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2023, Meriam Jemel, Nadia Ben Azzouna
Optimization of Multiple Scaling Factors for ECG Steganography Using Dynamic Thresholding GA
International Journal of Intelligent Systems and Applications in Engineering, 11(4), 01–10, 2023, 2023
Abstract
Protecting patient data has become a top priority for healthcare providers in the digital age. ECG steganography is a technique for concealing electrocardiogram (ECG) signals during Internet transmission along with other medical data. This strategy aims to recover all embedded patient data while minimizing degradation of the cover signal caused by embedding. Quantization techniques make it possible to include patient information in the ECG signal, and it has been discovered that multiple scaling factors (MSFs) provide a superior trade-off than uniform single scaling factors. In this paper, we present a novel contribution to the field: a discrete wavelet transforms and singular value decomposition-based dynamic Thresholding GA (DTGA)-based ECG steganography scheme. Using the MITIH database, we demonstrate the efficacy of this method, and our findings corroborate that DTGA significantly improves data security.
Chaima Romdhani, Jihene Tounsi, Said GattoufiLateral Transshipment in Two-Echelon Inventory Control for Sustainable Pharmaceutical Supply Chain
Conference: 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT), 2023
Abstract
Efficient inventory management (IM) presents an
important key driver for supply chain (SC) sustainability.
This latter becomes a crucial concern for decision-makers and
managers in all domains, particularly in the matter of sensitive
areas that affect human well-being, namely the pharmaceutical
industry. Medicines IM for a sustainable Pharmaceutical Sup-
ply Chain (PSC) brought further particularities compared to
the traditional SCs. Besides the economic preoccupation, social
and environmental issues might be considered. In this work, we
assess the impact of the Lateral Transshipment (LT) strategy on
the sustainability of the IM process. We compare the total costs
of two cases, IM with and without LT strategy. We propose an
IM model that seeks the optimal replenishment order quantity
of multiple types of products and the shipment time in a
two-echelon PSC under a centralized setting. The considered
PSC consists of a pharmaceutical company (PC), a Pharma-
distributor (PD), and multiple hospitals. The mathematical
model takes into account the transportation costs including LT
costs -in the case when LT is included- as well as shortage,
and products with high deterioration rate costs. We attempt
to minimize unused medicines leftover by minimizing the
deterioration rate of products at both distributor and hospital
sites.Mouna Karaja, Abir Chaabani, Ameni Azzouz, Lamjed Ben SaidDynamic bag-of-tasks scheduling problem in a heterogeneous multi-cloud environment: a taxonomy and a new bi-level multi-follower modeling
J Supercomput 79, 17716–17753 (2023), 2023
Abstract
Since more and more organizations deploy their applications through the cloud, an increasing demand for using inter-cloud solutions is noticed. Such demands could inherently result in overutilization of resources, which leads to resource starvation that is vital for time-intensive and life-critical applications. In this paper, we are interested in the scheduling problem in such environments. On the one hand, a new taxonomy of criteria to classify task scheduling problems and resolution approaches in inter-cloud environments is introduced. On the other hand, a bi-level multi-follower model is proposed to solve the budget-constrained dynamic Bag-of-Tasks (BoT) scheduling problem in heterogeneous multi-cloud environments. In the proposed model, the upper-level decision maker aims to minimize the BoT’ makespan under budget constraints. While each lower-level decision maker minimizes the completion time of tasks it received. Experimental results demonstrated the outperformance of the proposed bi-level algorithm and revealed the advantages of using a bi-level scheme with an improvement rate of 32%, 29%, and 21% in terms of makespan for the small, medium, and big size instances, respectively.
Rihab Said, Slim Bechikh, , Lamjed Ben SaidSolving the Discretization-based Feature Construction Problem using Bi-level Evolutionary Optimization
2023 IEEE Congress on Evolutionary Computation (CEC), Chicago, IL, USA, 2023, pp. 1-8, 2023
Abstract
Feature construction represents a crucial data preprocessing technique in machine learning applications because it ensures the creation of new informative features from the original ones. This fact leads to the improvement of the classification performance and the reduction of the problem dimensionality. Since many feature construction methods require discrete data, it is important to perform discretization in order to transform the constructed features given in continuous values into their corresponding discrete versions. To deal with this situation, the aim of this paper is to jointly perform feature construction and feature discretization in a synchronous manner in order to benefit from the advantages of each process. Thus, we propose here to model the discretization-based feature construction task as a bi-level optimization problem in which the constructed features are evaluated based on their optimized sequence of cut-points. The resulting algorithm is termed Discretization-Based Feature Construction (Bi-DFC) where the proposed model is solved using an improved version of an existing co-evolutionary algorithm, named I-CEMBA that ensures the variation of concatenation trees. Bi-DFC performs the selection of original attributes at the upper level and ensures the creation and the evaluation of constructed features at the upper level based on their optimal corresponding sequence of cut-points. The obtained experimental results on ten high-dimensional datasets illustrate the ability of Bi-DFC in outperforming relevant state-of-the-art approaches in terms of classification results.
