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arXiv 2021年

作者： Moosaei, Hossein Mousavi, Ahmad Hladík, Milan Gao, Zheming Department of Mathematics Faculty of Science University of Bojnord Bojnord Iran Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Institute for Mathematics and its Applications University of Minnesota MinneapolisMN55455 United States Department of Applied Mathematics Faculty of Mathematics and Physics Charles University Prague Czech Republic College of Information Science and Engineering Northeastern University ShenyangLiaoning110819 China

In binary classification, kernel-free linear or quadratic support vector machines are proposed to avoid dealing with difficulties such as finding appropriate kernel functions or tuning their hyper-parameters. Furthermore, Universum data points, which do not belong to any class, can be exploited to embed prior knowledge into the corresponding models so that the generalization performance is improved. In this paper, we design novel kernel-free Universum quadratic surface support vector machine models. Further, we propose the L1 norm regularized version that is beneficial for detecting potential sparsity patterns in the Hessian of the quadratic surface and reducing to the standard linear models if the data points are (almost) linearly separable. The proposed models are convex such that standard numerical solvers can be utilized for solving them. Nonetheless, we formulate a least squares version of the L1 norm regularized model and next, design an effective tailored algorithm that only requires solving one linear system. Several theoretical properties of these models are then reported/proved as well. We finally conduct numerical experiments on both artificial and public benchmark data sets to demonstrate the feasibility and effectiveness of the proposed models. Copyright © 2021, The Authors. All rights reserved.

关键词： Support vector machines

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Natural convection heat transport performance of nanofluids under the influence of inclined magnetic field

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Results in Physics 2024年 58卷

作者： Islam, Tarikul Fayz-Al-Asad, Md. Khatun, M.A. Parveen, N. Ahmad, Hijaz Askar, Sameh CMUP University of Porto Porto Portugal Department of Mathematics American International University –Bangladesh Khilkhet Kuratoli Dhaka-1229 Bangladesh Department of Mathematics Jagannath University Dhaka 1000 Bangladesh Department of Mathematics Bangladesh University of Engineering and Technology Dhaka-1000 Bangladesh Near East University Operational Research Center in Healthcare Nicosia PC: 99138 TRNC Mersin 10 Turkey Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology Mishref Kuwait Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Mathematics and Informatics Azerbaijan University Jeyhun Hajibeyli street 71 AZ1007 Baku Azerbaijan Department of Statistics and Operations Research College of Science King Saud University P.O. Box 2455 Riyadh 11451 Saudi Arabia

Two-dimensional free convective temperature transportation and fluid flow within the semi-circular cavity occupied by nanofluid with influence of inclined MHD is studied numerically. While bottom wall is warmed by three distinct thermal conditions (uniform, parabolic, and sinusoidal), the upper circular wall is warmed at lower temperatures. A slopping magnetic field acts upon the enclosure whose strength is unchanging. The governing non-linear equations is resolved with the numerical scheme Galerkin type finite element technique. A strong agreement is found with the earlier work. The outcomes for the model parameters likely Rayleigh number, sloping angle of magnetic field, nanoparticles volume, size, shape, and Brownian effects, and Hartmann number are demonstrated through streamlines, isothermal lines, and mean Nusselt number. In addition, investigation is conducted the thermal features of various nanoparticles and the nanofluid produced from them. The outcomes reveal that thermal transfer rate is significantly enhanced by volume fraction nanoparticles and buoyancy-driven parameter Rayleigh number whilst reduces for higher Hartman number. The inclined angles of magnetic field and different thermal conditions have a positive influence on thermal transport. For Cu-H₂O nanofluids, thermal transport is enhanced by 10.98 % in a uniform thermal system, where it increases by 14.42 % in the parabolic thermal system and by 15.53 % in a sinusoidal thermal system for the only 5 % volume of nanoparticles. Moreover, nano-sized particles’ shape factor has a noteworthy role in thermal transport. It is found that heat transfer rate rises by 6.79 % for blade-shaped nanoparticles. Additionally, as considering Brownian effects of nanoparticles, the temperature transport rate is improved by 21.45 % for small-sized nanoparticles. © 2024 The Authors

