Time: 10-10:25

Title: Signal processing and AI.

Speaker: Dr. Mohammed Abdulaal

Dr. Abdulaal is an assistant professor in the Department of Electrical and Computer Engineering at King Abdulaziz University, Saudi Arabia. His research interests include Brain-computer interfaces, crowd management in Makkah and Madinah, and image processing applications. His previous affiliations include the University of Manchester, UK, KAUST, SA, and University of Texas at Austin.

Abstract:

In order to use machine learning on a signal, it has to go through various signal processing stages such as filtering, features extraction and selection, and model training and testing. Firstly, the recorder, such as a camera, produce raw data, which require significant signal pre-processing before producing commands and information. This includes low-pass, high-pass, notch filtering, and sometime more sophisticated calculations to remove undesired noises. Secondly, the data need to be converted into a more recognizable form by extracting relevant features. Some features lie in the time or spatial domain, while others lie in the frequency domain. Both domains are equivalently important to extract relevant information from the raw data. Thirdly, data require feature selection in order to obtain the most useful information and reduce redundancy. Fourthly, the information is then passed to a machine-learning classifier that trains on the data and tests it several times in order to produce an average accuracy. This accuracy is the key performance indicator for AI systems.

Time : 10:30-10:55

Title: Making AI Ubiquitous through Edge Computing

Speaker: Dr. M. Eftekhar

Dr. M. Eftekhar is an educator, researcher and a maker. His research interests include Digital Image/Signal Processing, Machine Learning/AI, Digital/Analog circuit design, Embedded systems and Robotics. He is an Associate Professor in the Department of Electrical and Computer Engineering, KAU. Prior to joining KAU in 2014, he worked as a post-doctoral researcher at KAIST, South Korea.

Abstract:

AI is the center piece of the fourth industrial revolution taking the world by storm in the modern age. Moreover, AI is deemed essential to improve the day-to-day of citizens and healthcare facilities. This AI revolution is enabled by the support technologies such as Machine Learning, Computer Vision, IoT, Blockchain, 5G communications and cloud/fog processing etc. to bring the benefits within access of the masses. Thus, it has become essential to port the high-end AI algorithms developed on the ultra-fast and highly capable computing machines in the labs to smaller, cheaper and slower mobile and embedded devices. This is what is known as edge computing i.e. imparting intelligence to everyday use electronics such an ordinary toaster. These smart devices are at the forefront of the idea of smart cities and smart networks that is taking shape to modernize the lifestyle. Using the similar technologies, Computer Vison + AI-based techniques can be used to detect deformations/abnormalities in the produce/fields to promote industry and agriculture. Furthermore, security/surveillance applications can make use of such smart devices for small unmanned systems which can be equipped with secured/encrypted communication channels for monitoring/operating beyond visual range and in harsh terrains. In this talk, we will discuss how the engineering students can be a part of this revolution by learning and deploying the AI solutions on off-the-shelf and custom-built hardware/software frameworks. Deployment of AI inference engines on modern embedded devices based on ARM, RISC-V, ESP and AVR will be discussed in addition to rapid prototyping, hardware engines, and open-source software.

Time:11-11:25

Title: Computer engineering powering AI from cloud to IoT.

Speaker: Dr. Saud Wasly

Dr. Wasly received his bachelor's degree in Electrical and Computer Engineering from King Abdulaziz University, Jeddah, Saudi Arabia in 2007. He worked at the municipality of Jeddah as a software Engineer before completing his M.Sc. and Ph.D. in the field of Electrical and Computer Engineering from the University of Waterloo, Canada, in 2013 and 2018 respectively. He is currently working as an assistant professor at the Department of Electrical and Computer Engineering (Computer Group) at King Abdulaziz University.

Abstract:

With the increased use of Artificial Intelligence and Machine Learning in many application domains, engineering specialized hardware to accelerate AI tasks became a significant demand. In this talk, we will highlight some of the popular specially-engineered hardware architectures to accelerate AI tasks at different levels. In addition, we will show the advantages of using specialized hardware for AI tasks in embedded and IoT domains in contrast to using GPUs or powerful CPUs. Finally, we will show how FPGAs, as a flexible platform, are utilized to build efficient reconfigurable application-specific architectures for AI applications.

Time: 1-1:40

Title: The 6G roadmap, AI-aided wireless networks

Speaker: Dr. Abdulah Aljohani

Dr. Abdulah Aljohani received the B.Sc (Eng.) degree in electronics and communication engineering from King Abdulaziz University, Jeddah, Saudi Arabia, in 2006, and the M.Sc. degree with distinction and Ph.D. degree in wireless communication from the University of Southampton, Southampton, U.K., in 2010 and 2016, respectively. He is currently an Assistant Professor with the Department of Electrical and Computer Engineering, King Abdulaziz University.

