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Author: search.filters.author.Aida ChefrourStart date: 2020

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Now showing 1 - 4 of 4
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    CAE-CNN: Image Classification Using Convolutional AutoEncoder Pre-Training
    (2022) Aida Chefrour; Samia Drissi
    The work presented in this paper is in the general framework of classification using deep learning and, more precisely, that of convolutional neural networks (CNN). In particular, the convolutional autoencoder proposes an alternative for the processing of high-dimensional data, to facilitate their classification. In this paper, we propose the incorporation of convolutional autoencoders as a general unsupervised learning data dimension reduction method for creating robust and compressed feature representations in order to improve CNN performance on image classification tasks. For prediction reasons, we applied the two methods to the MNIST image databases. The use of CNN with the convolutional autoencoder gives better results compared to the individual use of each of them in terms of accuracy, to obtain a good classification of the data high-dimensional entrance.
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    A modified incremental density based clustering algorithm
    (IEEE, 2022) Aida Chefrour
    Cluster analysis, generally known as clustering, is a technique for separating data into groups (clusters) of similar objects. Except if the system is completely retrained, traditional clustering classifiers will be unable to learn new information and knowledge (attributes, examples, or classes). Only incremental learning, which outperforms when new data objects are introduced into an existing database, can solve this problem. These evolutionary strategies are applied to dynamic databases by updating the data. We’ll choose to study the Incremental Density- Based Spatial Clustering of Applications with Noise algorithm because of its capacity to discover arbitrary clusters and identify noise. In this study, a modified version of the Incremental Density Based Clustering Algorithm using an Adaptive Median Filtering Technique was used. The difference between our previous proposed AMF-IDBSCAN and the proposed algorithm developed in this work is in the evaluation performance stage. The key idea consists of a database change in the case of introducing new data items to an existing database in order to improve performance. We conducted several experiments on benchmark and synthetic data collected from the University of California Irvine repository in terms of the Generalized Dunn Index, Davies Bouldin Index, and change of time (milliseconds) with the increment of data in the original database. Experiments with datasets of various sizes and dimensions show that the proposed algorithm enhances clustering when compared to several current incremental wellknown techniques.
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    K-CAE: Image Classification Using Convolutional AutoEncoder Pre Training and K-means Clustering
    (Slovene Society Informatika, 2023) Aida Chefrour; Samia Drissi
    The work presented in this paper is in the general framework of classification using deep learning and, more precisely, that of convolutional Autoencoder. In particular, this last proposes an alternative for the processing of high-dimensional data, to facilitate their classification. In this paper, we propose the incorporation of convolutional autoencoders as a general unsupervised learning data dimension reduction method for creating robust and compressed feature representations for better storage and transmission to the classification process to improve K-means performance on image classification tasks. The experimental results on three image databases, MNIST, Fashion-MNIST, and CIFAR-10, show that the proposed method significantly outperforms deep clustering models in terms of clustering quality.
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    Unsupervised Deep Learning: Taxonomy and Algorithms
    (Slovene Society Informatika, 2022) Aida Chefrour; Labiba Souici-Meslati
    Clustering is a fundamental challenge in many data-driven application fields and machine learning techniques. The data distribution determines the quality of the outcomes, which has a significant impact on clustering performance. As a result, deep neural networks can be used to learn more accurate data representations for clustering. Many recent studies have focused on employing deep neural networks to develop a clustering-friendly representation, which has resulted in a significant improvement in clustering performance. We present a systematic survey of clustering with deep learning in this study. Then, a taxonomy of deep clustering is proposed, as well as some sample algorithms for our overview. Finally, we discuss some exciting future possibilities for clustering using deep learning and offer some remarks