Secure intelligent parking systems are complicated issues around the world. A secure parking system that is larceny-free is considered a luxury now. Parking is expensive and limited in almost all large cities. This research study presents a unique, safe, intelligent parking system based on deep image recognition techniques. To solve this tedious problem, an object detection deep learning model such as You Only Look Once (YOLO) is used. The proposed model is divided into three steps. The first step captures the car's image while entering the parking area. The second step is License plate recognition, in which the license plate is detected and saved the individual characters, and the recognized dataset is used when the driver leaves the parking area. The third step is facing detection and recognition of the taken plate. The fourth step is that the printer generates an invoice code used in case of a mismatch in the face of the driver. Evaluation of the proposed model indicates its efficiency. Accuracy of the following modules is achieved for vehicle detection (99%), number plate detection and recognition 98%, and 95%, respectively, and face detection and recognition 99%, and 97%, respectively. The proposed model's efficiency is shown through its evaluation. To get more security, a two-way screening procedure is used to keep the vehicles secure. So, the identification system's unique idea is to achieve better results by using two cameras and two-way authentication. It also forbids the entrance of those vehicles and drivers who are unauthorized or not allowed by the firm. Moreover, these modules were successfully implemented and evaluated using various performance measures, including precision, recall, and mean average precision.

This study was planned to conduct and to bring the novel approach of thoughts into 3D into reality by using AI techniques & Methods. The study was conducted on user EEG signals and processed into pre-trained tokens of NLP and EEG signals to identify the probability of the EEG signal approach. The translation of EEG and ECoG signals into NLP level and generate a better prompting to Solve the issue hidden in Tokens of Prompting. These Prompt Techniques produce Generative Art by using Stable diffusion to create an image. That 2 Dimension image has Raw data at the background which is removed by using u2net & ModNet to introduce a more refined format as a preprocess for 3-D artwork Generation. The study focused on using different model like shape-e by OpenAI to create a more general approach but our code used it to produce for specifically 3-D avatar character for Metaverse. Using PifuHD estimation model by Facebook and lightweight human pose estimation.3-D art produced a more refined artwork as 3-D object file that can later be used in terms of producing into 3-D printed stuff and enable 3-D Printers closer to User Access as an efficient way of Technology.