UMBC Data Science

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The Data Science graduate program at UMBC prepares students to respond to the growing demand for professionals with data science knowledge, skills, and abilities. Our program brings together faculty from a wide range of fields who have a deep understanding of the real-world applications of data analytics. UMBC’s Data Science programs prepare students to excel in data science roles through hands-on experience, rigorous academics, and access to a robust network of knowledgeable industry professionals.

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Recent Submissions

Now showing 1 - 20 of 156
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    Predictive Maintenance of Urban Metro Vehicles: Classification of Air Production Unit Failures Using Machine Learning
    (2023-03) Najjar, Ayat; Ashqar, Huthaifa; Hasasneh, Ahmad
    Predictive maintenance methods assist early detection of failures and errors in machinery before they reach critical stages. Predictive maintenance methods assist early detection of failures and errors in machinery before they reach critical stages. Predictive maintenance methods assist early detection of failures and errors in machinery before they reach critical stages. Predictive maintenance (PdM) is crucial for companies to avoid unplanned outages, increase overall reliability, and lower operating costs. Failure detection and classification is a key element of predictive maintenance. In this study, a novel framework for identifying failures in the Air Production Unit (APU) of metro vehicles in real-time was proposed. The framework can also be used to create a recommendation system for predicting APU failures. To the best of our knowledge, this is the first study that detect and classify the failures in APU's metro vehicle using a real-time approach that includes machine learning. Analog sensors were found to be more significant than digital sensors in providing real-time, continuous data that is crucial for maintaining safe and efficient train operation. The proposed framework resulted in promising results with the highest F-Score of about 85% for the binary classifier and 97% for the multiclassification using the RF algorithm on the MetroPT dataset. The framework can be beneficial for metro operators by reducing maintenance costs, increasing safety, improving reliability, better managing assets, and enhancing the passenger experience. By predicting when maintenance is needed, operators can address potential safety issues before they become serious problems, improve the reliability of the metro system, and reduce disruptions for passengers. The most important analog sensor-based features include the pressure within the trains' installed air tanks, oil temperature on the compressor, and flowmeter values. The proposed framework is applicable in the field and can help operators make more informed decisions about when to repair or replace assets.
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    Road sign classification using deep learning
    (National Academy of Sciences, 2023-09) Ashour, Karim; Nafaa, Selvia; Emad, Doaa; Mohamed, Rana; Essam, Hafsa; Elhenawy, Mohammed; Ashqar, Huthaifa; Hassan, Abdallah A.; Glaser, Sebastien; Rakotonirainy, Andry
    Road sign classification is essential for safety, especially with the development of autonomous vehicles and automated road asset management. Road sign classification is challenging because of several factors, including lighting, weather conditions, motion blur and car vibration. In this study, we developed an ensemble of fine-tuned pre-trained CCN networks. We used the German Traffic Sign Recognition Benchmark (GTSRB) to train and test the proposed ensemble. The proposed ensemble yielded a preliminary testing accuracy of 96.8%. Consequently, we customized the architecture of the worst-performing network in the ensemble, which boosted the accuracy to 99%.
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    Intersection detection using vehicle trajectories data: Deep Neural Network application
    (National Academy of Sciences, 2023-09) Kased, Abanoub; Rabee, Rana; Fahmy, Akram; Mohamed, Hussien; Yacoub, Marco; Elhenawy, Mohammed; Ashqar, Huthaifa; Hassan, Abdallah A.; Glaser, Sebastien; Rakotonirainy, Andry
    In 2021, intersection-adjacent crashes were stated to cause 7.7% of total annual road deaths in Australia (BITRE, n.d.). Generating intersection maps is essential for future Cooperative Intelligent Transport Systems (C-ITS) deployment. Nonetheless, crowdsourced vehicle trajectories are a viable and affordable data source that can be used to generate maps. However, intersection maps are changeable, and building one map inference model for all intersection types is challenging. Therefore, we need an object detector that can detect and classify the different intersections using the 2-D scatter plot of the crowdsourced trajectories. Consequently, each subset of trajectories data points passed to the suitable intersection map inference model. This study used two real-world vehicle trajectory datasets, T-Drive and ECML-PKDD 15, to classify the intersections by building an object detection model using Deep Neural Network (DNN). We created 2000 images to train a Single-Shot detector the initial testing results were promising.
