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Journal articles on the topic 'INTELLIGENT MODELLING'

Author: Grafiati
Published: 11 September 2023

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1

Green, H. S., and T. Triffet. "Modelling Intelligent Behavior." Journal of Intelligent Material Systems and Structures 4, no. 1 (January 1993): 35–42. http://dx.doi.org/10.1177/1045389x9300400104.

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2

Chin, Cheng Siong, and Rongxin Cui. "Intelligent Marine Robotics Modelling, Simulation and Applications." Journal of Marine Science and Engineering 8, no. 6 (May 27, 2020): 383. http://dx.doi.org/10.3390/jmse8060383.

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Creating this inaugural Special Issue on Intelligent Marine Robotics Modelling, Simulation, and Applications is important due to the rapid technological advancement and the aim to reduce human involvement via artificial intelligence [...]
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3

Hurrion, R. D. "Intelligent Visual Interactive Modelling." European Journal of Operational Research 54, no. 3 (October 1991): 349–56. http://dx.doi.org/10.1016/0377-2217(91)90110-h.

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4

Njeri, Ndung’u Rachael. "Data Preparation For Machine Learning Modelling." International Journal of Computer Applications Technology and Research 11, no. 06 (June 2022): 231–35. http://dx.doi.org/10.7753/ijcatr1106.1008.

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The world today is on revolution 4.0 which is data-driven. The majority of organizations and systems are using data to solve problems through use of digitized systems. Data lets intelligent systems and their applications learn and adapt to mined insights without been programmed. Data mining and analysis requires smart tools, techniques and methods with capability of extracting useful patterns, trends and knowledge, which can be used as business intelligence by organizations as they map their strategic plans. Predictive intelligent systems can be very useful in various fields as solutions to many existential issues. Accurate output from such predictive intelligent systems can only be ascertained by having well prepared data that suits the predictive machine learning function. Machine learning models learns from data input using the ‘garbage-in-garbage-out’ concept. Cleaned, pre-processed and consistent data would produce accurate output as compared to inconsistent, noisy and erroneous data.
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5

Verlan, A. A., and Jo Sterten. "Intelligent Object-Oriented Approach to Dynamic Energy Systems’ Modelling." Mathematical and computer modelling. Series: Technical sciences, no. 21 (November 2, 2020): 43–51. http://dx.doi.org/10.32626/2308-5916.2020-21.43-51.

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6

G, Manju. "Rule-based Cognitive Modelling for Multimodal Intelligent Tutoring Systems." International Journal of Psychosocial Rehabilitation 24, no. 1 (January 20, 2020): 1754–60. http://dx.doi.org/10.37200/ijpr/v24i1/pr200275.

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7

Griffin, S., W. Bauwens, and K. Ahmad. "Urban Drainage Modelling Intelligent Assistant." Water Science and Technology 29, no. 1-2 (January 1, 1994): 427–36. http://dx.doi.org/10.2166/wst.1994.0691.

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The work reported here focuses on research being conducted within the Artificial Intelligence Group at the University of Surrey and the Laboratory of Hydrology at Vrije Universiteit Brussel under the COMETT programme. The paper describes the “Urban Drainage Modelling Intelligent Assistant,” a computer-based tool kit which provides guidance, instruction and support for training on aspects of network modelling in urban drainage design and simulation models commonly used in Europe. The tool kit comprises four interrelated, interactive components: an expert system, a data preparation and model execution tool, a document browsing facility, and a term bank. The results of the work are illustrated with the aid of snapshots of the system in use. The specific emphasis here is on the role played by each component in a) the transfer of knowledge within both an inter-disciplinary field such as urban drainage modelling (hydrology, hydraulics, mathematics, computing), and a multilingual community (of particular importance in Europe at present) and b) the training of novices in urban drainage, enabling them to grasp the domain primitives, their interrelationships and meanings.
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8

Riviere, J.-M., M. Bayart, J.-M. Thiriet, A. Boras, and M. Robert. "Intelligent Instruments: Some Modelling Approaches." Measurement and Control 29, no. 6 (July 1996): 179–86. http://dx.doi.org/10.1177/002029409602900604.

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Thorpe, Christopher C., and Clifford M. Friend. "Modelling Intelligent Electronic Consumer Products." Journal of Intelligent Material Systems and Structures 10, no. 7 (July 1999): 552–57. http://dx.doi.org/10.1106/beha-5qhj-ua54-vdk9.

