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An effective stacked autoencoder based depth separable convolutional neural network model for face mask detection 一种有效的基于堆叠自动编码器的深度可分离卷积神经网络人脸检测模型
Q1 Computer Science Pub Date : 2023-09-01 DOI: 10.1016/j.array.2023.100294
Sundaravadivazhagan Balasubaramanian, Robin Cyriac, Sahana Roshan, Kulandaivel Maruthamuthu Paramasivam, Boby Chellanthara Jose

The COVID-19 pandemic has been infecting the entire world over the past years. To prevent the spread of COVID-19, people have acclimatised to the new normal, which includes working from home, communicating online, and maintaining personal cleanliness. There are numerous tools required to prepare to compact transmissions in the future. One of these elements for protecting individuals from fatal virus transmission is the mask. Studies have indicated that wearing a mask may help to reduce the risk of viral transmission of all kinds. It causes many public places to take efforts to ensure that its guests wear adequate face masks and keep a safe distance from one another. Screening systems need to be installed at the doors of businesses, schools, government buildings, private offices, and/or other important areas. A variety of face detection models have been designed using various algorithms and techniques. Most of the articles in the previously published research have not worked on dimensionality reduction in conjunction with depth-wise separable neural networks. The necessity of determining the identities of people who do not cover their faces when they are in public is the driving factor for the development of this methodology. This research work proposes a deep learning technique to determine if a person is wearing mask or not and identifies whether it is properly worn or not. Stacked Auto Encoder (SAE) technique is implemented by stacking the following components: Principal Component Analysis (PCA) and Depth-wise Separable Convolutional Neural Network (DWSC-NN). PCA is used to reduce the irrelevant features in the images and resulted high true positive rate in the detection of mask. We achieved an accuracy score of 94.16% and an F1 score of 96.009% by the application of the method described in this research.

过去几年,新冠肺炎大流行已经感染了整个世界。为了防止新冠肺炎的传播,人们已经适应了新常态,包括在家工作、在线交流和保持个人清洁。需要许多工具来准备将来的紧凑型变速器。口罩是保护个人免受致命病毒传播的要素之一。研究表明,戴口罩可能有助于降低各种病毒传播的风险。这导致许多公共场所努力确保客人佩戴足够的口罩,并保持安全距离。需要在企业、学校、政府大楼、私人办公室和/或其他重要区域的门口安装筛查系统。已经使用各种算法和技术设计了各种人脸检测模型。先前发表的研究中的大多数文章都没有将降维与深度可分离神经网络结合起来。确定那些在公共场合不遮脸的人的身份的必要性是这种方法发展的驱动因素。这项研究工作提出了一种深度学习技术来确定一个人是否戴口罩,并确定口罩是否正确佩戴。堆叠式自动编码器(SAE)技术通过堆叠以下组件来实现:主成分分析(PCA)和深度可分离卷积神经网络(DWSC-NN)。主成分分析用于减少图像中的不相关特征,使掩模检测的真阳性率较高。通过应用本研究中描述的方法,我们获得了94.16%的准确率分数和96.009%的F1分数。
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引用次数: 0
Capturing low-rate DDoS attack based on MQTT protocol in software Defined-IoT environment 在软件定义物联网环境中捕获基于MQTT协议的低速率DDoS攻击
Q1 Computer Science Pub Date : 2023-09-01 DOI: 10.1016/j.array.2023.100316
Mustafa Al-Fayoumi, Qasem Abu Al-Haija

