Bibliographies thématiques / Soft computing technology

Littérature scientifique sur le sujet « Soft computing technology »

Auteur : Grafiati
Publié le 18 mai 2024

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Articles de revues sur le sujet "Soft computing technology"

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Werbos, Paul J. « Quantum technology to expand soft computing ». Systems and Soft Computing 4 (décembre 2022) : 200031. http://dx.doi.org/10.1016/j.sasc.2022.200031.

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Pawar, Rajeshwari H. « Soft Computing in Advanced Robotics ». International Journal for Research in Applied Science and Engineering Technology 12, no 4 (30 avril 2024) : 1263–66. http://dx.doi.org/10.22214/ijraset.2024.59963.

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Abstract: Advanced robotics has witnessed significant advancements in recent years, enabling robots to perform complex tasks in various domains such as manufacturing, healthcare, and space exploration. Soft computing techniques have emerged as powerful tools to address the challenges associated with uncertainty, imprecision, and complexity inherent in robotic systems. This paper presents a comprehensive review of the application of soft computing techniques in advanced robotics. The review encompasses various soft computing paradigms including fuzzy logic, neural networks, evolutionary algorithms, and swarm intelligence. The paper discusses the integration of these techniques in different aspects of robotic systems such as perception, planning, control, and learning. Furthermore, it highlights the strengths, limitations, and future directions of soft computing in advancing robotics technology.
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Sadiku, Matthew N. O., Chandra M. M. Kotteti, Abayomi Ajayi-Majebi et Sarhan M. Musa. « Soft Computing in Decision Support Systems ». International Journal of Advances in Scientific Research and Engineering 08, no 02 (2022) : 153–58. http://dx.doi.org/10.31695/ijasre.2022.8.2.21.

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Magdalena, L. « Soft computing for students and for society [Technology Review] ». IEEE Computational Intelligence Magazine 4, no 1 (février 2009) : 47–50. http://dx.doi.org/10.1109/mci.2008.931074.

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Bosacchi, B. « On the role of Soft Computing in microelectronics technology ». Soft Computing - A Fusion of Foundations, Methodologies and Applications 1, no 2 (26 juin 1997) : 57–60. http://dx.doi.org/10.1007/s005000050006.

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I. Jeena Jacob. « Fuzzy Logic Decision Model for Robust Risk Management in ubiquitous environment- A Review ». December 2023 5, no 4 (décembre 2023) : 399–411. http://dx.doi.org/10.36548/jucct.2023.4.005.

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Soft computing models are invaluable resources for solving addressing real-world issues. Soft computing is the process of solving difficult computer problems with imprecise yet practical results through the application of approximate computations. The method enables the resolution of issues that might be too difficult or time-consuming to handle with the technology available today. The term ‘computational intelligence’ is often used interchangeably with soft computing. This article will discuss various soft computing strategies, with a particular focus on the fuzzy logic approach. The study delves into risk management in the real world using fuzzy logic techniques across multiple fields, providing a comprehensive review and comparing performance analyses.
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Beldjehem, Mokhtar, Lotfi A. Zadeh, Ronald Yager et Madan Gupta. « Special Issue on Soft Computing for Modeling and Simulation ». Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no 5 (20 septembre 2009) : 511. http://dx.doi.org/10.20965/jaciii.2009.p0511.