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10

Feray-Beaumont, S., R. Corea, M. T. Tham, and A. J. Morris. "Process modelling for intelligent control." Engineering Applications of Artificial Intelligence 5, no. 6 (November 1992): 483–92. http://dx.doi.org/10.1016/0952-1976(92)90025-f.

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11

Cojbasic, Zarko, Vlastimir Nikolic, Ivan Ciric, and Ljubica Cojbasic. "Computationally intelligent modeling and control of fluidized bed combustion process." Thermal Science 15, no. 2 (2011): 321–38. http://dx.doi.org/10.2298/tsci101205031c.

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In this paper modelling and control approaches for fluidized bed combustion process have been considered, that are based on the use of computational intelligence. Proposed adaptive neuro-fuzzy-genetic modeling and intelligent control strategies provide for efficient combining of available expert knowledge with experimental data. Firstly, based on the qualitative information on the desulphurization process, models of the SO2 emission in fluidized bed combustion have been developed, which provides for economical and efficient reduction of SO2 in FBC by estimation of optimal process parameters and by design of intelligent control systems based on defined emission models. Also, efficient fuzzy nonlinear FBC process modelling strategy by combining several linearized combustion models has been presented. Finally, fuzzy and conventional process control systems for fuel flow and primary air flow regulation based on developed models and optimized by genetic algorithms have also been developed. Obtained results indicate that computationally intelligent approach can be successfully applied for modelling and control of complex fluidized bed combustion process.
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12

Tham, H. J., S. Y. Tang, K. T. K. Teo, and S. P. Loh. "A review on intelligent sensory modelling." IOP Conference Series: Earth and Environmental Science 36 (June 2016): 012065. http://dx.doi.org/10.1088/1755-1315/36/1/012065.

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13

Elsom-Cook, Mark. "Student modelling in intelligent tutoring systems." Artificial Intelligence Review 7, no. 3-4 (August 1993): 227–40. http://dx.doi.org/10.1007/bf00849556.

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14

Asalkhanov, P. G., N. V. Bendik, and Ya M. Ivanyo. "Intelligent system for modelling climate variability." Herald of Dagestan State Technical University. Technical Sciences 47, no. 2 (August 8, 2020): 30–39. http://dx.doi.org/10.21822/2073-6185-2020-47-2-30-39.

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15

Guo, William, Chih-Cheng Hung, Ross Shepherd, Zhiqiang Ma, and Siddhivinayak Kulkarni. "Intelligent Techniques for Simulation and Modelling." Mathematical Problems in Engineering 2014 (2014): 1–3. http://dx.doi.org/10.1155/2014/179457.

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16

Brezovnik, Simon, Miran Brezocnik, Simon Klancnik, Joze Balic, Bogdan Sovilj, and Gregor Skorc. "Modelling of intelligent robot system by reverse engineering and swarm intelligence." International Journal of Mechatronics and Manufacturing Systems 3, no. 1/2 (2010): 79. http://dx.doi.org/10.1504/ijmms.2010.029876.

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17

Goyal, Dinesh. "Special Issue on : Mathematical Modelling for Intelligent Systems and Its Real-Time Applications." Journal of Interdisciplinary Mathematics 26, no. 3 (2023): i—vii. http://dx.doi.org/10.47974/jim-26-3-foreword.

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The 4th International Conference on Information Management & Machine Intelligence was held at Poornima Institute of Engineering and Technology, Jaipur on 23-24 December 2022. We are pleased to present this special issue on “Mathematical Modelling for Intelligent and Smart Systems” (containing the papers presented at the conference), which aspires to showcase the research and advancements in the development of mathematical modelling techniques in various domains. Mathematical modelling has become an essential activity in the study of simulating physical phenomena through computational processes, enabling us to predict the behaviour of artifacts within their environments. In recent years, machine learning (ML) and artificial intelligence (AI) have emerged as powerful tools in data science and decision-making processes. Mathematical approaches, such as ML, have proven instrumental in developing concise decision models, producing quick approximations, and forecasting changing phenomena based on known samples. The integration of mathematical modelling and machine learning has significantly improved machine intelligence and its applicability in diverse domains.
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18

Alhakeem, Mohammed Ridha H., and Dirja Nur Ilham. "Application of Artificial Intelligence in Mechanical Engineering." Brilliance: Research of Artificial Intelligence 2, no. 3 (September 13, 2022): 177–81. http://dx.doi.org/10.47709/brilliance.v2i3.1719.

