scholarly journals Classification of Hospital Web Security Efficiency Using Data Envelopment Analysis and Support Vector Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Han-Ying Kao ◽  
Tao-Ku Chang ◽  
Yi-Cheng Chang
Keyword(s):  
Support Vector Machine ◽  
Data Envelopment Analysis ◽  
Web Security ◽  
Support Vector ◽  
Data Envelopment ◽  
Learning Patterns ◽  
Dea Models ◽  
Efficiency Estimation ◽  
Hybrid Data ◽  
Using Data

This study proposes the hybrid data envelopment analysis (DEA) and support vector machine (SVM) approaches for efficiency estimation and classification in web security. In the proposed framework, the factors and efficiency scores from DEA models are integrated with SVM for learning patterns of web security performance and provide further decision support. The numerical case study of hospital web security efficiency is demonstrated to support the feasibility of this design.

Using Data Envelopment Analysis to Construct Human Development Index

2017 ◽  
pp. 298-323
Author(s):  
Paulo Nocera Alves Junior ◽  
Enzo Barberio Mariano ◽  
Daisy Aparecida do Nascimento Rebelatto
Keyword(s):  
Data Envelopment Analysis ◽  
Human Development ◽  
Human Development Index ◽  
Data Envelopment ◽  
Data Normalization ◽  
Weighting Schemes ◽  
Development Index ◽  
Dea Models ◽  
Kendall Correlation ◽  
Using Data

This chapter addresses problems related to methodological issues, such as data normalization, weighting schemes, and aggregation methods, encountered in the construction of composite indicators to measure socio-economic development and quality of life. It also addresses the use of several Data Envelopment Analysis (DEA) models to solve these problems. The models are discussed and applied in constructing a Human Development Index (HDI), derived from the most recent raw and normalized data, using arithmetic and geometric means to aggregate the indices. Issues related to data normalization and weighting schemes are emphasized. Kendall Correlation was applied to analyze the relationship between ranks obtained by DEA models and HDI. Recommendations regarding the advantages and disadvantages of using DEA models to construct HDI are offered.

ECOEFFICIENCY MEASUREMENT USING DATA ENVELOPMENT ANALYSIS: RESEARCH AND PRACTITIONER ISSUES

2004 ◽  
Vol 06 (01) ◽  
pp. 91-123 ◽  
Author(s):  
JOSEPH SARKIS ◽  
SRINIVAS TALLURI
Keyword(s):  
Data Envelopment Analysis ◽  
Environmental Performance ◽  
Published Data ◽  
Data Envelopment ◽  
Environmentally Conscious ◽  
Use Of Data ◽  
Dea Models ◽  
Using Data ◽  
Environmental Performance Evaluation ◽  
Insight Into

Ecoefficiency is critical for organisations that seek to be both environmentally conscious and profitable. Ecoefficiency has implications for a "win-win" situation to arise. Studying and managing organisations from this perspective requires an evaluation of ecoefficiency. To aid researchers and managers develop measures for ecoefficiency we review the use of data envelopment analysis (DEA) for this purpose. DEA theory and application has increased greatly. Its use as a tool for environmental performance evaluation has been limited. In this paper we provide a number of DEA models and some extensions and how they can be utilised from both the practitioner and researcher perspective. An illustrative example from published data helps to gain insight into the various models, their capabilities and limitations.

scholarly journals Evaluating Winter Barley Cultivar Using Data Envelopment Analysis Models

2019 ◽  
Vol 11 (6) ◽  
pp. 22
Author(s):  
Masoud Hashemi ◽  
Omid Reza Zandvakili ◽  
Roohollah Abbasi Shureshjani ◽  
Fatemeh Etemadi ◽  
Heather Darby
Keyword(s):  
Data Envelopment Analysis ◽  
Grain Yield ◽  
Quality Criteria ◽  
Grain Protein Content ◽  
Winter Barley ◽  
Data Envelopment ◽  
Testing Trial ◽  
Dea Models ◽  
Grain Yield And Quality ◽  
Using Data

