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Smart Attendance Management System Using Face Recognition 1

Smart Attendance Management System Using Face Recognition

引用

1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Rawat, Shivam Goyal, Shweta Rawat, Nikita Graphic Era Hill University Dept. Computer Science & Engineering Dehradun India Graphic Era deemed to be University Dept. of Electrical Engineering Dehradun India

ISBN: (纸本)9798331518578

Managing attendance in educational institutions is often a time-consuming and error-prone task, with traditional methods like roll calls or sign-in sheets being inefficient and susceptible to proxy attendance. This paper presents a Smart Attendance Management System that leverages facial recognition technology to automate the attendance process. By using cameras to capture images of students, the system recognizes their faces and marks their attendance in real time. Developed in Python, the system employs the K-Nearest Neighbours (KNN) algorithm for accurate face recognition. Once attendance is recorded, it is stored in a CSV file and can be visualized using the flask web application. The proposed system significantly reduces manual effort, ensures accuracy, and operates efficiently in real-time environments. © 2025 IEEE.

关键词： Nearest neighbor search

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Cross-Domain Adaptation Through Adversarial Teaching for Unmanned Ariel Vehicles in Smart Cities 22

Cross-Domain Adaptation Through Adversarial Teaching for Unm...

引用

22nd International Learning and Technology Conference, L and T 2025

作者： Mazhar Ul Haq, Qazi Yao, Leethar Lin, Bo-Yu Yuan Ze University Dept. of IBPI and Computer Science and Engineering Zhongli Taiwan National Taipei University of Technology Dept. of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798331532215

Person re-identification has been an important issue of surveillance systems in smart cities. However, this requires huge datasets to supervise deep learning models for accurately identifying and tracking people in smart cities. In this paper, we propose adversarial learning in integration with a mutual mean teaching model known as adversarial teaching to take the issue of domain adaptation. Initially, they learn a source dataset in a supervised way. This model information is then transferred to another model through unsupervised learning and pseudo-labels are generated by generative adversarial learning. The multi-class invariance is handled by adversarial learning. The performance of the proposed model is evaluated on two different datasets source and target datasets. Multiple experiments on Duke and Market datasets indicate higher accuracy of F-1 score on both datasets compared to previous methods, demonstrating the superior performance of the proposed method. © 2025 IEEE.

关键词： Adversarial machine learning

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Exploring emerging spreaders through GCN-based link prediction and a novel centrality method 17

Exploring emerging spreaders through GCN-based link predicti...

引用

17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Nandi, Suman Maji, Giridhar Dutta, Animesh National Institute of Technology Dept. of Computer Science and Engineering West Bengal Durgapur India Asansol Polytechnic Dept. of Electrical Engineering West Bengal India

ISBN: (纸本)9798331531195

Exploring influential spreaders and predicting missing links in complex networks is essential for understanding and effectively controlling network dynamics. This paper presents a Graph Convolutional Network (GCN)-based link prediction method to estimate the probability of future link formation. We incorporate node features that capture local and global topological connectivity structures and feed these into the GCN model, where convolutional layers aggregate neighboring information and transform node features. This approach enables the model to capture structural patterns by integrating local and global information from neighboring nodes. In the final layer, the GCN model computes a prediction score representing the likelihood of an edge's existence, using insights gained during training. Finally, considering the predicted links, we update the network structure and introduce a novel centrality method called Emerging Spreader Centrality (ESC) to identify emerging spreaders within this augmented network. We conduct two separate experiments to evaluate the performance of the GCN-based link prediction and the ESC method, comparing their effectiveness with various state-of-the-art methods. Results demonstrate that our approach not only effectively predicts future links but also identifies emerging spreaders in the augmented networks. © 2025 IEEE.

关键词： Prediction models

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Integrated Word2Vec-based Speech Annotations for Enhanced EEG Decoding of Speech Intentions 13

Integrated Word2Vec-based Speech Annotations for Enhanced EE...

引用

13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Jang, Se-Na Park, Hyeong-Yeong Kim, Seong-Wook Yu, Seong-Hyun Lee, Minji Jeong, Ji-Hoon Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of

ISBN: (纸本)9798331521929

Brain-computer interface (BCI) technology has promising applications as an intuitive communication tool and in fields such as language rehabilitation. This study aims to decode human speech intentions by analyzing EEG signals recorded during actual and imagined speech. EEG data were collected using a 64 channels system, with preprocessing to remove artifacts. Automatic speech recognition (ASR) was used to extract precise speech onset times, generating time-specific speech annotations corresponding with EEG data. Pretrained Word2Vec embeddings were integrated to provide semantic context, combining neural signals with high-level linguistic features. Support vector machine (SVM), linear discriminant analysis (LDA) were employed for decoding. The results demonstrate that integrating speech annotations improves decoding accuracy, even for imagined speech, highlighting the potential of BCI technology for advanced applications in communication and rehabilitation. © 2025 IEEE.