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The use of artificial intelligence (AI) is becoming more prevalent across many industries. Examples include intelligently based control, intelligently based mechanical systems, pattern recognition-based systems, and knowledge processing. Method/Statistical Analysis: In this paper, an extensive review was conducted on the applications of ANN in intelligent mechanical engineering systems, including fault diagnosis in machines, mechanical structure analysis, and geometry modelling of mechanical structures, mechanical design, and its optimization. Findings: The adaptation of artificial neural networks (ANN), particularly in the field of mechanical engineering, is still in its early stages of development. This paper highlights the different ways artificial neural networks (ANNs) are used in intelligent-based systems, as well as the potential for reducing costs and time and obtaining more efficient systems for mechanical-based design and defect detection. Application/Improvements: This work will be improved in the future by adding more AI applications to the design of mechanically based systems.
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19

Szczerbicki, Edward. "EDITORIAL FOR SPECIAL ISSUE ON INTELLIGENT MODELLING AND SIMULATION FOR COMPLEX SYSTEMS, VOLUME II: INTELLIGENT MODELLING TOOLS." Cybernetics and Systems 29, no. 7 (October 1998): 635–37. http://dx.doi.org/10.1080/019697298125452.

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20

Fougères, Alain-Jérôme, and Egon Ostrosi. "Intelligent agents for feature modelling in computer aided design." Journal of Computational Design and Engineering 5, no. 1 (November 6, 2017): 19–40. http://dx.doi.org/10.1016/j.jcde.2017.11.001.

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Abstract CAD modelling can be referred to as the process of generating an integrated multiple view model as a representation of multiple views of engineering design. In many situations, a change in the model of one view may conflict with the models of other views. In such situations, the model of some views needs to be adapted in order to make all models consistent. Thus, CAD models should be capable of adapting themselves to new situations. Recently, agent based technologies have been considered in order to increase both knowledge level and intelligence of real and virtual objects. The contribution of this paper consists in introducing the intelligent agents in intelligent CAD modelling. The proposed agents are elementary geometrical and topological objects. They incorporate the functions of observation, decision and action, and possess their own knowledge. Agents have the capacity of communication and inference based on the feature grammars. They are modelled as bio-dynamic objects that enjoy the properties of fusion, division and multiplication. Being aware of the context, the proposed agents interact to form potential regional transitory communities, called regions. Being aware of their belonging in a region, agents interact by generating virtual links (virtual extensions). These virtual links produce: (a) fusion of agents, (b) division of agents and c) multiplication of agents. The emerged agents interact with the other agents in a region to recognize each other and to form specific sub-communities, called intelligent features. From a CAD software development point of view, this paper advocates the idea of a new phase of CAD system development based on the agent-oriented programming (AOP) paradigm. Highlights This paper proposes the agent paradigm for intelligent CAD modelling. Second section presents the state of art. In the third section, using the linguistic hypothesis of product design, a feature modelling formalism is presented. Fourth section presents a formal model for agent modelling. In the fifth section, agents for feature generation and modelling are formalised and modelled. The sixth section presents the application of the method. Finally, in the last section, the conclusion and future developments are proposed.
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21

Korjagin, Sergey, Ksenia Polupan, Pavel Klachek, Alexey Pyatikop, and Evgeniy Koryagin. "Intelligent road traffic management based on the system of fuzzy situational management and virtual cyberspace." MATEC Web of Conferences 334 (2021): 01013. http://dx.doi.org/10.1051/matecconf/202133401013.

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Theoretical and applied ideas and tools of traffic intelligent management on the basis of fuzzy situational management and virtual cyberspace are developed through the system approach, artificial intelligence methods, modern achievements in the field of the creation of intelligent transport systems (ITS). The proposed scientifically-methodical foundations and software and hardware tools allow the creation of intelligent transport systems at a new level, synchronizing the development of road traffic infrastructure and virtual cyberspace, allowing to solve effectively a rather large range of theoretical and applied problems in the field of traffic management, transport modelling and planning.
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22

Khan, Qura-Tul-Ain, Sagheer Abbas, Muhammad Adnan Khan, Areej Fatima, Saad Alanazi, and Nouh Sabri Elmitwally. "Modelling Intelligent Driving Behaviour Using Machine Learning." Computers, Materials & Continua 68, no. 3 (2021): 3061–77. http://dx.doi.org/10.32604/cmc.2021.015441.

