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020 _a9789811519185
024 7 _a10.1007/978-981-15-1918-5
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aQ334
_b2020 EB
245 0 0 _aAdvances in Integrations of Intelligent Methods
_bPost-workshop volume of the 8th International Workshop CIMA 2018, Volos, Greece, November 2018 (in conjunction with IEEE ICTAI 2018)
_cedited by Ioannis Hatzilygeroudis, Isidoros Perikos, Foteini Grivokostopoulou.
250 _a1st ed. 2020.
264 1 _aSingapore
_bSpringer Singapore :
_bImprint: Springer
_c2020.
300 _a1 recurso en línea (XIV, 161 páginas)
_b60 ilustraciones, 45 ilustraciones a color.
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _atext file
_bPDF
490 0 _aSmart Innovation Systems and Technologies
_x2190-3018
_v170
490 0 _aIntelligent Technologies and Robotics (Springer-42732)
505 0 _aAligning Learning Materials and Assessment with Course Learning Outcomes in MOOCs using Data Mining Techniques -- Edge-Centric Queries Stream Management based on an Ensemble Model -- Bitcoin Price Prediction Combining Data and Text Mining -- Towards New Evaluation Metrics for Relational Learning -- Color Models for Skin Lesions Classification from Dermatoscopic Images -- Methods of Statistical Analysis and Machine Learning for the Evaluation of Generated Hardware and Firmware Designs -- Genetic Algorithms for Creating Large Job Shop Dispatching Rules.
520 3 _aThis book presents a number of research efforts in combining AI methods or techniques to solve complex problems in various areas. The combination of different intelligent methods is an active research area in artificial intelligence (AI), since it is believed that complex problems can be more easily solved with integrated or hybrid methods, such as combinations of different soft computing methods (fuzzy logic, neural networks, and evolutionary algorithms) among themselves or with hard AI technologies like logic and rules; machine learning with soft computing and classical AI methods; and agent-based approaches with logic and non-symbolic approaches. Some of the combinations are already extensively used, including neuro-symbolic methods, neuro-fuzzy methods, and methods combining rule-based and case-based reasoning. However, other combinations are still being investigated, such as those related to the semantic web, deep learning and swarm intelligence algorithms. Most are connected with specific applications, while the rest are based on principles.
988 _aPrimersemestre_2020_Robotics
650 7 _2embne
_aInteligencia artificial
_vCongresos y asambleas
_9413115
700 1 _aHatzilygeroudis, Ioannis
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_998065
700 1 _aPerikos, Isidoros.
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aGrivokostopoulou, Foteini.
_eeditor
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773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9789811519192
776 0 8 _iPrinted edition:
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856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-15-1918-5
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
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998 _b03/2020
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