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    Deep Learning-Based pavement defect detection
    (National Academy of Sciences, 2023-09) Mohamed, R.; Esam, H.; Nafaa, S.; Ashour, K.; Emad, D.; Elhenawy, M.; Ashqar, Huthaifa; Hassan, A. A.; Glaser, S.; Rakotonirainy, A.
    Pavement defects can significantly impact road safety, and detecting and repairing these defects is important. However, pavement defects detection by humans is time-consuming. With the advances in information and communication technology, many vehicles on the road are fitted with cameras, generating massive, crowdsourced data. This study demonstrates the usage of deep learning and computer vision to identify and classify pavement defects. We used the Road Damage Dataset 2022 (Arya et al., 2022) to train and test different object detectors, ensuring accurate and reliable detection. The initial results showed that it is possible to identify and classify pavement defects efficiently with results of 80% mAP50, reducing the risk of accidents, in addition, using these methods can lead to cost savings in maintenance and repair expenses, as well as reduce the environmental impact of routine road surveys.
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    Traffic Estimation of Various Connected Vehicle Penetration Rates: Temporal Convolutional Network Approach
    (IEEE, 2024-05) Ashqer, Mujahid; Ashqar, Huthaifa; Elhenawy, Mohammed; Rakha, Hesham A.; Bikdash, Marwan
    Traffic estimation using probe vehicle data is a crucial aspect of traffic management as it provides real-time information about traffic conditions. This study introduced a novel framework for traffic density estimation using Temporal Convolutional Network (TCN) for time series data. The study used two datasets collected from a three-leg intersection in Greece and a four-leg intersection in Germany. The model was built to predict the density in an approach of the signalized intersection using features extracted from the other approaches. The results showed that the highest accuracy was achieved when only probe vehicle data was used. This implies that relying solely on probe vehicle data from two approaches can effectively predict traffic density in the third approach, even when the Market Penetration Rate (MPR) is low. The results also indicated that having Signal Phase and Timing (SPaT) information may not be necessary for high accuracy in traffic estimation and that as the MPR increases, the model becomes more predictable.
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    Predictive Analytics in Mental Health Leveraging LLM Embeddings and Machine Learning Models for Social Media Analysis
    (IGI Global, 2024-01-01) Radwan, Ahmad; Amarneh, Mohannad; Alawneh, Hussam; Ashqar, Huthaifa; AlSobeh, Anas; Magableh, Aws Abed Al Raheem
    The prevalence of stress-related disorders has increased significantly in recent years, necessitating scalable methods to identify affected individuals. This paper proposes a novel approach utilizing large language models (LLMs), with a focus on OpenAI's generative pre-trained transformer (GPT-3) embeddings and machine learning (ML) algorithms to classify social media posts as indicative or not of stress disorders. The aim is to create a preliminary screening tool leveraging online textual data. GPT-3 embeddings transformed posts into vector representations capturing semantic meaning and linguistic nuances. Various models, including support vector machines, random forests, XGBoost, KNN, and neural networks, were trained on a dataset of >10,000 labeled social media posts. The top model, a support vector machine, achieved 83% accuracy in classifying posts displaying signs of stress.
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    Investigation of reusability of effluents from an organized industrial zone wastewater treatment plant using a pressure-driven membrane process
    (IWA, 2023-10-10) Ocal, Zehra Betül; Karagunduz, Ahmet; Keskinler, Bulent; Dizge, Nadir; Ashqar, Huthaifa
    The quantity of wastewater being discharged into the environment due to the rise in industrial activities is progressively growing over time. Aside from large environmental risk posed by untreated wastewater discharge, the reuse of treated water prevents wastage of large amounts of water. For this reason, in this study, the reuse potential of an organized industrial zone wastewater was investigated by membrane processes. The appropriate membrane type and rejection performance were determined for various pollutant parameters including conductivity, chemical oxygen demand (COD), total nitrogen (TN), chloride, and sulfate. Laboratory-scale batch membrane filtration experiments were performed by using three different membrane types (BW30, XLE, and X20). The experiments were conducted at 15 and 20 bar pressures and flux data were collected during the operations. The results showed that BW30 and X20 membranes could be operated comfortably with 80% recovery for the wastewater containing low and high sulfate concentrations. For the wastewater with low sulfate concentration, the fluxes of BW30 and X20 at 20 bar were 19.7 and 16.4 L/m²/h, respectively, at 80% recovery. On the other hand, for the wastewater with higher sulfate concentration, the fluxes of BW30 and X20 at 20 bar were 8.6 and 11.5 L/m²/h, respectively.