Unlike feed barley, malting barley must meet a specific set of quality standards for acceptability by maltsters. Multiple quality criteria in addition to the grain yield makes ranking of genotypes challenging. The objective of this study was to apply data envelopment analysis (DEA) models to rank the efficiencies of 27 winter barley entries based on grain yield and quality indices. Four methods of DEA including Charnes, Cooper and Rhodes (CCR), Färe and Grosskopf (FG), Banker, Charnes and Cooper (BCC), and Seiford and Thrall (ST) used for the ranking. Testing trial included 14 two-rows and 13 six-rows winter barley. All entries except two, demonstrated high winter survival ratings. Overall, the six-row cultivars out-yielded the two-row cultivars by 18%. However, in terms of brewing quality, the two-row entries performed better than six-row entries and had 40% lower thinness, 12% higher plump, and lower grain protein content. The six-row entries had 32% higher germinative energy than two-row entries. The ranking by four models were not similar, however, SU-Mateo and Calypso had the highest efficiency (1.0) by all four models followed by Wintmalt and Vincenta.

An Improved Method for Predicting and Ranking Suppliers Efficiency Using Data Envelopment Analysis

2014 ◽  
Vol 73 (2) ◽  
Author(s):  
Mohammadreza Farahmand ◽  
Mohammad Ishak Desa ◽  
Mehrbakhsh Nilashi ◽  
Antoni Wibowo
Keyword(s):  
Data Envelopment Analysis ◽  
Supplier Selection ◽  
Large Data ◽  
Computational Time ◽  
Support Vector ◽  
Data Sets ◽  
Data Envelopment ◽  
Process Data ◽  
Measuring Efficiency ◽  
Using Data

Supplier selection problem (SSP) is a problem to select the best among suppliers based on input and output data of the suppliers. Since different uncontrollable and unpredictable parameters are affecting selection, choosing the best supplier is a complicated process. Data Envelopment Analysis (DEA) is a method for measuring efficiency and inefficiencies of Decision Making Units (DMUs). DEA has been employed by many researchers for supplier selection and widely used in SSP with inputs for supplier evaluation. However, the DEA still has some disadvantages when it is solely used for SSP. Hence, in this paper, a combination of DEA and Neural Network (NN), DEA-NN, is proposed for SSP. We also develop a model for SSP based on Support Vector Regression (SVR) to improve the stability of DEA-NN. The proposed method was evaluated using small and large data sets. The experimental results showed that, the proposed method solve the problems connected to the previous methods. The results also showed that stability of proposed method is significantly better than DEA-NN method. In addition, CCR-SVR model overcome shortcomings such as instability and improves computational time and accuracy for predicting efficiency of new small and large DMUs.

scholarly journals Assessment of transit transport corridor efficiency of landlocked African countries using data envelopment analysis

2018 ◽  
Vol 114 (1/2) ◽  
Author(s):  
Enagnon H. Fanou ◽  
Xuping Wang
Keyword(s):  
Data Envelopment Analysis ◽  
Transport Systems ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
African Countries ◽  
Comparative Performance ◽  
Data Set ◽  
Dea Models ◽  
And Performance ◽  
Using Data

We used a data envelopment analysis (DEA) to examine the efficiency and performance of transport systems of landlocked African countries (LLACs). We conducted a comparative performance efficiency analysis of transfer transport systems for LLACs’ corridors. Three different types of DEA models were proposed and used to measure the relative efficiencies of transit transport using a 6-year data set (2008–2013) of some selected LLACs. The results show that the average pure technical and scale efficiency scores are 90.89% and 37.13%, respectively. Two units (13.33%) are technically efficient (technical and scale efficiency) while four units (26.66%) are only purely technically efficient over the observed period. Swaziland was the most efficient corridor while the Central African Republic corridor was the least efficient throughout the monitored years. The results indicate the relevance of minimising trade costs to stimulate landlocked countries’ exports.

