Fuzzy Logic


iDriveSense: Dynamic Route Planning Involving Roads Quality Information

arXiv.org Machine Learning

Owing to the expeditious growth in the information and communication technologies, smart cities have raised the expectations in terms of efficient functioning and management. One key aspect of residents' daily comfort is assured through affording reliable traffic management and route planning. Comprehensively, the majority of the present trip planning applications and service providers are enabling their trip planning recommendations relying on shortest paths and/or fastest routes. However, such suggestions may discount drivers' preferences with respect to safe and less disturbing trips. Road anomalies such as cracks, potholes, and manholes induce risky driving scenarios and can lead to vehicles damages and costly repairs. Accordingly, in this paper, we propose a crowdsensing based dynamic route planning system. Leveraging both the vehicle motion sensors and the inertial sensors within the smart devices, road surface types and anomalies have been detected and categorized. In addition, the monitored events are geo-referenced utilizing GPS receivers on both vehicles and smart devices. Consequently, road segments assessments are conducted using fuzzy system models based on aspects such as the number of anomalies and their severity levels in each road segment. Afterward, another fuzzy model is adopted to recommend the best trip routes based on the road segments quality in each potential route. Extensive road experiments are held to build and show the potential of the proposed system.


Plithogeny, Plithogenic Set, Logic, Probability, and Statistics

arXiv.org Artificial Intelligence

In this book we introduce the plithogenic set (as generalization of crisp, fuzzy, intuitionistic fuzzy, and neutrosophic sets), plithogenic logic (as generalization of classical, fuzzy, intuitionistic fuzzy, and neutrosophic logics), plithogenic probability (as generalization of classical, imprecise, and neutrosophic probabilities), and plithogenic statistics (as generalization of classical, and neutrosophic statistics). Plithogenic Set is a set whose elements are characterized by one or more attributes, and each attribute may have many values. An attribute value v has a corresponding (fuzzy, intuitionistic fuzzy, or neutrosophic) degree of appurtenance d(x,v) of the element x, to the set P, with respect to some given criteria. In order to obtain a better accuracy for the plithogenic aggregation operators in the plithogenic set, logic, probability and for a more exact inclusion (partial order), a (fuzzy, intuitionistic fuzzy, or neutrosophic) contradiction (dissimilarity) degree is defined between each attribute value and the dominant (most important) attribute value. The plithogenic intersection and union are linear combinations of the fuzzy operators tnorm and tconorm, while the plithogenic complement, inclusion, equality are influenced by the attribute values contradiction (dissimilarity) degrees. Formal definitions of plithogenic set, logic, probability, statistics are presented into the book, followed by plithogenic aggregation operators, various theorems related to them, and afterwards examples and applications of these new concepts in our everyday life.


Artificial intelligence saves water for water users associations

#artificialintelligence

Agriculture uses 70 percent of the water in the world, and this appears to be an upward trend regarding water needs. As the demand in other industry sectors is also increasing, and the effects of climate change exacerbate water shortages, water saving measures have become an unavoidable challenge for maintaining the sector and preserving life. Agronomy researcher Rafael González has developed a model to predict in advance the water that users will need each day. This tool came about from a drive to ally with water resource sustainability. The model applies artificial intelligence techniques including fuzzy logic, a system used to explain the behavior of decision making.


Artificial intelligence saves water for water users associations

#artificialintelligence

Agriculture uses 70% of the water in the world and this appears to be an upward trend regarding water needs. In this context in which the demand in other industry sectors is increasing as well and the effects of climate change influence ever-increasing water shortages, water saving measures have become an unavoidable challenge if we want to maintain the sector and preserve life. This is the challenge taken on by Agronomy Department researcher Rafael González when developing a model able to predict in advance the water that each water user will need each day. Therefore, this tool came about from a drive to ally with water resource sustainability. What is innovative about this model lies in the application of artificial intelligence techniques such as fuzzy logic, a system used to explain the behavior of decision making.


