models. Experiments with a state-of-the-art latent diffusion model and diverse prompts reveal that our method facilitates better intra-identity consistency and inter-identity separability than competing methods, while achieving higher image diversity. In turn, the produced data allows for effective augmentation of small-scale datasets and training of better-performing recognition models in a privacy-preserving manner. The source code for the ID-Booth framework is publicly available at https://***/dariant/ID-Booth. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

Assessing quality of decision reducts 24

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Assessing quality of decision reducts

24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Stanczyk, Urszula Zielosko, Beata Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland Institute of Computer Science University of Silesia in Katowice Bedzinska 39 Sosnowiec41-200 Poland

The paper presents research focused on decision reducts, a feature reduction mechanism inherent to rough sets theory. As a reduct enables to protect the discriminative properties of attributes with respect to described concepts, from the point of data representation, a reduct length is considered to be the most important measure of its quality. However, such approach is insufficient while taking into account the performance of a reduct-based rule classifier applied to test samples. When many reducts of the same length are available, they can lead to vastly different predictions. The paper provides a description for the proposed procedure for iterative reduct generation, which results in decrease of diversity in the observed levels of accuracy, supporting reduct selection. The procedure was applied for binary classification with balanced classes, for the stylometric task of authorship attribution. © 2020 The Authors. Published by Elsevier B.V.

关键词： Rough set theory

EFaR 2023: Efficient Face Recognition Competition

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arXiv 2023年

作者： Kolf, Jan Niklas Boutros, Fadi Elliesen, Jurek Theuerkauf, Markus Damer, Naser Alansari, Mohamad Hay, Oussama Abdul Alansari, Sara Javed, Sajid Werghi, Naoufel Grm, Klemen Štruc, Vitomir Alonso-Fernandez, Fernando Diaz, Kevin Hernandez Bigun, Josef George, Anjith Ecabert, Christophe Shahreza, Hatef Otroshi Kotwal, Ketan Marcel, Sébastien Medvedev, Iurii Jin, Bo Nunes, Diogo Hassanpour, Ahmad Khatiwada, Pankaj Toor, Aafan Ahmad Yang, Bian Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland Lausanne Switzerland Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal eHealth and Welfare Security Group Department of Information Security and Communication Technology Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well. © 2023, CC BY-NC-SA.

关键词： Face recognition

Direct Optical Probing of the Magnetic Properties of the Layered Antiferromagnet CrPS4

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arXiv 2024年

作者： Fąs, Tomasz Wlazlo, Mateusz Birowska, Magdalena Rybak, Milosz Zinkiewicz, Malgorzata Oleschko, Leon Goryca, Mateusz Gondek, Lukasz Camargo, Bruno Szczytko, Jacek Budniak, Adam K. Amouyal, Yaron Lifshitz, Efrat Suffczyński, Jan Institute of Experimental Physics Faculty of Physics University of Warsaw Pasteura St. 5 Warsaw02-093 Poland Chemical and Biological Systems Simulation Lab Centre of New Technologies University of Warsaw Warsaw02-097 Poland Institute of Theoretical Physics Faculty of Physics University of Warsaw Pasteura St. 5 Warsaw02-093 Poland Department of Semiconductor Materials Engineering Faculty of Fundamental Problems of Technology Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland Department of Physics University of Konstanz Universitätsstraße 10 KonstanzD-78457 Germany Faculty of Physics and Applied Computer Science AGH University of Kraków Mickiewicza 30 Kraków30-059 Poland Schulich Faculty of Chemistry Technion – Israel Institute of Technology Haifa3200003 Israel Department of Materials Science and Engineering Technion – Israel Institute of Technology Haifa3200003 Israel

Unusual magnetic properties of Van der Waals type antiferromagnetic semiconductors make them highly attractive for spintronics and optoelectronics. A link between the magnetic and optical properties of those materials, required for practical applications, has not been, however, established so far. Here, we report on a combined experimental and theoretical study of magnetic, optical, and structural properties of bulk CrPS4 samples. We find that the magnetic-field-dependent circular polarization degree of the photoluminescence is a direct measure of the net magnetization of CrPS4. Complementary, Raman scattering measured as a function of magnetic field and temperature enables the determination of the magnetic susceptibility curve of the material. Our experimental results are backed by Our experimental results are supported by density functional theory calculations that take as input the lattice parameters determined from temperature-dependent X-ray diffraction measurements. This allows us to explain the impact of spin ordering on the spectral position of Raman transitions in CrPS4, as well as anomalous temperature shifts of selected of them. The presented method for all-optical determination of the magnetic properties is highly promising for studies of spin ordering and magnetic phase transitions in single- or a few-layer samples of magnetic layered materials, for which a poor signal-to-noise ratio precludes any reliable neutron scattering or magnetometry measurements. © 2024, CC BY.

