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CSE faculty gives a keynote speech on AI applications in the Oil and Gas industry
Prof. Khaled Shaban participated as a keynote speaker and panelist in a workshop titled “Decarbonization and Energy Transition in the Oil and Gas Industry,” organized by the Gas Processing Center at Qatar University on Sunday, December 1, 2024. The workshop provided a vital platform to discuss the transformative role of advanced technologies in addressing the industry’s most pressing challenges.
Prof. Shaban delivered a keynote presentation on “AI and ML: Catalysts for Digital Transformation in the Oil and Gas Industry,” highlighting how artificial intelligence and machine learning are revolutionizing operations by driving efficiency, sustainability, and innovation. The presentation offered valuable insights into key applications, such as autonomous drilling, predictive maintenance, and supply chain optimization, along with actionable strategies to address challenges like data quality issues and resistance to change.
Following the keynote, Prof. Shaban contributed as a panelist, engaging with industry leaders and academics to explore collaborative approaches for implementing AI/ML technologies. The discussions emphasized their potential to enhance operational agility and environmental compliance. His participation underscores a strong commitment to fostering innovation and shaping the future of energy through cutting-edge research and practical solutions.
CSE faculties awarded QU grants
Three of our faculty members (Prof. Amr Mohmed, Dr. Moutaz Saleh and Dr. Ahmed Badawy) and their research teams have just been been awarded QU internal grants (Cycle 8) in the different categories.
Here are more details about the winning projects:
Project Team: Dr. Amr Mohamed
Project Title: “Next-Gen Health Systems: AI-Driven Edge Platform for Autonomous healthcare services”
Grant Category: Post-Doc grant
Project Abstract: Promoting smart and secure healthcare services is instrumental in ensuring that Qatari citizens receive efficient, timely, and high-quality healthcare, ultimately contributing to an improved quality of life. Coinciding with this vision, this project aims to adopt recent communication and Artificial Intelligence (AI) technologies, such as distributed collaborative learning, edge computing, and network function virtualization (NFV) to fulfil challenging healthcare services’ requirements. The proposed platform will integrate different, distributed AI schemes in healthcare infrastructures to enable a paradigm shift for improving efficiency of the healthcare systems in Qatar. In contrast to the literature on E-health, the adopted platform will consider context-aware, distributed AI approaches to be implemented over 6G networks. This aims to optimize medical data delivery and energy consumption while supporting intelligent healthcare services. In addition, our plan involves integrating distributed learning with resource allocation schemes over 6G networks. This aims to transmit critical health information in real-time and promptly alert the user in case of an emergency. Thus, a key aspect of this project is to develop intelligent platform and algorithms that consider: (i) all requirements of the supported healthcare services, (ii) the capabilities of the network and computing infrastructure, and (iii) the privacy and security of the acquired data. Such aspects will be thoroughly investigated and fully developed, thanks to the collaboration with Hamad Medical Corporation (HMC), which will further strengthen the scientific excellence of this project by providing the necessary datasets, use cases, and equipment if required. The developed techniques in this project will be implemented in various critical applications, such as remote surgery and emergency transmission of surveillance videos in ambient assisted living. We envision integrating these techniques with the prototypes of the remote surgery department at HMC, utilizing advanced communication over 6G networks. This integration aims to guarantee a high quality of service for delay-critical applications.
