Machine Learning for Disease Detection, Prediction, and Diagnosis | Challenges and Opportunities
Langue : anglais
Edité par Springer, 2025
- Livre relié
- Neuf
Vendeur : preigu, Osnabrück, Allemagnepreigu
Vendeur AbeBooks depuis 5 août 2024
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Ajouter au panierA propos de cet article
Machine Learning for Disease Detection, Prediction, and Diagnosis | Challenges and Opportunities | Tanupriya Choudhury (u. a.) | Buch | xviii | Englisch | 2025 | Springer | EAN 9789819642403 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
N° de réf. du vendeur 133437152
- Titre
- Machine Learning for Disease Detection, Prediction, and Diagnosis | Challenges and Opportunities
- Auteur
- Tanupriya Choudhury (u. a.)
- Éditeur
- Springer
- Année de publication
- 2025
- État de l'article
- Neu
- Reliure
- Buch
- Langue
- anglais
- ISBN à 10 chiffres
- 981964240X
- ISBN à 13 chiffres
- 9789819642403
- Poids de l'article
- 841 grammes
- Dimensions
- 241 x 160 x 26 mm
- Catalogues du vendeur
- Bücher
The book "Machine Learning for Disease Detection, Prediction, and Diagnosis" can be a comprehensive guide to the novel concepts, techniques, and frameworks essential for improving the viability of existing machine-learning practices. It provides an in-depth analysis of how these new technologies are helpful to detect, predict and diagnose diseases more accurately. The book covers various topics such as image classification algorithms, supervised learning methods like support vector machines (SVM), deep neural networks (DNNs), convolutional neural networks (CNNs), etc. unsupervised approaches such as clustering algorithms as well as reinforcement learning strategies.
This book is an invaluable resource for anyone interested in machine-learning applications related to disease detection or diagnosis. It explains different concepts and provides practical examples of how they can it implements using real-world data sets from medical imaging datasets or public health records databases, among others. Furthermore, it offers insights into recent advances made by researchers which have enabled automated decision-making systems based on AI models with improved accuracy over traditional methods. This text also discusses ways through which current models could improve further by incorporating domain knowledge during the model training phase, thereby increasing their efficacy even further.
Overall, this book serves as a great source of information about the latest advancements made in the field of Machine Learning & Artificial Intelligence towards efficient building systems capable enough detecting & diagnosing diseases automatically while avoiding human errors resulting due manual intervention at any stage along process pipeline thus ensuring better outcomes overall. Moreover, it helps readers understand the underlying principles behind each technique discussed so that they may apply them according to their own application scenarios efficiently without worrying much about the implementation details required to get the job done the right way the first time around itself!
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À propos de l’auteur
Dr. Katal has published extensively in reputed international journals and conferences, where her work has been recognized for its innovation and practical applications. She also serves as a reviewer for prestigious journals and conferences in her field. Her ongoing research aims to bridge the gap between theoretical advancements and their implementation in real-world cloud infrastructures, particularly in the context of scalability, reliability, and efficiency.
« A propos de ce titre » peut appartenir à une autre édition de cet ouvrage.
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