scholarly journals Supervised committee machine with artificial intelligence for prediction of fluoride concentration

2013 ◽  
Vol 15 (4) ◽  
pp. 1474-1490 ◽  
Author(s):  
Ata Allah Nadiri ◽  
Elham Fijani ◽  
Frank T.-C. Tsai ◽  
Asghar Asghari Moghaddam
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Ground Water ◽  
Intelligent System ◽  
Fluoride Concentration ◽  
Mixing Zone ◽  
Ann Model ◽  
Committee Machine ◽  
Hydrogeological Characteristics ◽  
Model Predictions

The study introduces a supervised committee machine with artificial intelligence (SCMAI) method to predict fluoride in ground water of Maku, Iran. Ground water is the main source of drinking water for the area. Management of fluoride anomaly needs better prediction of fluoride concentration. However, the complex hydrogeological characteristics cause difficulties to accurately predict fluoride concentration in basaltic formation, non-basaltic formation, and mixing zone. SCMAI predicts fluoride by a nonlinear combination of individual AI models through an artificial intelligent system. Factor analysis is used to identify effective fluoride-correlated hydrochemical parameters as input to AI models. Four AI models, Sugeno fuzzy logic, Mamdani fuzzy logic, artificial neural network (ANN), and neuro-fuzzy are employed to predict fluoride concentration. The results show that all of these models have similar fitting to the fluoride data in the Maku area, and do not predict well for samples in the mixing zone. The SCMAI employs an ANN model to re-predict the fluoride concentration based on the four AI model predictions. The result shows improvement to the CMAI method, a committee machine with the linear combination of AI model predictions. The results also show significant fitting improvement to individual AI models, especially for fluoride prediction in the mixing zone.

Artificial Intelligence Review

2019 ◽  
pp. 23-39
Author(s):  
Amal Kilani ◽  
Ahmed Ben Hamida ◽  
Habib Hamam
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent System ◽  
Route Planning ◽  
Turing Test ◽  
Future Research ◽  
Interaction Data ◽  
Research Directions ◽  
Future Research Directions

In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.

Artificial Intelligence Review

2018 ◽  
pp. 106-119 ◽  
Author(s):  
Amal Kilani ◽  
Ahmed Ben Hamida ◽  
Habib Hamam
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent System ◽  
Route Planning ◽  
Turing Test ◽  
Future Research ◽  
Interaction Data ◽  
Research Directions ◽  
Future Research Directions

In this paper, we present a profound literature review of the Artificial Intelligence (AI). After defining it, we briefly cover its history and enumerate its principal fields of application. We name for example information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called The Turing test, is also defined and detailed. Afterwards, we describe some AI tools such as Fuzzy logic, genetic algorithms and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. We also present the future research directions and ethics.

Expert Systems and Fuzzy Logic

2018 ◽  
pp. 48-75
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Expert System ◽  
Expert Systems ◽  
Intelligent System ◽  
Optimal Solution ◽  
Human Experience ◽  
Probabilistic Methods ◽  
Approximate Methods ◽  
Rules Of Thumb

An expert system is a system that employs human experience or knowledge captured in a computer to solve problems that ordinarily require human expertise. They may or may not have a learning component. Expert systems are a branch of Artificial intelligence. Truemper describes an expert system as an intelligent system which in an interactive setting asks a person for information and, based upon the response, draws conclusions or gives advice. Problems tend to be solved using heuristics (rules of thumb) or approximate methods or probabilistic methods which, unlike algorithmic solutions, are not guaranteed to result in a correct or optimal solution. The authors go further to clarify that expert systems usually have to provide explanations and justifications of their solutions or recommendations in order to convince the user that their reasoning is correct.

Artificial intelligence as a moving force of improvement and innovative development in education and pedagogic

2019 ◽  
pp. 21-30
Author(s):  
M. G. Koliada ◽  
T. I. Bugayova
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Interaction Analysis ◽  
Educational Data Mining ◽  
Main Task ◽  
Intellectual System ◽  
Will Force ◽  
To Come ◽  
Artificial Intelligence Systems

The article discusses the history of the development of the problem of using artificial intelligence systems in education and pedagogic. Two directions of its development are shown: “Computational Pedagogic” and “Educational Data Mining”, in which poorly studied aspects of the internal mechanisms of functioning of artificial intelligence systems in this field of activity are revealed. The main task is a problem of interface of a kernel of the system with blocks of pedagogical and thematic databases, as well as with the blocks of pedagogical diagnostics of a student and a teacher. The role of the pedagogical diagnosis as evident reflection of the complex influence of factors and reasons is shown. It provides the intelligent system with operative and reliable information on how various reasons intertwine in the interaction, which of them are dangerous at present, where recession of characteristics of efficiency is planned. All components of the teaching and educational system are subject to diagnosis; without it, it is impossible to own any pedagogical situation optimum. The means in obtaining information about students, as well as the “mechanisms” of work of intelligent systems based on innovative ideas of advanced pedagogical experience in diagnostics of the professionalism of a teacher, are considered. Ways of realization of skill of the teacher on the basis of the ideas developed by the American scientists are shown. Among them, the approaches of researchers D. Rajonz and U. Bronfenbrenner who put at the forefront the teacher’s attitude towards students, their views, intellectual and emotional characteristics are allocated. An assessment of the teacher’s work according to N. Flanders’s system, in the form of the so-called “The Interaction Analysis”, through the mechanism of fixing such elements as: the verbal behavior of the teacher, events at the lesson and their sequence is also proposed. A system for assessing the professionalism of a teacher according to B. O. Smith and M. O. Meux is examined — through the study of the logic of teaching, using logical operations at the lesson. Samples of forms of external communication of the intellectual system with the learning environment are given. It is indicated that the conclusion of the found productive solutions can have the most acceptable and comfortable form both for students and for the teacher in the form of three approaches. The first shows that artificial intelligence in this area can be represented in the form of robotized being in the shape of a person; the second indicates that it is enough to confine oneself only to specially organized input-output systems for targeted transmission of effective methodological recommendations and instructions to both students and teachers; the third demonstrates that life will force one to come up with completely new hybrid forms of interaction between both sides in the form of interactive educational environments, to some extent resembling the educational spaces of virtual reality.

