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2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA)最新文献

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Extended T-Type Topology of Single-Phase Multi-Level Inverter 单相多电平逆变器的扩展t型拓扑
A. Ouchatti, Azeddine Wahbi, A. Moutabir, Youness Benkhanous, A. Taouni, Redouane Majdoul
In recent years, the multilevel DC/AC static converters are increasingly used for their benefits especially in terms of reduction of total harmonic distortion (THD) of the output current and reduced voltage stress on semiconductors at switching moments. In this article, an Extended T-Type structure of multilevel inverter is proposed for photovoltaic systems. This structure has the advantage of being simple; on the one hand, it contains only switches (no switching capacitors and clamping diodes, etc.) and the number of these switches corresponds exactly to the required number of voltage levels, and on the other hand, the control scheme is much simpler to implement and suitable for variable RMS value operations. The modulation used is based on the technique of sinusoidal fundamental frequency pulse width modulation (PWM) with a single carrier. The performances (THD, voltage stress on the semiconductors) of the inverter are analyzed by simulations in the Matlab-Simulink environment on an example of a nine-level inverter.
近年来,多电平DC/AC静态变换器因其在降低输出电流的总谐波失真(THD)和减小开关时刻半导体的电压应力方面的优势而得到越来越多的应用。本文提出了一种适用于光伏系统的扩展t型多电平逆变器结构。这种结构的优点是简单;一方面,它只包含开关(没有开关电容器和箝位二极管等),这些开关的数量正好对应所需的电压电平数量,另一方面,控制方案实施起来简单得多,适合可变RMS值操作。所使用的调制是基于单载波正弦基频脉宽调制(PWM)技术。以一个九电平逆变器为例,在Matlab-Simulink环境下对逆变器的性能(THD、半导体电压应力)进行了仿真分析。
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引用次数: 0
Competenty-Based Approach for Learning Objects sequencing using DNA computing 基于能力的DNA计算学习对象排序方法
D. Amina, Mohamed Ben Ali Yamina
In the e- learning systems, a learning path is known as a sequence of learning materials linked to each others to help learners achieving their learning goals. As it is difficult to have the same learning path that suits different learners, the Curriculum Sequencing problem (CS) consists of the generation of a personalized learning path for each learner according to his learner profile. This last one is represented through competencies. The proposed approach includes two components: (1) competency based approach that is used to represent the knowledge model and to deliver to the learner the learning path; and (2) a DNA computing approach based on a weighted graph is used to deliver a learning path to each learner. An example regarding the course of algorithmic is presented to demonstrate the effectiveness of the proposed design. Results show that competency based DNA computing approach can generate a personalized learning path successfully.
在电子学习系统中,学习路径被称为一系列相互联系的学习材料,以帮助学习者实现他们的学习目标。由于很难有适合不同学习者的相同学习路径,课程排序问题(CS)包括根据每个学习者的学习概况为每个学习者生成个性化的学习路径。最后一个是通过能力来表现的。该方法包括两个组成部分:(1)基于能力的方法,用于表示知识模型并向学习者提供学习路径;(2)使用基于加权图的DNA计算方法为每个学习者提供学习路径。最后以算法过程为例,验证了所提设计的有效性。结果表明,基于胜任力的DNA计算方法可以成功地生成个性化的学习路径。
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引用次数: 1
A Survey of Spam Bots Detection in Online Social Networks 在线社交网络中垃圾邮件机器人检测的调查
Zineb Ellaky, F. Benabbou, Sara Ouahabi, N. Sael
Online Social networks (OSN) have become an integral part of people's lives. People from all over the world interact instantly between each other by sharing pictures and content. They can also express their opinion about politics, sport, and be part of influencing users in OSN. So, with the large growth of the number of users of OSN, it has become a target for the vicious people that post spam contents and messages. The malicious social bots (MSB) are one of the biggest threats that menace the social networks security and several studies have been conducted to detect them. In this work we focus on spam bots and reviewed all the existing bot detection techniques based on different features extracted from users' profiles and interactions. The paper analyzed and compared the proposed techniques between 2014 and 2021 to get the most relevant features that improve the spam bot detection and the most efficient Machine learning ML and Deep learning DL techniques from OSN. An investigation on existing datasets is proposed, some limitations of the studied approaches are outlined and future directions for social bot techniques detection improvement are proposed.