scholarly journals deaR-Shiny: An Interactive Web App for Data Envelopment Analysis

2021 ◽  
Vol 13 (12) ◽  
pp. 6774
Author(s):  
Rafael Benítez ◽  
Vicente Coll-Serrano ◽  
Vicente J. Bolós
Keyword(s):  
Data Envelopment Analysis ◽  
Web Application ◽  
Free Access ◽  
Data Envelopment ◽  
University Libraries ◽  
Dea Models ◽  
Web App ◽  
Using Data ◽  
Fuzzy Dea ◽  
Efficiency And Productivity

In this paper, we describe an interactive web application (deaR-shiny) to measure efficiency and productivity using data envelopment analysis (DEA). deaR-shiny aims to fill the gap that currently exists in the availability of online DEA software offering practitioners and researchers free access to a very wide variety of DEA models (both conventional and fuzzy models). We illustrate how to use the web app by replicating the main results obtained by Carlucci, Cirà and Coccorese in 2018, who investigate the efficiency and economic sustainability of Italian regional airport by using two conventional DEA models, and the results given by Kao and Liu in their papers published in 2000 and 2003, who calculate the efficiency scores of university libraries in Taiwan by using a fuzzy DEA model because they treat missing data as fuzzy numbers.

Evaluation of Heuristics Using Data Envelopment Analysis

2014 ◽  
Vol 13 (04) ◽  
pp. 795-810 ◽  
Author(s):  
Chung-Cheng Jason Lu ◽  
Yen-Chun Jim Wu
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Data Envelopment Analysis ◽  
Statistical Tests ◽  
Data Envelopment ◽  
Decision Making Unit ◽  
Dea Models ◽  
Using Data ◽  
Inputs And Outputs

This paper focuses on identifying relatively efficient configurations of algorithmic operators among a set of configurations in the development of heuristics or meta-heuristics. Each configuration is considered as a decision-making unit with multiple inputs and outputs. Then, data envelopment analysis (DEA) is adopted to evaluate relative and cross-efficiencies of a set of algorithmic configurations. The proposed approach differs from existing methods based on statistical tests in that multiple inputs and outputs are simultaneously considered in an integrated framework for the evaluation of algorithmic efficiency. A case study is presented to demonstrate the application of DEA for determining the efficient configurations of genetic algorithm operators. The evaluation results of two DEA models are also compared. The DEA evaluation results are consistent with those obtained by a commonly used statistical method.

Performance analysis of primary wood producers in British Columbia using data envelopment analysis

2005 ◽  
Vol 35 (2) ◽  
pp. 285-294 ◽  
Author(s):  
Neda Salehirad ◽  
Taraneh Sowlati
Keyword(s):  
British Columbia ◽  
Data Envelopment Analysis ◽  
Statistical Tests ◽  
Wood Products ◽  
Scale Efficiency ◽  
Business Practices ◽  
Data Envelopment ◽  
Significance Level ◽  
Dea Models ◽  
Using Data

Despite its importance, performance assessment of the Canadian primary wood products sector has received little attention in the academic literature and business practices. In this research a nonparametric technique, called data envelopment analysis (DEA), was used to evaluate the performance of sawmills in British Columbia in 2002. Individual mills were inspected using different DEA models to capture their technical, scale, and aggregate efficiencies. Log consumption and labor utilization were considered as the inputs and lumber and chip production as the outputs of these models. Although British Columbia sawmills enjoyed high scale efficiency, only 7% of them were aggregately efficient. The results showed possible efficiency improvements by increasing the production and decreasing the labor usage. Post-hoc analyses with two nonparametric statistical tests, median quartile and Kruskal–Wallis, revealed that the average efficiency of sawmills in different British Columbia forest regions varied significantly; however, the number of operating days had no effect on technical efficiency of sawmills at a 5% significance level.

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