Evolution of fuzzy logic: from intelligent systems and computation to human mind

Artificial intelligence and cognitive science must look at the world of industrial-process control to find the technological reifications of the concept of mind.

Brucellosis is an important zoonotic disease worldwide and it affects the health of animals, human and the livestock incomes negatively. Although it is an animal illness, it infects human by contact with infected animals or consuming contaminated milk or milk products. Modeling studies established to obtain seroprevalance of a disease is important in order to determine statistics and measuring strategies for this disease. Although fieldworks can represent data for a disease in a specific region, forming a model suits with associated fieldworks can offer a global strategy for it and this model can be used anywhere else. In this study, a model which is based on fuzzy logic and predicts seroprevalance of brucellosis in a specific localization is formed. In this model, fuzzy logic based prediction method for brucellosis seroprevalance is used for the first time. This model is used for determining brucellosis seroprevalance in sheep and goats in DiyarbakA±r and Van regions. Also, the results of the model show good fit with the data obtained in the regional studies, done in the same regions. Key words: Brucellosis, seroprevalance, fuzzy logic.

Identifying a strategy for legacy software system evolution requires analysis and assessment activities. Information on performance and costs of software systems must be considered when making decisions on the most suitable strategy to be applied. Many approaches have been defined for supporting this task, and several authors have proposed decision frameworks for aiding the selection of evolution strategy. These approaches often lack of techniques for the management of uncertainty, traditionally considered as unscientific and as a source of errors and arising from the confidence of the answers provided by respondents. An approach previously proposed is analyzed and extended with fuzzy logic concepts. The approach uses a measurement framework based on the Goal-Question-Metric (GQM) paradigm and a set of critiquing tables. Fuzzy logic principles have been introduced in both components, for obtaining a better insight of the analyzed software system quality and an indication of the risks to be assumed when one selected strategy is adopted instead of another.

This study aimed to analyze the business impact of the catering sector using the fuzzy logic method. The research was conducted at a catering company in Istanbul, utilizing document review and participant observation methods to evaluate the business impact. The nominal prioritization method was used to identify critical business processes, and a model along with a mathematical formula was developed for calculating the business impact. The Fuzzy Logic Designer Toolbox in MATLAB was utilized for this calculation. The study identified eight critical business processes: (1) material supply, (2) material storage, (3) pre-preparation process, (4) cooking process, (5) portioning, (6) shipping, (7) hygiene and food safety, and (8) customer relationship management. The business impact was assessed using classical and fuzzy logic methods, and the results were compared. The fuzzy logic method provided a more flexible and comprehensive assessment, managing uncertainty and variability more effectively than classical logic. Overall, it proved to be more effective in optimizing business processes, offering a more dynamic and holistic approach to improving and prioritizing these processes.

It is a great privilege to welcome you to the 2019 The 2nd International Conference on Computational Intelligence and Intelligent Systems (CIIS 2019) in Bangkok, Thailand during November 23-25, 2019. A major goal and feature of it is to bring academic scientists, engineers, industry researchers together to exchange and share their experiences and research results about most aspects of science and social research, and discuss the practical challenges encountered and the solutions adopted. We hope you will have a technically rewarding experience, and use this occasion to meet old friends and make many new ones.

This paper presents the fuzzy logic application with an approach to strategic management. The goals are to assign priority to the strategy adopted by a card solutions and services company by the Fuzzy Logic, using a technical analysis and judgment for decision making, that gives support to the executives in the development of indicators and organizational strategies. The methodologies used in this work are the literature search and case study applied in the card solutions and services company, located in Rio Grande do Sul, Brazil. In this study, it was applied the model of Strategic Alignment, developed and structured by Espin and Vanti (2005) in an intelligent information system, based on Fuzzy Logic concepts. At the end of this work, it is shown that Fuzzy Logic is an important technique to the prioritization of organization objectives and strategic actions.