Abstract:

Wireless communications have played a key role in creating the world as we know it, with enormous social and economic impact. To face the severe system capacity shortage due to the increasing data traffic in wireless networks, AI is a promising technology for alleviating the spectrum gridlock at lower frequencies, by exploiting the high bandwidth available. It is expected that, 6G will undergo an unprecedented transformation that will make it substantially different from the previous generations. AI will be a crucial solution in designing and optimizing 6G architectures, protocols, and operations.

In this talk, we will review the potential technologies for 6G to enable mobile AI applications, as well as AI-enabled methodologies for 6G network design and optimization.

Time: 1:45-2:25

Title: Deep convolutional neural network-based engineering solutions.

Speaker: Dr. Mohammed Shehzad Hanif

Muhammad Shehzad Hanif is working as associate professor at the Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia. In 2009, he received his Ph.D. in Computer Engineering from Sorbonne University, Paris, France. His research interests include machine learning and image understanding and analysis.

Abstract:

Deep learning has emerged as one of the most focused fields of research in recent years. In this field, a class of neural networks known as Deep Convolutional Neural Networks (DCNNs) have become extremely popular due to their superior performance over many other traditional machine learning algorithms. The success of DCNNs is mainly due to the novel architectures and improved training methodologies. In this talk, I will present the basic building blocks of a DCNN, explain their roles, and discuss training methodology. Moreover, some novel and sophisticated architectures along with challenges in the training will be discussed in detail. Application of DCNNs to solve different computer vision tasks like image classification, object detection, image matching, etc., will be presented.

Time: 10-10:40

Title: Artificial Intelligence Impact on Business

Speaker: Dr.Saeed Badghish

Dr. Saeed Badghish is an Associate Professor in marketing at King Abdulaziz University. Currently, he is the Vice Dean for Postgraduate Studies and Scientific Research at Faculty of Economics and Administration. He worked in industry with global companies as section and regional sales manager while he was holding bachelor’s degree in marketing from King Fahd University of Petroleum and Minerals. Saeed earned his MSc Degree in Marketing from University of Newcastle, Australia. Also, he has done his PhD at University of Western Sydney in International Marketing. He has completed a certificate program entitled Marketing Strategy from Cornell University and an online certified trainer for Marketplace Simulation. His current research interests appearing in sales management, consumer behaviour, and cross culture studies.

Abstract:

Artificial Intelligence is currently contributing to transforming the business world today. Artificial intelligence is enabling businesses to work smarter, doing more with significantly less. It has played significant position in the future of business. AI can be used in several sectors, including marketing, where artificial intelligence offers many benefits to companies that represent them with a competitive advantage. Professionals in marketing field can better understand consumers and build relationships with their customers. The technology has contributed to changing the buying behavior of consumers through huge amounts of data. On the other hand, using the AI technology has some challenges that we need to be aware about. In this talk, we will discuss how marketing and business can benefit from digitalization by presenting some latest examples of AI in Marketing.

Time: 10:45-11:25

Title: Using AI to Enhance Business Operations

Speaker : Dr. Asif Irshad Khan

Dr. Asif Irshad Khan is working as an Assistant Professor in the Computer Science Department, Faculty of Computing and Information Technology at the King Abdulaziz University, Saudi Arabia. He earned his PhD in Computer Science and Engineering from Singhania University, India. His research interest includes Component-Based Development, Software Security, and Machine Learning.

Abstract:

It is useful for companies to look at AI through the lens of business capabilities rather than technologies. the use of AI to enhance business operations — involves embedding algorithms into applications that support organizational processes. AI applications can automate repetitive, formulaic tasks and, in doing so, deliver orders-of-magnitude improvements in the speed of information analysis and in the reliability and accuracy of outputs. AI applications can enhance operational excellence, customer satisfaction, and employee experience. This talk will help students understand Application of AI in business and how to make easier for business to adopt, reuse, scale and realize value of AI through defined outcomes. The goal is putting AI to work for business. Journey to AI skills on scale will also be discussed in the talk.

Time:1-1:40

Title: Using Natural language Technology in Business

Speaker: Dr. Imtiaz H. Khan

Dr. Imtiaz H. Khan is a Professor in the Department of Computer Science at King Abdulaziz University, Jeddah, KSA. He received his master’s degree in computer science from the University of Essex, UK, in 2005. He earned his Ph.D. in artificial intelligence from the University of Aberdeen, UK, in 2010. His research interests are in natural language processing, particularly natural language generation and evolutionary computation.

