Cancer Prediction for Industrial IoT 4.0 (eBook, ePUB)

A Machine Learning Perspective
Redaktion: Gupta, Meenu; Al-Turjman, Fadi; Solanki, Arun; Jain, Rachna

• Format: ePub

Produktbeschreibung

Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective explores various cancers using Artificial Intelligence techniques. It presents the rapid advancement in the existing prediction models by applying Machine Learning techniques. Several applications of Machine Learning in different cancer prediction and treatment options are discussed, including specific ideas, tools and practices most applicable to product/service development and innovation opportunities. The wide variety of topics covered offers readers multiple perspectives on various disciplines.

Features



. Covers the fundamentals, history, reality and challenges of cancer

. Presents concepts and analysis of different cancers in humans

. Discusses Machine Learning-based deep learning and data mining concepts in the prediction of cancer

. Offers real-world examples of cancer prediction

. Reviews strategies and tools used in cancer prediction

. Explores the future prospects in cancer prediction and treatment

Readers will learn the fundamental concepts and analysis of cancer prediction and treatment, including how to apply emerging technologies such as Machine Learning into practice to tackle challenges in domains/fields of cancer with real-world scenarios. Hands-on chapters contributed by academicians and other professionals from reputed organizations provide and describe frameworks, applications, best practices and case studies on emerging cancer treatment and predictions.

This book will be a vital resource to graduate students, data scientists, Machine Learning researchers, medical professionals and analytics managers.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 217
• Erscheinungstermin: 30. Dezember 2021
• Englisch
• ISBN-13: 9781000508666
• Artikelnr.: 62912000

Autorenporträt

Dr. Meenu Gupta completed her PhD in Computer Science & Engineering with emphasis on Traffic Accident Severity problem from the Ansal University, Gurugram, India (2020), an M.Tech in Computer Sceince & Engineering from the M.D. U University, Rohtak, India (2010), and she graduated in Information Technology at the K.U.K University, Kurukshetra, India (2006). She is currently Associate Professor in Chandigarh University. She has 13 years of teaching experience. Her area of research are Machine Learning, Intelligent Systems, Data mining, with specific interest in, Artificial Intelligence, Image Processing and Analysis, Smart cities, Data Analysis, and human/brain-machine interaction. She also submitted one edited book of CRC press on Healthcare. She also have 4 authored book on engineering stream. She worked as a reviewer of many journal like, Big Data, CMC, Scientific Report, TSP, etc. She is a life member of ISTE and IAENG. She has authored or co-authored over 50 papers in refereed international journals (SCI/SCIE/WoS/Scopus/etc.), conferences, and more than 20 book chapters. She also chair IEEE international Conference and convened many workshop/FDP. Dr. Rachna Jain, currently working as Assistant Professor(Computer Science Department) in Bharati Vidyapeeth's College of Engineering (GGSIPU) since 2007.She did her PHD from Banasthali Vidyapith in Computer Science in 2017.She received ME degree in year 2011 from Delhi college of engineering (Delhi University) with specialisation in Computer Technology and Applications. She did her B.tech (Computer Science) in 2006 from N.C College of Engineering, Kurukshetra University. Her current research interests are Cloud Computing, Fuzzy Logic, Network and information security, Swarm Intelligence, Big Data and IoT, Deep Learning and Machine Learning. She has contributed with more than 10 book chapters in various books . She has also served as Session Chair in various International Conferences. She is CO-PI of DST Project titled "Design an autonomous intelligent drone for city surveillance".A total of 14+ Years of Academic / Research Experience with more than 50+ Publications in various National, International Conferences cum International Journals(Scopus/ISI/SCI) of High Repute. Dr. Arun Solanki is working as Assistant Professor in the Department of Computer Science and Engineering, Gautam Buddha University, Greater Noida, India where he has been working since 2009. He has worked as Time Table Coordinator, member Examination, Admission, Sports Council, Digital Information Cell, and other university teams from time to time. He has received M.Tech. Degree in Computer Engineering from YMCA University, Faridabad, Haryana, India. He has received his Ph.D. in Computer Science and Engineering from Gautam Buddha University in 2014. He has supervised more than 60 M.Tech. dissertations under his guidance. His research interests span Expert System, Machine Learning, and Search Engines. He has published many research articles in SCI/ Scopus indexed International journals/conferences like IEEE, Elsevier, Springer, etc. He has participated in many international conferences. He has been a technical and advisory committee member of many conferences. He has organized several FDP, Conferences, Workshops, and Seminars. He has chaired many sessions at International Conferences. Arun Solanki is working as Associate Editor in International Journal of Web-Based Learning and Teaching Technologies (IJWLTT)" IGI publisher. He has been working as Guest Editor for special issues in Recent Patents on Computer Science, Bentham Science Publishers. Arun Solanki is the editor of many Books with a reputed publisher like IGI Global, CRC and AAP. He is working as the reviewer in Springer, IGI Global, Elsevier, and other reputed publisher journals. Prof. Dr. Fadi Al-Turjman received his Ph.D. in computer science from Queen's University, Canada, in 2011. He is the associate dean for research and the founding director of the International Research Center for AI and IoT at Near East University, Nicosia, Cyprus. Prof. Al-Turjman is the head of Artificial Intelligence Engineering Dept., and a leading authority in the areas of smart/intelligent IoT systems, wireless, and mobile networks' architectures, protocols, deployments, and performance evaluation in Artificial Intelligence of Things (AIoT). His publication history spans over 350 SCI/E publications, in addition to numerous keynotes and plenary talks at flagship venues. He has authored and edited more than 40 books about cognition, security, and wireless sensor networks' deployments in smart IoT environments, which have been published by well-reputed publishers such as Taylor and Francis, Elsevier, IET, and Springer. He has received several recognitions and best papers' awards at top international conferences. He also received the prestigious Best Research Paper Award from Elsevier Computer Communications Journal for the period 2015-2018, in addition to the Top Researcher Award for 2018 at Antalya Bilim University, Turkey. Prof. Al-Turjman has led a number of international symposia and workshops in flagship communication society conferences. Currently, he serves as book series editor and the lead guest/associate editor for several top tier journals, including the IEEE Communications Surveys and Tutorials (IF 23.9) and the Elsevier Sustainable Cities and Society (IF 5.7), in addition to organizing international conferences and symposiums on the most up to date research topics in AI and IoT.

Inhaltsangabe

1. Investigation of IOMT based Cancer Detection and Prediction. 2.
Histopathological Cancer Detection using CNN. 3. Role of Histone
Methyltransferase In Breast Cancer. 4. Breast Cancer Detection Using
Machine Learning and Its Classification. 5. Diagnosis & Prediction of Type-
2 Chronic Kidney Disease Using Machine Learning Approaches. 6. Behavioural
Prediction of Cancer using Machine Learning. 7. Prediction of cervical
cancer using machine learningAshish Kumar. 8. Applications of Machine
Learning in Cancer prediction and Prognosis. 9. Significant advancements in
cancer diagnosis using machine learning. 10. Human papillomavirus and
cervical cancer. 11. Case Studies/ Success Stories on Machine Learning and
Data Mining for Cancer Prediction