Unmanned aerial vehicles (UAVs) have discovered a plethora of societal applications such as remote sensing, disaster management, medical emergency, security and surveillance, etc. UAVs require fast and reliable communication lines, and selecting the appropriate channel estimation (CE) technique is pivotal in reliable communication. Orthogonal time frequency space (OTFS) is an innovative modulation technique designed to support reliable communication in rapid mobility environments for 5G and beyond applications, effectively addressing challenges posed by Doppler shifts and multipath propagation. Current OTFS receivers utilize threshold methods such as least squares (LS) and minimum mean square error estimators for CE. To further enhance the accuracy and robustness of the CE process, this paper proposes a generative adversarial network (GAN) for learning channel parameters and performing CE in OTFS-based communication systems for high-speed UAVs (100-500 km/h). Firstly, a system model considering the Doppler effect has been modeled mathematically, and then the solution to the CE problem is presented for UAVs-assisted networks using GAN. The proposed GAN architectures comprise a U-Net-based generator and a PatchGAN discriminator for adversarial training of the model. The proposed model is compared with the baseline approaches in terms of bit error rate (BER), outage probability (OP), and normalized mean squared error (NMSE) for different velocities and modulation schemes. The proposed model has given an improvement of 70%, 55%, 45% in BER performance and 40%, 30%, 20% in OP compared to the conventional LS estimator, machine learning-based estimator, and deep learning-based estimator, respectively. The proposed model has also demonstrated robustness against the Doppler effect caused by the high mobility of UAVs, with only a minimal decrease in NMSE performance of 2-3 dB for every 50 km/h increase in speed. Additionally, the time complexity and processing time of the proposed model have been studied to test its suitability for UAV applications.

In the digital health infrastructure, cyber threats pose a long-term security concern for healthcare facilities, and the existing security measures are not capable enough to minimize the risks and security challenges in the healthcare systems. The concept of artificial intelligence (AI) is changing the way healthcare delivery, patient data management, and administrative tasks operate. At present, to elevate threat detection and prevention, we are using key mechanisms like machine learning, Natural language processing (NLP), and other data analytics. With the integrative concept of cybersecurity and generative AI, we can also investigate the impact of AI-driven solutions in healthcare systems. This study emphasizes AI's crucial role in reforming our response to escalating threats and explores the interdependent relationship between generative AI and cybersecurity and their applications in healthcare so that healthcare facilities can predict and mitigate emerging risks by integrating AI into existing security infrastructure. A total of five generative AI-based cybersecurity defense mechanisms are proposed in this study that can be used to enhance digital infrastructure security in healthcare. These five mechanisms represent dynamic strategies that could revolutionize defensive capabilities from real-time anomaly detection to combating zero-day exploits. The suggested technical approaches outline the organizational needs and select appropriate AI technologies and continuous improvement. Fast identification of irregularities and established security standards allow for proactive threat mitigation. Moreover, the proposed strategies ensure proactive threat mitigation and robust security standards for real-time anomaly detection to combat zero-day exploits. Hence, GenAI-based robust cybersecurity protocols are essential to protect digital healthcare facilities from determined cyber opponents. Received: 17 August 2024 | Revised: 6 March 2025 | Accepted: 7 March 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Shahazad Niwazi Qurashi: Conceptualization, Methodology, Investigation, Data curation, Writing – original draft, Supervision, Project administration. Farrukh Sobia: Conceptualization, Data curation, Writing – original draft. Wafa A. Hetany: Writing – review & editing. Hani Sultan: Visualization.

This study was intended to identify the genes responsible for ESBL- and carbapenemase-producing bacterial isolates obtained from Jizan region. A hospital-based cross-sectional study was conducted over a period of 3 months (15th November 2018–15th February 2019). Fifty non-duplicate, 3rd-generation cephalosporin and carbapenem-resistant isolates were collected from microbiology lab of a tertiary care hospital in Jizan province and were screened for ESBLs and MBLs by phenotypic methods (CDT). The positive isolates (by phenotypic method) were then scanned for the presence of bla_ESBLs and bla_NDM-₁ genes, respectively, by PCR.

With the advancement of modern healthcare systems, patients now utilize medical sensor nodes to generate health data. These data are then transmitted to healthcare providers for analysis and diagnosis of the patient. These data are sensitive in nature and, therefore, need to preserve their privacy, security, and confidentiality. Another crucial aspect of a healthcare system is the effective revocation of malicious users. Revocable encryption schemes, particularly those with ciphertext evolution, have emerged as promising solutions. However, all the existing revocable schemes with ciphertext evolution have expensive bilinear pairing, map-to-point hash, modular exponentiation operations, rendering them unsuitable for resource constrained medical sensor nodes. In this paper, we propose a novel pairing free revocable certificateless encryption scheme with ciphertext evolution. Also, we prove that our scheme is secure against adaptive chosen ciphertext attacks. Our comparative analysis demonstrates that our scheme achieves substantial reductions in total computational cost by 74.9% to 86.4% and reduces ciphertext communication cost by 45.9% to 72.9%, making it the most effective among related schemes.

The proliferation of medical data sourced from healthcare organizations has led to an increased utilization of Machine Learning (ML) methodologies within the medical domain. In order to uphold the effectiveness of healthcare professionals, it is imperative to design machine learning models that are dependable and credible. Machine learning (ML) models are widely employed in the field of healthcare to generate accurate disease predictions, while simultaneously ensuring the protection of data gathered via Internet of Medical Things (IoMT) devices. Federated Learning (FL) techniques are well-suited for the preservation of Internet of Medical Things (IoMT) data due to their ability to preserve only the trained models and advance with information from distributed users. Fluorescence techniques possess the potential to significantly revolutionize the medical business by enabling rapid disease diagnosis, hence improving the effectiveness of therapy. The efficacy of these FL approaches is diminished as a result of the substantial volume of data being transmitted between local and remote locations. To address this concern, a unique methodology called FedEDFA is introduced, which integrates Federated Machine Learning with a meta-heuristic optimization algorithm. The utilization of the Enhanced Dragonfly Optimization algorithm is employed to effectively choose the pertinent features and thereafter utilize them for disease prediction. This strategy enhances the system's resilience in networking situations that are prone to insecurity. The efficacy of the proposed FedEDFA is evaluated by its application to the UCI Cleveland dataset, with the objective of predicting cardiovascular illness while ensuring data security and privacy. The proposed approach demonstrates a greater level of accuracy, measuring at 98.3%, when compared to other existing methods.