Muhammad abulaish (40 Ergebnisse)

Taschenbuch. Zustand: Neu. Ontology Engineering for Imprecise Knowledge Management | Implementations, Findings and Frameworks | Muhammad Abulaish | Taschenbuch | 224 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783838306124 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. …

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: new.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

paperback. Zustand: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: New. In English.

hardcover. Zustand: Fine.

Zustand: New. 2025. 1st Edition. hardcover. . . . . .

Zustand: New.

Zustand: New.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New. 2025. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: New. Brand New ! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

Zustand: New. 1st edition NO-PA16APR2015-KAP.

Buch. Zustand: Neu. Neuware - Apply revolutionary deep learning technology to the fast-growing field of medical image segmentation Precise medical image segmentation is rapidly becoming one of the most important tools in medical research, diagnosis, and treatment. The potential for deep learning, a technology which is already revolutionizing practice across hundreds of subfields, is immense. The prospect of using deep learning to address the traditional shortcomings of image segmentation demands close inspection and wide proliferation of relevant knowledge. Deep Learning Applications in Medical Image Segmentation meets this demand with a comprehensive introduction and its growing applications. Covering foundational concepts and its advanced techniques, it offers a one-stop resource for researchers and other readers looking for a detailed understanding of the topic. It is deeply engaged with the main challenges and recent advances in the field of deep-learning-based medical image segmentation. Readers will also find: - Analysis of deep learning models, including FCN, UNet, SegNet, Dee Lab, and many more- Detailed discussion of medical image segmentation divided by area, incorporating all major organs and organ systems- Recent deep learning advancements in segmenting brain tumors, retinal vessels, and inner ear structures- Analyzes the effectiveness of deep learning models in segmenting lung fields for respiratory disease diagnosis- Explores the application and benefits of Generative Adversarial Networks (GANs) in enhancing medical image segmentation- Identifies and discusses the key challenges faced in medical image segmentation using deep learning techniques- Provides an overview of the latest advancements, applications, and future trends in deep learning for medical image analysis Deep Learning Applications in Medical Image Segmentation is ideal for academics and researchers working with medical image segmentation, as well as professionals in medical imaging, data science, and biomedical engineering.…

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New.

Zustand: New.

Zustand: New. In English.

Hardback. Zustand: New. This book addresses social and complex network analysis challenges, exploring social network structures, dynamic networks, and hierarchical communities. Emphasizing network structure heterogeneity, including directionality and dynamics, it covers community structure concepts like distinctness, overlap, and hierarchy. The book aims to present challenges and innovative solutions in community structure detection, incorporating diversity into problem-solving. Furthermore, it explores the applications of identified community structures within network analysis, offering insights into social network dynamics.Investigates the practical applications and uses of community structures identified from network analysis across various domains of real-world networksHighlights the challenges encountered in analyzing community structures and presents state-of-the-art approaches designed to address these challengesSpans into various domains like business intelligence, marketing, and epidemics, examining influential node detection and crime within social networksExplores methodologies for evaluating the quality and accuracy of community detection modelsExamines a diverse range of challenges and offers innovative solutions in the field of detecting community structures from social networksThe book is a ready reference for researchers and scholars of Computer Science and Computational Social Systems working in the area of Community Structure Analysis from Social Network Data.…

Hardcover. Zustand: Brand New. 280 pages. 9.18x6.12x9.45 inches. In Stock.

Hardback. Zustand: New. This book addresses social and complex network analysis challenges, exploring social network structures, dynamic networks, and hierarchical communities. Emphasizing network structure heterogeneity, including directionality and dynamics, it covers community structure concepts like distinctness, overlap, and hierarchy. The book aims to present challenges and innovative solutions in community structure detection, incorporating diversity into problem-solving. Furthermore, it explores the applications of identified community structures within network analysis, offering insights into social network dynamics.Investigates the practical applications and uses of community structures identified from network analysis across various domains of real-world networksHighlights the challenges encountered in analyzing community structures and presents state-of-the-art approaches designed to address these challengesSpans into various domains like business intelligence, marketing, and epidemics, examining influential node detection and crime within social networksExplores methodologies for evaluating the quality and accuracy of community detection modelsExamines a diverse range of challenges and offers innovative solutions in the field of detecting community structures from social networksThe book is a ready reference for researchers and scholars of Computer Science and Computational Social Systems working in the area of Community Structure Analysis from Social Network Data.…