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2023 International Conference on Information and Digital Technologies, IDT 2023

作者： Bris, Radim Jahoda, Pavel Vsb - Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Applied Mathematics Czech Republic

ISBN: (纸本)9798350305869

This paper deals with the exploration of a system which acts on demand in case of emergency matter. To verify functionality, each component of the system must be regularly inspected with a specified inspection interval and tested to find latent failures. A failure-based preventive maintenance (PM) is considered, in context with the imperfect corrective maintenance model. This means that PM brings full renew which is realized after a beforehand setting the number of failures. If a failure occurs, it is detected during the first follow-up inspection and the reparation process starts. The imperfect corrective maintenance model is intended where each repair deteriorates the system life-time whose probability distribution is gradually altered through rising failure rate. Reliability mathematics for unavailability analysis is briefly demonstrated. The new renewal process model, including the failure-based PM, has been termed as a real ageing process. The imperfect corrective maintenance as well as increasing length of the inspection interval result in unwanted increase of system unavailability function which can be eliminated as well as optimized by the properly selected failure-based PM applied to all system components. If the PM is fixed due to cost, inspection intervals of system components can be optimized to keep the maximal value of the unavailability function in its original level. Sensitivity analysis applied to the length of the optimal inspection intervals with regard to gradually changing PM is demonstrated. This optimization process is demonstrated on a four-component system adopted from references. © 2023 IEEE.

关键词： Preventive maintenance

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Advanced SMS Spam Detection using Deep Complex Models and Sine-Cosine Algorithm 14

Advanced SMS Spam Detection using Deep Complex Models and Si...

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14th International Conference on Information and Knowledge Technology, IKT 2023

作者： Rezaei, Sepehr Shams, Mohammadreza Meybodi, Mohsen Alambardar University of Isfahan Faculty of Mathematics and Statistics Department of Applied Mathematics and Computer Science Isfahan Iran University of Isfahan Shahreza Campus Department of Computer Engineering Isfahan Iran

ISBN: (纸本)9798350349412

With the increasing use of mobile phones and messaging services, SMS spam has become a significant issue for users. In this paper, we propose a novel approach1 to tackle this problem by using Sine-Cosine Algorithm (SCA) and Complex Multi-Layer Perceptron (C-MLP). Specifically, we apply the SCA method to reduce the dimensionality of the feature space and CMLP to improve the performance of spam detection. Also, in this paper, we investigate the effectiveness of different classification algorithms, including Support Vector Machines, Random Forests, K-nearest neighbors, Naive Bayes, bagging, and voting approaches. Our experimental results show that the proposed approach achieves high accuracy and outperforms existing methods in terms of both accuracy and F-measure. The proposed approach can be helpful in designing effective SMS spam filters and improving the overall user experience.1All the code used in this research is publicly available on the first author's GitHub repository: https://***/seper-sw/*** © 2023 IEEE.

关键词： Support vector machines

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Exploring household food security through institutional factors: A statistical and mathematical analysis

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9179-9195页

作者： Khan, Younas Ashraf, Shahzaib Farman, Muhammad Abdallah, Suhad Ali Osman Department of Sociology Kohat University of Science and Technology Kohat Pakistan Institute of Mathematics Khawaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Mathematics Faculty of Arts and Science Near East University Turkey Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Applied College Khamis Mushait King Khalid University Abha Saudi Arabia

Achieving household food security is the tumbling issue of the century. This article explores the factors affecting household food security and solutions by utilizing a synergy of statistical and mathematical models. The methodology section is divided into two portions namely sociological and mathematical methods. Sociologically, 379 household heads were interviewed through structured questions and further analyzed in terms of descriptive and binary logistic regression. The study found that 4 independent variables (poverty, poor governance, militancy, and social stratification) showed a significant association (P = 0.000) to explain variations in the dependent variable (household FS). The Omnibus test value (χ2= 102.386;P = 0.000) demonstrated that the test for the entire model against constant was statistically significant. Therefore, the set of predictor variables could better distinguish the variation in household FS. The Nagelkerke's R Square (R2 = .333) helps to interpret that the prediction variable and the group variables had a strong relationship. Moreover, 23% to 33% variation in FS was explained by the grouping variables (Cox and Snell R2 = 0.237 and Nagelkerke's R2 = 0.333). The significant value of Wald test results for each variable confirmed that the grouping variables (poor governance P = 0.004, militancy P = 0.000, social stratification P = 0.021 and poverty P = 0.000) significantly predicted FS at the household level. Mathematically, all the statistics were validated further through the application of spherical fuzzy mathematics (TOPIS and MADM) to explore what factors are affecting household FS. Thus, the study found that F3 (poverty) > F2 (militancy) > F4 (social stratification) > F1 (poor governance) respectively. Thus, it could be concluded from these findings that the prevalence of poverty dysfunctional all the channels of household FS at the macro and micro levels. Therefore, a sound and workable model to eradicate poverty in the study area by

