000			05603cam a2200481Ii 4500
001			96046
003			ES-MaUEC
005			20240111050134.0
006			m o d
007			cr cnu|||unuuu
008			170523s2017 si a o 100 0 eng d
020			_a9789811038730
020			_a9789811038747 _q(electronic bk.)
020			_a9811038732
020			_a9811038740 _q(electronic bk.)
020			_z9789811038730 _q(print)
040			_aN$T _cN$T _dGW5XE _dN$T _dEBLCP _dYDX _dUAB _dESU _dAZU _dUPM _dOCLCF _dIOG _dCOO _dMERER _dVT2 _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aQ342 _b.C667 2017 EB
111	2		_aInternational Conference on "Computational Intelligence in Data Mining" _n(3rd : _d2016 : _cBhubaneswar, India)
245	1	0	_aComputational intelligence in data mining : _bproceedings of the International Conference on CIDM, 10-11 December 2016 _cHimansu Sekhar Behera, Durga Prasad Mohapatra, editors.
264		1	_aSingapore _bSpringer _c2017.
300			_a1 recurso en línea (xix, 847 páginas) _bilustraciones
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF
490	0		_aAdvances in intelligent systems and computing _x2194-5357 _vvolume 556
500			_aIncluye índice de autor
500			_aActas de conferencias internacionales
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
505	0		_aPreface; Acknowledgements; About the Conference; Contents; About the Editors; 1 Safety and Crime Assistance System for a Fast Track Response on Mobile Devices in Bhubaneswar; Abstract; 1 Introduction; 2 Problem Statement; 3 Current Mechanism of Response to Crimes; 4 How the Situation can be Tackled?; 4.1 Victim at Center Approach; 4.2 Information Passing in Victim at Center Approach; 5 Safety and Crime Assistance System; 5.1 SCAS Architecture; 5.2 SCAS Advantages in Accidental Scenes; 5.3 Technical Architecture and Feasibility of SCAS; 6 Issues.
505	8		_a3 Proposed Algorithm4 Experimental Set up and Result Analysis; 4.1 Parameter Set up; 4.2 Dataset Information; 4.3 Experimental Results and Analysis; 5 Conclusion and Future Work; References; A Study of Dimensionality Reduction Techniques with Machine Learning Methods for Credit Risk Prediction; 1 Introduction; 2 Literature Review; 3 Methodology; 3.1 Information Gain; 3.2 Gain Ratio; 3.3 Principle Component Analysis; 3.4 Linear Discriminant Analysis; 3.5 Proposed Method; 4 Experiments; 4.1 Data Set Description; 4.2 Performance Measures; 5 Results and Analysis; 6 Conclusion; References.
505	8		_a3.2 Data Preprocessing3.3 Data Mining; 3.4 Data Visualization; 4 Experiments and Results; 5 Conclusion and Future Work; Acknowledgements; References; 4 Classical and Evolutionary Image Contrast Enhancement Techniques: Comparison by Case Studies; Abstract; 1 Introduction; 2 Procedure; 2.1 Transformation Function and Objective Function Used; 2.1.1 Algorithm for GA Based Approach; 2.1.2 Implementation of ABC Algorithm; 3 Results and Discussion; 3.1 Visual Comparison; 3.2 Quantitative Comparison; 3.2.1 Entropy; 3.2.2 Fitness Value; 3.2.3 SNR; 4 Conclusions; References.
505	8		_a7 Process Workflow, Login Page, and Registration Page8 Conclusion; References; 2 Major Global Energy (Biomass); Abstract; 1 Introduction; 2 Biomass as a Renewable Resource; 2.1 Biomass Energy -Bio Power; 2.2 Environment Impact; 2.3 Working of Biomass Heating Plant; 2.4 Various Issue; 2.5 Benefits of Biomass Heating; 2.6 Disadvantage; 2.7 Application; 3 Conclusion; Acknowledgements; References; 3 Detecting Targeted Malicious E-Mail Using Linear Regression Algorithm with Data Mining Techniques; Abstract; 1 Introduction; 2 Literature Survey; 3 Design and Implementation; 3.1 Data Importing.
505	8		_aCost Effectiveness Analysis of a Vertical Midimew-Connected Mesh Network (VMMN)1 Introduction; 2 Interconnection of Vertical-Midimew Connected Mesh Network; 2.1 Basic Module; 2.2 Higher Level Network; 3 Cost Effectiveness Analysis; 3.1 Cost Parameters; 3.2 Distance Parameters; 3.3 Packing Density; 3.4 Message Traffic Density; 3.5 Cost Effective Factor; 3.6 Time-Cost Effective Factor; 4 Conclusion; References; 6 Cluster Analysis Using Firefly-Based K-means Algorithm: A Combined Approach; Abstract; 1 Introduction; 2 Preliminaries; 2.1 K-means Algorithm; 2.2 Firefly Algorithm (FA).
520	3		_aThe book presents high quality papers presented at the International Conference on Computational Intelligence in Data Mining (ICCIDM 2016) organized by School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India during December 10 - 11, 2016. The book disseminates the knowledge about innovative, active research directions in the field of data mining, machine and computational intelligence, along with current issues and applications of related topics. The volume aims to explicate and address the difficulties and challenges that of seamless integration of the two core disciplines of computer science.
650		7	_aInteligencia artificial _2embne _0(OCoLC)fst00871995 _0 _9413115
700	1		_aBehera, Himansu Sekhar, _eeditor literario _9100565
700	1		_aMohapatra, Durga Prasad, _eeditor literario _9100497
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-981-10-3874-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017D
998			_b02/2018 _dz _e- _zSI
999			_c96046 _d96046 _x1