相關主題
商品描述
Applying artificial intelligence (AI) to new fields has made AI and data science indispensable to researchers in a wide range of fields. The proliferation and successful deployment of AI algorithms are fuelling these changes, which can be seen in fields as disparate as healthcare and emerging Internet of Things (IoT) applications. Machine learning techniques, and AI more broadly, are expected to play an ever-increasing role in the modelling, simulation, and analysis of data from a wide range of fields by the interdisciplinary research community. Ideas and techniques from multidisciplinary research are being utilised to enhance AI; hence, the connection between the two fields is a two-way street at a crossroads. Algorithms for inference, sampling, and optimisation, as well as investigations into the efficacy of deep learning, frequently make use of methods and concepts from other fields of study. Cloud computing platforms may be used to develop and deploy several AI models with high computational power. The intersection between multiple fields, including math, science, and healthcare, is where the most significant theoretical and methodological problems of AI may be found. To gather, integrate, and synthesise the many results and viewpoints in the connected domains, refer to it as interdisciplinary research. In light of this, the theory, techniques, and applications of machine learning and AI, as well as how they are utilised across disciplinary boundaries, are the main areas of this research topic.
- This book apprises the readers about the important and cutting-edge aspects of AI applications for interdisciplinary research and guides them to apply their acquaintance in the best possible manner
- This book is formulated with the intent of uncovering the stakes and possibilities involved in using AI through efficient interdisciplinary applications
- The main objective of this book is to provide scientific and engineering research on technologies in the fields of AI and data science and how they can be related through interdisciplinary applications and similar technologies
- This book covers various important domains, such as healthcare, the stock market, natural language processing (NLP), real estate, data security, cloud computing, edge computing, data visualisation using cloud platforms, event management systems, IoT, the telecom sector, federated learning, and network performance optimisation. Each chapter focuses on the corresponding subject outline to offer readers a thorough grasp of the concepts and technologies connected to AI and data analytics, and their emerging applications
商品描述(中文翻譯)
應用人工智慧（AI）於新領域，使得AI和數據科學對於各種領域的研究者變得不可或缺。AI演算法的普及和成功部署正在推動這些變化，這些變化可以在醫療保健和新興的物聯網（IoT）應用等截然不同的領域中看到。機器學習技術，以及更廣泛的AI，預計將在跨學科研究社群中，對來自各種領域的數據建模、模擬和分析發揮越來越重要的作用。來自多學科研究的思想和技術正在被用來增強AI；因此，這兩個領域之間的聯繫是一條雙向街道，正處於交叉路口。推理、取樣和優化的演算法，以及對深度學習有效性的研究，經常利用其他研究領域的方法和概念。雲計算平台可以用來開發和部署多個具有高計算能力的AI模型。數學、科學和醫療保健等多個領域的交集，可能是AI最重要的理論和方法問題所在。將其稱為跨學科研究，以收集、整合和綜合相關領域中的各種結果和觀點。基於此，機器學習和AI的理論、技術和應用，以及它們如何跨學科界限使用，是本研究主題的主要領域。
本書向讀者介紹AI應用於跨學科研究的重要和前沿方面，並指導他們以最佳方式應用其知識。
本書的編寫旨在揭示通過高效的跨學科應用使用AI所涉及的風險和可能性。
本書的主要目標是提供有關AI和數據科學領域技術的科學和工程研究，以及它們如何通過跨學科應用和類似技術相關聯。
本書涵蓋多個重要領域，如醫療保健、股市、自然語言處理（NLP）、房地產、數據安全、雲計算、邊緣計算、使用雲平台的數據可視化、事件管理系統、物聯網、電信行業、聯邦學習和網絡性能優化。每一章專注於相應的主題大綱，以提供讀者對與AI和數據分析相關的概念和技術及其新興應用的全面理解。
作者簡介
