RFOS - Repository of Faculty of Organizational Sciences Univ. of Belgrade
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Applications of the Multiattribute Decision-Making for the Development of the Tourism Industry Using Complex Intuitionistic Fuzzy Hamy Mean Operators
No full textIn the aggregation of uncertain information, it is very important to consider the interrelationship of the input information. Hamy mean (HM) is one of the fine tools to deal with such scenarios. This paper aims to extend the idea of the HM operator and dual HM (DHM) operator in the framework of complex intuitionistic fuzzy sets (CIFSs). The main benefit of using the frame of complex intuitionistic fuzzy CIF information is that it handles two possibilities of the truth degree (TD) and falsity degree (FD) of the uncertain information. We proposed four types of HM operators: CIF Hamy mean (CIFHM), CIF weighted Hamy mean (CIFWHM), CIF dual Hamy mean (CIFDHM), and CIF weighted dual Hamy mean (CIFWDHM) operators. The validity of the proposed HM operators is numerically established. The proposed HM operators are utilized to assess a multiattribute decision-making (MADM) problem where the case study of tourism destination places is discussed. For this purpose, a MADM algorithm involving the proposed HM operators is proposed and applied to the numerical example. The effectiveness and flexibility of the proposed method are also discussed, and the sensitivity of the involved parameters is studied. The conclusive remarks, after a comparative study, show that the results obtained in the frame of CIFSs improve the accuracy of the results by using the proposed HM operators
Social Alignment Contagion in Online Social Networks
No full textResearchers have already observed social contagion effects in both in-person and online interactions. However, such studies have primarily focused on users' beliefs, mental states, and interests. In this article, we expand the state of the art by exploring the impact of social contagion on social alignment, i.e., whether the decision to socially align oneself with the general opinion of the users on the social network is contagious to one's connections on the network or not. The novelty of our work in this article includes: 1) unlike earlier work, this article is among the first to explore the contagiousness of the concept of social alignment on social networks; 2) our work adopts an instrumental variable approach to determine reliable causal relations between observed social contagion effects on the social network; and 3) our work expands beyond the mere presence of contagion in social alignment and also explores the role of population heterogeneity on social alignment contagion. Based on the systematic collection and analysis of data from two large social network platforms, namely, Twitter and Foursquare, we find that a user's decision to socially align or distance from social topics and sentiments influences the social alignment decisions of their connections on the social network. We further find that such social alignment decisions are significantly impacted by population heterogeneity
Topological Data Analysis of m-Polar Spherical Fuzzy Information with LAM and SIR Models
No full textThe concept of m-polar spherical fuzzy sets (mPSFS) is a combination of m-polar fuzzy sets (mPFS) and spherical fuzzy sets (SFS). An mPSFS is an optimal strategy for addressing multipolarity and fuzziness in terms of ordered triples of positive membership grades (PMGs), negative membership grades (NMGs), and neutral grades (NGs). In this study, the innovative concept of m-polar spherical fuzzy topology (mPSF-topology) is proposed for data analysis and information aggregation. We look into the characteristics and results of mPSF-topology with the help of several examples. Topological structures on mPSFSs help with both the development of new artificial intelligence (AI) tools for different domain strategies and the study of different kinds of uncertainty in everyday life problems. These strategies make it possible to recognise and look into a situation early on, which helps professionals to reduce certain risks. In order to address various group decision-making issues in the m-polar spherical fuzzy domain, one suggestion has been to apply an extended linear assignment model (LAM) along with the SIR method known as superiority and inferiority ranking methodology in order to analyze road accident issues and dispute resolution. In addition, we examine the symmetry of optimal decision and perform a comparative study between the research carried out using the suggested methodology and several existing methods
Integrated FTA-risk matrix model for risk analysis of a mini hydropower plant's project finance
No full textInvestments in mini hydropower plants (MHPP) in Serbia are associated with numerous risks, both in the con-struction and exploitation phases. To assess these risks, this study proposes a methodology combining fault tree analysis and risk matrix using data obtained through a survey and semi-structured interviews with domain experts. Conducting qualitative fault tree analysis, 21 events have been identified that may jeopardize the success of the mini hydropower plant project finance. Based on expert assessments of probability of these events and their financial, reputational, and environmental impacts, risk matrix results showed that public protests, lack of projects sponsors' capital, and problems related to hydrology can be considered as events with the highest risk. In addition, quantitative analysis has been conducted to obtain the priority of measures for risk elimination or reduction. The quantitative fault tree analysis and three importance measures have indicated that measures must first be taken to prevent public protests during the implementation and exploitation phases, and a risk of watercourse abuse in the MHPP project
Employee Fluctuation in Quality Management Profession: Exploiting Social Professional Network Data
No full textDue to the lack of a significant volume of research on factors that affect employee fluctuation in the quality management profession, the paper is focused on exploring certain individual and organizational factors as possible influencers on quality management professionals' turnover. As a business and employment-oriented social networking service, LinkedIn was used to gain data on education, contacts, company ownership type, workplace type, and fluctuation behavior of 1014 quality management professionals. For statistical analysis, chi 2 contingency table, ANOVA with Tukey/Tamhane posthoc, and CHAID Decision Tree Analysis were employed. The results showed that employees' fluctuation depends on the degree of education, career development activities as well as the type of current examinee workplace. There is also an association between the type of company ownership and employee fluctuations. Further, the greater number of LinkedIn contacts was found to be related to more pronounced fluctuation. Since employees' fluctuation significantly influences company performance and competitiveness, the paper could provide valuable insight for managers to understand what could predict employees' fluctuation, which is especially important for knowledge-based contemporary organizations
Designing an extended smart classroom: An approach to game-based learning for IoT
