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46th International Symposium on Mathematical Foundations of computer science, MFCS 2021

作者： Kenison, George Lefaucheux, Engel Moree, Pieter Whiteland, Markus A. Klurman, Oleksiy Luca, Florian Ouaknine, Joël Worrell, James Institute for Logic and Computation Technical University of Vienna Austria Max Planck Institute for Software Systems Saarland Informatics Campus Saarbrücken Germany Max Planck Institute for Mathematics Bonn Germany School of Mathematics University of Bristol United Kingdom School of Mathematics University of Witwatersrand Johannesburg South Africa Research Group in Algebraic Structures and Applications King Abdulaziz University Riyadh Saudi Arabia Centro de Ciencias Matemáticas Unam Morelia Mexico Department of Computer Science University of Oxford United Kingdom

ISBN: (纸本)9783959772013

An infinite sequence unn of real numbers is holonomic (also known as P-recursive or P-finite) if it satisfies a linear recurrence relation with polynomial coefficients. Such a sequence is said to be positive if each un ≥ 0, and minimal if, given any other linearly independent sequence vnn satisfying the same recurrence relation, the ratio un/vn → 0 as n → ∞. In this paper we give a Turing reduction of the problem of deciding positivity of second-order holonomic sequences to that of deciding minimality of such sequences. More specifically, we give a procedure for determining positivity of second-order holonomic sequences that terminates in all but an exceptional number of cases, and we show that in these exceptional cases positivity can be determined using an oracle for deciding minimality. © George Kenison, Oleksiy Klurman, Engel Lefaucheux, Florian Luca, Pieter Moree, Joël Ouaknine, Markus A. Whiteland, and James Worrell;licensed under Creative Commons License CC-BY 4.0 46th International Symposium on Mathematical Foundations of computer science (MFCS 2021).

关键词： Holonomic sequences Minimal solutions Positivity problem

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Correction to: Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

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Multimedia Tools and applications 2024年第26期83卷 68415-68420页

作者： Ahammad, Shaik Hasane Jayaraj, R. Shibu, S. Sujatha, V. Prathima, Ch Leo, L. Megalan Prabu, R. Thandaiah Hossain, Md.Amzad Rashed, Ahmed Nabih Zaki Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Data Science and Buisness Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai India Department of Data Science School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Deptartment of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Chennai India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt

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Machine Learning Approaches for Efficient Traffic Flow in Smart Cities

Machine Learning Approaches for Efficient Traffic Flow in Sm...

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Electrical Power Engineering, Communication and Computing Technology(ODICON), Odisha International Conference on

作者： Aniket Kulkarni P. Anitha John Yesudas Valluri M V Sunena Rose U Hemavathi Omar M. Hussein Software QA Manager Drata Inc San Diego CA USA Department of Computer Applications Kalasalingam Academy of Research and Education Srivilliputhur Tamil Nadu India Faculty of Management Studies CMS Business School Jain (Deemed-to-be University) Bengaluru India Department of Computer Science and Engineering Jain (Deemed-to-be University) Bengaluru India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Medical Technical College Medical Instrumentation Engineering Al-Farahidi University Baghdad Iraq

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

These days, a lot of towns struggle with traffic jams during peak hours, which increment contamination, commotion, and feelings of anxiety for the general population. Because neural networks (NN) and machine learning (ML) techniques can handle large numbers of parameters in massive amounts of data and dynamic behaviour over time, they are replacing analytical and statistical methods in the solving of real-world problems. This research presents a convergence of machine-learning (ML) and deep-learning (DL) calculations for traffic stream prediction; this paves the way for flexible traffic signals, which can be implemented through the use of a calculation that adjusts the timing in accordance with the anticipated traffic flow or by controlling traffic signals to some degree. The proposed ML and DL models are constructed, validated, and tested using two publicly accessible datasets. The first depicts the total number of cars subjected to checks using various sensors at six junctions over the course of 56 days. In this research, ML and DL models are created using data from four of the six junctions. The excellent performance metrics obtained by all ML and DL computations suggest that their application to intelligent traffic light management is feasible.

关键词： Measurement Power engineering Machine learning algorithms Statistical analysis Smart cities Transportation Machine learning Prediction algorithms Timing Junctions

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On the transcendence of a series related to Sturmian words

arXiv

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arXiv 2022年

作者： Luca, Florian Ouaknine, Joël Worrell, James School of Mathematics University of the Witwatersrand Private Bag 3 Wits 2050 South Africa Research Group in Algebraic Structures and Applications King Abdulaziz University Jeddah Saudi Arabia Max-Planck Institute for Software Systems Saarbrücken Germany Max Planck Institute for Software Systems Saarland Informatics Campus Saarbrücken Germany Department of Computer Science University of Oxford OxfordOX13QD United Kingdom

Let b be an algebraic number with |b| > 1 and H a finite set of algebraic numbers. We study the transcendence of numbers of the form (Equation presented) where an ∈ H for all n ∈ N. We assume that the sequence (Equation presented) is generated by coding the orbit of a point under an irrational rotation of the unit circle. In particular, this assumption holds whenever the sequence is Sturmian. Our main result shows that, apart from some trivial exceptions, all numbers of the above form are transcendental. We moreover give sufficient conditions for a finite set of such numbers to be linearly independent over *** Codes 11J81 © 2022, CC BY.