Artificial Intelligence Saves Water for Water Users Associations

#artificialintelligence

Agriculture uses 70% of the water in the world and this appears to be an upward trend regarding water needs. In this context in which the demand in other industry sectors is increasing as well and the effects of climate change influence ever-increasing water shortages, water saving measures have become an unavoidable challenge if we want to maintain the sector and preserve life. This is the challenge taken on by Agronomy Department researcher Rafael González when developing a model able to predict in advance the water that each water user will need each day. Therefore, this tool came about from a drive to ally with water resource sustainability. What is innovative about this model lies in the application of artificial intelligence techniques such as fuzzy logic, a system used to explain the behavior of decision making.


Multi-View Fuzzy Logic System with the Cooperation between Visible and Hidden Views

arXiv.org Artificial Intelligence

Multi-view datasets are frequently encountered in learning tasks, such as web data mining and multimedia information analysis. Given a multi-view dataset, traditional learning algorithms usually decompose it into several single-view datasets, from each of which a single-view model is learned. In contrast, a multi-view learning algorithm can achieve better performance by cooperative learning on the multi-view data. However, existing multi-view approaches mainly focus on the views that are visible and ignore the hidden information behind the visible views, which usually contains some intrinsic information of the multi-view data, or vice versa. To address this problem, this paper proposes a multi-view fuzzy logic system, which utilizes both the hidden information shared by the multiple visible views and the information of each visible view. Extensive experiments were conducted to validate its effectiveness.


Creativity and Artificial Intelligence: A Digital Art Perspective

arXiv.org Artificial Intelligence

This paper describes the application of artificial intelligence to the creation of digital art. AI is a computational paradigm that codifies intelligence into machines. There are generally three types of artificial intelligence and these are machine learning, evolutionary programming and soft computing. Machine learning is the statistical approach to building intelligent systems. Evolutionary programming is the use of natural evolutionary systems to design intelligent machines. Some of the evolutionary programming systems include genetic algorithm which is inspired by the principles of evolution and swarm optimization which is inspired by the swarming of birds, fish, ants etc. Soft computing includes techniques such as agent based modelling and fuzzy logic. Opportunities on the applications of these to digital art are explored.


Fuzzy quantification for linguistic data analysis and data mining

arXiv.org Artificial Intelligence

Fuzzy quantification is a subtopic of fuzzy logic which deals with the modelling of the quantified expressions we can find in natural language. Fuzzy quantifiers have been successfully applied in several fields like fuzzy, control, fuzzy databases, information retrieval, natural language generation, etc. Their ability to model and evaluate linguistic expressions in a mathematical way, makes fuzzy quantifiers very powerful for data analytics and data mining applications. In this paper we will give a general overview of the main applications of fuzzy quantifiers in this field as well as some ideas to use them in new application contexts.


Decision method choice in a human posture recognition context

arXiv.org Artificial Intelligence

Human posture recognition provides a dynamic field that has produced many methods. Using fuzzy subsets based data fusion methods to aggregate the results given by different types of recognition processes is a convenient way to improve recognition methods. Nevertheless, choosing a defuzzification method to imple-ment the decision is a crucial point of this approach. The goal of this paper is to present an approach where the choice of the defuzzification method is driven by the constraints of the final data user, which are expressed as limitations on indica-tors like confidence or accuracy. A practical experimentation illustrating this ap-proach is presented: from a depth camera sensor, human posture is interpreted and the defuzzification method is selected in accordance with the constraints of the final information consumer. The paper illustrates the interest of the approach in a context of postures based human robot communication.


Fuzzy Logic Interpretation of Artificial Neural Networks

arXiv.org Artificial Intelligence

Over past several years, deep learning has achieved huge successes in various applications. However, such a data-driven approach is often criticized for lack of interpretability. Recently, we proposed artificial quadratic neural networks consisting of second-order neurons in potentially many layers. In each second-order neuron, a quadratic function is used in the place of the inner product in a traditional neuron, and then undergoes a nonlinear activation. With a single second-order neuron, any fuzzy logic operation, such as XOR, can be implemented. In this sense, any deep network constructed with quadratic neurons can be interpreted as a deep fuzzy logic system. Since traditional neural networks and second-order counterparts can represent each other and fuzzy logic operations are naturally implemented in second-order neural networks, it is plausible to explain how a deep neural network works with a second-order network as the system model. In this paper, we generalize and categorize fuzzy logic operations implementable with individual second-order neurons, and then perform statistical/information theoretic analyses of exemplary quadratic neural networks.