A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs.

Vehicle ad hoc networks (VANETs) are a vital part of intelligent transportation systems (ITS), offering a variety of advantages from reduced traffic to increased road safety. Despite their benefits, VANETs remain vulnerable to various security threats, including severe blackhole attacks. In this paper, we propose a deep-learning-based secure routing (DLSR) protocol using a deep-learning-based clustering (DLC) protocol to establish a secure route against blackhole attacks. The main features and contributions of this paper are as follows. First, the DLSR protocol utilizes deep learning (DL) at each node to choose secure routing or normal routing while establishing secure routes. Additionally, we can identify the behavior of malicious nodes to determine the best possible next hop based on its fitness function value. Second, the DLC protocol is considered an underlying structure to enhance connectivity between nodes and reduce control overhead. Third, we design a deep neural network (DNN) model to optimize the fitness function in both DLSR and DLC protocols. The DLSR protocol considers parameters such as remaining energy, distance, and hop count, while the DLC protocol considers cosine similarity, cosine distance, and the node's remaining energy. Finally, from the performance results, we evaluate the performance of the proposed routing and clustering protocol in the viewpoints of packet delivery ratio, routing delay, control overhead, packet loss ratio, and number of packet losses. Additionally, we also exploit the impact of the mobility model such as reference point group mobility (RPGM) and random waypoint (RWP) on the network metrics.

Diagnostic Accuracy of Lymph Nodes Fine Needle Aspiration Biopsy Based on The Sydney System for Reporting Lymph Node Cytology.

OBJECTIVE: To evaluate the diagnostic accuracy and malignancy risk of The Sydney System Reporting for Lymph Nodes Cytology. MATERIAL AND METHODS: This study utilized secondary data from 156 cases to conduct a retrospective analysis of a diagnostic test method. During 2019-2021, data were collected at Dr. Wahidin Sudirohusodo's Anatomical Pathology Laboratory in Makassar, Indonesia. The cytology slides of each case were split into five diagnostic groups using the Sydney method, which were then compared with the results of the histopathological diagnosis. RESULTS: There were six cases in the L1 category, thirty-two cases in the L2 category, thirteen patients in the L3 category, seventeen cases in the L4 category, and ninety-one cases in the L5 class. The malignant probability (MP) is computed for each diagnostic classification. L1 MP value is 66.7%, L2 MP value is 15.6%, L3 MP value is 76.9%, L4 MP value is 94.0%, and L5 MP value is 98.9%. The diagnostic value of the FNAB examination is as follows: 89.9% sensitivity, 92.9% specificity, 98.2% positive predictive value, 68.4% negative predictive value, and 90.47% diagnostic accuracy. CONCLUSION: The FNAB examination provides high sensitivity, specificity, and accuracy in diagnosing lymph node tumors. Using a classification based on the Sydney system promotes communication between laboratories and clinicians.
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