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[null,"1.","Record Nr.",null,"UNISA990000600800203316"]
[null,null,"Autore",null,"DI CATALDO, Vincenzo"]
[null,null,"Titolo",null,"I segni distintivi / Vincenzo Di Cataldo"]
[null,null,"Pubbl/distr/stampa",null,"Milano : A. Giuffrè, 1993"]
[null,null,"ISBN",null,"88-14-04506-2"]
[null,null,"Edizione",null,"[2. ed.]"]
[null,null,"Descrizione fisica",null,"VII, 201 p. ; 24 cm"]
[null,null,"Collana",null,"Corso di diritto industriale ; 4"]
[null,null,"Disciplina",null,"346.450486"]
[null,null,"Soggetti",null,"Proprietà industriale"]
[null,null,"Collocazione",null,"XXV.3.D 14 (TESTI 1195)"]
[{"$nulls":4},"XXV.3.D 14a (TESTI 1195)"]
[{"$nulls":4},"TESTI 1195"]
[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,"UNINA9910585795503321"]
[null,null,"Titolo",null,"Machine learning and data science : fundamentals and applications / / edited by Prateek Agrawal, Charu Gupta, Anand Sharma, Vishu Madaan and Nisheeth Joshi"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, NJ : , : Wiley"]
[{"$nulls":4},"Beverly, MA : , : Scrivener Publishing, , 2022"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"9781119776499"]
[{"$nulls":4},"111977649X"]
[{"$nulls":4},"9781119776482"]
[{"$nulls":4},"1119776481"]
[{"$nulls":4},"1-119-77649-X"]
[{"$nulls":4},"1-119-77648-1"]
[null,null,"Descrizione fisica",null,"1 online resource (271 pages)"]
[null,null,"Collana",null,"Advances in data engineering and machine learning"]
[null,null,"Disciplina",null,"006.3/1"]
[null,null,"Soggetti",null,"Machine learning"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- Book Description -- 1 Machine Learning: An Introduction to Reinforcement Learning -- 1.1 Introduction -- 1.1.1 Motivation -- 1.1.2 Machine Learning -- 1.1.3 How Machines Learn -- 1.1.4 Analogy -- 1.1.5 Reinforcement Learning Process -- 1.1.6 Reinforcement Learning Definitions: Basic Terminologies -- 1.1.7 Reinforcement Learning Concepts -- 1.2 Reinforcement Learning Paradigm: Characteristics -- 1.3 Reinforcement Learning Problem -- 1.4 Applications of Reinforcement Learning -- Conclusion -- References; 2 Data Analysis Using Machine Learning: An Experimental Study on UFC -- 2.1 Introduction -- 2.2 Proposed Methodology -- 2.2.1 Data Extraction: Preliminary -- 2.2.2 Pre-Processing Dataset -- 2.3 Experimental Evaluation and Visualization -- 2.4 Conclusion -- References -- 3 Dawn of Big Data with Hadoop and Machine Learning -- 3.1 Introduction -- 3.2 Big Data -- 3.2.1 The Life Cycle of Big Data "]
[{"$nulls":4},"-- 3.2.2 Challenges in Big Data -- 3.2.3 Scaling in Big Data Platforms -- 3.2.4 Factors to Understand Big Data Platforms and Their Selection Criteria -- 3.2.5 Current Trends in Big Data; 3.2.6 Big Data Use Cases -- 3.3 Machine Learning -- 3.3.1 Machine Learning Algorithms -- 3.4 Hadoop -- 3.4.1 Components of the Hadoop Ecosystem -- 3.4.2 Other Important Components of the Hadoop Ecosystem for Machine Learning -- 3.4.3 Benefits of Hadoop with Machine Learning -- 3.5 Studies Representing Applications of Machine Learning Techniques with Hadoop -- 3.6 Conclusion -- References -- 4 Industry 4.0: Smart Manufacturing in Industries The Future -- 4.1 Introduction -- Challenges or Responses -- Shared Infrastructure -- Security -- Costs or Profitability -- Future Proofing -- Conclusion; 6.3.3 Cluster-Based Mapping with Depth First Search (DFS) Algorithm -- 6.4 Proposed Methodology -- 6.4.1 Cluster-Based Mapping with FM Algorithm -- 6.4.2 Calculation of Total Power Consumption -- 6.4.3 Total Power Calculation by Using Tabu Search -- 6.5 Experimental Results and Discussion -- 6.5.1 Total Power Consumption in 2D NoC -- 6.5.2 Performance of Tabu Search for Power Optimization with Mesh Topology -- 6.5.3 Performance of Tabu Search for Power Optimization with Ring Topology -- 6.5.4 Average Hop Counts for 2D NoC -- 6.6 Conclusion -- References"]