RFOS - Repository of Faculty of Organizational Sciences Univ. of Belgrade
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Empirical (alpha,beta)-acceptable optimal values to full fuzzy linear fractional programming problems
No full textIn this paper we aim to provide empirical solutions to a special class of full fuzzy linear fractional programming problems. We use trapezoidal fuzzy numbers to describe the parameters and derive empirical shape of the membership of the goal function optimal values of the problem. Our approach essentially follows the extension principle, and is based on solving crisp quadratic optimization problems. The model we propose treats in different ways, through two independent parameters, the objective function coefficients and coefficients in the constraints. To illustrate our theory, we solve a relevant instance and compare our numerical results with the numerical results recalled from the recent literature
Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach
No full textGray-level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computational method for the assessment of textural patterns, applicable in almost any area of microscopy. The aim of our research was to perform the GLCM analysis of cell nuclei in Saccharomyces cerevisiae yeast cells after the induction of sublethal cell damage with ethyl alcohol, and to evaluate the performance of various machine learning (ML) models regarding their ability to separate damaged from intact cells. For each cell nucleus, five GLCM parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, and textural variance. Based on the obtained GLCM data, we applied three ML approaches: neural network, random trees, and binomial logistic regression. Statistically significant differences in GLCM features were observed between treated and untreated cells. The multilayer perceptron neural network had the highest classification accuracy. The model also showed a relatively high level of sensitivity and specificity, as well as an excellent discriminatory power in the separation of treated from untreated cells. To the best of our knowledge, this is the first study to demonstrate that it is possible to create a relatively sensitive GLCM-based ML model for the detection of alcohol-induced damage in Saccharomyces cerevisiae cell nuclei
Drivers of e-Relational Capital in the Retail Industry
No full textRelational capital in the retail industry is a paramount driver of growth and financial success. Although relational capital might not be a novel topic, measuring e-relational capital and tracing down its antecedents attracts immense scholarly attention worldwide. The aim of this paper is to measure the e-relational capital of the fast-moving consumer goods retailers (FMCG retailers) in Serbia and to explore and examine the predicting power of a number of drivers of e-relational capital. To fulfill this aim, we narrowed the components of the relational capital to the relationship with customers, on one side, and suppliers, on the other, and accordingly conducted two separate, but interrelated studies. By using the structured questionnaires, we collected primary data from customers (N-1 = 651) and suppliers (N-2 = 159). The results indicate that customer loyalty and brand awareness play pivotal role in the customers' side of e-relational capital formation, whereas supplier trust and relationship performance have crucial importance in the suppliers' side of e-relational capital building
Evaluation of critical risk factors in the implementation of modular construction
No full textModular construction is considered as a preferred construction method over conventional construction due to a number of benefits including reduction in project completion time, improved environmental performance, better quality, enhanced workers' safety and flexibility. However, successful implementation of modular construction is hindered by various risk factors and uncertainties. Therefore, it is imperative to perform a comprehensive risk assessment of critical risk factors that pose a negative impact on the implementation of modular construction. Moreover, there is also a relatively less rate of modular construction adoption in developing countries, highlighting the need to focus more on underdeveloped regions. This study aims to propose a risk assessment framework for identification, evaluation and prioritization of critical risk factors affecting the implementation of modular construction in Pakistan. 20 risk factors were identified from previous literature which were then evaluated to shortlist the most significant risks using Fuzzy Delphi. The most significant risk factors were then prioritized using a novel Full-Consistency Method (FUCOM). The results specified 'Inadequate skills and experience in modular construction', 'Inadequate capacity of modular manufacturers' and 'Inability to make changes in design during the construction stage' as top three critical risks in the implementation of modular construction. This is the first study to propose a risk assessment framework for modular construction in Pakistan. The results of the study are useful to provide insights to construction industry practitioners in highlighting and eliminating risks involved in modular construction planning and execution
Business system application in Serbian wood industry - implementation models analysis
No full textThis paper present analyzes the research results of business information systems (BIS) application in the wood industry companies in the Republic of Serbia. The main objective of this paper is to research the reasons for BIS application in the business of these companies. An additional objective is to determine the criteria for BIS introduction and selection in their company. Also, some of the additional objectives are to determine the introduction of the BIS strategy and analyze what software companies use. This paper aims to imply to wood industry companies the most popular way of BIS introduction and indicate to most popular software in the Serbian wood industry. The research results show that the largest number is decided for the Frontal introduction of BIS. Also, research results indicate that most number companies are deciding on Custom software development Microsoft Dynamics NAV, and Pantheon
Introduction to Fairness in Algorithmic Decision Making mini-track
No full textA vast application of machine learning and decision-making algorithms for decision support in various areas of life caused the need for the algorithms to take into account additional constraints, such as non-discriminatory behavior or imposing fairness, or social welfare prior to proposing decisions to decision makers. These constraints can be fulfilled by carefully guiding the whole decision-making and data governance process, by adjusting decision-making, data mining and machine learning algorithms to fulfill additional constraints. For example, by adapting CRISP-DM methodology to account for possible biases, by imposing instance-dependent cost-sensitive learning, or enforcing equality in data envelopment analysis as presented in this mini-track