A FORMALISM FOR REPRESENTING AND REASONING WITH LINGUISTIC INFORMATION

2002 ◽  
Vol 10 (03) ◽  
pp. 281-307 ◽  
Author(s):  
TRU H. CAO
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Natural Language ◽  
Functional Relation ◽  
Computing With Words ◽  
Conceptual Graphs ◽  
Graph Language ◽  
Conceptual Graph ◽  
Knowledge Representation Language ◽  
Representation Language

Conceptual graphs and fuzzy logic are two logical formalisms that emphasize the target of natural language, where conceptual graphs provide a structure of formulas close to that of natural language sentences while fuzzy logic provides a methodology for computing with words. This paper proposes fuzzy conceptual graphs as a knowledge representation language that combines the advantages of both the two formalisms for artificial intelligence approaching human expression and reasoning. Firstly, the conceptual graph language is extended with functional relation types for representing functional dependency, and conjunctive types for joining concepts and relations. Then fuzzy conceptual graphs are formulated as a generalization of conceptual graphs where fuzzy types and fuzzy attribute-values are used in place of crisp types and crisp attribute-values. Projection and join as basic operations for reasoning on fuzzy conceptual graphs are defined, taking into account the semantics of fuzzy set-based values.

Investigating Determinants of Industry–University Collaboration in an Open Innovation Context: Comparative Evidence from an Exploratory Study

2021 ◽  
pp. 097172182110204
Author(s):  
Calin Florin Baban ◽  
Marius Baban ◽  
Adalberto Rangone
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Open Innovation ◽  
Exploratory Study ◽  
Correlation Matrices ◽  
Related Literature ◽  
Industrial Areas ◽  
The Impact ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

In an open innovation (OI) paradigm, universities are considered as important sources of external scientific knowledge for industry, and comparative study of such collaboration can result in more effective and efficient employment of OI. Within this framework, this study explores how the determinants of collaboration between industry and universities in an open context of innovation are addressed by firms within industrial areas. For this purpose, a conceptual framework of industry–university determinants in an open context of innovation is developed from the related literature. Taking into consideration the determinants integrated into the framework, this study compares motives, barriers, channels of knowledge transfer, benefits and drawbacks of such collaboration in two Italian and Romanian industrial areas. Comparative differences in each OI determinant between the firms from the two Italian and Romanian industrial areas are analysed. The associations among the study determinants are also investigated based on correlation matrices among the five determinants in both Italian and Romanian firms. An artificial intelligence approach based on fuzzy logic was developed to predict the impact of the study determinants on the perception of universities as a source for OI activities of firms.

Artificial intelligence based prediction models: sales forecasting application in automotive aftermarket

2021 ◽  
pp. 1-13
Author(s):  
Mert Girayhan Türkbayrağí ◽  
Elif Dogu ◽  
Y. Esra Albayrak
Keyword(s):  
Artificial Intelligence ◽  
Computational Intelligence ◽  
Prediction Models ◽  
Predictive Analytics ◽  
Demand Characteristic ◽  
Product Portfolio ◽  
Ann Model ◽  
Nonlinear Relationships ◽  
Relationship Of ◽  
Automotive Aftermarket

Automotive aftermarket industry is possessed of a wide product portfolio range which is in the 4th rank by its worldwide trade volume. The demand characteristic of automotive aftermarket parts is volatile and uncertain. Nevertheless, the cause-and-effect relationship of automotive aftermarket industry has not been defined obviously heretofore. These conditions bring automotive aftermarket sales forecasting into a challenging process. This paper is composed to determine the relevant external factors for automotive aftermarket sales based on expert reviews and to propose a sales forecasting model for automotive aftermarket industry. Since computational intelligence techniques yield a framework to focus on predictive analytics and prescriptive analytics, an artificial neural network model constructed for Turkey automotive aftermarket industry. Artificial intelligence is a subset of computational intelligence that focused on problems which have complex and nonlinear relationships. The data which have complex and nonlinear relationships could be modelled successfully even though incomplete data in case of implementation of appropriate model. The proposed ANN model for sales forecast is compared with multiple linear regression and revealed a higher prediction performance.

Sign in / Sign up

Export Citation Format

Share Document