Anas M. Aloraiqat

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence, Computer Graphics and Computer-Aided Design

Scopus Publications

A Synergy between Machine Learning and Formal Concept Analysis for Crowd Detection
Anas M. Al-Oraiqat, Oleksandr Drieiev, Sattam Almatarneh, Mohammadnoor Injadat, Karim A. Al-Oraiqat, Hanna Drieieva, and Yassin M. Y. Hasan
Institute of Electrical and Electronics Engineers (IEEE)
To enhance public safety, crowd detection and prevention systems have essentially become a natural means to manage diverse crowded areas, such as urban settings, transportation hubs, and event venues. Recent systems take advantage of the synergy between machine learning, data mining, and image processing to extract/analyze features from crowded zones and recognize patterns and anomalies from the crowd behavior. Additionally, image processing tools play a key role in real-time monitoring by analyzing video feeds to detect crowd density, flow direction, and identify potential risks like overcrowding or emergencies. However, most existing solutions focus on the detection phase and often overlook integrated error handling and robust decision-making frameworks to ensure accurate and actionable crowd prevention. Aiming to solve these issues, we take advantage of the prediction capabilities of machine learning models and the analysis and clustering strengths of Formal Concept Analysis (FCA) chosen for its strong mathematical foundation and superior clustering capabilities compared to traditional methods, as highlighted in recent works such as K-means or hierarchical clustering. We used the first technique to extract useful knowledge from areas’ produced images while mitigating potential error accumulation through modular error-checking mechanisms. A neural network is used to mark human bodies, determine the position of walking individuals, and predict crowd levels. Such information is, thereafter, inputted to the FCA-based decision system to ensure an explicit representation and modelling of crowd data, thanks to lattice structures. These latter’s hierarchical view helped us identify the crowded areas and manage them as clustered zones, based on their common crowd information. We also define bottom-up parsing algorithms to recommend the suitable crowd prevention plan w.r.t. the crowd level. Experiments have successfully proved the ability of FCA to exclude low-crowd zones, locate crowded areas, and provide actionable crowd management insights, which may complement crowd counting techniques.


Spatiotemporal crowds features extraction of infrared images using neural network
Anas M. Al-Oraiqat, Oleksandr Drieiev, Hanna Drieieva, Yelyzaveta Meleshko, Hazim AlRawashdeh, Karim A. Al-Oraiqat, Yassin M. Y. Hasan, Noor Maricar, and Sheroz Khan
Springer Science and Business Media LLC



Efficient Road Status Monitoring in Vehicular Ad-Hoc Networks: An Optimization Framework
Ghassan Samara, Hussain Mohammed Turki, Mahmoud Odeh, Anas Al-Oraiqat, Mohammad Aljaidi, Raed Alazaidah, Mohammad Rasmi Al-Mousa, Ahmed BaniMustafa, and Mohammad Kanan
IEEE
The number of cars on the road in the world is currently increasing. With the widespread usage of vehicles, a slew of issues inevitably arises. The damage of the road can be annoying to the drivers, and congestion in traffic can waste both fuel and time. It's worth noting that one of the primary causes of car damage and aging is the state of the road surface. Driving comfort and vehicle controllability can be harmed by poor road conditions. As a result, the system of monitoring road status is crucial to reducing congestion in traffic, reducing accident rates, and protecting cars from hazardous road conditions. This paper proposes using a framework based on cloud-fog network architecture, The fog computing network architecture is a dispersed network that may process data at the fog layer to minimize cloud computing's computational load. In addition, using small and large threshold values ( ), Main Authority (MA) deals with the major conditions, and the Sub Authority (SA) deals with the minor conditions.


Bridging Machine Learning and Formal Concept Analysis for Effective Crowd Detection
Anas M. Al-Oraiqat, Oleksandr Drieiev, Ghassan Samara, Sattam Almatarneh, Karim A. Al-Oraiqat, Hazim Alrawashdeh, Hanna Drieieva, Ali Elrashidi, and Yassin M. Y. Hasan
IEEE
Crowd detection and prevention systems have become essential for managing densely populated areas. Modern systems leverage the combined power of machine learning, data mining, and image processing to extract and analyse features from crowded zones, enabling the identification of behavioural patterns and anomalies. However, most current solutions primarily focus on detection and lack robust decision-making and recommendation mechanisms for selecting appropriate crowd-prevention strategies. To address this gap, we integrate the predictive capabilities of machine learning models with the analytical and clustering strengths of Fuzzy Formal Concept Analysis (fuzzy FCA). Machine learning is employed to extract valuable insights from images of the area, with a neural network used to identify human figures, track individuals' positions, and predict crowd levels. This data is fed into an FFCA-based decision system, where crowd information is structured and clustered using lattice theory. This latter helped exclude low-crowd zones and cluster the rest of the monitored areas based on their crowd features. Additionally, we define bottom-up parsing algorithms to recommend suitable crowd-prevention plans based on crowd density levels within fuzzy formal concepts. Experiments confirmed not only the ability of fuzzy FCA to "exclude" low-crowd zones thanks to a used crowd threshold but also the efficient "feature-based clustering" of crowded zones into hierarchical formal concepts and these latter’s "bottom-up parsing" to finally identify the dense zones.


