标题： 基于物联网和数字孪生的生物技术产业CNN-FL 显示英文标题

标题： CNN-FL for Biotechnology Industry Empowered by Internet-of-BioNano Things and Digital Twins

摘要： 数字孪生（DTs）正在通过启用生物资产、微生物、药物开发过程和数字健康应用的复杂数字化表示来彻底改变生物技术行业。 然而，在微观和纳米尺度上进行数字孪生，特别是在建模像细菌这样复杂的实体时，需要先进的物联网（IoT）基础设施和计算方法以实现更高的准确性和可扩展性，这带来了显著的挑战。 在这项工作中，我们提出了一种新的框架，该框架将生物纳米物联网（IoBNT）与先进的机器学习技术相结合，特别是卷积神经网络（CNN）和联邦学习（FL），以有效应对这些挑战。 在我们的框架内，IoBNT设备被部署以在各种物理环境中收集基于图像的生物数据，利用CNNs的强大能力进行稳健的机器视觉和模式识别。 随后，FL用于从这些不同的数据源中聚合见解，创建一个不断改进准确性和预测可靠性的精炼全球模型，这对于在生物技术中有效部署DTs至关重要。 主要贡献在于开发了一种新颖的框架，该框架协同结合了CNN和FL，并由IoBNT的能力增强。 这种新方法专门针对提高生物技术行业的DTs。 结果显示，微小生物DTs的可靠性和安全性得到了提升，同时保持了其准确性。 此外，所提出的框架在能效和安全性方面表现出色，提供了一个用户友好且可适应的解决方案。 这扩大了它在生物技术、制药行业以及临床和医院环境中的适用范围。 显示英文摘要

摘要： Digital twins (DTs) are revolutionizing the biotechnology industry by enabling sophisticated digital representations of biological assets, microorganisms, drug development processes, and digital health applications. However, digital twinning at micro and nano scales, particularly in modeling complex entities like bacteria, presents significant challenges in terms of requiring advanced Internet of Things (IoT) infrastructure and computing approaches to achieve enhanced accuracy and scalability. In this work, we propose a novel framework that integrates the Internet of Bio-Nano Things (IoBNT) with advanced machine learning techniques, specifically convolutional neural networks (CNN) and federated learning (FL), to effectively tackle the identified challenges. Within our framework, IoBNT devices are deployed to gather image-based biological data across various physical environments, leveraging the strong capabilities of CNNs for robust machine vision and pattern recognition. Subsequently, FL is utilized to aggregate insights from these disparate data sources, creating a refined global model that continually enhances accuracy and predictive reliability, which is crucial for the effective deployment of DTs in biotechnology. The primary contribution is the development of a novel framework that synergistically combines CNN and FL, augmented by the capabilities of the IoBNT. This novel approach is specifically tailored to enhancing DTs in the biotechnology industry. The results showcase enhancements in the reliability and safety of microorganism DTs, while preserving their accuracy. Furthermore, the proposed framework excels in energy efficiency and security, offering a user-friendly and adaptable solution. This broadens its applicability across diverse sectors, including biotechnology and pharmaceutical industries, as well as clinical and hospital settings.