Spectrum sensing is of crucial importance in cognitive radio (CR) networks. In this paper, a reliable spectrum sensing scheme is proposed, which uses K-nearest neighbor, a machine learning algorithm. In the training phase, each CR user produces a sensing report under varying conditions and, based on a global decision, either transmits or stays silent. In the training phase the local decisions of CR users are combined through a majority voting at the fusion center and a global decision is returned to each CR user. A CR user transmits or stays silent according to the global decision and at each CR user the global decision is compared to the actual primary user activity, which is ascertained through an acknowledgment signal. In the training phase enough information about the surrounding environment, i.e., the activity of PU and the behavior of each CR to that activity, is gathered and sensing classes formed. In the classification phase, each CR user compares its current sensing report to existing sensing classes and distance vectors are calculated. Based on quantitative variables, the posterior probability of each sensing class is calculated and the sensing report is classified into either representing presence or absence of PU. The quantitative variables used for calculating the posterior probability are calculated through K-nearest neighbor algorithm. These local decisions are then combined at the fusion center using a novel decision combination scheme, which takes into account the reliability of each CR user. The CR users then transmit or stay silent according to the global decision. Simulation results show that our proposed scheme outperforms conventional spectrum sensing schemes, both in fading and in nonfading environments, where performance is evaluated using metrics such as the probability of detection, total probability of error, and the ability to exploit data transmission opportunities.

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String matching algorithms used in bioinformatics can be applied to scenarios in cognitive radios, where reports of cooperative spectrum sensing nodes need to be compared with each other. Cooperative spectrum sensing is susceptible to security risks, where malicious users who participate in the process falsify the spectrum sensing data, thus affecting cognitive radio network performance. In this paper, an efficient spectrum sensing system is developed where each cognitive radio (CR) user senses the spectrum multiple times within an allocated sensing period. Each CR user quantizes its decision to predefined levels so as to achieve a tradeoff between bandwidth utilization and decision reporting accuracy. The reports for all the CR users are compared at the fusion center using Smith-Waterman algorithm, an optimal algorithm for aligning biological sequences used in bioinformatics, and similarity indices are computed. Robust mean and robust deviation of the similarity indices are calculated and a threshold is determined by these values. The CR users who have similarity indices below the given threshold are declared malicious and their reports are discarded. The local decisions of the remaining CR users are combined using the modified rules of decision combination to take a global decision. Simulation results show that our proposed scheme performs better than conventional schemes with and without malicious users. IndexTerms-Bioinformatics, cooperative spectrum sensing, malicious user detection, quantized hard decision, Smith-Waterman algorithm.

Artificial Intelligence (AI) is increasingly used to support medical students’ learning journeys, providing personalized experiences and improved outcomes. We conducted a scoping review to explore the current application and classifications of AI in medical education. Following the PRISMA-P guidelines, we searched four databases, ultimately including 22 studies. Our analysis identified four AI methods used in various medical education domains, with the majority of applications found in training labs. The use of AI in medical education has the potential to improve patient outcomes by equipping healthcare professionals with better skills and knowledge. Post-implementation refers to the outcomes of AI-based training, which showed improved practical skills among medical students. This scoping review highlights the need for further research to explore the effectiveness of AI applications in different aspects of medical education.