关键词： Food supply

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Enhancing photovoltaic energy forecasting:a progressive approach using wavelet packet decomposition

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Clean Energy 2024年第3期8卷 95-108页

作者： Khaled Ferkous Mawloud Guermoui Abderahmane Bellaour Tayeb boulmaiz Nadjem Bailek LMTESE Laboratory of Materials Technology of Energy Systems and EnvironmentUniversitéde Ghardaia47000Algeria Renewable Energy Applied Research Unit(URAER) Ghardaia47000Algeria Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer ScienceFaculty of Sciences and TechnologyAhmed Draia University of AdrarAdrar 01000Algeria

Accurate photovoltaic(PV)energy forecasting plays a crucial role in the efficient operation of PV power *** study presents a novel hybrid machine-learning(ML)model that combines Gaussian process regression with wavelet packet decomposition to forecast PV power half an hour *** proposed technique was applied to the PV energy database of a station located in Algeria and its performance was compared to that of traditional forecasting *** evaluations demonstrate the superiority of the proposed approach over conventional ML methods,including Gaussian process regression,extreme learning machines,artificial neural networks and support vector machines,across all *** proposed model exhibits lower normalized root mean square error(nRMSE)(2.116%)and root mean square error(RMSE)(208.233 kW)values,along with a higher coefficient of determination(R^(2))of 99.881%.Furthermore,the exceptional performance of the model is maintained even when tested with various prediction ***,as the forecast horizon extends from 1.5 to 5.5 hours,the prediction accuracy decreases,evident by the increase in the RMSE(710.839 kW)and nRMSE(7.276%),and a decrease in R2(98.462%).Comparative analysis with recent studies reveals that our approach consistently delivers competitive or superior *** study provides empirical evidence supporting the effectiveness of the proposed hybrid ML model,suggesting its potential as a reliable tool for enhancing PV power forecasting accuracy,thereby contributing to more efficient grid management.

关键词： short photovoltaic power forecasting wavelet packet decomposition sub-series reconstruction machine learning in energy forecasting sustainable power stations renewable energy

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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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Firefly Algorithm Approach for Recovering the Mathematical Structure of One-Dimensional Chaotic Maps from Time Series Data 3

Firefly Algorithm Approach for Recovering the Mathematical S...

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3rd International Conference on computer Technologies, ICCTech 2024

作者： Galvez, Akemi Fister, Iztok Fister, Iztok Iglesias, Andres University of Cantabria Dept. of Applied Mathematics & Computational Sciences Santander Spain University of Maribor Faculty of Electrical Engineering & Computer Science Maribor Slovenia

ISBN: (纸本)9798350395419

Uncovering the mathematical structure of unknown chaotic systems from limited time series data poses a significant challenge in the field of dynamical systems, with broad applications across various domains. In this paper, we propose a swarm intelligence approach to tackle this issue, specifically focusing on one-dimensional discrete maps. Our approach leverages the firefly algorithm, a popular nature-inspired metaheuristic, known for its optimization capabilities. We assess the performance of the method by applying it to two illustrative examples of time series derived from one-dimensional maps: the Hénon map and the Burger map. The results show that this approach is promising for recovering the equations of motion of one-dimensional chaotic maps, even in situations where no additional information is available beyond the provided data points. © 2024 IEEE.

关键词： Time series

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Improving Transfer Learning and Domain Adaptation in Smart Homes: The Role of Dynamic Domain Adaptation Layer

Improving Transfer Learning and Domain Adaptation in Smart H...

