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2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2022

作者： Sreelaja, N.K. Psg College of Technology Department of Applied Mathematics and Computational Sciences Coimbatore India

ISBN: (纸本)9781665477192

Tor network is used to power the connections through Tor. The Tor network has several proxy servers through which the online requests are encrypted and routed. Thus it is difficult to identify the users in a tor network. Tor network is used to host hidden websites which are utilized by the cybercriminals to hide their identity and engage in malicious behavior. To prevent the hackers from malicious behavior, the connections from tor IP addresses must be blocked. An Ant Colony Optimization based approach to filter tor IP address (ACOFT) algorithm is proposed in this paper which filters the incoming tor IP address based on the IP addresses of the tor exit nodes in a database. The advantage of ACOFT algorithm is that the search space is categorized and the incoming IP address is searched in the reduced search space. This approach of searching reduces the search time compared to the existing search techniques. The time complexity of ACOFT is O(log2s) where ' s ' denotes the count of tor IP addresses in the search space and s © 2022 IEEE.

关键词： Ant colony optimization

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Traveling antiferromagnetic domain walls in a magnetic field

arXiv

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arXiv 2025年

作者： Theodorou, George Komineas, Stavros Department of Mathematics and Applied Mathematics University of Crete Crete Heraklion70013 Greece Institute of Applied and Computational Mathematics FORTH Crete Heraklion Greece

We consider an antiferromagnet in one space dimension with easy-axis anisotropy in a perpendicular magnetic field. We study propagating domain wall solutions that can have a velocity up to a maximum vc. The width of the domain wall is a non-monotonic function of the velocity and it diverges to infinity at vc. Both features are in contrast to the case of the Lorentz invariant model in the absence of the field. We further study the modification of the wall profile when a Dzyaloshinskii-Moriya interaction is added. Finally, we present a propagating spiral expected to form when the system is forced at a velocity higher than the maximum velocity for domain walls and we give numerical results for the effect of the Dzyaloshinskii-Moriya interaction. © 2025, CC BY.

关键词： Magnetic fields

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PCOS Perception analysis prediction using Machine learning algorithms 7

PCOS Perception analysis prediction using Machine learning a...

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7th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2022

作者： Dhinakaran, Sakthipriya Thangavel, Chandrakumar Shivayavashilaxmipriya, S. Harinee, V.S. Thiagarajar College of Engineering Department of Applied Mathematics and Computational Science Madurai India

ISBN: (纸本)9781665489102

PCOS-Polycystic ovary syndrome is a prevalent disorder among women in their reproductive years, characterised by irregular periods, pelvic pain, and other symptoms like excessive hair growth on the face, stomach, thighs, and chest, excessive weight gain, and thickened skin patches. According to a large-scale national survey on PCOS, 65 percent of Indian women were unaware of PCOS signs and symptoms. This paper focuses on disseminating knowledge by developing a machine learning-based model for diagnosing whether individuals exhibit PCOS symptoms or not. As PCOS grows, a woman's ovaries and adrenal glands produce an imbalanced amount of hormones. Based on the visible changes in their bodies caused by a hormonal imbalance, this model predicts whether they should take a PCOS test or not. In this paper, we use machine learning algorithms such as logistic regression, random forest, decision tree, K nearest neighbour,naïve bayes and support vector machine (SVM) to diagnose PCOS based on network-obtained data. With the help of performance metrics like accuracy,F-statistics, recall, and precision, the performance of the algorithms was validated after an analysis of the results. The results of logistic regression with an accuracy of 90% have proven to be the most accurate prediction model out of all the possible approaches. © 2022 IEEE.

关键词： Decision trees

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Parameter-robust preconditioners for Biot’s model

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SeMA Journal 2024年第1期81卷 51-80页

作者： Rodrigo, Carmen Gaspar, Francisco J. Adler, James Hu, Xiaozhe Ohm, Peter Zikatanov, Ludmil Department of Applied Mathematics University of Zaragoza Pedro Cerbuna 12 Zaragoza Zaragoza 50009 Spain Department of Mathematics Tufts University 177 College Avenue Medford 02155 MA United States Department of Mathematics RIKEN Center for Computational Science 7-1-26 Minatojima-minamimachi Chuo-ku Hyogo Kobe 650-0047 Japan Department of Mathematics The Pennsylvania State University 107 McAllister Building University Park State College 16802 PA United States

This work presents an overview of the most relevant results obtained by the authors regarding the numerical solution of the Biot’s consolidation problem by preconditioning techniques. The emphasis here is on the design of parameter-robust preconditioners for the efficient solution of the algebraic system of equations resulting after proper discretization of such poroelastic problems. The classical two- and three-field formulations of the problem are considered, and block preconditioners are presented for some of the discretization schemes that have been proposed by the authors for these formulations. These discretizations have been proved to be well-posed with respect to the physical and discretization parameters, what provides a framework to develop preconditioners that are robust with respect to such parameters as well. In particular, we construct both norm-equivalent (block diagonal) and field-of-value-equivalent (block triangular) preconditioners, which are proved to be parameter-robust. The theoretical results on this parameter-robustness are demonstrated by considering typical benchmark problems in the literature for Biot’s model. © 2023, The Author(s).

