### Special Sessions

Authors who wish to participate in a special session are asked to submit their papers directly to the special session organizers by email. Special session organizers will be responsible for handling the review process for the papers in their sessions. The final papers will be uploaded to the easychair website once the papers are accepted. The following special Sessions are endorsed by the organizing committee:

### Similarity and association measures

#### Ildar Batyrshin (batyr1@gmail.com) and Vladik Kreinovich (vladik@utep.edu)

Similarity, association measures and related measures of relationship between features, variables and objects of different nature play important role in data mining, information retrieval, clustering, pattern recognition, machine learning etc. Recent years there is increasing interest both in development of application methods based on similarity and association measures, and in theoretical analysis of the general properties of similarity and association measures used in different applications. The goal of the session is to discuss the similarity and association measures on different domains and their relationships with operations of fuzzy logic, fuzzy relations, aggregation functions, kernels, possibility and probability definitions, data transformations, applications of similarity and association measures etc.

### Complex Fuzzy Sets and Complex Fuzzy Logic

#### Scott Dick (sdick@ualberta.ca), Sarah Greenfield

Complex fuzzy sets are an extension to type-1 fuzzy sets in which membership grades are complex-valued. Likewise, complex fuzzy logic is an isomorphic family of multi-valued logics whose truth values are complex numbers. In the ten years since these concepts were first proposed, further theoretical investigations and a number of applications have made complex fuzzy sets and logic a lively and growing research area. This special session will provide a forum to consolidate the community of researchers in this area, share our current ideas, reflect on future directions, and communicate our ideas and vision to the larger Computational Intelligence community. As such, we welcome submissions on all aspects of complex fuzzy sets or complex fuzzy logic, including but not limited to:

- Theory of complex fuzzy logic
- Complex fuzzy sets
- Complex fuzzy inferential systems
- Elicitation of complex fuzzy rules
- Machine learning for complex fuzzy inferential systems
- Hybridizations of complex fuzzy sets and logic with other CI technologies
- Data mining with complex fuzzy sets and logic
- Applications of complex fuzzy sets and logic
- Complex fuzzy logic hardware

### Fuzzy Logic Applications in Construction Engineering and Management

#### Aminah Robinson Fayek (aminah.robinson@ualberta.ca)

Construction engineering and management research has seen significant growth in fuzzy logic applications to solve numerous problems. Fuzzy logic has been used to model subjective uncertainty in construction and address the lack of comprehensive data sets available for modeling. In the construction domain, fuzzy logic has been combined with other techniques, such as simulation, system dynamics, genetic algorithms, and artificial neural networks to create hybrid systems. This session will focus on recent applications of fuzzy logic and fuzzy hybrid techniques for applications related to planning and scheduling, estimating and bidding, productivity, project control, structuring projects, process improvement, risk analysis, and others. In particular, challenges related to applying fuzzy logic in the construction domain will be discussed and ideas generated on how to adapt fuzzy logic and fuzzy hybrid techniques to better suit construction applications.

### Inter-Relation Between Interval and Fuzzy Techniques

#### Vladik Kreinovich (vladik@utep.edu), Martine Ceberio (mceberio@utep.edu)

Objectives: The relation between fuzzy and interval techniques is well known; e.g., due to the fact that a fuzzy number can be represented as a nested family of intervals (alpha-cuts), level-by-level interval techniques are often used to process fuzzy data.

At present, researchers in fuzzy data processing mainly used interval techniques originally designed for non-fuzzy applications, techniques which are often taken from textbooks and are, therefore, already outperformed by more recent and more efficient methods.

One of the main objectives of the proposed special session is to make the fuzzy community at-large better acquainted with the latest, most efficient interval techniques, especially with techniques specifically developed for solving fuzzy-related problems.

Another objective is to combine fuzzy and interval techniques, so that we will be able to use the combined techniques in (frequent) practical situations where both types of uncertainty are present: for example, when some quantities are known with interval uncertainty (e.g., coming from measurements), while other quantities are known with fuzzy uncertainty (coming from expert estimates).

### Soft Computing for Unmanned Aerial Vehicles (UAVs)

#### Vladik Kreinovich (vladik@utep.edu), Rodrigo Romero (raromero2@utep.edu)

There are many potential applications of commercial UAVs (aka drones), from widely publicized ideas of delivering goods to more mundane tasks of collecting information about the weather, environment, traffic, agriculture, etc., so as to make better decisions. Until now, the use of UAVs in the US is limited by FAA regulations, but the FAA Modernization and Reform Act of 2012[ sets a deadline of 30 September 2015, for the agency to establish regulations to allow the use of commercial drones. This will hopefully lead to a boom in UAV applications.

In anticipation of this boom, many universities and companies are doing research on different types of UAVs and on their applications. This is a completely new application areas, with a lot of uncertainty, and with a need for intelligent techniques to avoid potential problems with autonomous UAVs. Soft computing (in particular, fuzzy) techniques play an important role both in handling uncertainty and in intelligent reasoning; we therefore expect that these techniques will be useful for UAVs. In this section, we expect researchers presenting their results, and – even more important – practitioners describing problems in which soft computing methods can be helpful.