在线社交网络(OSN)已经成为人们生活中不可或缺的一部分。来自世界各地的人们通过分享图片和内容即时互动。他们还可以表达自己对政治、体育的看法,成为OSN影响用户的一部分。因此,随着OSN用户数量的大量增长,它也成为了恶意分子发布垃圾内容和信息的目标。恶意社交机器人(MSB)是威胁社交网络安全的最大威胁之一,已经进行了一些研究来检测它们。在这项工作中,我们专注于垃圾邮件机器人,并基于从用户配置文件和交互中提取的不同特征回顾了所有现有的机器人检测技术。本文分析和比较了2014年和2021年之间提出的技术,以获得最相关的特征,以改进垃圾邮件机器人检测以及最有效的机器学习ML和深度学习DL技术。对现有的数据集进行了调查,概述了研究方法的一些局限性,并提出了社交机器人技术检测改进的未来方向。
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引用次数: 0
An Evidence-Based Approach on Public Health Decisions in Low-Middle Income Countries: Use Case of Senegal at the Verge of COVID-19 中低收入国家公共卫生决策的循证方法:以处于COVID-19边缘的塞内加尔为例
Jesus Ekie, Bassirou Gueye, Tresor Ekie, I. Niang
The advent of health crises like COVID-19 in West Africa, and more particularly in Senegal, led the MoHSA, with the support of several development partners, to set up a real-time alert and information reporting system through the mInfoSante project. These informations are retrieved from Chief Nursing Officers, Chief Veterinary Officers, and more than 2,000 Community Watch & Alert Committees dispatched throughout the national territory. The ease of use of mInfoSante and its advantages has seduced professionals in the health sector. However, one will quickly realize that the exploitation of information can be tedious without the support of a system dedicated to this task. Due to the specificity of the sector as well as the technical and institutional environment, we had to adapt to a certain number of constraints in the identification and implementation of such a solution. To do this, we have studied a set of open-source and powerful Business Intelligence and Dashboarding solutions. According to the performed analysis, our solution, which make use of REST-based service composition, presents advantages of having functionalities related to the distributed storage & processing of large data sets, a capacity for rapid processing of all data types, good computing power offered by its optimized components, and fault tolerance due to integration of distributed infrastructure model.
在西非,特别是塞内加尔出现COVID-19等卫生危机的背景下,卫生部在几个发展伙伴的支持下,通过mInfoSante项目建立了一个实时警报和信息报告系统。这些信息是从全国各地派出的首席护理官、首席兽医官和2000多个社区观察和警报委员会中检索的。mInfoSante的易用性及其优势吸引了卫生部门的专业人员。然而,人们很快就会意识到,如果没有专门用于这项任务的系统的支持,信息的利用可能是乏味的。由于该部门的特殊性以及技术和体制环境,我们在确定和执行这种解决办法时必须适应若干限制。为此,我们研究了一组强大的开源商业智能和仪表板解决方案。通过分析,我们的解决方案利用基于rest的服务组合,具有与大型数据集的分布式存储和处理相关的功能,具有快速处理所有数据类型的能力,其优化组件提供的良好计算能力,以及由于集成了分布式基础设施模型而产生的容错性等优点。
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引用次数: 1
Sentiment analysis through word embedding using AraBERT: Moroccan dialect use case 基于AraBERT摩洛哥方言用例的词嵌入情感分析
Yassir Matrane, F. Benabbou, N. Sael
Nowadays, Sentiment Analysis (SA) represents a big chunk of Natural Language Processing (NLP) problems. The latter makes it possible to assign feelings and polarity to portions of text, which comes handy in multiple areas of social conduct such as product reviewing in business, determining political opinions of the masses and other uses. Nevertheless, sentiment analysis can be tricky when dealing with unstructured languages due to the lack of conventional syntactic and morphological structures. In this paper, we discuss several attempts of the literature at solving the challenge of Sentiment analysis of regional dialects, and we propose an approach based on AraBERT word embedding for Moroccan dialect (MD) sentiment analysis. The method goes through a pipeline of steps starting with preprocessing, lexicon-based translation and feature extraction. Afterwards we conduct a comparative study, in 2-way classification, of machine learning algorithms as SVM, DT, LR, RF, NB and deep learning algorithms such as LSTM, BiLSTM and LSTM-CNN from state of art. On the other hand, we managed to train our model with four different outputs in 4 way classification. As a result, BiLSTM proved to be the best in both 2-way classification scoring 83% accuracy, and in 4-way classification achieving scores ranging between 62% and 92% of accuracy for each of the 4 classes.
