1,132 research outputs found
Editor’s Note
The International Journal of Interactive Multimedia and Artificial Intelligence provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence and Interactive Multimedia techniques.
The research works presented in this issue are based on various topics of interest, among which are included: 3D image reconstruction, Persian texts, usability evaluation methods, user experience, oriented matroids, flexible job-shop scheduling, business and social behavior, mobile computing and mobile devices, intelligent tutoring systems and geography optimization
Measurements of Consensus in Multi-granular Linguistic Group Decision-making
The reaching of consensus in group decision-making (GDM) problems is a common task in group decision processes. In this contribution, we consider GDM with linguistic information. Different experts may have different levels of knowledge about a problem and, therefore, different linguistic term sets (multi-granular linguistic information) can be used to express their opinions.
The aim of this paper is to present different ways of measuring consensus in order to assess the level of agreement between the experts in multi-granular linguistic GDM problems. To make the measurement of consensus in multi-granular GDM problems possible and easier, it is necessary to unify the information assessed in different linguistic term sets into a single one. This is done using fuzzy sets defined on a basic linguistic term set (BLTS). Once the information is uniformed, two types of measurement of consensus are carried out: consensus degrees and proximity measures. The first type assesses the agreement among all the experts' opinions, while the second type is used to find out how far the individual opinions are from the group opinion. The proximity measures can be used by a moderator in the consensus process to suggest to the experts the necessary changes to their opinions in order to be able to obtain the highest degree of consensus possible. Both types of measurements are computed in the three different levels of representation of information: pair of alternatives, alternatives and experts.TIC2002-0334
Latent Dirichlet Allocation (LDA) for improving the topic modeling of the official bulletin of the spanish state (BOE)
Since Internet was born most people can access fully free to a lot sources of information. Every day a lot of web pages are created and new content is uploaded and shared. Never in the history the humans has been more informed but also uninformed due the huge amount of information that can be access. When we are looking for something in any search engine the results are too many for reading and filtering one by one. Recommended Systems (RS) was created to help us to discriminate and filter these information according to ours preferences. This contribution analyses the RS of the official agency of publications in Spain (BOE), which is known as "Mi BOE'. The way this RS works was analysed, and all the meta-data of the published documents were analysed in order to know the coverage of the system. The results of our analysis show that more than 89% of the documents cannot be recommended, because they are not well described at the documentary level, some of their key meta-data are empty. So, this contribution proposes a method to label documents automatically based on Latent Dirichlet Allocation (LDA). The results are that using this approach the system could recommend (at a theoretical point of view) more than twice of documents that it now does, 11% vs 23% after applied this approach
A cloud-based tool for sentiment analysis in reviews about restaurants on TripAdvisor
The tourism industry has been promoting its products and services based on the reviews that people often write on travel websites like TripAdvisor.com, Booking.com and other platforms like these. These reviews have a profound effect on the decision making process when evaluating which places to visit, such as which restaurants to book, etc.
In this contribution is presented a cloud based software tool for the massive analysis of this social media data (TripAdvisor.com). The main characteristics of the tool developed are: i) the ability to aggregate data obtained from social media; ii) the possibility of carrying out combined analyses of both people and comments; iii) the ability to detect the sense (positive, negative or neutral) in which the comments rotate, quantifying the degree to which they are positive or negative, as well as predicting behaviour patterns from this information; and iv) the ease of doing everything in the same application (data downloading, pre-processing, analysis and visualisation).
As a test and validation case, more than 33.500 revisions written in English on restaurants in the Province of Granada (Spain) were analyse
A Review on Information Accessing Systems Based on Fuzzy Linguistic Modelling
This paper presents a survey of some fuzzy linguistic information access systems. The review shows
information retrieval systems, filtering systems, recommender systems, and web quality evaluation tools,
which are based on tools of fuzzy linguistic modelling. The fuzzy linguistic modelling allows us to
represent and manage the subjectivity, vagueness and imprecision that is intrinsic and characteristic of the
processes of information searching, and, in such a way, the developed systems allow users the access to
quality information in a flexible and user-adapted way.European Union (EU)
TIN2007-61079
PET2007-0460Ministry of Public Works
90/07Excellence Andalusian Project
TIC529
Predicting missing pairwise preferences from similarity features in group decision making
In group decision-making (GDM), fuzzy preference relations (FPRs) refer to pairwise preferences in
the form of a matrix. Within the field of GDM, the problem of estimating missing values is of utmost
importance, since many experts provide incomplete preferences. In this paper, we propose a new
method called the entropy-based method for estimating the missing values in the FPR. We compared
the accuracy of our algorithm for predicting the missing values with the best candidate algorithm
from state of the art achievements. In the proposed entropy-based method, we took advantage of
pairwise preferences to achieve good results by storing extra information compared to single rating
scores, for example, a pairwise comparison of alternatives vs. the alternative’s score from one to five
stars. The entropy-based method maps the prediction problem into a matrix factorization problem, and
thus the solution for the matrix factorization can be expressed in the form of latent expert features
and latent alternative features. Thus, the entropy-based method embeds alternatives and experts in
the same latent feature space. By virtue of this embedding, another novelty of our approach is to
use the similarity of experts, as well as the similarity between alternatives, to infer the missing values
even when only minimal data are available for some alternatives from some experts. Note that current
approaches may fail to provide any output in such cases. Apart from estimating missing values, another
salient contribution of this paper is to use the proposed entropy-based method to rank the alternatives.
