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Approximating reproduction numbers: a general numerical method for age-structured models

Mathematical Biosciences and Engineering 2024年第4期21卷 5360-5393页

作者： De Reggi, Simone Scarabel, Francesca Vermiglio, Rossana Department of Mathematics Computer Science and Physics University of Udine via delle Scienze 206 Udine33100 Italy Department of Applied Mathematics University of Leeds Woodhouse LeedsLS2 9JT United Kingdom CDLab – Computational Dynamics Laboratory University of Udine Italy

In this paper, we introduce a general numerical method to approximate the reproduction numbers of a large class of multi-group, age-structured, population models with a finite age span. To provide complete flexibility in the definition of the birth and transition processes, we propose an equivalent formulation for the age-integrated state within the extended space framework. Then, we discretize the birth and transition operators via pseudospectral collocation. We discuss applications to epidemic models with continuous and piecewise continuous rates, with different interpretations of the age variable (e.g., demographic age, infection age and disease age) and the transmission terms (e.g., horizontal and vertical transmission). The tests illustrate that the method can compute different reproduction numbers, including the basic and type reproduction numbers as special cases. c 2024 the Author(s), licensee AIMS Press.

关键词： Numerical methods

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Salp Swarm Incorporated Adaptive Dwarf Mongoose Optimizer with Lévy Flight and Gbest-Guided Strategy

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Journal of Bionic Engineering 2024年第4期21卷 2110-2144页

作者： Gang Hu Yuxuan Guo Guanglei Sheng Department of Applied Mathematics Xi’an University of TechnologyXi’an710054People’s Republic of China School of Computer Science and Engineering Xi’an University of TechnologyXi’an710048People’s Republic of China Department of Electronics and Information Engineering Bozhou UniversityBozhou236800People’s Republic of China

In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as ***,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local *** addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in *** results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test ***,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization *** simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.

关键词： Dwarf mongoose optimization algorithm Gbest-guided Lévy flight Adaptive parameter Salp swarm algorithm Engineering optimization Truss topological optimization

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Orbital refinement of ERS-1 using dual crossover arc techniques with TOPEX/POSEIDON

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manuscripta geodaetica 2024年第5期18卷 249-262页

作者： Moore, P. Ehlers, S. Department of Computer Science and Applied Mathematics Aston University Birmingham UK

Optimal interpretation of altimetry requires radial positioning of the satellite to accuracy approaching that of the instrumentation. For ERS–1 this is currently unrealistic due to uncertainties in the gravitational and surface force modelling but close to realisation for the higher orbit of TOPEX/POSEIDON. From August 1992 the altimeters on board ERS–1 and TOPEX/POSEIDON will be in simultaneous operation, enabling exploitation of the merged datasets and refinement of the less accurate ERS–1 radial ephemeris to accuracies hopefully not far short of those of TOPEX/POSEIDON. Within this study simulations have been undertaken to examine a procedure to refine the less precise ERS–1 radial ephemeris by use of single and dual satellite crossovers with TOPEX/POSEIDON. The procedure recovers a time series expansion of the radial error through least–squares minimisation of crossover residuals. Simulations consider refinement of ERS–1 by itself and of ERS–1 and TOPEX/POSEIDON simultaneously. Deficiencies leading to singularities in the least–square refinement are identified. The simulations confirm that the methodology has the capability of refining ERS–1 orbits to accuracies nearly comparable to TOPEX/POSEIDON.

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On the Practical Power of Automata in Pattern Matching

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SN computer science 2024年第4期5卷 400页

作者： Amir, Ora Amir, Amihood Fraenkel, Aviezri Sarne, David Department of Computer Science Bar-Ilan University Ramat-Gan 52900 Israel Camtek Migdal HaEmek Israel College of Computing Georgia Tech Atlanta 30332 GA United States Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot 76100 Israel

