Publications Scientifiques

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Author: search.filters.author.Boudane, Fatima

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    Machine learning in the medical field: A comprehensive overview
    (Institute of Electrical and Electronics Engineers Inc, 2023) Belgacem, Ali; Khoudi, Asmaa; Boudane, Fatima; Berrichi, Ali
    Machine learning utilization in medicine has increased interest over the last few years. With its impressive results in treating diseases and medical conditions, it will be important to understand and analyze how the scientific community has used it. Thus, opening up space for new research and opportunities in medicine. The objective of this study is to review the literature on machine learning applications in the medical sector. Therefore, we conducted an extensive research by reviewing recent studies and surveys on machine-learning health solutions. As a result, we offer, in this paper, a fresh study affirming the foundations and necessities of a machine learning application in the medical field. We also provide a breakdown of current research trends, which highlights future research opportunities.
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    Multi-Objective artificial bee colony algorithm for Parameter-Free Neighborhood-Based clustering
    (IGI Global, 2021) Boudane, Fatima; Berrichi, Ali
    Although various clustering algorithms have been proposed, most of them cannot handle arbitrarily shaped clusters with varying density and depend on the user-defined parameters which are hard to set. In this paper, to address these issues, the authors propose an automatic neighborhood-based clustering approach using an extended multi-objective artificial bee colony (NBC-MOABC) algorithm. In this approach, the ABC algorithm is used as a parameter tuning tool for the NBC algorithm. NBC-MOABC is parameter-free and uses a density-based solution encoding scheme. Furthermore, solution search equations of the standard ABC are modified in NBC-MOABC, and a mutation operator is used to better explore the search space. For evaluation, two objectives, based on density concepts, have been defined to replace the conventional validity indices, which may fail in the case of arbitrarily shaped clusters. Experimental results demonstrate the superiority of the proposed approach over seven clustering methods
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    Gabriel graph-based connectivity and density for internal validity of clustering
    (Springer link, 2020) Boudane, Fatima; Berrichi, A.
    Clustering has an important role in data mining field. However, there is a large variety of clustering algorithms and each could generate quite different results depending on input parameters. In the research literature, several cluster validity indices have been proposed to evaluate clustering results and find the partition that best fits the input dataset. However, these validity indices may fail to achieve satisfactory results, especially in case of clusters with arbitrary shapes. In this paper, we propose a new cluster validity index for density-based, arbitrarily shaped clusters. Our new index is based on the density and connectivity relations extracted among the data points, based on the proximity graph, Gabriel graph. The incorporation of the connectivity and density relations allows achieving the best clustering results in the case of clusters with any shape, size or density. The experimental results on synthetic and real datasets, using the well-known neighborhood-based clustering (NBC) algorithm and the DBSCAN (density-based spatial clustering of applications with noise) algorithm, illustrate the superiority of the proposed index over some classical and recent indices and show its effectiveness for the evaluation of clustering algorithms and the selection of their appropriate parameters