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On the Granular Representation of Fuzzy Quantifier-Based Fuzzy Rough Sets

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arXiv 2023年

作者： Theerens, Adnan Cornelis, Chris Computational Web Intelligence Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium

Rough set theory is a well-known mathematical framework that can deal with inconsistent data by providing lower and upper approximations of concepts. A prominent property of these approximations is their granular representation: that is, they can be written as unions of simple sets, called granules. The latter can be identified with "if..., then..." rules, which form the backbone of rough set rule induction. It has been shown previously that this property can be maintained for various fuzzy rough set models, including those based on ordered weighted average (OWA) operators. In this paper, we will focus on some instances of the general class of fuzzy quantifier-based fuzzy rough sets (FQFRS). In these models, the lower and upper approximations are evaluated using binary and unary fuzzy quantifiers, respectively. One of the main targets of this study is to examine the granular representation of different models of FQFRS. The main findings reveal that Choquet-based fuzzy rough sets can be represented granularly under the same conditions as OWA-based fuzzy rough sets, whereas Sugeno-based FRS can always be represented granularly. This observation highlights the potential of these models for resolving data inconsistencies and managing noise. © 2023, CC BY-NC-ND.

关键词： Machine learning

A Novel Perspective on Brain Tumor Classification Using Hybrid Algorithm

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A Novel Perspective on Brain Tumor Classification Using Hybr...

2023 International Conference on computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023

作者： Jayanthi, R. Christinal, A. Hepzibah Hephzibah, R. Shekinah, T. Bajaj, Chandrajit Chandy, D. Abraham Karunya University Department of Mathematics Coimbatore India Loyola - Icam College of Engineering and Technology Department of Computer Science and Engineering Chennai India University of Texas Computational Applied Mathematics Chair in Visualization Austin United States Karunya University Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350338003

Brain tumor classification is a critical task in medical imaging, where the early and accurate detection of brain tumors is crucial for patient treatment and survival. In recent years, Machine Learning techniques have emerged as a promising approach for automated brain tumor classification. In this paper, we investigate the performance of three ML classifiers - Support Vector Machine, Naïve Bayes, and an ensemble of SVM and Naïve Bayes - for the two-class classification of brain tumor data into a tumor and no tumor classes. Our experiments demonstrate that the ensemble of SVM and Naïve Bayes classifier achieved an accuracy of 97% outperforming the other classifiers. These results indicate that our ensemble classifier can be a powerful tool for the accurate and efficient classification of brain tumor data, potentially leading to improved patient outcomes. © 2023 IEEE.

关键词： Tumors

Proof of Activity Protocol for IoMT Data Security

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computer Systems science & Engineering 2023年第1期44卷 339-350页

作者： R.Rajadevi K.Venkatachalam Mehedi Masud Mohammed A.AlZain Mohamed Abouhawwash Department of Information Technology Kongu Engineering CollegeErode638060TamilnaduIndia Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The Internet of Medical Things(IoMT)is an online device that senses and transmits medical data from users to physicians within a time ***,recent years,IoMT has rapidly grown in the medicalfield to provide healthcare services without physical *** the use of sensors,IoMT applications are used in healthcare *** such applications,one of the most important factors is data security,given that its transmission over the network may cause *** data security in IoMT systems,blockchain is used due to its numerous blocks for secure data *** this study,Blockchain-assisted secure data management framework(BSDMF)and Proof of Activity(PoA)protocol using malicious code detection algorithm is used in the proposed data security for the healthcare *** main aim is to enhance the data security over the *** PoA protocol enhances high security of data from the literature *** replacing the malicious node from the block,the PoA can provide high security for medical data in the *** with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio,precision ratio,security,and efficiency and less response time for Blockchain-enabled healthcare systems.

关键词： Blockchain IoMT malicious code detection security secure data management framework data management PoA

New developments in MatCont: delay equation importer and Lyapunov exponents

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arXiv 2025年

作者： Liessi, Davide Santi, Enrico Vermiglio, Rossana Thakur, Mayank Meijer, Hil G.E. Scarabel, Francesca CDLab – Computational Dynamics Laboratory Department of Mathematics Computer Science and Physics University of Udine Italy Department of Applied Mathematics University of Twente Netherlands School of Mathematics University of Leeds United Kingdom CDLab – Computational Dynamics Laboratory University of Udine Italy

MatCont is a powerful toolbox for numerical bifurcation analysis focussing on smooth ODEs. A user can study equilibria, periodic and connecting orbits, and their stability and bifurcations. Here, we report on additional features in version 7p6. The first is a delay equation importer enabling MatCont users to study a much larger class of models, namely delay equations with finite delay (including delay differential and renewal equations). This importer translates the delay equation into a system of ODEs using a pseudospectral approximation with an order specified by the user. We also implemented Lyapunov exponent computations, event functions for Poincaré maps, and enhanced homoclinic continuation. We demonstrate these features with test cases, such as the Mackey–Glass equation and a renewal equation, and provide additional examples in online tutorials. © 2025, CC BY.

