Excellence in Research: Research in Machine Learning and Its Application

卓越研究：机器学习及其应用研究

• 批准号: 1954532
• 负责人: Negash Begashaw
• 金额: $ 46.62万
• 依托单位: Benedict College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1954532&HistoricalAwards=false
• 关键词: Excellence; Research; Machine; Learning; Its

This project is aimed at developing a data-driven machine learning research program accompanied by an integrated undergraduate educational curriculum in data science and machine learning. The research team, consisting of faculty from Benedict College, a historically black college and university (HBCU), University of South Carolina, and North Carolina State University with complementary expertise in optimization, control theory, statistics, applied and computational mathematics, and engineering will develop new methods in machine learning and employ these methods in a variety of applications, including modeling malaria epidemics and diabetes; discovering constitutive laws and mechanisms for some selected materials and life science problems such as modeling heart tissues and designing synthetic ion separating membranes; incorporating machine learning modules into a hybrid multiscale model for simulating angiogenesis (angiogenesis is the physiological process through which new blood vessels form from preexisting vessels, formed in the earlier stage of formation and development of vascular system) of various organs and tissue constructs in 3D biofabrication. Additionally this project will create an innovative training program to educate undergraduate STEM students and to retool affiliated faculty in Benedict College to prepare them for data science and artificial intelligence related jobs and for conducting research using data-driven approaches in the future. The computing and learning laboratory will provide the necessary computing facility for participating faculty and students to carry out the research as well as educational activities. The project team will focus on several application problems that can be solved and improved using data science and machine learning tools: (1) using a multi-objective optimization approach to improve machine learning outcome in clustering, feature selection, knowledge extraction, and ensemble generation in modeling malaria epidemics and diabetes disease; (2) using Grey models to improve predictions in financial analysis; (3) developing forecasting models for disease epidemics including malaria and other diseases; (4) discovering constitutive laws and mechanisms in selected materials and life science problems such as stress-strain constitutive relations based on deep neural networks for heterogeneous heart tissues and organs and ion transport mechanisms in synthetic ion separating membranes; (5) coupling machine and deep learning tools to calibrate interaction energies and accelerate Monte Carlo simulations in a hybrid multiscale model for angiogenesis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在开发一个数据驱动的机器学习研究计划，并提供数据科学和机器学习的综合本科教育课程。该研究团队由本尼迪克特学院的教师组成，本尼迪克特学院是一所历史悠久的黑人学院和大学（HBCU），南卡罗来纳州大学和北卡罗来纳州州立大学在优化，控制理论，统计学，应用和计算数学以及工程方面具有互补的专业知识，将开发机器学习的新方法，并将这些方法应用于各种应用中，包括疟疾流行病和糖尿病的建模，一些选定材料的本构关系和机理的发现，以及心脏组织建模和合成离子分离膜设计等生命科学问题;将机器学习模块并入混合多尺度模型以模拟血管生成（血管生成是新血管从血管系统形成和发育的早期阶段形成的预先存在的血管形成的生理过程）。此外，该项目还将创建一个创新的培训计划，以教育本科STEM学生，并重组本尼迪克特学院的附属教师，为他们从事数据科学和人工智能相关工作做好准备，并在未来使用数据驱动的方法进行研究。电脑及学习实验室将提供所需的电脑设施，供参与计划的师生进行研究及教育活动。 该项目团队将专注于使用数据科学和机器学习工具可以解决和改进的几个应用问题：（1）使用多目标优化方法来改善疟疾流行病和糖尿病建模中聚类，特征选择，知识提取和集成生成的机器学习结果;（2）使用灰色模型来改善金融分析中的预测;（3）使用灰色模型来改善预测。（3）开发包括疟疾和其他疾病在内的疾病流行预测模型;（4）发现选定材料中的本构规律和机制以及生命科学问题，如基于深度神经网络的异质心脏组织和器官的应力-应变本构关系以及合成离子分离膜中的离子传输机制;（5）结合机器和深度学习工具，在混合多尺度模型中校准相互作用能并加速血管生成的蒙特卡罗模拟。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Switching Strategy for Connected Vehicles Under Variant Harsh Weather Conditions

• DOI: 10.1109/jrfid.2023.3274602
• 发表时间: 2023
• 期刊: IEEE Journal of Radio Frequency Identification
• 影响因子: 3.1
• 作者: Jian Liu;A. Nazeri;Chunheng Zhao;Esmail M. M. Abuhdima-Esmail-M.-M.-Abuhdima-31030309;G. Comert;Chin-Tser Huang;P. Pisu

Cycle-to-Cycle Queue Length Estimation from Connected Vehicles with Filtering on Primary Parameters

• DOI: 10.1016/j.ijtst.2021.04.009
• 发表时间: 2020-11
• 期刊: ArXiv
• 作者: G. Comert;N. Begashaw

Modeling Covid-19 Epidemic with Quarantine and Lockdown and Analysis

通过隔离、封锁和分析对 Covid-19 流行病进行建模

• DOI: 10.46719/dsa2023.32.15
• 发表时间: 2023
• 期刊: Dynamic Systems and Applications
• 作者: Begashaw, N;Comert, Gurcan;Medhin, N G
• 通讯作者: Medhin, N G

Fractional Differential Equation Model For COVID-19 Epidemic

COVID-19 流行病的分数阶微分方程模型

• 发表时间: 2022
• 期刊: Dynamic systems and applications
• 作者: N. Begashaw, G. Comert

Boundary-to-Solution Mapping for Groundwater Flows in a Toth Basin

• DOI: 10.1016/j.advwatres.2023.104448
• 发表时间: 2023-03
• 期刊: ArXiv
• 作者: Jin-Jin Sun-Jin;Jun Li;Y. Hao;Cuiting Qi;Chunmei Ma;Huazhi Sun;N. Begashaw;Gurcan Comet;Yi-mei Sun;Qi Wang