关键词： Magnetic susceptibility

Exploring Machine Learning Techniques to Identify Important Factors Leading to Injury in Curve Related Crashes

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arXiv 2023年

作者： Moeinaddini, Mehdi Pourmoradnasseri, Mozhgan Hadachi, Amnir Cools, Mario ITS Lab Institute of Computer Science University of Tartu Narva mnt 18 Tartu51009 Estonia LEMA Research Group Urban & Environmental Engineering Department University of Liège Liège Belgium Department of Informatics Simulation and Modelling KULeuven Campus Brussels Brussels Belgium Faculty of Business Economics Hasselt University Diepenbeek Belgium

Different factors have effects on traffic crashes and crash-related injuries. These factors include segment characteristics, crash-level characteristics, occupant level characteristics, environment characteristics, and vehicle level characteristics. There are several studies regarding these factors’ effects on crash injuries. However, limited studies have examined the effects of pre-crash events on injuries, especially for curve-related crashes. The majority of previous studies for curve-related crashes focused on the impact of geometric features or street design factors. The current study tries to eliminate the aforementioned shortcomings by considering important pre-crash events related factors as selected variables and the number of vehicles with or without injury as the predicted variable. This research used CRSS data from the National Highway Traffic Safety Administration (NHTSA), which includes traffic crash-related data for different states in the USA. The relationships are explored using different machine learning algorithms like the random forest, C5.0, CHAID, Bayesian Network, Neural Network, C&R Tree, Quest, etc. The random forest and SHAP values are used to identify the most effective variables. The C5.0 algorithm, which has the highest accuracy rate among the other algorithms, is used to develop the final model. Analysis results revealed that the extent of the damage, critical pre-crash event, pre-impact location, the trafficway description, roadway surface condition, the month of the crash, the first harmful event, number of motor vehicles, attempted avoidance maneuver, and roadway grade affect the number of vehicles with or without injury in curve-related crashes. © 2023, CC BY.

关键词： Machine learning

Reduct-based ranking of attributes 24

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Reduct-based ranking of attributes

24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Zielosko, Beata Stanczyk, Urszula Institute of Computer Science University of Silesia in Katowice Bedzi nska 39 Sosnowiec41-200 Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The paper is dedicated to the area of feature selection, in particular a notion of attribute rankings that allow to estimate importance of variables. In the research presented for ranking construction a new weighting factor was defined, based on relative reducts. A reduct constitutes an embedded mechanism of feature selection, specific to rough set theory. The proposed factor takes into account the number of reducts in which a given attribute exists, as well as lengths of reducts. Two approaches for reduct generation were employed and compared, with search executed by a genetic algorithm. To validate the usefulness of the reduct-based rankings in the process of feature reduction, for gradually decreasing subsets of attributes, selected through rankings, sets of decision rules were induced in classical rough set approach. The performance of all rule classifiers was evaluated, and experimental results showed that the proposed rankings led to at least the same, or even increased classification accuracy for reduced sets of features than in the case of operating on the entire set of condition attributes. The experiments were performed on datasets from stylometry domain, with treating authorship attribution as a classification task, and stylometric descriptors as characteristic features defining writing styles. © 2020 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

Dynamic task allocation algorithm based on D-NSGA3

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Dynamic task allocation algorithm based on D-NSGA3

2021年第二届控制、机器人和智能系统国际会议

作者： Jing Zhou Xiaozhe Zhao Zhen Xu Siyun Peng Zhong Lin Faculty of Management and Economics Dalian University of Technology Operation Software and Simulation Institute Dalian Navy Academy School of Computer Science and Technology Wuhan University of Technology

Task allocation is a key part of unmanned aerial vehicle(UAV)*** a large number of solving algorithms have been developed,there are few technologies that support task allocation algorithms in dynamic ***,this is not in accord with the actual *** battlefield is changing rapidly,which may lead to the failure of the allocated tasks and the inability to allocate new *** order to deal with this problem,this paper improves the original D-NSGA3 algorithm to adapt to the dynamic *** experimental results show that,compared with the original static algorithm,the proposed algorithm has better effect in solving the task allocation problem of high-dimensional multi-objective agent based on maximizing the number of successfully allocated tasks,maximizing the benefits of executing tasks,minimizing the consumption cost,and minimizing the time cost.

关键词： multi-agent system dynamic task allocation evolutionary algorithm acquaintance

CrowdSim2: an Open Synthetic Benchmark for Object Detectors

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arXiv 2023年

作者： Foszner, Pawel Szczęsna, Agnieszka Ciampi, Luca Messina, Nicola Cygan, Adam Bizon, Bartosz Cogiel, Michal Golba, Dominik Macioszek, Elżbieta Staniszewski, Michal Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Institute of Information Science and Technologies National Research Council Pisa Italy QSystems.pro sp. z o.o. Mochnackiego 34 Bytom41-907 Poland Blees sp. z o.o. Zygmunta Starego 24a/10 Gliwice44-100 Poland Department of Transport Systems Traffic Engineering and Logistics Faculty of Transport and Aviation Engineering Silesian University of Technology Katowice Poland

Data scarcity has become one of the main obstacles to developing supervised models based on Artificial Intelligence in computer Vision. Indeed, Deep Learning-based models systematically struggle when applied in new scenarios never seen during training and may not be adequately tested in non-ordinary yet crucial real-world situations. This paper presents and publicly releases CrowdSim2, a new synthetic collection of images suitable for people and vehicle detection gathered from a simulator based on the Unity graphical engine. It consists of thousands of images gathered from various synthetic scenarios resembling the real world, where we varied some factors of interest, such as the weather conditions and the number of objects in the scenes. The labels are automatically collected and consist of bounding boxes that precisely localize objects belonging to the two object classes, leaving out humans from the annotation pipeline. We exploited this new benchmark as a testing ground for some state-of-the-art detectors, showing that our simulated scenarios can be a valuable tool for measuring their performances in a controlled environment. © 2023, CC BY.

关键词： Object detection