Forecasting Sovereign Credit Ratings Using Differential Evolution and Logic Aggregation in IBA Framework
No full textThe sovereign credit rating is considered as a quantified assessment of country's economic and political stability. Due to its importance and increasing amount of available information, the sovereign credit rating is considered as a hot topic in the last few years. However, the models that predict the credit ratings used by the several big credit rating agencies are unavailable, and can therefore be considered as the black boxes. In this paper, we are tackling this problem of predicting sovereign credit ratings by proposing a hybrid model based on interpolative Boolean algebra (IBA) and differential evolution (DE). Namely, we aim to obtain a logical/pseudo-logical function in IBA framework using DE metaheuristic that could underline connections of chosen macroeconomic indicators and sovereign credit ratings. Such functions are easy to interpret and able to make a subtle fuzzy gradation among countries. Country's economic indicators together with credit ratings from 2000 to 2016 are used for the model training. Acquired model is further tested on the data for 2017 and 2018
Effect of UV-B radiation on chlorophyll fluorescence, photosynthetic activity and relative chlorophyll content of five different corn hybrids
No full textThis research presents an experimental study of the effect of UV-B radiation (7.5 Wm−2) on the change of the total concentration of chlorophyll ΔChl and energy that a plant can store during the process of photosynthesis. The aim was to investigate the effect of UV-B radiation to spectral lines of five genetically different corn hybrids and find the lines with better resistance. Chlorophyll fluorescence from plant leaves was used as experimental method. The plants were exposed to UV-B radiation for 19 days. The following results were obtained: a) there is a significant variation between different corn hybrids regarding the effect of UV-B radiation, b) an indicative element of change in the functioning of the photosynthetic apparatus is represented in variations in the relative composition of photosynthetic pigments, c) regardless of what may be the cause of the change of the plant's ability to deposit a part of absorbed energy in the primary products of photosynthesis, it has been shown that two out of five investigated corn hybrids show great resistance to UV-B radiation, and d) relative change of photosynthesis can be used as a measure of the plant's resistance to the harmful effect of UV-B radiation
Social recruiting: an application of social network analysis for preselection of candidates
No full textPurpose: The paper aims to studiy social recruiting for finding suitable candidates on social networks. The main goal is to develop a methodological approach that would enable preselection of candidates using social network analysis. The research focus is on the automated collection of data using the web scraping method. Based on the information collected from the users' profiles, three clusters of skills and interests are created: technical, empirical and education-based. The identified clusters enable the recruiter to effectively search for suitable candidates. Design/methodology/approach: This paper proposes a new methodological approach for the preselection of candidates based on social network analysis (SNA). The defined methodological approach includes the following phases: Social network selection according to the defined preselection goals; Automatic data collection from the selected social network using the web scraping method; Filtering, processing and statistical analysis of data. Data analysis to identify relevant information for the preselection of candidates using attributes clustering and SNA. Preselection of candidates is based on the information obtained. Findings: It is possible to contribute to candidate preselection in the recruiting process by identifying key categories of skills and interests of candidates. Using a defined methodological approach allows recruiters to identify candidates who possess the skills and interests defined by the search. A defined method automates the verification of the existence, or absence, of a particular category of skills or interests on the profiles of the potential candidates. The primary intention is reflected in the screening and filtering of the skills and interests of potential candidates, which contributes to a more effective preselection process. Research limitations/implications: A small sample of the participants is present in the preliminary evaluation. A manual revision of the collected skills and interests is conducted. The recruiters should have basic knowledge of the SNA methodology in order to understand its application in the described method. The reliability of the collected data is assessed, because users provide data themselves when filling out their social network profiles. Practical implications: The presented method could be applied on different social networks, such as GitHub or AngelList for clustering profile skills. For a different social network, only the web scraping instructions would change. This method is composed of mutually independent steps. This means that each step can be implemented differently, without changing the whole process. The results of a pilot project evaluation indicate that the HR experts are interested in the proposed method and that they would be willing to include it in their practice. Social implications: The social implication should be the determination of relevant skills and interests during the preselection phase of candidates in the process of social recruitment. Originality/value: In contrast to previous studies that were discussed in the paper, this paper defines a method for automatic data collection using the web scraper tool. The described method allows the collection of more data in a shorter period. Additionally, it reduces the cost of creating an initial data set by removing the cost of hiring interviewers, questioners and people who collect data from social networks. A completely automated process of data collection from a particular social network stands out from this model from currently available solutions. Considering the method of data collection implemented in this paper, the proposed method provides opportunities to extend the scope of collected data to implicit data, which is not possible using the tools presented in other papers
