Research on intelligent wireless network aims at the development of a human society which is ubiquitous and mobile, simultaneously providing solutions to the coverage, capacity, and computing issues. These networks will focus on provisioning intelligent use-cases through higher data-rates over the millimeter waves and the Tera-Hertz frequency. However, at such high frequencies, multiple non-desired phenomena such as, atmospheric absorption and blocking occur which create a bottleneck owing to resource scarcity. Hence, existing trend of exactly reproducing transmitted data at the receiver will result in a constant need for higher bandwidth. A possible solution to such a challenge lies in semantic communications which focuses on meaning (relevance or context) of the received data. This article presents a detailed survey on the recent technological trends in regard to semantic communications for intelligent wireless networks. Initially, the article focuses on the semantic communications architecture including the model, and source and channel coding. Next, cross-layer interaction, and various goal-oriented communication applications are detailed. Further, overall semantic communications trends are presented following which, the key challenges and issues are detailed. Lastly, this survey article is an attempt to significantly contribute towards initiating future research in the area of semantic communications for the intelligent wireless networks.

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There is a broad range of novel Coronaviruses (CoV) such as the common cold, cough, and severe lung infections. The mutation of this virus, which originally started as COVID-19 in Wuhan, China, has continued the rapid spread globally. As the mutated form of this virus spreads across the world, testing and screening procedures of patients have become tedious for healthcare departments in largely populated countries such as India. To diagnose COVID-19 pneumonia by radiological methods, high-resolution computed tomography (CT) of the chest has been considered the most precise method of examination. The use of modern artificial intelligence (AI) techniques on chest high-resolution computed tomography (HRCT) images can help to detect the disease, especially in remote areas with a lack of specialized physicians. This article presents a novel metaheuristic algorithm for automatic COVID-19 detection using a least square support vector machine (LSSVM) classifier for three classes namely normal, COVID, and pneumonia. The proposed model results in a classification accuracy of 87.2% and an F1-score of 86.3% for multiclass classifications from simulations. The analysis of information transfer rate (ITR) revealed that the modified quantum-based marine predators algorithm (Mq-MPA) feature selection algorithm reduces the classification time of LSSVM by 23% when compared to the deep learning models.

Brain Computer Interface (BCI) systems are able to communicate directly between the brain and computer using neural activity measurements without the involvement of muscle movements. For BCI systems to be widely used by people with severe disabilities, long-term studies of their real-world use are needed, along with effective and feasible dissemination models. In addition, the robustness of the BCI systems' performance should be improved, so they reach the same level of robustness as natural muscle-based health monitoring. In this chapter, we review the recent BCI-related studies, followed by the most relevant applications. We also present the key issues and challenges which exist in regard to the BCI systems and also provide future directions.<br>