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Browsing Conference Articles by Author "Abu Zitar, Raed"
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- PublicationA Lower Complexity Deep Learning Method for Drones Detection(2023)
;Mohamad Kassab ;Amal El Fallah Seghrouchni ;Frederic BarbarescoDetecting 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.38Scopus© Citations 1 - PublicationA Review for the Genetic Algorithm and the Red Deer Algorithm Applications(2021)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 37 - PublicationA Review of the Genetic Algorithm and JAYA Algorithm Applications(2022)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.
16Scopus© Citations 4 - PublicationAnalysis of the Performance of Four Filter Types for Drone Tracking(2023)
; ;Segrouchni, Amal El Fallah ;Barbaresco, FredericIn 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.16 - PublicationApplication of Red Deer Algorithm in Optimizing Complex functions(2021)
; Abualigah, LaithThe 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 - PublicationDrone 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, StephenThis 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 - PublicationDrone/Bird Classification Based on Features of Tracks Trajectories(2023)
;Kengeskanov, Maksat ;Seghrouchni, Amal El Fallah; Barbaresco, FredericThis 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 show32 - PublicationEnd to End Camera only Drone Detection and Tracking Demo within a Multi-agent Framework with a CNN-LSTM Model for Range Estimation(2024)
;De Rochechouart, Maxence; ;Seghrouchni, Amal El FallahBarbaresco, FredericWe present an end-to-end camera-only drone tracking approach in a multi-agent framework. We show implementation and simulation of such a system and test the tracking components utilizing a CNN-LSTM model for range estimation tested on real data. A video of the demo is available at this link (https://drive.google.com/file/d/1AlV89lgfi5nqwZCHTXC0pLm6BsZh_cBk/view?usp=drive_link).21 2 - PublicationMachine Learning Approach for the Design of an Assessment Outcomes Recommendation System(2021)
; ;Fatime Al-Zahra ;Shaimaa MounirLamees DalbahIt is believed that the evaluation of the outcomes of the course, based on grades, is necessary to improve the teaching and learning process. Our research processes and workflows supported by AI utilize machine learning technology in order to interpret big data, analyze broad data sets and recognize associations with more reliably. The course learning outcomes will be assessed on the basis of QF-Emirates guidelines and use it to suggest teaching and learning measures. It will be used to determine courses learning results based on the empirical knowledge presented. We research and test the design of the right neural networks that achieves our goal. A modern algorithm was improvised for this reason. For our proposed recommendation system, a database program was created to store data and include details in the analysis of course learning outcomes. As a machine-learning system, the proposed approach is tested and results are competitive.20 - PublicationMulti-Target Tracking Resources Allocation Using Multi-Agent Modeling and Auction Algorithm(2023)
;De Rochechouart, Maxence ;Seghrouchni, Amal El Fallah ;Barbaresco, FredericThis paper proposes a multi-agent technique for modeling and simulating multiple objects and multiple sensor tracking for drones. The goal is to achieve efficient resource allocation based on different preset cost functions that take into account several local and global attributes. The target dynamics, the sensor selection, and the measurements are simulated by way of an agent-based modeling framework called MESA. The results show an optimum resource allocation with an information-based cost function that is optimized by an Auction Algorithm.15 - PublicationOptimum Track to Track Fusion Using CMA-ES and LSTM Techniques(2024)
;Fares, Samar ;Seghrouchni, Amal El Fallah ;Barbaresco, FredericThis paper presents two different methods for track-to-track fusion of drone tracks. The sensors are unbiased radars with fixed locations. The first method uses an offline technique based on a global optimizer called the CMA-ES algorithm and the second one uses LSTM in its different forms to learn the online adjustment of the fusion weights between the two tracks. An objective function utilizing the covariance of the fused tracks is used by the first algorithm while a cost function based on the Kullback-Leibler (KL) divergence measure is used in the second case for training the LSTM. The two methods are compared with other baseline methods using performance metrics such as SIAP and OSPA. Simulations are done for a single object (drone) and repeated for multiple objects in the presence of two radars to demonstrate the validity of the two proposed techniques. The JPDA (Joint Probability Data Association) with fixed gating and moderate clutter is used in the case of multiple objects. Stone Soup was chosen as the radar simulation environment.8 - PublicationPredictive Model of Psychoactive Drugs Consumption using Classification Machine Learning Algorithms(2023)
