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008 230228t20212021gw fo d z eng d
020 _a9783110676112
024 7 _a10.1515/9783110676112
_2doi
035 _a(DE-B1597)536238
040 _aDE-B1597
_beng
_cDE-B1597
_erda
041 0 _aeng
044 _agw
_cDE
050 4 _aQA402.5
_b.K43 2021
072 7 _aCOM004000
_2bisacsh
082 0 4 _a519.3
_223
084 _aWC 7700
_qSEPA
_2rvk
_0(DE-625)rvk/148144:
245 0 0 _aNature-Inspired Optimization Algorithms :
_bRecent Advances in Natural Computing and Biomedical Applications /
_ced. by Aditya Khamparia, Ashish Khanna, Nhu Gia Nguyen, Bao Le Nguyen.
264 1 _aBerlin ;
_aBoston :
_bDe Gruyter,
_c[2021]
264 4 _c©2021
300 _a1 online resource (XIV, 154 p.)
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 0 _aIntelligent Biomedical Data Analysis ,
_x2629-7140 ;
_v4
505 0 0 _tFrontmatter --
_tPreface --
_tContents --
_tAbout the editors --
_tList of contributors --
_t1 Selecting and assessing the importance of malware analysis methods for web-based biomedical services through fuzzy-based decision-making procedure --
_t2 A medical intelligent system for diagnosis of chronic kidney disease using adaptive neuro-fuzzy inference system --
_t3 Contrast enhancement approach for satellite images using hybrid fusion technique and artificial bee colony optimization --
_t4 Role of intelligent IoT applications in fog computing --
_t5 Energy-efficient routing employing neural networks along with vector-based pipeline in underwater wireless sensor networks --
_t6 A review of global optimization problems using meta-heuristic algorithm --
_t7 Secure indexing and storage of big data --
_t8 Genetic algorithm and normalized text feature based document classification --
_t9 Nature-inspired optimization techniques --
_tIndex
506 0 _arestricted access
_uhttp://purl.org/coar/access_right/c_16ec
_fonline access with authorization
_2star
520 _aThis book will focus on the involvement of data mining and intelligent computing methods for recent advances in Biomedical applications and algorithms of nature-inspired computing for Biomedical systems. The proposed meta heuristic or nature-inspired techniques should be an enhanced, hybrid, adaptive or improved version of basic algorithms in terms of performance and convergence metrics. In this exciting and emerging interdisciplinary area a wide range of theory and methodologies are being investigated and developed to tackle complex and challenging problems. Today, analysis and processing of data is one of big focuses among researchers community and information society. Due to evolution and knowledge discovery of natural computing, related meta heuristic or bio-inspired algorithms have gained increasing popularity in the recent decade because of their significant potential to tackle computationally intractable optimization dilemma in medical, engineering, military, space and industry fields. The main reason behind the success rate of nature inspired algorithms is their capability to solve problems. The nature inspired optimization techniques provide adaptive computational tools for the complex optimization problems and diversified engineering applications. Tentative Table of Contents/Topic Coverage: - Neural Computation - Evolutionary Computing Methods - Neuroscience driven AI Inspired Algorithms - Biological System based algorithms - Hybrid and Intelligent Computing Algorithms - Application of Natural Computing - Review and State of art analysis of Optimization algorithms - Molecular and Quantum computing applications - Swarm Intelligence - Population based algorithm and other optimizations
530 _aIssued also in print.
538 _aMode of access: Internet via World Wide Web.
546 _aIn English.
588 0 _aDescription based on online resource; title from PDF title page (publisher's Web site, viewed 28. Feb 2023)
650 0 _aArtificial intelligence
_xMedical applications.
_94809
650 0 _aBiomedical engineering.
_93292
650 0 _aMathematical optimization.
_94112
650 0 _aNatural computation.
_910477
650 0 _aNature-inspired algorithms.
_912606
650 0 _aProgram transformation (Computer programming).
_977352
650 4 _aAlgorithmus.
_963838
650 4 _aBig Data.
_94174
650 4 _aKünstliche Intelligenz.
_975263
650 4 _aMaschinelles Lernen.
_975264
650 7 _aCOMPUTERS / Intelligence (AI) & Semantics.
_2bisacsh
_977353
700 1 _aAgrawal, Alka,
_econtributor.
_4ctb
_4https://id.loc.gov/vocabulary/relators/ctb
_977354
700 1 _aBatra, Isha,
_econtributor.
_4ctb
_4https://id.loc.gov/vocabulary/relators/ctb
_977355
700 1 _aBhardwaj, Ramakant,
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_977356
700 1 _aBhardwaj, Reeta,
_econtributor.
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_977357
700 1 _aBhushan, Bharat,
_econtributor.
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_977358
700 1 _aGoyal, Anjali,
_econtributor.
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_977359
700 1 _aHarika, Gunturu,
_econtributor.
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_977360
700 1 _aKaur, Balwinder,
_econtributor.
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_977361
700 1 _aKaur, Harpreet,
_econtributor.
_4ctb
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_977362
700 1 _aKhamparia, Aditya,
_econtributor.
_4ctb
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_977363
700 1 _aKhamparia, Aditya,
_eeditor.
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_977363
700 1 _aKhan, Raees Ahmad,
_econtributor.
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_977364
700 1 _aKhanna, Ashish,
_eeditor.
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_977365
700 1 _aKumar Mishra, Amit,
_econtributor.
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_977366
700 1 _aKumar Pandey, Abhishek,
_econtributor.
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_977367
700 1 _aKumar Singh, Sanjay,
_econtributor.
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_977368
700 1 _aKumar, Rajeev,
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_977369
700 1 _aKumari, Poonam,
_econtributor.
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_977370
700 1 _aLe Nguyen, Bao,
_eeditor.
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_977371
700 1 _aMalik, Arun,
_econtributor.
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_977372
700 1 _aMalik, Rahul,
_econtributor.
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_977373
700 1 _aMishra, D. K.,
_econtributor.
_4ctb
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_977374
700 1 _aNguyen, Nhu Gia,
_eeditor.
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_977375
700 1 _aPande, Sagar,
_econtributor.
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_977376
700 1 _aSahu, Vishal,
_econtributor.
_4ctb
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_977377
700 1 _aSharma, Vivek,
_econtributor.
_4ctb
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_977378
700 1 _aShinde, Vikas,
_econtributor.
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_977379
700 1 _aShukla, Pratyush,
_econtributor.
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_977380
700 1 _aSingla, Jimmy,
_econtributor.
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_977381
700 1 _aTripathi, Ashutosh,
_econtributor.
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_977382
773 0 8 _iTitle is part of eBook package:
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856 4 0 _uhttps://doi.org/10.1515/9783110676112
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856 4 2 _3Cover
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