Abstract:

Natural language processing (NLP) is a subfield of artificial intelligence where computer science meets with linguistics. NLP has been widely used in many areas including business and healthcare. NLP tools are important for businesses that deal with large amounts of unstructured text, whether emails, social media conversations, online chats, survey responses, customer feedback and so on. In this talk, we will discuss how NLP can help businesses by automating time-consuming processes to gain a competitive advantage. Some challenges and potential opportunities will also be discussed.

Time : 1:45-2:25

Title: AI in Business: Building Sustainable Innovation Driven Capabilities in KSA business ecosystem

Speaker: Dr. Miltiadis D. Lytras

Dr. Miltiadis D. Lytras is an expert in advanced computer science and information management, editor, lecturer, and research consultant, with extensive experience in academia and the business sector in Europe, Saudi Arabia and Asia. Dr. Lytras is Research Professor at Deree College – The American College of Greece and serves as Distinguished Scientist at the King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia and Visiting scholar in Effat University, KSA.

Abstract:

In this webinar the emphasis in on the innovation capabilities of Artificial Intelligence in Business. A thorough presentation of key worldwide initiatives for sophisticated AI driven services in the business sectors integrated with an open discussion on the emerging business models and innovation cluster of Artificial Intelligence in business. The webinar serves as a unique venue for communicating state of the Art in AI for Business and as a robust debate for the new era of interdisciplinary driven innovation. In our discussion we will discuss five different areas for business empowerment in KSA directly related to Vision 2030. Finally, we will conceptualize at list 15 new AI-driven startup ideas for businesses in the Kingdom of Saudi Arabia. The concluding message is that KSA must be a core partner of the global leadership in the AI for Business Innovation Ecosystem.

Time:10-10:25

Title: Artificial Intelligence Enhancement of Medical Diagnosis

Speaker: Prof. Wadee Alhalabi

Wadee Alhalabi received his master and PhD in electrical and computer engineering from the University of Miami in 2004 and 2008 respectively in machine learning. He is a professor of artificial intelligence in the computer science department KAU, a former member of KAU scientific council, 2016-2020. A member of KAU academic promotion committee since 2016. His research focuses on virtual reality and machine learning Director of the house of expertise of virtual reality and artificial intelligence.

Abstract:

: Medical diagnosis is one of the most complicated skills with large diversity in term of number of diseases and complexity of the disease. The task requires experts in medical diagnosis, and sophisticated machines for the medical imaging or lab samples’ analysis. Artificial Intelligence (AI) and Machine Learning (ML) are being used for decades to decipher the patterns that might be found in human body and understand those messages. Many techniques were used starting from simple statistical approach with number of samples to advance Deep Learning (DL) with large datasets to recent algorithms that use very small number of samples. Approaches differ from state-of-the-art imaging technologies to very simple low-resolution B/W cameras. In this talk, we explore the revolution of medical diagnosis using AI.

Time :10:30-10:55

Title: Applications of ML and AI for Covid-19 pandemic

Speaker: Dr. Emad Nabil

Dr. Emad Nabil: Received Ph.D. and master’s degrees in the field of computer science and machine learning from the faculty of computers and information, Cairo University, Egypt. Worked as an assistant professor at Cairo University and currently is working as an associate professor at the Islamic University of Madinah, KSA. He published more than 20 peer-reviewed journal and conference papers in machine learning, soft computing, health informatics, optimization, and bioinformatics.

Abstract:

Machine learning proved to be a solution key for many of today’s problems and dilemmas. The medical domain is one of the areas where ML has a lot of success stories. In this talk, we will show how ML helped in overcoming the COVID-19 pandemic. Some applications are related to the diagnosis using radiology images or numerical readings that came from an analysis. Other applications covered time series prediction of infection spread. Some research worked in predicting the reflection of the government’s measures on human mobility. Other researchers utilized ML to show how ML can accelerate the process of vaccine design and drug discovery. The above applications helped the government’s decision-makers to determine when to make lockdown or not, also helped hospitals’ managers to manage their resources giving high priority to severe cases and helped doctors to make an accurate diagnosis and drug and vaccine discovery.

Time:11-11:25

Title: Self-Explainable Deep Learning at the Edge, Internet of Health Things, Beyond 5G, and Blockchain Based Digital Twin: Personalized Healthcare During Pandemic

Speaker: Dr. Abdur Rahman

Dr. Md. Abdur Rahman is an Associate Professor and former Chairman of the Department of Cyber Security and Forensic Computing, College of Computer and Cyber Sciences, University of Prince Muqrin (UPM), Madinah Al Munawwarah, Kingdom of Saudi Arabia. Dr. Rahman is currently serving as the Director of the Research and Postgraduate Studies Department at UPM. In 2018, 2019, and 2020.

