arXiv subjects

Huthaifa I. Ashqar

Publications and source records attributed to Huthaifa I. Ashqar.

At least 37 records · Page 2Linked to original sources

arXiv2024

Influential Factors in Increasing an Amazon products Sales Rank

Amazon is the world number one online retailer and has nearly every product a person could need along with a treasure trove of product reviews to help consumers make educated purchases. Companies want to find a way to increase their sales in a very crowded market, and using this data is key. A very good indicator of how a product is selling is its sales rank; which is calculated based on all-time sales of a product where recent sales are weighted more than older sales. Using the data from the Amazon products and reviews we determined that the most influential factors in determining the sales rank of a product were the number of products Amazon showed that other customers also bought, the number of products Amazon showed that customers also viewed, and the price of the product. These results were consistent for the Digital Music category, the Office Products category, and the subcategory Holsters under Cell Phones and Accessories.

cs.CY↗

arXiv2024

The Impact of Medicaid Expansion on Medicare Quality Measures

The Affordable Care Act was signed into law in 2010, expanding Medicaid and improving access to care for millions of low-income Americans. Fewer uninsured individuals reduced the cost of uncompensated care, consequently improving the financial health of hospitals. We hypothesize that this amelioration in hospital finances resulted in a marked improvement of quality measures in states that chose to expand Medicaid. To our knowledge, the impact of Medicaid expansion on the Medicare population has not been investigated. Using a difference-in-difference analysis, we compare readmission rates for four measures from the Hospital Readmission Reduction Program: acute myocardial infarction, pneumonia, heart failure, and coronary artery bypass graft surgery. Our analysis provides evidence that between 2013 and 2021 expansion states improved hospital quality relative to non-expansion states as it relates to acute myocardial infarction readmissions (p = 0.015) and coronary artery bypass graft surgery readmissions (p = 0.039). Our analysis provides some evidence that expanding Medicaid improved hospital quality, as measured by a reduction in readmission rates. Using visualizations, we provide some evidence that hospital quality improved for the other two measures as well. We believe that a refinement of our estimation method and an improved dataset will increase our chances of finding significant results for these two other measures.

cs.CY↗

arXiv2024

Identifying Economic Factors Affecting Unemployment Rates in the United States

In this study, we seek to understand how macroeconomic factors such as GDP, inflation, Unemployment Insurance, and S&P 500 index; as well as microeconomic factors such as health, race, and educational attainment impacted the unemployment rate for about 20 years in the United States. Our research question is to identify which factor(s) contributed the most to the unemployment rate surge using linear regression. Results from our studies showed that GDP (negative), inflation (positive), Unemployment Insurance (contrary to popular opinion; negative), and S&P 500 index (negative) were all significant factors, with inflation being the most important one. As for health issue factors, our model produced resultant correlation scores for occurrences of Cardiovascular Disease, Neurological Disease, and Interpersonal Violence with unemployment. Race as a factor showed a huge discrepancies in the unemployment rate between Black Americans compared to their counterparts. Asians had the lowest unemployment rate throughout the years. As for education attainment, results showed that having a higher education attainment significantly reduced one chance of unemployment. People with higher degrees had the lowest unemployment rate. Results of this study will be beneficial for policymakers and researchers in understanding the unemployment rate during the pandemic.

cs.CY↗

arXiv2024

The Relationship Between Smartphone Usage and Sleep Quality Amongst University Students

Gender differences were examined in sensitivity to sleep quality, in the context of blue light exposure from smartphones. Our hypothesis was created based on our journal article findings that females are more prone to be inclined to the prolonged usage of smartphones at bedtime and thus had less quality of sleep than males. The theory that usage affects sleep quality was due to the belief that the blue light emanating from the smartphone screen would disrupt our body natural circadian rhythm, or sleep cycle, due to the blue light ability to block a hormone called melatonin that controls and aids sleep. However, upon conducting regression tests and statistical analysis on our dataset, we found that our hypothesis was incorrect. Our dataset and analysis showed no relationship between smartphone usage and sleep quality in both males and females in young adults.

cs.CY↗

arXiv2024

Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing

This study aims to comprehensively review and empirically evaluate the application of multimodal large language models (MLLMs) and Large Vision Models (VLMs) in object detection for transportation systems. In the first fold, we provide a background about the potential benefits of MLLMs in transportation applications and conduct a comprehensive review of current MLLM technologies in previous studies. We highlight their effectiveness and limitations in object detection within various transportation scenarios. The second fold involves providing an overview of the taxonomy of end-to-end object detection in transportation applications and future directions. Building on this, we proposed empirical analysis for testing MLLMs on three real-world transportation problems that include object detection tasks namely, road safety attributes extraction, safety-critical event detection, and visual reasoning of thermal images. Our findings provide a detailed assessment of MLLM performance, uncovering both strengths and areas for improvement. Finally, we discuss practical limitations and challenges of MLLMs in enhancing object detection in transportation, thereby offering a roadmap for future research and development in this critical area.