It is worth mentioning that ranking alternatives have many possible applications in GDM, especially
in group recommendation systems (GRS).Andalusian Government P20 00673
PID2019-103880RB-I00
MCIN/AEI/10.13039/50110001103
Hospitality brand management by a score-based q-rung orthopair fuzzy V.I.K.O.R. method integrated with the best worst method
Hospitality brand management is a primary concern in the hotel
industry and the evaluation of brands can be considered as a decision-
making problem with multiple criteria. The evaluation information
of brands may be uncertain sometimes. The q-rung
orthopair fuzzy set (q-R.O.F.S.), which represents the preference
degree of a person from the positive and negative aspects, has
turned out to be an efficient tool in depicting uncertainty and
vagueness in the decision-making process. This article dedicates to
presenting an integrated multiple criteria decision-making method
with q-R.O.F.S.. Firstly, a score function of the q-R.O.F.S. is proposed
to solve the deficiencies of two existing score functions.
Then, a weight-determining method based on the additive consistency
of the preference relation is developed. A decision-making
method integrating the score function, the best worst method
and the VIsekriterijumska optimizacija I KOmpromisno Resenje
(V.I.K.O.R.) which means multiple criteria compromise optimisation
in English) method is further proposed. Finally, a case study
regarding the hospitality brand management is provided to show
the applicability and validity of the proposed method.The work was supported by the National Natural Science Foundation of China (71771156,
71971145), the Scholarship from China Scholarship Council (No. 201906240161) and the
Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah (No. RG-10-611-
39, No. RG-7-135-38)
Assessment of Energy Systems Using Extended Fuzzy AHP, Fuzzy VIKOR, and TOPSIS Approaches to Manage Non-Cooperative Opinions
Energy systems planning commonly involves the study of supply and demand of power,
forecasting the trends of parameters established on economics and technical criteria of models.
Numerous measures are needed for the fulfillment of energy system assessment and the investment
plans. The higher energy prices which call for diversification of energy systems and managing the
resolution of conflicts are the results of high energy demand for growing economies. Due to some
challenging problems of fossil fuels, energy production and distribution from alternative sources
are getting more attention. This study aimed to reveal the most proper energy systems in Saudi
Arabia for investment. Hence, integrated fuzzy AHP (Analytic Hierarchy Process), fuzzy VIKOR
(Vlse Kriterijumska Optimizacija Kompromisno Resenje) and TOPSIS (Technique for Order Preferences
by Similarity to Idle Solution) methodologies were employed to determine the most eligible energy
systems for investment. Eight alternative energy systems were assessed against nine criteria—power
generation capacity, efficiency, storability, safety, air pollution, being depletable, net present value,
enhanced local economic development, and government support. Data were collected using the Delphi
method, a team of three decision-makers (DMs) was established in a heterogeneous manner with the
addition of nine domain experts to carry out the analysis. The fuzzy AHP approach was used for
clarifying the weight of criteria and fuzzy VIKOR and TOPSIS were utilized for ordering the alternative
energy systems according to their investment priority. On the other hand, sensitivity analysis was
carried out to determine the priority of investment for energy systems and comparison of them using
the weight of group utility and fuzzy DEA (Data Envelopment Analysis) approaches. The results
and findings suggested that solar photovoltaic (PV) is the paramount renewable energy system
for investment, according to both fuzzy VIKOR and fuzzy TOPSIS approaches. In this context our
findings were compared with other works comprehensively.This research was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz
University, Jeddah, under grant no. (RG-7-135-38). The authors, therefore, acknowledge with thanks DSR technical
and financial support
A personality-aware group recommendation system based on pairwise preferences
Human personality plays a crucial role in decision-making and it has paramount importance
when individuals negotiate with each other to reach a common group decision.
Such situations are conceivable, for instance, when a group of individuals want to watch
a movie together. It is well known that people influence each other’s decisions, the more
assertive a person is, the more influence they will have on the final decision. In order to
obtain a more realistic group recommendation system (GRS), we need to accommodate
the assertiveness of the different group members’ personalities. Although pairwise preferences
are long-established in group decision-making (GDM), they have received very little
attention in the recommendation systems community. Driven by the advantages of pairwise
preferences on ratings in the recommendation systems domain, we have further pursued
this approach in this paper, however we have done so for GRS. We have devised a
three-stage approach to GRS in which we 1) resort to three binary matrix factorization
methods, 2) develop an influence graph that includes assertiveness and cooperativeness
as personality traits, and 3) apply an opinion dynamics model in order to reach consensus.
We have shown that the final opinion is related to the stationary distribution of a Markov
chain associated with the influence graph. Our experimental results demonstrate that our
approach results in high precision and fairness.Spanish Government PID2019-10380RBI00/AEI/10. 13039/501100011033Andalusian Government P20_0067
- …