A Study of Fractional Order Financial Crime Model Using the Gegenbauer Wavelet Collocation Method

IF 2.9 4区 工程技术 Q1 MULTIDISCIPLINARY SCIENCES Advanced Theory and Simulations Pub Date : 2024-12-19 DOI:10.1002/adts.202400998
Manohara G, Kumbinarasaiah S
{"title":"A Study of Fractional Order Financial Crime Model Using the Gegenbauer Wavelet Collocation Method","authors":"Manohara G, Kumbinarasaiah S","doi":"10.1002/adts.202400998","DOIUrl":null,"url":null,"abstract":"The manuscript investigates the numerical approximation of the fractional mathematical model of the financial crime population dynamics by the Gegenbauer wavelet collocation method. The study aims to enhance the accuracy and efficiency of solving the underlying differential equations that describe these phenomena by utilizing the proposed technique. The financial crime model is a nonlinear coupled system of ordinary differential equations. Using the Gegenbauer wavelets, the novel operational matrices of integration are created. A nonlinear system of ordinary differential equations are transformed into a system of algebraic equations using the characteristics of the Gegenbauer wavelet expansions and the operational matrix of integration, which speeds up processing. Then, this system of algebraic equations is solved using the Newton-iterative technique to find the unknown Gegenbauer coefficients that help to obtain the approximate solution for the system. A numerical illustration is presented to show the efficacy and precision of the approach. The numerical results obtained from the projected approach are compared with the existing methods, such as NDSolve and Runge Kutta methods. These results show that the projected scheme is simple, reliable, and resilient. The findings suggest that this approach can be a powerful tool for researchers and practitioners in the financial sector, aiding in developing crime prevention and intervention strategies. The study concludes with suggestions for future research directions.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"1 1","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Theory and Simulations","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1002/adts.202400998","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

The manuscript investigates the numerical approximation of the fractional mathematical model of the financial crime population dynamics by the Gegenbauer wavelet collocation method. The study aims to enhance the accuracy and efficiency of solving the underlying differential equations that describe these phenomena by utilizing the proposed technique. The financial crime model is a nonlinear coupled system of ordinary differential equations. Using the Gegenbauer wavelets, the novel operational matrices of integration are created. A nonlinear system of ordinary differential equations are transformed into a system of algebraic equations using the characteristics of the Gegenbauer wavelet expansions and the operational matrix of integration, which speeds up processing. Then, this system of algebraic equations is solved using the Newton-iterative technique to find the unknown Gegenbauer coefficients that help to obtain the approximate solution for the system. A numerical illustration is presented to show the efficacy and precision of the approach. The numerical results obtained from the projected approach are compared with the existing methods, such as NDSolve and Runge Kutta methods. These results show that the projected scheme is simple, reliable, and resilient. The findings suggest that this approach can be a powerful tool for researchers and practitioners in the financial sector, aiding in developing crime prevention and intervention strategies. The study concludes with suggestions for future research directions.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
使用格根鲍尔小波配位法的分数阶金融犯罪模型研究
本文研究了基于Gegenbauer小波搭配法的金融犯罪人口动态分数数学模型的数值逼近。本研究旨在利用所提出的技术,提高求解描述这些现象的潜在微分方程的准确性和效率。金融犯罪模型是一个常微分方程的非线性耦合系统。利用Gegenbauer小波,建立了新的积分运算矩阵。利用Gegenbauer小波展开式和积分运算矩阵的特点,将非线性常微分方程组转化为代数方程组,提高了处理速度。然后,利用牛顿迭代法求解该代数方程组,求出未知的Gegenbauer系数,从而得到系统的近似解。算例表明了该方法的有效性和准确性。将投影法得到的数值结果与现有的NDSolve法和Runge Kutta法进行了比较。结果表明,该方案简单、可靠,具有较强的弹性。研究结果表明,这种方法可以成为金融部门研究人员和从业人员的有力工具,有助于制定预防犯罪和干预战略。最后,对今后的研究方向提出了建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Advanced Theory and Simulations
Advanced Theory and Simulations Multidisciplinary-Multidisciplinary
CiteScore
5.50
自引率
3.00%
发文量
221
期刊介绍: Advanced Theory and Simulations is an interdisciplinary, international, English-language journal that publishes high-quality scientific results focusing on the development and application of theoretical methods, modeling and simulation approaches in all natural science and medicine areas, including: materials, chemistry, condensed matter physics engineering, energy life science, biology, medicine atmospheric/environmental science, climate science planetary science, astronomy, cosmology method development, numerical methods, statistics
期刊最新文献
Numerical Analysis and Artificial Neural Networks for Solving Nonlinear Tuberculosis Model in SEITR Framework Rolling Bearing Fault Diagnosis Based on 2D CNN and Hybrid Kernel Fuzzy SVM A Variability-Aware Behavioral Model of Monolayer MoS2 RRAM for Tunable Stochastic Sources Numerical Approximation of the Fractional Model of Atmospheric Dynamics of CO2 Using the Gegenbauer Wavelet Collocation Method First-Principles Study on Introducing Fluorine Doping and Sulfur Vacancy into MoS2 for Advanced Lithium Storage
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1