Project Team: Dr. Moutaz Saleh (Lead PI) (Department of Computer Science & Engineering, College of Engineering, Qatar University) Dr. Sumaya AlMaadeed (PI) (Department of Computer Science & Engineering, College of Engineering, Qatar University) Dr. Asma Al-Attiyah (PI) (Dean of College of Education, College of Education, Qatar University) Dr. Hanan Khalil (PI) (Department of Rehabilitation Sciences, College of Health Sciences, Qatar University) Dr. Achraf Othman (PI) (Head of Research & Innovation, Mada Center, Qatar
Project Title: “Multimodal Explainable AI for Inclusive Education in Qatar: A Novel Approach for Specific Learning Disability Diagnosis and Personalized Learning Enhancement”
Grant Category: High Impact Grant
Project Abstract: Specific Learning Disability (SLD) is defined as a disorder in one or more of the basic learning processes involved in understanding or using language, spoken or written, that may manifest in significant difficulties affecting the ability to listen, speak, read, write, spell, or do mathematics. Particularly, Dyslexia which refers to difficulties in reading; Dysgraphia which refers to difficulties in writing; and Dyscalculia which refers to difficulties in math. While Qatar has focused on promoting inclusive education to support students with SLD, the development of a general system that can automatically diagnose and assist is an essential step for improving its education system. This project aims to develop expert systems that use artificial intelligence (AI) algorithms to improve diagnosis and assistance evaluation for children with SLD. The project aims to use multimodal data based on standard activities including physiological, neurological, and behavioral data, to provide accurate and personalized assessments to improve the overall quality of care and assistance outcomes. Generally, two main steps are considered: Diagnosis and Assistance Evaluation. In the diagnosis, data collection for normal and SLD is planned, and an AI expert system based on multimodal data will be implemented. This system should analyze the degree of normality and severity of disability. In the second step, based on the analysis of the diagnosis and the suggestions of the psychologist, intelligent decision-making models will be developed to generate tailored assistance plans for each individual by considering the severity of diagnostic disability, level of motor control impairment, and other related factors that contribute to the learning disability. This step is followed until a significant improvement is seen. The project utilizes advanced technologies and data analysis techniques to develop intelligent decision-making models that enhance the accuracy and efficiency of the assessment process. By integrating innovative methods, such as machine learning algorithms, computer vision, multimodal learning, explainable AI, the system can provide objective and reliable measurements for early identification of SLD in children and suggest appropriate improvement plans in an efficient and timely manner that will contribute to the advancements of the education system in Qatar.
Project Team: Dr. Ahmed Badawy(LPI), Prof. Saeed Salem
Project Title: “Distributed Edge Learning for Secure Healthcare IoT Networks”
Grant Category: Collaborative grant
Project Abstract: Digital healthcare is a rapidly growing domain with a variety of devices incorporated to monitor, assess, and maintain a healthy lifestyle. Therefore, the Healthcare Internet of Things (HIoT) domain presents a great opportunity to enhance the quality of patient care and potentially save lives; however, comes with unique challenges related to network security and data privacy. On the other hand, Distributed Edge Learning (DEL) represents a paradigm shift in data processing by decentralizing both the learning and the model aggregation processes across multiple edge devices allowing them to collaboratively aggregate a single learning model while keeping the data local. DEL’s decentralized nature allows for faster model aggregation, deployment, and later on model updates, which are key advantages for HIoT applications, allowing for faster response time and reduced network congestion. However, given that DEL is a relatively new field, significant research gaps remain, especially in security and data privacy—crucial for healthcare data—requiring further work to ensure the reliable and secure implementation of HIoT systems.
Wishing continued success to all other colleagues in the upcoming cycles!
CSE Faculty Awarded QRDI Grant to Advance Food Security Innovations
Congratulation to Dr. Cagatay Catal and his team for securing a grant through Qatar Research, Development and Innovation Council (QRDI) for food security call.
The granted project is titled “Artificial Intelligence-Empowered Aquaponics: Empowering Food Security for a Sustainable Future”.
The Project team includes:
- Prof. Dr. Cagatay Catal (Lead PI) (Department of Computer Science & Engineering, College of Engineering, Qatar University)
- Prof. Dr. Nader Meskin (PI) (Department of Electrical Engineering, College of Engineering, Qatar University)
- Prof. Dr. Ahmed Khalifa (PI) (Department of Finance and Economics, College of Business and Economics, Qatar University)
- Assoc. Prof. Dr. Mohammed Hussain S A Alsafran (PI) (Agriculture Research Station, Qatar University)
- Dr. Yamine Bouzembrak (PI) (Information Technology Group, Wageningen University & Research, The Netherlands)
- Assoc. Prof. Dr. Turker Bodur (PI) (Department of Aquaculture, Faculty of Fisheries, Akdeniz University, Türkiye)
Project Abstract: The objectives of this project are to advance the efficiency, scalability, and reliability of aquaponics systems through the integration of Artificial Intelligence (AI), Machine Learning (ML), Computer Vision (CV), and Internet of Things (IoT) solutions. The project aims to address key challenges in aquaponics, including fish health monitoring, plant disease detection, and environmental monitoring. The project team, in collaboration with Wageningen University & Research in the Netherlands and Akdeniz University in Türkiye will develop Qatar’s first comprehensive research setup for aquaponics. This research setup will be deployed at the Agricultural Research Station of Qatar University. The expected outcomes of this project are two-fold. Firstly, it will result in the development of novel prediction models and innovative technologies that address the challenges in aquaponics. These models and technologies will contribute to enhanced fish health, optimized crop production, and improved overall system efficiency. Secondly, the project aims to empower sustainable food security by minimizing the environmental impact of agriculture. By reducing water usage, nutrient runoff, and pollution, AI-empowered aquaponics will offer a more sustainable alternative to traditional farming methods.