关键词： Algebra

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Transcendence of Sturmian Numbers over an Algebraic Base

arXiv

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arXiv 2023年

作者： Luca, Florian Ouaknine, Joël Worrell, James School of Mathematics University of the Witwatersrand Private Bag 3 Wits2050 South Africa Research Group in Algebraic Structures and Applications King Abdulaziz University Jeddah Saudi Arabia Max Planck Institute for Software Systems Saarland Informatics Campus Saarbrücken Germany Department of Computer Science University of Oxford OxfordOX1 3QD United Kingdom

We consider numbers of the form (Equation presented) for (Equation presented) a Sturmian sequence over a binary alphabet and β an algebraic number with |β| > 1. We show that every such number is transcendental. More generally, for a given base β and given irrational number θ we characterise the Q-linear independence of sets of the form (Equation presented), where (Equation presented) are Sturmian sequences having slope θ. We give an application of our main result to the theory of dynamical systems, showing that for a contracted rotation on the unit circle with algebraic slope, its limit set is either finite or consists exclusively of transcendental elements other than its endpoints 0 and 1. This confirms a conjecture of Bugeaud, Kim, Laurent, and Nogueira [3]. © 2023, CC BY.

关键词： Dynamical systems

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia computer science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) computer-aided diagnosis (CAD)

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FEniCS-preCICE: Coupling FEniCS to other simulation software

arXiv

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arXiv 2021年

作者： Rodenberg, Benjamin Desai, Ishaan Hertrich, Richard Jaust, Alexander Uekermann, Benjamin Scientific Computing in Computer Science Department of Informatics Technical University of Munich Germany Usability and Sustainability of Simulation Software Institute for Parallel and Distributed Systems University of Stuttgart Germany Simulation of Large Systems Institute for Parallel and Distributed Systems University of Stuttgart Germany

The new software FEniCS-preCICE is a middle software layer, sitting in between the existing finite-element library FEniCS and the coupling library preCICE. The middle layer simplifies coupling (existing) FEniCS application codes to other simulation software via preCICE. To this end, FEniCS-preCICE converts between FEniCS and preCICE mesh and data structures, provides easy-to-use coupling conditions, and manages data checkpointing for implicit coupling. The new software is a library itself and follows a FEniCS-native style. Only a few lines of additional code are necessary to prepare a FEniCS application code for coupling. We illustrate the functionality of FEniCS-preCICE by two examples: a FEniCS heat conduction code coupled to OpenFOAM and a FEniCS linear elasticity code coupled to SU2. The results of both scenarios are compared with other simulation software showing good agreement. © 2021, CC BY-NC-ND.

关键词： Finite element method

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Work-in-Progress: Using the PerFECt Framework to Design and Implement Blended Learning Activities to Introduce the Binary System in Primary School Students

Work-in-Progress: Using the PerFECt Framework to Design and ...

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9th International Conference on Interactive Collaborative and Blended Learning, ICBL 2020

作者： Moumoutzis, Nektarios Rigas, Nikolaos Apostolos Xanthaki, Chara Maragkoudakis, Yiannis Christodoulakis, Christina Paneva-Marinova, Desislava Pavlova, Lilia Laboratory of Distributed Multimedia Information Systems and Applications School of Electrical and Computer Engineering Technical University of Crete Chania Crete73100 Greece Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia1113 Bulgaria Directorate of Secondary Education Chania Crete Greece Department of Computer Science University of Toronto Toronto Canada Laboratory of Telematics Bulgarian Academy of Sciences Sofia1113 Bulgaria

ISBN: (纸本)9783030672089

This paper presents how the PerFECt framework is employed to enable innovative teaching and learning mathematics in schools using drama-based approaches. The topic addressed lies in the cross-section between mathematics and computer science: teaching and learning the binary system of arithmetic that constitutes the basis of modern computing. The work builds on previous research efforts targeting gamification of mathematics and is seen under new light offered by the Theatre in Mathematics (TIM) Methodology. The result is an engaging game to teach the binary system to children using their bodies and simple rules for interacting with each other. Following these simple rules the participants enact the operation of a calculator that is able to transform integers into their binary representations and compute the result of the four arithmetic operations (addition, subtraction, multiplication and division). The game can be combined with online activities using a simulator of the game, so that learners can explore it for themselves and discover how the arithmetic operations are done, even when they are not in the same physical space with their peers. This way, an effective blended learning approach is ultimately offered combining face-to-face and online activities. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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eShadow+: Mixed Reality Storytelling Inspired by Traditional Shadow Theatre

eShadow+: Mixed Reality Storytelling Inspired by Traditional...

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IEEE Annual International computer software and applications Conference (COMPSAC)

作者： Nektarios Moumoutzis Marios Christoulakis Chara Xanthaki Yiannis Maragoudakis Stavros Christodoulakis Desislava Paneva-Marinova Lilia Pavlova Bulgarian Academy of Sciences Institute of Mathematics and Informatics Sofia Bulgaria Lab. of Distr. Multimedia Inf. Systems &#x0026 Applications School of Electrical &#x0026 Computer Eng. Technical University of Crete Chania Greece Computer Science Teacher Directorate of Secondary Education Chania Greece Laboratory of Telematics Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

eShadow is a digital storytelling platform inspired by traditional Shadow Theatre. It enables the creation of digital stories within a project-based approach that may start from scenario development and include the creation of digital puppets and sceneries, the set-up and recording of story scenes and the final assembly of a digital story. This paper presents how eShadow can be enhanced to solve the problem of creating mixed reality installations to offer rich learning experiences in informal learning settings. This enhanced version is eShadow+ and it is evaluated via two installations which are described and compared. The evaluation results demonstrate the effectiveness of the approach thus offering new learning opportunities that are aligned with current trends in the use of mixed reality technologies.

关键词： Conferences Mixed reality Market research software Recording

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Attia Shabbir Raja Hashim Ali Muhammad Zeeshan Shabbir Zain Ul Abideen Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Muhammad Imad Muhammad Abu Bakar Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Department of Electrical Engineering University of Engineering & Technology (UET) Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models.

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