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23

Zacarias, Fernando, Rosalba Cuapa, Luna Jimenez, and Noemi Vazquez. "Modelling of Intelligent Agents Using A–Prolog." International Journal of Artificial Intelligence & Applications 10, no. 02 (March 31, 2019): 47–57. http://dx.doi.org/10.5121/ijaia.2019.10205.

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24

Juuso, Esko K. "Modelling and Simulation in Adaptive Intelligent Control." SNE Simulation Notes Europe 26, no. 2 (June 2016): 109–16. http://dx.doi.org/10.11128/sne.26.on.10338.

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25

Masood, Syed H. "Intelligent rapid prototyping with fused deposition modelling." Rapid Prototyping Journal 2, no. 1 (March 1996): 24–33. http://dx.doi.org/10.1108/13552549610109054.

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26

Ameri, Farhad, and Christian McArthur. "Semantic rule modelling for intelligent supplier discovery." International Journal of Computer Integrated Manufacturing 27, no. 6 (September 20, 2013): 570–90. http://dx.doi.org/10.1080/0951192x.2013.834467.

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27

Lopes, J. A., and J. C. Menezes. "Intelligent Systems for Penicillin Fermentation Process Modelling." IFAC Proceedings Volumes 31, no. 8 (May 1998): 307–12. http://dx.doi.org/10.1016/s1474-6670(17)40203-5.

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28

Petrov, Tibor, Milan Dado, and Karl Ernst Ambrosch. "Computer Modelling of Cooperative Intelligent Transportation Systems." Procedia Engineering 192 (2017): 683–88. http://dx.doi.org/10.1016/j.proeng.2017.06.118.

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29

Molin, Eric J. E., and Karel A. Brookhuis. "Modelling acceptability of the intelligent speed adapter." Transportation Research Part F: Traffic Psychology and Behaviour 10, no. 2 (March 2007): 99–108. http://dx.doi.org/10.1016/j.trf.2006.06.008.

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30

Anderson, J., and M. Evans. "Intelligent agent modelling for natural resource management." Mathematical and Computer Modelling 20, no. 8 (October 1994): 109–19. http://dx.doi.org/10.1016/0895-7177(94)90235-6.

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31

Wu, Yanjun Sun, Fang Zhong, D. J. "Organizational Agent Systems for Intelligent Enterprise Modelling." Electronic Markets 10, no. 4 (October 1, 2000): 272–81. http://dx.doi.org/10.1080/10196780010001303.

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32

Clark, G., D. Rossiter, and P. W. H. Chung. "Intelligent Modelling Interface for Dynamic Process Simulators." Chemical Engineering Research and Design 78, no. 6 (September 2000): 823–39. http://dx.doi.org/10.1205/026387600528021.

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33

BELLINI, PIERFRANCESCO, IVAN BRUNO, and PAOLO NESI. "EXPLOITING INTELLIGENT CONTENT VIA AXMEDIS/MPEG-21 FOR MODELLING AND DISTRIBUTING NEWS." International Journal of Software Engineering and Knowledge Engineering 21, no. 01 (February 2011): 3–32. http://dx.doi.org/10.1142/s0218194011005141.

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The content technology needs to attain forms with more intelligence, flexibility and complete features than those being currently on the market or proposed by standards. In this paper, an analysis of the state of the art about intelligent and complex content models is presented. The analysis allowed identifying a number of topics and features which models and formats should evolve according to. The work has been used to extend AXMEDIS content model and format which in turn is grounded on MPEG-21, SMIL, HTML, and other standards. The Extended AXMEDIS format presents a set of new features among them: semantic descriptors, extended annotations, intelligent behavioral and semantic computing capabilities. The newly obtained format has been compared against NewsML which is one of the most widespread formats for news production and distribution. The management of news has some peculiarities such as container, production tools and players, that may take advantage of the intelligent content features and applications. Moreover, news have to be massively processed for ingestion and repurposing, and present relevant requirements on right control. Also these features may be satisfied by AXMEDIS tools. To this end, a comparative analysis of processing and modeling NewsML with AXMEDIS tools and format has been performed and reported to verify the usage. In addition, AXMEDIS format can be profitably used for a range of innovative applications of intelligent content.
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34

Kolesnikov, A., and A. Yashin. "HYBRID MODELLING IN STRATIFIED DECISION SUPPORT SYSTEMS. I." Mathematical Modelling and Analysis 5, no. 1 (December 15, 2000): 108–18. http://dx.doi.org/10.3846/13926292.2000.9637133.