The QRDI and the Ministry of Municipality (MoM) have launched the Food Security Call to support research that aligns with Qatar’s National Food Security Strategy, fostering collaboration among experts to develop solutions for the country’s food supply challenges.
Project to Enhance ‘Digital Citizenship’ in Qatar
Dr. Osama Halabi an associate professor in Computer Science and Engineering department is one of the leads of a project titled “Future of Digital Citizenship in Qatar: A Socio-Technical Approach”.
The project is a four-year research initiative done by Msheireb Museums and Hamad Bin Khalifa University (HBKU) as a significant step towards promoting responsible digital citizenship and enhancing positive online community engagement in Qatar.
The project is funded by the Qatar Research, Development, and Innovation Council (QRDI). The initiative comprises six sub-projects led by experts in their respective fields, bringing together more than 60 researchers.
The project’s tangible outputs include web interfaces for public analysis of social media, detection of hate speech, discrimination, and propaganda, as well as training materials and ambassadors to educate students on digital literacy, propaganda detection, safety, well-being, and social inclusion.
Dr. Osama Halabi, highlighted the importance of social media literacy and the project’s focus on assessing public attitudes towards social media in Qatar. The project’s findings, which will be primarily presented in Arabic, are expected to provide deeper insights into the variations between Qatar, the Arab world, and Western countries regarding technology and social media acceptance.
Msheireb Museums and HBKU’s commitment to integrating technology with academia and emphasis on sharing research findings with the public through events, exhibitions, and community engagement activities demonstrate their dedication to fostering a more sustainable and inclusive digital environment in Qatar.
This collaborative effort between academia, museums, and the community is a promising endeavor that has the potential to impact digital citizenship and online community engagement in Qatar significantly. The initiative’s focus on responsible digital citizenship and positive online engagement sets a commendable example for other regions and countries.
CSE faculties awarded with Internal Grants (2024, Cycle 7)
Five of our faculty members (Dr. Noora Fetais, Dr. Abdelaziz Bouras, Dr. Mahmoud Barhamgi, Dr.Khaled Shaban, and Dr. Elias Yacoub) and their research teams have just been been awarded QU internal grants 2024 (Cycle 7) in the different categories. Congratulations!
Here are more details about the winning projects:
Award Category: Collaborative grants (CG)
Project title: Practical Adversarial Machine Learning for Network Intrusion Detection Systems
Project team: LPI: Dr. Noora Fetais (QU), PIs: Dr. Khaled Khan (QU), Dr. Armstrong Nhlabatsi (QU), Consultant: Dr. Dan Dongseong Kim (University of Queensland)
Project abstract:
An adversarial example exploits an imperceptible attribute of the input to cause a deep-learning (DL) algorithm to misclassify. Attackers intentionally design these adversarial examples to confuse the model so that it makes a mistake. Some researchers hypothesize that adversarial examples are caused by highly abstract representations, which make the decision function extremely discontinuous. In contrast, others claim that adversarial examples occur due to the locally linear nature of neural networks. As a result of adversarial examples, researchers are developing countermeasures to defend against adversarial attacks against DL-based applications, especially in security-critical domains such as Network Intrusion Detection Systems (NIDS). However, the security of NIDS under adversarial attacks has not been well explored. The aim of this research is to enhance the understanding of practical adversarial attacks on NIDS. Here, a “practical” adversarial attack means the output of the attack is a set of replayable network packets, and adversarial attacks specifically refer to evasion attacks that slightly modify the input to bypass detection. To achieve these goals, this research proposal seeks to:
- Develop an adversarial attack generator tailored for NIDS and present a comprehensive evaluation framework to determine the severity of the attack on the NIDS.
- Propose a defense mechanism to defend against adversarial attacks and formulate another evaluation framework to examine the strength of the defense.
The proposed project is expected to bridge previously identified limitations in adversarial NIDS attacks/defenses. To enable automation and deployment of future attacks/defenses, software toolbox/frameworks will be designed and developed. More importantly, the resulting framework, methods, techniques, and tools of this project are expected to be readily applicable in real world settings.