The MQTT (Message Queue Telemetry Transport) protocol has recently been standardized to provide a lightweight open messaging service over low-bandwidth and resource-constrained communication environments. Hence, it is the primary messaging protocol used by Internet of Things (IoT) devices to disseminate telemetry data in a machine-to-machine approach. Despite its advantages in providing reliable, scalable, and timely delivery, the MQTT protocol is widely vulnerable to flooding and denial of service attacks, specifically, the low-rate distributed denial of services (LR-DDoS). Unlike conventional DDoS, the LR-DDoS attack tends to appear as normal traffic at a very slow rate, which makes it difficult to differentiate from legitimate packets, allowing the packets to move undetected by traditional detection policies. This paper presents an intelligent lightweight detection scheme that can capture LR-DDoS attacks based on MQTT protocol in a software-defined IoT environment. The proposed scheme examines the performance of four machine learning models on a modern dataset (LRDDoS-MQTT-2022) with a minimum feature set (i.e., two features only) and a balanced dataset, namely: decision tree classifier (DTC), multilayer perceptron (MLP), artificial neural networks (ANN), and naïve Bayes classifier (NBC). Our exploratory assessment demonstrates the arrogance of the DTC detection scheme achieving an accuracy of 99.5% with peak detection speed. Eventually, our best outcomes outdo existing models with higher prediction rates.

MQTT(消息队列遥测传输)协议最近已经标准化,以便在低带宽和资源受限的通信环境中提供轻量级的开放消息传递服务。因此,它是物联网(IoT)设备使用的主要消息传递协议,用于以机器对机器的方式传播遥测数据。尽管MQTT协议在提供可靠、可扩展和及时的交付方面具有优势,但它很容易受到洪水攻击和拒绝服务攻击,特别是低速率分布式拒绝服务攻击(LR-DDoS)。与传统的DDoS攻击不同,LR-DDoS攻击往往以非常慢的速度呈现为正常流量,难以与合法报文区分,从而使其无法被传统的检测策略检测到。本文提出了一种在软件定义物联网环境下基于MQTT协议捕获LR-DDoS攻击的智能轻量级检测方案。该方案通过最小特征集(即只有两个特征)和平衡数据集,即决策树分类器(DTC)、多层感知器(MLP)、人工神经网络(ANN)和naïve贝叶斯分类器(NBC),检验了四种机器学习模型在现代数据集(LRDDoS-MQTT-2022)上的性能。我们的探索性评估证明了DTC检测方案的傲慢,在峰值检测速度下实现了99.5%的准确率。最终,我们的最佳结果会以更高的预测率超越现有的模型。
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引用次数: 0
Fault detection and state estimation in robotic automatic control using machine learning 基于机器学习的机器人自动控制故障检测与状态估计
Q1 Computer Science Pub Date : 2023-09-01 DOI: 10.1016/j.array.2023.100298
Rajesh Natarajan , Santosh Reddy P , Subash Chandra Bose , H.L. Gururaj , Francesco Flammini , Shanmugapriya Velmurugan

In the commercial and industrial sectors, automatic robotic control mechanisms, which include robots, end effectors, and anchors containing components, are often utilized to enhance service quality. Robotic systems must be installed in manufacturing lines for a variety of industrial purposes, which also increases the risk of a robot, end controller, and/or device malfunction. According to its automated regulation, this may hurt people and other items in the workplace in addition to resulting in a reduction in quality operation. With today's advanced systems and technology, security and stability are crucial. Hence, the system is equipped with fault management abilities for the identification of developing defects and assessment of their influence on the system's activity in the upcoming utilizing fault diagnostic methodologies. To provide adaptive control, fault detection, and state estimation for robotic automated systems intended to function dependably in complicated contexts, efficient techniques are described in this study. This paper proposed a fault detection and state estimation using Accelerated Gradient Descent based support vector machine (AGDSVM) and gaussian filter (GF) in automatic control systems. The Proposed system is called (AGDSVM + GF). The proposed system is evaluated with the following metrics accuracy, fault detection rate, state estimation rate, computation time, error rate, and energy consumption. The result shows that the proposed system is effective in fault detection and state estimation and provides intelligent control automatic control.

