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Khaled Hossam Mahfouz
- Instructor
- Ext: 6723
Khaled Mahfouz is an Instructor in the Department of Electrical and Computer Engineering at Ajman University, specializing in Electronics Engineering and Artificial Intelligence. He holds a B.Sc. in Electrical Engineering/Electronics (2016) and an M.Sc. in Artificial Intelligence (2023) from Ajman University. Since 2017, Mahfouz has contributed to the department, initially as an Assistant Instructor and currently as an instructor. His teaching areas include Electronic Devices and Circuits, Circuit Analysis, Sensors and Instrumentation, Digital Logic Design, Renewable Energy Systems, and Power Electronics. His research interests focus on Evolutionary Computations, Artificial Intelligence, Machine Learning, and Embedded Systems.
Education
- B.Sc. in Electrical Engineering/Electronics, Ajman University, United Arab Emirates, 2016.
- M.Sc. in Artificial Intelligence, Ajman University, United Arab Emirates, 2023.
B.Sc. in Electrical Engineering/Electronics, Ajman University, United Arab Emirates, 2016.
M.Sc. in Artificial Intelligence, Ajman University, United Arab Emirates, 2023.
Experience
- Assistant Instructor, Electrical and Computer Engineering Department, Ajman University, United Arab Emirates, 2017 - 2022.
- Instructor, Electrical and Computer Engineering Department, Ajman University, United Arab Emirates, 2022 - Present.
Assistant Instructor, Electrical and Computer Engineering Department, Ajman University, United Arab Emirates, 2017 - 2022.
Instructor, Electrical and Computer Engineering Department, Ajman University, United Arab Emirates, 2022 - Present.
Teaching Area
- Electronic Devices and Circuits
- Circuit Analysis
- Sensors and Instrumentation
- Digital Logic Design
- Renewable Energy Systems
- Design with Integrated Circuits
- Power Electronics
- Power Systems and Electrical Machines
Electronic Devices and Circuits
Circuit Analysis
Sensors and Instrumentation
Digital Logic Design
Renewable Energy Systems
Design with Integrated Circuits
Power Electronics
Power Systems and Electrical Machines
Research
- Evolutionary Computations
- Artificial Intelligence
- Machine Learning
- Embedded Systems
Evolutionary Computations
Artificial Intelligence
Machine Learning
Embedded Systems
Publications
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ConferenceK. Mahouz, S. Rameshi, M. Rafat, M.Elsayed, M. Sheikh, H. Zidan, Route Mapping and Biometric Attendance System in School Buses, 2020 Advances in Science and Engineering Technology International Conferences (ASET), Dubai
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ConferenceK. Mahfouz, S. Ali, M. Al-Betar, M. Awadallah, Solving 0-1 Knaosack Problems Using Sine-Cosine Algorithm, 2021 Palestinian International Conference on Information and Communication Technology (PICICT), State of Gaza
K. Mahouz, S. Rameshi, M. Rafat, M.Elsayed, M. Sheikh, H. Zidan, Route Mapping and Biometric Attendance System in School Buses, 2020 Advances in Science and Engineering Technology International Conferences (ASET), Dubai
Many students are reported to be left behind in school buses unwittingly due to several reasons. Locked-in students might face adverse health issues and might even die due to the suffocation and extreme climate conditions. Hence, tracking a student's entry and exit to a school bus helps avoid the problem. This paper illustrates an attendance approach using biometrics. The system comprises of the smart tablet, a microcontroller-based fingerprint sensor, an automated gate and a developed android application. When integrated in school buses, a student can only board/exit the school bus when his/her fingerprint is recognized. Once students are to be dropped off back home, the system generates a route based on the attended students and furthermore detects and alerts the bus driver if an attended student has not boarded/exited the bus.
About the journal
K. Mahfouz, S. Ali, M. Al-Betar, M. Awadallah, Solving 0-1 Knaosack Problems Using Sine-Cosine Algorithm, 2021 Palestinian International Conference on Information and Communication Technology (PICICT), State of Gaza
The task of optimization is no easy task and from a computational point of view, it often involves scanning a large search space to find the best solution that adheres to all the constraints and desired specifications. Designing a customized algorithm to solve several optimization problems is also a challenging task, therefore scientists and engineers utilize metaheuristic algorithms that can provide an optimal solution within a reasonable time. This optimal solution may or may not be the best solution in the search space, but it is usually good enough to satisfy the requirements without spending a lot of computational resources or time. The 0–1 knapsack problem is an constraint-based optimization problem in which a number of items have to be packed into a container by maximizing the value of the items in the container while also adhering to the weight limit of the container. In this paper, sine-cosine algorithm (SCA) is adopted to solve 0–1 knapsack problems. The proposed algorithm is called binary sine-cosine algorithm (BSCA). Due to the binary nature of 0–1 knapsack problem, the SCA is manipulated using a mapping function. The performance of the proposed BSCA is evaluated using 15 well-known datasets. Furthermore, the performance of the proposed BSCA is compared with other comparative algorithms (i.e., GA, PSO, and BFPA) from the literature using the same datasests. It can be observed from the results that the performance of the proposed BSCA is similar to other algorithms by obtaining the optimal results on 10 datasets. While the results of the proposed BSCA are convergent with others for the remaining five datasets.
About the journal
Memberships, Awards and Honors
- Excellence with Honors and Head of the Electrical Engineering Class of 2012, Ajman University, United Arab Emirates.