Abstract:

COVID-19 pandemic has shown the weaknesses of our existing healthcare systems, as a city, state, country, and global village. To flatten the curve, the healthcare providers have resorted to traditional clinical solutions, which does not scale to mass level. Thanks to the recent advancements in AI-based Healthcare technologies in the areas such as Self-Explainable Deep Learning at the Edge, Blockchain, Internet of Health Things (IoHT), and Beyond 5G (B5G), to name a few, researchers have shown that technological advancement can play a key role in managing the digital twin of each individual during the pandemic. In this talk, I will first present different branches of deep learning that have contributed to healthcare system automation. I will then present how we leveraged these 4 key technologies during the recent development of more than 58 COVID-19 related applications for COVID-19 diagnosis and pandemic management. The running hands-on demonstrations have been presented to Prince Sultan Military Hospital at Madinah Al Munawwarah during a one-day workshop in which we have trained more than 60 medical practitioners from the hospital and got their feedback on clinical trials. I will also show you how we designed the final model, from dataset collection, training, testing, validating, and deploying to our LOCAL SECURE CLOUD as web interface and smartphones – to be ready for clinical trials. Finally, I will share some recommendations regarding the way forward.

Time: 1-1:40

Title: Dealing with the Shortage of Data in Deep learning algorithms for Medical Applications

Speaker: Dr. Manal AlGhamdi

Manal AlGhamdi is an Associate Professor at the Department of Computer Science, UQU, Saudi Arabia. She received her Ph.D. degree in Computer Vision from University of Sheffield, United Kingdom in 2015. Manal's research interests include Machine Learning, Computer Vision and Security. She focuses on developing and evaluating video and image processing techniques for various applications including video representation, similarity measurements and crowed analysis.

Abstract:

Convolutional Neural Network (CNN) models have been successfully utilized in many computer vision applications such as classification and semantic segmentation. These models have the ability to learn the deep features of the input without pre-processing steps such as segmentation. However, constructing these models to achieve desirable results is restricted by the availability of large and annotated training sets. In several fields, large amount of data can be available to build competitive systems, which is not the case for the medical applications. Many techniques have been successfully developed to address this issue including data augmentation, semi-supervised and transfer learning methods. However, medical data problem is full of challenges and far from being solved.

Time: 1:45-2:25

Title: Machine learning for genomic medicine

Speaker: Dr. Nouf Alghanmi

Dr. Alganmi is deputy director for the Center of Genomic Medicine Research at King AbdulAziz University and an assistant professor at the faculty of computing. She established the first genomics data center in KAU, developed pipelines to analyze clinical data and performed researches such as functional characterization of genetic variants.

Abstract:

Many types of researches today utilized genomics technologies (such as Next Generation Sequencing technologies), creating large datasets. The more data we collect, the more it is important to extract information. Machine learning is a powerful and practical form of artificial intelligence. Applying machine learning techniques to genomics medicine aims to find disease-related markers, patterns in massive datasets and infer computer models for better understanding across many different clinical fields, including prenatal and reproductive health, rare diseases, cancer, infectious diseases, regenerative medicine (gene editing and gene therapies) and pharmacotherapy. Advances in the supervised and unsupervised learning techniques can manage the high dimensional nature of the genomics data and build complex disease prediction models. Therefore, machine learning is playing an important role in improving human health by integrating patients' genetic features into personalized medicine.

Time: 10-10.40

Title: Artificial Intelligence in Education: Applications and Implications

Speaker: Prof. Abdullah Basuhail

Abdullah Basuhail, Professor of Computer Engineering, FCIT-KAU, member of AI-EC. He worked as JIC faculty member, E-Training Center Director, Technical Education Council member, E-Learning-EC-member, ICDL-Center Director, JCT Computer Center Director, WSA Consultant. He holds several courses in e-learning and higher education development. He presented several short-courses for faculty members.

