Automatic attendance system using face recognition with deep learning algorithm

This project aims to develop an attendance system that is more efficient and convenient than traditional attendance methods currently used in schools and universities. Therefore, this paper proposes an automatic attendance system using face recognition. In this face recognition attendance system, the university does not need to install any additional devices in the classroom, which makes it a cost-effective system. The system consists of three parts: attendance system, student profile system, and training. First is the training stage where the student’s photo should be captured and stored in a separate folder. Second is the attendance system. Here the lecturer needs to take a photograph of the student and then upload it to the system. The system will automatically recognize the student’s face and store his/her name in an excel sheet (CVS file). The third system is the student’s profile. This system is to help the lecturer retrieve the student’s data by only capturing a picture of the student. A GUI has been made to simplify the usage of the system. The face recognition system has been developed using a combination of two deep learning algorithms: Multi-Task Cascaded Convolutional Neural Network (MTCNN) and FaceNet. To train the system, 908 pictures from 21 different students were collected and used, and 108 pictures were used for testing. The testing result showed 100% for face detection and 87.03% for face recognition

Retrograde Snaring for Left Ventricular Lead Placement in the Presence of a Persistent Left Superior Vena Cava

Left ventricular lead positioning is technically demanding in cardiac resynchronization therapy (CRT) device implantation, especially in patients with complex cardiac venous anatomies. We report a case in which retrograde snaring was employed to successfully deliver the left ventricular lead through a persistent left superior vena cava for CRT implantation

The physical and biogeochemical parameters along the coastal waters of Saudi Arabia during field surveys in summer, 2021

<jats:p>Abstract. During the last decades, the coastal areas of the Kingdom of Saudi Arabia, on the Red Sea and the Arabian Gulf, have been subjected to intense economic and industrial growth. As a result, it may be expected that the overall environmental status of Saudi Arabian coastal marine waters has been affected by human activities. As a consequence, adequate management of the Saudi Arabian coastal zone requires an assessment of how the various pressures within this zone impact the quality of seawater and sediments. To this end, environmental surveys were conducted over 15 hotspot areas (areas subject to environmental pressures) in the Saudi Arabian coastal zone of the Red Sea and over three hotspot areas in the Saudi Arabian waters of the Arabian Gulf. The survey in the Red Sea, conducted in June/July 2021, acquired measurements from hotspot areas spanning most of the Saudi coastline, extending from near the Saudi–Jordanian border in the north to Al Shuqaiq and Jizan Economic City (close to the Saudi–Yemen border) in the south. The survey in the Arabian Gulf, carried out in September 2021, included the areas of Al Khobar, Dammam and Ras Al Khair. The main objective of both cruises was to record the physical and biogeochemical parameters along the coastal waters of the kingdom, tracing the dispersion of contaminants related to specific pressures. Taken together, these cruises constitute the first multidisciplinary and geographically comprehensive study of contaminants within the Saudi Arabian coastal waters and sediments. The measurements acquired revealed the influence of various anthropogenic pressures on the coastal marine environment of Saudi Arabia and also highlighted a strong influence of hydrographic conditions on the distribution of biochemical properties in the Red Sea and the Arabian Gulf. The data can be accessed at SEANOE https://doi.org/10.17882/96463 (Abualnaja et al., 2023), whereas the details of the sampling stations are available at https://mcep.kaust.edu.sa/cruise-postings (last access: 25 March 2024). The dataset includes the parameters shown in Tables 1a, b and 2a. </jats:p&gt