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    Detection of vehicle with Infrared images in Road Traffic using YOLO computational mechanism
    (IOP Publishing Ltd, 2020) Mahmood, M.T.; Ahmed, S.R.A.; Ahmed, M.R.A.
    Vehicle counting is an important process in the estimation of road traffic density to evaluate the traffic conditions in intelligent transportation systems. With increased use of cameras in urban centers and transportation systems, surveillance videos have become central sources of data. Vehicle detection is one of the essential uses of object detection in intelligent transport systems. Object detection aims at extracting certain vehicle-related information from videos and pictures containing vehicles. This form of information collection in intelligent systems is faced with low detection accuracy, inaccuracy in vehicle type detection, slow processing speeds. In this research, we propose a vehicle detection system from infrared images using YOLO (You Look Only Once) computational mechanism. The YOLO mechanism can apply different machine or deep learning algorithms for accurate vehicle type detection. In this study we propose an infrared based technique to combine with YOLO for vehicle detection in traffic. This method will be compared with a machine learning technique of K-means++ clustering algorithm, a deep learning mechanism of multitarget detection and infrared imagery using convolutional neutral network © Published under licence by IOP Publishing Ltd.
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    Using Machine Learning to Secure IOT Systems
    (Institute of Electrical and Electronics Engineers Inc., 2020) Mahmood, M.T.; Ahmed, S.R.A.; Ahmed, M.R.A.
    In this paper we will first find out the issues that are arises when we implement IOT systems and later we will fix these issues using Machine Learning techniques. we will implement an RFID (radio frequency identification) system which is seen as the prerequisite for the IOT and the research will also show a number of different technologies available when implementing such a system, showing their differences and why certain ones can be chosen over others for certain functional or security requirements. As stated the prototype IoT system will serves as a running example. The system implemented serves as a way for passengers at an airport to track their baggage after checking it in. The findings of implementing this system, combined with a literature study led us to find five main differences between IoT and traditional systems. Briefly summarized these differences are the following:1. Technical limitations of IoT devices.2. Physical environment plays a larger role in IoT systems. Many components of an IoT system will not be in a controlled environment.3. Lack of security-focus during design and implementation process.4. IoT devices are an interesting target for attackers as tools for DDoS attacks.5. The use cases of IoT systems are more often privacy sensitive. For the training, testing and validation of KDD (Knowledge Discovery and Data Mining) Cup 1999 dataset which is an IoT and cybersecurity based dataset, a well-known MATLAB R2019a software was used for this purpose. Furthermore, this works shows that the accuracy of machine learning models can mitigated to some degree with artificial neural network technique and achieving the accuracy of up to 97.2% with execution time of 2.11s only. © 2020 IEEE.