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This study offers a comparative analysis of different diagnostic techniques for lung pathologies from an engineering standpoint. The review concentrates on three primary categories of methods: electronic nose-based (E-nose) detection, computer vision or image processing, and biosensors such as Graphene-FET (GFET). The e-nose-based detection technique uses electronic sensors to recognize Volatile Organic Compounds (VOCs) in the exhaled breath. These VOCs can aid in the diagnosis of lung pathologies such as pneumonia. The computer vision processing method involves the application of advanced imaging techniques and Machine Learning algorithms to scrutinize and diagnose lung pathologies and Ventilator-Associated Pneumonia (VAP). Lastly, biosensors employ the exceptional properties of these materials to identify specific biomarkers in biological samples. This information can be used to diagnose lung pathologies and VAP. The present paper examines the current state-of-the-art in each methods and offers a comprehensive analysis of their advantages and disadvantages from an engineering perspective. The study underscores the potential of these techniques to enhance the diagnosis of lung pathologies and VAP. Additionally, it emphasizes the necessity for further research to optimize their performance and clinical usefulness.

Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while improving grid stability and meeting service demand. This is possible by adopting next-generation energy systems, which leverage artificial intelligence, the Internet of things (IoT), and communication technologies to collect and analyze big data in real-time and effectively run city services. However, training machine learning algorithms to perform various energy-related tasks in sustainable smart cities is a challenging data science task. These algorithms might not perform as expected, take much time in training, or do not have enough input data to generalize well. To that end, transfer learning (TL) has been proposed as a promising solution to alleviate these issues. To the best of the authors’ knowledge, this paper presents the first review of the applicability of TL for energy systems by adopting a well-defined taxonomy of existing TL frameworks. Next, an in-depth analysis is carried out to identify the pros and cons of current techniques and discuss unsolved issues. Moving on, two case studies illustrating the use of TL for (i) energy prediction with mobility data and (ii) load forecasting in sports facilities are presented. Lastly, the paper ends with a discussion of the future directions.