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    A Generic and Extendable Framework for Benchmarking and Assessing the Change Detection Models
    (2024-03-20) Hassouna, Ahmed Alaa Abdelbaky; Ismail, Mohamed Badr; Alqahtani, Ali; Alqahtani, Nayef; Hassan, Amany Shaban; Ashqar, Huthaifa; AlSobeh, Anas M. R.; Hassan, Abdallah A.; Elhenawy, Mohammed
    Change Detection (CD) of aerial images refers to identifying and analyzing changes between two or more aerial images of the same location taken at different times. The CD is a highly challenging task due to the need to distinguish relevant changes, such as urban expansion, deforestation, or post-disaster damage assessment, from irrelevant ones, such as light conditions, shadows, and seasonal variations. Many CD papers have recently been published, where most of the papers that proposed a new model contained a comparison between their proposed and state-of-the-art (SOTA) models. While many recent studies propose new deep learning (DL) models for improving CD performance, their comparative analyses are often restricted, lacking comprehensive insights into the proposed models' real-world generalizability, robustness, and performance trade-offs across diverse change characteristics. This paper presents a novel generic framework to systematically benchmark and assess DL-based CD models through three parallel pipelines: 1) cross-testing models on diverse benchmark datasets to evaluate generalization, 2) robustness analysis against different image corruptions, and 3) multi-faceted contour-level analytics evaluating detection sensitivity to change size/complexity. The framework is applied to comparatively evaluate five state-of-the-art DL-based CD models - Changeformer, BIT, Tiny, SNUNet, and CSA-CDGAN. Extensive experiments unveil each model's strengths, limitations and biases, highlighting their relative proficiencies in generalizing across data distributions, resilience to noise corruption, and discriminative capabilities for changes of varying characteristics. The proposed benchmarking framework demonstrates significant potential for guiding the selection of suitable CD models tailored to specific application requirements by comprehensively evaluating their generalizability, robustness, and detection capabilities across diverse real-world scenarios. This systematic evaluation approach can drive future research into developing more robust and versatile CD solutions aligned with practical needs.
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    Factors influencing bikeshare service and usage in a rural college town: A case study of Montgomery County, VA
    (Taylor & Francis, 2024-01-03) Woodson, Cat; Ashqar, Huthaifa; Almannaa, Mohammed; Elhenawy, Mohammed; Buehler, Ralph
    While much of the bikeshare boom has centered around larger cities, smaller, lower-density, and even some rural communities have also implemented bikeshare systems successfully. Using a bikeshare dataset of more than 14,000 trips that cover the period from July 2018 to December 2021 for both pedal and e-bikes, this paper describes the structure and performance of ROAM NRV, a bikeshare system in Montgomery County, Virginia—which is home to Virginia Tech university and has many areas classified as rural. The paper presents bikeshare users’ travel behaviors and usage trends (including during the COVID-19 pandemic). Moreover, compares the usage of the system’s pedal bicycles to electric bicycles (e-bikes) that were introduced in 2021. Findings indicated that residents of Blacksburg and Christiansburg regularly use and benefit from bikeshare much like their urban counterparts do. Ridership was noted to likely be more common among university affiliates with trips more likely to start/end on or around campus due to the number of stations located within campus grounds. Trail usage was also high among bikeshare users due to the extensive trail network within and between the towns. As rural bikeshare users tend to travel greater distances and encounter more varying terrains throughout their commutes, considering e-bikes instead of pedal bike systems should increase the utilization of such mobility systems in rural areas. When electric assist bicycles were first introduced to the system, initially replacing some and then all former pedal bicycles, utilization increased significantly compared to pedal bike usage.
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    Automated Pavement Cracks Detection and Classification Using Deep Learning
    (IEEE, 2024-07-11) Nafaa, Selvia; Ashour, Karim; Mohamed, Rana; Essam, Hafsa; Emad, Doaa; Elhenawy, Mohammed; Ashqar, Huthaifa; Hassan, Abdallah A.; Alhadidi, Taqwa I.
