Mr. Khaled Mahfouz is an Instructor at the Department of Electrical and Computer Engineering majoring in Electronics Engineering and an extended interest in Artificial Intelligence.
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.
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.