;Almahmood, Mothanna ;Najadat, Hassan ;Alzubi, Dalia ;Abualigah, Laith; ;Abualigah, SayelAL-Saqqar, FaisalIt is difficult to predict the effect of drugs on the individuals, as its results are unpredictable and most often dangerous. For a police purpose that concerned with the protection of individuals, the problem of predicting drug abusing is highly important. A dataset was used from open-source website UCI, that includes specific attributes about using up of eighteen different psychoactive drugs. Our study aimed to use data mining classification techniques, in order to classify the individual into two categories: user or non-user. Eighteen classification models were built using different classification algorithms such as Gaussian Naive Bais, Logistic Regression, k-nearest neighbors, Random Forest, and Decision Tree. The accurate classifier was chosen by studying the accuracy, recall, precision, and f1-score measures for each one, and it was evaluated by the Holdout method. The results were obtained optimally, and we got 18 models, where each one had different high accurate outputs, that classify an individual to user and non-user. The final model is a combination of 18 models for 18 critical psychoactive drugs: Alcohol, Amphet, Amyl, Benzos, Caff, Cannabis, Choc, Coke, Crack, Ecstasy, Heroin, Ketamine, Legalh, LSD, Meth, Mushrooms, Nicotine and VSA. This study in turn may give a chance for the decision makers to reduce the risk of these drugs consumption, in order to avoid healthcare issues and keep the community in safe.20 1 - PublicationResources Allocation for Drones Tracking Utilizing Agent-Based Proximity Policy Optimization(2023)
;De Rochechouart, Maxence ;Segrouchni, Amal El Fallah ;Barbaresco, FredericThis paper presents a reinforcement learning agent-based model that works by incorporating the MESA environment with the Stone Soup radar systems simulator. In particular, the Proximity Policy Optimization (PPO) reinforcement algorithm is used to discover a policy for sensor selection that results in optimum sensor resource allocation. In this work, one radar and one camera collaborate to generate tracks of a drone. The sequential measurements processing method is used to merge the inputs from the camera and the radar. An Extended Kalman Filter (EKF) is used to estimate the track. The learned system can apply sensor allocation online, works in real-time, and selects one radar and one camera at a time without having to reevaluate a cost function at every time step. Comparisons are done with the straight minimum entropy method and the random selection baseline method. The work demonstrates how machine learning techniques can capture resource allocation policy and help avoid the complexity of having to re-calculate cost function at every time step, especially when we have many radars and many cameras.11 - PublicationSmart Learning and Fourth Industrial Age Effects on Higher Education(2021)The shift toward e-learning is imminent. The world is heading to the fourth industrial age. What are the implications of the fourth industrial age on higher education? What can a trend such as artificial intelligence contribute to education? Can Blockchain technology play part in opening a new education model? Can credentials be awarded and granted online? Experts believe that market forces will push higher education institutes to expand online education. More hybrid learning spaces will be created and different degrees awarded, and curricula structures will be created in the few coming years. However, how will this affect the education, for the better or for the worse? We have investigated this major question through extensive research within available literature and through personal encounters with many stakeholders. What are the implications of the fourth industrial age on education? That would be a major question to answer.
24 3 - PublicationTemporal Machine Learning Payload Prediction for DJI Matrice 100 Quadcopter Drone Based on Tracking Data(2024)
;Kashkash, Mariam ;Seghrouchni, Amal El Fallah ;Barbaresco, FredericThis paper presents three different machine-learning techniques to predict the payloads of DJI Matrice 100 quadcopter drones. The tracking data is based on real-life experimentation that is provided as open source. The payloads are 0.0, 250, 500, and 750 grams. The Machine Learning techniques are LSTM, TCN, and GRU. The values of the tracks’ kinematics come from several different flights for the different loads. The tracks’ kinematics in addition to some flight parameters are used in training the three models. The temporal nature of the data values triggers the need for machine learning methods that use history/memory as part of inputs. The input variables went through the data reduction stage as some variables turned out to be less relevant than others for the prediction operation. The original data has more than 1400 records for every flight with more than 22 variable values. More than 270 flights were conducted with the 4 payloads. The flight/track data is accessed in parallel at every time stamp by the learning models and the model converges after a few epochs to the payload label. The training/validation/testing values show that the three models captured the predicted load efficiently. Comparisons between the three models as predictors for the carried payloads are presented and discussed in the paper. The TCN showed slight superiority over the other models7