    Monitoring asset conditions is a crucial factor in building efficient transportation asset management. Because of substantial advances in image processing, traditional manual classification has been largely replaced by semi-automatic/automatic techniques. As a result, automated asset detection and classification techniques are required. This paper proposes a methodology to detect and classify roadway pavement cracks using the well-known You Only Look Once (YOLO) version five (YOLOv5) and version 8 (YOLOv8) algorithms. Experimental results indicated that the precision of pavement crack detection reaches up to 67.3% under different illumination conditions and image sizes. The findings of this study can assist highway agencies in accurately detecting and classifying asset conditions under different illumination conditions. This will reduce the cost and time that are associated with manual inspection, which can greatly reduce the cost of highway asset maintenance.
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    Advancing Roadway Sign Detection with YOLO Models and Transfer Learning
    (IEEE, 2024-04) Nafaa, Selvia; Ashour, Karim; Mohamed, Rana; Essam, Hafsa; Emad, Doaa; Elhenawy, Mohammed; Ashqar, Huthaifa; Hassan, Abdallah A.; Alhadidi, Taqwa I.
    Roadway signs detection and recognition is an essential element in the Advanced Driving Assistant Systems (ADAS). Several artificial intelligence methods have been used widely among of them YOLOv5 and YOLOv8. In this paper, we used a modified YOLOv5 and YOLOv8 to detect and classify different roadway signs under different illumination conditions. Experimental results indicated that for the YOLOv8 model, varying the number of epochs and batch size yields consistent MAP50 scores, ranging from 94.6% to 97.1% on the testing set. The YOLOv5 model demonstrates competitive performance, with MAP50 scores ranging from 92.4% to 96.9%. These results suggest that both models perform well across different training setups, with YOLOv8 generally achieving slightly higher MAP50 scores. These findings suggest that both models can perform well under different training setups, offering valuable insights for practitioners seeking reliable and adaptable solutions in object detection applications.
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    Determining the Location of Shared Electric Micro-Mobility Stations in Urban Environment
    (MDPI, 2024-06-06) Jaber, Ahmed; Ashqar, Huthaifa; Csonka, Bálint
    Locating shared electric micro-mobility stations in urban environments involves balancing multiple objectives, including accessibility, profitability, sustainability, operational costs, and social considerations. This study investigates traveler preferences regarding shared electric micro-mobility stations, focusing on factors influencing their location decisions. The study used the Analytic Hierarchy Process (AHP) model to analyze the criteria and determine their relative importance in influencing the location decisions of shared electric micro-mobility stations as evaluated by experts in transportation fields. The examined criteria are proximity to public transportation, accessibility to key destinations, demographics (e.g., age, and income), safety, land use, and pedestrian and cyclist infrastructure. Using the AHP model, the importance and ranking of each criterion were established. Results indicate that the availability and quality of sidewalks and bike lanes in the vicinity, along with the proximity to popular destinations like shopping centers and tourist attractions, emerge as the most influential criteria. The least important criteria were the demographics such as the young age percentage in the area and the average income of the surrounding population. These findings underscore the critical importance of well-maintained infrastructure for pedestrian and cyclist mobility, as well as the need for convenient access to high-traffic areas. Such insights provide valuable guidance for informed decision making regarding the optimal placement of shared electric micro-mobility stations.
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    Temporal Shifts in E-Scooter Rider Perspectives: A Longitudinal Investigation in Riyadh, Saudi Arabia
    (MDPI, 2024-04-30) Almannaa, Mohammed; Alyahya, Asim; Ashqar, Huthaifa; Elhenawy, Mohammed
    Shared electric scooters (e-scooters) have rapidly gained prominence as a first/last-mile mobility solution globally, with over 66,000 systems operating in 88 cities across 21 countries in 2019. While recognized for their flexibility, accessibility, and environmental benefits, concerns such as safety, parking issues, and infrastructural challenges accompany the operation of shared e-scooter systems. This research investigates the evolving perceptions of e-scooter users in Riyadh, Saudi Arabia, comparing pre-survey results with a recent study following the official deployment of e-scooters as a transportation mode in 2022. The analysis reveals significant shifts in user behavior, preferences, and perceptions. The findings indicate increased familiarity with e-scooters, heightened usage rates, and notable changes in domestic e-scooter use. Furthermore, the study identifies variations in willingness to use e-scooters across genders. A notable shift is observed in riders’ perceptions, transforming from viewing e-scooters primarily as entertainment tools to embracing them as a reliable mode of transportation. The results show that the percentage of female respondents using e-scooters increased from 3% to 13%, representing over four times the post-survey numbers. Additionally, the percentage of individuals perceiving e-scooters as safe decreased from 28.2% in the pre-survey to 14.9% in the current survey (post-survey) among those who had used e-scooters. The regression analysis demonstrates a historical uptrend in the utilization of e-scooters, juxtaposed with a discernible decline projected for forthcoming usage (odds ratio [OR] = 0.74). Intriguingly, there is evidence indicating an enhancement of riders’ confidence towards e-scooters, as reflected by an augmented perception of safety (OR = 1.48).
