AttendanceKit: A set of Role-Based Mobile Applications for Automatic Attendance Checking with UHF RFID Using Realtime Firebase and Face Recognition

Tran, Trung Dung; Huynh, Kha-Tu; Nguyen, Phu-Quang; Ly-Tu, Nga

doi:10.1007/978-981-19-8069-5_29

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…Pada tingkat dasar, status ini bisa berupa sesuatu yang sederhana seperti apakah pengguna tertentu sedang online atau offline. Firebase hebat dalam melakukan hal berbagi status kehadiran (Tran et al, 2022).…”

Section: Pendahuluanunclassified

Aplikasi Sistem Presensi Pegawai PT. Berkah Pena Ilmu dengan Metode Location Based Service (LBS) Berbasis Android Menggunakan Firebase

2023

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PT. Pena Berkah Ilmu is a medium-sized company engaged in Food & Beverage, social and humanitarian institutions, event organizers, travel and merchandise sales where this company still implements attendance methods such as signatures for employees. The attendance of each employee is recorded through the existing attendance system. However, in its implementation, sometimes there are some persons who do not attend or forget to attend. This results in the employee being considered absent. This desire arose because this company experienced difficulties in processing attendance information data, resulting in more and more hardcopy archives and making it difficult when the company's management intended to recap and re-collect data and employee attendance lists. This study uses the waterfall method which describes a systematic and sequential approach to software, starting with the specification of employee requirements, design, modeling, construction, delivery of the system to employees and complete software support that is produced. With this attendance application, it will be easier for employees to carry out attendance attendance even though they are outside the office and attendance data will later be stored in the database so that it will make it easier for the personnel department to be able to recapitulate employee data.

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Section: Pendahuluanunclassified

Aplikasi Sistem Presensi Pegawai PT. Berkah Pena Ilmu dengan Metode Location Based Service (LBS) Berbasis Android Menggunakan Firebase

2023

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“…(3) Cloud Firebase Cloud: Firebase is a real-time cloud-hosted NoSQL database platform that Google develops [36,37]. It lets users store and sync data in real-time across multiple end devices.…”

Section: Iot Systemsmentioning

confidence: 99%

IoT-Based Object-Detection System to Safeguard Endangered Animals and Bolster Agricultural Farm Security

Raiaan,

Fahad,

Chowdhury

et al. 2023

Future Internet

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Significant threats to ecological equilibrium and sustainable agriculture are posed by the extinction of animal species and the subsequent effects on farms. Farmers face difficult decisions, such as installing electric fences to protect their farms, although these measures can harm animals essential for maintaining ecological equilibrium. To tackle these essential issues, our research introduces an innovative solution in the form of an object-detection system. In this research, we designed and implemented a system that leverages the ESP32-CAM platform in conjunction with the YOLOv8 object-detection model. Our proposed system aims to identify endangered species and harmful animals within farming environments, providing real-time alerts to farmers and endangered wildlife by integrating a cloud-based alert system. To train the YOLOv8 model effectively, we meticulously compiled diverse image datasets featuring these animals in agricultural settings, subsequently annotating them. After that, we tuned the hyperparameter of the YOLOv8 model to enhance the performance of the model. The results from our optimized YOLOv8 model are auspicious. It achieves a remarkable mean average precision (mAP) of 92.44% and an impressive sensitivity rate of 96.65% on an unseen test dataset, firmly establishing its efficacy. After achieving an optimal result, we employed the model in our IoT system and when the system detects the presence of these animals, it immediately activates an audible buzzer. Additionally, a cloud-based system was utilized to notify neighboring farmers effectively and alert animals to potential danger. This research’s significance lies in its potential to drive the conservation of endangered species while simultaneously mitigating the agricultural damage inflicted by these animals.

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“…To overcome these challenges, it was necessary to design models that could extract appropriate features from data sets with low intra-class and high inter-class correlation to increase the generalization power of the models. Fortunately, in recent years, with the introduction of various deep architectures, many problems of face recognition systems have been solved [2][3][4]. Tran et al [2] proposed an attendance kit, which integrates real-time Ultra-High Frequency (UHF) RFID technology with face recognition within a suite of mobile applications tailored for institutions, lecturers, parents, and students.…”

Section: Introductionmentioning

confidence: 99%

“…Fortunately, in recent years, with the introduction of various deep architectures, many problems of face recognition systems have been solved [2][3][4]. Tran et al [2] proposed an attendance kit, which integrates real-time Ultra-High Frequency (UHF) RFID technology with face recognition within a suite of mobile applications tailored for institutions, lecturers, parents, and students. This tool is powered by the FaceNet model [5], renowned for its innovations, including the introduction of the triplet loss function, utilization of the Siamese Network Architecture, and implementation of triplet Selection Strategies.…”

Section: Introductionmentioning

confidence: 99%

SNResNet: A New Architecture Based on SqNxt Blocks and Rish Activation for Efficient Face Recognition

Diba,

Khosravi

2024

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In this paper, we present a novel face recognition architecture based on the Inception-ResNet framework, called SNResNet. The Inception-ResNet architecture, is effective in computer vision applications but exhibits limitations such as computational complexity, high memory consumption, and data dependency. It uses the ReLU activation function and softmax loss function which are not best-suited for face recognition. The proposed SNResNet uses triplet loss as the loss function to be able to train the model on large datasets. The advantages of the triplet loss over the softmax are handling one-shot learning, robustness to class imbalance and fine-grained discrimination. The ReLU activation function rejects all negative values that in some applications reduce the accuracy of the model. To overcome this problem, we introduced a new activation function called Rish which has better performance. In addition, we optimized the Inception-ResNet-B block using the SqNxt block to control the model's computational costs. The CASIA-WebFace dataset is used to train the models. This dataset has some challenges; e.g., some photos have more than one face, and all faces have a background. Preprocessing conditions are defined to identify and align the correct face. SNResNet achieves 94.63% accuracy on CASIA-WebFace. Performance evaluation on the LFW benchmark database yields an impressive accuracy of 99.68%, surpassing the standard model's accuracy of 98.85%. Further, we reduced the FLOPS of the Inception-ResNet model by 15.61% which indicates a lower computational cost and a faster model for face recognition.

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AttendanceKit: A set of Role-Based Mobile Applications for Automatic Attendance Checking with UHF RFID Using Realtime Firebase and Face Recognition

Cited by 4 publications

References 11 publications

Aplikasi Sistem Presensi Pegawai PT. Berkah Pena Ilmu dengan Metode Location Based Service (LBS) Berbasis Android Menggunakan Firebase

Aplikasi Sistem Presensi Pegawai PT. Berkah Pena Ilmu dengan Metode Location Based Service (LBS) Berbasis Android Menggunakan Firebase

IoT-Based Object-Detection System to Safeguard Endangered Animals and Bolster Agricultural Farm Security

SNResNet: A New Architecture Based on SqNxt Blocks and Rish Activation for Efficient Face Recognition

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