关键词： 65F08 65F10 65N12 65N22 65N30 Biot’s model Parameter-robust preconditioners Poroelasticity Preconditioning Well-posedness

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Securing Smart City Networks - Intelligent Detection of DDoS Cyber Attacks 5

Securing Smart City Networks - Intelligent Detection of DDoS...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Bennet, Deepthi Tabitha Bennet, Preethi Samantha Anitha, D. Thiagarajar College of Engineering Department of Applied Mathematics and Computational Science Madurai India

ISBN: (纸本)9798350398267

A distributed denial-of-service (DDoS) is a malicious attempt by attackers to disrupt the normal traffic of a targeted server, service or network. This is done by overwhelming the target and its surrounding infrastructure with a flood of Internet traffic. The multiple compromised computer systems (bots or zombies) then act as sources of attack traffic. Exploited machines can include computers and other network resources such as IoT devices. The attack results in either degraded network performance or a total service outage of critical infrastructure. This can lead to heavy financial losses and reputational damage. These attacks maximise effectiveness by controlling the affected systems remotely and establishing a network of bots called bot networks. It is very difficult to separate the attack traffic from normal traffic. Early detection is essential for successful mitigation of the attack, which gives rise to a very important role in cybersecurity to detect the attacks and mitigate the effects. This can be done by deploying machine learning or deep learning models to monitor the traffic data. We propose using various machine learning and deep learning algorithms to analyse the traffic patterns and separate malicious traffic from normal traffic. Two suitable datasets have been identified (DDoS attack SDN dataset and CICDDoS2019 dataset). All essential preprocessing is performed on both datasets. Feature selection is also performed before detection techniques are applied. 8 different Neural Networks/ Ensemble/ Machine Learning models are chosen and the datasets are analysed. The best model is chosen based on the performance metrics (DEEP NEURAL NETWORK MODEL). An alternative is also suggested (Next best - Hypermodel). Optimisation by Hyperparameter tuning further enhances the accuracy. Based on the nature of the attack and the intended target, suitable mitigation procedures can then be deployed. © 2022 IEEE.

关键词： Network security

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Depth Separation in Norm-Bounded Infinite-Width Neural Networks 37

Depth Separation in Norm-Bounded Infinite-Width Neural Netwo...

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37th Annual Conference on Learning Theory, COLT 2024

作者： Parkinson, Suzanna Ongie, Greg Willett, Rebecca Shamir, Ohad Srebro, Nathan Committee on Computational and Applied Mathematics University of Chicago United States Department of Mathematical and Statistical Sciences Marquette University United States Department of Statistics Department of Computer Science University of Chicago United States Department of Computer Science and Applied Mathematics Weizmann Institute of Science Israel Toyota Technological Institute Chicago United States

We study depth separation in infinite-width neural networks, where complexity is controlled by the overall squared 2-norm of the weights (sum of squares of all weights in the network). Whereas previous depth separation results focused on separation in terms of width, such results do not give insight into whether depth determines if it is possible to learn a network that generalizes well even when the network width is unbounded. Here, we study separation in terms of the sample complexity required for learnability. Specifically, we show that there are functions that are learnable with sample complexity polynomial in the input dimension by norm-controlled depth-3 ReLU networks, yet are not learnable with sub-exponential sample complexity by norm-controlled depth-2 ReLU networks (with any value for the norm). We also show that a similar statement in the reverse direction is not possible: any function learnable with polynomial sample complexity by a norm-controlled depth-2 ReLU network with infinite width is also learnable with polynomial sample complexity by a norm-controlled depth-3 ReLU network. © 2024 S. Parkinson, G. Ongie, R. Willett, O. Shamir & N. Srebro.

关键词： Source separation

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Agricultural Advancement: Leveraging EfficientNetB4 for Enhanced Paddy Disease Classification

Agricultural Advancement: Leveraging EfficientNetB4 for Enha...

