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Realistic mobility dynamics and underlying PHY/MAC layer implementation affect real deployment of routing protocols in vehicular ad hoc network (VANET). Currently, dedicated short range communication devices are using wireless access in vehicular environment (WAVE) mode of operation, but now IEEE is standardizing 802.11p WAVE. This work presents an in-depth simulation-based analysis of two reactive routing protocols, i.e., dynamic source routing (DSR) and ad hoc on-demand distance vector (AODV) with modified IEEE 802.11a PHY/MAC layers (comparable to 802.11p) in modified VANET mobility models (freeway, stop sign, and traffic sign) in terms of load, throughput, delay, number of hops, and retransmission attempts. Results obtained using OPNET simulator show that in urban/highway mobility scenarios, AODV's performance with forthcoming 802.11p at high bit rate would be better than DSR in terms of high throughput, less delay, and retransmission attempts. Moreover, this comprehensive evaluation will assist to address challenges associated with future deployment of routing protocols integrated upon devices with upcoming IEEE 802.11p, concerning specific macro-/micro-mobility scenarios.

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Dengue is one of the most significant diseases transmitted by arthropods in the world. Dengue phenotypes are focused on documented inaccuracies in the laboratory and clinical studies. In countries with a high incidence of this disease, early diagnosis of dengue is still a concern for public health. Deep learning has been developed as a highly versatile and accurate methodology for classification and regression, which requires small adjustment, interpretable results, and the prediction of risk for complex diseases. This work is motivated by the inclusion of the Particle Swarm Optimization (PSO) algorithm for the fine-tuning of the model's parameters in the convolutional neural network (CNN). The use of this PSO was used to forecast patients with extreme dengue, and to refine the input weight vector and CNN parameters to achieve anticipated precision, and to prevent premature convergence towards local optimum conditions.