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Topic review
Updated time: 30 May 2021
Submitted by: Thyago Nepomuceno
Definition: The first Data Envelopment Analysis (DEA) model developed by Charnes, Cooper and Rhodes (1978) under the assumption of a Constant Returns to Scale production technology, i.e., when an increase in the production resources results in a proportional increase in the output.
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Topic review
Updated time: 08 Apr 2021
Submitted by: Thyago Nepomuceno
Definition: Data Envelopment Analysis (DEA) is a powerful non-parametric engineering tool for estimating technical efficiency and the production capacity of service units. The Complex-of-Needs Allocation Model proposed by Nepomuceno et al. (2020) is a two-step methodology for prioritizing hospital bed vacancy and reallocation during the COVID-19 pandemic. The framework determines the production capacity of hospitals through Data Envelopment Analysis and incorporates the Complexity of Needs in two categories for the reallocation of beds throughout the medical specialties. As a result, we have a set of inefficient health-care units presenting less complex bed slacks to be reduced, i.e. to be allocated for patients presenting more severe conditions.
Entry Collection : COVID-19
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Topic review
Updated time: 01 May 2021
Submitted by: Thyago Nepomuceno
Definition: Conditional Frontier Analysis is part of the Nonparametric Robust Estimators proposed to overcome some drawbacks in the traditional Data Envelopment Analysis (DEA) and Free Disposal Hull (FDH) measures for the technical efficiency. In special, this methodology extends the nonparametric input/output production technology to robustly account for extreme values or outliers in the data, and allow measuring the effect of external environmental variables on the efficiency of Decision Making Units (DMUs).
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Topic review
Updated time: 12 Jul 2021
Submitted by: Aaron Maxwell
Definition: Convolutional neural network (CNN)-based deep learning (DL) has a wide variety of applications in the geospatial and remote sensing (RS) sciences, and consequently has been a focus of many recent studies.
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Topic review
Updated time: 02 Feb 2021
Submitted by: Amir Mosavi
Definition: Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to the lack of essential data and uncertainty, the epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19, and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are proposed to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for 9 days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.
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Topic review
Updated time: 08 Apr 2021
Submitted by: Thyago Nepomuceno
Definition: Data Envelopment Analysis (DEA) is a non-parametric methodology for measuring the efficiency of Decision Making Units (DMUs) using multiple inputs to outputs configurations. This is the most commonly used tool for frontier estimations in assessments of productivity and efficiency applied to all fields of economic activities.
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Topic review
Updated time: 08 Apr 2021
Submitted by: Amir Mosavi
Definition: The popularity of deep reinforcement learning (DRL) applications in economics has increased exponentially. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated dynamic economics systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into the state-of-the-art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems in the presence of risk parameters and the ever-increasing uncertainties. View Full-Text
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Biography
Updated time: 10 Nov 2020
Submitted by: Delfim F.M. Torres
Abstract: Professor Dr. Delfim F. M. Torres D.Sc. (Habilitation) in Mathematics, Ph.D. in Mathematics Web of Science Highly Cited Researcher (2015, 2016, 2017 and 2019). Full Professor of Mathematics (Professor Catedrático) Department of Mathematics, University of Aveiro, 3810-193 Aveiro, Portugal. Director of the R&D unit CIDMA (http://cidma.ua.pt). Coordinator of the Systems and Control Group (http://systems.cidma.ua.pt). Director of the Doctoral/PhD Programme in Applied Mathematics (MAP-PDMA). Lifetime Member of The American Mathematical Society . ISI Web of Science: http://www.researcherid.com/rid/A-7682-2008Scopus: http://scopus.com/authid/detail.url?authorId=7102800859MathSciNet: http://www.ams.org/mathscinet/MRAuthorID/657307zbMATH: http://zbmath.org/authors/?q=ai:torres.delfim-f-mORCID: http://orcid.org/0000-0001-8641-2505Google Scholar: http://scholar.google.com/citations?user=PAeWCi8AAAAJResearchGate: https://www.researchgate.net/profile/Delfim_F_M_TorresarXiv: http://arxiv.org/a/torres_d_1.htmlUniversity of Aveiro: http://www.ua.pt/dmat/pageperson.aspx?id=1107
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Biography
Updated time: 16 Apr 2021
Submitted by: Ephraim Suhir
Abstract: Bell Laboratories, Physical Sciences and Engineering Research Division, Murray Hill, NJ, USA (ret); Portland State University, Depts. of Mech. and Mat., and Elect. and Comp. Engineering, Portland, OR, USA;Technical University, Dept. of Applied Electronic Materials, Inst. of Sensors and Actuators, Vienna, Austria; James Cook University, Mackay Institute of Research and Innovation, Townsville, Queensland, Australia; and ERS Co., 727 Alvina Ct., Los Altos, CA 94024, USA, www.ERSuhir.com, Tel. 650.969.1530, Cell. 408-410-0886, e-mail: suhire@aol.com and e.suhir@ieee.org RESEARCH GATE (RG) AND H-INDEX DATA (as of March 30, 2021): Profile strength: all-star; RG score: 41.96 (higher than 98% of RG members); Downloads (“Reads”): 27,288 Citations: 5,531 H-index (Scopus): 32
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Topic review
Updated time: 01 Nov 2020
Definition: The main purpose is to identify among variables that constitute water resources consumption at public schools, the link between consumption and expenditures oscillations. It was obtained a theoretical model of how oscillations patterns are originated and how time lengths have an important role over expenditures oscillations ergodicity and non-ergodicity.
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