关键词： Finite Element Method Inclined magnetic field Nanoparticles Natural convection Semi-circular cavity

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Three-dimensional radiative flow of tetra-hybrid nanofluid via oil rig solar panel sheet with catalysis reaction using Yamad-Ota and Hamilton-Crosser models

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Journal of Radiation Research and applied Sciences 2025年第1期18卷

作者： Munawar Abbas Abdullah A. Faqihi Ilyas Khan Faisal Salah Mastoor M. Abushaega Abdulhadi A. Altherwi Muhammad Shafique Department of Mathematics The Islamia University of Bahawalpur Bahawalpur 63100 Pakistan Department of Industrial Engineering College of Engineering and Computer Science Jazan University P. O. Box 706 Jazan 45142 Kingdom of Saudi Arabia Department of Mathematics Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Mathematics College of Science Al-Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Department of Mathematics College of Science and Arts King Abdulaziz University Rabigh 21911 Saudi Arabia Industrial Engineering Department College of Engineering and Computer Science Jazan University Jazan 45142 Saudi Arabia Department of Industrial Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Chemistry Government Graduate College Taunsa Sharif 32100 Pakistan

In this study, a framework for examining how solar radiation affects the MHD tetra hybrid nanofluid flow in an extending solar module sheet situated on an offshore solar oil field is presented. A tetra hybrid nanofluid consisting of propylene glycol ( C 3 H 8 O 2 ) as the improper fluid and A g , T i O 2 , C u and F e 3 O 4 nanoparticles are used. Renewable energy has the amazing capacity to replace itself more quickly than it is consumed, especially when it comes from natural sources like the sun and wind. This article compares the performance of the Yamada–Ota thermal conductivity tetra hybrid nanofluid model to the Hamilton–Crosser model. In addition to temperature-dependent components like thermal radiation and exponentially and temperature-dependent heat generation, the concentration aspect of tetra hybrid nanofluid is analyzed by considering both heterogeneous and homogeneous chemical processes. The temperature rise of the tetra hybrid nanofluid, which simulates the migration of nanoparticles caused by homogeneous chemical reactions inside the liquid, is investigated. Similarity factors are then used to convert the resulting partial differential equations into ordinary differential equations. After that, numerical results are acquired using the bvp4c scheme, an ODE solver. The higher heat absorbed by the PV sheet due to the increased volume percentage of tetra-hybrid nanoparticles and heat-generating features allows for a variety of applications on the offshore solar rig, such as drilling and navigation.

关键词： Cubic autocatalysis Tetra hybrid nanofluid Inclined magnetic Yamada–Ota and Hamilton–Crosser models Renewable energy Heat generation

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Exploring Tidal Force Effects and Shadow Constraints for Schwarzschild-like Black Hole in Starobinsky-Bel-Robinson Gravity

arXiv

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arXiv 2023年

作者： Arora, Dhruv Molla, Niyaz Uddin Chaudhary, Himanshu Debnath, Ujjal Atamurotov, Farruh Mustafa, G. Sagara Odisha Sambalpur768224 India Department of Mathematics Indian Institute of Engineering Science and Technology Shibpur Howrah711 103 India Department of Applied Mathematics Delhi Technological University Delhi110042 India Department of Mathematics Shyamlal College University of Delhi Delhi110032 India New Uzbekistan University Mustaqillik ave. 54 Tashkent100007 Uzbekistan Central Asian University Milliy Bog’ Street 264 Tashkent111221 Uzbekistan Faculty of Computer Engineering Tashkent University of Applied Sciences Gavhar Str. 1 Tashkent100149 Uzbekistan Institute of Theoretical Physics National University of Uzbekistan Tashkent100174 Uzbekistan Department of Physics Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Photoelectronics Zhejiang Institute for Advanced Light Source Zhejiang Normal University Zhejiang Jinhua321004 China