关键词： Semantics

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A New Method for One-Pedal Driving With Fuzzy-Based Efficiency-Optimization Algorithm

A New Method for One-Pedal Driving With Fuzzy-Based Efficien...

引用

2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Heydrich, Marius Hammer, Tim Mitsching, Thomas Ivanov, Valentin Technische Universitä t Ilmenau Dept. of Computer Science and Automation Ilmenau Germany Technische Universitä t Ilmenau Dept. of Mechanical Engineering Ilmenau Germany

ISBN: (纸本)9798331533892

This paper presents a case study on adaptive one pedal driving for a battery electric sport utility vehicle with in wheel based rear-wheel drive that is able to adjust the drive pedal curve automatically. In addition, a fuzzy logic observes the state of the electric machines to adapt the motor torque to the most efficient region. The control method was tested in a co-simulation using a validated vehicle model and a driver-in-the-loop strategy. Within the experiments, an improvement in energy recovery of up to 4.8 % was achieved compared to the classic brake blending. In addition, the total number of brake pedal applications is ten times lower, which translates into less driving stress and greater driving comfort, especially on long distance trips, proving the effectiveness of the controller. © 2025 IEEE.

关键词： Vehicle wheels

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An Edge AI and Adaptive Embedded System Design for Agricultural Robotics Applications 25

An Edge AI and Adaptive Embedded System Design for Agricultu...

引用

30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Huang, Chun-Hsian Chen, Zhi-Rui Hsu, Huai-Shu Dept. Electrical Engineering National Changhua University of Education Changhua Taiwan Dept. Computer Science and Information Engineering National Taitung University Taitung Taiwan

ISBN: (纸本)9798400706356

This work presents the AgrBot, an agricultural robot designed to intelligently estimate and predict crop pest and disease severity (PDS). The AgrBot incorporates two binarized neural network (BNN) hardware modules for recognizing target crops and estimating their PDS. In a resource-constrained FPGA-based design, these BNN hardware modules can be configured on-demand, showcasing system adaptivity. Furthermore, a multimodal model that integrates crop images, sensor data, and time features is presented for predicting PDS. Employing edge artificial intelligence (AI) through the BNN hardware modules and the multimodal model enables the AgrBot to determine if biological agents are applied to protect crops from pests and diseases, creating a comprehensive agricultural cyber-physical system (CPS). Experimental results demonstrate accuracies of 76.3% for recognizing target crops, 65.3% for estimating PDS, and 67% for predicting PDS. In comparison to existing microprocessor-based design methods, the AgrBot's BNN hardware modules improve frames per second (FPS) by a factor of 790, while the multimodal model reduces processing time by up to 50.9%. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Digital storage

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Empowering E-Commerce Decisions Through AI-Enhanced Review Analytics

Empowering E-Commerce Decisions Through AI-Enhanced Review A...

引用

2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Tembhurnikar, Atharv Rahinj, Shreyash Mali, Siddhesh Desai, Vishwajeet Yadav, Gitanjali Vishwakarma Institute of Information Technology Dept. of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798331527495

Due to huge customer market product reviews are large in number and complicated. Thus, understanding the actual essence of the feedback is difficult. The system proposed by us is complex but it tackles the issues faced by previous systems. The paper serves as guideline for creating such system. For gathering data the method of web scraping is used, followed by preprocessing data using Natural Language Processing (NLP) techniques. For prediction purpose deep learning models LSTM and Convolutional Neural Networks (CNNs) are used along with Classification model XGBoost. Through our system we have presented the results visually so that consumers as well as Business stakeholders can take actionable insights. The system has great potential in variety of sectors as it can capture the sentiment of the reviews for a product and also its genuineness with precise accuracy. It has achieved fabulous training accuracy of 89.19% and validation accuracy of 81.49%. It represents the true sentiment of the customer feedback eliminating the fake reviews. © 2025 IEEE.

关键词： Emotion Recognition

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Predicting the Likelihood of a Recurrent Febrile Seizure Using Thermographic Images and Deep Learning with Rules Generation 1

Predicting the Likelihood of a Recurrent Febrile Seizure Usi...

