COVID-19 (Corona Virus Disease 19) is an infectious disease which is having a significant health and economic impact across the world. The primary source for the transmission of the disease, its detection and treatment methods are still unknown. Hence, a scientific response to this new corona virus is being hampered by a lack of knowledge on how it spreads, possible prevention measures and vaccinations, which all need to be investigated further.

Artificial Intelligence (AI) and computer communication networks have a role to play, especially Machine Learning (ML) due to its learning-from samples capability and applicability over distributed computer systems and networks. Such intelligent techniques have the potential to achieve an effective diagnosis of COVID-19 and similar infectious diseases, potentially surpassing current physicians' capability, according to recent reports, due to their ability to analyse vast numbers of possibilities and exchange findings in real time. Current computer communication infrastructures include smart devices having the sensing and data routing capabilities to communicate with each other using various protocols, potentially allowing disease updates to be accessed anytime from anywhere, and on which innovative ML-powered services could be developed. Due to their advanced technical capabilities, intelligent communication paradigms are making their way into the detection and treatment of COVID-19 and similar diseases: crisis reaction and coordination, open or private infrastructure control, and urban monitoring and treatment systems, are all examples of medical diagnosis areas in which intelligent communication systems may play an important role very soon.

This Special Issue aims to solicit original research which contributes to the state of the art on the application of ML techniques to the problem of computer communication-assisted diagnosis of COVID-19 and similar diseases: we are interested in the latest theoretical developments, real-world applications and future perspectives on this topic. This Special Issue will bring together a broad multidisciplinary Expert Systems community, aiming to integrate ideas, theories, models and techniques from across different disciplines on intelligent solutions/systems, to inform how cognitive systems in Next Generation Networks (NGN) should be designed, developed, and evaluated while exchanging and processing critical health information.

The topics of interests include, but are not limited to

• Deep Learning for the medical diagnosis of COVID-19 and similar diseases.

• Neural Network for medical diagnosis of COVID-19 and similar diseases.

• Integration of Image Progressing and ML for medical diagnosis of COVID-19 and similar diseases.

• Integration of Computer Communication and ML for medical diagnosis of COVID-19 and similar diseases.

• Use cases of computer-assisted detection systems for COVID-19 and similar diseases.

• COVID-19 patient care and treatment using ML-oriented systems.

• Emerging networks solutions for improved medical diagnosis of COVID-19 and similar diseases.

• Intelligent hardware solutions for medical diagnosis of COVID-19 and similar diseases.

• Effective use of computer communication and ML for solving open medical problems.

• Next Generation Networks (NGNs) and ML solutions for medical diagnosis.

COVID-19（冠状病毒病 19）是一种传染病，对全世界的健康和经济产生了重大影响。该疾病传播的主要来源，其检测和治疗方法仍然未知。因此，由于缺乏关于它如何传播、可能的预防措施和疫苗接种的知识，阻碍了对这种新冠病毒的科学反应，所有这些都需要进一步调查。

人工智能 (AI) 和计算机通信网络可以发挥作用，尤其是机器学习 (ML)，因为它具有从样本中学习的能力以及在分布式计算机系统和网络上的适用性。根据最近的报告，这种智能技术有可能实现对 COVID-19 和类似传染病的有效诊断，可能超过当前医生的能力，因为它们能够实时分析大量可能性并交换发现。当前的计算机通信基础设施包括具有传感和数据路由能力的智能设备，可以使用各种协议相互通信，可能允许随时随地访问疾病更新，并且可以在其上开发创新的 ML 驱动的服务。

本期特刊旨在征集原始研究，这些研究有助于将 ML 技术应用于计算机通信辅助诊断 COVID-19 和类似疾病的问题：我们对最新的理论发展感兴趣，真正的关于该主题的世界应用和未来展望。本期特刊将汇集一个广泛的多学科专家系统社区，旨在整合来自不同学科的智能解决方案/系统的思想、理论、模型和技术，以告知如何设计、开发下一代网络 (NGN) 中的认知系统，并在交换和处理关键健康信息时进行评估。

感兴趣的话题包括但不限于

• 深度学习用于 COVID-19 和类似疾病的医学诊断。

• 用于 COVID-19 和类似疾病医学诊断的神经网络。

• 图像处理和机器学习的集成，用于 COVID-19 和类似疾病的医学诊断。

• 计算机通信和机器学习的集成，用于 COVID-19 和类似疾病的医学诊断。

• 针对 COVID-19 和类似疾病的计算机辅助检测系统的使用案例。

• 使用面向机器学习的系统对 COVID-19 患者进行护理和治疗。

• 用于改进 COVID-19 和类似疾病的医学诊断的新兴网络解决方案。

• 用于 COVID-19 和类似疾病的医学诊断的智能硬件解决方案。

• 有效利用计算机通信和机器学习来解决开放的医疗问题。

• 用于医疗诊断的下一代网络 (NGN) 和 ML 解决方案。