Many papers in the intersection of theoretical and applied algorithms show that the simple, asymptotically less efficient algorithm, performs better than the bestcomplex theoretical algorithms on random data or in specialized “real world” applications. This paper considers the Knuth–Morris–Pratt automaton, and shows a counter-intuitive practical result. The classical pattern matching paradigm is that of seeking occurrences of one string—the pattern, in another—the text, where both strings are drawn from an alphabet set Σ. Assuming the text length is n and the pattern length is m, this problem can naively be solved in time O(nm). In Knuth, Morris and Pratt’s seminal paper of 1977, an automaton, was developed that allows solving this problem in time O(n) for any alphabet. This automaton, which we will refer to as the KMP-automaton, has proven useful in solving many other problems. A notable example is the parameterized pattern matching model. In this model, a consistent renaming of symbols from Σ is allowed in a match. The parameterized matching paradigm has proven useful in problems in software engineering, computer vision, and other applications. It has long been believed that for texts where the symbols are uniformly random, the naive algorithm will perform as well as the KMP algorithm. In this paper, we examine the practical efficiency of the KMP algorithm versus the naive algorithm on a randomly generated text. We analyze the time under various parameters, such as alphabet size, pattern length, and the distribution of pattern occurrences in the text. We do this for both the original exact matching problem and parameterized matching. While the folklore wisdom is vindicated by these findings for the exact matching case, surprisingly, the KMP algorithm always works significantly faster than the naive in the parameterized matching case. We check this hypothesis for DNA texts and image data and observe a similar behavior as in the random text. We also show a very stru

关键词： Knuth–Morris–Pratt automaton Parameterized matching Simple algorithms Uniformly random text

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Multi Spectral Feature Extraction to Improve Lung Sound Classification using CNN 10

Multi Spectral Feature Extraction to Improve Lung Sound Clas...

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10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Rishabh Kumar, Dhirendra University of Delhi Department of Computer Science Delhi India Delhi Technological University Department of Applied Mathematics Delhi India

ISBN: (纸本)9781665490993

The principal method for screening and diagnosing lung disorders is auscultation of respiratory sounds. In the medical field, automated auscultation is a new research field that helps with faster disease diagnosis. The task of automated auscultation is to classify the respiratory sound, which requires a dataset to build a classification model. Deep learning networks are a popular method for producing such classification models, which require a large number of samples to train. Unfortunately, even the largest publicly accessible respiratory sound dataset (ICBHI 2017) of 5.5 hours total length contains only 6898 respiratory cycles, which is still insufficient for training deep learning networks. In this research work, data augmentation and smart padding techniques are utilised to account for the ICBHI dataset's class imbalance. Further, we have suggested to extract several features from the audio signal, such as MFCC, Chroma STFT, Mel-Spectrogram, Spectral Contrast, and Tonnetz, for better representation of the samples in a class. Further, we have obtained the statistical features such as mean and standard deviation for developing the classification model. We have utilised a deep learning-based CNN architecture for the classification of the learned features. The proposed method achieved a score of 74.08% for a 60-40 train-test split and 75.04% for an 80-20 train-test split, outperforming all state-of-the-art lung sound classification methods. © 2023 IEEE.

关键词： Deep learning

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Multiview Orthonormalized Partial Least Squares: Regularizations and Deep Extensions

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IEEE Transactions on Neural Networks and Learning Systems 2023年第8期34卷 4371-4385页

作者： Wang, Li Li, Ren-Cang Lin, Wen-Wei The University of Texas at Arlington Department of Mathematics ArlingtonTX76019 United States The University of Texas at Arlington Department of Computer Science and Engineering ArlingtonTX76019 United States National Chiao Tung University Department of Applied Mathematics Hsinchu300 Taiwan

In this article, we establish a family of subspace-based learning methods for multiview learning using least squares as the fundamental basis. Specifically, we propose a novel unified multiview learning framework called multiview orthonormalized partial least squares (MvOPLSs) to learn a classifier over a common latent space shared by all views. The regularization technique is further leveraged to unleash the power of the proposed framework by providing three types of regularizers on its basic ingredients, including model parameters, decision values, and latent projected points. With a set of regularizers derived from various priors, we not only recast most existing multiview learning methods into the proposed framework with properly chosen regularizers but also propose two novel models. To further improve the performance of the proposed framework, we propose to learn nonlinear transformations parameterized by deep networks. Extensive experiments are conducted on multiview datasets in terms of both feature extraction and cross-modal retrieval. Results show that the subspace-based learning for a common latent space is effective and its nonlinear extension can further boost performance, and more importantly, one of two proposed methods with nonlinear extension can achieve better results than all compared methods. © 2012 IEEE.

