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Approximate solutions to fractional differential equations

Applied Mathematics and Mechanics(English Edition) 2023年第10期44卷 1791-1802页

作者： Yue LIU Zhen ZHAO Yanni ZHANG Jing PANG College of Sciences Inner Mongolia University of TechnologyHohhot 010051China Department of Mathematics and Computer Science Hetao CollegeBayannur 015000Inner Mongolia Autonomous RegionChina Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling Hohhot 010051China College of Mathematics and Statistics Ningbo UniversityNingbo 315211Zhejiang ProvinceChina College of Science Liaoning University of TechnologyJinzhou 121001Liaoning ProvinceChina

In this paper, the time-fractional coupled viscous Burgers' equation(CVBE)and Drinfeld-Sokolov-Wilson equation(DSWE) are solved by the Sawi transform coupled homotopy perturbation method(HPM). The approximate series solutions to these two equations are obtained. Meanwhile, the absolute error between the approximate solution given in this paper and the exact solution given in the literature is analyzed. By comparison of the graphs of the function when the fractional order α takes different values, the properties of the equations are given as a conclusion.

关键词： Caputo fractional derivative coupled viscous Burgers'equation(CVBE) Drinfeld-Sokolov-Wilson equation(DSWE) Sawi transform homotopy perturbation method(HPM)

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p, q-Spherical fuzzy sets and their aggregation operators with application to third-party logistic provider selection

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Journal of Intelligent and Fuzzy Systems 2024年第1期46卷 505-528页

作者： Rahim, Muhammad Amin, Fazli Tag Eldin, ElSayed M. Abd El-Wahed Khalifa, Hamiden Ahmad, Sadique Department of Mathematics and Statistics Hazara University KP Mansehra Pakistan Future University in Egypt New Cairo Egypt Department of Mathematics College of Science and Arts Qassim University Al-Badaya Saudi Arabia Department of Operations and Management Research Faculty of Graduate Studies for Statistical Research Cairo University Giza Egypt EIAS: Data Science and Blockchain Laboratory College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

The selection of an appropriate third-party logistics (3PL) provider has become an inescapable option for shippers in today's business landscape, as the outsourcing of logistics activities continues to increase. Choosing the 3PL supplier that best meets their requirements is one of the most difficult difficulties that logistics consumers face. Effective decision-making (DM) is critical in dealing with such scenarios, allowing shippers to make well-informed decisions within a restricted timeframe. The importance of DM arises from the possible financial repercussions of poor decisions, which can result in significant financial losses. In this regard, we introduce p, q-spherical fuzzy set (p, q-SFS), a novel concept that extends the concept of T-spherical fuzzy sets (T-SFSs). p, q-SFS is a comprehensive representation tool for capturing imprecise information. The main contribution of this article is to define the basic operations and a series of averaging and geometric AOs under p, q-spherical fuzzy (p, q-SF) environment. In addition, we establish several fundamental properties of the proposed aggregation operators (AOs). Based on these AOs, we propose a stepwise algorithm for multi-criteria DM (MCDM) problems. Finally, a real-life case study involving the selection of a 3PL provider is shown to validate the applicability of the proposed approach. © 2024 - IOS Press. All rights reserved.

关键词： Decision making

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Hybrid support vector regression in electric load during national holiday season

Hybrid support vector regression in electric load during nat...

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2017 International Conference on Innovative and Creative Information Technology: Computational Intelligence and IoT, ICITech 2017

作者： Caraka, Rezzy Eko Bakar, Sakhinah Abu Pardamean, Bens Budiarto, Arif School of Mathematical Sciences Faculty of Science and Technology National University of Malaysia Malaysia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538640456

This paper studies non-parametric time-series approach to electric load in national holiday seasons based on historical hourly data in state electric company of Indonesia consisting of historical data of the Northern Sumatera also South and Central Sumatra electricity load. Given a baseline for forecasting performance, we apply our hybrid models and computation platform with combining parameter of the kernel. To facilitate comparison to results of our analysis, we highlighted the results around MAPE-based and R2-based techniques. In order to get more accurate results, we need to improve, investigate, also develop the appropriate statistical tools. Electric load forecasting is a fundamental aspect of infrastructure development decisions and can reduce the energy usage of the nation. © 2017 IEEE.

