The security-reliability tradeoff (SRT) in free-space optical (FSO) communications is the most critical property to highlight, especially with respect to the development of wireless optical communications. In this paper, opportunistic scheduling selection techniques are used to improve the SRT of multiuser FSO systems under the combined influence of atmospheric turbulence with Fisher-Snedecor F distribution, generalized pointing error, and path losses due to foggy weather. Due to the broadcast nature of wireless optical propagation, the optical transmission from the transmitting users to the legitimate receiver can be easily intercepted by eavesdroppers. Therefore, an optimal user scheduling (OUS) scheme is proposed in this work to protect the legitimate wireless transmission from eavesdroppers, where a user with the highest secrecy capacity is scheduled to transmit his confidential information to the receiver. Closed-form expressions for the outage probability, interception probability, and SRT are derived for the conventional round-robin scheduling (RRS) and the proposed OUS. In addition, an asymptotic analysis for the outage probability, interception probability, and SRT is performed to provide insight into the impact of user scheduling on the system performance. We also propose the use of ''friendly jamming'' techniques, where the user with the lowest secrecy capacity is selected by the authorized receiver to jam the existing interceptor. Finally, another SRT is formulated to determine the impact of a friendly jammer on the secrecy performance of the system. The results show that the proposed OUS outperforms the RRS in terms of intercept probability and SRT performance. The obtained exact and asymptotic results are validated by Monte-Carlo simulations.
A keyphrase can be described as a brief phrase comprising between one to five words that correspond to significant perceptions in an article. Text summarization, automatic indexing, classification and text mining are some of the many activities that involve the function of keyphrases. A wide range of techniques have been generated over time for the purpose of keyphrase extraction and much emphasis has been placed on the automatic extraction of keyphrases involving manuscripts in English and a variety of other dialects. However, on the other side of the coin, keyphrase extraction for documents in the Arabic language has largely been neglected. Thus, for the purpose of Arabic keyphrase extraction, this study recommends a hybrid approach which involves the merger of statistical and machine learning methods. The statistical methods involve Term Frequency (TF), First Occurrence in text (FO), Sentence Count (SC), C-Value and TF-IDF, while the machine learning algorithms comprise Linear Logistic Regression (LLR), Linear Discriminant Analysis (LDA) and Support Vector Machines (SVMs). The execution of this undertaking was initiated by the utilization of Part of Speech (POS) for the extraction of noun phrases. Following this, the outcomes generated through the application of statistical methods are employed as features for the purpose of classification. The hybrid model, which is based on SVM achieves the best result with 93.9% accuracy. Through several tests, it has been substantiated that the recommended model is appropriate for extracting Arabic keyphrase.
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