An integrated artificial intelligence model for efficiency assessment in pharmaceutical companies during the COVID-19 pandemic

Mirmozaffari, Mirpouya; Yazdani, Reza; Shadkam, Elham; Khalili, Seyed Mohammad; Mahjoob, M. J.; Boskabadi, Azam

doi:10.1016/j.susoc.2022.01.003

Cited by 23 publications

(8 citation statements)

References 83 publications

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“…The DEA is used quite frequently in scientific papers to analyze productive efficiency. For instance, M. Mirmozaffari et al [18] apply data envelopment analysis (DEA) to measure productivity and effectiveness of pharmaceutical companies during the COVID-19 pandemic. In 2018 B.N.…”

Section: Methodsmentioning

confidence: 99%

How Do External Shocks Affect the Economic Efficiency of Companies with Foreign Direct Investment?

2022

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The purpose of this study is to assess the effectiveness of export and import activities of domestic companies with foreign direct investment during the external shocks. The relevance of the research is due to the fact that the impact of side effects of FDI on domestic companies and economic uncertainty after the sanctions and the outbreak of the pandemic was ambiguous. The empirical base contains about 170,000 observations on 18,799 operating companies with a foreign capital share of at least 10% for the period from 2012 to 2020, obtained from the Ruslana database - Bureau Van Dijk. For the purposes of the study, the companies are grouped by the industry and their roles in international trade. Efficiency assessment is measured by using the data envelopment analysis (DEA), taking into account the spillover effects from foreign direct investment. The results of the study confirm that firms that trade in both directions have better performance. Next come companies focused only on export or import. Companies that are not involved in international trade are the least efficient (hypothesis 1). Industries that benefit from FDI inflows include more capital-intensive sectors (hypothesis 2). External shocks have a negative impact on the efficiency of companies with FDI (hypothesis 3). This understanding has important implications for long-term economic growth and the recovery of the Russian economy in the current external shocks.

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Section: Methodsmentioning

confidence: 99%

How Do External Shocks Affect the Economic Efficiency of Companies with Foreign Direct Investment?

2022

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“…Accordingly, for future research, the DEASE approach can be proposed under uncertain data, including fuzzy [76][77][78][79][80][81][82][83][84][85][86][87][88][89][90], stochastic [91][92][93][94][95][96][97][98][99][100][101][102], and interval [103][104][105][106][107][108][109][110][111][112][113] data. Additionally, the DEASE approach can be combined with machine learning approaches for the prediction of input and output data, and consequently, evaluation of the future performance of DMUs [114][115][116][117][118][119][120][121][122][123]…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Input/Output Variables Selection in Data Envelopment Analysis: A Shannon Entropy Approach

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Esmaeili

Mirmozaffari

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The purpose of this study is to provide an efficient method for the selection of input–output indicators in the data envelopment analysis (DEA) approach, in order to improve the discriminatory power of the DEA method in the evaluation process and performance analysis of homogeneous decision-making units (DMUs) in the presence of negative values and data. For this purpose, the Shannon entropy technique is used as one of the most important methods for determining the weight of indicators. Moreover, due to the presence of negative data in some indicators, the range directional measure (RDM) model is used as the basic model of the research. Finally, to demonstrate the applicability of the proposed approach, the food and beverage industry has been selected from the Tehran stock exchange (TSE) as a case study, and data related to 15 stocks have been extracted from this industry. The numerical and experimental results indicate the efficacy of the hybrid data envelopment analysis–Shannon entropy (DEASE) approach to evaluate stocks under negative data. Furthermore, the discriminatory power of the proposed DEASE approach is greater than that of a classical DEA model.

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“…The nature-inspired FS methods select the best optimal feature subset using heuristic search to maximize the classification accuracy in binary and multiclass classification problems [34]. Metaheuristics algorithms have also been used to solve many NP-hard problems in various fields, such as function optimization [35][36][37], feature extraction for the image-based classification of cancer [38], feature selection for cancer diagnosis [39,40], and biomedical engineering [41][42][43][44] and circuit design [45,46].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data

2023

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The high dimensionality and sparsity of the microarray gene expression data make it challenging to analyze and screen the optimal subset of genes as predictors of breast cancer (BC). The authors in the present study propose a novel hybrid Feature Selection (FS) sequential framework involving minimum Redundancy-Maximum Relevance (mRMR), a two-tailed unpaired t-test, and meta-heuristics to screen the most optimal set of gene biomarkers as predictors for BC. The proposed framework identified a set of three most optimal gene biomarkers, namely, MAPK 1, APOBEC3B, and ENAH. In addition, the state-of-the-art supervised Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Neural Net (NN), Naïve Bayes (NB), Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR) were used to test the predictive capability of the selected gene biomarkers and select the most effective breast cancer diagnostic model with higher values of performance matrices. Our study found that the XGBoost-based model was the superior performer with an accuracy of 0.976 ± 0.027, an F1-Score of 0.974 ± 0.030, and an AUC value of 0.961 ± 0.035 when tested on an independent test dataset. The screened gene biomarkers-based classification system efficiently detects primary breast tumors from normal breast samples.

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An integrated artificial intelligence model for efficiency assessment in pharmaceutical companies during the COVID-19 pandemic

Cited by 23 publications

References 83 publications

How Do External Shocks Affect the Economic Efficiency of Companies with Foreign Direct Investment?

How Do External Shocks Affect the Economic Efficiency of Companies with Foreign Direct Investment?

Input/Output Variables Selection in Data Envelopment Analysis: A Shannon Entropy Approach

A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data

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