الصفحة الرئيسية

كلية المجتمع \ علوم الحاسب

خالد سليمان زين المحمدي

السيرة الذاتية
نسبة اكتمال الملف الشخصي
الجنسية السعودية
التخصص العام علوم الحاسب
التخصص الدقيق الذكاء الاصطناعي والأنظمة والبيانات
المسمى الوظيفي أستاذ مشارك
الدرجة العلمية (المرتبة) دكتوراه

نبذه مختصرة

Khalid Almohammadi completed his bachelor’s degree in computer science from Taibah University in the Kingdom of Saudi Arabia He then pursued a profession in teaching at Tabuk University Building on this experience, he was awarded a scholarship to complete his master’s in computer science (MSc) at Newcastle University in England, which he obtained with distinction in 2011 He recently completed his PhD in computer science (Artificial intelligence) from Essex University in England, which enables him to incorporate all his previous knowledge and experience in education and e-learning The focus of his research is the development of theoretical and practical environments based on type-2 fuzzy logic theory, Intelligent autonomous systems He currently works as an associate professor in the Computer Science Department at Community college Tabuk University and works as the dean of Information Technology deanship in Tabuk University

المؤهلات العلمية

بكالوريوس حاسب آلي , كلية علوم وهندسة الحاسبات - جامعه طيبه - بالمدينة المنورة , سبتمر 2007 ماجستير في علوم الحاسب – جامعة نيوكاسل (University of Newcastle) - With Distinction)) – أكتوبر 2011 دكتوراة في علوم الحاسب الالي (الذكاء الاصطناعي) – جامعة إيسكس (University of Essex) – يوليو 2016

الاهتمامات البحثية

Artificial intelligence theory and applications Computational intelligence, including fuzzy logic, neural networks, and genetic algorithms Type-2 fuzzy logic theory and applications Machine learning/pattern recognition Intelligent autonomous systems Computer vision Data analytics Data mining techniques

الخبرات والمناصب الإدارية

ديسمبر 2020– حاليا: عميد عمادة تقنية المعلومات ، عمادة تقنية المعلومات، جامعة تبوك، تبوك، المملكة العربية السعودية 2011 - 2016 : محاضر, جامعة تبوك, تبوك, المملكة العربية السعودية 2016 – 2020: أستاذ الذكاء الاصطناعي المساعد, قسم علوم الحاسب، كلية المجتمع ، جامعة تبوك, تبوك, المملكة العربية السعودية 2020 – حاليا: أستاذ الذكاء الاصطناعي المشارك, قسم علوم الحاسب، كلية المجتمع ، جامعة تبوك, تبوك, المملكة العربية السعودية 2016 – 2017: مشرف وحدة الارشاد الاكاديمي، كلية المجتمع ، جامعة تبوك, تبوك, المملكة العربية السعودية أبريل 2017-مارس 2018 : وكيل عمادة التطوير والجودة للجودة, جامعة تبوك , تبوك, المملكة العربية السعودية مارس 2018 –أكتوبر 2019: وكيل عمادة تقنية المعلومات, جامعة تبوك , تبوك, المملكة العربية السعودية نوفمبر 2019 – ديسمبر 2020: وكيل عمادة تقنية المعلومات للخدمات الالكترونية، عمادة تقنية المعلومات، جامعة تبوك، تبوك، المملكة العربية السعودية 2008 - 2011 : معيد, جامعة تبوك, تبوك, المملكة العربية السعودية