在商业和工业部门,自动机器人控制机制,包括机器人,末端执行器和锚包含组件,经常被用来提高服务质量。机器人系统必须安装在各种工业用途的生产线上,这也增加了机器人、终端控制器和/或设备故障的风险。根据其自动调节,这可能会伤害工作场所的人员和其他物品,并导致质量下降。在当今先进的系统和技术下,安全和稳定至关重要。因此,系统配备了故障管理能力,用于识别开发中的缺陷,并在即将使用故障诊断方法时评估其对系统活动的影响。为了为机器人自动化系统提供自适应控制、故障检测和状态估计,以便在复杂环境中可靠地运行,本研究描述了有效的技术。提出了一种基于加速梯度下降的支持向量机(AGDSVM)和高斯滤波(GF)的自动控制系统故障检测和状态估计方法。该系统被称为(AGDSVM + GF)。系统的评估指标包括准确率、故障检测率、状态估计率、计算时间、错误率和能耗。结果表明,该系统能有效地进行故障检测和状态估计,并提供智能控制和自动控制。
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引用次数: 3
SSFuzzyART: A Semi-Supervised Fuzzy ART through seeding initialization and a clustered data generation algorithm to deeply study clustering solutions SSFuzyART:一种通过种子初始化的半监督模糊ART和聚类数据生成算法来深入研究聚类解决方案
Q1 Computer Science Pub Date : 2023-09-01 DOI: 10.1016/j.array.2023.100319
Siwar Jendoubi, Aurélien Baelde, Thong Tran

Semi-supervised clustering is a machine learning technique that was introduced to boost clustering performance when labeled data is available. Indeed, some labeled data are usually available in real use cases, and can be used to initialize the clustering process to guide it and to make it more efficient. Fuzzy ART is a clustering technique that is proved to be efficient in several real cases, but as an unsupervised algorithm, it cannot use available labeled data. This paper introduces a semi-supervised variant of the FuzzyART clustering algorithm (SSFuzzyART). The proposed solution uses the available labeled data to initialize clusters centers. In another hand, to deeply evaluate the characteristics of the proposed algorithm, a clustered binary data generation algorithm with controlled partitioning is also introduced in this paper. Indeed, the controlled generated clusters allows studying the characteristics of the proposed SSFuzzyART. Furthermore, a set of experiments is carried out on some available benchmarks. SSFuzzyART demonstrated better clustering prediction results than its classic counterpart.

半监督聚类是一种机器学习技术,用于在标记数据可用时提高聚类性能。事实上,一些标记的数据通常在实际用例中是可用的,并且可以用于初始化集群过程,以指导它并使它更高效。模糊ART是一种聚类技术,在一些实际情况下被证明是有效的,但作为一种无监督算法,它不能使用可用的标记数据。本文介绍了FuzzyART聚类算法的一个半监督变体(SSFuzzyART)。所提出的解决方案使用可用的标记数据来初始化集群中心。另一方面,为了深入评估该算法的特点,本文还介绍了一种具有控制分区的聚类二进制数据生成算法。事实上,受控生成的簇允许研究所提出的SSFuzyART的特性。此外,还在一些可用的基准上进行了一系列实验。SSFuzyART的聚类预测结果优于传统的聚类预测方法。
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引用次数: 0
The study of the hyper-parameter modelling the decision rule of the cautious classifiers based on the Fβ measure 基于Fβ测度的谨慎分类器决策规则的超参数建模研究
Q1 Computer Science Pub Date : 2023-09-01 DOI: 10.1016/j.array.2023.100310
Abdelhak Imoussaten

In some sensitive domains where data imperfections are present, standard classification techniques reach their limits. To avoid misclassifications that have serious consequences, recent works propose cautious classification algorithms to handle this problem. Despite of the presence of uncertainty and/or imprecision, a point prediction classifier is forced to bet on a single class. While a cautious classifier proposes the appropriate subset of candidate classes that can be assigned to the sample in the presence of imperfect information. On the other hand, cautiousness should not be at the expense of precision and a trade-off has to be made between these two criteria. Among the existing cautious classifiers, two classifiers propose to manage this trade-off in the decision step by the mean of a parametrized objective function. The first one is the non-deterministic classifier (ndc) proposed within the framework of probability theory and the second one is “evidential classifier based on imprecise relabelling” (eclair) proposed within the framework of belief functions. The theoretical aim of the mentioned hyper-parameters is to control the size of predictions for both classifiers. This paper proposes to study this hyper-parameter in order to select the “best” value in a classification task. First the utility for each candidate subset is studied related to the values of the hyper-parameter and some thresholds are proposed to control the size of the predictions. Then two illustrations are proposed where a method to choose this hyper-parameters based on the calibration data is proposed. The first illustration concerns randomly generated data and the second one concerns the images data of fashion mnist. These illustrations show how to control the size of the predictions and give a comparison between the performances of the two classifiers for a tuning based on our proposition and the one based on grid search method.

