Knowledge Engineering and Data Mining

• Beste egile batzuk: Konys, Agnieszka (Argitaratzailea), Nowak-Brzezińska, Agnieszka (Argitaratzailea)
• Formatua: Baliabide elektronikoa Liburu kapitulua
• Hizkuntza: ingelesa
• Argitaratua: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Gaiak: Information technology industries; computing-intensive data; dynamic programming; loop nest tiling; parallel code; OpenMP C/C++; credit scoring; cash loans; machine learning; decision model; classification; feature selection; resampling; discretization; knowledge representation; formal ontologies; graph databases; evaluation feature selection; evaluation model; psychosocial education; recommendation systems; rank aggregation; differential evolution; supervised learning; matrix factorization; metaheuristic; clinical named entity recognition; Chinese medical text; pre-trained model; systematic review; multicriteria; MCDA; MCDM; MADM; MODM; AHP; TOPSIS; VIKOR; PROMETHEE; ANP; computer-aided design (CAD); educational data mining; engineering education; online and hybrid learning environments; social media analytics; soft tissue; gamma correction; landmark detection; X-ray images; facial profile; prediction; artificial neural network; support vector machine; random forest; regression; offshore wave; wind speed; unmanned aerial vehicle; UAV smoke show; mobile networks; artificial intelligence; healthcare; database design; geospatial data; software; knowledge management; reasoning; information extraction; rule mining; knowledge acquisition; engineering; ontology; knowledge base; sustainable supplier selection; ontology population; knowledge acquisition from text; knowledge mining; drug-drug interaction; graph convolutional network; self-attention; deep learning; n/a
• Sarrera elektronikoa: DOAB: download the publication; DOAB: description of the publication

Knowledge engineering and data mining are fundamental topics in the area of artificial intelligence and knowledge-based systems. This Special Issue covers the entire knowledge engineering pipeline: from data acquisition and data mining to knowledge extraction and exploitation. The reader will find t...