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The paper studies the basic problems of Artificial Intelligence, such as integration of difference attributes of human intellect. For this purpose we have been created synergetic systems that are hybrid intelligent systems (HYIS). The paper shows the world of decision support problems and the world of modelling approaches evolution. The term ‘heterogeneous problem’ for decision support systems is discussed. Two models of interaction between the problems world and the methods world also the results of HYIS creating are discussed. The formalism of HYIS is introduced.
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35

Li, Shijin, and Fucai Wang. "Research on Optimization of Improved Gray Wolf Optimization-Extreme Learning Machine Algorithm in Vehicle Route Planning." Discrete Dynamics in Nature and Society 2020 (October 6, 2020): 1–7. http://dx.doi.org/10.1155/2020/8647820.

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With the rapid development of intelligent transportation, intelligent algorithms and path planning have become effective methods to relieve traffic pressure. Intelligent algorithm can realize the priority selection mode in realizing traffic optimization efficiency. However, there is local optimization in intelligence and it is difficult to realize global optimization. In this paper, the antilearning model is used to solve the problem that the gray wolf algorithm falls into local optimization. The positions of different wolves are updated. When falling into local optimization, the current position is optimized to realize global optimization. Extreme Learning Machine (ELM) algorithm model is introduced to accelerate Improved Gray Wolf Optimization (IGWO) optimization and improve convergence speed. Finally, the experiment proves that IGWO-ELM algorithm is compared in path planning, and the algorithm has an ideal effect and high efficiency.
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36

Yang, C.-J., J.-F. Zhang, Y. Chen, Y.-M. Dong, and Y. Zhang. "A Review of exoskeleton-type systems and their key technologies." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 222, no. 8 (August 1, 2008): 1599–612. http://dx.doi.org/10.1243/09544062jmes936.

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The exoskeleton-type system is a brand new type of man—machine intelligent system. It fully combines human intelligence and machine power so that machine intelligence and human operator's power are both enhanced. Therefore, it achieves a high-level performance that neither could separately. This paper describes the basic exoskeleton concepts from biological system to man—machine intelligent systems. It is followed by an overview of the development history of exoskeleton-type systems and their two main applications in teleoperation and human power augmentation. Besides the key technologies in exoskeleton-type systems, the research is presented from several viewpoints of the biomechanical design, system structure modelling, cooperation and function allocation, control strategy, and safety evaluation.
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37

Lendel, Viliam, Lucia Pancikova, Lukas Falat, and Dusan Marcek. "Intelligent Modelling with Alternative Approach: Application of Advanced Artificial Intelligence into Traffic Management." Communications - Scientific letters of the University of Zilina 19, no. 4 (December 31, 2017): 36–42. http://dx.doi.org/10.26552/com.c.2017.4.36-42.

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38

Kok, André J. "A review and synthesis of user modelling in intelligent systems." Knowledge Engineering Review 6, no. 1 (March 1991): 21–47. http://dx.doi.org/10.1017/s0269888900005567.

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AbstractThis paper gives a state-of-the-art overview of the rapidly expanding field of user modelling in artificially intelligent systems. After showing how a user modelling component can improve parts of the processing of, and the interaction with, a large number of systems, the current situation in this field is sketched by means of a number of short descriptions of systems employing user modelling techniques. Next, a synthesis of this review is made by discussing the aspects on which the existing methods differ. It turns out that these aspects can best be approached from two points of view: the technical level and a more abstract, functional level. This dichotomy results in eight dimensions on which to compare the modelling methods. The synthesis is then completed by describing the existing modelling techniques, and classifying the reviewed systems. In the final section, current trends in the field are outlined and prerequisites for acceptance of user modelling on a larger scale are discussed.
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39

Zhang, Xia, Youchao Sun, and Yanjun Zhang. "Ontology modelling of intelligent HCI in aircraft cockpit." Aircraft Engineering and Aerospace Technology 93, no. 5 (June 10, 2021): 794–808. http://dx.doi.org/10.1108/aeat-11-2020-0255.