在一些存在数据缺陷的敏感领域,标准分类技术达到了极限。为了避免产生严重后果的错误分类,最近的工作提出了谨慎的分类算法来处理这个问题。尽管存在不确定性和/或不精确性,点预测分类器还是被迫将赌注押在单个类别上。而谨慎的分类器提出了在存在不完美信息的情况下可以分配给样本的候选类的适当子集。另一方面,谨慎不应以牺牲准确性为代价,必须在这两个标准之间进行权衡。在现有的谨慎分类器中,有两个分类器提出通过参数化的目标函数来管理决策步骤中的这种权衡。第一种是在概率论框架内提出的非确定性分类器(ndc),第二种是在置信函数框架下提出的“基于不精确重新标记的证据分类器”(eclair)。上述超参数的理论目的是控制两个分类器的预测大小。本文提出研究这个超参数,以便在分类任务中选择“最佳”值。首先,研究了每个候选子集与超参数值相关的效用,并提出了一些阈值来控制预测的大小。然后给出了两个例子,其中提出了一种基于校准数据选择该超参数的方法。第一个图示涉及随机生成的数据,第二个图示涉及时尚mnist的图像数据。这些插图展示了如何控制预测的大小,并对基于我们的命题和基于网格搜索方法的两个分类器的性能进行了比较。
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引用次数: 0
Ensuring network security with a robust intrusion detection system using ensemble-based machine learning 使用基于集成的机器学习的强大入侵检测系统确保网络安全
Q1 Computer Science Pub Date : 2023-09-01 DOI: 10.1016/j.array.2023.100306
Md. Alamgir Hossain, Md. Saiful Islam

Intrusion detection is a critical aspect of network security to protect computer systems from unauthorized access and attacks. The capacity of traditional intrusion detection systems (IDS) to identify unknown sophisticated threats is constrained by their reliance on signature-based detection. Approaches based on machine learning have shown promising results in identifying unknown malicious attacks. No learning algorithm-based model, however, is able to accurately and consistently detect all different kinds of attacks. Besides that, the existing models are tested for a specific dataset. In this research, a novel ensemble-based machine-learning technique for intrusion detection is presented. Numerous public datasets and multiple ensemble strategies, including Random Forest, Gradient Boosting, Adaboost, Gradient XGBoost, Bagging, and Simple Stacking, will be employed to evaluate the performance of the proposed approach. The most relevant features for the detection of intrusion are selected using correlation analysis, mutual information, and principal component analysis. Our research using different ensemble methods demonstrates that the proposed approach using the Random Forest technique outperforms existing approaches in terms of accuracy and FPR, typically exceeding 99% with better evaluation metrics like Precision, Recall, F1-score, Balanced Accuracy, Cohen's Kappa, etc. This strategy may be a useful tool for strengthening the safety of computer systems and networks against emerging cyber threats.