Award Category: Collaboration Co-Fund (IRCC) Grants
Project title: MESledger: A decentralized intelligent control of production systems based on AI and Blockchain technologies.
Project team: LPI: Dr. Abdelaziz Bouras (QU), PIs: Dr. Khaled Benfriha and Améziane Aoussat (ENSAM Institute of technology, Paris-France). Dr. Abdelhak Belhi (JBJADS, Qatar). Dr. Mahmoud Barhamgi and Dr. Loay Ismael Sabry (QU)
Project Abstract: The project focuses on the development of a new system for the intelligent control of digitalized production systems. Indeed, the old production control systems, based essentially on automats (Programable Logical Controllers), are not adapted to the new manufacturing processes, said digitalized. Such new system will be confronted with a mass of data generated by the IoT layer and the various IT systems required for operation.
It becomes important to think about a new “manufacturing execution system” able to analyze the data, to order dynamically the operations and to take the adequate decisions in front of malfunctions. Moreover, such system will be able to optimize production operations according to priorities, such as cost, energy or production time. To do this, it would be necessary to review the design of a manufacturing operation, known as standard and parametric. Thus, the system should be intelligent enough to configure the values of the parameters according to the priorities.
The industrial stakes are high. Companies will not be able to reach the expected performances without the intelligent exploitation of the generated data. From a scientific point of view, the challenge is to develop intelligent and scalable algorithms that can drive a digital production system with a high level of autonomy.
Project title: Building Transparent, Fair and Privacy-preserving Smart City Applications
Project team: LPI: Dr. Mahmoud Barhamgi (QU), PIs: Dr. Saeed Salem, Dr. Qutaibah m. Malluhi, Dr. Noora Fetais (QU), Dr. Daniela Grigory (Paris-Dauhine University, France), Dr. David Camacho (Madrid University, Spain)
Project abstract: In Qatar, as well as in all advanced countries, Intelligent Cyber-Physical Systems (ICPSs) are increasingly becoming an integral part of people’s life. Their applications are exploited today to optimize many aspects of our daily lives including in healthcare (e.g., remote patient monitoring networks), smart food supply chains, smart road infrastructures (e.g. for efficient real-time regulation of traffic), and smart grids (e.g., for greener energy consumption). All of these applications are collectively called smart city applications.
They collect huge amounts of data about us, process and exploit them to make important decisions that affect our lives both positively or negatively. As these smart systems and applications are expected to grow and stay with us, it is important to ensure they are designed and operationalized in a way to respect the fundamental rights of their users.
In this project, we focus on how three fundamental rights of users including Transparency, Fairness, and Privacy can be ensured in ICPSs. Specifically, we propose models, mechanisms and software tools allowing the users of such systems to monitor how and why a decision affecting them was taken by the system, based on what data as well as how fairly they have been treated by the system compared to their peers or other groups of users. The provided justifications are computed by our solution while respecting the privacy of all users or data subjects impacted by the system. We intend to apply and validate our solution in two specific smart city applications including smart patient monitoring and traffic optimization.
Project title: DeIN: A Drone-based Inspection System for Outdoor Insulators in Qatar.
Project team: LPI: Dr. Khaled Shaban (QU), PIs: Dr. Saeed Salem (QU), Dr. Ayman El-Hag (University of Waterloo)
Project abstract: This research proposal aims to enhance the effectiveness of inspection systems for outdoor, high voltage insulators utilized in the electric power transmission networks of Qatar, the Gulf region, and beyond. The proposed research is significant due to the criticality of outdoor insulators in supporting and insulating overhead power lines (OHPL) and the impact of their poor electrical performance on the resilience of the entire transmission network. Insulators are subjected to harsh environmental factors such as hot and humid weather, heavy dust and sand depositions, and salt buildup, leading to accelerated aging and increased maintenance costs. Insulators are responsible for over 70% of power line outages and require continuous monitoring to prevent premature failures. While traditional inspection methods are expensive, time-intensive, and laborious, aerial-based robots, such as drones, equipped with sensors and advanced algorithms offer a promising solution. However, the data obtained from drones is susceptible to noise and uncertainties, affecting their accuracy and limiting their inspection capabilities. The proposed solution, DeIN, aims to develop a novel drone-based inspection system that combines vision and radiation-based sensors with state-of-the-art representation learning, fusion, and deep learning (DL) algorithms. DeIN offers advantages such as reduced inspection time and cost, increased personnel safety, enhanced precision, repeatability, and improved access to OHPL, eliminating user subjectivity. Moreover, the use of radiation-based sensors provides enhanced inspection capabilities for detecting surface cracks and internal voids, complementing the vision-based sensors. The proposed research contributes to the development of a reliable and efficient inspection system for high voltage insulators in Qatar, the Gulf region, and beyond, ensuring the reliability and security of power transmission infrastructure.