Dr. Sukhpal Singh Gill (FHEA) is a Assistant Professor in Cloud Computing at School of Electronic Engineering and Computer Science (EECS), Queen Mary University of London (QMUL), UK and he is a member of Network Research Group. Prior to this, Dr. Gill has held positions as a Research Associate at Evolving Distributed Systems Lab at the School of Computing and Communications, Lancaster University, UK and also as a Postdoctoral Research Fellow at the Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Australia. He was awarded Fellow of the Higher Education Academy (FHEA) in 2022 after passing PGCAP/PGCert with Distinction. He has published his PGCAP/PGCert work in highly-ranked Education Conferences and Journals such as IEEE EDUCON (top conference for education papers with acceptance rate 26%), Wiley Computer Applications in Engineering Education (Impact Factor = 2.1) and IT NOW - British Computer Society (BCS). Before joining CLOUDS Lab, Dr. Gill also worked in Computer Science and Engineering Department of Thapar University, India, as a Lecturer. Dr. Gill received his Bachelor's degree in Computer Science and Engineering from Punjab Technical University with Distinction in 2010. Then, he obtained the Degree of Master of Engineering in Software Engineering (Gold Medalist), as well as a Doctoral Degree specialization in Autonomic Cloud Computing from Thapar University. He was a DST (Department of Science & Technology) Inspire Fellow during Doctorate and worked as a Senior Research Fellow (Professional) on DST Project, Government of India. Dr. Gill was a research visitor at Monash University, University of Manitoba, University of Manchester and Imperial College London. He was a recipient of several awards, including the Distinguished Reviewer Award from Software: Practice and Experience (Wiley), 2018, Best Paper Award AusPDC at ACSW 2021, and the EECS Award for the "Widest Academic Staff Contribution" at EECS, QMUL in 2023. He has also served as the PC member for venues such as IEEE PerCom, UCC, CCGRID, CLOUDS, ICFEC, AusPDC. His one review paper has been nominated and selected for the ACM 21st annual Best of Computing Notable Books and Articles as one of the notable items published in computing - 2016. He has co-authored 150+ peer-reviewed papers (with Citations 7500+ and H-index 45+ as per Google Scholar) and has published in prominent international journals and conferences such as IEEE TCC, IEEE TSC, IEEE TSUSC, IEEE TCE, ACM TOIT, IEEE TII, IEEE TNSM, IEEE IoT Journal, Elsevier JSS/FGCS, IEEE/ACM UCC and IEEE CCGRID. Dr. Gill served as a Guest Editor for SPE (Wiley), JCC Springer Journal, Sustainability Journal (MDPI) and Sensors Journal (MDPI). He is a regular reviewer for IEEE TPDS, IEEE TSC, IEEE TNSE, IEEE TSC, ACM CSUR and Wiley SPE. Dr. Gill has reviewed 570+ research articles of high ranked journals and prestigious conferences as per Web of Science. He has edited a research books for Elsevier, Springer and CRC Press. Dr. Gill is serving as an Associate Editor in IEEE IoT Journal, Elsevier IoT Journal, Wiley SPE Journal, Wiley ETT Journal and IET Networks Journal. and Area Editor for Springer Cluster Computing Journal. He is a professional member of ACM. His name appears in the list of the World's Top 2% of Scientists released by Stanford University and Elsevier BV (2022 and 2023). Dr. Gill has been serving as an editorial board member for IGIGLOBAL JOEUC, IGIGLOBAL IJAEC, and MECS IJEME. One of his articles published by the IEEE IoT Journal is highlighted in IEEE Spectrum (the world's leading engineering magazine). Dr. Gill wrote articles for international magazines such as Ars Technica, Tech Monitor, Cutter Consortium and ICT Academy. He has been interviewed by Tallinn University, Estonia, to talk about "The capabilities and limitations of ChatGPT for Education". His research interests include Cloud Computing, Fog Computing, Software Engineering, Internet of Things and Energy Efficiency. For further information, please visit www.ssgill.me.