入侵检测是保护计算机系统免受未经授权的访问和攻击的一个重要方面。传统入侵检测系统对未知复杂威胁的识别能力受到基于签名检测的限制。基于机器学习的方法在识别未知恶意攻击方面显示出有希望的结果。然而,没有一种基于学习算法的模型能够准确、一致地检测到所有不同类型的攻击。此外,针对特定数据集对现有模型进行了测试。本文提出了一种新的基于集成的入侵检测机器学习技术。将使用大量公共数据集和多种集成策略(包括Random Forest、Gradient Boosting、Adaboost、Gradient XGBoost、Bagging和Simple Stacking)来评估所提出方法的性能。利用相关分析、互信息分析和主成分分析,选择与入侵检测最相关的特征。我们使用不同的集成方法进行的研究表明,使用随机森林技术的方法在准确性和FPR方面优于现有方法,通常超过99%,具有更好的评估指标,如Precision, Recall, F1-score, Balanced accuracy, Cohen's Kappa等。这一战略可能是加强计算机系统和网络安全以抵御新出现的网络威胁的有用工具。
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引用次数: 4
SSFuzzyART: A Semi-Supervised Fuzzy ART through seeding initialization and a clustered data generation algorithm to deeply study clustering solutions SSFuzyART:一种通过种子初始化的半监督模糊ART和聚类数据生成算法来深入研究聚类解决方案
Q1 Computer Science Pub Date : 2023-09-01 DOI: 10.1016/j.array.2023.100319
Siwar Jendoubi, Aurélien Baelde, Thong Tran

Semi-supervised clustering is a machine learning technique that was introduced to boost clustering performance when labeled data is available. Indeed, some labeled data are usually available in real use cases, and can be used to initialize the clustering process to guide it and to make it more efficient. Fuzzy ART is a clustering technique that is proved to be efficient in several real cases, but as an unsupervised algorithm, it cannot use available labeled data. This paper introduces a semi-supervised variant of the FuzzyART clustering algorithm (SSFuzzyART). The proposed solution uses the available labeled data to initialize clusters centers. In another hand, to deeply evaluate the characteristics of the proposed algorithm, a clustered binary data generation algorithm with controlled partitioning is also introduced in this paper. Indeed, the controlled generated clusters allows studying the characteristics of the proposed SSFuzzyART. Furthermore, a set of experiments is carried out on some available benchmarks. SSFuzzyART demonstrated better clustering prediction results than its classic counterpart.

半监督聚类是一种机器学习技术,用于在标记数据可用时提高聚类性能。事实上,一些标记的数据通常在实际用例中是可用的,并且可以用于初始化集群过程,以指导它并使它更高效。模糊ART是一种聚类技术,在一些实际情况下被证明是有效的,但作为一种无监督算法,它不能使用可用的标记数据。本文介绍了FuzzyART聚类算法的一个半监督变体(SSFuzzyART)。所提出的解决方案使用可用的标记数据来初始化集群中心。另一方面,为了深入评估该算法的特点,本文还介绍了一种具有控制分区的聚类二进制数据生成算法。事实上,受控生成的簇允许研究所提出的SSFuzyART的特性。此外,还在一些可用的基准上进行了一系列实验。SSFuzyART的聚类预测结果优于传统的聚类预测方法。
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引用次数: 0
ResneSt-Transformer: Joint attention segmentation-free for end-to-end handwriting paragraph recognition model ResneSt-Transformer:用于端到端手写段落识别的无联合注意分割模型
Q1 Computer Science Pub Date : 2023-09-01 DOI: 10.1016/j.array.2023.100300
Mohammed Hamdan, Mohamed Cheriet

Offline handwritten text recognition (HTR) typically relies on segmented text-line images for training and transcription. However, acquiring line-level position and transcript information can be challenging and time-consuming, while automatic line segmentation algorithms are prone to errors that impede the recognition phase. To address these issues, we introduce a state-of-the-art solution that integrates vision and language models using efficient split and multi-head attention neural networks, referred to as joint attention (ResneSt-Transformer), for end-to-end recognition of handwritten paragraphs. Our proposed novel one-stage, segmentation-free pipeline employs joint attention mechanisms to process paragraph images in an end-to-end trainable manner. This pipeline comprises three modules, with the output of one serving as the input for the next. Initially, a feature extraction module employing a CNN with a split attention mechanism (ResneSt50) is utilized. Subsequently, we develop an encoder module containing four transformer layers to generate robust representations of the entire paragraph image. Lastly, we designed a decoder module with six transformer layers to construct weighted masks. The encoder and decoder modules incorporate a multi-head self-attention mechanism and positional encoding, enabling the model to concentrate on specific feature maps at the current time step. By leveraging joint attention and a segmentation-free approach, our neural network calculates split attention weights on the visual representation, facilitating implicit line segmentation. This strategy signifies a substantial advancement toward achieving end-to-end transcription of entire paragraphs. Experiments conducted on paragraph-level benchmark datasets, including RIMES, IAM, and READ 2016 test datasets, demonstrate competitive results compared to recent paragraph-level models while maintaining reduced complexity. The code and pre-trained models are available on our GitHub repository here: HTTPS link.

