Now showing 1 - 10 of 19
  • Publication
    A Lower Complexity Deep Learning Method for Drones Detection
    (2023)
    Mohamad Kassab
    ;
    Amal El Fallah Seghrouchni
    ;
    Frederic Barbaresco
    ;
    Detecting objects such as drones is a challenging task as their relative size and maneuvering capabilities can deceive machine learning models and cause them to misclassify drones as birds or other objects. In this work, we investigate applying several deep-learning techniques to benchmark real data sets of flying drones. A Deep learning paradigm is proposed for the purpose of mitigating the complexity of those systems. The proposed paradigm consists of a hybrid between the AdderNet deep learning paradigm and the SSD paradigm. The goal was to minimize multiplication operations numbers in the filtering layers within the proposed system and, hence, reduce complexity. Some standard machine learning techniques such as SVM is also tested and compared to other deep learning systems. The data sets used for training and testing were either complete or filtered in order to remove the images with mall objects. The types of data were either RGB or IR data. Comparisons were made between all these types and conclusions are presented.
      31Scopus© Citations 1
  • Publication
    A real-time automatic pothole detection system using convolution neural networks
    (2023)
    Bharat, Ricardo
    ;
    Ikotun, Abiodun M
    ;
    Ezugwu, Absalom E.
    ;
    Abualigah, Laith
    ;
    Shehab, Mohammad
    ;
    Detecting a pothole can help prevent damage to your vehicle and potentially prevent an accident. Different techniques, including machine learning, deep learning models, sensor methods, stereo vision, the internet of things (IoT), and black-box cameras, have already been applied to address the problem. However, studies have shown that machine learning and deep learning techniques successfully detect potholes. However, because most of these successful attempts are peculiar to the location of the study, we found no study which has addressed the peculiarity of potholes in South Africa using a tailored-trained deep learning model. In this study, we propose using a convolutional neural network (CNN), a type of deep learning model, to address this growing problem on South African roads. To achieve this, a CNN model was designed from scratch and trained with image samples obtained from the context of the study. The classifier was adapted to distinguish between a binary class which identifies the presence or absence of potholes. Results showed a significant performance enhancement at a classification accuracy of 92.72%. The outcome of this study showed that this machine learning approach holds great potential for addressing the challenge of potholes and road bumps in the region and abroad.
      17
  • Publication
    A Review for the Genetic Algorithm and the Red Deer Algorithm Applications
    The Red Deer algorithm (RD), a contemporary population-based meta heuristic algorithm, applications are thoroughly examined in this paper. The RD algorithm blends evolutionary algorithms' survival of the fittest premise with the productivity and richness of heuristic search approaches. On the other a well-known and relatively older evolutionary based algorithm called the Genetic Algorithm applications are also shown. The contemporary algorithm; the RDA, and the older algorithm; the GA have wide applications in computer science and engineering. This paper sheds the light on all those applications and enable researchers to exploit the possibilities of adapting them in any applications they may have either in engineering, computer science, or business.
    Scopus© Citations 8  36
  • Publication
    A Review of the Genetic Algorithm and JAYA Algorithm Applications
    This study throws the light on two metaheuristic algorithms and enable researchers to leverage the potential of adapting them in whatever applications they may have either in engineering, computer science, or business. The two algorithms are the GA and the JAYA. The JAYA algorithm is a modern population-based meta heuristic algorithm, its applications are presented in this work. The JA Y A algorithm integrates evolutionary algorithms' survival of the fittest concept with the productivity and richness of heuristic search methodologies. On the other a well-known and somewhat older evolutionary based method called the Genetic Algorithm with applications is also presented here. The recent two algorithms; the JA Y A and the GA have broad comparable applications in computer science and engineering applications.
      12Scopus© Citations 3
  • Publication
    Analysis of the Performance of Four Filter Types for Drone Tracking
    (2023) ;
    Segrouchni, Amal El Fallah
    ;
    Barbaresco, Frederic
    ;
    In this work, extensive simulations are done to compare the performance of the 4 filter types; Linear Kalman filter (LKF), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). A simple nearly constant velocity (NCV) motion model is used with a Gaussian noise measurement model. Simulations were done with different ground truths, different measurements covariance matrices, and different speeds of the drone. Stone soup software was used in the simulations. The analyses revealed informative results that gave us more understanding of the behavior of the four filters when a common type of motion model such as the NCV model is used.