情感分析是当今自然语言处理(NLP)问题的重要组成部分。后者可以将情感和极性分配到文本的各个部分,这在社会行为的多个领域都很方便,比如商业中的产品评论,确定大众的政治观点和其他用途。然而,由于缺乏传统的句法和形态结构,情感分析在处理非结构化语言时可能会很棘手。在本文中,我们讨论了一些文献在解决区域方言情感分析挑战方面的尝试,并提出了一种基于AraBERT词嵌入的摩洛哥方言情感分析方法。该方法从预处理、基于词典的翻译和特征提取开始,经历了一系列步骤。随后,我们对SVM、DT、LR、RF、NB等机器学习算法和LSTM、BiLSTM、LSTM- cnn等深度学习算法进行了双向分类对比研究。另一方面,我们设法用四种方式分类的四种不同输出训练我们的模型。结果表明,BiLSTM在双向分类中准确率最高,达到83%;在四向分类中准确率最高,达到62% - 92%。
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引用次数: 3
Elearning 4.0 for higher education: literature review, trends and perspectives 高等教育的电子学习4.0:文献综述,趋势和观点
Abdelghani Babori, Khalid Ghoulam, N. Falih, Hicham Ouchitachen
Over the last years, various studies have concentrated on E-learning and its impact on the performance of students. This learning context has raised and guided several studies of E-learning from educational and technical perspectives. However, there is a paucity of literature review about web 4.0 research for teaching and learning. Thus, this study aims to examine the research trends on E-learning 4.0 (web 4.0 destined for learning purposes). More particularly, this literature review presents the major topics, techniques and tools identified by analyzing the research focusing on the use of Artificial intelligence (AI) and the Internet of things (IoT) in distance learning contexts. Implications for future research, especially during the period of the Covid 19 pandemic, are described as well.
在过去的几年里,各种研究都集中在电子学习及其对学生表现的影响上。这种学习背景从教育和技术的角度提出并指导了一些关于电子学习的研究。然而,关于web 4.0在教学和学习方面的研究文献很少。因此,本研究旨在探讨电子学习4.0(以学习为目的的web 4.0)的研究趋势。更具体地说,这篇文献综述通过分析在远程学习环境中使用人工智能(AI)和物联网(IoT)的研究,提出了主要的主题、技术和工具。本文还描述了对未来研究的影响,特别是在2019冠状病毒病大流行期间。
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引用次数: 2
Towards precision agriculture in Morocco: A machine learning approach for recommending crops and forecasting weather 摩洛哥走向精准农业:推荐作物和预测天气的机器学习方法
Chouaib El Hachimi, S. Belaqziz, S. Khabba, A. Chehbouni
Statistical models predict that the world's population will reach 8.5 billion by the end of 2030. This represents a real threat to our food security and puts the current food production system under pressure. Efficient use of Earth's natural resources is the only solution to facing future challenges such as global hunger. The implementation of precision agriculture using new technologies such as artificial intelligence, big data, IoT and remote sensing is the first step towards this goal. In this paper, we investigated several machine learning models to create two services: one for recommending the best crop to grow based on soil and the region's weather characteristics, and another for the forecasting of the hourly average air temperature. Performance evaluation results for the first service show that Random Forest has the best metrics as a classifier (accuracy = 100%, precision = 100%, recall = 100%) compared to K-Nearest Neighbors (KNN), Decision Tree, Naive Bayes, Logistic Regression, Convolutional Neural Network, and Feed Forward Neural Network. This is a confirmation that classic machine learning algorithms perform better on small-size datasets. In our case, we used a dataset of 2200 instances available online. On the other hand, Facebook Prophet was more accurate (R2 = 0.81, RMSE = 3.74) than our proposed LSTM architecture in time series forecasting at hourly scale using historical weather data provided by the weather station of our study area. These two optimal models are then integrated as the first building blocks in our decision support platform, intended for both farmers and policymakers with the aim of making agriculture in Morocco more efficient and more sustainable.