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    Real-Time Traffic Density Estimation Using Various Connected Vehicle Penetration Rates: A New Predictive Approach
    (2024-01-16) Ashqer, Mujahid; Ashqar, Huthaifa; Elhenawy, Mohammed; Rakha, Hesham A.; Bikdash, Marwan
    Traffic density estimation using various Market Penetration Rates (MPRs) of Connected Vehicle (CV) data represents an area in need of continued research and refinement to fully leverage its potential in addressing complex real-world traffic scenarios. This study introduces an innovative approach, the Predictive Approach, employing the Temporal Convolutional Network (TCN) algorithm to estimate traffic density. This method calculates the densities of input approaches at intersections with non-uniform MPRs, using these predictions to estimate the target approach density. Using the Predictive Approach, results showed that improving traffic density predictions can be achieved through factors like accounting for MPR variations between different intersection approaches and considering specific scenarios. Results also highlighted that excluding Signal Phase and Timing (SPaT) data in certain cases can enhance model performance. It offers practical applications in optimizing traffic flow and reducing congestion in smart cities and traffic control centres, particularly when rapid and real-time computations are required. Additionally, it serves as a valuable solution in areas lacking SPaT information and experiencing varying levels of vehicle connectivity, collectively providing versatile tools for efficient traffic management and urban mobility enhancement. These insights have the potential to make real-world traffic management more efficient, responsive, and adaptable, ultimately leading to safer and more effective transportation systems.
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    Exploring Traffic Crash Narratives in Jordan Using Text Mining Analytics
    (2024-06-11) Jaradat, Shadi; Alhadidi, Taqwa I.; Ashqar, Huthaifa; Hossain, Ahmed; Elhenawy, Mohammed
    This study explores traffic crash narratives in an attempt to inform and enhance effective traffic safety policies using text-mining analytics. Text mining techniques are employed to unravel key themes and trends within the narratives, aiming to provide a deeper understanding of the factors contributing to traffic crashes. This study collected crash data from five major freeways in Jordan that cover narratives of 7,587 records from 2018-2022. An unsupervised learning method was adopted to learn the pattern from crash data. Various text mining techniques, such as topic modeling, keyword extraction, and Word Co-Occurrence Network, were also used to reveal the co-occurrence of crash patterns. Results show that text mining analytics is a promising method and underscore the multifactorial nature of traffic crashes, including intertwining human decisions and vehicular conditions. The recurrent themes across all analyses highlight the need for a balanced approach to road safety, merging both proactive and reactive measures. Emphasis on driver education and awareness around animal-related incidents is paramount.
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    Transformer Models in Education: Summarizing Science Textbooks with AraBART, MT5, AraT5, and mBART
    (2024-06-11) Masri, Sari; Raddad, Yaqeen; Khandaqji, Fidaa; Ashqar, Huthaifa; Elhenawy, Mohammed
    Recently, with the rapid development in the fields of technology and the increasing amount of text t available on the internet, it has become urgent to develop effective tools for processing and understanding texts in a way that summaries the content without losing the fundamental essence of the information. Given this challenge, we have developed an advanced text summarization system targeting Arabic textbooks. Relying on modern natu-ral language processing models such as MT5, AraBART, AraT5, and mBART50, this system evaluates and extracts the most important sentences found in biology textbooks for the 11th and 12th grades in the Palestinian curriculum, which enables students and teachers to obtain accurate and useful summaries that help them easily understand the content. We utilized the Rouge metric to evaluate the performance of the trained models. Moreover, experts in education Edu textbook authoring assess the output of the trained models. This approach aims to identify the best solutions and clarify areas needing improvement. This research provides a solution for summarizing Arabic text. It enriches the field by offering results that can open new horizons for research and development in the technologies for understanding and generating the Arabic language. Additionally, it contributes to the field with Arabic texts through creating and compiling schoolbook texts and building a dataset.