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Welcome to this special issue on Soft Computing for Modeling and Simulation of the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII). We are pleased to introduce 5 selected papers and 4 regular papers presented at NASTEC (North-American Simulation Technology Conference), which is a series of conferences initiated by Eurosis after in-depth discussions with Prof. Mokhtar Beldjehem and North-American Simulationists, Soft Computing Scientists, addressing issues regarding the interplays and synergies between Modeling, Simulation and Soft Computing. The first NASTEC 2008 was organized at Mc Gill University, Montré{e}al, Canada, which is its birth place. It has attracted Simulationists, researchers and practitioners of Soft Computing, attendees from academic, industry and government agencies in an exchange of ideas and shared experiences. The intent of the NASTEC'2008 event is to nurture the spirit of cooperation and strive to improve the quality of life in this global village through excellence in hybrid Soft Computing research and education by engineering of next-generation intelligent hybrid Soft Computing systems for Modeling, Simulation, Simulation-based and Data-driven Software Engineering, Web-centric Computing, E-learning and Virtual Reality Systems at the service and for the benefits of the humankind. Computer Simulation is being acknowledged as the “third leg” of scientific discovery and analysis, along with theory and experimentation. Simulation Technology aims at building the “software digital factory.” The fields of Modeling, Simulation, and Soft Computing in general have made significant progress; parts of them were reflected in the present Special issue. This issue was able to attract top-level and forefront research. The themes center on novel issues in connection with Modeling, Simulation, Soft Computing, Simulation-based Software Engineering, Web-centric Computing, Virtual Reality Systems, their interplays and synergies. We are grateful to a number of people without whom we would not have been able to put this special issue together. They include our NASTEC'08 IPC and JACIII reviewers for making this special issue possible; they have done an excellent job: We got 4.5 reviews per paper on the average. We are grateful to authors of selected papers who have considered JACIII as the target for their work, and even though we could not accommodate every submission in this issue, we hope that the reviews will be helpful to many people. We are also grateful to Prof. Toshio Fukuda, Nagoya University, and Prof. Kaoru Hirota, Tokyo Institute of Technology, the editors-in-chief, and the NASTEC 2008 conference staff for inviting us to guest-edit this Journal. Last, but not least, we are indebted to the staff of JACIII and Fuji Technology Press for making this a reality.
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Mahmudova, Shafagat. « MACHINE LEARNING FOR SOFTWARE ». International Conference on Technics, Technologies and Education, ICTTE 2019 (2019) : 251–55. http://dx.doi.org/10.15547/ictte.2019.04.061.

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The study machine learning for software based on Soft Computing technology. It analyzes Soft Computing components. Their use in software, their advantages and challenges are studied. Machine learning and its features are highlighted. The functions and features of neural networks are clarified, and recommendations were given.
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Kumar, Deepak. « Advances in Data Analytics, Soft Computing and Info-Comm Technology ». Recent Advances in Computer Science and Communications 15, no 5 (juin 2022) : 652. http://dx.doi.org/10.2174/266625581505220315111711.

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Liao, Xiangyun, Zhiyong Yuan, Pengfei Hu et Qianfeng Lai. « GPU-assisted energy asynchronous diffusion parallel computing model for soft tissue deformation simulation ». SIMULATION 90, no 11 (13 octobre 2014) : 1199–208. http://dx.doi.org/10.1177/0037549714552708.

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Soft tissue deformation simulation is a key technology of virtual surgical simulation. In this work, we present a graphics processing unit (GPU)-assisted energy asynchronous diffusion parallel computing model which is stable and fast in processing complex models, especially concave surface models. We adopt hexahedral voxels to represent the physical model of soft tissue to improve the visual realistic quality and computing efficiency of deformation simulation. We also adopt the concept of free boundary to simulate soft tissue geometric characteristics more precisely during the deformation process and introduce asynchronous diffusion by using the mechanical energy of mass points to achieve realistic soft tissue deformation effects. In order to meet the requirement of real-time surgery simulation, we accelerate the soft tissue deformation by using OpenCL (Open Computing Language) and optimize the parallel computing process in several means. Experimental results have shown that the GPU-assisted energy asynchronous diffusion parallel computing model for soft tissue deformation simulation implements satisfactory effects on deformation in visual realistic and real-time quality.
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Thèses sur le sujet "Soft computing technology"

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Mitra, Malay. « Medical diognosis using soft computing technology ». Thesis, University of North Bengal, 2018. http://hdl.handle.net/123456789/2722.

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Mitra, Malay. « Medical diognosis using soft computing technologies ». Thesis, University of North Bengal, 2018. http://ir.nbu.ac.in/handle/123456789/3659.

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Fernando, Kurukulasuriya Joseph Tilak Nihal. « Soft computing techniques in power system analysis ». full-text, 2008. http://eprints.vu.edu.au/2025/1/thesis.pdf.