The current manuscript deals with the tidal force effects, geodesic deviation, and shadow constraints of the Schwarzschild-like black hole theorised in Starobinsky-Bel-Robinson gravity exhibiting M-theory compactification. In the current analysis, we explore the radial and angular tidal force effects on a radially in-falling particle by the central black hole, which is located in this spacetime. We also numerically solve the geodesic deviation equation and study the variation of the geodesic separation vector with the radial coordinate for two nearby geodesics using suitable initial conditions. All the obtained results are tested for Sag A* and M87* by constraining the value of the stringy gravity parameter β using the shadow data from the event horizon telescope observations. All the results are compared with Schwarzschild black hole spacetime. In our study, we found that both the radial and angular tidal forces experienced by a particle switch their initial behaviour and turn compressive and stretching, respectively, before reaching the event horizon. The geodesic deviation shows an oscillating trend as well for the chosen initial condition. For the constrained value of β, we see that the spacetime geometry generated by Sag A* and M87* is effectively same for both Schwarzschild and Starobinsky-Bel-Robinson black hole. Furthermore, we also calculated the angular diameter of the shadow in Starobinsky-Bel-Robinson black hole and compared with the Schwarzschild black hole. It is observed that the angular diameter of shadow for M87* and Sgr A* in Starobinsky-Bel-Robinson black hole is smaller than the Schwarzschild black hole. The calculated results satisfy the event horizon telescope observational constraints. Copyright © 2023, The Authors. All rights reserved.

关键词： Black holes

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VOLUME COMPARISON THEOREM WITH RESPECT TO SIGMA-2 CURVATURE

arXiv

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arXiv 2021年

作者： Chen, Jiaqi Fang, Yi He, Yan Zhong, Jingyang School of Electrical Engineering and Automation Xiamen University of Technology Xiamen361024 China Department of Mathematics Anhui University of Technology Ma’anshan243002 China Faculty of Mathematics and Statistics Hubei Key Laboratory of Applied Mathematics Hubei University Wuhan430062 China College of Mathematics and Computer Sciences Fuzhou University Fuzhou350108 China

In this paper, we investigate the volume comparison theorem related to σ2curvature. In particular, we show that volume comparison theorem with respect to σ2curvature holds for metrics close to strictly stable positive Einstein metrics. By applying similar techniques, we derive the local rigidity theorem for strictly stable Ricci flat manifolds with respect to σ2-curvature, which shows it admits no metric with positive σ2-curvature near strictly stable Ricci-flat metrics. © 2021, CC BY.

关键词： Gravitation

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An efficient algorithm for fuzzy frequent itemset mining

An efficient algorithm for fuzzy frequent itemset mining

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作者： Wu, Tsu-Yang Lin, Jerry Chun-Wei Yun, Unil Chen, Chun-Hao Srivastava, Gautam Lv, Xianbiao College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Computer Engineering Sejong University Seoul Korea Republic of Department of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan Department of Mathematics Computer Science Brandon University Brandon Canada Shenzhen China

Association-rule mining (ARM) has concerned as an important and critical research issue in the field of data analytics and mining that aims at finding the correlations among the items in binary databases. However, the conventional algorithms considered the frequency of the item(set) in binary databases for ARM, which is not sufficient in real-life situations. Mining of useful information is not an easy task especially if the item(set) consists of the added values. Moreover, the discovered knowledge is not easy to understand if you are not the domain experts. For the past decades, several intelligent systems involved the fuzzy-set theory for many domains and applications due to it is interpretable for human reasoning. Before, the Apriori-based method for discovering fuzzy frequent itemsets (FFIs) based on the type-2 fuzzy-set theory was proposed, which requires the amount of computations with enormous candidates. In this study, we then first present a fast list-based multiple fuzzy frequent itemset mining (named as LFFT2)algorithm under type-2 fuzzy-set theory. It is developed by the type-2 membership functions to retrieve the multiple fuzzy frequent itemsets for presenting more useful and meaningful knowledge for making the efficient strategies or decisions. From the results shown in the experiments, it is clear to see that the developed LFFT2 outperforms the conventional Apriori-based approach regarding the execution time and the number of examined nodes in the search space. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Fuzzy set theory