关键词： Time series

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Ibaqpy: A scalable Python package for baseline quantification in proteomics leveraging SDRF metadata

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Journal of Proteomics 2025年 317卷

作者： Zheng, Ping Audain, Enrique Webel, Henry Dai, Chengxin Klein, Joshua Hitz, Marc-Phillip Sachsenberg, Timo Bai, Mingze Perez-Riverol, Yasset Chongqing Key Laboratory of Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing China Institute of Medical Genetics University Medicine Oldenburg Carl von Ossietzky University Oldenburg Germany The Novo Nordisk Foundation Center for Biosustainability Technical University of Denmark Lyngby Denmark State Key Laboratory of Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Life Omics Beijing China Program for Bioinformatics Boston University Boston United States Department of Computer Science Applied Bioinformatics University of Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Tübingen Germany European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Cambridge United Kingdom

Intensity-based absolute quantification (iBAQ) is essential in proteomics as it allows for the assessment of a protein's absolute abundance in various samples or conditions. However, the computation of these values for increasingly large-scale and high-throughput experiments, such as those using DIA, TMT, or LFQ workflows, poses significant challenges in scalability and reproducibility. Here, we present ibaqpy (https://***/bigbio/ibaqpy), a Python package designed to compute iBAQ values efficiently for experiments of any scale. Ibaqpy leverages the Sample and data Relationship Format (SDRF) metadata standard to incorporate experimental metadata into the quantification workflow. This allows for automatic normalization and batch correction while accounting for key aspects of the experimental design, such as technical and biological replicates, fractionation strategies, and sample conditions. Designed for large-scale proteomics datasets, ibaqpy can also recompute iBAQ values for existing experiments when an SDRF is available. We showcased ibaqpy's capabilities by reanalyzing 17 public proteomics datasets from ProteomeXchange, covering HeLa cell lines with 4921 samples and 5766 MS runs, quantifying a total of 11,014 proteins. In our reanalysis, ibaqpy is a key component in automating reproducible quantification, reducing manual effort and making quantitative proteomics more accessible while supporting FAIR principles for data reuse. Significance: Proteomics studies often rely on intensity-based absolute quantification (iBAQ) to assess protein abundance across various biological conditions. Despite its widespread use, computing iBAQ values at scale remains challenging due to the increasing complexity and volume of proteomics experiments. Existing tools frequently lack metadata integration, limiting their ability to handle experimental design intricacies such as replicates, fractions, and batch effects. Our work introduces ibaqpy, a scalable Python package that lev

关键词： Big data Bioinformatics data integration Proteomics Quantification

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UAV-Aided data Acquisition Using Gaining-Sharing Knowledge Optimization Algorithm

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computers, Materials & Continua 2022年第9期72卷 5999-6013页

作者： Rania M Tawfik Hazem A.A.Nomer M.Saeed Darweesh Ali Wagdy Mohamed Hassan Mostafa Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12677Egypt School of Information Technology and Computer Science(ITCS) Nile UniversityGiza12677Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Science and EngineeringThe American University in CairoEgypt Electronics and Communications Engineering Department Cairo UniversityEgypt University of Science and Technology Nanotechnology and Nanoelectronics ProgramZewal City of Science and TechnologyOctober Gardens6th of OctoberGiza12578Egypt

Unmanned Aerial Vehicles(UAVs)provide a reliable and energyefficient solution for data collection from the Narrowband Internet of Things(NB-IoT)***,the UAV’s deployment optimization,including locations of the UAV’s stop points,is a necessity to minimize the energy consumption of the UAV and the NB-IoT devices and also to conduct the data collection *** this regard,this paper proposes GainingSharing Knowledge(GSK)algorithm for optimizing the UAV’s *** GSK,the number of UAV’s stop points in the three-dimensional space is encapsulated into a single individual with a fixed length representing an entire *** superiority of using GSK in the tackled problem is verified by simulation in seven *** provides significant results in all seven scenarios compared with other four optimization algorithms used before with the same ***,the NB-IoT is proposed as the wireless communication technology between the UAV and IoT devices.

