Bio-Inspired Algorithms in Machine Learning and Deep Learning for Disease Detection
暫譯: 生物啟發算法在機器學習與深度學習中的疾病檢測應用

S, Balasubramaniam, Kadry, Seifedine, Tk, Manoj Kumar

Bio-Inspired Algorithms in Machine Learning and Deep Learning for Disease Detection
  • 出版商: CRC
  • 出版日期: 2025-03-13
  • 售價: $5,830
  • 貴賓價: 9.5 折 $5,539
  • 語言: 英文
  • 頁數: 250
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1032865482
  • ISBN-13: 9781032865485
  • 相關分類: Machine Learning、DeepLearning、Algorithms-data-structures

    • 尚未上市，無法訂購

商品描述

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.

商品描述(中文翻譯)

目前，計算智能方法被應用於各種科學和工程應用中，以分析資訊、做出決策並實現優化目標。在過去幾十年中，各種技術和演算法在遺傳演算法、人工神經網絡、進化演算法和模糊演算法等領域中相繼被創造出來。在未來幾年，智能優化演算法預期將在工程、科學、醫療、太空和人造衛星等領域中更有效地解決各種問題，特別是在早期疾病診斷方面。計算機科學中的元啟發式演算法旨在發現能夠解決複雜問題的優化演算法。元啟發式演算法是模仿動物或鳥類生物行為的優化演算法，用於尋找特定問題的最佳解決方案。元啟發式是一種進階方法，利用啟發式來處理複雜的優化問題。在數學規劃中，元啟發式是一種尋求優化問題解決方案的方法。元啟發式利用啟發式函數來協助搜尋過程。啟發式搜尋可以分為盲目搜尋或有資訊搜尋。由於其簡單性、獨立於數據趨勢、尋找最佳解的能力以及在不同領域的多功能性，元啟發式優化演算法在各種應用中越來越受歡迎。最近，許多受自然啟發的計算演算法被用於診斷不同疾病的人。受自然啟發的方法論現在在數據分析、決策制定和優化等多個領域中被廣泛應用。受自然啟發的技術可分為基於生物的或基於自然現象的。近年來，生物啟發計算涵蓋了計算機科學、數學和生物學的各種主題。生物啟發的計算優化演算法是一種發展中的方法，利用生物發展的概念和靈感來創造新的和具有韌性的競爭策略。生物啟發的優化演算法在機器學習和深度學習中獲得了認可，用於解決科學和工程中的複雜問題。將生物啟發演算法的學習方法與機器學習和深度學習結合使用，顯示出準確分類醫療狀況的巨大潛力。本書探討了生物啟發演算法的歷史發展及其在機器學習和深度學習模型中應用於疾病診斷的情況，包括 COVID-19、心臟病、癌症、糖尿病及其他一些疾病。它討論了在疾病診斷中使用生物啟發演算法的優勢，並以該領域的研究方向和未來前景作結。

作者簡介

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, Thiruvananthapuram, Kerala, India. He has totally around 15+ years of experience in teaching, research and industry. He has completed his Post Doctoral Research in Department of Applied Data Science, Noroff University College, Kristiansand, Norway. He holds a Ph.D degree in Computer Science and Engineering from Anna University, Chennai, India in 2015. He has published nearly 25+ research papers in reputed SCI/WoS/Scopus indexed Journals. He has also granted with 1 Australian patent and 2 Indian Patents and published 2 Indian patents. He has presented papers at conferences, contributed chapters to the edited books and editor in few books published by international publishers. His research and publication interests include machine learning and deep learning-based disease diagnosis, cloud computing security, Generative AI and Electric Vehicles.

Prof. Seifedine Kadry has a bachelor's degree in 1999 from Lebanese University, MS degree in 2002 from Reims University (France) and EPFL (Lausanne), PhD in 2007 from Blaise Pascal University (France), HDR degree in 2017 from Rouen University (France). At present his research focuses on Data Science, education using technology, system prognostics, stochastic systems, and applied mathematics. He is an ABET program evaluator for computing, and ABET program evaluator for Engineering Tech. he is a full professor of data science at Noroff University College, Norway and Department of Computer Science, Lebanese American University, Beirut, Lebanon.