引用

1st International Conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Patil, Pratima Deshpande, Deepa S. MGM University Jawaharlal Nehru Engineering College Dept. of Computer Science and Engineering Chhatrapati Sambhajinagar431003 India

ISBN: (纸本)9798350355611

Recurrent febrile seizures are a common concern in children, often triggered by rapid changes in body temperature during fever episodes. For rapid action and care, early and accurate seizure recurrence prediction is crucial. Limited techniques were developed for predicting the likelihood of a Recurrent Febrile Seizure using a Thermographic image. Several drawbacks were considered for recurrent febrile seizure prediction such as lower performance, high time consumption, and error rate. Hence, the proposed technique is introduced to provide an efficient Deep Learning (DL)-based technique. Initially, the data is gathered from datasets, and the Intra-Attentional Convolutional ResNet (InAtt-CRNet) with the Yolov5-Face model detects the face and reduces the processing time by tacking the face with a bounding box. The patient's forehead or nose temperature is monitored using the Region of Interest (ROI) finding approach following face detection and tracking in each frame. Then, the temperature is predicted using the Enhanced Convolutional Capsule Network (EC2Net). The body temperature, age, duration of fever, and family history are used to construct the clinical decision rule. The Twin-directional Gated Long Short-term memory (TdG-LSTM) is used to predict the likelihood of a subsequent febrile seizure. The proposed technique achieves 98.16% accuracy, 98.45% precision, 0.12 RMSE, and 29 seconds as processing time. © 2025 IEEE.

关键词： Long short-term memory

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A Pipeline for Immersive data Visualization in CAVE Systems

A Pipeline for Immersive Data Visualization in CAVE Systems

引用

2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Challa, Koundinya Jaiswal, Chandra Pendem, Abhinav Gokaraju, Balakrishna North Carolina A&T State University Dept. Computational Data Science and Engineering Greensboro United States

ISBN: (纸本)9798331504847

Virtual Reality (VR) has transformed data visualization by enabling immersive and interactive experiences. CAVE (Cave Automatic Virtual Environment) systems provide a unique platform for exploring complex datasets in an intuitive, spatially immersive manner. However, a standardized pipeline for developing VR scripts tailored to CAVE-based data visualization remains underexplored. This paper presents a structured pipeline for creating and deploying immersive data visualization in CAVE systems, specifically using the WorldViz Prism CAVE environment. Our approach facilitates seamless integration of multi-dimensional datasets, enabling real-time interaction with 27 visual graphs within a single VR scene. The system allows dynamic graph manipulation, interactive exploration, and enhanced user engagement. By streamlining VR script development, this pipeline improves accessibility for researchers and practitioners, fostering a more intuitive understanding of complex datasets. Experimental results demonstrate the effectiveness of this approach in enhancing data interpretability and decision-making. © 2025 IEEE.

关键词： Video analysis

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Electric Vehicle Charging System Using RFID and IoT for Enhanced User Experience 5

Electric Vehicle Charging System Using RFID and IoT for Enha...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Hassain, Md Mehedi Haque Jamil, Injamul Hoque, Md Mahmudul Hasan, Md. Jahid International Islamic University Chittagong Dept. of Electrical and Electronics Engineering Chittagong Bangladesh University of Greenwich Dept. of Computing and Mathematical Sciences London United Kingdom CCN University of Science and Technology Dept. of Computer Science and Engineering Cumilla Bangladesh Comilla University Dept. of Information and Communication Technology Cumilla Bangladesh

ISBN: (纸本)9798331519957

Electric vehicles (EVs) are surging in popularity globally, offering a greener and more energy-efficient alternative to traditional cars. Our research aims to develop an RFID based system designed to automate EV charging with minimal user input. This system integrates RFID readers, tags, a microcontroller, and a relay module to manage and monitor charging sessions efficiently. An IoT platform supports real time monitoring and remote control, while a secure payment mechanism handles transactions seamlessly. RFID tags identify vehicles for automatic charging initiation, and the relay module prevents overcharging by disconnecting power after a set time. The system uses MQTT for data transmission and offers real-Time insights into energy use and account balances. All information is updated and displayed in real-Time on the server. MySQL ensures scalability, enabling effective handling of large volumes of data. The mobile application dashboard provides a view of the charging and discharging processes for user monitoring, and an email notification is sent after the recharge. EV battery charging status is monitored through visual graphs. Key benefits include a streamlined user experience, improved charging management, and support for EV adoption. This innovation advances sustainable mobility with a user friendly, efficient charging solution. © 2025 IEEE.

关键词： Radio frequency identification (RFID)

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