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Soft computing is a concept that has come into prominence in recent times and its application to power system analysis is still more recent. This thesis explores the application of soft computing techniques in the area of voltage stability of power systems. Soft computing, as opposed to conventional “hard” computing, is a technique that is tolerant of imprecision, uncertainty, partial truth and approximation. Its methods are based on the working of the human brain and it is commonly known as artificial intelligence. The human brain is capable of arriving at valid conclusions based on incomplete and partial data obtained from prior experience. It is an approximation of this process on a very small scale that is used in soft computing. Some of the important branches of soft computing (SC) are artificial neural networks (ANNs), fuzzy logic (FL), genetic computing (GC) and probabilistic reasoning (PR). The soft computing methods are robust and low cost. It is to be noted that soft computing methods are used in such diverse fields as missile guidance, robotics, industrial plants, pattern recognition, market prediction, patient diagnosis, logistics and of course power system analysis and prediction. However in all these fields its application is comparatively new and research is being carried out continuously in many universities and research institutions worldwide. The research presented in this thesis uses the soft computing method of Artificial Neural Networks (ANN’s) for the prediction of voltage instability in power systems. The research is very timely and current and would be a substantial contribution to the present body of knowledge in soft computing and voltage stability, which by itself is a new field. The methods developed in this research would be faster and more economical than presently available methods enabling their use online.
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Fernando, Kurukulasuriya Joseph Tilak Nihal. « Soft computing techniques in power system analysis ». Thesis, full-text, 2008. https://vuir.vu.edu.au/2025/.

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Soft computing is a concept that has come into prominence in recent times and its application to power system analysis is still more recent. This thesis explores the application of soft computing techniques in the area of voltage stability of power systems. Soft computing, as opposed to conventional “hard” computing, is a technique that is tolerant of imprecision, uncertainty, partial truth and approximation. Its methods are based on the working of the human brain and it is commonly known as artificial intelligence. The human brain is capable of arriving at valid conclusions based on incomplete and partial data obtained from prior experience. It is an approximation of this process on a very small scale that is used in soft computing. Some of the important branches of soft computing (SC) are artificial neural networks (ANNs), fuzzy logic (FL), genetic computing (GC) and probabilistic reasoning (PR). The soft computing methods are robust and low cost. It is to be noted that soft computing methods are used in such diverse fields as missile guidance, robotics, industrial plants, pattern recognition, market prediction, patient diagnosis, logistics and of course power system analysis and prediction. However in all these fields its application is comparatively new and research is being carried out continuously in many universities and research institutions worldwide. The research presented in this thesis uses the soft computing method of Artificial Neural Networks (ANN’s) for the prediction of voltage instability in power systems. The research is very timely and current and would be a substantial contribution to the present body of knowledge in soft computing and voltage stability, which by itself is a new field. The methods developed in this research would be faster and more economical than presently available methods enabling their use online.
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Patton, Michael Dean. « Seedlet Technology for anomaly detection ». Diss., Mississippi State : Mississippi State University, 2002. http://library.msstate.edu/etd/show.asp?etd=etd-08022002-142101.

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Livres sur le sujet "Soft computing technology"

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Luo, Jia, dir. Soft Computing in Information Communication Technology. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29452-5.

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Luo, Jia, dir. Soft Computing in Information Communication Technology. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29148-7.

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Chattopadhyay, Jayeeta, Rahul Singh et Vandana Bhattacherjee, dir. Innovations in Soft Computing and Information Technology. Singapore : Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3185-5.

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Abraham, Ajith, Yasuhiko Dote, Takeshi Furuhashi, Mario Köppen, Azuma Ohuchi et Yukio Ohsawa, dir. Soft Computing as Transdisciplinary Science and Technology. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-32391-0.

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1939-, Tzafestas S. G., dir. Soft computing in systems and control technology. Singapore : World Scientific, 1999.

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Charles, Hsu, dir. Advanced signal processing technology by soft computing. Singapore : World Scientific, 2001.

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Luo, Jia. Soft Computing in Information Communication Technology : Volume 2. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012.

