Proceedings of the 3rd International Conference on Networking, Information Systems & Security最新文献_第5页

Adaptive E-learning: Adaptation of Content According to the Continuous Evolution of the Learner During his Training 适应性网络学习:根据学习者在训练过程中的不断演变对内容进行适应

Proceedings of the 3rd International Conference on Networking, Information Systems & Security Pub Date : 2020-03-31 DOI: 10.1145/3386723.3387890

Mohammed Zaoudi, H. Belhadaoui

{"title":"Adaptive E-learning: Adaptation of Content According to the Continuous Evolution of the Learner During his Training","authors":"Mohammed Zaoudi, H. Belhadaoui","doi":"10.1145/3386723.3387890","DOIUrl":"https://doi.org/10.1145/3386723.3387890","url":null,"abstract":"Higher education has always been based on a traditional education system. All learners have to attend all training courses without taking into consideration human and logistical constraints. This situation leads to major problems of massification, which unfortunately subsequently lead to problems of demotivation or even abandonment of a large number of students. A solution to these problems resides in introduction of distance learning. The implementation of online courses, such as MOOC (Massive Open Online Courses), and the emergence of educational platforms such as LMS (Learning Management System) or LCMS (Learning Content Management System), have made it possible to introduce the notion of e-learning into Higher education. Nevertheless, if e-learning has left the Stone Age elsewhere, it is still an emerging field in some countries as Morocco where it is far from having reached maturity. Like any new system or proposal, e-learning has its own detractors who need to be more reassured on certain aspects. In this article, we deal with some major issues related to e-learning platforms, which offer pre-established pedagogical content without really taking into account the particularity or evolution of each learner during the training path. We will therefore talk about a customised or adaptive e-learning. By combining UBA (User Behavior Analytics) and AI (Artificial Intelligence), we will propose during this article an LBA (Learner Behavior Analytics) model based on an a system called SBAN (Score and Behavior ANalytics).","PeriodicalId":139072,"journal":{"name":"Proceedings of the 3rd International Conference on Networking, Information Systems & Security","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124530003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Three-level binary tree structure for sentiment classification in Arabic text 面向阿拉伯语文本情感分类的三层二叉树结构

Proceedings of the 3rd International Conference on Networking, Information Systems & Security Pub Date : 2020-03-31 DOI: 10.1145/3386723.3387844

H. A. Addi, R. Ezzahir, A. Mahmoudi

引用次数: 5

An Adaptive Learning Approach for Better Retention of Learners in MOOCs 一种提高mooc学习者留存率的适应性学习方法

Proceedings of the 3rd International Conference on Networking, Information Systems & Security Pub Date : 2020-03-31 DOI: 10.1145/3386723.3387845

Smaili El Miloud, Sraidi Soukaina, Salma Azzouzi, M. E. H. Charaf

引用次数: 6

Machine Learning Methods for Air Quality Monitoring 空气质量监测的机器学习方法

Proceedings of the 3rd International Conference on Networking, Information Systems & Security Pub Date : 2020-03-31 DOI: 10.1145/3386723.3387835

Akram Zaytar, Chaker El Amrani

引用次数: 0

Handwriting Recognition with Artificial Neural Networks a Decade Literature Review 人工神经网络手写识别的十年文献综述

Proceedings of the 3rd International Conference on Networking, Information Systems & Security Pub Date : 2020-03-31 DOI: 10.1145/3386723.3387884

Ahmed Remaida, A. Moumen, Y. Idrissi, Zineb Sabri

{"title":"Handwriting Recognition with Artificial Neural Networks a Decade Literature Review","authors":"Ahmed Remaida, A. Moumen, Y. Idrissi, Zineb Sabri","doi":"10.1145/3386723.3387884","DOIUrl":"https://doi.org/10.1145/3386723.3387884","url":null,"abstract":"Deep Learning Artificial Neural Networks has pushed forward researches in the field of pattern recognition, furthermore in human handwriting recognition. From online to offline approach, signature verification, writing or writer identification, segmentation or features extraction, a multitude of Artificial Neural Networks (ANNs) models are applied in the process. This paper focuses on the literature review of human handwriting recognition with ANN's over the last decade. We propose an exploratory analysis of 294 research papers collected from five indexed research engines: ACM Digital Library, IEEE digital library, Science Direct, Scopus and Web of Science. Our aim is to provide a research papers distribution across years and journals, a Keywords frequency analysis using cloud visualization, and a Natural Language Processing Topic Modeling using Non-Negative Matrix Factorization (NMF). The results of this study show that the number of research papers reached noticeably a peak in the 2010 with 44 published papers; also Pattern Recognition was the top publishing journal with 12 published papers. As for the topic modeling using NMF we obtained 3 topics listed as follows: 1) Feature Extraction and segmentation techniques for Handwritten Texts Recognition; 2) Signature Verification in Biometric security for Off-line Authentication; 3) Assessment Systems for Student Identification","PeriodicalId":139072,"journal":{"name":"Proceedings of the 3rd International Conference on Networking, Information Systems & Security","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125114939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

