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Chapter and Conference Paper
A Bayesian Interpretation of Fuzzy C-Means
In Explainable Artificial Intelligence, the interpretation of the decisions provided by a model is of primary importance. In this context, we consider Fuzzy C-Means (FCM), which is a clustering algorithm that ...
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Article
Open AccessEffect of fuzziness in fuzzy rule-based classifiers defined by strong fuzzy partitions and winner-takes-all inference
We study the impact of fuzziness on the behavior of Fuzzy Rule-Based Classifiers (FRBCs) defined by trapezoidal fuzzy sets forming Strong Fuzzy Partitions. In particular, if an FRBC selects the class related t...
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Chapter and Conference Paper
Human Detection in Drone Images Using YOLO for Search-and-Rescue Operations
Today, unmanned aerial vehicles, more commonly known as drones, can be equipped with high-resolution cameras and embedded GPUs powerful enough to provide effective and efficient aid to Search-and-Rescue (SAR) ...
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Chapter and Conference Paper
Connections Between Granular Counts and Twofold Fuzzy Sets
Twofold fuzzy sets model ill-known collections of objects, where, for each element of a given domain, both necessity and possibility degrees of membership are specified. It is proved that the cardinality of a ...
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Chapter
Remarks and Prospects on Explainable Fuzzy Systems
Since 2016, there is an increasing interest on research topics such as fairness, accountability and interpretability, in the entire community of researchers in Artificial Intelligence. However, there were rese...
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Chapter
An Overview of Fuzzy Systems
Fuzzy systems have found widespread application in several contexts and proved their suitability in tackling a number of diverse real-world problems. However, the realization of such systems must be well groun...
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Chapter
Revisiting Indexes for Assessing Interpretability of Fuzzy Systems
Interpretability is one of the most valuable properties of fuzzy systems. Despite the effort made by the research community for characterizing interpretability, there is not a consensus about how to measure in...
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Chapter
Design and Validation of an Explainable Fuzzy Beer Style Classifier
We describe step by step how to design, implement and validate an interpretable fuzzy rule-based beer style classifier endowed with explanation capability. First, we revise some preliminary work regarding both...
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Chapter
Toward Explainable Artificial Intelligence Through Fuzzy Systems
Explainable Artificial Intelligence is a novel paradigm conjugating the effectiveness of machine learning with the new requirements coming from the integration of intelligent systems in the human society. Expl...
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Chapter
Interpretability Constraints and Criteria for Fuzzy Systems
Fuzzy systems are commonly considered suitable tools to express knowledge in a human comprehensible fashion. This kind of characterization makes them eligible for being applied in several contexts where interp...
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Chapter
Designing Interpretable Fuzzy Systems
Fuzzy sets and fuzzy logic are powerful tools widely used to represent human knowledge and mimic human reasoning capabilities, being the main constituents of fuzzy systems. Among the different approaches to fu...
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Chapter and Conference Paper
Possibilistic Bounds for Granular Counting
Uncertain data are observations that cannot be uniquely mapped to a referent. In the case of uncertainty due to incompleteness, possibility theory can be used as an appropriate model for processing such data. ...
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Chapter and Conference Paper
Multi-view Convolutional Network for Crowd Counting in Drone-Captured Images
This paper proposes a novel lightweight and fast convolutional neural network to learn a regression model for crowd counting in images captured from drones. The learning system is initially based on a multi-in...
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Chapter and Conference Paper
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint. However, there are few algori...
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Chapter and Conference Paper
Crowd Detection for Drone Safe Landing Through Fully-Convolutional Neural Networks
In this paper, we propose a novel crowd detection method for drone safe landing, based on an extremely light and fast fully convolutional neural network. Such a computer vision application takes advantage of t...
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Article
Open AccessFine-Tuning the Fuzziness of Strong Fuzzy Partitions through PSO
We study the influence of fuzziness of trapezoidal fuzzy sets in the strong fuzzy partitions (SFPs) that constitute the database of a fuzzy rule-based classifier. To this end, we develop a particular represent...
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Chapter and Conference Paper
Paving the Way to Explainable Artificial Intelligence with Fuzzy Modeling
Explainable Artificial Intelligence (XAI) is a relatively new approach to AI with special emphasis to the ability of machines to give sound motivations about their decisions and behavior. Since XAI is human-ce...
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Chapter and Conference Paper
Enhancing the DISSFCM Algorithm for Data Stream Classification
Analyzing data streams has become a new challenge to meet the demands of real time analytics. Conventional mining techniques are proving inefficient to cope with challenges associated with data streams, includ...
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Chapter and Conference Paper
Looking at the Branches and Roots
When looking at the future of Fuzzy Logic (FL), it is immediate to think at applications where FL could be used to compute with perceptions, possibly expressed in natural language, thus enabling Explainable Ar...
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Chapter
The Role of Interpretable Fuzzy Systems in Designing Cognitive Cities
In recent years, there has been a huge effort connecting all kind of devices to Internet. From small devices (e.g., e-health monitoring sensors or mobile phones) that we carry daily in what is called the body-...