cs.CV↗

arXiv2024

Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarios

Urban traffic management faces significant challenges due to the dynamic environments, and traditional algorithms fail to quickly adapt to this environment in real-time and predict possible conflicts. This study explores the ability of a Large Language Model (LLM), specifically, GPT-4o-mini to improve traffic management at urban intersections. We recruited GPT-4o-mini to analyze, predict position, detect and resolve the conflicts at an intersection in real-time for various basic scenarios. The key findings of this study to investigate whether LLMs can logically reason and understand the scenarios to enhance the traffic efficiency and safety by providing real-time analysis. The study highlights the potential of LLMs in urban traffic management creating more intelligent and more adaptive systems. Results showed the GPT-4o-mini was effectively able to detect and resolve conflicts in heavy traffic, congestion, and mixed-speed conditions. The complex scenario of multiple intersections with obstacles and pedestrians saw successful conflict management as well. Results show that the integration of LLMs promises to improve the effectiveness of traffic control for safer and more efficient urban intersection management.

cs.CL↗

arXiv2024

Visual Reasoning and Multi-Agent Approach in Multimodal Large Language Models (MLLMs): Solving TSP and mTSP Combinatorial Challenges

Multimodal Large Language Models (MLLMs) harness comprehensive knowledge spanning text, images, and audio to adeptly tackle complex problems, including zero-shot in-context learning scenarios. This study explores the ability of MLLMs in visually solving the Traveling Salesman Problem (TSP) and Multiple Traveling Salesman Problem (mTSP) using images that portray point distributions on a two-dimensional plane. We introduce a novel approach employing multiple specialized agents within the MLLM framework, each dedicated to optimizing solutions for these combinatorial challenges. Our experimental investigation includes rigorous evaluations across zero-shot settings and introduces innovative multi-agent zero-shot in-context scenarios. The results demonstrated that both multi-agent models. Multi-Agent 1, which includes the Initializer, Critic, and Scorer agents, and Multi-Agent 2, which comprises only the Initializer and Critic agents; significantly improved solution quality for TSP and mTSP problems. Multi-Agent 1 excelled in environments requiring detailed route refinement and evaluation, providing a robust framework for sophisticated optimizations. In contrast, Multi-Agent 2, focusing on iterative refinements by the Initializer and Critic, proved effective for rapid decision-making scenarios. These experiments yield promising outcomes, showcasing the robust visual reasoning capabilities of MLLMs in addressing diverse combinatorial problems. The findings underscore the potential of MLLMs as powerful tools in computational optimization, offering insights that could inspire further advancements in this promising field. Project link: https://github.com/ahmed-abdulhuy/Solving-TSP-and-mTSP-Combinatorial-Challenges-using-Visual-Reasoning-and-Multi-Agent-Approach-MLLMs-.git

cs.AI↗

arXiv2024

The Use of Multimodal Large Language Models to Detect Objects from Thermal Images: Transportation Applications

The integration of thermal imaging data with Multimodal Large Language Models (MLLMs) constitutes an exciting opportunity for improving the safety and functionality of autonomous driving systems and many Intelligent Transportation Systems (ITS) applications. This study investigates whether MLLMs can understand complex images from RGB and thermal cameras and detect objects directly. Our goals were to 1) assess the ability of the MLLM to learn from information from various sets, 2) detect objects and identify elements in thermal cameras, 3) determine whether two independent modality images show the same scene, and 4) learn all objects using different modalities. The findings showed that both GPT-4 and Gemini were effective in detecting and classifying objects in thermal images. Similarly, the Mean Absolute Percentage Error (MAPE) for pedestrian classification was 70.39% and 81.48%, respectively. Moreover, the MAPE for bike, car, and motorcycle detection were 78.4%, 55.81%, and 96.15%, respectively. Gemini produced MAPE of 66.53%, 59.35% and 78.18% respectively. This finding further demonstrates that MLLM can identify thermal images and can be employed in advanced imaging automation technologies for ITS applications.

cs.CV↗

arXiv2024

Using Multimodal Large Language Models for Automated Detection of Traffic Safety Critical Events

Traditional approaches to safety event analysis in autonomous systems have relied on complex machine learning models and extensive datasets for high accuracy and reliability. However, the advent of Multimodal Large Language Models (MLLMs) offers a novel approach by integrating textual, visual, and audio modalities, thereby providing automated analyses of driving videos. Our framework leverages the reasoning power of MLLMs, directing their output through context-specific prompts to ensure accurate, reliable, and actionable insights for hazard detection. By incorporating models like Gemini-Pro-Vision 1.5 and Llava, our methodology aims to automate the safety critical events and mitigate common issues such as hallucinations in MLLM outputs. Preliminary results demonstrate the framework's potential in zero-shot learning and accurate scenario analysis, though further validation on larger datasets is necessary. Furthermore, more investigations are required to explore the performance enhancements of the proposed framework through few-shot learning and fine-tuned models. This research underscores the significance of MLLMs in advancing the analysis of the naturalistic driving videos by improving safety-critical event detecting and understanding the interaction with complex environments.