关键词： Bifurcation (mathematics)

Cryptography: Recent research trends of encrypting mathematics

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Materials Today: Proceedings 2022年 56卷 3247-3253页

作者： Kikani, Ria Jatin Verma, Kirti Navalakhe, Rachna Shrivastava, Gourav Shrivastava, Vineeta Department of Information Technology Silver Oak College of Engineering and Technology GTU Gujarat India Department of Engineering Mathematics Lakshmi Narain College of Technology MP Bhopal India Department of Applied Mathematics and Computational Science Shri G.S. Institute of Technology and Science MP Indore India Department of Computer Science and Engineering SAGE University MP Bhopal India Department of Computer Science and Engineering Lakshmi Narain College of Technology MP Bhopal India

In today's world where technology and mathematics are progressing hand in hand there are so many things to be considered and thought of when it comes to network security. Cryptography plays a prominent and an important role when it comes to securing the system from unwanted hazardous attacks. As the Internet has made great strides in different domains it has impacted the human life in several ways and hence data security has become the question of the hour. In such a scenario cryptography plays a big role as it ensures that the data transmitted is received as it is without any alteration or modification. However, we have discussed the algorithms and various methods required in order to maintain the security. In the subsequent sections we shall be discussing about theoretical computer science, cryptography attacks, algorithms and its application in everyday life. © 2021

关键词： Cryptography

Inferring the Parameters of Ultrafast Lasers from Images with Surface Patterns 13

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Inferring the Parameters of Ultrafast Lasers from Images wit...

13th International Symposium on Image and Signal Processing and Analysis, ISPA 2023

作者： Litsas, Anastasis Fraggelakis, Fotis Tsibidis, George D. Stratakis, Emmanuel Pantazis, Yannis University of Crete Computer Science Department Heraklion Crete Greece Institute of Electronic Structure and Laser Foundation for Research and Technology - Hellas Heraklion Crete Greece Institute of Applied and Computational Mathematics Foundation for Research and Technology - Hellas Heraklion Crete Greece

ISBN: (纸本)9798350315363

In this paper, we explore the potential of deep learning techniques in the field of ultra-fast laser processing. More specifically, we trained convolutional neural networks on an in-house dataset with the aim of predicting the laser parameters from images which capture the morphological characteristics of the material surface after being laser fabricated. There are several challenges related with the collected dataset stemming from stochastic variations of the material and the laser, the sampling scheme and the overall low sample size. Therefore, to further test our models we additionally construct a synthetic dataset. The results on the synthetic dataset are almost perfect showing that the proposed predictive models have the capacity to learn the assigned tasks. As expected, the predictive performance decreases when the real dataset is utilized. Nevertheless, we show that both the accuracy in the classification task and the mean square error in the majority of the regression tasks are satisfactory (e.g., classification accuracy drops from 99.9% to 94.8%). © 2023 IEEE.

关键词： Convolutional neural networks

Classifying token frequencies using angular Minkowski p-distance

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arXiv 2023年

作者： Lenz, Oliver Urs Cornelis, Chris Research Group for Computational Web Intelligence Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

Angular Minkowski p-distance is a dissimilarity measure that is obtained by replacing Euclidean distance in the definition of cosine dissimilarity with other Minkowski p-distances. Cosine dissimilarity is frequently used with datasets containing token frequencies, and angular Minkowski p-distance may potentially be an even better choice for certain tasks. In a case study based on the 20-newsgroups dataset, we evaluate clasification performance for classical weighted nearest neighbours, as well as fuzzy rough nearest neighbours. In addition, we analyse the relationship between the hyperparameter p, the dimensionality m of the dataset, the number of neighbours k, the choice of weights and the choice of classifier. We conclude that it is possible to obtain substantially higher classification performance with angular Minkowski p-distance with suitable values for p than with classical cosine dissimilarity. © 2023, CC BY-NC-ND.

关键词： Classification (of information)

Hardware Acceleration for HPS Algorithms in Two and Three Dimensions

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arXiv 2025年

作者： Melia, Owen Fortunato, Daniel Hoskins, Jeremy Willett, Rebecca Department of Computer Science University of Chicago United States Center for Computational Mathematics Flatiron Institute United States Center for Computational Biology Flatiron Institute United States Data Science Institute University of Chicago United States Computational and Applied Mathematics Department of Statistics University of Chicago United States

We provide a flexible, open-source framework for hardware acceleration, namely massively-parallel execution on general-purpose graphics processing units (GPUs), applied to the hierarchical Poincaré–Steklov (HPS) family of algorithms for building fast direct solvers for linear elliptic partial differential equations. To take full advantage of the power of hardware acceleration, we propose two variants of HPS algorithms to improve performance on two- and three-dimensional problems. In the two-dimensional setting, we introduce a novel recomputation strategy that minimizes costly data transfers to and from the GPU;in three dimensions, we modify and extend the adaptive discretization technique of Geldermans and Gillman [2019] to greatly reduce peak memory usage. We provide an open-source implementation of these methods written in JAX, a high-level accelerated linear algebra package, which allows for the first integration of a high-order fast direct solver with automatic differentiation tools. We conclude with extensive numerical examples showing our methods are fast and accurate on two- and three-dimensional problems. © 2025, CC BY.

关键词： Graphics processing unit

Browsing without Third-Party Cookies: What Do You See?

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arXiv 2024年

作者： Lin, Maxwell Lin, Shihan Wu, Helen Wang, Karen Yang, Xiaowei Duke University Computer Science DurhamNC United States Vanderbilt University Computer Science NashvilleTN United States Duke Kunshan University Applied Mathematics and Computational Sciences China

Third-party web cookies are often used for privacy-invasive behavior tracking. Partly due to privacy concerns, browser vendors have started to block all third-party cookies in recent years. To understand the effects of such third-party cookieless browsing, we crawled and measured the top 10,000 Tranco websites. We developed a framework to remove third-party cookies and analyze the differences between the appearance of web pages with and without these cookies. We find that disabling third-party cookies has no substantial effect on website appearance including layouts, text, and images. This validates the industry-wide shift towards cookieless browsing as a way to protect user privacy without compromising on the user experience. © 2024, CC BY.

关键词： Differential privacy