COMP 1336 - Computer Science I: PVAMU Professor Works

"Automatic Number Plate Recognition (ANPR) has been widely used in different domains, such as car park management, traffic management, tolling, and intelligent transport systems. Despite this technology’s importance, the existing ANPR approaches suffer from the accurate identification of number plats due to its different size, orientation, and shapes across different regions worldwide. In this paper, we are studying these challenges by implementing a case study for smart car towing management using Machine Learning (ML) models. The developed mobile-based system uses different approaches and techniques to enhance the accuracy of recognizing number plates in real-time. First, we developed an algorithm to accurately detect the number plate’s location on the car body. Then, the bounding box of the plat is extracted and converted into a grayscale image. Second, we applied a series of filters to detect the alphanumeric characters’ contours within the grayscale image. Third, the detected the alphanumeric characters’ contours are fed into a K-Nearest Neighbors (KNN) model to detect the actual number plat. Our model achieves an overall classification accuracy of 95% in recognizing number plates across different regions worldwide. The user interface is developed as an Android mobile app, allowing law-enforcement personnel to capture a photo of the towed car, which is then recorded in the car towing management system automatically in real-time. The app also allows owners to search for their cars, check the case status, and pay fines. Finally, we evaluated our system using various performance metrics such as classification accuracy, processing time, etc. We found that our model outperforms some state-of-the-art ANPR approaches in terms of the overall processing time."

"The increasing ubiquity of network traffic and the new online applications’ deployment has increased traffic analysis complexity. Traditionally, network administrators rely on recognizing well-known static ports for classifying the traffic flowing their networks. However, modern network traffic uses dynamic ports and is transported over secure application-layer protocols (e.g., HTTPS, SSL, and SSH). This makes it a challenging task for network administrators to identify online applications using traditional port-based approaches. One way for classifying the modern network traffic is to use machine learning (ML) to distinguish between the different traffic attributes such as packet count and size, packet inter-arrival time, packet send–receive ratio, etc. This paper presents the design and implementation of NetScrapper, a flow-based network traffic classifier for online applications. NetScrapper uses three ML models, namely K-Nearest Neighbors (KNN), Random Forest (RF), and Artificial Neural Network (ANN), for classifying the most popular 53 online applications, including Amazon, Youtube, Google, Twitter, and many others. We collected a network traffic dataset containing 3,577,296 packet flows with different 87 features for training, validating, and testing the ML models. A web-based user-friendly interface is developed to enable users to either upload a snapshot of their network traffic to NetScrapper or sniff the network traffic directly from the network interface card in real time. Additionally, we created a middleware pipeline for interfacing the three models with the Flask GUI. Finally, we evaluated NetScrapper using various performance metrics such as classification accuracy and prediction time. Most notably, we found that our ANN model achieves an overall classification accuracy of 99.86% in recognizing the online applications in our dataset."

"With recent advances in both AI and IoT capabilities, it is possible than ever to implement surveillance systems that can automatically identify people who might represent a potential security threat to the public in real-time. Imagine a surveillance camera system that can detect various on-body weapons, masked faces, suspicious objects and traffic. This system could transform surveillance cameras from passive sentries into active observers which would help in preventing a possible mass shooting in a school, stadium or mall. In this paper, we present a prototype implementation of such systems, Hawk-Eye, an AI-powered threat detector for smart surveillance cameras. Hawk-Eye can be deployed on centralized servers hosted in the cloud, as well as locally on the surveillance cameras at the network edge. Deploying AI-enabled surveillance applications at the edge enables the initial analysis of the captured images to take place on-site, which reduces the communication overheads and enables swift security actions. At the cloud side, we built a Mask R-CNN model that can detect suspicious objects in an image captured by a camera at the edge. The model can generate a high-quality segmentation mask for each object instance in the image, along with the confidence percentage and classification time. The camera side used a Raspberry Pi 3 device, Intel Neural Compute Stick 2 (NCS 2), and Logitech C920 webcam. At the camera side, we built a CNN model that can consume a stream of images directly from an on-site webcam, classify them, and displays the results to the user via a GUI-friendly interface. A motion detection module is developed to capture images automatically from the video when a new motion is detected. Finally, we evaluated our system using various performance metrics such as classification time and accuracy. Our experimental results showed an average overall prediction accuracy of 94% on our dataset."