cs.CV↗

arXiv2024

Automated Pavement Cracks Detection and Classification Using Deep Learning

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.

cs.CV↗

arXiv2024

Transformer Models in Education: Summarizing Science Textbooks with AraBART, MT5, AraT5, and mBART

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.

cs.CL↗

arXiv2024

Question-Answering (QA) Model for a Personalized Learning Assistant for Arabic Language

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.

cs.CL↗

arXiv2024

Automated Question Generation for Science Tests in Arabic Language Using NLP Techniques

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%.

cs.CL↗

arXiv2024

Advancing Roadway Sign Detection with YOLO Models and Transfer Learning

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.

cs.CV↗

arXiv2024

Exploring Traffic Crash Narratives in Jordan Using Text Mining Analytics

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.

cs.CL↗

arXiv2023

Effect of roundabout design on the behavior of road users: A case study of roundabouts with application of Unsupervised Machine Learning

This research aims to evaluate the performance of the rotors and study the behavior of the human driver in interacting with the rotors. In recent years, rotors have been increasingly used between countries due to their safety, capacity, and environmental advantages, and because they provide safe and fluid flows of vehicles for transit and integration. It turns out that roundabouts can significantly reduce speed at twisting intersections, entry speed and the resulting effect on speed depends on the rating of road users. In our research, (bus, car, truck) drivers were given special attention and their behavior was categorized into (conservative, normal, aggressive). Anticipating and recognizing driver behavior is an important challenge. Therefore, the aim of this research is to study the effect of roundabouts on these classifiers and to develop a method for predicting the behavior of road users at roundabout intersections. Safety is primarily due to two inherent features of the rotor. First, by comparing the data collected and processed in order to classify and evaluate drivers' behavior, and comparing the speeds of the drivers (bus, car and truck), the speed of motorists at crossing the roundabout was more fit than that of buses and trucks. We looked because the car is smaller and all parts of the rotor are visible to it. So drivers coming from all directions have to slow down, giving them more time to react and mitigating the consequences in the event of an accident. Second, with fewer conflicting flows (and points of conflict), drivers only need to look to their left (in right-hand traffic) for other vehicles, making their job of crossing the roundabout easier as there is less need to split attention between different directions.

cs.LG↗

arXiv2023

How Do Drivers Behave at Roundabouts in a Mixed Traffic? A Case Study Using Machine Learning

Driving behavior is considered a unique driving habit of each driver and has a significant impact on road safety. Classifying driving behavior and introducing policies based on the results can reduce the severity of crashes on the road. Roundabouts are particularly interesting because of the interconnected interaction between different road users at the area of roundabouts, which different driving behavior is hypothesized. This study investigates driving behavior at roundabouts in a mixed traffic environment using a data-driven unsupervised machine learning to classify driving behavior at three roundabouts in Germany. We used a dataset of vehicle kinematics to a group of different vehicles and vulnerable road users (VRUs) at roundabouts and classified them into three categories (i.e., conservative, normal, and aggressive). Results showed that most of the drivers proceeding through a roundabout can be mostly classified into two driving styles: conservative and normal because traffic speeds in roundabouts are relatively lower than in other signalized and unsignalized intersections. Results also showed that about 77% of drivers who interacted with pedestrians or cyclists were classified as conservative drivers compared to about 42% of conservative drivers that did not interact or about 51% from all drivers. It seems that drivers tend to behave abnormally as they interact with VRUs at roundabouts, which increases the risk of crashes when an intersection is multimodal. Results of this study could be helpful in improving the safety of roads by allowing policymakers to determine the effective and suitable safety countermeasures. Results will also be beneficial for the Advanced Driver Assistance System (ADAS) as the technology is being deployed in a mixed traffic environment.

cs.LG↗

arXiv2023

Detection of DDoS Attacks in Software Defined Networking Using Machine Learning Models

The concept of Software Defined Networking (SDN) represents a modern approach to networking that separates the control plane from the data plane through network abstraction, resulting in a flexible, programmable and dynamic architecture compared to traditional networks. The separation of control and data planes has led to a high degree of network resilience, but has also given rise to new security risks, including the threat of distributed denial-of-service (DDoS) attacks, which pose a new challenge in the SDN environment. In this paper, the effectiveness of using machine learning algorithms to detect distributed denial-of-service (DDoS) attacks in software-defined networking (SDN) environments is investigated. Four algorithms, including Random Forest, Decision Tree, Support Vector Machine, and XGBoost, were tested on the CICDDoS2019 dataset, with the timestamp feature dropped among others. Performance was assessed by measures of accuracy, recall, accuracy, and F1 score, with the Random Forest algorithm having the highest accuracy, at 68.9%. The results indicate that ML-based detection is a more accurate and effective method for identifying DDoS attacks in SDN, despite the computational requirements of non-parametric algorithms.

cs.LG↗