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2024 IEEE Students Conference on Engineering and Systems, SCES 2024

作者： Johnson, B. Chandrakumar, T. Vijayakumar, A. Thiagarajar College of Engineering Department of Applied Mathematics and Computational Science TamilNadu Madurai India S.T. Hindu College Department of Compuetr Science TamilNadu Nagercoil India

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374711

In agricultural settings, the threat of paddy diseases looms large, precipitating considerable yield losses and economic hardships for farmers. Timely and precise disease detection is crucial for implementing effective control measures. This study seeks to tackle this challenge by leveraging convolutional neural networks (CNNs) to devise an automated framework for paddy disease classification. With a focus on constructing a resilient model capable of accurately discerning various paddy diseases from images and harnessing transfer learning to optimize model performance, the project aims to develop a robust and efficient classification system. The methodology entails a concise overview of the methods employed, encompassing dataset preparation, model implementation, and evaluation metrics. Through rigorous evaluation, the study achieved an impressive accuracy of 98.2 % in paddy disease classification, signifying a significant advancement in automated disease detection in agriculture and holding promise for enhancing crop yield protection and ensuring food security. © 2024 IEEE.

关键词： Convolutional neural networks

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Preface to the Focused Issue on WENO Schemes

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Communications on applied mathematics and Computation 2023年第1期5卷 1-2页

作者： Sigal Gottlieb Jan S.Hesthaven Jianxian Qiu Chi-Wang Shu Qiang Zhang Yong-Tao Zhang Department of Mathematics University of Massachusetts DartmouthNorth DartmouthMA 02747USA Ecole Polytechnique Federale de Lausanne(EPFL) CH-1015 LausanneSwitzerland School of Mathematical Sciences Xiamen UniversityXiamen 361005FujianChina Division of Applied Mathematics Brown UniversityProvidenceRI 02912USA Department of Mathematics Nanjing UniversityNanjing 210093JiangsuChina Department of Applied and Computational Mathematics and Statistics University of Notre DameNotre DameIN 46556USA

The weighted essentially-oscillatory(WENO)schemes are a class of finite volume and finite difference methods for solving convection-dominated problems,mainly hyperbolic conservation *** idea comes from the earlier essentially-oscillatory(ENO)schemes,first developed in[1]in finite volume version and in[6]in finite difference version,for solving hyperbolic conservation laws.

关键词： hyperbolic finite essentially

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MOMENT-BASED METRICS FOR MOLECULES COMPUTABLE FROM CRYO-EM IMAGES

arXiv

引用

arXiv 2024年

作者： Zhang, Andy Mickelin, Oscar Kileel, Joe Verbeke, Eric J. Marshall, Nicholas F. Gilles, Marc Aurèle Singer, Amit Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mathematics Oden Institute University of Texas At Austin AustinTX78712 United States Department of Mathematics Oregon State University CorvallisOR97331 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Single particle cryogenic electron microscopy (cryo-EM) is an imaging technique capable of recovering the high-resolution 3-D structure of biological macromolecules from many noisy and randomly oriented projection images. One notable approach to 3-D reconstruction, known as Kam's method, relies on the moments of the 2-D images. Inspired by Kam's method, we introduce a rotationally invariant metric between two molecular structures, which does not require 3-D alignment. Further, we introduce a metric between a stack of projection images and a molecular structure, which is invariant to rotations and reflections and does not require performing 3-D reconstruction. Additionally, the latter metric does not assume a uniform distribution of viewing angles. We demonstrate uses of the new metrics on synthetic and experimental datasets, highlighting their ability to measure structural similarity. Copyright © 2024, The Authors. All rights reserved.

关键词： Method of moments

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Single-ancilla ground state preparation via Lindbladians

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Physical Review Research 2024年第3期6卷 033147-033147页

作者： Zhiyan Ding Chi-Fang Chen Lin Lin Department of Mathematics Institute for Quantum Information and Matter Applied Mathematics and Computational Research Division Challenge Institute of Quantum Computation

We design a quantum algorithm for ground state preparation in the early fault tolerant regime. As a Monte Carlo style quantum algorithm, our method features a Lindbladian where the target state is stationary. The construction of this Lindbladian is algorithmic and should not be seen as a specific approximation to some weakly coupled system-bath dynamics in nature. Our algorithm can be implemented using just one ancilla qubit and efficiently simulated on a quantum computer. It can prepare the ground state even when the initial state has zero overlap with the ground state, bypassing the most significant limitation of methods like quantum phase estimation. As a variant, we also propose a discrete-time algorithm, demonstrating even better efficiency and providing a near-optimal simulation cost depending on the desired evolution time and precision. Numerical simulations using Ising and Hubbard models demonstrate the efficacy and applicability of our method.

关键词： Quantum algorithms Quantum algorithms & computation Quantum computation

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