Design of Multi Agent Based Crowd Injury Model

A major concern of many government agencies is to predict and control the behavior of crowds in different situations. Many times such gatherings are legal, legitimate, and peaceful. But there are times when they can turn violent, run out of control, result in material damages and even casualties. It then becomes the duty of governments to bring them under control using a variety of techniques, including non-lethal and lethal weapons, if necessary. In order to aid decision makers on the course of action in crowd control, there are modeling and simulation tools that can provide guidelines by giving programmed rules to computer animated characters and to observe behaviors over time in appropriate scenarios. A crowd is a group of people attending a public gathering, with some joint purpose, such as protesting government or celebrating an event. In some countries these kinds of activities are the only way to express public\u27s displeasure with their governments. The governments\u27 reactions to such activities may or may not be tolerant. For these reasons, such situations must be eliminated by recognizing when and how they occur and then providing guidelines to mitigate them. Police or military forces use non-lethal weapons (NLWs), such as plastic bullets or clubs, to accomplish their job. In order to simulate the results of such actions in a computer, there is a need to determine the physical effects of NLWs over the individuals in the crowd. In this dissertation, a fuzzy logic based crowd injury model for determining the physical effects of NLWs is proposed. Fuzzy logic concepts can be applied to a problem by using linguistic rules, which are determined by problem domain experts. In this case, a group of police and military officers were consulted for a set of injury model rules and those rules were then included in the simulation platform. As a proof of concept, a prototype system was implemented using the Repast Simphony agent based simulation toolkit. Simulation results illustrated the effectiveness of the simulation framework

Parameter Optimization for Image Denoising Based on Block Matching and 3D Collaborative Filtering

Clinical MRI images are generally corrupted by random noise during acquisition with blurred subtle structure features. Many denoising methods have been proposed to remove noise from corrupted images at the expense of distorted structure features. Therefore, there is always compromise between removing noise and preserving structure information for denoising methods. For a specific denoising method, it is crucial to tune it so that the best tradeoff can be obtained. In this paper, we define several cost functions to assess the quality of noise removal and that of structure information preserved in the denoised image. Strength Pareto Evolutionary Algorithm 2 (SPEA2) is utilized to simultaneously optimize the cost functions by modifying parameters associated with the denoising methods. The effectiveness of the algorithm is demonstrated by applying the proposed optimization procedure to enhance the image denoising results using block matching and 3D collaborative filtering. Experimental results show that the proposed optimization algorithm can significantly improve the performance of image denoising methods in terms of noise removal and structure information preservation

Finding Smoothness Area on the Topographic Maps for the Unmanned Aerial Vehicle's Landing Site Estimation

In this study, determining of the suitable landing areas on the topographical maps were determined for emergency landing of the Unmanned Air Vehicles (UAVs) during flight. In order to reach desired goals of this work, Shuttle radar topography mission maps (SRTM) was used. Nowodays, UA V have been intensively used in civillian and military applications. There are urgent needs on increasing of autonomy of the UAVs, decreasing human expertise and making smarter of UA V systems has become an inevitable necessity. Unexpectable stiuations (i.e. motor or comminatication failure, etc ... ) can arise while missions of the UAV. Emergency landing system must be activated by autonomously and then landed on the ground in safely while occuring some failure mentioned above. Two different techniques were chosed for determining probable landing areas by using digital elevation maps (SRTM). Firstly, surface fitting approximation was applied by using Least Squares Error (LSE). The slope of the points were calculated to specify of the smoothness rate of the landing areas. Smooth areas were signed by using SRTM datas. Image processing techniques were utilized for marking of the smooth areas and determinig boundries of the landing areas. The smooth landing areas were groupped with Blob analysis. The noise of the ground specified as landing areas were reduced with morphological image processing (performs morphological openning). UAVs system can be made smarter with specifying of the landing areas and planning of the path according to emergency cases. With designed systems, the UA V could be guided to the suitable landing zones vice versa undesirable areas by limiting of the landing path in the emergency cases of the UAV

DEPHIDES: Deep Learning Based Phishing Detection System

In today’s digital landscape, the increasing prevalence of internet-connected devices, including smartphones, personal computers, and IoT devices, has enabled users to perform a wide range of daily activities such as shopping, banking, and communication in the online world. However, cybercriminals are capitalizing on the Internet’s anonymity and the ease of conducting cyberattacks. Phishing attacks have become a popular method for acquiring sensitive user information, including passwords, bank account details, social security numbers and more, often through social engineering and messaging tools. To protect users from such threats, it is essential to establish sophisticated phishing detection systems on computing devices. Many of these systems leverage machine learning techniques for accurate classification. In recent years, deep learning algorithms have gained prominence, especially when dealing with large datasets. This study presents the development of a phishing detection system based on deep learning, employing five different algorithms: artificial neural networks, convolutional neural networks, recurrent neural networks, bidirectional recurrent neural networks, and attention networks. The system primarily focuses on the fast classification of web pages using URLs. To assess the system’s performance, a relatively extensive dataset of labeled URLs, comprising approximately five million records, was collected and shared. The experimental results indicate that convolutional neural networks achieved the highest performance, boasting a detection accuracy of 98.74% for phishing attacks. This research underscores the effectiveness of deep learning algorithms, particularly in enhancing cybersecurity in the face of evolving cyber threats