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Luo, Jia. Soft Computing in Information Communication Technology : Volume 1. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012.

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Tinku, Acharya, dir. Data mining : Multimedia, soft computing, and bioinformatics. Hoboken, NJ : John Wiley, 2003.

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Richard, Chbeir, dir. The Fifth International Conference on Soft Computing as Transdisciplinary Science and Technology : October 28th-October 31st 2008, University of Cergy-Pontoise, France : proceedings. Danvers, MA : Association for Computing Machinery, 2008.

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Chapitres de livres sur le sujet "Soft computing technology"

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Souran, Davood Mohammadi, Hossein Hoshmandi Safa, Behrooz Gohari Moghadam, Mehran Ghasempour et Parisa Tavakkoli Heravi. « Smart Grid Technology in Power Systems ». Dans Soft Computing Applications, 1367–81. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18416-6_109.

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Mamdani, Abe, Arash G. Sichanie et Jeremy Pitt. « Soft Agent Computing : Towards Enhancing Agent Technology with Soft Computing ». Dans Intelligent Systems and Soft Computing, 110–35. Berlin, Heidelberg : Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/10720181_5.

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Berdie, Adela Diana, Mihaela Osaci et Nicolae Budişan. « A Multicriteria Approach in the Selection of a SAP UI Technology ». Dans Soft Computing Applications, 699–706. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33941-7_62.

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Al-Khannak, Rafid. « E-Learning Web Based Grid by Utilising P2P Technology in Presentation Broadcasting ». Dans Soft Computing Applications, 395–407. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62524-9_30.

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Chiriac, Ionut Adrian, Lăcrămioara Stoicu-Tivadar et Elena Podoleanu. « Romanian Sign Language Oral Health Corpus in Video and Animated Avatar Technology ». Dans Soft Computing Applications, 279–93. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18296-4_24.

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Prakash, Om, Saurabh Ranjan, Anil Kumar et P. P. Tripathy. « Applications of Soft Computing in Solar Drying Systems ». Dans Solar Drying Technology, 419–38. Singapore : Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3833-4_14.

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Tița, Victor, Doru Anastasiu Popescu et Nicolae Bold. « Integrating Technology in Training : System Dynamic-Based Model Considerations Regarding Smart Training and Its Relationship with Educational Components ». Dans Soft Computing Applications, 165–76. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51992-6_14.

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Godínez Borja, Juan Sebastián Guadalupe, Marco Antonio Corona Ruiz et Carlos Pérez Leguízamo. « ADSOA - Fault Detection and Recovery Technology Based on Collective Intelligence ». Dans Advances in Soft Computing, 176–86. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33749-0_15.

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Deng, Zhicong, Martin Stommel et Weiliang Xu. « Soft Robotics Technology and a Soft Table for Industrial Applications ». Dans Advances in Intelligent Systems and Computing, 397–409. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31293-4_32.

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Xi, Ziqiang, Ruili Tang, Wencong Huang, Dandan Huang, Lizhi Zheng et Pan Shen. « Harmonic Current Detection Based on Neural Network Adaptive Noise Cancellation Technology ». Dans Advances in Soft Computing, 829–35. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01216-7_88.

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Actes de conférences sur le sujet "Soft computing technology"

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« Session YC3 : Soft Computing 1 ». Dans 2005 IEEE Instrumentationand Measurement Technology Conference Proceedings. IEEE, 2005. http://dx.doi.org/10.1109/imtc.2005.1604555.

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« Session YC4 : Soft Computing 2 ». Dans 2005 IEEE Instrumentationand Measurement Technology Conference Proceedings. IEEE, 2005. http://dx.doi.org/10.1109/imtc.2005.1604584.

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Kostrzewski, Andrew A., Tomasz P. Jannson et Stephen A. Kupiec. « Soft computing and wireless communication ». Dans International Symposium on Optical Science and Technology, sous la direction de Bruno Bosacchi, David B. Fogel et James C. Bezdek. SPIE, 2001. http://dx.doi.org/10.1117/12.448328.