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An Explainable Deep Neural Network Model Based on Multi-Granularity Co-Attention to Improve Prediction of Drug-Target Binding Affinity

SSRN

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SSRN 2024年

作者： Lin, Yanmei Cao, Aiqing Deng, Lei Liao, Mingzhi Zhang, Ru Zhang, Hao Peng, Yuzhong Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning530001 China Guangxi Academy of Sciences Nanning530007 China School of Computer Science and Engineering Central South University Changsha410083 China Center of Bioinformatics College of Life Sciences Northwest A&F University Shaanxi Yangling712100 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518000 China Center for Applied Mathematics of Guangxi Nanning Normal University Nanning530100 China

The prediction of drug-target interactions is pivotal in modern new drug development and drug repositioning. Nonetheless, current methods for predicting drug-target interactions often lose essential molecular structure information during the modeling process, leading to low prediction accuracy. This work proposes rMolGIN, a novel graph neural network with residual connectivity for molecular feature learning. rMolGIN not only takes into account the differences in chemical bonding properties and atomic neighborhood interactions, but also captures global and local structural information of drug molecules. Furthermore, we propose a novel drug-target affinity model based on rMolGIN and deep neural networks, coupled with a multi-granularity co-attention mechanism called HMCoDTA to capture comprehensive structural insights from both drugs and proteins. The proposed innovative model allows for comprehensive insight into the structural relationships between drugs and proteins, facilitating a more accurate prediction of binding affinities. To validate the effectiveness of our proposed methods, we conducted extensive experiments on three benchmark datasets of drug-target binding affinity, comparing HMCoDTA against 19 state-of-the-art models. Notably, the results show that HMCoDTA outperforms existing methods, demonstrating the effectiveness and advantages of our model in predicting drug-target binding affinity. © 2024, The Authors. All rights reserved.

关键词： Prediction models

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Analysis of Twitter Data Using Evolutionary Clustering during the COVID-19 Pandemic

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computers, Materials & Continua 2020年第10期65卷 193-203页

作者： Ibrahim Arpaci Shadi Alshehabi Mostafa Al-Emran Mahmoud Khasawneh Ibrahim Mahariq Thabet Abdeljawad Aboul Ella Hassanien Department of Computer Education and Instructional Technology Tokat Gaziosmanpasa UniversityTokatTurkey Department of Computer Engineering University of Turkish Aeronautical AssociationAnkaraTurkey Applied Computational Civil and Structural Engineering Research Group Faculty of Civil EngineeringTon Duc Thang UniversityHo Chi MinhVietnam College of Engineering and Technology American University of the Middle EastKuwaitKuwait Department of Mathematics and General Sciences Prince Sultan UniversityRiyadh66833Saudi Arabia Department of Medical Research China Medical UniversityTaichung40402Taiwan Department of Computer Science and Information Engineering Asia UniversityTaichungTaiwan Faculty of Computers and AI Cairo UniversityGizaEgypt Scientific Research Group in Egypt CairoEgypt

People started posting textual tweets on Twitter as soon as the novel coronavirus(COVID-19)*** these tweets can assist institutions in better decision-making and prioritizing their ***,this study aimed to analyze 43 million tweets collected between March 22 and March 30,2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering *** results indicated that unigram terms were trended more frequently than bigram and trigram terms.A large number of tweets about the COVID-19 were disseminated and received widespread public attention during the *** high-frequency words such as“death”,“test”,“spread”,and“lockdown”suggest that people fear of being infected,and those who got infection are afraid of *** results also showed that people agreed to stay at home due to the fear of the spread,and they were calling for social distancing since they become aware of the *** can be suggested that social media posts may affect human psychology and *** results may help governments and health organizations to better understand the psychology of the public,and thereby,better communicate with them to prevent and manage the panic.

