Foundations of Computing and Decision Sciences最新文献_第6页

A MCDM Based on Goal Programming and GIS-Based Risk Assessment for the Evaluation of Educational Locations in Conflict Areas 基于目标规划和gis风险评估的冲突地区教育选址MCDM

IF 1.1

Foundations of Computing and Decision Sciences Pub Date : 2021-12-01 DOI: 10.2478/fcds-2021-0022

J. Hallak, Elifcan Göçmen Polat

{"title":"A MCDM Based on Goal Programming and GIS-Based Risk Assessment for the Evaluation of Educational Locations in Conflict Areas","authors":"J. Hallak, Elifcan Göçmen Polat","doi":"10.2478/fcds-2021-0022","DOIUrl":"https://doi.org/10.2478/fcds-2021-0022","url":null,"abstract":"Abstract Conflict is recognized as a major barrier in socio-economic development. In conflict situations, most sectors such as health, food, shelter and education are adversely affected. The provision of education services to conflict-affected children saves them from becoming a lost generation and contributes to community building. Thus, we conducted this research to investigate the potential of a GIS (Geographic Information Systems) approach and risk assessment based multi-criteria decision making (MCDM) for the allocation of displaced dropped-out children to the most appropriate educational centres, taking into account multiple goals related to cost, distance, risk, etc. A two-stage approach was adopted, utilizing a risk assessment approach, and a location-allocation approach. The risk assessment approach was carried out using GIS and F-AHP (Fuzzy Analytic Hierarchy Process) to determine the risk value of each candidate educational centre in the conflict area. In the location-allocation stage, a mathematical model was developed to allocate all demands to the chosen centres. All presented methods were computationally conducted on real case data provided by direct beneficiaries and stakeholders in the 26 sub-districts in the Idleb governorate, Syria. The computational results demonstrate that the proposed approaches ensure practical and theoretical impacts.","PeriodicalId":42909,"journal":{"name":"Foundations of Computing and Decision Sciences","volume":"46 1","pages":"361 - 391"},"PeriodicalIF":1.1,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48852898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessment Methods for Evaluation of Recommender Systems: A Survey 评价推荐系统的评估方法:综述

IF 1.1

Foundations of Computing and Decision Sciences Pub Date : 2021-12-01 DOI: 10.2478/fcds-2021-0023

Madhusree Kuanr, Puspanjali Mohapatra

引用次数: 2

An Introduction to the Special Issue on Numerical Techniques Meet with OR - Part II 介绍数值技术与OR的特殊问题-第二部分

IF 1.1

Foundations of Computing and Decision Sciences Pub Date : 2021-09-01 DOI: 10.2478/fcds-2021-0013

Burcu Gürbüz, G. Weber

引用次数: 0

New Numerical Approach for Solving Abel’s Integral Equations 求解阿贝尔积分方程的新数值方法

IF 1.1

Foundations of Computing and Decision Sciences Pub Date : 2021-09-01 DOI: 10.2478/fcds-2021-0017

Ayşe Anapalı Şenel, Yalçın Öztürk, Mustafa Gülsu

引用次数: 1

A Soft Interval Based Decision Making Method and Its Computer Application 一种基于软区间的决策方法及其计算机应用

IF 1.1

Foundations of Computing and Decision Sciences Pub Date : 2021-09-01 DOI: 10.2478/fcds-2021-0018

Gözde Yaylalı, N. Polat, B. Tanay

{"title":"A Soft Interval Based Decision Making Method and Its Computer Application","authors":"Gözde Yaylalı, N. Polat, B. Tanay","doi":"10.2478/fcds-2021-0018","DOIUrl":"https://doi.org/10.2478/fcds-2021-0018","url":null,"abstract":"Abstract In today’s society, decision making is becoming more important and complicated with increasing and complex data. Decision making by using soft set theory, herein, we firstly report the comparison of soft intervals (SI) as the generalization of interval soft sets (ISS). The results showed that SIs are more effective and more general than the ISSs, for solving decision making problems due to allowing the ranking of parameters. Tabular form of SIs were used to construct a mathematical algorithm to make a decision for problems that involves uncertainties. Since these kinds of problems have huge data, constructing new and effective methods solving these problems and transforming them into the machine learning methods is very important. An important advance of our presented method is being a more general method than the Decision-Making methods based on special situations of soft set theory. The presented method in this study can be used for all of them, while the others can only work in special cases. The structures obtained from the results of soft intervals were subjected to test with examples. The designed algorithm was written in recently used functional programing language C# and applied to the problems that have been published in earlier studies. This is a pioneering study, where this type of mathematical algorithm was converted into a code and applied successfully.","PeriodicalId":42909,"journal":{"name":"Foundations of Computing and Decision Sciences","volume":"7 9","pages":"273 - 296"},"PeriodicalIF":1.1,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41330216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Some Generalized Estimating Equations Models Based on Causality Tests for Investigation of The Economic Growth of The Country Groups 基于因果检验的若干广义估计方程模型在农村经济增长调查中的应用