Since the last decade, several studies have shown the ability of Artificial Neural Networks (ANNs) in modeling of rainfall-runoff process. From methodological viewpoint, ANN belongs to more general paradigm, i.e., soft computing or computational intelligence, in which independent methodologies, mostly Fuzzy Logic (FL), ANN, and Genetic Algorithms (GAs), are combined together in order to provide an intelligent behavior in computational frameworks. Consequently, in the context of rainfall-runoff modeling, this question rises whether hybridization of ANNs with other soft computing-related methodologies improves the overall performance of modeling or not. In this study, based on the idea of structure and/or parameter identification of ANNs with GAs, the evolutionary neural networks modeling paradigm is examined for describing the rainfall-runoff process. One of the benchmark data set of current literature, i.e., Leaf River basin (near Collins, Mississippi) data set, is used for simulation. The results show that on the one hand, the overall accuracy is improved; but one the other hand, in evolutionary neural modeling, the computational time is increased significantly. Hence the modeler may be faced with a trade-off problem between accuracy and computational difficulties which may have different importance in a particular rainfall-runoff problem.

In this study, the aim is to combine the goalscoring with the robotics and it was also discussed that one-on-one positions in detailed. When a forward player is one-on-one with a goalkeeper, in such limited time and space, it was observed that there are many ways that a forward player could use. When there is a one-on-one position between a striker and a goalkeeper, the simulation of the forward player's dribbling and crossing process had been aimed to be combined with the robotics. The aim of the project is that, when the robot encounter with an obstacle in the way of goalscoring; having the robot enable to calculate the distances between the right and left sides and the distance between the goalkeeper and itself so as to cross the goalkeeper and score the goal. Thanks to this study, different options of goalscoring have been introduced in real life football competitions. This shows that there may be alternative ways to finish positions with more goals, in a real football match, there could be many positions but most of them do not result in goals many times. Because there are countless ways and methods to score goals. The fuzzy logic algorithm, which is a branch of artificial intelligence, was used in order to lead the robot to perform these actions. Thanks to the simulink environment of the Matlab program, the results were interpreted and, thanks to these algorithms, our robot determined the area that it would go depending on the position of the obstacle. It also measured the distance and determined the speed of the kick. The robot succeeded in scoring the goalscoring in the final stage of the project.

Wireless body area network (WBAN) is an emerging technology which is getting a lot of attention these days due to its application in medical field and health monitoring system. In this technology by using different bio-sensors which could be implanted or worn by patient, he/she get his/her health conditions at regular intervals. Wireless connectivity makes it more popular because of its patient could use it in his daily life activities also. There are various fields in WBAN where a lot of development could be made, as this field is still in its developing stages. Efficient energy utilisation techniques have been explained. Routing metrices which consists of residual energy, delay, packet collision, network lifetime, complexity, network traffic etc. is also an interesting area where we will be working to develop intelligent routing metrices for wireless body Area Network (WBAN).Fuzzy Logic(FL) is a powerful tool used to develop solution of various problems today which maps the crisps inputs to give corresponding outputs. Momdani Fuzzy Inference System (FIS) provides the atmosphere needed for Fuzzy Logics where depending on degree of membership functions maps the variables and a surface is generated with respect to various factors in routing metrics. In this paper we had reviewed its basic aspects like its constituents, layered architecture and Issues as well as requirements of WBAN with Fuzzy Inference System (FIS). Keywords: Wireless Body Area Network, WBAN, Routing Metrices, Layered Architecture, Fuzzy Logic, Energy efficiency, Fuzzy Inference System (FIS).

We discuss a scheme for defining and reasoning about partial recursive functions within a classical two-valued logic in which all terms denote. We show how a total extension of the partial function introduced by a recursive declaration may be axiomatized within a classical logic, and illustrate by an example the kind of reasoning that our scheme supports. By presenting a naive set-theoretic semantics, we show that the system we propose is logically consistent. Our work is motivated largely by the pragmatic issues arising from mechanical theorem proving – we discuss some of the practical benefits and limitations of our scheme for mechanical verification of software and hardware systems.

