为在线考试环境中的学生活动识别而进行联合学习的生物启发超参数调整

AI Pub Date : 2024-07-01 DOI:10.3390/ai5030051
Ramu Shankarappa, Nandini Prasad, R. R. Guddeti, Biju R. Mohan
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引用次数: 0

摘要

如今,在线考试(简称 "考试")平台正变得越来越流行,这就要求数字学习环境采取强有力的安全措施。这包括应对头部姿态检测和估计等关键挑战,这些挑战是自动人脸识别、高级监控系统、直观人机界面和增强驾驶安全措施等应用不可或缺的。拟议的工作在提高在线考试平台的安全性和可靠性方面具有巨大潜力。它通过基于不同的头部姿势对学生的注意力进行准确分类来实现这一目标,这种新方法利用了联合学习和深度学习模型等先进技术。所提议的工作旨在借助不同的头部姿势对学生的专注度进行分类。在这项工作中,我们考虑了五种头部姿势:前脸、下脸、右脸、上脸和左脸。联合学习(FL)框架与预先训练好的深度学习模型(ResNet50)被用来完成分类任务。为了使用 FL 框架的本地客户端设备对学生在在线考试环境中的活动(行为)进行分类,我们考虑了 ResNet50 模型。然而,在本地客户端 ResNet50 模型中确定最佳超参数具有挑战性。因此,在本研究中,我们提出了两种混合生物启发优化方法,即遗传算法粒子群优化(PSOGA)和遗传算法精英粒子群优化(PSOEGA),用于微调 ResNet50 模型的超参数。ResNet50 模型采用的生物启发优化方法将对在线考试环境中的学生行为进行训练和分类。FL 框架在本地训练客户端模型,并将更新的权重发送到服务器模型。所提出的混合生物启发算法在独立使用时优于 GA 和 PSO。提议的 PSOGA 不仅优于提议的 PSOEGA,而且优于性能评估所考虑的基准算法,准确率达到 95.97%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bio-Inspired Hyperparameter Tuning of Federated Learning for Student Activity Recognition in Online Exam Environment
Nowadays, online examination (exam in short) platforms are becoming more popular, demanding strong security measures for digital learning environments. This includes addressing key challenges such as head pose detection and estimation, which are integral for applications like automatic face recognition, advanced surveillance systems, intuitive human–computer interfaces, and enhancing driving safety measures. The proposed work holds significant potential in enhancing the security and reliability of online exam platforms. It achieves this by accurately classifying students’ attentiveness based on distinct head poses, a novel approach that leverages advanced techniques like federated learning and deep learning models. The proposed work aims to classify students’ attentiveness with the help of different head poses. In this work, we considered five head poses: front face, down face, right face, up face, and left face. A federated learning (FL) framework with a pre-trained deep learning model (ResNet50) was used to accomplish the classification task. To classify students’ activity (behavior) in an online exam environment using the FL framework’s local client device, we considered the ResNet50 model. However, identifying the best hyperparameters in the local client ResNet50 model is challenging. Hence, in this study, we proposed two hybrid bio-inspired optimized methods, namely, Particle Swarm Optimization with Genetic Algorithm (PSOGA) and Particle Swarm Optimization with Elitist Genetic Algorithm (PSOEGA), to fine-tune the hyperparameters of the ResNet50 model. The bio-inspired optimized methods employed in the ResNet50 model will train and classify the students’ behavior in an online exam environment. The FL framework trains the client model locally and sends the updated weights to the server model. The proposed hybrid bio-inspired algorithms outperform the GA and PSO when independently used. The proposed PSOGA not only outperforms the proposed PSOEGA but also outperforms the benchmark algorithms considered for performance evaluation by giving an accuracy of 95.97%.
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