000 06043nam a2200505 i 4500
710 2 _aSpringerLink (Online service)
_9106996
999 _c118474
_d118474
001 118474
003 ES-MaUEC
005 20230102113855.0
006 a||||fo|||| 10| 0
007 cr nn nnnaamaa
008 200121s2020 gw a o |1|| 0|eng d
020 _a9783030390334
024 7 _a10.1007/978-3-030-39033-4
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
041 0 _aeng
050 4 _aT57.95
_b2020 EB
245 0 0 _aBiologically Inspired Techniques in Many-Criteria Decision Making :
_bInternational Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM-2019)
_cedited by Satchidananda Dehuri, Bhabani Shankar Prasad Mishra, Pradeep Kumar Mallick, Sung-Bae Cho, Margarita N. Favorskaya.
250 _aFirst edition
264 1 _aCham
_bSpringer International Publishing :
_bImprint Springer
_c2020.
300 _a1 recurso en línea (XV, 258 páginas)
_b 97 ilustraciones, 65 ilustraciones a color
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _atext file
_bPDF
490 0 _aLearning and Analytics in Intelligent Systems
_x2662-3447
_v10
490 0 _aIntelligent Technologies and Robotics (Springer-42732)
505 0 _aChapter 1: Classification of Arrhythmia Using Artificial Neural Network with Grey Wolf Optimization -- Chapter 2: Multi-objective Biogeography-Based Optimization for Influence Maximization-Cost Minimization in Social Networks -- Chapter 3: Classification of Credit Dataset Using Improved Particle Swarm Optimization Tuned Radial Basis Function Neural Networks -- Chapter 4: Multi-verse Optimization of Multilayer Perceptrons (MV-MLPs) for Efficient Modeling and Forecasting of Crude Oil Prices Data -- Chapter 5: Application of machine learning to predict diseases based on symptoms in rural India -- Chapter 6: Classıfıcatıon of Real Tıme Noısy Fıngerprınt Images Usıng FLANN -- Chapter 7: Software Reliability Prediction with Ensemble Method and Virtual Data Point Incorporation -- Chapter 8: Hyperspectral Image Classification using Stochastic Gradient Descent based Support Vector Machine -- Chapter 9: A Survey on Ant Colony Optimization for Solving Some of the Selected NP-Hard Problem -- Chapter 10: Machine Learning Models for Stock Prediction using Real-Time Streaming Data -- Chapter 11: Epidemiology of Breast Cancer (BC) and its Early Identification via Evolving Machine Learning Classification Tools (MLCT)-A Study -- Chapter 12: Ensemble Classification Approach for Cancer Prognosis and Prediction -- Chapter 13: Extractive Odia Text Summarization System: An OCR based Approach -- Chapter 14: Predicting sensitivity of local news articles from Odia dailies -- Chapter 15: A systematic frame work using machine learning approaches in supply chain forecasting -- Chapter 16: An Intelligent system on computer-aided diagnosis for Parkinson's disease with MRI using Machine Learning -- Chapter 17: Operations on Picture Fuzzy Numbers and their Application in Multi-Criteria Group Decision Making Problems -- Chapter 18: Some Generalized Results on Multi-Criteria Decision Making Model using Fuzzy TOPSIS Technique -- Chapter 19: A Survey on FP-Tree Based Incremental Frequent Pattern Mining -- Chapter 20: Improving Co-expressed Gene Pattern Finding Using Gene Ontology -- Chapter 21: Survey of Methods Used for Differential Expression Analysis on RNA Seq Data -- Chapter 22: Adaptive Antenna Tilt for Cellular Coverage Optimization in Suburban Scenario -- Chapter 23: A survey of the different itemset representation for candidate.
520 3 _aThis book addresses many-criteria decision-making (MCDM), a process used to find a solution in an environment with several criteria. In many real-world problems, there are several different objectives that need to be taken into account. Solving these problems is a challenging task and requires careful consideration. In real applications, often simple and easy to understand methods are used; as a result, the solutions accepted by decision makers are not always optimal solutions. On the other hand, algorithms that would provide better outcomes are very time consuming. The greatest challenge facing researchers is how to create effective algorithms that will yield optimal solutions with low time complexity. Accordingly, many current research efforts are focused on the implementation of biologically inspired algorithms (BIAs), which are well suited to solving uni-objective problems. This book introduces readers to state-of-the-art developments in biologically inspired techniques and their applications, with a major emphasis on the MCDM process. To do so, it presents a wide range of contributions on e.g. BIAs, MCDM, nature-inspired algorithms, multi-criteria optimization, machine learning and soft computing.
988 _aPrimersemestre_2020_Robotics
650 7 _2embne
_aToma de decisiones
_vCongresos y asambleas
_9141176
650 7 _2embne
_9669177
_aIngeniería
_xProceso de datos
_vCongresos y asambleas
700 1 _aDehuri, Satchidananda
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_998235
700 1 _aMishra, Bhabani Shankar Prasad.
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_998234
700 1 _aMallick, Pradeep Kumar.
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aCho, Sung-Bae.
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aFavorskaya, Margarita N.
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783030390327
776 0 8 _iPrinted edition:
_z9783030390341
776 0 8 _iPrinted edition:
_z9783030390358
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-39033-4
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
_n0
998 _b03/2020
_dz
_eb
_zSI