Machine learning disease detection (25 Ergebnisse)

Paperback. Zustand: Brand New. 48 pages. 6.00x0.11x9.00 inches. In Stock.

Paperback. Zustand: Brand New. 102 pages. 6.00x0.23x9.00 inches. In Stock.

Zustand: New.

Taschenbuch. Zustand: Neu. DETECTION OF AUTISM DISEASE USING MACHINE LEARNING ALGORITHMS | Autism Disease Detection | Nirmalakumari K (u. a.) | Taschenbuch | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786203025378 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Taschenbuch. Zustand: Neu. Early Detection of Disease in Tomatoes | Using Artificial Intelligence and Machine Learning | Azeezat Ojoogun | Taschenbuch | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786208011864 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Taschenbuch. Zustand: Neu. Image-Based Plant Disease Detection | A Machine Learning Perspective | Jayapriya Palanisamy | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208429010 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.…

Taschenbuch. Zustand: Neu. Application of Machine Learning for Detection of Heart Disease | S. Ramacharan | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206740513 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Taschenbuch. Zustand: Neu. Machine Learning based Analysis and Detection of Malaria Disease | Malaria Disease Detection | Samir Kumar Bandyopadhyay | Taschenbuch | 172 S. | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202524308 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Paperback. Zustand: Brand New. 250 pages. 6.14x0.59x9.21 inches. In Stock.

Zustand: New. Dr. Balasubramaniam S (IEEE Senior Member) is working as an Assistant Professor in School of Computer Science and Engineering, Kerala University of Digital Sciences, Innovation and Technology (Formerly IIITM-K), Digital University Kerala, Thiruvan.

Taschenbuch. Zustand: Neu. Neuware - Currently, computational intelligence approaches are utilised in various science and engineering applications to analyse information, make decisions, and achieve optimisation goals. Over the past few decades, various techniques and algorithms have been created in disciplines such as genetic algorithms, artificial neural networks, evolutionary algorithms, and fuzzy algorithms. In the coming years, intelligent optimisation algorithms are anticipated to become more efficient in addressing various issues in engineering, scientific, medical, space, and artificial satellite fields, particularly in early disease diagnosis. A metaheuristic in computer science is designed to discover optimisation algorithms capable of solving intricate issues. Metaheuristics are optimisation algorithms that mimic biological behaviours of animals or birds and are utilised to discover the best solution for a certain problem. A meta-heuristic is an advanced approach used by heuristics to tackle intricate optimisation problems. A metaheuristic in mathematical programming is a method that seeks a solution to an optimisation problem. Metaheuristics utilise a heuristic function to assist in the search process. Heuristic search can be categorised as blind search or informed search. Meta-heuristic optimisation algorithms are gaining popularity in various applications due to their simplicity, independence from data trends, ability to find optimal solutions, and versatility across different fields.Recently, many nature-inspired computation algorithms have been utilised to diagnose people with different diseases. Nature-inspired methodologies are now widely utilised across several fields for tasks such as data analysis, decision-making, and optimisation. Techniques inspired by nature are categorised as either biology-based or natural phenomena-based. Bioinspired computing encompasses various topics in computer science, mathematics, and biology in recent years. Bio-inspired computer optimisation algorithms are a developing method that utilises concepts and inspiration from biological development to create new and resilient competitive strategies. Bio-inspired optimisation algorithms have gained recognition in machine learning and deep learning for solving complicated issues in science and engineering. Utilising BIAs learning methods with machine learning and deep learning shows great promise for accurately classifying medical conditions.This book explores the historical development of bio-inspired algorithms and their application in machine learning and deep learning models for disease diagnosis, including COVID-19, heart diseases, cancer, diabetes and some other diseases. It discusses the advantages of using bio-inspired algorithms in disease diagnosis and concludes with research directions and future prospects in this field.…

Zustand: New.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Zustand: New.

Buch. Zustand: Neu. 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.…

Buch. Zustand: Neu. Neuware - 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.…

Zustand: New. Prof. (Dr.) Nebojsa Bacanin obtained a PhD in 2015 from the Faculty of Mathematics, University of Belgrade, Serbia (in Computer Science, average grade 10,00). He started his university career in Serbia 18 years ago at the Graduate School of Comput.

Zustand: New.

Hardcover. Zustand: Brand New. 542 pages. 6.69x1.25x9.61 inches. In Stock.

Buch. Zustand: Neu. Machine Learning for Medical Applications | Volume II: Computer Vision, Image Processing, Disease Detection | Ranjith Rajamanickam (u. a.) | Buch | XII | Englisch | 2025 | De Gruyter | EAN 9783119147828 | Verantwortliche Person für die EU: Walter de Gruyter GmbH, De Gruyter GmbH, Genthiner Str. 13, 10785 Berlin, productsafety[at]degruyterbrill[dot]com | Anbieter: preigu. …

Buch. Zustand: Neu. Neuware - Machine Learning for Medical Applications - Volume II delves into the intersection of artificial intelligence, computer vision, and healthcare, offering a comprehensive exploration of how machine learning is revolutionizing disease detection and diagnostics. With a focus on deep learning methods, the volume covers a wide spectrum of innovations including medical image segmentation, predictive modeling, tissue engineering, smart biomaterials, and personalized implant design through 3D printing. Contributors from academia and industry present state-of-the-art applications involving quantum dot functionalization, AI-enhanced diagnostic materials, and real-time image analysis. Each chapter provides both foundational knowledge and practical insight into how advanced algorithms can drive medical breakthroughs. Ideal for medical technologists, data scientists, biomedical engineers, and clinical practitioners, this volume emphasizes the role of machine learning in developing faster, smarter, and more accurate diagnostic tools for the next generation of personalized medicine.…

Buch. Zustand: Neu. Neuware - The book explores how machine learning (ML), deep learning (DL), and metaheuristic optimization techniques can revolutionize plant disease detection and agricultural intelligence. Unlike traditional agronomic approaches, this book bridges advanced computational methods with real-world agricultural needs. It emphasizes both the scientific and practical dimensions-focusing on image-based disease detection, sensor data interpretation, optimization of predictive models, and real-world deployment strategies. The book looks at the subject from a technology, biological & agricultural, computational optimization, practical and social impact perspective.This edited book is designed for researchers, academicians, and professionals working in Artificial Intelligence, Machine Learning, Deep Learning, Metaheuristics, and agricultural sciences. It will also benefit agricultural engineers, data scientists, agritech industries, policymakers, and undergraduate, postgraduate, and doctoral students interested in AI-driven plant disease detection and smart agriculture applications.…

Zustand: New. In English.

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

Hardcover. Zustand: Brand New. 260 pages. 9.18x6.12x9.21 inches. In Stock.