Cantitate/Preț
Produs

Prognostic Models in Healthcare: AI and Statistical Approaches (Studies in Big Data, nr. 109)

Editat de Tanzila Saba, Amjad Rehman, Sudipta Roy
Notă GoodReads:
en Limba Engleză Hardback – 07 Jul 2022
This book focuses on contemporary technologies and research in computational intelligence that has reached the practical level and is now accessible in preclinical and clinical settings. This book's principal objective is to thoroughly understand significant technological breakthroughs and research results in predictive modeling in healthcare imaging and data analysis. Machine learning and deep learning could be used to fully automate the diagnosis and prognosis of patients in medical fields. The healthcare industry's emphasis has evolved from a clinical-centric to a patient-centric model. However, it is still facing several technical, computational, and ethical challenges. Big data analytics in health care is becoming a revolution in technical as well as societal well-being viewpoints.  Moreover, in this age of big data, there is increased access to massive amounts of regularly gathered data from the healthcare industry that has necessitated the development of predictive models and automated solutions for the early identification of critical and chronic illnesses. The book contains high-quality, original work that will assist readers in realizing novel applications and contexts for deep learning architectures and algorithms, making it an indispensable reference guide for academic researchers, professionals, industrial software engineers, and innovative model developers in healthcare industry.
Citește tot Restrânge

Din seria Studies in Big Data

Preț: 99090 lei

Preț vechi: 123862 lei
-20%

Puncte Express: 1486

Preț estimativ în valută:
19187 20150$ 16550£

Carte tipărită la comandă

Livrare economică 26 ianuarie-09 februarie 23
Livrare express 20-28 decembrie pentru 75155 lei

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9789811920561
ISBN-10: 9811920567
Ilustrații: XXII, 504 p. 211 illus., 161 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.91 kg
Ediția: 1st ed. 2022
Editura: Springer Nature Singapore
Colecția Springer
Seria Studies in Big Data

Locul publicării: Singapore, Singapore

Cuprins

​Segmentation of White Blood Cells in Acute Myeloid Leukaemia Microscopic Images: The Current Challenges and Future Solutions.- Computer Vision Based Prognostic Modeling of COVID-19 from Medical Imaging.- Skin Lesion Classification From Dermoscopic Images with Deep Residual Network based Fused Pigmented Deep Feature Extraction and Entropy Based Best Features Selection Approach.- Computer Vision Technologies for COVID-19 Prediction, Diagnosis and Prevention.- Health monitoring methods in heart diseases based on data mining approach, a directional survey.- Machine learning based brain diseases diagnosing in electroencephalogram signals, Alzheimer and Parkinson's.- Skin Lesion Detection Using Recent Machine Learning Approaches.- Improving monitoring and controling parameters for Alzheimer's patients based on IoT.-  A Novel Method for Lung Segmentation of Chest with Convolutional Neural Network.- Leukemia Detection Using Machine and Deep Learning Through Microscopic Images-A Review.

Notă biografică

Prof. Tanzila Saba earned her Ph.D. in document information security and management from the Faculty of Computing, Universiti Teknologi Malaysia (UTM), Malaysia, in 2012. She won the best student award in the Faculty of Computing UTM for 2012. Currently, she serves as Research Professor and Associate Chair of the Information Systems Department in the College of Computer and Information Sciences, Prince Sultan University, Riyadh, KSA. Her primary research focus in recent years is medical imaging, pattern recognition, data mining, MRI analysis, and soft computing. She led more than fifteen research-funded projects. She has above two hundred research publications that have around 7376 citations with h-index 53. Her most publications are in biomedical research published in ISI/SCIE indexed. Due to her excellent research achievement, she is included in Marquis Who’s Who (S & T) 2012. She is Editor and Reviewer of reputed journals and on the panel of TPC of international conferences. She has full command of various subjects and taught several courses at the graduate and postgraduate levels. On the accreditation side, she is a skilled lady with ABET & NCAAA quality assurance. She is Senior Member of IEEE. Dr. Tanzila is Leader of Artificial Intelligence & Data Analytics Research Lab at PSU and Active Professional Member of ACM, AIS, and IAENG organizations. She is PSU WiDS (Women in Data Science) Ambassador at Stanford University.

Dr. Amjad Rehman is Senior Researcher in the Artificial Intelligence & Data Analytics Lab CCIS, Prince Sultan University, Riyadh, Saudi Arabia. He received his Ph.D. & postdoc from Faculty of Computing Universiti Teknologi Malaysia with a specialization in Forensic Documents Analysis and Security with honor in 2010 and 2011, respectively. He received rector award for 2010 best student in the university.  Currently, he is PI in several funded projects and also completed projects funded from MOHE, Malaysia, and Saudi Arabia. His keen interests are in data mining, health informatics, and pattern recognition. He is Author of more than 200 ISI journal papers and conferences and is Senior Member of IEEE.

Dr. Sudipta Roy is working as Assistant Professor in Artificial Intelligence & Data Science department at JIO institute, Navi Mumbai. Prior to that, he was Postdoctoral Research Associate at Washington University in St. Louis, MO, USA. He has received his Ph.D. in Computer Science & Engineering from the Department of Computer Science and Engineering, University of Calcutta. He is Author of more than 50 publications in refereed international journals and conferences including IEEE, Springer, Elsevier, and many others. He has authored/edited four books and many chapters. He holds a US patent in medical image processing and filed an Indian patent in the smart agricultural system. He has served as Regular Reviewer of many international journals including IEEE, Springer, Elsevier, IET, and many others, and international conferences. He has served as International Advisory Committee Member and Program Committee Member of INDIAcom-2020, AICAE-2019, INDIACom-2019, CAAI 2018, ICAITA-2018, ICSESS-2018, INDIACom-2018, ISICO-2017, AICE-2017, and many more conferences. Currently, he is serving as Associate Editor of IEEE Access (IEEE), International Journal of Computer Vision and Image Processing (IJCVIP-IGI Global), and Topic Editor of Journal of Imaging (MDPI). In recognition of his exceptional contributions to the IEEE access journal as Associate Editor, the IEEE Access Editorial Board and Editorial Office honored him as Outstanding Associate Editor of 2020. He has more than five years of experience in teaching and research. His fields of research interests are biomedical image analysis, image processing, steganography, artificial intelligence, big data analysis, machine learning, and big data technologies.

Textul de pe ultima copertă

This book focuses on contemporary technologies and research in computational intelligence that has reached the practical level and is now accessible in preclinical and clinical settings. This book's principal objective is to thoroughly understand significant technological breakthroughs and research results in predictive modeling in healthcare imaging and data analysis. Machine learning and deep learning could be used to fully automate the diagnosis and prognosis of patients in medical fields. The healthcare industry's emphasis has evolved from a clinical-centric to a patient-centric model. However, it is still facing several technical, computational, and ethical challenges. Big data analytics in health care is becoming a revolution in technical as well as societal well-being viewpoints.  Moreover, in this age of big data, there is increased access to massive amounts of regularly gathered data from the healthcare industry that has necessitated the development of predictive models and automated solutions for the early identification of critical and chronic illnesses. The book contains high-quality, original work that will assist readers in realizing novel applications and contexts for deep learning architectures and algorithms, making it an indispensable reference guide for academic researchers, professionals, industrial software engineers, and innovative model developers in healthcare industry.

Caracteristici

Presents significant technological breakthroughs and research results in predictive modeling
Contains high-quality, original work that will assist readers in realizing novel applications and contexts
Serves as a reference resource for researchers and practitioners in academia and healthcare industry