2022 5th International Symposium on Informatics and its Applications (ISIA)最新文献_第3页

Super-resolution of document images using transfer deep learning of an ESRGAN model 使用ESRGAN模型的迁移深度学习实现文档图像的超分辨率

2022 5th International Symposium on Informatics and its Applications (ISIA) Pub Date : 2022-11-29 DOI: 10.1109/ISIA55826.2022.9993497

Zakia Kezzoula, Djamel Gaceb, Nadjat Gritli

引用次数: 0

EEG signals analysis using SVM and MLPNN classifiers for epilepsy detection 基于SVM和MLPNN分类器的脑电信号分析与癫痫检测

2022 5th International Symposium on Informatics and its Applications (ISIA) Pub Date : 2022-11-29 DOI: 10.1109/ISIA55826.2022.9993577

G. Chekhmane, R. Benali

引用次数: 1

Sarcasm Detection in Arabic Tweets: A comparison Between deep learning and Pre trained Transformers-based Models 阿拉伯语推文中的讽刺检测:深度学习和基于预训练的transformer模型的比较

2022 5th International Symposium on Informatics and its Applications (ISIA) Pub Date : 2022-11-29 DOI: 10.1109/ISIA55826.2022.9993553

R. Bouguesri, Khadidja Habelhames, H. Aliane, A. A. Aliane

引用次数: 0

A Deep Learning Approach to Recognize Mixed Fonts Printed Arabic Characters 一种识别混合字体印刷阿拉伯字符的深度学习方法

2022 5th International Symposium on Informatics and its Applications (ISIA) Pub Date : 2022-11-29 DOI: 10.1109/ISIA55826.2022.9993489

Rahima Bentrcia, Meriem Tallai, Asma Mekdour

引用次数: 0

Plant-Leaf Diseases Classification using CNN, CBAM and Vision Transformer 基于CNN、CBAM和Vision Transformer的植物叶片病害分类

2022 5th International Symposium on Informatics and its Applications (ISIA) Pub Date : 2022-11-29 DOI: 10.1109/ISIA55826.2022.9993601

Abdeldjalil Chougui, Achraf Moussaoui, A. Moussaoui

{"title":"Plant-Leaf Diseases Classification using CNN, CBAM and Vision Transformer","authors":"Abdeldjalil Chougui, Achraf Moussaoui, A. Moussaoui","doi":"10.1109/ISIA55826.2022.9993601","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993601","url":null,"abstract":"Detecting plant diseases is usually difficult without an experts knowledge. In this study we want to propose a new classification model based on deep learning that will be able to classify and identify different plant-leaf diseases with high accuracy that outperforms the state of the art approaches and previous works. Using only training images, CNN can automatically extract features for classification, and achieve high classification performance. We used two datasets in this study, PlantVillage dataset containing 54,303 healthy and unhealthy leaf images divided into 38 categories by species and disease, and Tomato dataset containing 11,000 healthy and unhealthy tomato leaf images with nine diseases to train the models. We propose a deep convolutional neural network architecture, with and without attention mechanism, and we tuned 4 pretrained models that have been trained on large dataset such as MobileNet, VGG-16, VGG-19 and ResNET. We also tuned 2 ViT models, the vit b32 from keras and the base patch 16 from google. Our porposed model obtained an accuracy up to 97.74%. The pretrained models gave an accuracy up to 99.52%. And the ViT models obtained an accuracy up to 99.7%. This study may aid in detecting the plant leaf diseases and improve life conditions to plants which will improve quality of humans life.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132180435","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 New Deep Reinforcement Learning-Based Adaptive Traffic Light Control Approach for Isolated Intersection 基于深度强化学习的孤立交叉口自适应红绿灯控制新方法

2022 5th International Symposium on Informatics and its Applications (ISIA) Pub Date : 2022-11-29 DOI: 10.1109/ISIA55826.2022.9993598

Tarek Amine Haddad, D. Hedjazi, Sofiane Aouag

引用次数: 0

Study and comparison of machine learning models for air PM 2.5 concentration prediction 空气中pm2.5浓度预测的机器学习模型研究与比较