关键词： NB-IoT UAV GSK stop points optimization

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Performance of multimodal prediction models for intracerebral hemorrhage outcomes using real-world data

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International Journal of Medical Informatics 2025年 202卷 105989-105989页

作者： Matsumoto, Koutarou Suzuki, Masahiro Ishihara, Kazuaki Tokunaga, Koki Matsuda, Katsuhiko Chen, Jenhui Yamashiro, Shigeo Soejima, Hidehisa Nakashima, Naoki Kamouchi, Masahiro Department of Health Care Administration and Management Graduate School of Medical Sciences Kyushu University Fukuoka Japan Institute for Medical Information Research and Analysis Saiseikai Kumamoto Hospital Kumamoto Japan Graduate Degree Program of Applied Data Sciences Sophia University Tokyo Japan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Pharmacy Saiseikai Kumamoto Hospital Kumamoto Japan Department of Radiology Saiseikai Kumamoto Hospital Kumamoto Japan Division of Neurosurgery Saiseikai Kumamoto Hospital Kumamoto Japan Medical Information Center Kyushu University Hospital Fukuoka Japan Department of Medical Informatics Graduate School of Medical Sciences Kyushu University Fukuoka Japan Center for Cohort Studies Graduate School of Medical Sciences Kyushu University Fukuoka Japan

Graphical abstract

关键词： Multimodal model Deep learning Machine learning Intracerebral hemorrhage

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Comparative Analysis of Machine Learning Models for Customer Churn Prediction in the U.S. Banking and Financial Services: Economic Impact and Industry-Specific Insights

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Journal of data Analysis and Information Processing 2024年第3期12卷 388-418页

作者： Omoshola S. Owolabi Prince C. Uche Nathaniel T. Adeniken Oghenekome Efijemue Samuel Attakorah Oluwabukola G. Emi-Johnson Emmanuel Hinneh Department of Data Science Carolina University Winston-Salem NC USA Department of Computer Science Austin Peay State University Clarkesville TN USA Department of Statistical Sciences Wake Forest University Winston-Salem NC USA

Customer churn poses a significant challenge for the banking and finance industry in the United States, directly affecting profitability and market share. This study conducts a comprehensive comparative analysis of machine learning models for customer churn prediction, focusing on the U.S. context. The research evaluates the performance of logistic regression, random forest, and neural networks using industry-specific datasets, considering the economic impact and practical implications of the findings. The exploratory data analysis reveals unique patterns and trends in the U.S. banking and finance industry, such as the age distribution of customers and the prevalence of dormant accounts. The study incorporates macroeconomic factors to capture the potential influence of external conditions on customer churn behavior. The findings highlight the importance of leveraging advanced machine learning techniques and comprehensive customer data to develop effective churn prevention strategies in the U.S. context. By accurately predicting customer churn, financial institutions can proactively identify at-risk customers, implement targeted retention strategies, and optimize resource allocation. The study discusses the limitations and potential future improvements, serving as a roadmap for researchers and practitioners to further advance the field of customer churn prediction in the evolving landscape of the U.S. banking and finance industry.

关键词： Churn Prediction Machine Learning Economic Impact Industry-Specific Insights Logistic Regression Random Forest Neural Networks

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Decision fusion of circulating markers for breast cancer detection in premenopausal women

Decision fusion of circulating markers for breast cancer det...

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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Jesneck, Jonathan L. Mukherjee, Sayan Nolte, Loren W. Lokshin, Anna E. Marks, Jeffrey R. Lo, Joseph Department of Biomedical Engineering Department of Statistical Science Computational Biology and Bioinformatics Program Duke Advanced Imaging Labs. Institute for Genome Sciences and Policy Department of Computer Science Duke University Department of Electrical and Computer Engineering Division of Hematology/Oncology University of Pittsburgh School of Medicine University of Pittsburgh Cancer Institute Pittsburgh PA 15213 United States Department of Experimental Surgery Duke University Medical Center Medical Physics Graduate Program Duke University Durham NC 27708 United States

