000			04356cam a2200493M 4500
001			9781003467199
003			FlBoTFG
005			20251030122251.0
006			m o d
007			cr |n|||||||||
008			240731s2024 xx o 0|| 0 eng d
040			_aOCoLC-P _beng _cOCoLC-P
020			_a9781040110492 _q(electronic bk.)
020			_a1040110495 _q(electronic bk.)
020			_a9781003467199 _q(electronic bk.)
020			_a1003467199 _q(electronic bk.)
020			_a9781040110553 _q(electronic bk. : EPUB)
020			_a104011055X _q(electronic bk. : EPUB)
020			_z1032733306
020			_z9781032733302
024	7		_a10.1201/9781003467199 _2doi
035			_a(OCoLC)1450349557
035			_a(OCoLC-P)1450349557
050		4	_aQ335
072		7	_aCOM _x094000 _2bisacsh
072		7	_aCOM _x016000 _2bisacsh
072		7	_aMED _x062000 _2bisacsh
072		7	_aUYQ _2bicssc
245	0	0	_aAPPLICATIONS OF AI FOR INTERDISCIPLINARY RESEARCH _h[electronic resource].
260			_a[S.l.] : _bCRC PRESS, _c2024.
300			_a1 online resource
520			_aApplying artificial intelligence (AI) to new fields has made AI and data science indispensable to researchers in a wide range of fields. The proliferation and successful deployment of AI algorithms are fuelling these changes, which can be seen in fields as disparate as healthcare and emerging Internet of Things (IoT) applications. Machine learning techniques, and AI more broadly, are expected to play an ever-increasing role in the modelling, simulation, and analysis of data from a wide range of fields by the interdisciplinary research community. Ideas and techniques from multidisciplinary research are being utilised to enhance AI; hence, the connection between the two fields is a two-way street at a crossroads. Algorithms for inference, sampling, and optimisation, as well as investigations into the efficacy of deep learning, frequently make use of methods and concepts from other fields of study. Cloud computing platforms may be used to develop and deploy several AI models with high computational power. The intersection between multiple fields, including math, science, and healthcare, is where the most significant theoretical and methodological problems of AI may be found. To gather, integrate, and synthesise the many results and viewpoints in the connected domains, refer to it as interdisciplinary research. In light of this, the theory, techniques, and applications of machine learning and AI, as well as how they are utilised across disciplinary boundaries, are the main areas of this research topic. This book apprises the readers about the important and cutting-edge aspects of AI applications for interdisciplinary research and guides them to apply their acquaintance in the best possible manner This book is formulated with the intent of uncovering the stakes and possibilities involved in using AI through efficient interdisciplinary applications The main objective of this book is to provide scientific and engineering research on technologies in the fields of AI and data science and how they can be related through interdisciplinary applications and similar technologies This book covers various important domains, such as healthcare, the stock market, natural language processing (NLP), real estate, data security, cloud computing, edge computing, data visualisation using cloud platforms, event management systems, IoT, the telecom sector, federated learning, and network performance optimisation. Each chapter focuses on the corresponding subject outline to offer readers a thorough grasp of the concepts and technologies connected to AI and data analytics, and their emerging applications
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aArtificial intelligence. _958441
650		0	_aResearch _xMethodology. _959814
650		7	_aCOMPUTERS / Computer Vision & Pattern Recognition _2bisacsh _992738
650		7	_aMEDICAL / Oncology _2bisacsh _964358
700	1		_aGill, Sukhpal Singh. _994908
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003467199
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942			_cE-Books
999			_c67923 _d67923