统计模型预测,到2030年底,世界人口将达到85亿。这对我们的粮食安全构成了真正的威胁,并使当前的粮食生产系统面临压力。有效利用地球的自然资源是面对诸如全球饥饿等未来挑战的唯一解决办法。利用人工智能、大数据、物联网和遥感等新技术实施精准农业是实现这一目标的第一步。在本文中,我们研究了几个机器学习模型,以创建两种服务:一种用于根据土壤和该地区的天气特征推荐最佳作物,另一种用于预测每小时平均气温。第一次服务的性能评估结果表明,与k近邻(KNN)、决策树、朴素贝叶斯、逻辑回归、卷积神经网络和前馈神经网络相比,随机森林具有最佳的分类器指标(准确率= 100%,精度= 100%,召回率= 100%)。这证实了经典的机器学习算法在小型数据集上表现更好。在我们的示例中,我们使用了一个包含2200个在线实例的数据集。另一方面,在利用研究区气象站提供的历史天气数据进行小时尺度的时间序列预报时,Facebook Prophet比我们提出的LSTM架构更准确(R2 = 0.81, RMSE = 3.74)。这两个最优模型随后被整合为我们决策支持平台的第一块积木,旨在为农民和政策制定者提供支持,以提高摩洛哥农业的效率和可持续性。
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引用次数: 8
Real-time Facemask Detector using Deep Learning and Raspberry Pi 实时面具检测器使用深度学习和树莓派
Ikram Ben abdel ouahab, Lotfi Elaachak, M. Bouhorma, Yasser A. Alluhaidan
Medical staffs wear face masks to prevent the spread of the disease. Nowadays, with the coronavirus pandemic everyone must wear a facemask for the same reason. When a person near to you coughs, talks, sneezes he could release germs into the air that may infect you or anyone nearby. Wearing a facemask is a part of an infection control strategy to avoid and eliminate cross-contamination. Even so, people are getting tired of wearing facemasks or they are not conscious enough of the seriousness of the actual covid19. In this paper, we propose a facemask detector based on IoT embedded devices and deep learning algorithm. Our main goal is to warn people in real-time if they are not wearing a facemask or they are not wearing it correctly. The proposed solution generates loud vocal alerts after detection disrespect of facemask wear in real-time for a fast reaction. To have the most efficient detector in real-time we tested the facemask detection model using various versions of the Raspberry Pi and NCS2. As a result, the facemask detector works perfectly on powerful devices, however its performance decrease in realtime using less powerful devices such as an old version of the Raspberry Pi.
