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Fuzzy Multiple Attribute Decision Making Methods And Applications

Natural resource abundance, technological innovation, and human capital nexus with financial development: A case study of China

Decision making techniques have been widely used in healthcare and medical industry. This study systematically reviews the conventional and fuzzy decision-making techniques applied in healthcare and medical problems. In total, we evaluated 202 published studies selected from 85 high-ranking journals, in which 130 of those published studies were directly related to the decision-making processes in healthcare and medical issues. Selected studies were classified into nine application areas: environmental sustainability, waste management, service quality, risk management, medical equipment and material selection, health technology, operation researches in healthcare, hospital healthcare services and other application areas. Furthermore, we classified the selected studies by numerous significant perspectives such as the authors, application areas, utilized techniques, publication time periods, decision-making and study goals, and significant results of the papers. The statistical results show that the year 2012 was ranked first in terms of developed studies within the period of 1989–2018. The techniques of AHP and hybrid approaches were the most frequently implemented decision-making technique in healthcare fields. Notably, the results also show that decision-making approaches and techniques are mostly applied to evaluate and rank different applications of service quality in healthcare and medical industries.

Application of decision making and fuzzy sets theory to evaluate the healthcare and medical problems: A review of three decades of research with recent developments

Group recommendation has recently drawn a lot of attention to the recommender system community. Currently, several deep learning-based approaches are leveraged to learn preferences of groups for items and predict next items in which groups may be interested. Yet, their recommendation performance is still unsatisfactory due to sparse group–item interactions. To address this challenge, this study presents a novel model, called group recommendation model with two-stage deep learning (GRMTDL), which encompasses two sequential stages: 1) group representation learning (GRL) and 2) group preference learning (GPL). In GRL, we first construct an undirected tripartite graph over group–user–item interactions, and then employ it to accurately learn group semantic features through a spatial-based variational graph autoencoder network. While in GPL, we first introduce a dual PL-network that contains two structure-sharing subnetworks: 1) group PL-network employed for GPL and 2) user PL-network utilized for user preference learning. Then, we design a novel layered transfer learning (LTL) method to learn group preferences by alternately optimizing these two subnetworks. In particular, it can effectively absorb knowledge of user preferences into the process of GPL. Furthermore, extensive experiments on four real-world datasets demonstrate that the proposed GRMTDL model outperforms the state-of-the-art baselines for group recommendation.

A Novel Group Recommendation Model With Two-Stage Deep Learning

Basic econometrics / Damodar N. Gujarati, Dawn C. Porter.

Gradient-based algorithms have been widely used in optimizing parameters of deep neural networks’ (DNNs) architectures. However, the vanishing gradient remains as one of the common issues in the parameter optimization of such networks. To cope with the vanishing gradient problem, in this article, we propose a novel algorithm, evolved gradient direction optimizer (EVGO), updating the weights of DNNs based on the first-order gradient and a novel hyperplane we introduce. We compare the EVGO algorithm with other gradient-based algorithms, such as gradient descent, RMSProp, Adagrad, momentum, and Adam on the well-known Modified National Institute of Standards and Technology (MNIST) data set for handwritten digit recognition by implementing deep convolutional neural networks. Furthermore, we present empirical evaluations of EVGO on the CIFAR-10 and CIFAR-100 data sets by using the well-known AlexNet and ResNet architectures. Finally, we implement an empirical analysis for EVGO and other algorithms to investigate the behavior of the loss functions. The results show that EVGO outperforms all the algorithms in comparison for all experiments. We conclude that EVGO can be used effectively in the optimization of DNNs, and also, the proposed hyperplane may provide a basis for future optimization algorithms.

A Novel Learning Algorithm to Optimize Deep Neural Networks: Evolved Gradient Direction Optimizer (EVGO)

Financial performance evaluation of banks is a kind of multi-criteria decision making (MCDM) problem which has developed rapidly. It is very important for a firm to monitor a wide range of performance indicators in order to ensure that appropriate and timely decisions and plans can be made. Suitable performance measures can ensure that managers adopt a long-term perspective and allocate the company’s resources to the most effective activities. The aim of this study is to evaluate the financial performance model of Turkish Banks during 2002-2011 periods. Both Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodologies are used for the outranking of banks. In this study, subjective and objective opinions of financial actors turn into quantitative form with AHP.