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    Eyeballing Combinatorial Problems: A Case Study of Using Multimodal Large Language Models to Solve Traveling Salesman Problems
    (2024-06-11) Elhenawy, Mohammed; Abdelhay, Ahmed; Alhadidi, Taqwa I.; Ashqar, Huthaifa; Jaradat, Shadi; Jaber, Ahmed; Glaser, Sebastien; Rakotonirainy, Andry
    Multimodal Large Language Models (MLLMs) have demonstrated proficiency in processing di-verse modalities, including text, images, and audio. These models leverage extensive pre-existing knowledge, enabling them to address complex problems with minimal to no specific training examples, as evidenced in few-shot and zero-shot in-context learning scenarios. This paper investigates the use of MLLMs' visual capabilities to 'eyeball' solutions for the Traveling Salesman Problem (TSP) by analyzing images of point distributions on a two-dimensional plane. Our experiments aimed to validate the hypothesis that MLLMs can effectively 'eyeball' viable TSP routes. The results from zero-shot, few-shot, self-ensemble, and self-refine zero-shot evaluations show promising outcomes. We anticipate that these findings will inspire further exploration into MLLMs' visual reasoning abilities to tackle other combinatorial problems.
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    Question-Answering (QA) Model for a Personalized Learning Assistant for Arabic Language
    (2024-06-11) Sammoudi, Mohammad; Habaybeh, Ahmad; Ashqar, Huthaifa; Elhenawy, Mohammed
    This paper describes the creation, optimization, and assessment of a question-answering (QA) model for a personalized learning assistant that uses BERT transformers customized for the Arabic language. The model was particularly finetuned on science textbooks in Palestinian curriculum. Our approach uses BERT's brilliant capabilities to automatically produce correct answers to questions in the field of science education. The model's ability to understand and extract pertinent information is improved by finetuning it using 11th and 12th grade biology book in Palestinian curriculum. This increases the model's efficacy in producing enlightening responses. Exact match (EM) and F1 score metrics are used to assess the model's performance; the results show an EM score of 20% and an F1 score of 51%. These findings show that the model can comprehend and react to questions in the context of Palestinian science book. The results demonstrate the potential of BERT-based QA models to support learning and understanding Arabic students questions.
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    Automated Question Generation for Science Tests in Arabic Language Using NLP Techniques
    (2024-06-11) Tami, Mohammad; Ashqar, Huthaifa; Elhenawy, Mohammed
    Question generation for education assessments is a growing field within artificial intelligence applied to education. These question-generation tools have significant importance in the educational technology domain, such as intelligent tutoring systems and dialogue-based platforms. The automatic generation of assessment questions, which entail clear-cut answers, usually relies on syntactical and semantic indications within declarative sentences, which are then transformed into questions. Recent research has explored the generation of assessment educational questions in Arabic. The reported performance has been adversely affected by inherent errors, including sentence parsing inaccuracies, name entity recognition issues, and errors stemming from rule-based question transformation. Furthermore, the complexity of lengthy Arabic sentences has contributed to these challenges. This research presents an innovative Arabic question-generation system built upon a three-stage process: keywords and key phrases extraction, question generation, and subsequent ranking. The aim is to tackle the difficulties associated with automatically generating assessment questions in the Arabic language. The proposed approach and results show a precision of 83.50%, a recall of 78.68%, and an Fl score of 80.95%, indicating the framework high efficiency. Human evaluation further confirmed the model efficiency, receiving an average rating of 84%.
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    Object Detection using Oriented Window Learning Vi-sion Transformer: Roadway Assets Recognition
    (2024-06-15) Alhadidi, Taqwa; Jaber, Ahmed; Jaradat, Shadi; Ashqar, Huthaifa; Elhenawy, Mohammed
    Object detection is a critical component of transportation systems, particularly for applications such as autonomous driving, traffic monitoring, and infrastructure maintenance. Traditional object detection methods often struggle with limited data and variability in object appearance. The Oriented Window Learning Vision Transformer (OWL-ViT) offers a novel approach by adapting window orientations to the geometry and existence of objects, making it highly suitable for detecting diverse roadway assets. This study leverages OWL-ViT within a one-shot learning framework to recognize transportation infrastructure components, such as traffic signs, poles, pavement, and cracks. This study presents a novel method for roadway asset detection using OWL-ViT. We conducted a series of experiments to evaluate the performance of the model in terms of detection consistency, semantic flexibility, visual context adaptability, resolution robustness, and impact of non-max suppression. The results demonstrate the high efficiency and reliability of the OWL-ViT across various scenarios, underscoring its potential to enhance the safety and efficiency of intelligent transportation systems.