2019 1st International Conference on Smart Systems and Data Science (ICSSD)最新文献

Enhancement of supply chain management by integrating Blockchain technology 整合区块链技术，加强供应链管理

2019 1st International Conference on Smart Systems and Data Science (ICSSD) Pub Date : 2019-10-01 DOI: 10.1109/ICSSD47982.2019.9002771

S. Nasih, Sara Arezki, T. Gadi

引用次数: 3

Decomposition and Visualization of High-Dimensional Data in a Two Dimensional Interface 二维界面中高维数据的分解与可视化

2019 1st International Conference on Smart Systems and Data Science (ICSSD) Pub Date : 2019-10-01 DOI: 10.1109/ICSSD47982.2019.9002846

Mimoun Lamrini, M. Chkouri

{"title":"Decomposition and Visualization of High-Dimensional Data in a Two Dimensional Interface","authors":"Mimoun Lamrini, M. Chkouri","doi":"10.1109/ICSSD47982.2019.9002846","DOIUrl":"https://doi.org/10.1109/ICSSD47982.2019.9002846","url":null,"abstract":"Data visualization has a crucial role in understanding and processing voluminous data (i.e., Big Data) and subsequently has become more important with the coincidence of the exponential growth of data analysis need.The problem of high-dimensional data visualization in a data processing software interface cannot be entirely displayed, in consideration that once the data size exceeds two-dimension, it cannot be projected into a two-dimension interface. Furthermore, the rough analysis and evaluation of high-dimensional data become considerably ambiguous, thus, making a precise decision on that data cannot be achieved. In order to overcome this anomaly, resorting to data dimensionality reduction is a plausible solution.In this paper, the integration of Principal Component Analysis (PCA) combined with the Matrix by Block Decomposition (MBD) method(A.K.A block segmentation). According to the literature, the MBD method turned out quite efficient in data segmentation, wherein a huge data can be divided into regular blocks. By doing so, it becomes easier to access and visualize a given part of data. In order to further enhance the visualization understanding, K-means segmentation has been integrated in our proposed algorithm.In our study, we took into account other data dimensionality reduction techniques such as Linear Discriminant Analysis (LDA), Multi-Dimensional Scaling(MDS).","PeriodicalId":342806,"journal":{"name":"2019 1st International Conference on Smart Systems and Data Science (ICSSD)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114846805","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: 2

A Multi-Agent Model for Network Intrusion Detection 网络入侵检测的多智能体模型

2019 1st International Conference on Smart Systems and Data Science (ICSSD) Pub Date : 2019-10-01 DOI: 10.1109/ICSSD47982.2019.9003119

Said Ouiazzane, M. Addou, Fatimazahra Barramou

引用次数: 11

An Empirical Study of Deep Neural Networks Models for Sentiment Classification on Movie Reviews 电影评论情感分类的深度神经网络模型实证研究

2019 1st International Conference on Smart Systems and Data Science (ICSSD) Pub Date : 2019-10-01 DOI: 10.1109/ICSSD47982.2019.9003171

Oumaima Hourrane, Nouhaila Idrissi, E. Benlahmar

引用次数: 1

A lightweight risk analysis of a critical infrastructure based ICSs 基于集成电路的关键基础设施的轻量级风险分析

2019 1st International Conference on Smart Systems and Data Science (ICSSD) Pub Date : 2019-10-01 DOI: 10.1109/ICSSD47982.2019.9002902

O. El Idrissi, Abdellatif Mezrioui, A. Belmekki

引用次数: 0

Big Data Dependability Opportunities & Challenges 大数据可靠性的机遇与挑战

2019 1st International Conference on Smart Systems and Data Science (ICSSD) Pub Date : 2019-10-01 DOI: 10.1109/ICSSD47982.2019.9002676

Mdarbi Fatima Ezzahra, Afifi Nadia, Hilal Imane

引用次数: 1

A Restricted Boltzmann Machine-based Recommender System For Alleviating Sparsity Issues 一种基于受限玻尔兹曼机的推荐系统缓解稀疏性问题