关键词： Twitter social media evolutionary clustering COVID-19 coronavirus

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Application of Kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal CT images

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Information Sciences 2023年 640卷

作者： Patro, Kiran Kumar Allam, Jaya Prakash Neelapu, Bala Chakravarthy Tadeusiewicz, Ryszard Acharya, U Rajendra Hammad, Mohamed Yildirim, Ozal Plawiak, Pawel Department of Electronics and Communication Engineering Aditya Institute of Technology and Management AP Tekkali532201 India Department of Electronics and Communication Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India Department of Biotechnology and Medical Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India AGH University of Science and Technology Department of Biocybernetics and Biomedical Engineering Krakow Poland School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology Faculty of Computers and Information Menoufia University Shibin El Kom32511 Egypt Department of Software Engineering Firat University Elazig Turkey Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland

Kidney stone disease is a serious public health concern that is getting worse with changes in diet, obesity, medical conditions, certain supplements etc. A kidney stone also called a renal calculus, is a hard buildup of urine minerals that form in the kidneys. Computed tomography (CT) is one of the imaging models used to identify kidney stones by clinical experts. Due to the low resolution of these images, sometimes detecting kidney stones is tedious with the naked eye, which may lead to false alarms. In this work, a computer-based diagnosis system with a deep learning technique has been developed as a practical solution to aid clinicians in their diagnosis. The traditional convolutional neural network (CNN)-based deep learning technology can detect stones in the kidney. Still, it suffers from the performance and standard implementation of the convolution operations in convolution layers. A Kronecker product-based convolution technique is incorporated in the proposed deep learning architecture to reduce the redundancy in feature maps without convolution overlapping. Our proposed method helps to make the network more effective by extracting abstract and in-depth features from the input images. The publicly available GitHub kidney stone CT scans are utilized to develop the proposed architecture. Our automated model detected kidney stones with an accuracy of 98.56% utilizing CT images. Our system is more effective than the most recent and cutting-edge techniques developed for identifying kidney stones of any size, including the smallest ones. © 2023 The Author(s)

关键词： Convolution

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Some inequalities for weighted and integral means of operator convex functions

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SUT Journal of mathematics 2020年第2期56卷 109-127页

作者： Dragomir, Silvestru Sever College of Engineering & Science Victoria University PO Box 14428 Melbourne City MC 8001 Australia DST-NRF Centre of Excellence in the Mathematical and Statistical Sciences School of Computer Science & Applied Mathematics University of the Witwatersrand Private Bag 3 Johannesburg 2050 South Africa

Let f be an operator convex function on I and A, B ∈ SAI (H), the convex set of selfadjoint operators with spectra in I. If A ≠ B and f, as an operator function, is Gâteaux differentiable on [A, B]:= {(1 − t) A... 详细信息

Let f be an operator convex function on I and A, B ∈ SA_I (H), the convex set of selfadjoint operators with spectra in I. If A ≠ B and f, as an operator function, is Gâteaux differentiable on [A, B]:= {(1 − t) A + tB | t ∈ [0, 1]}, while p: [0, 1] → [0, ∞) is Lebesgue integrable satisfying the condition 0 ≤ ∫_τ 0 p (s) ds ≤ ∫₁ 0 p (s) ds for all τ ∈ [0, 1] and symmetric, namely p (1 − t) = p (t) for all t ∈ [0, 1], then [formula presented] Some particular examples of interest are also given. © 2020, Tokyo University of Science. All rights reserved.

关键词： Féjer’s inequalities. integral inequalities Hermite-Hadamard inequality Operator convex functions

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