### Fuzzy mathematical analysis and applications

#### Luciano Stefanini (luciano.stefanini@uniurb.it) Yurilev Chalco-Cano (yurichalco@gmail.com)

The goal of this session is to bring together researchers interested in recent advances in fuzzy mathematical analysis and applications. The topics of this special session include, but are not limited to, the following:

- Spaces of fuzzy sets
- Fuzzy arithmetic
- Fuzzy metric spaces
- General fuzzy-valued functions
- Convexity and fuzzy-valued functions
- Differentiability of fuzzy-valued functions
- Fuzzy integrals
- Fuzzy differential equations
- Fuzzy optimal control
- Fuzzy systems
- Fuzzy optimization problems

### Fuzzy Pattern Recognition and Data Mining

#### Mohammad H. Fazel Zarandi (zarandi@aut.ac.ir) Burhan Turksen, Reyhaneh Gamasaee

By the advent of pattern recognition techniques, data processing and making intelligent decisions has been facilitated. This is because of their capability of discovering regularities in data using mathematical techniques. Pattern recognition tries to classify observations such as objects, symptoms of patients, speech, or images. Classification, data clustering, regression, sequence labeling, and parsing, which assigns a parse tree to an input sentence, are some pattern recognition methods. Hence, because of its capability of discovering patterns from data, there is an increasing need to do more research in the area of pattern recognition to handle complex problems. However, uncertainty is a prevalent phenomenon in such intricate problems. Therefore, fuzzy theory is applied to handle the uncertainty in real world applications such as time series, financial forecasting, image/signal processing, and speech recognition. Regarding to the increasing need for developing fuzzy pattern recognition techniques to manage complex systems, this session welcomes the researchers and papers in the area of theory and applications of fuzzy pattern recognition. The topics of this session include but are not limited to

the following areas:

- Fuzzy Clustering Techniques
- Fuzzy Classification Methods
- Fuzzy Support Vector Machines (SVM)
- Fuzzy Relevance Vector Machines (RVM)
- Fuzzy logic in Kernel Methods
- Fuzzy Neural Networks (NN)
- Type2 Fuzzy Clustering Techniques
- Type2 Fuzzy Classification Methods
- Type1 and Type2 Fuzzy Evolutionary Methods
- Type1 and Type2 Fuzzy Regression Analysis
- Type1 and Type2 Fuzzy Time Series
- Fuzzy Sequential Data Analysis
- Fuzzy techniques for the monitoring process such as quality control charts and reject options in fuzzy pattern recognition
- Fuzzy Logic in Machine Learning
- Real-World applications of pattern recognition in finance, economics, engineering, medicine, image processing, and computer vision.

### Type-2 Fuzzy Systems and Modeling

#### Mohammad H. Fazel Zarandi (zarandi@aut.ac.ir) Burhan Turksen, Reyhaneh Gamasaee

In many real world problems, we encounter uncertain information based on which decision making should be considered. In such situations, fuzzy set theory can be used to model and solve problems with vague information. In some problems, the information is too vague to model the problem with type-1 fuzzy sets, so type-2 fuzzy sets are used to model these systems. These kind of fuzzy sets were introduced by Zadeh (1975) as an extension of type-1 fuzzy theory. Type-2 fuzzy sets and systems are divided into two groups: (I) Interval Type-2 Fuzzy Sets and Systems; (II) General Type-2 Fuzzy Sets and Systems. In type-2 fuzzy sets, each element is represented by two memberships, which are named primary and secondary memberships. This fact shows the

capability of type-2 fuzzy sets in containing and representing more information than type-1 fuzzy sets. Because of that capability, real problems with higher degree of uncertainty are solvable. Hence, there is an increasing need to do more research in the area of type-2 fuzzy systems and modeling to manage uncertain problems. Regarding to the increasing need for developing type-2 fuzzy systems, this session welcomes the researchers and papers in the area of theory and applications of type-2 fuzzy systems. The topics of this session include but are not limited to the following areas:

- Operations on interval type-2 fuzzy sets
- Interval type-2 fuzzy systems
- Operations on general type-2 fuzzy sets
- General type-2 fuzzy systems
- Type reduction methods
- Direct and indirect approaches in type-2 fuzzy systems and modeling
- Type-2 fuzzy pattern recognition methods such as type-2 fuzzy clustering, type-2 fuzzy classification, type-2 fuzzy support vector machines, and type-2 fuzzy neural networks.
- Type-2 fuzzy TSK systems
- Type-2 fuzzy functions
- Type-2 fuzzy membership function generation techniques
- Real-World applications of type-2 fuzzy systems in control, robotics, finance, economics, engineering, medicine, image processing, and computer vision.