医务人员戴上口罩,防止疾病传播。如今,由于冠状病毒大流行,每个人都必须戴口罩。当你附近的人咳嗽、说话、打喷嚏时,他可能会向空气中释放细菌,感染你或附近的任何人。佩戴口罩是避免和消除交叉污染的感染控制策略的一部分。即便如此,人们还是厌倦了戴口罩,或者没有意识到实际的covid - 19的严重性。在本文中,我们提出了一种基于物联网嵌入式设备和深度学习算法的面罩检测器。我们的主要目标是实时警告人们,如果他们没有戴口罩,或者他们没有正确佩戴口罩。该解决方案在检测到不戴口罩的行为后,会实时发出响亮的声音警报,以便快速做出反应。为了获得最有效的实时检测器,我们使用不同版本的树莓派和NCS2测试了面罩检测模型。因此,面罩检测器在功能强大的设备上工作完美,但是在使用功能较弱的设备(如旧版本的树莓派)时,其性能会实时下降。
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引用次数: 4
Predicting the Traffic Congestion and Optimal Route in a Smart City Exploiting IoT Devices 利用物联网设备预测智慧城市交通拥堵和最优路径
Abderrahim Zannou, Abdelhak Boulaalam, E. Nfaoui
Monitoring and managing traffic congestion is the most challenging problem for many cities today. It has an effect on the environment and disrupts our everyday lives. As the population expands, the number of roads and cars, creating a slew of issues such as travel time delays, fuel waste, air pollution, and transportation-related issues. On another side, the Internet of Things (IoT) provides different devices and systems to monitor and manage the real-time traffic for smart cities. In this paper, we propose a new approach to avoid traffic congestion and obtain an optimal route for vehicles in the smart city exploiting IoT devices. To do this, we create a map of all possible sources and destinations, secondly and we suggested new parameters to determine the optimal path for the vehicle's traffic. The first phase is to obtain a set of candidate paths for each possible source and destination using Ant Colony Optimization based on the unvaried constraints. The second phase is to obtain the principal path for the vehicle to achieve its destination. The simulation results show that our solution reduces the distance and the time of travel and avoids traffic congestion.
监控和管理交通拥堵是当今许多城市面临的最具挑战性的问题。它对环境有影响,扰乱了我们的日常生活。随着人口的增长,道路和汽车的数量增加,产生了一系列问题,如旅行时间延误、燃料浪费、空气污染和交通相关问题。另一方面,物联网(IoT)为智能城市提供了不同的设备和系统来监控和管理实时交通。在本文中,我们提出了一种利用物联网设备来避免交通拥堵并获得智慧城市中车辆最优路线的新方法。为了做到这一点,我们创建了所有可能的来源和目的地的地图,其次,我们提出了新的参数来确定车辆交通的最佳路径。第一阶段是利用基于不变约束的蚁群优化方法,对每个可能的源和目的地进行候选路径的集合。第二阶段是获得飞行器到达目的地的主要路径。仿真结果表明,该方案减少了交通距离和时间,避免了交通拥堵。
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引用次数: 0
Enhanced VGG19 Model for Accident Detection and Classification from Video 视频事故检测与分类的增强VGG19模型
S. Bouhsissin, N. Sael, F. Benabbou
Over the last years, the number of cars used in road traffic growth at a staggering rate. This situation had resulted in a significant increase of accidents and several traffic problems resulting huge losses. One of the most important road safety technologies is to automatically recognize dangerous situations and quickly share this information with nearby vehicles. In this work, we first, analyze various researches in the detection and classification of traffic anomalies and then propose to explore the potential of VGG19, which is a transfer-learning model to classify anomalies (accidents). In addition, we have compared the proposed algorithm to the other methods used. Our experience shows that our enhanced VGG19 model gives the best performance with 96% accuracy, and 0.99 AUC compared to the Convolutional Neural Network (CNN), which is the most widely used deep learning technique for image (accident image) classification, and the VGG19 models proposed over the last researches.
在过去的几年里,道路交通中使用的汽车数量以惊人的速度增长。这种情况造成了事故和若干交通问题的显著增加,造成了巨大的损失。最重要的道路安全技术之一是自动识别危险情况,并迅速与附近的车辆共享这些信息。在本研究中,我们首先分析了交通异常检测和分类的各种研究,然后提出探索VGG19的潜力,VGG19是一种用于异常(事故)分类的迁移学习模型。此外,我们还将所提出的算法与其他使用的方法进行了比较。我们的经验表明,与卷积神经网络(CNN)相比,我们的增强VGG19模型的性能最好,准确率为96%,AUC为0.99,卷积神经网络是图像(事故图像)分类中使用最广泛的深度学习技术,并且在过去的研究中提出了VGG19模型。
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引用次数: 2
期刊
2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA)
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