IDS-Previous conferences                                  In memory of late Prof. Lotfi Zadeh   

Keynote speakers

Prof. M. M. Zahedi

Shahid Bahonar University, Kerman, Iran

Head of the High Commission on Education and Research of the Parliament.

He is the invited speaker for the opening of the conference.


Prof. Bernard De Baets

KERMIT, Ghent University, Belgium


Monotonicity, an overlooked property in data science



In many modelling problems, there exists a monotone relationship between one or more of the input variables and the output variable, although this may not always be fully the case in the observed input-output data due to data imperfections. Monotonicity is also a common property of evaluation and selection procedures. In contrast to a local property such as continuity, monotonicity is of a global nature and any violation of it is therefore simply unacceptable. We explore several problem settings where monotonicity matters, including fuzzy modelling, machine learning and decision making. Central to the above three settings is the cumulative approach, which matches nicely with the monotonicity requirement.

By far the most popular fuzzy modelling paradigm, despite its weak theoretical foundations, is the rule-based approach of Mamdani and Assilian. In numerous applied papers, authors innocently assume that given a fuzzy rule base that appears monotone at the linguistic level, this will be the case for the generated input-output mapping as well. Unfortunately, this assumption is false, and we will show how to counter it. Moreover, we will show that an implication-based interpretation, accompanied with a cumulative approach based on at-least and/or at-most quantifiers, might be a much more reasonable alternative.

Next, we deal with a particular type of classification problem, in which there exists a linear ordering on the label set (as in ordinal regression) as well as on the domain of each of the features. Moreover, there exists a monotone relationship between the features and the class labels. Such problems of monotone classification typically arise in a multi-criteria evaluation setting. When learning such a model from a data set, we are confronted with data impurity in the form of reversed preference. We present the Ordinal Stochastic Dominance Learner framework, which permits to build various instance-based algorithms able to process such data.

Finally, we explore a pairwise preference setting where each stakeholder expresses his/her preferences in the shape of a reciprocal relation that is monotone w.r.t. a linear order on the set of alternatives. The goal is to come up with an overall monotone reciprocal relation reflecting `best' the opinions. We formulate the problem as an optimization problem, where the aggregated linear order is that for which the implied stochastic monotonicity conditions are closest to being satisfied by the distribution of the input monotone reciprocal relations. Interesting links with social choice will be pointed out.


Prof. Janusz Kacprzyk

Polish academy of science, Poland.


Prof. Mohammad-R. Akbarzadeh

University of Mashhad


It is increasingly clear that artificial intelligence (AI) will be making a profound contribution to the future of humanity, the planet, and the world. One could only hope that this synergism of technologies address humanity’s many rising ills such as in health, education, food, water, pollution, transportation, housing, as well as the natural tragedies such as the earth’s rising temperature, the massive desertification of lands, loss of land due to rising sea levels, and the loss of diversity in animal/plant life. 

Among the above problems, there are some with cost effective solution strategies in the areas of education & media, healthcare, finances, robotics, security, and autonomous vehicles.  These have already attracted the private industry and funded the academia, giving rise to a number of highly successful AI corporations around the world such as the Bytendance in China, C3IoT in USA, Sportlogiq in Canada, Upstart in USA, and LeapMind in Japan. 

But there are also problems that do not have seemingly cost-effective solutions and are hence neglected for the most part by the private sector. Yet, these are also often problems that address the needs of the voiceless masses such as the extremely poor, the least educated, and the most ill. This voiceless majority also includes the nature and animal life, where man’s wrongdoings are recognized only when it is too late and the depth of the tragedy is too great. To be fair, governments and non-profit organizations have traditionally tended to these problems, hoping to do some ‘public good.’ But public funds are scarce and the problems are many.

The central question then is if AI along with its related technologies can offer new cost-effective solutions to do ‘public good,’ that were not previously thought of as profitable. This would change the dynamics of ‘doing good,’ making it possible for the large private sector to join the governments and non-profit organization in further addressing the ills of humanity. In other words, while we recognize that AI technologies can do much good everywhere, we define ‘for good’ here as the intention to address problems that are traditionally not profitable and yet could positively impact humanity at large. 

It is the author’s contention in this lecture that great strides could be made in this realm by AI and its synergistic technologies such as in computing, communication and energy. In other words, these modern technologies are indeed changing the game for doing ‘public good.’   But for this to happen, we need a concerted effort to identify and solve suitable problems. In this talk, we will review several technological breakthroughs and their original intents. We will then introduce few instances in which profitable solutions are successfully obtained by addressing the concerns of the voiceless majority. We conclude by emphasizing the strength of the current technologies in making connections and using the resources of the crowd, as well as recognizing the importance of solutions that are in tune with cultural frames and norms.