关键词： Mathematical transformations

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Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

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computer Systems science & Engineering 2023年第3期44卷 1869-1880页

作者： S.Siamala Devi K.Venkatachalam Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Sri Krishna College of TechnologyCoimbatore641042India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced *** diagnosis of many diseases will improve the patient *** cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things(IoT),Wireless Sensor Networks(WSN),Embedded systems,Deep learning approaches and Optimization and aggregation *** data generated through these technologies will demand the bandwidth,data rate,latency of the *** this proposed work,efficient discrete grey wolf optimization(DGWO)based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code(ECEMAC)has been used to aggregate the parameters generated from the wearable sensor devices of the *** nodes that are far away from edge node will forward the data to its neighbor cluster head using *** scheme will reduce the number of transmissions over the *** aggregated data are preprocessed at edge node to remove the noise for better *** node will reduce the overhead of cloud *** aggregated data are forward to cloud server for central storage and *** proposed smart diagnosis will reduce the transmission cost through aggrega-tion scheme which will reduce the energy of the *** cost for proposed system for 300 nodes is 0.34μ*** energy cost of existing approaches such as secure privacy preserving data aggregation scheme(SPPDA),concealed data aggregation scheme for multiple application(CDAMA)and secure aggregation scheme(ASAS)are 1.3μJ,0.81μJ and 0.51μJ *** optimization approaches and encryption method will ensure the data privacy.

关键词： Discrete grey wolf optimization data aggregation cloud computing IoT WSN smart healthcare elliptic curve elgamal energy optimization

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Categorizing methods for integrating machine learning with executable specifications

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science China(Information sciences) 2024年第1期67卷 5-19页

作者： David HAREL Raz YERUSHALMI Assaf MARRON Achiya ELYASAF Department of Computer Science and Applied Mathematics Weizmann Institute of Science Department of Software and Information Systems Engineering Ben-Gurion University of the Negev

Deep learning(DL),which includes deep reinforcement learning(DRL),holds great promise for carrying out real-world tasks that human minds seem to cope with quite *** promise is already delivering extremely impressive results in a variety of ***,while DL-enabled systems achieve excellent performance,they are far from *** has been demonstrated,in several domains,that DL systems can err when they encounter cases they had not hitherto ***,the opacity of the produced agents makes it difficult to explain their behavior and ensure that they adhere to various requirements posed by human *** the other end of the software development spectrum of methods,behavioral programming(BP) facilitates orderly system development using self-standing executable modules aligned with how humans intuitively describe desired system *** this paper,we elaborate on different approaches for combining DRL with BP and,more generally,machine learning(ML) with executable specifications(ES).We begin by defining a framework for studying the various approaches,which can also be used to study new emerging approaches not covered *** then briefly review state-of-the-art approaches to integrating ML with ES,continue with a focus on DRL,and then present the merits of integrating ML with *** conclude with guidelines on how this categorization can be used in decision making in system development,and outline future research challenges.

关键词： machine learning artificial intelligence grey box learning domain knowledge rules behavioral programming deep reinforcement learning survey

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Towards new realities: Implications of personalized online layers in our daily lives

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i-com 2024年第2期23卷 221-229页

作者： Herder, Eelco Stojko, Laura Strecker, Jannis Neumayr, Thomas Yigitbas, Enes Augstein, Mirjam Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Department of Computer Science University of the Bundeswehr Munich Neubiberg Germany Institute of Computer Science University of St. Gallen St. Gallen Switzerland Research Group Personalized Environments and Collaborative Systems University of Applied Sciences Upper Austria Hagenberg Austria Faculty of Electrical Engineering Computer Science and Mathematics Paderborn University Paderborn Germany

We are currently in a period of upheaval, as many new technologies are emerging that open up new possibilities to shape our everyday lives. Particularly, within the field of Personalized Human-computer Interaction we observe high potential, but also challenges. In this article, we explore how an increasing amount of online services and tools not only further facilitates our lives, but also shapes our lives and how we perceive our environments. For this purpose, we adopt the metaphor of personalized 'online layers' and show how these layers are and will be interwoven with the lives that we live in the 'human layer' of the real world. © 2024 the author(s), published by De Gruyter, Berlin/Boston.

关键词： Human computer interaction

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Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

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computer Modeling in Engineering & sciences 2024年第4期139卷 801-816页

作者： D.Dorathy Prema Kavitha L.Francis Raj Sandeep Kautish Abdulaziz S.Almazyad Karam M.Sallam Ali Wagdy Mohamed Department of Mathematics Voorhees CollegeVelloreTamil NaduIndia Chandigarh Group of Colleges JhanjeriMohaliPunjabIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of Science and Technology School of IT and SystemsUniversity of CanberraCanberraAustralia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

The intuitive fuzzy set has found important application in decision-making and machine *** enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images *** image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye *** degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal *** proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between *** methodology was used to clarify the input images and make them adequate for the process of glaucoma *** objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were *** the peak regions were identified,the recurrence relationships among those peaks were then *** partitioning was done due to varying degrees of similar and dissimilar concentrations in the *** and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and *** distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

关键词： Convolutional Neural Network(CNN) glaucomatous eyes fuzzy difference equation intuitive fuzzy sets image segmentation retinal images

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