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E-raamat: Machine Learning Algorithms and Applications in Engineering [Taylor & Francis e-raamat]

Edited by , Edited by , Edited by (Universidad de Malaga, Departamento de Lengujes y Ciencias de la Computación, Spain), Edited by
  • Formaat: 314 pages, 20 Tables, black and white; 63 Line drawings, black and white; 10 Halftones, black and white; 73 Illustrations, black and white
  • Sari: Smart and Intelligent Computing in Engineering
  • Ilmumisaeg: 28-Feb-2023
  • Kirjastus: CRC Press
  • ISBN-13: 9781003104858
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  • Taylor & Francis e-raamat
  • Hind: 170,80 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 244,00 €
  • Säästad 30%
Machine Learning Algorithms and Applications in EngineeringSuurem pilt
  • Formaat: 314 pages, 20 Tables, black and white; 63 Line drawings, black and white; 10 Halftones, black and white; 73 Illustrations, black and white
  • Sari: Smart and Intelligent Computing in Engineering
  • Ilmumisaeg: 28-Feb-2023
  • Kirjastus: CRC Press
  • ISBN-13: 9781003104858
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781003104858
"Machine Learning (ML) is a sub field of artificial intelligence that uses soft computing and algorithms to enable computers to learn on their own and identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models. This book discusses various applications of ML in engineering fields and the use of ML algorithms in solving challenging engineering problems ranging from biomedical, transport, supply chain and logistics, to manufacturing and industrial. Through numerous case studies, it will assist researchers and practitioners in selecting the correct options and strategies for managing organizational tasks"--

Discusses various applications of ML in engineering fields and the use of ML algorithms in solving challenging engineering problems ranging from biomedical to manufacturing and industrial. Through numerous case studies, it will assist researchers and practitioners in selecting the correct options and strategies for managing organizational tasks.

Machine Learning (ML) is a sub field of artificial intelligence that uses soft computing and algorithms to enable computers to learn on their own and identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models. This book discusses various applications of ML in engineering fields and the use of ML algorithms in solving challenging engineering problems ranging from biomedical, transport, supply chain and logistics, to manufacturing and industrial. Through numerous case studies, it will assist researchers and practitioners in selecting the correct options and strategies for managing organizational tasks.
Preface vii
Organization of the Book ix
The Editors xiii
1 Machine Learning for Smart Health Care
1(16)
Rehab A. Rayan
2 Predictive Analysis for Flood Risk Mapping Utilizing Machine Learning Approach
17(18)
Aditya Singh
Sunil Khatri
Sandhya Save
Hemant Kasturiwale
3 Machine Learning for Risk Analysis
35(20)
Parita Jain
Puneet Kumar Aggarwal
Kshirja Makar
Riya Garg
Jaya Mehta
Poorvi Chaudhary
4 Machine Learning Techniques Enabled Electric Vehicle
55(18)
Shyamalagowri Murugesan
Revathy Jayabaskar
5 A Comparative Analysis of Established Techniques and Their Applications in the Field of Gesture Detection
73(20)
Muskan Jindal
Eshan Bajal
Shilpi Sharma
6 Brain-Computer Interface for Dream Visualization using Deep Learning
93(24)
Brijesh K. Soni
Akhilesh A. Waoo
7 Machine Learning and Data Analysis Based Breast Cancer Classification
117(14)
Souvik Das
Rama Chaitanya Karanam
Obilisetty Bala Krishna
Jhareswar Maiti
8 Accurate Automatic Functional Recognition of Proteins: Overview and Current Computational Challenges
131(10)
Javier Perez-Rodriguez
Morteza Yazdani
Prasenjit Chatterjee
9 Taxonomy of Shilling Attack Detection Techniques in Recommender System
141(20)
Abhishek Majumder
Keya Chowdhury
Joy Lal Sarkar
10 Machine Learning Applications in Real-World Time Series Problems
161(16)
Antonio Manuel Durdn-Rosal
David Guijo-Rubio
11 Prediction of Selective Laser Sintering Part Quality Using Deep Learning
177(18)
Lokesh Kumar Saxena
Pramod Kumar Jain
12 CBPP: An Efficient Algorithm for Privacy-Preserving Data Publishing of 1:M Micro Data with Multiple Sensitive Attributes
195(18)
Jayapradha Jayaram
Prakash Manickam
Apoorva Gupta
Madhuri Rudrabhatla
13 Classification of Network Traffic on ISP Link and Analysis of Network Bandwidth during COVID-19
213(26)
V. Ajantha Devi
Yogendra Malgundkar
Bandana Mahapatra
14 Integration of AI/MI in 5G Technology toward Intelligent Connectivity, Security, and Challenges
239(16)
Devasis Pradhan
Prasanna Kumar Sahu
Rajeswari
Hla Myo Tun
Naw Khu Say Wah
15 Electrical Price Prediction using Machine Learning Algorithms
255(16)
Swastik Mishra
Kanika Prasad
Anand Mukut Tigga
16 Machine Learning Application to Predict the Degradation Rate of Biomedical Implants
271(12)
Pradeep Bedi
Shyam Bihari Goyal
Prasenjit Chatterjee
Jugnesh Kumar
17 Predicting the Outcomes of Myocardial Infarction Using Neural Decision Forest
283(14)
Akashdeep Singh Chaudhary
Ravinder Saini
18 Image Classification Using Contrastive Learning
297(18)
Abhyuday Trivedi
Anjali Hembrom
Arkajit Saha
Tahreem Fatima
Shreya Dey
Monideepa Roy
Sujoy Datta
Index 315
Prasenjit Chatterjee is an Associate Professor of Mechanical Engineering Department at MCKV Institute of Engineering, India. He has published over 80 research papers in various international journals and has received numerous awards including Outstanding Researcher Award and University Gold Medal. He has been the Guest Editor of several special issues and has edited and authored several books on decision-making approaches and sustainability. He is the Lead Series Editor of International Perspectives on Decision Analysis and Operations Research, Emerald Group Publishing. Dr. Chatterjee is one of the developers of a new data-driven multiple-criteria decision-making method called Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS).

Morteza Yazdani works at Universidad Loyola Andalucia, Spain. Previously he finished his post-doctoral research at the University of Toulouse and was a lecturer the European University of Madrid. He participates in the editorial board of the International Journal of Decision Support System Technology and is a reviewer in different journals. His main research areas are decision-making modelling and fuzzy decision system in application of supply chain and energy systems and has published several journal articles.

Francisco de Asís Fernández Navarro has earned his PhD in Computer Science and Artificial Intelligence from the University of Malaga, Spain. He also obtained a degree in Market Research from the Open University of Catalonia (UOC). He was awarded at the European Space Agency Noordwijk, The Netherlands with a postdoctoral fellowship in computational management and currently works as an Associate Professor at the Loyola University of Andalusia, Department of Quantitative Methods.

Javier Pérez-Rodríguez earned his PhD in ICT from the University of Granada, Spain. In 2018 he joined the Department of Quantitative Methods at the University of Loyola, Andalucía as an Associate Professor. His research is focused on Computer Science and Artificial Intelligence and Bioinformatics. Within the area of machine learning, specifically, his works have been about pattern recognition and classification and has published several papers in reputable journals. His residency at the Institut für Mathematik und Informatik of the University of Greifswald, Germany was with Professor Stanke, who develops and maintains one of the most prestigious automatic gene recognition systems at present at an international level.
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