2019 1st International Conference on Smart Systems and Data Science (ICSSD) Pub Date : 2019-10-01 DOI: 10.1109/ICSSD47982.2019.9003149

Nouhaila Idrissi, Oumaima Hourrane, A. Zellou, E. Benlahmar

{"title":"A Restricted Boltzmann Machine-based Recommender System For Alleviating Sparsity Issues","authors":"Nouhaila Idrissi, Oumaima Hourrane, A. Zellou, E. Benlahmar","doi":"10.1109/ICSSD47982.2019.9003149","DOIUrl":"https://doi.org/10.1109/ICSSD47982.2019.9003149","url":null,"abstract":"With the explosive growth of the Internet and the Web, assisting users and facilitate their access to resources that might be of their interest and that are adapted to their personal needs is a tedious task. Efficient management of large amounts of information becomes an increasingly significant challenge. Hence, recommender systems have proved, in recent years, to be a valuable asset to dealing with the problem of information overload by assisting the users and providing them with more effective access to information. To this end, these systems must be able to predict users’ interests based on their prior feedback. However, sparsity issues arise when necessary transactional information is not available for inferring users and items similarities, which deteriorate the quality and accuracy of the recommender system. To fill these gaps, we propose in this paper a Restricted Boltzmann Machine-based model to learn hidden factors and reconstruct sparse input rating data. Experimental results show that our proposed approach can effectively deal with data sparsity in MovieLens dataset, containing a massive amount of scarce information.","PeriodicalId":342806,"journal":{"name":"2019 1st International Conference on Smart Systems and Data Science (ICSSD)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124669331","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: 0

ICSSD 2019 Committees ICSSD 2019委员会

2019 1st International Conference on Smart Systems and Data Science (ICSSD) Pub Date : 2019-10-01 DOI: 10.1109/icssd47982.2019.9002832

引用次数: 0

Propelling motion modeling of an Hexapod robot 六足机器人推进运动建模

2019 1st International Conference on Smart Systems and Data Science (ICSSD) Pub Date : 2019-10-01 DOI: 10.1109/ICSSD47982.2019.9003014

M. Atify, M. Bennani, A. Abouabdellah

引用次数: 0

Artificial Neural Networks Optimized with Unsupervised Clustering for IDS Classification 基于无监督聚类优化的人工神经网络IDS分类

2019 1st International Conference on Smart Systems and Data Science (ICSSD) Pub Date : 2019-10-01 DOI: 10.1109/ICSSD47982.2019.9002827

I. Lafram, N. Berbiche, Jamila El Alami

{"title":"Artificial Neural Networks Optimized with Unsupervised Clustering for IDS Classification","authors":"I. Lafram, N. Berbiche, Jamila El Alami","doi":"10.1109/ICSSD47982.2019.9002827","DOIUrl":"https://doi.org/10.1109/ICSSD47982.2019.9002827","url":null,"abstract":"Information systems are becoming more and more complex and closely linked. These systems are encountering an enormous amount of nefarious traffic while ensuring real - time connectivity. Therefore, a defense method needs to be in place. One of the commonly used tools for network security is intrusion detection systems (IDS). An IDS tries to identify fraudulent activity using predetermined signatures or pre-established user misbehavior while monitoring incoming traffic. Intrusion detection systems based on signature and behavior cannot detect new attacks and fall when small behavior deviations occur. Many researchers have proposed various approaches to intrusion detection using machine learning techniques as a new and promising tool to remedy this problem. In this paper, the authors present a combination of two machine learning methods, unsupervised clustering followed by a supervised classification framework as a Fast, highly scalable and precise packets classification system. This model’s performance is assessed on the new proposed dataset by the Canadian Institute for Cyber security and the University of New Brunswick (CICIDS2017). The overall process was fast, showing high accuracy classification results.","PeriodicalId":342806,"journal":{"name":"2019 1st International Conference on Smart Systems and Data Science (ICSSD)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116441158","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: 0