ISBN: (纸本)1424415098

Current mammographic screeningfor breast cancer is less effective for younger women. To complement mammography for premenopausal women, we investigated the feasibility screening test using 98 blood serum proteins. Because the data set was very noisy and contained only weak features, we used a classifier designed for noisy data: decision fusion. Decision fusion outperformed both a support vector machine (SVM) and linear regression with forward stepwise feature selection on all three two-class classification tasks: normal tissue vs. cancer, normal tissue vs. benign lesions, and benign lesions vs. cancer. Decision fusion detected cancer moderately well (AUC=0.84 on normal vs. cancer), demonstrating promise as a screening tool. Decision fusion also detected benign lesions similarly well (AUC=0.83 on normal vs. benign lesions) and was the only classifier to achieve any success in separating benign from malignant lesions (AUC=0.64 on benign vs. cancer). The classification results suggest that the assayed proteins are more indicative of a secondary effect, such as immune response, rather than specific for breast cancer. In conclusion, the decision fusion classifier demonstrated some promise in detecting breast lesions and outperformed other classifiers, especially for the very noisy classification problem of distinguishing benign from malignant lesions. ©2007 IEEE.

关键词： Tissue

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Tractably modelling dependence in networks beyond exchangeability

arXiv

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arXiv 2020年

作者： Wu, Weichi Olhede, Sofia Wolfe, Patrick Center for Statistical Sciences Department of Industrial Engineering Tsinghua University China Institute of Mathematics Chair of Statistical Data Science EPFL Swaziland Departments of Statistics and Computer Science Purdue University United States

We propose a general framework for modelling network data that is designed to describe aspects of non-exchangeable networks. Conditional on latent (unobserved) variables, the edges of the network are generated by their finite growth history (with latent orders) while the marginal probabilities of the adjacency matrix are modeled by a generalization of a graph limit function (or a graphon). In particular, we study the estimation, clustering and degree behavior of the network in our setting. We determine (i) the minimax estimator of a composite graphon with respect to squared error loss;(ii) that spectral clustering is able to consistently detect the latent membership when the block-wise constant composite graphon is considered under additional conditions;and (iii) we are able to construct models with heavy-tailed empirical degrees under specific scenarios and parameter choices. This explores why and under which general conditions non-exchangeable network data can be described by a stochastic block model. The new modelling framework is able to capture empirically important characteristics of network data such as sparsity combined with heavy tailed degree distribution, and add understanding as to what generative mechanisms will make them *** Codes 62G05, 62R07, 62E20, 62G20, secondary 53C20 Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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Advising big data transfer over dedicated connections based on profiling optimization

Advising big data transfer over dedicated connections based ...

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作者： Yun, Daqing Wu, Chase Q. Rao, Nageswara S. V. Kettimuthu, Rajkumar Computer and Information Sciences Program Harrisburg University HarrisburgPA17101 United States Department of Computer Science New Jersey Institute of Technology NewarkNJ07102 United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Data Science and Learning Division Argonne National Laboratory ArgonneIL60439 United States

Big data transfer in next-generation scientific applications is now commonly carried out over dedicated channels in high-performance networks (HPNs), where transport protocols play a critical role in maximizing application-level throughput. Optimizing the performance of these protocols is challenging: i) transport protocols perform differently in various network environments, and the protocol choice is not straightforward;ii) even for a given protocol in a given environment, different parameter settings of the protocol may lead to significantly different performance and oftentimes the default setting does not yield the best performance. However, it is prohibitively time-consuming to conduct exhaustive transport profiling due to the large parameter space. In this paper, we propose a PRofiling Optimization Based data Transfer Advisor (Probdata) to help end users determine the most effective transport method with the most appropriate parameter settings to achieve satisfactory performance for big data transfer over dedicated connections in HPNs. Probdata employs a fast profiling scheme based on the Simultaneous Perturbation Stochastic Approximation algorithm, namely, FastProf, to accelerate the exploration of the optimal operational zones of various transport methods to improve profiling efficiency. We first present a theoretical background of the optimized profiling approach in Probdata and then detail its design and implementation. The advising procedure and performance benefits of FastProf and Probdata are illustrated and evaluated by both extensive emulations based on real-life performance measurements and experiments over various physical connections in existing production HPNs. © 1993-2012 IEEE.

关键词： data transfer

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