2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS)最新文献_第2页

Feature selection based on attributes clustering 基于属性聚类的特征选择

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626770

Youssef Asnaoui, Yassine Akhiat, Ahmed Zinedine

引用次数: 3

Towards a Blockchain based Intelligent and Secure Voting

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626751

Asmae El Fezzazi, Amina Adadi, M. Berrada

{"title":"Towards a Blockchain based Intelligent and Secure Voting","authors":"Asmae El Fezzazi, Amina Adadi, M. Berrada","doi":"10.1109/ICDS53782.2021.9626751","DOIUrl":"https://doi.org/10.1109/ICDS53782.2021.9626751","url":null,"abstract":"Blockchain and machine learning (ML) have emerged as two disruptive technologies that could transform various sectors. Blockchain is known as peer to peer decentralized, distributed ledger technology that enables to store and exchange anything of value, it is deterministic, permanent and immutable. On the other hand, ML is the ability of computers to learn without being programmed, and it is probabilistic. Hence, when blockchain and ML converge, they surely would benefit from each other. Blockchain can enhance security of ML platforms, and ML can provide automation and optimization to the blockchain solutions. In this work, we advocate the importance of enhancing blockchain with ML algorithms, as a proof of principle we address the issue of secure and intelligent e-voting.The use of blockchain technology has brought tremendous different application domains, e-voting is one of them. Most existing e-Voting systems require central authority during the process of authentication and verification of the voter. In this paper we propose a safe online voting approach based on blockchain and ML to provide a solution to this issue. We use blockchain to ensure integrity and transparency of the votes, and ML for automating the verification process of eligible voters based on AI-powered oracle platform for face authentication. The proposed solution offers automation, security, and mobility to the voting system.","PeriodicalId":351746,"journal":{"name":"2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130771332","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: 3

Acceptance and Rejection Zones for a Classifier’s Predictions in Deep Learning 深度学习中分类器预测的接受区和拒绝区

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626739

A. Samake, L. Boulmane

引用次数: 2

An effective modified possibilistic Fuzzy C-Means clustering algorithm for noisy data problems 一种有效的改进可能性模糊c均值聚类算法

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626706

Souad Azzouzi, J. EL-Mekkaoui, A. Hjouji, A. E. Khalfi

引用次数: 0

Filter design for two-dimensional discrete-time T-S fuzzy systems described by FM LSS model 用FM LSS模型描述二维离散T-S模糊系统的滤波器设计

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626748

R. Chakib, A. El‐Amrani

引用次数: 0

Facial expression recognition Using Machine Learning 使用机器学习进行面部表情识别

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626709

Hajar Chouhayebi, J. Riffi, Mohamed Adnane Mahraz, Ali Yahyaouy, H. Tairi

引用次数: 1

Sentiment-Based Neural Network Approach for Predicting the Severity of Bug Reports 基于情绪的预测Bug报告严重性的神经网络方法

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626730

A. Baarah, Ahmad Aloqaily, Hala Zyod, Nasser Mustafa

{"title":"Sentiment-Based Neural Network Approach for Predicting the Severity of Bug Reports","authors":"A. Baarah, Ahmad Aloqaily, Hala Zyod, Nasser Mustafa","doi":"10.1109/ICDS53782.2021.9626730","DOIUrl":"https://doi.org/10.1109/ICDS53782.2021.9626730","url":null,"abstract":"During the software maintenance process, bugs encountered by software users need to be solved according to their severity level to improve the quality of the software. Therefore, bug reports with high severity should have the highest priority to be fixed. A numerous number of bug reports are submitted daily through Bug Tracking Systems (BTS) such as Bugzilla. The bug triager examines the incoming bug reports manually and verifies whether the assigned severity level is correct or not. This manual process is time-consuming, requires much effort and is possibly error-prone. However, a limited number of research works have considered the sentiments of the bug reporters in predicting the severity of bug reports. This paper proposes two approaches based on sentiment analysis, the Lexicon-based and Multilayer Perceptron (MLP) neural network approaches. The sentiment analysis process determines and measures the sentiment words and their sentiment scores according to the popular sentiment lexicon called SentiWordNet. The proposed approaches are validated on the Eclipse open-source project, and the sentiment-based models performance is evaluated. According to the experimental results, the sentiment-based MLP outperforms the Lexicon-based approach (baseline approach). The F-measure has been improved significantly from 0.52 for the Lexicon-based approach to 0.81 when applying the MLP approach.","PeriodicalId":351746,"journal":{"name":"2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130839549","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