2022 5th International Symposium on Informatics and its Applications (ISIA) Pub Date : 2022-11-29 DOI: 10.1109/ISIA55826.2022.9993569

Leila Abbad, Djallel Brahmia, Mohamed Nadir Cherfia

{"title":"Study and comparison of machine learning models for air PM 2.5 concentration prediction","authors":"Leila Abbad, Djallel Brahmia, Mohamed Nadir Cherfia","doi":"10.1109/ISIA55826.2022.9993569","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993569","url":null,"abstract":"In the last several decades and as a result of various kinds of man-made activities, industrialization and human urbanization, the atmospheric environment pollution became a real threat to the human's health. The particles with a diameter of less than 2.5µm, one of the most harmful pollutants present in the air as it causes diseases in the respiratory system as well as cardiovascular ones. Consequently, it is beneficial to predict the particulate matter PM2.5 concentrations with high accuracy for the purpose of to alert people to make the right decision in order to fix the situation and improve the air quality especially in environments where it is essential. The prediction of the PM2.5 concentration have to pass throw a pre-processing stage then fed to the multiple models by passing a data chunk of twelve days to get the prediction for the next day. In this article, a comparative study between different Artificial Intelligence predictions models is presented: Bidirectional Long Short-Term Memory (Bi-LSTM), Time Distributed Convolutional Neural Network (CNN), and a hybrid model combining both CNN and Bi-LSTM. For this purpose, several architectures were used for the different models: Multi Inputs - Multi Outputs, Multi Inputs - Single Output and the univariate. The CNN extracts the internal spatial correlation between variables and the Bi-LSTM extracts the temporal patterns, the hybridization process proposed of those two models with the multiple Inputs -Single Output architecture gave us the most accurate results.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131080060","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

Face Kinship Verification Based VGG16 and new Gabor Wavelet Features 基于VGG16和新的Gabor小波特征的人脸亲缘关系验证

2022 5th International Symposium on Informatics and its Applications (ISIA) Pub Date : 2022-11-29 DOI: 10.1109/ISIA55826.2022.9993565

A. Chouchane, Mohcene Bessaoudi, A. Ouamane, Oussama Laouadi

引用次数: 2

An approach with flexible choice of model for customer churn prediction and retention help 灵活选择模型的方法对客户流失预测和留存率有帮助

2022 5th International Symposium on Informatics and its Applications (ISIA) Pub Date : 2022-11-29 DOI: 10.1109/ISIA55826.2022.9993540

Mahdia Azzouz, Saïda Boukhedouma, Z. Alimazighi

{"title":"An approach with flexible choice of model for customer churn prediction and retention help","authors":"Mahdia Azzouz, Saïda Boukhedouma, Z. Alimazighi","doi":"10.1109/ISIA55826.2022.9993540","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993540","url":null,"abstract":"Customer churn is one of the most critical issues faced by companies. These turn towards prediction techniques to predict the churn of their customers, because it is more expensive to acquire a new customer inside of retaining existing one. In this paper, we propose a process-based approach to detect potential customer churn and provide early warning indicator of problems that could lead to customer's loss and open up opportunities to implement effective retention strategies. The predictive churn model is determined by applying a set of data mining and machine learning algorithms, in order to keep flexible choice of the best prediction algorithm. Once the categories of churners are determined, association rule mining algorithm is applied to analyze and detect customer churn causes. The proposed approach is based on the CRISP-DM process with flexible choice of predictive model since it implements different machine learning algorithms and allows the selection of the most appropriate one for better churn prediction (the best model). The proposed approach is illustrated on a case study and the results indicate that the system is effective in detecting customer churners and addressing appropriate retention solutions.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134357365","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

Egocentric Scene Description for the Blind and Visually Impaired 盲人和视障人士的自我中心场景描述

2022 5th International Symposium on Informatics and its Applications (ISIA) Pub Date : 2022-11-29 DOI: 10.1109/ISIA55826.2022.9993531

Khadidja Delloul, S. Larabi

引用次数: 3