离线手写文本识别(HTR)通常依赖于分割的文本行图像进行训练和转录。然而,获取线水平位置和转录信息可能是具有挑战性和耗时的,而自动线段算法容易出现阻碍识别阶段的错误。为了解决这些问题,我们引入了一种最先进的解决方案,该解决方案集成了视觉和语言模型,使用高效的分裂和多头注意力神经网络,称为联合注意力(ResneSt-Transformer),用于手写段落的端到端识别。我们提出的新型单阶段无分割管道采用联合注意机制以端到端可训练的方式处理段落图像。该管道由三个模块组成,其中一个模块的输出作为下一个模块的输入。最初,我们使用了一个带有分裂注意机制的CNN特征提取模块(ResneSt50)。随后,我们开发了一个包含四个变压器层的编码器模块,以生成整个段落图像的鲁棒表示。最后,我们设计了一个具有六层变压器的解码器模块来构建加权掩模。编码器和解码器模块结合了多头自注意机制和位置编码,使模型能够专注于当前时间步长的特定特征映射。通过利用联合注意和无分割方法,我们的神经网络计算视觉表示上的分裂注意权重,促进隐式线分割。这一策略标志着实现整个段落的端到端转录的实质性进步。在段落级基准数据集(包括RIMES、IAM和READ 2016测试数据集)上进行的实验显示,与最近的段落级模型相比,实验结果具有竞争力,同时保持了较低的复杂性。代码和预训练模型可以在我们的GitHub存储库中找到:HTTPS链接。
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引用次数: 0
A novel lossy image compression algorithm using multi-models stacked AutoEncoders 一种基于多模型堆叠自编码器的有损图像压缩算法
Q1 Computer Science Pub Date : 2023-09-01 DOI: 10.1016/j.array.2023.100314
Salam Fraihat, Mohammed Azmi Al-Betar

The extensive use of images in many fields increased the demand for image compression algorithms to overcome the transfer bandwidth and storage limitations. With image compression, disk space, and transmission speed can be efficiently reduced. Some of the traditional techniques used for image compression are the JPEG and ZIP formats. The compression rate (CR) in JPEG can be high but to the detriment of the quality factor of the image. ZIP has a low compression rate, where the quality remains almost unaffected. Machine learning (ML) is considered an essential technique for image compression using different algorithms. The most widely used algorithm is Deep Learning (DL), which represents the features of the image at different scales by using different types of layers. In this research, an AutoEncoder (AE) deep learning-based compression algorithm is proposed for lossy image compression and experimented with using three standard dataset types: MNIST, Grayscale, and Color images datasets. A Stacked AE (SAE) for image compression and a binarized content-based image filter are used with a high compression rate while keeping the quality above 85% using structural similarity index metric (SSIM) compared to traditional techniques. In addition, a convolutional neural network (CNN) classification model has been utilized as SAEs compression model selector for each image class. Experimental results demonstrate that the proposed SAE image compression algorithm outperforms the JPEG-encoded algorithm in terms of compression rate (CR) and image quality. The CR that the proposed model achieved with an acceptable reconstruction accuracy was about 85%, which is almost 20% higher than the standard JPEG’s compression rate, with an accuracy of 94.63% SSIM score.