A Theoretical Study on Project Delivery and Leadership Style 项目交付与领导风格的理论研究

Proceedings of the 3rd International Conference on Networking, Information Systems & Security Pub Date : 2020-03-31 DOI: 10.1145/3386723.3387842

Muhammad Azman, A. Mohamed, E. E. Odzaly

引用次数: 1

Evaluation and Comparative Study of the Both Algorithm LEACH and PEGASIS Based on Energy Consumption 基于能耗的LEACH和PEGASIS算法的评价与比较研究

Proceedings of the 3rd International Conference on Networking, Information Systems & Security Pub Date : 2020-03-31 DOI: 10.1145/3386723.3387838

Fatima Es-Sabery, Abdellatif Hair

{"title":"Evaluation and Comparative Study of the Both Algorithm LEACH and PEGASIS Based on Energy Consumption","authors":"Fatima Es-Sabery, Abdellatif Hair","doi":"10.1145/3386723.3387838","DOIUrl":"https://doi.org/10.1145/3386723.3387838","url":null,"abstract":"The main purpose of hierarchical routing is to effectively maintain the energy consumption of sensor nodes by involving them in multi-hop communication within a cluster and by aggregating and merging data to reduce the number of messages sent to the destination. Cluster formation is usually based on the energy reserve of sensors and on sensors that are close to the cluster head. LEACH (Low Energy Adaptive Clustering Hierarchy) is one of the first routing approaches for sensor networks. The principle of LEACH consists of dividing the network into distributed clusters at one pop to faster data delivery. An improved version of the LEACH protocol is proposed; it is called PEGASIS (Power-Efficient Gathering in Sensor Information Systems). PEGASIS forms chains of sensor nodes rather than clusters so that each node transmits and receives only data from a neighbor. The aims of this paper are first to study both protocols LEACH and PEGASIS. Secondly, to estimate the energy consumption by both protocols in Wireless Sensor Networks, Finally to show the results of comparative study between the LEACH and PEGASIS.","PeriodicalId":139072,"journal":{"name":"Proceedings of the 3rd International Conference on Networking, Information Systems & Security","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116053192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Natural Convection in a Differentially Heated Cavity with a Heated and Cooled Circular Cylinders 自然对流在不同的加热腔与加热和冷却圆柱

Proceedings of the 3rd International Conference on Networking, Information Systems & Security Pub Date : 2020-03-31 DOI: 10.1145/3386723.3387885

L. E. Moutaouakil, M. Boukendil, Z. Zrikem, A. Abdelbaki

引用次数: 0

Advanced Recommendation Systems Through Deep Learning 通过深度学习的高级推荐系统

Proceedings of the 3rd International Conference on Networking, Information Systems & Security Pub Date : 2020-03-31 DOI: 10.1145/3386723.3387870

Mohamed Khoali, Abdelhak Tali, Yassin Laaziz

引用次数: 8

Big Data Solutions Proposed for Cluster Computing Systems Challenges: A survey 针对集群计算系统挑战提出的大数据解决方案:调查

Proceedings of the 3rd International Conference on Networking, Information Systems & Security Pub Date : 2020-03-31 DOI: 10.1145/3386723.3387826

Fatima Es-Sabery, Abdellatif Hair

{"title":"Big Data Solutions Proposed for Cluster Computing Systems Challenges: A survey","authors":"Fatima Es-Sabery, Abdellatif Hair","doi":"10.1145/3386723.3387826","DOIUrl":"https://doi.org/10.1145/3386723.3387826","url":null,"abstract":"CCS (Cluster Computing System) is coming to solve the problems of standard technology. Whose, objective is to improve the performance/power efficiency of a single processor for storing and mining the large data sets, using the parallel programming to read and process the massive data sets on multiple disks and CPUs. The thing which makes these systems somewhat performant than the standard technology is the physical organization of computing nodes in the cluster. Currently, this kind of cluster does not entirely solve the problem because it comes with its challenges, which are Node failures, Computations, Network Bottleneck, and Distributed programming. All these problems are coming when we are mining and storing the massive volume of data using cluster computing. To solve these challenges, Google invented a new Big Data framework of data processing called MapReduce, to manage large scale data processing across large clusters of commodity servers. The paper outlines the running of CCS and presents its challenges in this era of Big Data. Moreover, it introduces the most popular Big Data solutions proposed to overcome the CCS challenges. Also, it shows how Big Data technologies solve CCS issues. Generally, the main goal of this work is to provide a better understanding of the challenges of CCS and identify the essential big data solutions in this increasingly important area.","PeriodicalId":139072,"journal":{"name":"Proceedings of the 3rd International Conference on Networking, Information Systems & Security","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127412068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7