الجدول الدراسي
اليوم المادة الوقت
من إلى
الأحد ساعات مكتبية 00:00 13:00
الأحد ساعات مكتبية 08:00 11:00
الأبحاث والمؤلفات
  • A. Tarawneh, A. Hassanat, K. Almohammadi, D. Chetverikov and, C. Bellinger, “SMOTEFUNA: Synthetic Minority Over-sampling Technique based on Furthest Neighbour Algorithm.” IEEE Access, March 2020.
  • K. Almohammadi, “Conceptual Framework Based On Type-2 Fuzzy Logic Theory for Predicting Childhood Obesity Risk.” International Journal of Online and Biomedical Engineering (iJOE), Vol 16, No 03, March 2020.
  • A. Tarawneh, A. Hassanat, I. Elkhadiri, D. Chetverikov and, K. Almohammadi, “Automatic Gamma Correction Based on Root-Mean-Square-Error Maximization.” 2020-International Conference on Computing and Information Technology (ICCIT-1441), September 2020.
  • K. Almohammadi, “Type -1 Fuzzy logic based system for predicating the best Combination of Requirements Elicitation Techniques.” IJCSNS International Journal of Computer Science and Network Security, VOL.20 No.1, January 2020.
  • A. Hassanat, K. Almohammadi, E. Alkafaween, E. Abunawas, A. Hammouri, V. Prasath, “ Choosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach." Information, 10, 39, December 2019.
  • K. Almohammadi, “Quality Prediction Model Based on Artificial Neural Networks for Mobile 3D Video Streaming.” International Journal of Computer Science and Information Security (IJCSIS), Vol 17, No 11, November 2019.
  • P. Narloch, A. Hassanat, A. Tarawneh,H. Anysz, J. Kotowski and, K. Almohammadi, "Predicting Compressive Strength of Cement-Stabilized Rammed Earth Based on SEM Images Using Computer Vision and Deep Learning". Applied Sciences,9(23):5131, November 2019.
  • Alnajdi, S. M., Alrashidi, M. Q., & K. Almohammadi, "The effectiveness of using augmented reality (AR) on assembling and exploring educational mobile robot in pedagogical virtual machine (PVM)". Interactive Learning Environments,1-27, 2018.
  • K. Almohammadi, H. Hagras, D. Alghazzawi, G. Aldabbagh, “A Survey of the Artificial Intelligence Techniques Employed for Adaptive Educational Systems.” Journal of Artificial Intelligence and Soft Computing Research, 2016.
  • K. Almohammadi, H. Hagras, D. Alghazzawi, G. Aldabbagh, “zSlices-Based Type-2 Fuzzy-Logic System for Users-Centric Adaptive Learning in Large Scale E-Learning Platforms.” Journal of Soft Computing, 2016.
  • K. Almohammadi, H. Hagras, D. Alghazzawi, G. Aldabbagh, “Users-Centric Adaptive Learning System Based on Interval Type-2 Fuzzy Logic for Massively Crowded E-learning Platforms.” Journal of Artificial Intelligence and Soft Computing Research, March 2016.
  • K. Almohammadi, H. Hagras, B. Yao, A. Alzahrani, D. Alghazzawi, G. Aldabbagh, “A Type-2 Fuzzy Logic Recommendation System for Adaptive Teaching.” Journal of Soft Computing, August 2015.
  • A. Alzahrani, A. Alzahrani, K. Fawaz, A. Alarfaj, K. Almohammadi, M. Alrashidi, “AutoScor: An Automated System for Essay Questions Scoring.” IJHSSE, 2015.
  • M. Alrashidi, K. Almohammadi, M. Gardner, V. Callaghan, “Experiment on Assembling and Exploring Educational Mobile Robot Using PVM Framework with Augmented Reality.” Proceedings of the International Conference on Robotics in Education Sofia, Bulgaria, 2017.
  • K. Almohammadi, B. Yao, A. Alzahrani, H. Hagras, D. Alghazawi, “An Interval Type-2 Fuzzy Logic Based System for Improved Instruction within Intelligent E-Learning Platforms.” Proceedings of the 2015 IEEE International Conference on Fuzzy Systems, Istanbul, Turkey, August 2015.
  • K. Almohammadi, B. Yao, H. Hagras, “An Interval Type-2 Fuzzy Logic Based System with User Engagement Feedback for Customized Knowledge Delivery within Intelligent E-Learning Platforms.” Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, Beijing, China, July 2014.
  • A. Alzahrani, A. Alzahrani, F. Alarfaj, K. Almohammadi, M. Alrashidi, “An Automated Scoring Approach for Essay Questions.” Proceedings of the International Conference on Education in Mathematics, Science and Technology (ICEMST 2014), Konya, Turkey, May 2014.
  • K. Almohammadi, H. Hagras, “An Interval Type-2 Fuzzy Logic Based System for Customised Knowledge Delivery within Pervasive E-Learning Platforms.” Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics, Manchester, UK, October 2013.
  • K. Almohammadi, H. Hagras, “An Adaptive Fuzzy Logic Based System for Improved Knowledge Delivery within Intelligent ELearning Platforms.” Proceedings of the 2013 IEEE International Conference on Fuzzy Systems, Hyderabad, India, 2013.
جوائز التميز
  • Two academic distinction awards from the Saudi Arabian Cultural Bureau, Saudi Embassy in London, UK, for distinction in the 2010–2011 academic year
  • Academic distinction certificate and award from the Saudi Arabian Cultural Bureau, Saudi Embassy in London, UK, for scientific research in March 2014
  • Academic distinction certificate and award from the Saudi Arabian Cultural Bureau, Saudi Embassy in London, UK, for scientific research in September 2014
  • Academic distinction certificate and award from the Saudi Arabian Cultural Bureau, Saudi Embassy in London, UK for scientific research in August 2015