IF 1.1

Foundations of Computing and Decision Sciences Pub Date : 2021-09-01 DOI: 10.2478/fcds-2021-0019

H. Yonar, Neslihan Iyit

{"title":"Some Generalized Estimating Equations Models Based on Causality Tests for Investigation of The Economic Growth of The Country Groups","authors":"H. Yonar, Neslihan Iyit","doi":"10.2478/fcds-2021-0019","DOIUrl":"https://doi.org/10.2478/fcds-2021-0019","url":null,"abstract":"Abstract In this study, investigation of the economic growth of the Organization for Economic Cooperation and Development (OECD) countries and the countries in different income groups in the World Data Bank is conducted by using causality analyses and Generalized Estimating Equations (GEEs) which is an extension of Generalized Linear Models (GLMs). Eight different macro-economic, energy and environmental variables such as the gross domestic product (GDP) (current US$), CO2 emission (metric tons per capita), electric power consumption (kWh per capita), energy use (kg of oil equivalent per capita), imports of goods and services (% of GDP), exports of goods and services (% of GDP), foreign direct investment (FDI) and population growth rate (annual %) have been used. These countries have been categorized according to their OECD memberships and income groups. The causes of the economic growth of these countries belonging to their OECD memberships and income groups have been determined by using the Toda-Yamamoto causality test. Furthermore, various GEE models have been established for the economic growth of these countries belonging to their OECD membership and income groups in the aspect of the above variables. These various GEE models for the investigation of the economic growth of these countries have been compared to examine the contribution of the causality analyses to the statistical model establishment. As a result of this study, the highlight is found as the use of causally-related variables in the causality-based GEE models is much more appropriate than in the non-causality based GEE models for determining the economic growth profiles of these countries.","PeriodicalId":42909,"journal":{"name":"Foundations of Computing and Decision Sciences","volume":"46 1","pages":"297 - 315"},"PeriodicalIF":1.1,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46708502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Legendre Matrix Method for Legendre Curve in Sasakian 3-Manifold Sasakian 3流形中Legendre曲线的Legendre矩阵法

IF 1.1

Foundations of Computing and Decision Sciences Pub Date : 2021-09-01 DOI: 10.2478/fcds-2021-0014

Tuba Ağirman Aydin, M. Sezer, H. Kocayiğit

引用次数: 0

Some New Characterizations of The Harmonic and Harmonic 1-Type Curves in Euclidean 3-Space 欧几里得三维空间中调和曲线和调和1型曲线的一些新特征

IF 1.1

Foundations of Computing and Decision Sciences Pub Date : 2021-09-01 DOI: 10.2478/fcds-2021-0016

H. K. Samanci, Sedat Ayaz, H. Kocayiğit

引用次数: 0

A Computational Technique for Solving Singularly Perturbed Delay Partial Differential Equations 求解奇摄动时滞偏微分方程的一种计算方法

IF 1.1

Foundations of Computing and Decision Sciences Pub Date : 2021-09-01 DOI: 10.2478/fcds-2021-0015

Burcu Gürbüz

引用次数: 4

Effect or Program Constructs on Code Readability and Predicting Code Readability Using Statistical Modeling 程序结构对代码可读性的影响及使用统计建模预测代码可读性

IF 1.1

Foundations of Computing and Decision Sciences Pub Date : 2021-06-01 DOI: 10.2478/fcds-2021-0009

Aisha Batool, Muhammad Bilal Bashir, M. Babar, Adnan Sohail, N. Ejaz

{"title":"Effect or Program Constructs on Code Readability and Predicting Code Readability Using Statistical Modeling","authors":"Aisha Batool, Muhammad Bilal Bashir, M. Babar, Adnan Sohail, N. Ejaz","doi":"10.2478/fcds-2021-0009","DOIUrl":"https://doi.org/10.2478/fcds-2021-0009","url":null,"abstract":"Abstract In software, code is the only part that remains up to date, which shows how important code is. Code readability is the capability of the code that makes it readable and understandable for professionals. The readability of code has been a great concern for programmers and other technical people in development team because it can have a great influence on software maintenance. A lot of research has been done to measure the influence of program constructs on the code readability but none has placed the highly influential constructs together to predict the readability of a code snippet. In this article, we propose a novel framework using statistical modeling that extracts important features from the code that can help in estimating its readability. Besides that using multiple correlation analysis, our proposed approach can measure dependencies among di erent program constructs. In addition, a multiple regression equation is proposed to predict the code readability. We have automated the proposals in a tool that can do the aforementioned estimations on the input code. Using those tools we have conducted various experiments. The results show that the calculated estimations match with the original values that show the effectiveness of our proposed work. Finally, the results of the experiments are analyzed through statistical analysis in SPSS tool to show their significance.","PeriodicalId":42909,"journal":{"name":"Foundations of Computing and Decision Sciences","volume":"46 1","pages":"127 - 145"},"PeriodicalIF":1.1,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47103283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0