Performance Evaluation of Turkish Banks Using Fuzzy Analytical Hierarchy Process and TOPSIS Methods

The new International Financial Reporting Standard (IFRS) 16 issued by the International Accounting Standards Board will significantly change accounting for leases. The most important issue is that operating leases beyond one year will be capitalized, which means Off-TheBalance Sheet (OBS) financing via operating lease is effectively eliminated. “IFRS 16: Leases” will fundamentally change the way that leases are accounted for and reported in financial statements. This paper tries to illustrate the impact of IFRS 16 on financial statements and financial ratios. The study is an ex ante research, simulating a predicted outcome of the new lease standard, which will be in effect after 1 January 2019. For this purpose, this research is applied to the Turkish retailing companies whose shares are publicly traded in the Istanbul Stock Exchange. The study uses the constructive capitalization method for these companies. The results indicate that new standard will have a statistically significant effect on some of the financial ratios tested (debt/asset, debt/equity, return on assets (ROA) and return on equity (ROE)) for 2010 – 2013.

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The effect of the ifrs 16: constructive capitalization of operating leases in the turkish retailing sector

During last 10 years some EU countries had economic instability. They have
short and long term challenges such as unemployment, population ageing,
globalization etc. In this study it is aimed to analyze macroeconomic
indicators of EU countries’ economic growth using panel data approach.
Static and dynamic panel data models were used for determining the
effects of independent macro-economic variables on gross domestic
product (GDP) of EU member countries including Austria, Belgium,
Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France,
Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg,
Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain,
Sweden, United Kingdom; acceding country: Croatia; and candidate
countries: Iceland, Montenegro, Serbia, The former Yugoslav Republic of
Macedonia and Turkey. While dependent variable of analyze is gross
domestic product (volume), the independent variables are current account
balance, general government gross debt, general government revenue,
general government total expenditure, gross national savings, inflation,
average consumer prices, population, total investment, unemployment
rate, volume of exports of goods and services, volume of imports of goods
and services. The analysis proposed is based on a panel data (cross
sectional time series data) approach. The dataset of this research involves
33 EU member and EU candidate countries (units). The effects of 12
macroeconomic indicators on gross domestic product volume were
examined. The paper also empirically analyzes the negative impacts of
global financial crisis (the 2007 U.S. Subprime Financial Crisis) into EU
member and candidate countries’ economic growth during the 2002–2012 crisis is, the factors that promote a financial crisis, and the dynamics of a
financial crisis. Thus, the effects of macroeconomic parameters are
analyzed using panel data series. The findings of this research are
especially useful for EU candidate countries such as Iceland, Montenegro,
Serbia, The Former Yugoslav Republic of Macedonia and Turkey for
developing convenient economic strategies.
Keywords: European Union and Candidate Countries, Financial Crisis,
Macro Economic Parameters, Panel Data Analysis, Gross Domestic Product,
Economic Growth.

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Analyzing Macroeconomic Indicators of Economic Growth Using Panel Data

Identifying the Importance Level of Factors Influencing the Selection of Nursing as a Career Choice Using AHP: Survey to Compare the Precedence of Private Vocational High School Nursing Students and their Parents

Nihat Taş

Papers

A Novel Learning Algorithm to Optimize Deep Neural Networks: Evolved Gradient Direction Optimizer (EVGO)

Performance Evaluation of Turkish Banks Using Fuzzy Analytical Hierarchy Process and TOPSIS Methods

The effect of the ifrs 16: constructive capitalization of operating leases in the turkish retailing sector

Analyzing Macroeconomic Indicators of Economic Growth Using Panel Data

Identifying the Importance Level of Factors Influencing the Selection of Nursing as a Career Choice Using AHP: Survey to Compare the Precedence of Private Vocational High School Nursing Students and their Parents