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.

Anomaly detection is essential in cybersecurity for identifying abnormal activities, a requirement that has grown increasingly critical with the complexity of cyberthreats. This study leverages the BPF-Extended Tracking Honeypot (BETH) dataset, a comprehensive resource designed to benchmark robustness in detecting anomalous behavior in kernel-level process and network logs. The symmetry of the proposed system lies in its ability to identify balanced and consistent patterns within kernel-level process logs, which form the foundation for accurately distinguishing anomalies. This study focuses on anomaly detection in kernel-level process logs by introducing an enhanced Isolation Forest (iForest) model, which is integrated into a structured framework that includes exploratory data analysis (EDA), data pre-processing, model training, validation, and evaluation. The proposed approach achieves a significant performance improvement in the anomaly detection results, with an area under the receiver operating characteristic curve (AUROC) score of 0.917—an approximate 7.88% increase over the baseline model’s AUROC of 0.850. Additionally, the model demonstrates high precision (99.57%), F1-score (91.69%), and accuracy (86.03%), effectively minimizing false positives while maintaining balanced detection capabilities. These results underscore the role of leveraging symmetry in designing advanced intrusion detection systems, offering a structured and efficient solution for identifying cyberthreats.

In recent years, the use of massive multiple-input multiple-output (MIMO) systems and higher frequency bands for next-generation urban rail transportation systems has emerged as an intriguing research topic due to its potential to significantly increase network capacity by utilizing available narrowband and broadband spectrums. In metro and mining applications, the high-reliability wireless sensor network (WSN) plays a vital role in providing personal safety, channel optimization, and improving operational performance. Through the duration of 1921–2023, this paper provides the survey on the progress of fifth-generation (5G) and beyond-fifth-generation (B5G) wireless communication systems in underground environments such as tunnels and mines, the evolution of the earliest technologies, development in channel modeling for vehicle-to-vehicle (V2V) communications, and realization of different wireless propagation channels in high-speed train (HST) environments. In addition, the most recent advanced channel modeling methods are examined, including the development of new algorithms and their use in intelligent transportation systems (ITS); mathematical, analytical, and experimental techniques for propagation design; and the significance of the radiation characteristics, antenna placing, and physical environment effect on wireless communications. Leaky coaxial cable (LCX) and distributed antenna system (DAS) designs are introduced in the demonstrated systems for improving the channel capacity of narrowband and wideband channels as well as the spatial characteristics of various MIMO systems. The review article concludes by figuring out open research directions for future technologies.

The session initiation protocol (SIP) is widely used for multimedia communication as a signaling protocol for managing, establishing, maintaining, and terminating multimedia sessions among participants. However, SIP is exposed to a variety of security threats. To overcome the security flaws of SIP, it needs to support a number of security services: authentication, confidentiality, and integrity. Few solutions have been introduced in the literature to secure SIP, which can support these security services. Most of them are based on internet security standards and have many drawbacks. This work introduces a new protocol for securing SIP called secure-SIP (S-SIP). S-SIP consists of two protocols: the SIP authentication (A-SIP) protocol and the key management and protection (KP-SIP) protocol. A-SIP is a novel mutual authentication protocol. KP-SIP is used to secure SIP signaling messages and exchange session keys among entities. It provides different security services for SIP: integrity, confidentiality, and key management. A-SIP is based on the secure remote password (SRP) protocol, which is one of standard password-based authentication protocols supported by the transport layer security (TLS) standard. However, A-SIP is more secure and efficient than SRP because it covers its security flaws and weaknesses, which are illustrated and proven in this work. Through comprehensive informal and formal security analyses, we demonstrate that S-SIP is secure and can address SIP vulnerabilities. In addition, the proposed protocols were compared with many related protocols in terms of security and performance. It was found that the proposed protocols are more secure and have better performance.

Fault-tolerance of a system measures its working capability in the presence of faulty components in the system. The fault-tolerant partition dimension of a network computes the least number of subcomponents of network required to distinctively identify each node in the presence of faults, having promising applications in telecommunication, robot navigation and geographical routing protocols. In this paper, certain triangular mesh networks including, triangular ladder (Tl_s), triangular mesh (T_s), reflection triangular mesh (rl(T_s)), tower triangular mesh (Tr_s) and reflection tower triangular mesh (rl(Tr_s)) networks are discussed for their partition and fault-tolerant partition resolvability. In this regard, it is shown that the partition dimension of these networks is 3, whereas their fault-tolerant partition dimension is 4.