 Mohammad-R. Akbarzadeh-T. is currently a professor and director of the Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), departments of electrical engineering and computer engineering, Ferdowsi University of Mashhad. He received his PhD on Evolutionary Optimization and Fuzzy Control of Complex Systems from the department of electrical and computer engineering at the University of New Mexico (UNM) in 1998.  From 1996-2003, he was also affiliated with the NASA Center for Autonomous Control Engineering at UNM. In 2006 and 2017, he was also with the Berkeley Initiative on Soft Computing (BISC), UC Berkeley as a visiting scholar. In 2007, he also served as a consulting faculty at the Department of Aerospace and Aeronautic Engineering, Purdue University.

Prof. Akbarzadeh is the founding president of the Intelligent Systems Scientific Society of Iran and the founding councilor representing the Iranian Coalition on Soft Computing in IFSA. He is a senior member of the IEEE and the founding faculty councilor of the IEEE student branch until 2008. He is also a life member of Eta Kappa Nu (The Electrical Engineering Honor Society), Kappa Mu Epsilon (The Mathematics Honor Society), and the Golden Key National Honor Society. He has received several awards including: the IDB Excellent Leadership Award in 2010, The IDB Excellent Performance Award in 2009, the Outstanding Faculty Award in 2008 and 2002, the IDB Merit Scholarship for High Technology in 2006, the Outstanding Faculty Award in Support of Student Scientific Activities in 2004, Outstanding Graduate Student Award in 1998, and Service Award from the Mathematics Honor Society in 1989. His research interests are in the areas of bio-inspired computing/optimization, fuzzy logic and control, soft computing, multi-agent systems, complex systems, robotics, cognitive sciences and medical informatics. He has published over 450 peer-reviewed articles in these and related research fields.


Prof. R. Farzipoor Saen

Adjunct Professor, Maastricht School of Management, Netherlands

Visiting Professor of Nottingham Trent University

Full Professor of Islamic Azad University, Karaj Branch



How to assess sustainability of suppliers in the presence of dual-role factor and volume discounts? A data envelopment analysis approach



Sustainability factors play critical role for long-term achievement of a supply chain management and purchasing process becomes more complicated with social and environmental pressures. Managing supplier selection process is a necessary step for companies seeking to manage their corporate legitimacy and reputations. Data envelopment analysis (DEA) has been widely used for supplier selection problems. In this speech, we propose a new super-efficiency method for evaluating sustainability of suppliers in presence of dual-role factors and volume discounts. We show that enhanced Russell model (ERM) fails to present a complete ranking of suppliers. Our new model presents a complete ranking and also preserves properties of ERM. Capabilities of our proposed method are shown using a couple of examples. A case study is presented to illustrate our proposed approach. The proposed method is used to select the best sustainable suppliers.


Prof. R. A. Borzooei

Department of Mathematics, Shahid Beheshti University, Tehran, Iran


Fuzzy Graphs with Applications



Fuzzy graph theory is nding an increasing number of applications in modeling real time systems where the level of information inherent in the system varies with different levels of precision. Fuzzy models are becoming useful because of their aim in reducing the differences between the traditional numerical models used in engineering and sciences and the symbolic
models used in expert systems. Rosenfeld, introduced fuzzy graph in 1975. It has been growing fast and has numerous applications in various eld. Now, in this talk we investigate some concepts in the fuzzy graphs and we introduce some real applications.

Short CV:

R. A. Borzooei is Full Professor at Shahid Beheshti University, Tehran, Iran. He has been published more than two hundred contributions in refereed journals, mainly devoted to the theory of fuzzy graphs with applications and logical algebraic structures such as BCK, BL and MV-algebra. He has served the fuzzy community as President of the Iranian Fuzzy Systems Society and head of Research Center of Fuzzy Systems in University of Sistan and Baluchistan. He currently is Managing Editor of Iranian Journal of Fuzzy Systems and President of the University Campus 2 of Shahid Beheshti University.


Prof. Javier Montero

Complutense University of Madrid, Spain.


Computing information aggregation.


In this talk we shall discuss the concept of aggregation function, particularly the standard mathematical assumptions and its relevance in any management of information, to finally focus on the computational issue. In this way, we will stress how the way we reckon each aggregation provides a key description of the tool in our hand, if it is really useful in practice. As a consequence, we claim that more effort should be devoted to the design and analysis of the algorithms for aggregation we use in practice, that quite frequently need to be adapted to each specific situation.     
Short CV:
Javier Montero is Full Professor at Complutense University of Madrid, Spain. He has been leading competitive research projects since 1987 and has published more than one hundred contributions in refereed journals, mainly devoted to the theory and applications of fuzzy sets, plus a similar number of papers as book chapters. He has served the fuzzy community as President of the European Society for Fuzzy Logic and Technologies (EUSFLAT) and as Vicepresident of the International Fuzzy Systems Association (IFSA). He has been also Vicedean and Dean of the Faculty of Mathematics and Vicerrector at his University. He currently is Head of the Department of Statistics and Operational Research, and President of IFSA. Javier Montero has been also acknowledged as "IFSA Fellow”.