图像在许多领域的广泛使用增加了对图像压缩算法的需求,以克服传输带宽和存储的限制。通过图像压缩,可以有效地减少磁盘空间和传输速度。用于图像压缩的一些传统技术是JPEG和ZIP格式。JPEG中的压缩率(CR)可以很高,但会损害图像的质量因子。ZIP具有较低的压缩率,其质量几乎不受影响。机器学习(ML)被认为是使用不同算法进行图像压缩的基本技术。使用最广泛的算法是深度学习(DL),它通过使用不同类型的层来表示不同尺度下图像的特征。在本研究中,提出了一种基于AutoEncoder (AE)深度学习的有损图像压缩算法,并使用三种标准数据集类型进行了实验:MNIST、灰度和彩色图像数据集。与传统技术相比,采用堆叠AE (SAE)图像压缩和基于内容的二值化图像滤波器,压缩率高,同时使用结构相似指数度量(SSIM)将质量保持在85%以上。此外,利用卷积神经网络(CNN)分类模型作为每个图像类别的压缩模型选择器。实验结果表明,所提出的SAE图像压缩算法在压缩率(CR)和图像质量方面都优于jpeg编码算法。在可接受的重构精度下,该模型实现的重构率约为85%,比标准JPEG的压缩率提高了近20%,准确率为94.63%。
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引用次数: 0
Optimizing student engagement in edge-based online learning with advanced analytics 通过高级分析优化学生在基于边缘的在线学习中的参与度
Q1 Computer Science Pub Date : 2023-09-01 DOI: 10.1016/j.array.2023.100301
Rasheed Abdulkader , Firas Tayseer Mohammad Ayasrah , Venkata Ramana Gupta Nallagattla , Kamal Kant Hiran , Pankaj Dadheech , Vivekanandam Balasubramaniam , Sudhakar Sengan

Edge-Based Online Learning (EBOL), a technique that combines the practical, hands-on approach of EBOL with the convenience of Online Learning (OL), is growing in popularity. But accurately monitoring student engagement to enhance teaching methodologies and learning outcomes is one of the difficulties of OL. To determine this challenge, this paper has put forth an Edge-Based Student Attentiveness Analysis System (EBSAAS) method, which uses a Face Detection (FD) algorithm and a Deep Learning (DL) model known as DLIP to extract eye and mouth landmark features. Images of the eye and mouth are used to extract landmarks using DLIP or Deep Learning Image Processing. Landmark Localization pre-trained models for Facial Landmark Localization (FLL) are one well-liked DL model for facial landmark recognition. The Visual Geometry Group-19 (VGG-19) learning model then uses these features to classify the student's level of attentiveness as fatigued or focused. Compared to a server-based model, the proposed model is developed to execute on an Edge Device (ED), enabling a swift and more effective analysis. The EBOL achieves 95.29% accuracy and attains 2.11% higher than existing model 1 and 4.41% higher than existing model 2. The study's findings have shown how successful the proposed method is at assisting teachers in changing their teaching methodologies to engage students better and enhance learning outcomes.

基于边缘的在线学习(EBOL)是一种结合了EBOL的实用、动手方法和在线学习(OL)的便利性的技术,正越来越受欢迎。但准确监测学生的参与,以提高教学方法和学习成果是OL的难点之一。为了解决这一挑战,本文提出了一种基于边缘的学生注意力分析系统(EBSAAS)方法,该方法使用人脸检测(FD)算法和深度学习(DL)模型DLIP来提取眼睛和嘴巴的标志性特征。眼睛和嘴巴的图像用于使用DLIP或深度学习图像处理提取地标。人脸标记定位(FLL)预训练模型是一种很受欢迎的人脸标记识别深度学习模型。视觉几何组19 (VGG-19)学习模型然后使用这些特征将学生的注意力水平分为疲劳或集中。与基于服务器的模型相比,所提出的模型可在边缘设备(ED)上执行,从而实现快速有效的分析。EBOL的准确率达到95.29%,比现有模型1高2.11%,比现有模型2高4.41%。这项研究的结果表明,所提出的方法在帮助教师改变教学方法以更好地吸引学生和提高学习成果方面是多么成功。
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