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Mastacan, Lucian, et Constantin-Catalin Dosoftei. « Temperature intelligent control based on soft computing technology ». Dans 2016 International Conference and Exposition on Electrical and Power Engineering (EPE). IEEE, 2016. http://dx.doi.org/10.1109/icepe.2016.7781295.

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Bonissone, Piero P. « Soft computing applications at General Electric ». Dans International Symposium on Optical Science and Technology, sous la direction de Bruno Bosacchi, David B. Fogel et James C. Bezdek. SPIE, 2001. http://dx.doi.org/10.1117/12.448335.

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Jannson, Tomasz P., Andrew A. Kostrzewski, Kang S. Lee, Todd J. Hester, Thomas C. Forrester et Gajendra D. Savant. « Soft computing and small system integration ». Dans Optical Science and Technology, the SPIE 49th Annual Meeting, sous la direction de Andrew G. Tescher. SPIE, 2004. http://dx.doi.org/10.1117/12.562332.

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Vijayalakshmi, D. P., K. S. Jinesh Babu, R. B. Patel et B. P. Singh. « Soft computing tools for floodplain modelling ». Dans INTERNATIONAL CONFERENCE ON METHODS AND MODELS IN SCIENCE AND TECHNOLOGY (ICM2ST-10). AIP, 2010. http://dx.doi.org/10.1063/1.3526251.

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Deng, Lujuan, Kanyu Zhang, Youmin Gong et Shengxue Wang. « Soft computing technology for modeling of greenhouse climate control ». Dans Fifth International Symposium on Instrumentation and Control Technology, sous la direction de Guangjun Zhang, Huijie Zhao et Zhongyu Wang. SPIE, 2003. http://dx.doi.org/10.1117/12.522080.

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Jannson, Tomasz P., Andrew A. Kostrzewski, Sookwang N. Ro, Thomas Forrester et T. Hester. « Soft computing techniques in network packet video ». Dans Optical Science and Technology, SPIE's 48th Annual Meeting, sous la direction de Bruno Bosacchi, David B. Fogel et James C. Bezdek. SPIE, 2004. http://dx.doi.org/10.1117/12.511580.

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Chen, Peiyou, et Jing Zhang. « Research on Applications of Soft Computing Agents ». Dans 2008 International Symposium on Intelligent Information Technology Application Workshops. IEEE, 2008. http://dx.doi.org/10.1109/iita.workshops.2008.132.

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Rapports d'organisations sur le sujet "Soft computing technology"

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Canto, Patricia, dir. The role of vocational training knowledge intensive business services. (Main conclusions). Universidad de Deusto, 2020. http://dx.doi.org/10.18543/vyqr9353.

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In the global economic model, the service sector continues to gain ground on the manufacturing sector and trends such as the integration of new technologies into production processes are advancing inexorably. Advanced economies are pushed to specialise, supported by their regional innovation systems, and cities are emerging as key and strategic centres of activity. In this context, Knowledge Intensive Business Services (KIBS) are presented as critical due to their capacity to promote innovation within the regional productive fabric and smart specialisation strategies, the promotion of advanced manufacturing, the generation of quality employment and the stimulation of economic growth, especially in urban environments. This is why many cities, prioritizing KIBS to stimulate their economy, need to create and retain talent for this sort of industry. Likewise, vocational and education training (VET) systems, such as the Basque VET system, have so far developed their greatest strengths in the field of manufacturing knowledge. Due to this, VET seems to be obliged to adapt to this new scenario, in which KIBS and cities stand out, in order to continue to maintain their level of excellence. KIBS have been extensively examined, but until now no one had posed the following questions: What is the role of vocational training in KIBS? To what extent are VET profiles (and will VET profiles be) relevant in KIBS? This study will show an emerging trend in the labour market. This is the growing relevance of technology profiles with VET background in KIBS, especially in technology-based KIBS. VET technology profiles can be consolidated as one of the main implementing agents of the digital transformation (cybersecurity, blockchain, cloud computing, UX design, artificial intelligence, scientific computing...). To this end, hybridisation with other fields of knowledge but also with studies of other kinds such as university studies may be essential.
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