<p>Human evolution has included the development of transportation systems. People are currently driving a significant number of fuel-powered automobiles. This resulted in an increase in the number of accidents as well as pollution in the environment. To address the disadvantages of gasolinebased vehicles, this study presents an IoT-based E-vehicle monitoring system (E-VMS) for early accident detection and to make the environment cleaner and greener by using alternative energy. E-VMS employs internet of things (IoT) technology to continuously monitor the vehicle as well as to access and control it remotely. The IoT devices installed in vehicles are built using an Arduino microcontroller and sensors to detect accidents quickly. When an accident occurs, the E-VMS recognizes it quickly and determines the severity of the incident. The machine will then promptly alert the authorities. The E-VMS is also familiar with the GPS system. This will allow the E-VMS to maintain track of the cars' location in real time. This information will be used to locate the car in the event of an accident or theft. The E-VMS system's results were promising in terms of accurately identifying accidents, determining the severity of the accident, and determining the position of the vehicle.</p>

Computer tomography is an extensively used method for the detection of the disease in the subjects. Basically, computer-aided tomography depending on the artificial intelligence reveals its significance in smart health care monitoring system. Owing to its security and the private issue, analyzing the computed tomography dataset has become a tedious process. This study puts forward the convolutional autoencrypted deep learning neural network to assist unsupervised learning technique. By carrying out various experiments, our proposed method produces better results comparative to other traditional methods, which efficaciously solves the issues related to the artificial image description. Hence, the convolutional autoencoder is widely used in measuring the lumps in the bronchi. With the unsupervised machine learning, the extracted features are used for various applications.