LEADER 01417nam--2200457---450- 001 990000600800203316 005 20100519155555.0 010 $a88-14-04506-2 035 $a0060080 035 $aUSA010060080 035 $a(ALEPH)000060080USA01 035 $a0060080 100 $a20010830d1993----km-y0itay0103----ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $a<> segni distintivi$fVincenzo Di Cataldo 205 $a2. ed. 210 $aMilano$cA. 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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 -- 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 606 $aMachine learning 606 $aData mining 608 $aElectronic books. 615 0$aMachine learning. 615 0$aData mining. 676 $a006.3/1 702 $aAgrawal$b Prateek 702 $aGupta$b Charu 702 $aSharma$b Anand 702 $aMadaan$b Vishu 702 $aJoshi$b Nisheeth 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910585795503321 996 $aMachine learning and data science$92963040 997 $aUNINA LEADER 01999nas 2200529 a 450 001 996213096103316 005 20231101154203.0 011 $a1744-3571 035 $a(OCoLC)38908314 035 $a(CKB)954925273433 035 $a(CONSER) 2008233604 035 $a(DE-599)ZDB2008001-3 035 $a(EXLCZ)99954925273433 100 $a19980407a19919999 sy 101 0 $aeng 135 $aurmnu||||| 200 00$aFood and bioproducts processing $etransactions of the Institution of Chemical Engineers, Part C 210 $a[Rugby, Warwickshire] $cInstitution of Chemical Engineers 300 $aRefereed/Peer-reviewed 311 $a0960-3085 517 3 $aTransactions of the Institution of Chemical Engineers. 517 1 $aTrans IChemE, Part C 517 1 $aTrans IChemE, Part C 517 $aFood & Bioproducts Processing: Transactions of the Institution of Chemical Engineers Part C 531 $aFOOD & BIOPRODUCTS PROCESSING 531 $aFOOD BIOPROD PROCESS 531 $aFOOD BIO PR 531 $aFOOD BIOPROD PROCESS TRANS INST CHEM ENG PART C 531 $aFOOD AND BIOPRODUCTS PROCESSING PART C TRANSACTIONS OF THE INSTITUTION OF CHEM 531 $aFOOD AND BIOPRODUCTS PROCESSING TRANSACTIONS OF THE INSTITUTION OF CHEMICAL ENG 531 0 $aFood bioprod. process. 531 $aFOOD BIOPROD. PROCESS 606 $aFood industry and trade$vPeriodicals 606 $aBiochemical engineering$vPeriodicals 606 $aAliments$xIndustrie et commerce$vPe?riodiques 606 $aGe?nie biochimique$vPe?riodiques 615 0$aFood industry and trade 615 0$aBiochemical engineering 615 6$aAliments$xIndustrie et commerce 615 6$aGe?nie biochimique 676 $a664 712 02$aInstitution of Chemical Engineers (Great Britain) 712 02$aEuropean Federation of Chemical Engineering. 906 $aJOURNAL 912 $a996213096103316 996 $aFood and bioproducts processing$9795288 997 $aUNISA