No full textThis article presents a game-based learning model implemented in a smart learning environment. The goal is to determine students' interest, willingness to participate, and impressions about game-based learning, which takes place in interaction with the smart classroom. The educational game was designed and implemented by harnessing advanced web, mobile, Internet of Things (IoTs) technologies, and augmented reality and integrated into an e-learning ecosystem based on Moodle platform. The game aimed to test students' knowledge in the field of IoT. For the evaluation, an experiment was conducted within the IoT-related courses at the Faculty of Organizational Sciences, University of Belgrade. The results show that the proposed approach has a positive impact on the learning process and that the integration of educational games into common learning management systems is a good practice for extending common formal learning models. The proposed approach is expected to contribute to introducing smart learning environments into the learning process in higher education
Target recognition approach using image local features in rehabilitation robots
No full textFrom the computer science literature, it can be seen that many different technologies are used in target recognition, which is one of the most significant areas in the artificial intelligence field. Target recognition is applied in a variety of disciplines, including healthcare, robot vision, vehicular traffic, and virtual reality. Target recognition techniques involve a robotic vision system that must perform with high accuracy and efficiency in real time; additionally, it must have the capacity to handle difficult identification contexts. In one existing target recognition system, the Harris algorithm is used; it provides a higher accuracy compared to more traditional algorithms. In order to improve its achieved accuracy, we focus on the target detection algorithm of a rehabilitation robot that is based on the local features of images. Considering the feature points of the images and target identification technology, a rehabilitation robotic recognition method is developed in this work. Initially, it collects the images, and then, adaptive weighted symplectic geometry decomposition is used for pre-processing. This method helps to reduce the noise in the images. Next, the features are extracted, and the vectors of the features are separated and identified. Afterward, one-to-many rehabilitation modes and actual system monitors are implemented to precisely select the target condition based on the functional criteria of the rehabilitation robot recognition method. Finally, an invertible color-to-grayscale conversion method using clustering and reversible watermarking is applied. It converts images into grayscale. The Gaussian distribution is consistently utilized to define the position and the quantity of the extracted feature points. Related images are retrieved as well. According to experimental findings, the proposed method improves the accuracy and the recall rate compared with the Harris algorithm
A Multi-Attribute Decision-Making Approach for the Analysis of Vendor Management Using Novel Complex Picture Fuzzy Hamy Mean Operators
No full textVendor management systems (VMSs) are web-based software packages that can be used to manage businesses. The performance of the VMSs can be assessed using multi-attribute decision-making (MADM) techniques under uncertain situations. This article aims to analyze and assess the performance of VMSs using MADM techniques, especially when the uncertainty is of complex nature. To achieve the goals, we aim to explore Hany mean (HM) operators in the environment of complex picture fuzzy (CPF) sets (CPFSs). We introduce CPF Hamy mean (CPFHM) and CPF weighted HM (CPFWHM) operators. Moreover, the reliability of the newly proposed HM operators is examined by taking into account the properties of idempotency, monotonicity, and boundedness. A case study of VMS is briefly discussed, and a comprehensive numerical example is carried out to assess VMSs using the MADM technique based on CPFHM operators. The sensitivity analysis and comprehensive comparative analysis of the proposed work are discussed to point out the significance of the newly established results
Employing Trait Emotional Intelligence in an Adaptive E-learning Environment
No full textThis paper investigates possibilities of harnessing trait emotional intelligence in e-learning ecosystems with particular focus on enhancing adaptivity. The goal of the paper is to develop a model for adaptive e-education based on trait emotional intelligence as a criterion. Employing Trait Emotional Intelligence (TraitEI) as a model and agglomerative hierarchical cluster analysis technique, we identify segments of students attending the online course Digital Marketing within the e-learning platform at University of Belgrade, Faculty of Organizational Sciences, Department for E-Business. We found out three important clusters of students exist: those that have "Average TraitEI, Average Performers"; those that have "Slightly above Average TraitEI, High Performers"; and those that have "Above Average TraitEI, Super Performers." The characteristics that most differentiate the super performers group from the rest is the extent to which cluster members have high score of well-being, self-control, emotionality, sociability and a higher record of global TraitEI profiles in general. Comparative analysis using python machine learning packages is used to validate the relevant clusters based on Achievement Emotions Questionnaire (AEQ). The method is found to be useful tool to assist educators in segmenting students and by doing so, online course designers will have the ability to design and develop intervention course materials tailored to better meet the needs of different groups of students. The contribution of this study is reflected in the fact that the proposed model for segmenting students into relevant groups based on emotional intelligence can provide better adaptivity in e-education. In addition, the study can contribute to building more effective educational strategies in an e-learning environment
More than words: Rethinking sustainability communications through neuroscientific methods
No full textIn the era when the overarching problem of climate change, threatening the entire humanity and all life on earth, demands actions and behavioural change from all of the societal agents, including governments, organizations, companies and individuals, the world fails to achieve unity on the matter of existence of the problem, cause of the problem and on the solution of the problem. Since the scientific consensus on climate change is achieved, this article tries to examine why it is so hard to convey the message of needed behavioural change. The neuromarketing study presented in this article focuses on marketing communications that try to convey the message that would lead to the consumers' mental, emotional and behavioural change. Effectiveness of the branded environmental videos was evaluated utilizing the neuroscience approach. Electroencephalogram and an eye-tracking device were used to register the implicit brain reactions of the study participants viewing the branded videos. For comparison reasons, the branded videos selected for the study use two different approaches for conveying the message. The first approach relies on narrative, words and logic, whilst the second one appeals primarily to emotions. The aim of the study was to answer the question whether the words are enough or there more to it