  • Academic distinction certificate and award from the Saudi Arabian Cultural Bureau, Saudi Embassy in London, UK for scientific research in May 2016
المشاريع البحثية
اسم المشروع وصف المشروع
CPIT100 iTutor: Computer Skills Unit–Faculty of Computing and Information Technology–King Abdulaziz University I developed a system that can learn users’ preferred knowledge delivery needs and preferred learning style based on their characteristics and engagement levels to generate a customized learning environment. This system, the CPIT100 Intelligent Tutor (iTutor), is a novel approach that uses visual information to automatically calculate students’ degree of engagement. This differs from traditional methods that usually employ expensive and invasive sensors. This approach only requires a low cost RGB-D video camera (Kinect, Microsoft) operating in a nonintrusive mode whereby users can act and move without restrictions. The proposed system’s efficiency was tested in various real-world experiments with the participation of 10,000 students from King Abdulaziz University. These experiments indicated the ability of the proposed type-2 fuzzy logic-based system to handle linguistic uncertainties to improve learning and user engagement as compared to type-1 fuzzy logic-based systems and nonadaptive systems.
• Intelligent classroom: A Type-2 Fuzzy Logic Recommendation System for Adaptive Teaching o I developed a method based on type-2 FL utilising visual RGB-D features, including head pose direction and facial expressions captured from Kinect (v2), a low-cost but robust 3D camera, to measure the engagement degree of students in both remote and onsite education for small scale e-learning platforms. This system augments another self-learning type-2 FLS that helps teachers with recommendations of how to adaptively vary their teaching methods to suit the level of students and enhance their instruction delivery. o The rules are learnt from the student and teacher learning/teaching behaviours, and the system is continuously updated to give the teacher the ability to adapt the delivery approach to varied learner engagement levels. o The efficiency of the proposed system has been tested through various real-world experiments in the University of Essex intelligent classroom (iClassroom). These experiments demonstrated the capabilities—compared to type 1 fuzzy systems and non-adaptive systems—of the proposed interval type 2 FL-based system to handle uncertainties and improve the average learner motivation to engage during learning
Using augmented reality (AR) on assembling and exploring educational mobile robot in pedagogical virtual machine (PVM Augmented reality (AR) has shown potential for aiding users in their assembly tasks It provides a magic-lens view of the physical object Thus, assembly tasks become less demanding An assembly task using AR provides a pre-defined sequence of actions with a minimum amount of information to do the task Thus, there is a need to exploit the advances in AR technology to provide assemblers with a more sophisticated learning experience Using AR to turn the physical object into a smart object that communicates and interacts with a user can result in an assembler achieving a more knowledge and a greater awareness of the technology and provide insights and a deeper understanding of the software components inside the embedded computing instead of only learning the assembly hardware components
Conceptual and Practical Frameworks Based On Type-2 Fuzzy Logic Theory for Predicting Childhood Obesity Risk Obesity is a critical public health concern affecting a wide range of people globally The rise in obesity is limited to not only the wealthiest countries but also the poorest Childhood obesity has grown exponentially in the last few years, and its progression is significant contribution to the increase in mortality rates Childhood obesity is linked with a wide range of risk factors These include individual and parental biological factors, sedentary behavior or decreased physical activity, and parent restriction This paper focuses on reviewing the techniques of artificial intelligence (AI) utilized in the management of obesity in children This project will also propose a conceptual framework to use novel type-1 and type-2 fuzzy logic methods capable of predicting risks for developing childhood obesity The proposed approach will address factors such as family characteristics, unhealthy food choices and lack of exercise, and others related to children and their home environment The procedure will help in the prevention of childhood obesity, promote public health, and reduce treatment costs for a wide range of obesity-related conditions The project will also plan an examination of type-1 and type-2 fuzzy logic systems on approximately one thousand families in Saudi Arabia The proposed methods can handle the encountered uncertainties to enhance modeling and promote the accuracy of predictions of the risk for childhood obesity Type-1 and type-2 fuzzy logic systems can also encode extracted rules comprehensively to provide insight into the best childhood obesity prevention behaviors /p
معلومات التواصل
البريد الإلكتروني : kalmohammadi@UT.EDU.SA
0144563300