Many real-life decision making problems incorporate higher-order structure, involving interdependencies between different stimuli, actions, and subsequent rewards. It is not known whether brain regions implicated in decision making, such as ventromedial prefrontal cortex, employ a stored model of the task structure to guide choice (model-based decision making) or merely learn action or state values without assuming higher-order structure, as in standard reinforcement learning. To discriminate between these possibilities we scanned human subjects with fMRI while they performed two different decision making tasks with higher-order structure: probabilistic reversal learning, in which subjects had to infer which of two choices was the more rewarding and then flexibly switch their choice when contingencies changed; and the inspection game, in which subjects had to successfully compete against an intelligent adversary by mentalizing the opponent’s state of mind in order to anticipate the opponent’s behavior in future. For both tasks we found that neural activity in a key decision making region: ventromedial prefrontal cortex, was more consistent with computational models that exploit higher-order structure, than with simple reinforcement learning. Moreover, in the social interaction game, subjects were found to employ a sophisticated strategy whereby they used knowledge of how their actions would influence the actions of their opponent to guide their choices. Specific computational signals required for the implementation of such a strategy were present in medial prefrontal cortex and superior temporal sulcus, providing insight into the basic computations underlying competitive strategic interactions. These results suggest that brain regions such as ventromedial prefrontal cortex employ an abstract model of task structure to guide behavioral choice, computations that may underlie the human capacity for complex social interactions and abstract strategizing.

The fourth edition of this bestselling textbook explains the principles of artificial intelligence (AI) and its practical applications. Using clear and concise language, it provides a solid grounding across the full spectrum of AI techniques, so that its readers can implement systems in their own domain of interest. The coverage includes knowledge-based intelligence, computational intelligence (including machine learning), and practical systems that use a combination of techniques. All the key techniques of AI are explained—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), agents, objects, frames, symbolic learning, case-based reasoning, genetic algorithms and other optimization techniques, shallow and deep neural networks, hybrids, and the Lisp, Prolog, and Python programming languages. The book also describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control. Fully updated and revised, Intelligent Systems for Engineers and Scientists: A Practical Guide to Artificial Intelligence, Fourth Edition features: A new chapter on deep neural networks, reflecting the growth of machine learning as a key technique for AI A new section on the use of Python, which has become the de facto standard programming language for many aspects of AI The rule-based and uncertainty-based examples in the book are compatible with the Flex toolkit by Logic Programming Associates (LPA) and its Flint extension for handling uncertainty and fuzzy logic. Readers of the book can download this commercial software for use free of charge. This resource and many others are available at the author’s website: adrianhopgood.com. Whether you are building your own intelligent systems, or you simply want to know more about them, this practical AI textbook provides you with detailed and up-to-date guidance.

This research is aimed to empower the logical mathematical intelligence of the inclusive group of the 4-6 years old children by implementing the neourosensory instructional approach through holistic integrative instructional process The mix method research, which is carried out by action research is applied in order to do this study. Rooting in the mix method research. there are two targets to achieve in applying the research method, they are (1) quantitative method is aimed to get to know the improvement of logical mathematical intellligence, and (2) qualitative method is apllied in order to know the implementation of the neurosensory instructional approach through holistic integrative instructional process to improve the logical mathematical intelligence. Therefore, the research is started by measuring the actual abilities of the logical mathematical intelligence of the children by using valid and realiable formal multiple intelligences assessment instruments for the 4-6 years old children, especially, in the part of the logical mathematical intelligence.. The research is ended by measuring the improvement of the logical mathematical intelligence of the children after participating the neurosensory instructional approach. Purposive sampling is apllied in choosing 15 research sample which involves 12 normal children and 3 special need children. The study reveals that the inclusive group of the 4-6 years old children’s logical mathematical intelligence are improved from under average up to above excellent after participating the neurosensory instructional approch.

An efficient intelligent system (FUZZY LOGIC) for the power transformer residual life assessment is presented in this paper. In the fuzzy method, different inputs and one output is used, that strictly depends on the number of membership function and their rule base and the type of the defuzzification method. The residual life of the transformers greatly influenced by the condition of the transformer insulation system. The insulation of winding de-teriorates during the normal ageing of the transformers. Paper insulation that contains cellulose molecules degrades due to heating or electrical breakdown dissolved in the transformer oil. Hence, the analysis of the Transformer oil gives correct evidence of variation in the property of the winding insulation during operation. Deterioration in transformer oil-paper insulation decreases both its electrical and mechanical strength that influence the age of the transformer. This paper presents a fuzzy logic method that has been implemented on three samples of power transformer oil for the residual life assessment of the transformers.

Flexible manufacturing cell model of multiple workshops is constructed, which combines the advantages of physical manufacturing cell and logical manufacturing cell. A cell formation method based on adaptive inertia weight PSO is worked out, which considers the layout of manufacturing resources, the selection of processing path and the setting of processing batches. The experiment proves that the method can effectively reduce the total manufacturing cost.