LEADER 02362nam 2200433 450 001 9910583485003321 005 20211119154052.0 010 $a0-12-811478-9 035 $a(CKB)4100000005249353 035 $a(MiAaPQ)EBC5471101 035 $a(PPN)233366210 035 $a(EXLCZ)994100000005249353 100 $a20180801d2018 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSmart, resilient and transition cities $eemerging approaches and tools for a climate-sensitive urban development /$fAdriana Galderisi, Angela Colucci 205 $aFirst edition. 210 1$aCambridge, Massachusetts :$cElsevier,$d[2018] 210 4$dİ2018 215 $a1 online resource (322 pages) 311 $a0-12-811477-0 330 $aFocuses on the need for enhancing cities' capacities to cope with the multiple and heterogeneous challenges threatening contemporary cities and their future development and, above all, with climate issues. The authors provide an overview of current large-scale and urban strategies to counterbalance climate change so far undertaken in different geographical contexts (Europe, United States, China, Africa and Australia), shedding light on the different approaches, on the different weights assigned to mitigation and adaptation issues as well as on the main barriers hindering their effectiveness and translation into measurable outcomes. Opportunities and criticalities arising from the rich, 'sprawled' and 'blurred' landscape of current strategies and initiatives in the face of climate change pave the way to a discussion on the lessons learned from current initiatives and provide new hints for developing integrated climate strategies, capable to guide planners and decision makers towards a climate sensitive urban development. 606 $aCity planning$xEnvironmental aspects 606 $aSustainable development 615 0$aCity planning$xEnvironmental aspects. 615 0$aSustainable development. 676 $a307.1216 700 $aGalderisi$b Adriana$035305 702 $aColucci$b Angela 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910583485003321 996 $aSmart, resilient and transition cities$92160287 997 $aUNINA LEADER 03905nam 22006015 450 001 9910254346103321 005 20250609110057.0 010 $a3-319-54597-3 024 7 $a10.1007/978-3-319-54597-4 035 $a(CKB)3710000001124852 035 $a(DE-He213)978-3-319-54597-4 035 $a(MiAaPQ)EBC4830420 035 $a(PPN)199767823 035 $a(MiAaPQ)EBC6242025 035 $a(EXLCZ)993710000001124852 100 $a20170325d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTime-series prediction and applications $ea machine intelligence approach /$fby Amit Konar, Diptendu Bhattacharya 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XVIII, 242 p. 69 illus., 13 illus. in color.) 225 1 $aIntelligent Systems Reference Library,$x1868-4394 ;$v127 311 08$a3-319-54596-5 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aAn Introduction to Time-Series Prediction -- Prediction Using Self-Adaptive Interval Type-2 Fuzzy Sets -- Handling Multiple Factors in the Antecedent of Type-2 Fuzzy Rules -- Learning Structures in an Economic Time-Series for Forecasting Applications -- Grouping of First-Order Transition Rules for Time-Series Prediction by Fuzzy-induced Neural Regression -- Conclusions and Future Directions. . 330 $aThis book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at the end of each chapter to the readers? ability and understanding of the topics covered. 410 0$aIntelligent Systems Reference Library,$x1868-4394 ;$v127 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputer science$xMathematics 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputational Mathematics and Numerical Analysis$3https://scigraph.springernature.com/ontologies/product-market-codes/M1400X 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aComputer science$xMathematics. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aComputational Mathematics and Numerical Analysis. 676 $a519.55 700 $aKonar$b Amit$4aut$4http://id.loc.gov/vocabulary/relators/aut$0542703 702 $aBhattacharya$b Diptendu$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254346103321 996 $aTime-Series Prediction and Applications$92188116 997 $aUNINA