Among the different synthetic polymers developed for biomedical applications, poly­(lactic-co-glycolic acid) (PLGA) has attracted considerable attention because of its excellent biocompatibility and biodegradability. Nanocomposites based on PLGA and metal-based nanostructures (MNSs) have been employed extensively as an efficient strategy to improve the structural and functional properties of PLGA polymer. The MNSs have been used to impart new properties to PLGA, such as antimicrobial properties and labeling. In the present review, the different strategies available for the fabrication of MNS/PLGA nanocomposites and their applications in the biomedical field will be discussed, beginning with a description of the preparation routes, antimicrobial activity, and cytotoxicity concerns of MNS/PLGA nanocomposites. The biomedical applications of these nanocomposites, such as carriers and scaffolds in tissue regeneration and other therapies are subsequently reviewed. In addition, the potential advantages of using MNS/PLGA nanocomposites in treatment illnesses are analyzed based on in vitro and in vivo studies, to support the potential of these nanocomposites in future research in the biomedical field.

Summary The Internet of Things (IoT) has a significant impact on our daily lives as applications, services, devices, and industries become more intelligent. Artificial intelligence (AI) is expected to significantly influence machine learning training on IoT devices without data sharing. Federated learning (FL) is a distributed machine learning method used in many IoT smart devices; however, FL ensures IoT security and privacy. The systematic literature review (SLR) method is used in this paper to review recently published articles in the FL‐based IoT domain. We analyzed 39 papers that were published between 2018 and March 2022. According to the evaluation factors, the accuracy factor has a high percentage in the FL‐based IoT domain by 29%, and the epoch has 23%, the time has 18%, the energy consumption has 9%, the delay has 9%, the communication overhead has 6%, and the privacy has 6%. Finally, we discuss future research challenges and open issues in the context of FL‐based IoT.

Since early 2020, Coronavirus Disease 2019 (COVID-19) has spread widely around the world. COVID-19 infects the lungs, leading to breathing difficulties. Early detection of COVID-19 is important for the prevention and treatment of pandemic. Numerous sources of medical images (e.g., Chest X-Rays (CXR), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI)) are regarded as a desirable technique for diagnosing COVID-19 cases. Medical images of coronavirus patients show that the lungs are filled with sticky mucus that prevents them from inhaling. Today, Artificial Intelligence (AI) based algorithms have made a significant shift in the computer aided diagnosis due to their effective feature extraction capabilities. In this survey, a complete and systematic review of the application of Machine Learning (ML) methods for the detection of COVID-19 is presented, focused on works that used medical images. We aimed to evaluate various ML-based techniques in detecting COVID-19 using medical imaging. A total of 26 papers were extracted from ACM, ScienceDirect, Springerlink, Tech Science Press, and IEEExplore. Five different ML categories to review these mechanisms are considered, which are supervised learning-based, deep learningbased, active learning-based, transfer learning-based, and evolutionary learningbased mechanisms. A number of articles are investigated in each group. Also, some directions for further research are discussed to improve the detection of COVID-19 using ML techniques in the future. In most articles, deep learning is used as the ML method. Also, most of the researchers used CXR images to diagnose COVID-19. Most articles reported accuracy of the models to evaluate model performance. The accuracy of the studied models ranged from 0.84 to 0.99. The studies demonstrated the current status of AI techniques in using AI potentials in the fight against COVID-19.

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