Artificial Intelligence and Cloud Computing Applications in Biomedical Engineering
暫譯: 生物醫學工程中的人工智慧與雲端運算應用

H. S., Madhusudhan, Gupta, Punit, Rawat, Pradeep Singh

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商品描述

Biomedical engineering is undergoing a transformation because of AI, which is allowing creative solutions that enhance patient outcomes, diagnosis, treatment planning, and healthcare delivery. Artificial Intelligence and Cloud Computing Applications in Biomedical Engineering examines the salient characteristics of AI in biomedical engineering, highlighting its practical applications and new directions. Highlights of the book include:

  • Genome sequence and visualization
  • The role of AI and cloud in detection of diseases
  • Nature-inspired algorithms for disease detection
  • Frameworks for disease classification

With a focus on designing AI techniques for disease detection, the book explores the role of AI in biomedical engineering. It discusses how machine learning (ML) and deep learning (DL) are at the heart of AI applications in biomedical engineering. ML algorithms, particularly those based on neural networks, enable computers to learn from large datasets, identify patterns, and make predictions or decisions without explicit programming, and implementing ML algorithms is a focus of the book. Another focus is on DL, a subset of ML, and how it uses multi-layered neural networks to achieve high accuracy in such complex tasks as image and speech recognition. Biomedical engineering generates massive amounts of data from medical imaging, genomic sequencing, wearable devices, electronic health records (EHR), and other sources. This book also discusses AI-driven big data analytics, which allows researchers and clinicians to derive from data meaningful insights, aiding in early disease detection, personalized treatment plans, and patient monitoring.

商品描述(中文翻譯)

生物醫學工程正因為人工智慧(AI)的發展而經歷轉型,這使得創新的解決方案能夠提升病人的治療效果、診斷、治療計劃及醫療服務的交付。《人工智慧與雲端計算在生物醫學工程中的應用》探討了AI在生物醫學工程中的顯著特徵,強調其實際應用及新方向。這本書的重點包括:

- 基因組序列與可視化
- AI與雲端在疾病檢測中的角色
- 受自然啟發的算法用於疾病檢測
- 疾病分類的框架

本書專注於設計用於疾病檢測的AI技術,探討AI在生物醫學工程中的角色。它討論了機器學習(ML)和深度學習(DL)如何成為生物醫學工程中AI應用的核心。ML算法,特別是基於神經網絡的算法,使計算機能夠從大型數據集中學習、識別模式,並在沒有明確編程的情況下做出預測或決策,而實施ML算法是本書的一個重點。另一個重點是DL,作為ML的一個子集,如何利用多層神經網絡在圖像和語音識別等複雜任務中實現高準確度。生物醫學工程從醫學影像、基因組測序、可穿戴設備、電子健康紀錄(EHR)及其他來源產生大量數據。本書還討論了AI驅動的大數據分析,這使研究人員和臨床醫生能夠從數據中獲得有意義的見解,幫助早期疾病檢測、個性化治療計劃及病人監測。

作者簡介

Dr. Madhusudhan H S is an associate professor in the Department of Computer Science and Engineering at Vidyavardhaka College of Engineering, Mysuru, India.

Dr. Punit Gupta is an associate professor in the department of Computer and Communication Engineering at Pandit Deendayal Energy University, India.

Dr. Pradeep Singh Rawat is an assistant professor with the Department of Computer Science and Engineering at DIT University, Dehradun, India.

Dr. Dinesh Kumar Saini is a full professor at the School of Computing and Information Technology, Manipal University

作者簡介(中文翻譯)

Dr. Madhusudhan H S 是印度邁索爾Vidyavardhaka工程學院計算機科學與工程系的副教授。
Dr. Punit Gupta 是印度潘迪特·丁達亞能源大學計算機與通信工程系的副教授。
Dr. Pradeep Singh Rawat 是印度德拉敦DIT大學計算機科學與工程系的助理教授。
Dr. Dinesh Kumar Saini 是馬尼帕爾大學計算與信息技術學院的正教授。