Abstract:

Today, educational institutions are required to keep pace with technological development through the adoption of new creative and more efficient techniques in education. Innovative educational technologies have revolutionized the methods of teaching and learning. In recent times, with advancements of computer technologies, higher education institutions have begun to introduce new technologies in their plans and curriculum to develop the educational process. Artificial intelligence is one of the most prominent technologies in the field of education that help to achieve this goal. Artificial intelligence applications are central for educational institutions as they are no longer limited to science and technology, but rather have become an essential part of towards sustainable education, where governments are pouring enormous budgets into a very wide array of implementations in this field. This session intends to demonstrate some of the artificial intelligence applications and technologies and the trends of their use in educational contexts. It aims to highlight the emergence of using artificial intelligence in teaching, learning and related processes. Moreover, it explores the educational implications of emergent technologies on the styles of teaching and learning. The session intends to show the effective application of artificial intelligence methods as a means of improving the quality of teaching and learning, in addition, it intends to foresee the role of artificial intelligence in the future nature of education. The session aims to present a concise overview of some recent studies to the application and implications of artificial intelligence in educational contexts for further research and investigation.

Time :10.45-11.25

Title: Students Authentication and Engagement Assessment in Online Learning

Speaker: Dr. Areej Alhothali

Areej Alhothali is currently an assistant professor in the faculty of computer science and information technology at King Abdul-Aziz University. She earned her master's and Ph.D. degrees in computer science (artificial intelligence) from the University of Waterloo, Canada. Her research interest lies in the areas of machine learning, deep learning, natural language processing, intelligent agent systems, affective computing, and sentiment analysis.

Abstract:

During the COVID-19 home quarantine, many universities have moved towards greater blended or online deliveries via video call and learning management systems. Online educators often face challenges associated with student engagement in virtual classrooms and student authentication in online exams. Assessing student engagement is essential to ensure student satisfaction and the quality of student experience. Continuous students engagements assessment can be performed using vision-based methodologies that add to the current challenges facing online learning platforms. Online examination also poses a unique obstacle for distance educators. Due to the nature of online examinations, students may attempt to artificially boost their examination scores by having another individual take the exam for them, which typical authentication schemes fail to detect. Keystroke dynamics can help predict students satisfaction and engagement and provide continuous biometric authentication in online examinations.

Time :1.00-1.40

Title: Artificial Intelligence & English Language Instruction: Use cases and potential

Speaker: Dr. Norah Ahmed Almalki

Asst. Prof. of English literature, and head of the development dept. in the Deanship of E-learning and Distance Education, female section at King Abdulaziz University. Research interests include cultural studies, comparative literature, digital humanities, and machine and deep learning application with relevance to literary studies.

Abstract:

The potential of an effective integration of Artificial Intelligence (AI) applications in the field of foreign language (FL) instruction is still yet to garner enough interest from teachers and researchers in the field of education. For instance, the hype over language apps that utilize AI technologies is still neither sufficiently researched, nor mainstreamed as a potential educational technology. This presentation aims to provide some accessible and, hopefully, reproducible scenarios for both language instructors and students who want to integrate AI in their instructional, learning and research activities in higher or public education. We will be reflecting on the following:

• Current and widely used AI applications that can be utilized in novel ways to introduce/learn AI concepts in the FL classrooms
• Lesser know AI applications that can be integrated in FL instructional design and relevant educational research practices.
• The development of a tentative roadmap for an AI-enhanced language instruction paradigm.

Time : 1:45-2:25

Title: Predicting Student Performance Using Machine Learning

Speaker: Dr. Abdullah Alshanqiti

Abdullah Alshanqiti received the M.Sc. and Ph.D. degrees from the University of Leicester, U.K. He is currently the Vice Dean of FCIS, Islamic University of Madina, recognized for his work on machine learning, software reverse engineering based on dynamic analysis, model/graph transformations using intelligent learning, and inference approaches. His research interests include research cooperation in different cutting-edge disciplines, including quantum machine learning, hybrid AI approaches that focus on solving NLP, computer vision challenges, and interpretability of deep learning models using graph transformations rules.

Abstract:

Understanding, modeling, and predicting student performance in higher education poses significant challenges concerning the design of accurate and robust diagnostic models. While numerous studies attempted to develop intelligent classifiers for anticipating student achievement, they overlooked the importance of identifying the key factors that lead to the achieved performance. Such identification is essential to empower program leaders to recognize their academic programs' strengths and weaknesses and thereby take the necessary corrective interventions to facilitate student achievements. In this talk, I will present our approach that consists of principally two main parts: (1) a hybrid regression model (used for optimizing the prediction accuracy of student academic performance) and (2) an optimized multi-label classifier based on a self-organizing map model (used for predicting the qualitative values for the influence of various factors associated with the obtained student performance). I will also show our experimental results and findings based on seven publicly available and varying datasets, emphasizing the need to predict not just the student performances but also their attainments w.r.t the program learning outcomes.

Time: 11:30-12:30

The Clinical Skills and Simulation Center (CSSC)

https://csc.kau.edu.sa/Default-140073-EN

Time: 2:30-04:30

AI in Education Poster Sessions