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Purpose Semantic modelling is an essential prerequisite for designing the intelligent human–computer interaction in future aircraft cockpit. The purpose of this paper is to outline an ontology-based solution to this issue. Design/methodology/approach The scenario elements are defined considering the cognitive behaviours, system functions, interaction behaviours and interaction situation. The knowledge model consists of a five-tuple array including concepts, relations, functions, axioms and instances. Using the theory of belief-desire-intention, the meta-model of cognitive behaviours is established. The meta-model of system functions is formed under the architecture of sub-functions. Supported by information flows, the meta-model of interaction behaviours is presented. Based on the socio-technical characteristics, the meta-model of interaction situation is proposed. The knowledge representation and reasoning process is visualized with the semantic web rule language (SWRL) on the Protégé platform. Finally, verification and evaluation are carried out to assess the rationality and quality of the ontology model. Application scenarios of the proposed modelling method are also illustrated. Findings Verification results show that the knowledge reasoning based on SWRL rules can further enrich the knowledge base in terms of instance attributes and thereby improve the adaptability and learning ability of the ontology model in different simulations. Evaluation results show that the ontology model has a good quality with high cohesion and low coupling. Practical implications The approach presented in this paper can be applied to model complex human–machine–environment systems, from a semantics-driven perspective, especially for designing future cockpits. Originality/value Different from the traditional approaches, the method proposed in this paper tries to deal with the socio-technical modelling issues concerning multidimensional information semantics. Meanwhile, the constructed model has the ability of autonomous reasoning to adapt to complex situations.
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Gadallah, Mohamed H., Khaled Abdel Hamid El Sayed, and Keith Hekman. "Intelligent process modelling using radial basis neural network." International Journal of Machining and Machinability of Materials 8, no. 1/2 (2010): 177. http://dx.doi.org/10.1504/ijmmm.2010.034495.

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41

Ahmed Hasan, Ibtisam, Mohammed Jawad Mohammed, and Fatima Attya Lafta. "Intelligent Nero modelling methods for PV panel system." IOP Conference Series: Materials Science and Engineering 765 (March 17, 2020): 012044. http://dx.doi.org/10.1088/1757-899x/765/1/012044.

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42

Sozhamadevi, N., and S. Sathiyamoorthy. "Intelligent modelling of continuous stirred tank reactor process." International Journal of Automation and Control 9, no. 2 (2015): 143. http://dx.doi.org/10.1504/ijaac.2015.070005.

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43

Lazarević, A., Ž. Ćojbašić, and D. Lazarević. "Computationally intelligent modelling of the plasma cutting process." International Journal of Computer Integrated Manufacturing 33, no. 3 (March 3, 2020): 252–64. http://dx.doi.org/10.1080/0951192x.2020.1736635.

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44

Li, Xiang, Christopher P. Jobling, and Philip W. Grant. "An Object-Oriented Information Model for Intelligent Modelling." IFAC Proceedings Volumes 29, no. 1 (June 1996): 4410–15. http://dx.doi.org/10.1016/s1474-6670(17)58375-5.

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45

Wong, Kok Wai, and László T. Kóczy. "Guest editorial: uncertainty modelling and intelligent information processing." Memetic Computing 2, no. 4 (November 5, 2010): 247–48. http://dx.doi.org/10.1007/s12293-010-0052-5.

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46

Van Joolingen, Wouter R., and Ton De Jong. "Modelling domain knowledge for intelligent simulation learning environments." Computers & Education 18, no. 1-3 (January 1992): 29–37. http://dx.doi.org/10.1016/0360-1315(92)90033-2.

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47

Gadallah, Mohamed H., Khaled Abdel Hamid El Sayed, and Keith Hekman. "Intelligent process modelling using Feed-Forward Neural Networks." International Journal of Manufacturing Technology and Management 19, no. 3/4 (2010): 238. http://dx.doi.org/10.1504/ijmtm.2010.031371.

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48

Herrero, Pilar, Chris Greenhalgh, and Angélica de Antonio. "Modelling the Sensory Abilities of Intelligent Virtual Agents." Autonomous Agents and Multi-Agent Systems 11, no. 3 (November 2005): 361–85. http://dx.doi.org/10.1007/s10458-005-2921-8.

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49

Chang, Fi-John, Yu-Chung Wang, and Wen-Ping Tsai. "Modelling Intelligent Water Resources Allocation for Multi-users." Water Resources Management 30, no. 4 (January 11, 2016): 1395–413. http://dx.doi.org/10.1007/s11269-016-1229-6.

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50

Gallardo, María-del-Mar, Leticia Lavado, Laura Panizo, and Laura Titolo. "A constraint-based language for modelling intelligent environments." Journal of Reliable Intelligent Environments 3, no. 1 (May 12, 2017): 55–79. http://dx.doi.org/10.1007/s40860-017-0040-3.

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