Feature Fusion for Next-Generation AI: Building Intelligent Solutions from Medical Data
暫譯: 下一代 AI 的特徵融合:從醫療數據構建智能解決方案
Nag, Anindya, Hassan, MD Mehedi, Bairagi, Anupam Kumar
商品描述
This book delves into the fundamental concepts, methodologies, and practical implementations of feature fusion, providing valuable perspectives on how merging several data aspects might augment the decision-making skills of artificial intelligence. Feature fusion is inherently connected to the advancement of intelligent solutions from medical data as it enables the incorporation of various and complementary data sources to construct more advanced AI models. Within the medical domain, data manifests in diverse formats, including electronic health records (EHRs), medical imaging, genomic data, and real-time sensor metrics. Although each of these data kinds offers distinct perspectives, they may have limitations in terms of their breadth or depth when considered independently. The application of feature fusion enables the integration of diverse data sources into a unified model, hence improving the AI's capacity to detect patterns, make precise predictions, and produce significant insights. The fusion process facilitates the development of intelligent solutions that exhibit enhanced reliability and effectiveness by using a more extensive reservoir of knowledge. For example, an artificial intelligence system that combines imaging data with clinical history might enhance the precision of disease diagnosis, forecast patient outcomes, and suggest tailored treatment strategies. Feature fusion is the crucial factor in unleashing the complete capabilities of medical data, enabling artificial intelligence to provide intelligent solutions that not only enhance the provision of healthcare but also stimulate advancements in medical research and practice. The proposed book explores the advanced notion of feature fusion within the field of artificial intelligence, with a particular emphasis on its implementation in physiological data. The integration of many data sources is crucial in the development of more precise, dependable, and understandable AI models as the healthcare industry becomes more data-driven.
商品描述(中文翻譯)
這本書深入探討特徵融合的基本概念、方法論和實際應用,提供有價值的觀點,說明如何合併多個數據方面可能增強人工智慧的決策能力。特徵融合與智能解決方案的發展密切相關,特別是在醫療數據領域,因為它使得能夠整合各種互補的數據來源,以構建更先進的 AI 模型。在醫療領域,數據以多種格式呈現,包括電子健康紀錄(EHRs)、醫學影像、基因組數據和即時感測器指標。雖然這些數據類型各自提供獨特的觀點,但在獨立考量時,可能在廣度或深度上存在限制。特徵融合的應用使得能夠將多樣的數據來源整合到一個統一的模型中,從而提高 AI 檢測模式、做出精確預測和產生重要見解的能力。融合過程通過利用更廣泛的知識庫,促進了智能解決方案的發展,這些解決方案展現出更高的可靠性和有效性。例如,結合影像數據與臨床歷史的人工智慧系統可能提高疾病診斷的精確性、預測病人結果並建議量身定制的治療策略。特徵融合是釋放醫療數據全部潛能的關鍵因素,使人工智慧能夠提供智能解決方案,不僅提升醫療服務的質量,還促進醫學研究和實踐的進步。本書探討了人工智慧領域中特徵融合的先進概念,特別強調其在生理數據中的應用。隨著醫療行業變得越來越依賴數據,整合多個數據來源對於開發更精確、可靠和易於理解的 AI 模型至關重要。
作者簡介
Anindya Nag obtained an M.Sc. in Computer Science and Engineering from Khulna University in Khulna, Bangladesh, and a B.Tech. in Computer Science and Engineering from Adamas University in Kolkata, India. He is currently a Lecturer in the Department of Computer Science and Engineering at the Northern University of Business and Technology in Khulna, Bangladesh. His research focuses on health informatics, medical Internet of Things, neuro-science, and machine learning. He serves as a reviewer for numerous prestigious journals and international conferences. He has authored and co-authored about 36publications, including journal articles, conference papers, book chapters, and has co-edited 7 books.
Md. Mehedi Hassan (Member, IEEE) is a dedicated and accomplished researcher, completed the Master of Science (M.Sc.) degree in computer science and engineering at Khulna University, Khulna, Bangladesh. Mehedi completed his BSc degree in Computer Science and Engineering from North Western University, Khulna in 2022. As the founder and CEO of The Virtual BD IT Firm and VRD Research Laboratory, Bangladesh, Mehedi has established himself as a highly respected leader in the fields of biomedical engineering, data science, and expert systems. As a young researcher, Mehedi has published 52 articles and 2 books in various international top journals and conferences, which is a remarkable achievement. His accomplishments to date are impressive, and his potential for future contributions to his field is very promising. Additionally, he serves as a reviewer for 56 prestigious journals. He has filed more than 3 patents out of which 2 are granted to his name. Anupam Kumar Bairagi, PhD is a professor in the discipline of Computer Science and Engineering, at Khulna University, Bangladesh. He received his Ph.D. degree in Computer Engineering from Kyung Hee University, South Korea, and his B.Sc. and M.Sc. degree in Computer Science and Engineering from Khulna University, Bangladesh. His research interests include wireless resource management in 5G, game theory, Health Informatics, IIoT, Agri Informatics, etc. He obtained the Vice Chancellor's Award in 2023 for his contribution in research and academic excellence. He is a senior member of IEEE.作者簡介(中文翻譯)
Anindya Nag 於孟加拉國庫爾納的庫爾納大學獲得計算機科學與工程碩士學位(M.Sc.),並於印度加爾各答的阿達馬斯大學獲得計算機科學與工程學士學位(B.Tech.)。他目前是孟加拉國庫爾納北方商業與科技大學計算機科學與工程系的講師。他的研究專注於健康資訊學、醫療物聯網、神經科學和機器學習。他擔任多本知名期刊和國際會議的審稿人,並已發表和合著約36篇出版物,包括期刊文章、會議論文和書籍章節,並共同編輯了7本書。
Md. Mehedi Hassan(IEEE 會員)是一位專注且成就卓越的研究者,於孟加拉國庫爾納的庫爾納大學完成計算機科學與工程碩士學位(M.Sc.)。Mehedi 於2022年在庫爾納的西北大學獲得計算機科學與工程學士學位(BSc)。作為孟加拉國虛擬BD IT公司和VRD研究實驗室的創始人兼首席執行官,Mehedi 在生物醫學工程、數據科學和專家系統領域建立了高度尊重的領導地位。作為一名年輕的研究者,Mehedi 在各大國際頂尖期刊和會議上發表了52篇文章和2本書,這是一項了不起的成就。他迄今為止的成就令人印象深刻,未來在其領域的貢獻潛力非常可觀。此外,他還擔任56本知名期刊的審稿人。他已申請超過3項專利,其中2項已獲得授權。
Anupam Kumar Bairagi 博士是孟加拉國庫爾納大學計算機科學與工程學科的教授。他在韓國京畿大學獲得計算機工程博士學位(Ph.D.),並在孟加拉國庫爾納大學獲得計算機科學與工程的學士(B.Sc.)和碩士(M.Sc.)學位。他的研究興趣包括5G中的無線資源管理、博弈論、健康資訊學、工業物聯網(IIoT)、農業資訊學等。他因在研究和學術卓越方面的貢獻而於2023年獲得副校長獎。他是IEEE的資深會員。