Sadok Ben Yahia

Tunis University

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Engelbert Mephu Nguifo

Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes

Tarek Hamrouni

Tunis El Manar University

Sami Zghal

University of Jendouba

Marouen Kachroudi

Tunis University

Yahya Slimani

Manouba University

Dirk Draheim

Tallinn University of Technology

Hanen Brahmi

Tunis El Manar University

Amel Bouzeghoub

Département d'Informatique

Sana Hamdi

Tunis El Manar University

Chiraz Trabelsi

Université de Mayotte

Cooperative Institutions

Tunis El Manar University

Centre National de la Recherche Scientifique

Tunis University

Tallinn University of Technology

Département d'Informatique

Université Toulouse III - Paul Sabatier

National Engineering School of Tunis

Institut de Recherche en Informatique de Toulouse

Université de Montpellier

Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier

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Pregnancy Associated Breast Cancer Gene Expressions : New Insights on Their Regulation Based on Rare Correlated Patterns

IEEE/ACM Transactions on Computational Biology and Bioinformatics (2020)

Souad Bouasker Wissem Inoubli Sadok Ben Yahia Gayo Diallo

Breast-cancer (BC) is the most common invasive cancer in women, with considerable death. Given that, BC is classified as a hormone-dependent cancer, when it collides with pregnancy, different questions may arise for which there are still no convincing answers. To deal with this issue, two new frameworks are proposed within this paper: CoRaM and Dist-CoRaM. The former is the first unified framework dedicated to the extraction of a generic basis of Correlated-Rare Association rules from gene expression data. The proposed approach has been successfully applied on a breast-cancer Gene Expression Matrix (GSE1379) with very promising results. The latter, the Dist-CoRaM approach, is a big-data processing based on Apache spark framework, dealing with correlation mining from micro-array pregnancy associated breast-cancer assays (PABC) data. It is successfully applied on the (GSE31192) gene expression matrix (GEM). The correlated patterns of gene-sets shed light on the fact that PABC exhibits heightened aggressiveness compared to cancers for Non-PABC women. Our findings suggest that higher levels of estrogen and progesterone hormones, unfortunately, are very keen to the increase of the tumor aggressiveness and the proliferation of the cancer.

10.1109/tcbb.2020.3015236

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Citations (9)

A SDN-based IoT Architecture Framework forEfficient Energy Management in Smart Buildings

Akram Hakiri Bassem Sellami Sadok Ben Yahia Pascal Berthou

Source

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Fuzzy set-based formalization of gradual patterns

Sarra Ayouni Sadok Ben Yahia

In this paper, we attempt at providing a new formalization of gradual patterns based on fuzzy sets. In fact, in the last few years and over the last decade, the extraction of gradual patterns grasped the interest of the data mining community and several approaches were proposed to deal with such patterns. Gradual patterns of the form "The more/less A, the more/less B" express the co-variation between the attributes A and B. All the proposed approaches differ depending on the way they count the frequency (i.e.; the support) of a pattern, or depending on the way the attributes are treated, i.e.; if they are considered as monotonically growing within the range of values or if they are considered as a fuzzy partition. For instance, the pattern "The older the age, the higher the salary" is called to be a fuzzy gradual pattern. In this paper, we propose a novel formalization of such patterns.

10.1109/socpar.2014.7008046

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Citations (2)

Special Issue on Concept Lattices and their Applications (CLA 2020)

International Journal of Approximate Reasoning (2023)

Francisco José Valverde-Albacete Martin Trnečka Sadok Ben Yahia

10.1016/j.ijar.2023.109079

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Distributed Scalable Association Rule Mining over Covid-19 Data

Lecture notes in computer science (2021)

Mahtab Shahin Wissem Inoubli Syed Attique Shah Sadok Ben Yahia Dirk Draheim

SPARK (programming language)

10.1007/978-3-030-91387-8_3

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Citations (7)

Hypergraph fuzzy minimals transversals mining: A new approach for social media recommendation

2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2017)

Hazem Souid Chiraz Trabelsi Gabriella Pasi Sadok Ben Yahia

User preference discovery aims to detect the patterns of user preferences for various topics of interest or items such as movie genre or category. Preferences discovery is a crucial stage in the development of intelligent personalization systems. Although a variety of studies have been proposed in the literature addressing a wide range of applications such as recommender systems or personalized search, only a few of them have considered the management of imprecision in the representation of user and item features. This paper aims to address the above issue by using fuzzy sets. The paper proposes a general framework for preferences discovery through fuzzy sets and fuzzy models and it introduces a new algorithm for representing and discovering fuzzy user interest profile. Based on the results of the empirical evaluation, the proposed approach outperforms two well-known recommendation approaches in terms of well-known quality assessment metrics, namely: discounted cumulative gain, precision, recall, as well as F1-measure.

Hypergraph

10.1109/fuzz-ieee.2017.8015639

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Citations (1)

A Systematic Assessment of Numerical Association Rule Mining Methods

SN Computer Science (2021)

Minakshi Kaushik Rahul Sharma Sijo Arakkal Peious Mahtab Shahin Sadok Ben Yahia

Categorical variable

Association (psychology)

10.1007/s42979-021-00725-2

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Citations (45)

Special Issue on Digital Twin for Future Networks and Emerging IoT Applications (DT4IoT)

Future Generation Computer Systems (2024)

Akram Hakiri Sadok Ben Yahia Aniruddha Gokhale Nédra Mellouli

10.1016/j.future.2024.06.056

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Grand Reports: A Tool for Generalizing Association Rule Mining to Numeric Target Values

Lecture notes in computer science (2020)

Sijo Arakkal Peious Rahul Sharma Minakshi Kaushik Syed Attique Shah Sadok Ben Yahia

Association (psychology)

10.1007/978-3-030-59065-9_3

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Citations (10)

Transnational Innovation Networks, Population Aging and The Silver Economy: Challenges, Opportunities and the State of Play

AHFE international (2023)

Eric Jackson Ingrid Pappel Sadok Ben Yahia

Globally, lower fertility rates combined with increased life expectancy and the transition of “baby boomers” toward retirement have contributed to an aging population in many societies. Ultimately, these demographic developments contain immense societal and economic implications for the public and private sectors. Subsequently, the term “Silver Economy” has been used to describe the delivery of products and services to older people, with a particular focus on leveraging Information Communication Technologies (ICTs) to improve the well-being of older citizens in multi-faceted domains: healthcare, the labor market, social care, mobility, housing, and many others. In an increasingly globalized knowledge economy, regional knowledge sharing, integration, and synergy can enable innovation in the Silver Economy and combat population aging. Transnational innovation networks (TINs) represent a pathway for achieving these goals. Although research exploring population aging, primarily from the EU, has grown in the literature over the past five years, there is a knowledge gap regarding transnational innovation approaches, platforms, and initiatives in the Silver Economy domain. Thus, this paper utilizes a literature review approach from a global perspective to survey the Silver Economy and population aging transnational innovation landscape. Our state-of-the-art review shows the importance of transnational knowledge flows enabled by TINs and subsequent innovations.

Population Ageing

Knowledge Economy

10.54941/ahfe1003895

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