Arabic handwritten text line segmentation using a multi-agent system and a directed CNN 使用多智能体系统和有向CNN的阿拉伯手写文本线分割

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626747

Mohsine Elkhayati, Y. Elkettani

{"title":"Arabic handwritten text line segmentation using a multi-agent system and a directed CNN","authors":"Mohsine Elkhayati, Y. Elkettani","doi":"10.1109/ICDS53782.2021.9626747","DOIUrl":"https://doi.org/10.1109/ICDS53782.2021.9626747","url":null,"abstract":"Segmentation of text lines is a critical phase in Arabic handwriting recognition systems. Incorrect line segmentation generates errors in later phases, leading to total changes in the meaning of the text. Line segmentation in printed Arabic documents is an easier task compared to handwritten ones. Segmentation of lines from Arabic handwriting is difficult due to many challenging issues including overlaps, touches, skew/tilt in lines, diacritics, etc. To implement this phase, this paper proposes a robust approach (A2) that is based on a multi-agent system and Convolutional Neural Networks (CNN). The proposed approach is an enhanced version of the approach proposed in [1] (A1). In A1, the agents rely mainly on a morphological analysis algorithm to segment lines. Instead of that, the new approach adapts the External Features-based CNN (EFNet) architecture [2] inside the multi-agent system in order to enhance the performance of the segmentation algorithm. In terms of segmentation score, A2 brought better results compared to A1 on two benchmark databases (KHATT and HAPD). It also outperformed other recent methods in the literature.","PeriodicalId":351746,"journal":{"name":"2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121981178","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

Word Embedding for Social Bot Detection Systems 用于社交机器人检测系统的词嵌入

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626752

Zineb Ellaky, F. Benabbou, Sara Ouahabi, N. Sael

{"title":"Word Embedding for Social Bot Detection Systems","authors":"Zineb Ellaky, F. Benabbou, Sara Ouahabi, N. Sael","doi":"10.1109/ICDS53782.2021.9626752","DOIUrl":"https://doi.org/10.1109/ICDS53782.2021.9626752","url":null,"abstract":"In recent years, the growth of online social network (OSN) has been very phenomenal with great social and economic impact. However, some accounts are created for malicious activities whose objective is to influence elections, sully reputations, spread fake information or attack legitimate users. Practitioners as well as researchers are attracted by this problem and attempt to propose some solutions to prevent any OSN malicious activity. Fake account can be managed by bots, but all bots are not malicious systematically. In this paper we study the impact of using word embedding with different Machine Learning (ML) techniques on the of social bot detection performance. The experiments are based only on comment features from Cresci-17 dataset. For this purpose, we used the most ML algorithms such as Logistic Regression (LR), Decision Tree (DT), k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB),Adaboost, XGBoost and MultiLayer Perceptron (MLP) with different word embedding methods such as BOW, TF-IDF, Doc2Vec, Bert, Word2Vec, and FastText. The results showed that RF and DT algorithms performed the highest precision of 99.96% with Bert, and Doc2Vec gave performed a precision score of100%.","PeriodicalId":351746,"journal":{"name":"2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121154044","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: 1

Preordonance correlation filter for feature selection in the high dimensional classification problem 基于先验相关滤波的高维分类问题特征选择

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626705

Hasna Chamlal, T. Ouaderhman, F. Aaboub

引用次数: 2