作者簡介(中文翻譯)
Dr. Sukhpal Singh Gill (FHEA) 是英國倫敦女王瑪莉大學電子工程與計算機科學學院 (EECS) 的雲端計算助理教授，並且是網路研究小組的成員。在此之前，Gill 博士曾擔任英國蘭卡斯特大學計算與通信學院的演變分散系統實驗室研究助理，以及澳大利亞墨爾本大學計算與資訊系統學院的雲端計算與分散系統 (CLOUDS) 實驗室的博士後研究員。他於 2022 年獲得高等教育學院 (FHEA) 的院士資格，並以優異成績通過 PGCAP/PGCert。他的 PGCAP/PGCert 研究成果發表於多個高排名的教育會議和期刊，如 IEEE EDUCON（教育論文的頂尖會議，接受率 26%）、Wiley Computer Applications in Engineering Education（影響因子 = 2.1）以及 IT NOW - 英國計算機學會 (BCS)。在加入 CLOUDS 實驗室之前，Gill 博士還曾在印度 Thapar 大學的計算機科學與工程系擔任講師。Gill 博士於 2010 年以優異成績獲得旁遮普技術大學的計算機科學與工程學士學位，隨後在 Thapar 大學獲得軟體工程碩士學位（金獎得主）及自動雲端計算專業的博士學位。在攻讀博士學位期間，他是 DST（科學與技術部）啟發獎學金得主，並在印度政府的 DST 項目中擔任高級研究員（專業）。Gill 博士曾在莫納什大學、曼尼托巴大學、曼徹斯特大學和倫敦帝國學院擔任研究訪客。他獲得多項獎項，包括 2018 年來自 Wiley 的《Software: Practice and Experience》的傑出審稿人獎、2021 年 ACSW 的 AusPDC 最佳論文獎，以及 2023 年 EECS 的「最廣泛學術人員貢獻」獎。他還擔任過 IEEE PerCom、UCC、CCGRID、CLOUDS、ICFEC、AusPDC 等會議的程序委員會成員。他的一篇評論文章被提名並選為 ACM 第 21 屆年度計算領域傑出書籍和文章之一，作為 2016 年計算領域的顯著出版物之一。他共同撰寫了 150 多篇經過同行評審的論文（根據 Google Scholar，引用次數超過 7500，H 指數超過 45），並在 IEEE TCC、IEEE TSC、IEEE TSUSC、IEEE TCE、ACM TOIT、IEEE TII、IEEE TNSM、IEEE IoT Journal、Elsevier JSS/FGCS、IEEE/ACM UCC 和 IEEE CCGRID 等知名國際期刊和會議上發表。Gill 博士曾擔任 Wiley SPE、Springer JCC、MDPI Sustainability Journal 和 MDPI Sensors Journal 的客座編輯。他是 IEEE TPDS、IEEE TSC、IEEE TNSE、ACM CSUR 和 Wiley SPE 的定期審稿人。根據 Web of Science，他已審查超過 570 篇高排名期刊和知名會議的研究文章。他為 Elsevier、Springer 和 CRC Press 編輯過研究書籍。Gill 博士目前擔任 IEEE IoT Journal、Elsevier IoT Journal、Wiley SPE Journal、Wiley ETT Journal 和 IET Networks Journal 的副編輯，以及 Springer Cluster Computing Journal 的區域編輯。他是 ACM 的專業會員。他的名字出現在史丹佛大學和 Elsevier BV 發布的全球前 2% 科學家名單中（2022 和 2023 年）。Gill 博士一直擔任 IGIGLOBAL JOEUC、IGIGLOBAL IJAEC 和 MECS IJEME 的編輯委員會成員。他在 IEEE IoT Journal 發表的一篇文章被 IEEE Spectrum（全球領先的工程雜誌）報導。Gill 博士為 Ars Technica、Tech Monitor、Cutter Consortium 和 ICT Academy 等國際雜誌撰寫文章。他曾接受愛沙尼亞塔林大學的訪問，討論「ChatGPT 在教育中的能力與限制」。他的研究興趣包括雲端計算、霧計算。
