Razvoj i prikaz funkcionalnosti programske aplikacije za rješavanje problema predviđanja ponašanja potrošača

Markovljevi lanci imaju široku primjenu u predviđanju kretanja raznih pojava. Cilj ovog rada je prikazati razvoj i funkcionalnost programske aplikacije za rješavanje problema predviđanja ponašanja potrošača primjenom metode Markovljevih lanaca. Programska aplikacija je prvenstveno namijenjena za predviđanje korištenja elektroničkih usluga i prognoziranje broja korisnika. Rad programske aplikacije testiran je na primjeru organizacije čija je djelatnost pružanje elektroničkih i financijskih usluga. Važnost predviđanja korištenja usluga i prognoziranje broja korisnika ogleda se u procjeni kapaciteta servera na kojima se nalaze usluge organizacije. Programska aplikacija sadrži korisničke upute za jednostavnije korištenje i interpretaciju dobivenih rezultata

A pre-planning for hotel locating according to the sustainability perspective based on BWM-WASPAS approach

Finding an appropriate location from any kind of perspectives is a really vital issue for businesses. Searching for the best location to meet a business perspective which can be also a sustainable one would be really challenging and it needs pre-planning. Locating for business issues in tourism industry for instance hotel locating is one of the major complicated topics in this area. Locating can come along with pre-planning and kind of feasibility studies which can cover all necessities of current and future needs of an issue. Locating with sustainability point of view is one the newest approach in different studies which this research is also working on that based on a hybrid Multiple Attribute Decision Making (MADM) model. MADM methods are really suitable ways in making complicated decisions in different areas. A hybrid MADM model based on BWM-WASPAS is applied for locating problem in finding the best location for the hotel locating challenge. To meet the aim of this research, a case study for evaluating probable locations of a five star hotel examined for the Shahrekord city, Iran. As locations were prioritized based on sustainability perspective, business goals can also be seen as a pre-planning project

Neighborhood selection for a newcomer via a novel BWM-based revised MAIRCA integrated model: a case from the Coquimbo-La Serena conurbation, Chile

Nowadays, cities are developing differently according to their needs, limitations and certain strategic plans. Moreover, conurbation areas will be more common in so many countries like Chile when there are two or more cities developing one next to another, leaning on each other. In this atmosphere, typical residents live in a region or a neighborhood based on certain criteria, so they know how and where they are going to live. From another point of view, a newcomer is usually faced with a city full of contrasts which make things completely and surprisingly complicated. In order to illustrate this, a real case was selected based on the research field, qualitative and quantitative (real) data. The Coquimbo-La Serena conurbation and it’s regions as “Comuna (in Chile)” is a really suitable case to show the complexity of the study. In order to face the challenge, a new hybrid Multiple-Attribute Decision-Making (MADM) method is introduced based on the Best-Worst Method (BWM) and Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA). The five best different neighborhoods as Comunas of the conurbation were analyzed based on the two main scenarios: having a private car or using only public transportation. To obtain more reliable results, a sensitivity analysis was made so as to determine the behavior of the proposed model against weight changes. Besides, the final results were compared with the other MADM methods, for example: Multi-Attributive Border Approximation Area Comparison (MABAC), VIsekriterijumsko KOmpromisno Rangiranje (VIKOR) and COmbinative Distance-based ASsessment (CODAS)

An MCDM method for cloud service selection using a Markov chain and the best-worst method

© 2018 Elsevier B.V. Due to the increasing number of cloud services, service selection has become a challenging decision for many organisations. It is even more complicated when cloud users change their preferences based on the requirements and the level of satisfaction of the experienced service. The purpose of this paper is to overcome this drawback and develop a cloud broker architecture for cloud service selection by finding a pattern of the changing priorities of User Preferences (UPs). To do that, a Markov chain is employed to find the pattern. The pattern is then connected to the Quality of Service (QoS) for the available services. A recently proposed Multi Criteria Decision Making (MCDM) method, Best Worst Method (BWM), is used to rank the services. We show that the method outperforms the Analytic Hierarchy Process (AHP). The proposed methodology provides a prioritized list of the services based on the pattern of changing UPs. The methodology is validated through a case study using real QoS performance data of Amazon Elastic Compute (Amazon EC2) cloud services

An MCDM method for cloud service selection using a Markov chain and the best-worst method

Due to the increasing number of cloud services, service selection has become a challenging decision for many organisations. It is even more complicated when cloud users change their preferences based on the requirements and the level of satisfaction of the experienced service. The purpose of this paper is to overcome this drawback and develop a cloud broker architecture for cloud service selection by finding a pattern of the changing priorities of User Preferences (UPs). To do that, a Markov chain is employed to find the pattern. The pattern is then connected to the Quality of Service (QoS) for the available services. A recently proposed Multi Criteria Decision Making (MCDM) method, Best Worst Method (BWM), is used to rank the services. We show that the method outperforms the Analytic Hierarchy Process (AHP). The proposed methodology provides a prioritized list of the services based on the pattern of changing UPs. The methodology is validated through a case study using real QoS performance data of Amazon Elastic Compute (Amazon EC2) cloud services

How digital data are used in the domain of health: A short review of current knowledge

In the era of digitalization, digital data is available about every aspect of our daily lives, including our physical and mental health. Digital data has been applied in the domain of healthcare for the detection of an outbreak of infectious diseases, clinical decision support, personalized care, and genomics. This paper will serve as a review of the rapidly evolving field of digital health. More specifically, we will discuss (1) big data and physical health, (2) big data and mental health, (3) digital contact tracing during the COVID-19 pandemic, and finally, (4) ethical issues with using digital data for health-related purposes. With this review, we aim to stimulate a public debate on the appropriate usage of digital data in the health sector