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Attaullah, Hasina Kiani, Anum Jungeblut, Thorsten Faculty of Eng. and Mathematics Uni. of Applied Sciences and Arts Bielefeld Germany Department of Computer Science Rawalpindi Women University Rawalpindi Pakistan

ISBN: (纸本)9798331510503

The advent of smart home technologies has opened new avenues for personalized healthcare, energy management, and enhanced convenience. However, variations in resident behavior, sensor types, and home layouts cause domain shifts, posing significant challenges to the deployment of machine learning models across diverse smart home environments. This paper describes a new method that uses domain adaptation layers in a transfer learning framework to change domain-specific features on the fly. This makes cross-data set generalization better. We compare the proposed method against Standard Transfer Learning, Domain-Adversarial Neural Networks (DANN), and Correlation Alignment (CORAL) using multiple smart home data sets. Our evaluation demonstrates that the proposed method significantly outperforms these existing approaches in terms of predictive performance, highlighting its effectiveness in addressing domain shifts in smart home environments. © 2024 IEEE.

关键词： Smart homes

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On the Maximum Number of Crossings in Star-Simple Drawings of Kn with No Empty Lens

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Journal of Graph Algorithms and Applications 2022年第3期26卷 381-399页

作者： Felsner, Stefan Hoffmann, Michael Knorr, Kristin Kynčl, Jan Parada, Irene Institute of Mathematics Technische Universität Berlin Germany Department of Computer Science ETH Zürich Switzerland Department of Computer Science Freie Universität Berlin Germany Department of Applied Mathematics Faculty of Mathematics and Physics Charles University Prague Czech Republic Department of Mathematics and Computer Science TU Eindhoven Netherlands

A star-simple drawing of a graph is a drawing in which adjacent edges do not cross. In contrast, there is no restriction on the number of crossings between two independent edges. We forbid empty lenses, i.e., every lens is required to enclose a vertex, and show that with this restriction 3 · (n − 4)! is an upper bound on the number of crossings between two edges of a star-simple drawing of Kn. It follows that n! bounds the total number of crossings in the drawing. This is the first finite upper bound on the number of crossings in star-simple drawings of the complete graph Kn with no empty lens. For a lower bound we construct a star-simple drawing of Kn with no empty lens in which a pair of edges contributes 5n/2−2 crossings. © 2022, Brown University. All rights reserved.

关键词： Stars

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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism

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Mathematical Biosciences and Engineering 2025年第1期22卷 73-105页

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00–99.00%) and specificity (89.63–99.00%) across all tasks highlighted the model’s robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life. © 2025 American Institute of Mathematical sciences. All rights reserved.

关键词： Electroencephalography

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Yang-Lee zeros for real-space condensation

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Physical Review E 2025年第1期111卷 L012101-L012101页

作者： Zdzislaw Burda Desmond A. Johnston Mario Kieburg Faculty of Physics and Applied Computer Science AGH University of Krakow al. Mickiewicza 30 30-059 Kraków Poland School of Mathematical and Computer Sciences Heriot-Watt University Riccarton Edinburgh EH14 4AS United Kingdom and Maxwell Institute of Mathematical Sciences Edinburgh United Kingdom School of Mathematics and Statistics University of Melbourne Parkville VIC 3010 Australia Faculty of Physics and Applied Computer Science AGH University of Krakow al. Mickiewicza 30 30-059 Kraków Poland and Faculty of Physics Bielefeld University P.O. Box 100131 D-33501 Bielefeld Germany

Using the electrostatic analogy, we derive an exact formula for the limiting Yang-Lee zero distribution in the random allocation model of general weights. This exhibits a real-space condensation phase transition, which is induced by a pressure change. The exact solution allows one to read off the scaling of the density of zeros at the critical point and the angle at which the locus of zeros hits the critical point. Since the order of the phase transition and critical exponents can be tuned with a single parameter for several families of weights, the model provides a useful testing ground for verifying various relations between the distribution of zeros and the critical behavior, as well as for exploring the behavior of physical quantities in the mesoscopic regime, i.e., systems of large but finite size. The main result is that asymptotically the Yang-Lee zeros are images of a conformal mapping, given by the generating function for the weights, of uniformly distributed complex phases.

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