Method for Determining Treated Metal Surface Quality Using Computer Vision Technology
Anas M. Al-Oraiqat, Tetiana Smirnova, Oleksandr Drieiev, Oleksii Smirnov, Liudmyla Polishchuk, Sheroz Khan, Yassin M. Y. Hasan, Aladdein M. Amro, and Hazim S. AlRawashdeh
MDPI AG
Computer vision and image processing techniques have been extensively used in various fields and a wide range of applications, as well as recently in surface treatment to determine the quality of metal processing. Accordingly, digital image evaluation and processing are carried out to perform image segmentation, identification, and classification to ensure the quality of metal surfaces. In this work, a novel method is developed to effectively determine the quality of metal surface processing using computer vision techniques in real time, according to the average size of irregularities and caverns of captured metal surface images. The presented literature review focuses on classifying images into treated and untreated areas. The high computation burden to process a given image frame makes it unsuitable for real-time system applications. In addition, the considered current methods do not provide a quantitative assessment of the properties of the treated surfaces. The markup, processed, and untreated surfaces are explored based on the entropy criterion of information showing the randomness disorder of an already treated surface. However, the absence of an explicit indication of the magnitude of the irregularities carries a dependence on the lighting conditions, not allowing to explicitly specify such characteristics in the system. Moreover, due to the requirement of the mandatory use of specific area data, regarding the size of the cavities, the work is challenging in evaluating the average frequency of these cavities. Therefore, an algorithm is developed for finding the period of determining the quality of metal surface treatment, taking into account the porous matrix, and the complexities of calculating the surface tensor. Experimentally, the results of this work make it possible to effectively evaluate the quality of the treated surface, according to the criterion of the size of the resulting irregularities, with a frame processing time of 20 ms, closely meeting the real-time requirements.


Modeling strategies for information influence dissemination in social networks
Anas M. Al-Oraiqat, Oleksandr S. Ulichev, Yelyzaveta V. Meleshko, Hazim S. AlRawashdeh, Oleksii O. Smirnov, and Liudmyla I. Polishchuk
Springer Science and Business Media LLC



MODIS derived sea surface salinity, temperature, and chlorophyll-a data for potential fish zone mapping: West red sea coastal areas, Saudi Arabia
Saleh Daqamseh, A’kif Al-Fugara, Biswajeet Pradhan, Anas Al-Oraiqat, and Maan Habib
MDPI AG
In this study, a multi-linear regression model for potential fishing zone (PFZ) mapping along the Saudi Arabian Red Sea coasts of Yanbu’ al Bahr and Jeddah was developed, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived parameters, such as sea surface salinity (SSS), sea surface temperature (SST), and chlorophyll-a (Chl-a). MODIS data was also used to validate the model. The model expanded on previous models by taking seasonal variances in PFZs into account, examining the impact of the summer, winter, monsoon, and inter-monsoon season on the selected oceanographic parameters in order to gain a deeper understanding of fish aggregation patterns. MODIS images were used to effectively extract SSS, SST, and Chl-a data for PFZ mapping. MODIS data were then used to perform multiple linear regression analysis in order to generate SSS, SST, and Chl-a estimates, with the estimates validated against in-situ data obtained from field visits completed at the time of the satellite passes. The proposed model demonstrates high potential for use in the Red Sea region, with a high level of congruence found between mapped PFZ areas and fish catch data (R2 = 0.91). Based on the results of this research, it is suggested that the proposed PFZ model is used to support fisheries in determining high potential fishing zones, allowing large areas of the Red Sea to be utilized over a short period. The proposed PFZ model can contribute significantly to the understanding of seasonal fishing activity and support the efficient, effective, and responsible use of resources within the fishing industry.


Specialized Computer Systems for Environment Visualization
Anas M. Al-Oraiqat, Evgeniy A. Bashkov, and Sergii A. Zori
Springer Science and Business Media LLC



Parallel Computer System for 3D Visualization Stereo on GPU
Anas M. Al-Oraiqat and Sergii A. Zori
Springer Science and Business Media LLC



Realistic stereo visualization system architecture using ray tracing
Piotr A. Kisała, Evgeniy Bashkov, Sergii A. Zori, Akmaral Tleshova, and Anas M. Al-Oraiqat
SPIE
The article considers experience of creating of system of realistic 3D stereo visualization by raytracing method on GPU basis. The basic organization of such 3D stereo visualization systems and parallel architecture of computing systems for realistic synthesis of stereo images by raytracing method were proposed. The developed architecture of 3D stereo visualization systems is able to solve the problem associated with middle 3D scene complexity image synthesis in real-time mode.

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