Award Category: CD-IRCC Sustainable Development Goals (SDGs)/Local Projects)
Project title: Wearable Bracelet and Machine Learning for Remote Diagnosis and Pandemic Infection Detection.
Project team: LPI: Prof. Elias Yaacoub (QU), PIs: Dr. Ahmed Badawy and Dr. Khalid Abualsaud (QU)
Project abstract: More than three years into the coronavirus disease 2019 (COVID-19) pandemic, it can be noted that the measures put in place for societies to manage the spread of this disease could have been better. For example, contact tracing mobile applications used to curb the spread of COVID-19 need additional enhancements to allow health care professionals to better understand the proliferation of the disease and to lessen the burden on hospitals and medical centers. In this project, we present an intelligent solution to remotely self-monitor COVID-19 symptoms to help rapidly identify and detect suspected positives. The proposed intelligent solution is based on using a near-field communications (NFC) wristband that collects body temperature, heart rate and SpO2 levels. It is connected to a dedicated mobile application to intelligently draw conclusions from the data (COVID-19 symptoms) it collects. Moreover, the application is trained to analyze cough sounds and detect the probability of infection. Current results show more than 90% of detection accuracy. The work in the project aims to deliver a prototype at TRL 5 or 6, where the bracelet with embedded sensors will be fully functional and the readings are sent to the mobile application, where they can be jointly analyzed with coughing sounds to further enhance performance. The proposed system can be adapted to future pandemics based on respiratory symptoms.
CSE Faculty Wins Two Best Poster Awards at QU Annual Research Forum 2023
Prof. Sumaya Al-Maadeed and her research team have just won two Best Poster Awards at the Qatar University Annual Research Forum & Exhibition 2023. The awards were received in Information and Communications Technologies discipline for Undergraduate Students and Faculty and Postdoc categories. Congratulation!
Category: Undergraduate Studeents
Project’s title: “Enhancing Ultrasound Intima-Media Complex (IMC) Segmentation: Leveraging four Deep Learning Models and Self-ONN integration”.
Project’s team: Hanadi Hassen, Omar Elharrouss, Najmath Ottakath, Somaya Al Maadeed, Mohammed E.H. Chowdhury, Ahmed Bouridane, Susu Zughaier
Project’s abstract: Carotid intima-media thickness (CIMT) is a commonly used indicator for atherosclerosis, typically evaluated through carotid ultrasound images. The utilization of advanced deep learning techniques for the analysis, segmentation, and CIMT measurement in these images remains relatively unexplored. This research aims to assess the effectiveness of four recent deep learning models, including a convolutional neural network (CNN), a self-organizing operational neural network (self-ONN), a transformer-based network, and a pixel difference convolution-based network. The evaluation is conducted on segmenting the intima-media complex (IMC) using the CUBS dataset, which comprises ultrasound images from both sides of the neck of 1088 participants. The findings reveal that the self-ONN model surpasses the traditional CNN-based approach, while the pixel difference and transformer-based models exhibit the most robust performance in segmentation.
Category: Faculty and Postdoc
Project’s title: “Automated device for detection and grading of carotid artery plaque deposit”.
Project’s team: Fatma Al-Mannai, Maryam Al-Kuwari, Ala El-Bardini, Najmath Ottakath, Somaya Al-Maadeed
Project’s abstract: The escalating global incidence of lifestyle-related risk factors, including conditions such as diabetes and elevated cholesterol levels has heightened the prevalence of cardiovascular diseases and stroke. Notably, the accumulation of plaque within the carotid artery is a primary precursor to stenosis, a condition closely associated with the onset of strokes. In response to this pressing health concern, we present the development of a state-of-the-art system engineered system to acquire ultrasound images and precisely delineate the carotid artery intima-media using artificial intelligence. Furthermore, our innovative system provides a graded assessment of stenosis risk within the carotid artery, offering timely notifications to both patients and medical practitioners. Operating on the Raspberry Pi platform, our solution incorporates a user-friendly display and communication interface, capable of promptly alerting patients and doctors to any impending stenosis-related risks.