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[null,"1.","Record Nr.",null,"UNISALENTO991003164219707536"]
[null,null,"Autore",null,"Tinelli, Antonio"]
[null,null,"Titolo",null,"Algoritmi e strutture di dati per la gestione dei grafi. Tesi di laurea / laureando Antonio Tinelli ; relat. Paola Vocca"]
[null,null,"Pubbl/distr/stampa",null,"Lecce : Università degli Studi. Facoltà di Scienze. Corso di laurea in Matematica, a.a. 2002-03"]
[null,null,"Descrizione fisica",null,"83 p. ; 30 cm"]
[null,null,"Classificazione",null,"AMS 05C85"]
[{"$nulls":4},"AMS 68P05"]
[{"$nulls":4},"AMS 68W40"]
[null,null,"Disciplina",null,"511.5"]
[null,null,"Soggetti",null,"Graph algorithms"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9911044026503321"]
[null,null,"Autore",null,"López-Flores Francisco Javier"]
[null,null,"Titolo",null,"Machine learning tools for chemical engineering : methodologies and applications / / Francisco Javier López-Flores, Rogelio Ochoa-Barragán, Alma Yunuen Raya-Tapia, César Ramírez-Márquez, José Maria Ponce-Ortega"]
[null,null,"Pubbl/distr/stampa",null,"Elsevier Science [Imprint]"]
[{"$nulls":4},"San Diego, : Elsevier Science & Technology Books"]
[{"$nulls":4},"Saint Louis, : Elsevier [Distributor]"]
[{"$nulls":4},"Chantilly : , : Elsevier Science & Technology, , 2025"]
[{"$nulls":4},"©2026"]
[null,null,"ISBN",null,"0-443-29059-8"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (630 pages)"]
[null,null,"Disciplina",null,"660.0285631"]
[null,null,"Soggetti",null,"Chemical engineering - Data processing"]
[{"$nulls":4},"Machine learning - Industrial applications"]
[{"$nulls":4},"Génie chimique - Informatique"]
[{"$nulls":4},"Apprentissage automatique - Applications industrielles"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","Machine Learning Tools for Chemical Engineering: Methodologies and Applications examines how machine learning (ML) techniques are applied in the field, offering precise, fast, and flexible solutions to address specific challenges.ML techniques and methodologies offer significant advantages (such as accuracy, speed of execution, and flexibility)."]