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Vehicular ad hoc networks (VANETs) are recent advancements that permit vehicles to communicate with infrastructure and other vehicles, improving road safety and traffic efficiency. One of the difficulties in constructing and maintaining VANETs deals with the consequences of blockage, it may occur when buildings, trees, or other obstructions block radio signals between vehicles. However, the presence of vehicles as obstacles can severely impact the performance of VANETs. In this paper, an efficient machine learning (ML) model is developed to identify the impact of vehicle obstacles in VANETs. The proposed optimizable tree ML model showed better results in comparison to the other existing models. The results of the proposed model are superior as compared with other existing models in terms of nine performance measures namely, recall, specificity, balanced accuracy, accuracy, error rate, precision, F1 score, FNR and FPR. The values of these nine performance matrices for the proposed optimizable tree ML model are 0.99, 0.99, 0.99, 0.99, 0.01, 0.99, 0.99, 0.01, and 0.01 respectively. [ABSTRACT FROM AUTHOR]

The rapid technological advancements in the current modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients' health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient's health data by exploiting the primary user's (PU) spectrum. In this paper, tree-based algorithms (TBAs) of machine learning (ML) are investigated to evaluate spectrum sensing in CR-based smart healthcare systems. The required data sets for TBAs are created based on the probability of detection ( P d ) and probability of false alarm ( P f ). These data sets are used to train and test the system by using fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree. Training and testing accuracies of all TBAs are calculated for both simulated and theoretical data sets. The comparison of training and testing accuracies of all classifiers is presented for the different numbers of received signal samples. Results depict that optimizable tree gives the best accuracy results to evaluate the spectrum sensing with minimum classification error (MCE). [ABSTRACT FROM AUTHOR]

The rapid technological advancements in the current modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients’ health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient’s health data by exploiting the primary user’s (PU) spectrum. In this paper, tree-based algorithms (TBAs) of machine learning (ML) are investigated to evaluate spectrum sensing in CR-based smart healthcare systems. The required data sets for TBAs are created based on the probability of detection (Pd) and probability of false alarm (Pf). These data sets are used to train and test the system by using fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree. Training and testing accuracies of all TBAs are calculated for both simulated and theoretical data sets. The comparison of training and testing accuracies of all classifiers is presented for the different numbers of received signal samples. Results depict that optimizable tree gives the best accuracy results to evaluate the spectrum sensing with minimum classification error (MCE).