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Journal of Big Data, 1, 1.We believe that our money box for cash system, Multinational Currency Coin Counting Machine, electronic time attendance machine and services will bring more satisfactory results and more surprises to customers. A hybrid framework combining background subtraction and deep neural networks for rapid person detection. International Journal of Engineering and Advanced Technology (IJEAT), 8(3S), 1–4. Geethapriya, S., Duraimurugan, N., & Chokkalingam, S. What is object detection? Introduction to YOLO algorithm. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212–3232. Object detection with deep learning: A review. Retrieved Septemfrom alexnet-vgg-googlenet-resnet-and-more-666091488df5. CNN architectures: LeNet, AlexNet, VGG, GoogLeNet, ResNet and more …. In First international conference on sustainable technologies for computational intelligence, pp. Using RFID technology in vaccination cards for Saudi children.
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In 2019 international conference on machine learning, big data, cloud and parallel computing (Com- IT- Con), India, Feb 14th–16th, 2019.Īlbuhairi, T., & Altameem, A. Cloud based smart attendance system for educational institutions. The Pacific Journal of Science and Technology, 13(1), 300–307. Development of attendance management system using biometrics. In Online international conference on green engineering and technologies (IC- GET). Automatic attendance management system using face detection. Varadharajan, E., Dharani, R., Jeevitha, S., Kavinmathi, B., & Hemalatha, S. University of Novi Sad, SISY 2017, September 14–16, Subotica, Serbia. In IEEE 15th international symposium on intelligent systems and informatics. FaceTime-Deep learning based face recognition attendance system. Applied Sciences, 9, 3750.Īrsenovic, M., Sladojevic, S., Anderla, A., & Stefanovic, D. Application research of improved YOLO V3 algorithm in PCB electronic component detection. Reducing chronic absenteeism: An assessment of an early truancy initiative. In 18th international symposium INFOTEH- JAHORINA, March 20–22, 2019. An improved version of student attendance management system based on RFID. Mijić, D., Durutović, J., Bjelica, O., & Ljubojević, M. In IEEE 4th international conference on cloud computing and big data analytics. In IEEE international conference on automatic control and intelligent systems (I2CACIS 2019), Selangor, Malaysia, June 29, 2019. Face recognition automatic class attendance system (FRACAS). In IEEE integrated STEM education conference (ISEC). A smart classroom of wireless sensor networks for students time attendance system. Our entire system has proven to gather high accuracy in face detection and performance.įawaz, A. The designed system performs efficient in real time implementation for counting and detection.
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YOLO V3 will first count the students in an image followed by identifying faces as known and unknown generating spreadsheets separately and an email is send at the end of month to students, parents and faculty. The unique part is camera installed in classroom will take picture twice one at the start and one at the end to ensure students has attended complete class. Our unified structure is based on YOLO V3 (You only look once) algorithm for face detection and Microsoft Azure using face API for face recognition (face database). Some of popular object detection algorithms are back propagation neural network, region based convolution network (RCNN), faster RCNN, single shot detector. In this paper attendance monitoring will be done through smart phone available with almost all faculty members. In Modern era everyone has Smartphone and connected via internet every time. Current biometric attendance system is not automatic that’s why wastes a lot of time, difficult to maintain and requires a queue for scanning fingerprints to mark their attendance. Traditionally, the attendance of students has been a major concern for the colleges and the faculty has to spend a lot of time and is a tedious job to mark attendance manually.