SARS-CoV-2 (COVID-19) has been one of the worst global health crises in the 21st century. The currently available rollout vaccines are not 100% effective for COVID-19 due to the evolving nature of the virus. There is a real need for a concerted effort to fight the virus, and research from diverse fields must contribute. Artificial intelligence-based approaches have proven to be significantly effective in every branch of our daily lives, including healthcare and medical domains. During the early days of this pandemic, artificial intelligence (AI) was utilized in the fight against this virus outbreak and it has played a major role in containing the spread of the virus. It provided innovative opportunities to speed up the development of disease interventions. Several methods, models, AI-based devices, robotics, and technologies have been proposed and utilized for diverse tasks such as surveillance, spread prediction, peak time prediction, classification, hospitalization, healthcare management, heath system capacity, etc. This paper attempts to provide a quick, concise, and precise survey of the state-of-the-art AI-based techniques, technologies, and datasets used in fighting COVID-19. Several domains, including forecasting, surveillance, dynamic times series forecasting, spread prediction, genomics, compute vision, peak time prediction, the classification of medical imaging—including CT and X-ray and how they can be processed—and biological data (genome and protein sequences) have been investigated. An overview of the open-access computational resources and platforms is given and their useful tools are pointed out. The paper presents the potential research areas in AI and will thus encourage researchers to contribute to fighting against the virus and aid global health by slowing down the spread of the virus. This will be a significant contribution to help minimize the high death rate across the globe.

Introduction: As the number of Internet of Things (IoT) devices grows quickly, cyber threats are becoming more complex and increasingly sophisticated; thus, we need a more robust network security solutions. Traditional deep learning approaches often suffer in identifying effectively anomalies in IoT network. To tackle this evolving challenge, this research proposes a hybrid architecture of Neural Network (NN) models that combine Recurrent-NN (RNN) and Spiking-NN (SNN), referred to as HRSNN, to improve IoT the security.Methods: The proposed HRSNN technique has five steps: preprocessing data, extracting features, equalization classes, features optimization and classification. Data processing step makes sure that input data is accurate and consistent and by employing normalization and the removal of outliers’ techniques. Feature extraction makes use of the RNN part to automatically detect abnormal patterns and high-level features, which are then turned into spike trains for the SNN to process over time. In class equalization step, the Synthetic Minority-Oversampling Technique (SMOTE) is being used resulting in balanced classes. Recursive Feature Elimination (RFE) is used to keep the important features for feature optimization. Then, the dataset is split into sets for testing and training so that the model can be tested properly.Results: The hybrid model integrates the spatial feature learning skills of RNNs with the temporal adaptability of SNNs, results in an improved accuracy and resilience in identifying IoT network abnormalities. The proposed HRSNN approach, which was tested on the CIC-IoT23 and TON_IoT data sets, achieved better performance compared to current deep learning (DL) models. In particular, experimental assessments show that the model attained an accuracy rate of 99.5% on the “CICIoT2023” dataset and 98.75% on the “TON_IoT” dataset.Discussion: These results confirm demonstrate that the proposed architecture of RNN and SSN can achieve significant advancement to IoT security. By combining both spatial and temporal feature learning, HRSNN can improve accuracy detection against diverse security threats. The model is reliable, accurate, and adaptable for safeguarding IoT networks against diverse security threats. Thus, the model addresses the potential solutions in the challenging problem of secured IoT networks.