      10
  • Publication
    Application of Red Deer Algorithm in Optimizing Complex functions
    (2021) ;
    Abualigah, Laith
    The Red Deer algorithm (RDA), a recently developed population-based meta-heuristic algorithm, is examined in this paper with the optimization task of complex functions. The RD algorithm blends evolutionary algorithms' survival of the fittest concept with heuristic search techniques' productivity and richness. It is critical to assess this algorithm's performance in comparison with other well-known heuristic methods. The findings are presented along with additional recommendations for increasing RDA performance based on the analysis. The readers of this paper will gain a grasp of the RD algorithm and its optimization ability to determine whether this algorithm is appropriate for their particular business, research, or industrial needs.
    Scopus© Citations 4  19
  • Publication
    Cybers Security Analysis and Measurement Tools Using Machine Learning Approach
    (2022) ;
    Ghazal, Taher M.
    ;
    Hasan, Mohammad Kamrul
    ;
    Al-Dmour, Nidal A.
    ;
    Al-Sit, Waleed T.
    ;
    Shayla Islam
    Artificial intelligence (AI) and machine learning (ML) have been used in transforming our environment and the way people think, behave, and make decisions during the last few decades [1]. In the last two decades everyone connected to the Internet either an enterprise or individuals has become concerned about the security of his/their computational resources. Cybersecurity is responsible for protecting hardware and software resources from cyber attacks e.g. viruses, malware, intrusion, eavesdropping. Cyber attacks either come from black hackers or cyber warfare units. Artificial intelligence (AI) and machine learning (ML) have played an important role in developing efficient cyber security tools. This paper presents Latest Cyber Security Tools Based on Machine Learning which are: Windows defender ATP, DarckTrace, Cisco Network Analytic, IBM QRader, StringSifter, Sophos intercept X, SIME, NPL, and Symantec Targeted Attack Analytic.
      10Scopus© Citations 4
  • Publication
    Drone Tracking Based on the Fusion of Staring Radar and Camera Data: An Experimental Study
    (2023) ;
    Ahmad, Bashar I.
    ;
    Seghrouchni, Amal El Fallah
    ;
    Barbaresco, Frederic
    ;
    Harman, Stephen
    ;
    This paper presents an experimental study on tracking a small drone target with a high resolution camera and a staring radar. The objective is to assess the benefits of fusing the outputs of both sensors using real data collected during live drone trials. We examine the impact of losing the signal from one sensor, which often occurs in practice for various reasons such as occlusions, high background noise-clutter, target sharp maneuvers, etc. We demonstrate that fusion with filtering, namely employing interacting multiple models with unscented Kalman filter in modified spherical coordinates or a simple extended Kalman filter, can deliver improved overall target tracking performance under such degraded sensing conditions.
      19Scopus© Citations 1
  • Publication
    Drone/Bird Classification Based on Features of Tracks Trajectories
    (2023)
    Kengeskanov, Maksat
    ;
    Seghrouchni, Amal El Fallah
    ;
    ;
    Barbaresco, Frederic
    This paper presents the outcome of several machine learning techniques used for the task of bird/drone classification based on their tracks. Instead of using static images, the dynamics and features extracted from the trajectories captured in videos are used to provide a more accurate and reliable recognition task. Standard Machine Learning methods such as SVM and Random Forest are used for learning this classification. Features based on the kinematics, Gabor filter, and Gray Level Co-occurrence Matrix are utilized. Several comparisons and experiments based on benchmark data sets are show
      32
  • Publication
    Drones Tracking Adaptation Using Reinforcement Learning: Proximal Policy optimization
    (2023) ;
    Seghrouchni, Amal El Fallah
    ;
    Barbaresco, Frederic
    ;
    This paper presents a reinforcement learning approach for automatic adaptation of the process noise covariance (Q). The Q value plays a crucial role in estimating future state values within a Kalman filter tracking system. Proximal Policy Optimization (PPO), a state-of-the-art policy optimization algorithm, was employed to determine the optimal Q value that enhances tracking performance, as measured by Root Mean Square Error (RMSE). Our results demonstrate the successful learning capability of the PPO agent over time, enabling it to suggest the optimal Q value by effectively capturing the policy of appropriate rewards under varying environmental conditions. These outcomes were compared with those of a feed-forward neural network learning, the Castella innovation/ Q values mapping, and fixed Q values. The PPO algorithm yielded promising results. We employed the Stone Soup library to simulate ground truths, measurements, and the Kalman filter tracking process.
      11Scopus© Citations 1