Novel Hybrid Intelligence Techniques in Engineering

doi:10.3390/books978-3-0365-7107-2

dc.contributor.editor	Armaghani, Danial Jahed
dc.contributor.editor	Zhang, Yixia
dc.contributor.editor	Samui, Pijush
dc.contributor.editor	Elshafie, Ahmed Hussein Kamel Ahmed
dc.contributor.editor	Azizi, Aydin
dc.date.accessioned	2023-05-11T17:15:58Z
dc.date.available	2023-05-11T17:15:58Z
dc.date.issued	2023
dc.identifier	ONIX_20230511_9783036571065_15
dc.identifier.uri	https://directory.doabooks.org/handle/20.500.12854/99998
dc.description.abstract	The focus of this reprint is the development of novel intelligence techniques for solving various problems in engineering. These techniques, due to their ability to create complex relationships between dependent and independent variables, can be implemented in a faster and more reliable way. Such techniques utilise algorithms/approaches such as artificial neural networks, fuzzy logic, evolutionary theory, learning theory, and probabilistic theory, making them a suitable and useful fit for real-life complex problems. This reprint introduces the process of selecting, applying, and developing such techniques in different engineering designs and applications. In addition, the validation process of intelligence systems as an alternative is discussed in this reprint. Overall, this reprint forms an excellent introduction to these systems for engineers who are not familiar with them.
dc.language	English
dc.subject.classification	thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries	en_US
dc.subject.other	confinement of concrete
dc.subject.other	CFST composite column
dc.subject.other	artificial intelligence
dc.subject.other	gene-expression programming
dc.subject.other	hybrid techniques
dc.subject.other	finite element method (FEM)
dc.subject.other	imbalanced data
dc.subject.other	travel mode choice data
dc.subject.other	hybrid support vector machine-based model
dc.subject.other	rock excavation
dc.subject.other	soft computing
dc.subject.other	cutter life index
dc.subject.other	rock strength
dc.subject.other	brittleness
dc.subject.other	classification
dc.subject.other	slope stability
dc.subject.other	tree-based models
dc.subject.other	random forest
dc.subject.other	AdaBoost
dc.subject.other	decision tree
dc.subject.other	3D bridge model
dc.subject.other	IFC-based bridge model
dc.subject.other	engineering document
dc.subject.other	document fragment
dc.subject.other	integrated operation
dc.subject.other	granular model
dc.subject.other	incremental granular model
dc.subject.other	interval-based fuzzy c-means clustering
dc.subject.other	coverage
dc.subject.other	specificity
dc.subject.other	performance index
dc.subject.other	piezocone
dc.subject.other	soil classification
dc.subject.other	fuzzy C-means clustering
dc.subject.other	neuro-fuzzy
dc.subject.other	scratch-resistant
dc.subject.other	hydrophobic
dc.subject.other	GPTES
dc.subject.other	transparent
dc.subject.other	sol–gel
dc.subject.other	blasting
dc.subject.other	ground vibration
dc.subject.other	PPV prediction
dc.subject.other	whale optimization algorithm
dc.subject.other	salp swarm optimizer
dc.subject.other	spread foundation
dc.subject.other	retaining structures
dc.subject.other	economic design
dc.subject.other	rock brittleness
dc.subject.other	linear genetic programming
dc.subject.other	bagged regression tree
dc.subject.other	lazy machine learning method
dc.subject.other	SCC
dc.subject.other	compressive strength
dc.subject.other	fly ash
dc.subject.other	statistical analysis
dc.subject.other	modeling
dc.subject.other	blockchain technology
dc.subject.other	intelligent technology
dc.subject.other	internet of vehicles
dc.subject.other	malicious nodes
dc.subject.other	identification algorithm
dc.subject.other	inverse analysis
dc.subject.other	hydraulic conductivities
dc.subject.other	Gray Wolf Optimizer
dc.subject.other	thermal conductivity
dc.subject.other	geothermal systems
dc.subject.other	gene expression programming (GEP)
dc.subject.other	non-linear multivariable regression (NLMR)
dc.subject.other	P-wave
dc.subject.other	porosity
dc.subject.other	backpropagation neural network
dc.subject.other	blast-induced ground vibration
dc.subject.other	Gaussian process regression
dc.subject.other	green campus
dc.subject.other	shared free-floating electric scooter
dc.subject.other	usage frequency prediction
dc.subject.other	battery electric
dc.subject.other	battery pack
dc.subject.other	energy performance
dc.subject.other	simulation
dc.subject.other	second life batteries
dc.subject.other	off-grid PV system
dc.subject.other	residential building
dc.subject.other	EV charging station
dc.subject.other	optimization
dc.subject.other	metaheuristic algorithms
dc.subject.other	streamflow forecasting
dc.subject.other	concrete
dc.subject.other	water-reducer contents
dc.subject.other	workability
dc.subject.other	slump retention
dc.subject.other	conceptual framework
dc.subject.other	crowd-machine hybrid interaction
dc.subject.other	design implications
dc.subject.other	hybrid intelligence
dc.subject.other	survey
dc.subject.other	taxonomy
dc.title	Novel Hybrid Intelligence Techniques in Engineering
dc.type	book
oapen.identifier.doi	10.3390/books978-3-0365-7107-2
oapen.relation.isPublishedBy	46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn	9783036571065
oapen.relation.isbn	9783036571072
oapen.pages	456
oapen.place.publication	Basel

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