The current world faces a considerable traffic rate on roads due to the increase in various types of vehicles. It caused emergency vehicles to delay and increasing the patients' health risk factor. Internet of vehicle-based artificial neural network (IoV-ANN) has been proposed for effective health autonomous system in medical things. The proposed IoV-ANN provides a secure network to monitor and track the vehicle's motion using the global positioning system. It consists of an autonomous system which is enabled with an artificial neural network (ANN). ANN model has three layers. First layers collect the data using IoV sensors. Second or hidden layers process the sensor data, predict the road's traffic condition and reroute the emergency vehicle to an exact route. IoV-ANN helps to reduce road congestion in this article to enhance the timely functioning of an emergency vehicle. ANN categorizes the congestion networks of traffic. Traffic restrictions such as changing the queue gap in the road signals and the alternative roads are altered automatically due to congestion. It allows the government to develop ideas for alternate routes to enhance traffic control. The output layer gives commands to the driver autonomously. The simulation analysis of the proposed method proved that the system could work independently. The IoV-ANN achieves the highest performance rate of (97.89%), with a reduced error rate (9.12%) traffic congestion rate (10.31%), travel period (32 s), vehicle detection rate (93.61%), classification accuracy (95.02%), MAPE (8.4%), throughput rate (93.50%) when compared to other popular methods.

中文翻译：

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撤回文章：用于医疗领域的具有 AI 的车辆互联网和自主系统。

由于各种类型的车辆的增加，当今世界在道路上面临着相当大的交通流量。导致急救车辆延误，增加了患者的健康风险因素。基于车联网的人工神经网络 (IoV-ANN) 已被提出用于医疗物联网中有效的健康自主系统。拟议的 IoV-ANN 提供了一个安全网络，以使用全球定位系统监视和跟踪车辆的运动。它由一个由人工神经网络 (ANN) 启用的自治系统组成。ANN 模型具有三层。第一层使用 IoV 传感器收集数据。第二层或隐藏层处理传感器数据，预测道路交通状况并将紧急车辆重新安排到准确的路线。本文中的 IoV-ANN 有助于减少道路拥堵，以增强应急车辆的及时运行。ANN 对交通拥塞网络进行分类。交通限制，例如改变道路信号中的队列间隙和替代道路会因拥堵而自动改变。它允许政府制定替代路线的想法，以加强交通管制。输出层自主向驱动程序发出命令。仿真分析证明该系统能够独立工作。IoV-ANN实现了最高的性能率（97.89%）、错误率（9.12%）、交通拥堵率（10.31%）、行驶周期（32 s）、车辆检测率（93.61%）、分类准确率（ 95.02%)、MAPE (8.4%)、吞吐率 (93.50%) 与其他流行方法相比。