Prof. Manoj Kumar T K, currently serving as Dean (Research) and Professor at Kerala University of Digital Sciences, Innovation and Technology, Thiruvananthapuram, Kerala, India. He is having 5 years of post-doctoral research experience in prestigious institutions like IIT-Madras and Pohang University of Science & Technology, Korea. With an impressive 17-year track record in post-graduate teaching, Dr Manoj has imparted knowledge across a diverse range of subjects including Data Analytics, Deep Learning, Computational Sciences, Predictive Analytics, Big data technologies and Cloud computing, Discrete mathematics, Ordinary differential Equations, Automata, Data Structure and Algorithm, Artificial Intelligence, and Quantum Chemistry. Their scholarly contributions extend to 80 publications in international journals of high impact, marking a significant impact in their respective fields. Previously, he has holding key administrative roles such as Chair of the School of Digital Sciences; Registrar, Digital University Kerala; Registrar, Indian Institute of Information Technology and Management - Kerala and Director of the International Centre for Free and Open-Source Systems, Kerala, India.

Prof. K. Satheesh Kumar presently holds the role of Visiting Professor at the Kerala University of Digital Sciences, Innovation, and Technology, Thiruvananthapuram Kerala, India. Previously, he served as Professor and Head of the Department of Futures Studies at the University of Kerala, Kerala, India. Dr. Kumar's academic journey began with a degree in mathematics, followed by doctoral research in suspension rheology and chaotic dynamics at the CSIR Lab in Thiruvananthapuram. He subsequently pursued post-doctoral research positions at Monash University, Australia, and POSTECH, South Korea. Dr. Kumar's research interests span suspension and polymer rheology, chaotic dynamics, nonlinear time series analysis, geophysics, complex network analysis, and wind energy modeling and forecasting.

作者簡介(中文翻譯)

Balasubramaniam S 博士（IEEE 高級會員）目前擔任印度喀拉拉邦數位科學、創新與技術大學（前身為 IIITM-K）計算機科學與工程學院的助理教授。他在教學、研究和產業方面擁有超過 15 年的經驗。他在挪威克里斯蒂安桑的 Noroff 大學學院應用數據科學系完成了博士後研究。他於 2015 年在印度金奈的安娜大學獲得計算機科學與工程的博士學位。他在知名的 SCI/WoS/Scopus 索引期刊上發表了近 25 篇研究論文。他還獲得了 1 項澳大利亞專利和 2 項印度專利，並發表了 2 項印度專利。他在會議上發表論文，為編輯書籍貢獻章節，並擔任幾本國際出版社出版的書籍的編輯。他的研究和出版興趣包括基於機器學習和深度學習的疾病診斷、雲計算安全、生成式 AI 和電動車。

Seifedine Kadry 教授於 1999 年獲得黎巴嫩大學的學士學位，2002 年在法國的蘭斯大學和洛桑聯邦理工學院獲得碩士學位，2007 年在法國布雷斯帕斯卡大學獲得博士學位，2017 年在法國魯昂大學獲得 HDR 學位。目前他的研究重點是數據科學、利用技術進行教育、系統預測、隨機系統和應用數學。他是計算機的 ABET 課程評估員和工程技術的 ABET 課程評估員。他是挪威 Noroff 大學學院的數據科學全職教授，以及黎巴嫩貝魯特的黎巴嫩美國大學計算機科學系的教授。

Manoj Kumar T K 教授目前擔任印度喀拉拉邦數位科學、創新與技術大學的研究院院長和教授。他在 IIT-Madras 和韓國浦項科技大學等知名機構擁有 5 年的博士後研究經驗。擁有 17 年的研究生教學經驗，Manoj 博士在數據分析、深度學習、計算科學、預測分析、大數據技術和雲計算、離散數學、常微分方程、自動機、數據結構與演算法、人工智慧和量子化學等多個領域傳授知識。他的學術貢獻在高影響力的國際期刊上發表了 80 篇論文，對其各自領域產生了顯著影響。此前，他曾擔任數位科學學院院長、喀拉拉數位大學註冊主任、印度信息技術與管理學院（喀拉拉）註冊主任以及喀拉拉自由與開源系統國際中心主任等重要行政職位。

K. Satheesh Kumar 教授目前擔任印度喀拉拉邦數位科學、創新與技術大學的訪問教授。此前，他曾擔任喀拉拉大學未來研究系的教授和系主任。Kumar 博士的學術之旅始於數學學位，隨後在喀拉拉邦的 CSIR 實驗室進行懸浮流變學和混沌動力學的博士研究。他隨後在澳大利亞莫納什大學和韓國 POSTECH 追求博士後研究職位。Kumar 博士的研究興趣涵蓋懸浮液和聚合物流變學、混沌動力學、非線性時間序列分析、地球物理學、複雜網絡分析以及風能建模和預測。

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