Sami Rojbi, ,Enhancing the Accessibility of Images on the Web: A Holistic View and New Perspectives
49th PATTAYA International Conference on “Science, Engineering, Technology & Healthcare” (PSETH-23), Tech Rep, 2023
Abstract
None of the available web specifications consider images as purely visual elements. However, their online accessibility remains challenging. This issue has taken on an entirely new dimension with the evolution of mobile technology. Nowadays, most Internet users use mobile phones equipped with an increasing number of cameras. Obviously, this has helped democratize access to the web. However, it has made images the major medium of communication, and thus it has increased the challenge of image accessibility for people with disabilities. In light of the above, we present a holistic view of the accessibility of images for use on the internet. Precisely, we seek to provide an overview of the various accessibility strategies while highlighting an implementation gap that research has attempted to address. In addition, we discuss new research perspectives that can lead to the design of a new alternative to the image. Soon, it may be possible to supplement the ALT text with non-speech sound alternatives or even tactile alternatives.
Hamida Labidi, Nadia Ben Azzouna, ,An improved genetic algorithm for solving the multi-objective vehicle routing problem with environmental considerations
This paper presents an improved genetic algorithm for addressing the multi-objective vehicle routing problem with environmental considerations, published at KES 2023., 2023
Abstract
In recent years, the negative impacts of neglecting the environment, particularly global warming caused by greenhouse gases, have gained attention. Many countries and organizations are taking steps to reduce their greenhouse gas emissions and promote sustainable practices. In this paper, we aim to address the gap in the classical Vehicle Routing Problem (VRP) by taking into consideration the environmental effects of vehicles. To find a balance between cost-efficiency and environmental impact, we propose a Hybrid Genetic Algorithm (HGA) to address the Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) and a heterogeneous fleet, taking into account new orders that arrive dynamically during the routing process. This approach takes into consideration the environmental effects of the solutions by optimizing the number and type/size of vehicles used to fulfill both static and dynamic orders. The goal is to provide a solution that is both cost-effective and environmentally friendly, addressing the issue of over-exploitation of energy and atmospheric pollution that threaten our ecological environment. Computational results prove that the hybridization of a genetic algorithm with a greedy algorithm can find high-quality solutions in a reasonable run time.
Marwa Chabbouh, Slim Bechikh, Lamjed Ben Said,Imbalanced multi-label data classification as a bi-level optimization problem: application to miRNA-related diseases diagnosis
Neural Comput. Appl. 35(22): 16285-16303 (2023), 2023
Abstract
In multi-label classification, each instance could be assigned multiple labels at the same time. In such a situation, the relationships between labels and the class imbalance are two serious issues that should be addressed. Despite the important number of existing multi-label classification methods, the widespread class imbalance among labels has not been adequately addressed. Two main issues should be solved to come up with an effective classifier for imbalanced multi-label data. On the one hand, the imbalance could occur between labels and/or within a label. The “Between-labels imbalance” occurs where the imbalance is between labels however the “Within-label imbalance” occurs where the imbalance is in the label itself and it could occur across multiple labels. On the other hand, the labels’ processing order heavily influences the quality of a multi-label classifier. To deal with these challenges, we propose in this paper a bi-level evolutionary approach for the optimized induction of multivariate decision trees, where the upper-level role is to design the classifiers while the lower-level approximates the optimal labels’ ordering for each classifier. Our proposed method, named BIMLC-GA (Bi-level Imbalanced Multi-Label Classification Genetic Algorithm), is compared to several state-of-the-art methods across a variety of imbalanced multi-label data sets from several application fields and then applied on the miRNA-related diseases case study. The statistical analysis of the obtained results shows the merits of our proposal.
Kalthoum Rezgui, Hédia SellemiUsing Innovation Diffusion Theory to Understand the Factors Impacting the Adoption of Competency-Based Applications
-, 2023
Abstract
This paper presents a case study based on Rogers’ diffusion of innovations theory designed to gather learner opinions regarding the use of competency-based applications in technology-enhanced learning. In particular, a questionnaire was designed to measure the satisfaction of learners with a Competency Management, Development and Tracking Tool, called CMD2T, which can be integrated into virtual learning environments in order to provide users with a practical way to assess, track, and prove competencies associated with a given course. To test the reliability and validity of the proposed questionnaire, two statistical methods have been used. The Cronbach’s alpha test was performed to check the internal consistency of each subscale comprising the questionnaire as well as the entire questionnaire. Besides, different validity procedures have been used to assess its validity, including construct validity using factor analysis, convergent and discriminant validity. Results of factor analysis and learner satisfaction measurement indicated that the questionnaire is proved to be a reliable instrument to measure learners’ satisfaction and that the proposed CMD2T tool is very useful and highly appreciated by teachers and students. This paper, also, aims to show how a new questionnaire to assess learners’ satisfaction can be validated. Hence, researchers who have a similar issue related to measuring learners’ satisfaction about an innovative competence-based tool can potentially benefit from the paper’s results.
Lilia Rejeb, Lamjed Ben Said,Evidential Supervised Classifier System: A New Learning Classifier System Dealing with Imperfect Information
International Journal of Information Technology & Decision Making, 23(02), 917-938., 2023
Abstract
Learning Classifier Systems (LCSs) are a kind of evolutionary machine learning algorithms that provide highly adaptive components to deal with real world problems. They have been widely used in resolving complex problems such as decision making and classification. LCSs are flexible algorithms that are able to construct, incrementally, a set of rules and evolve them through the Evolutionary Algorithm (EA). Despite their efficiency, LCSs are not capable of handling imperfect information, which may lead to reduced performance in terms of classification accuracy. We propose a new accuracy-based Michigan-style LCS that integrates the belief function theory in the supervised classifier system. The belief function or evidence theory represents an efficient framework for treating imperfect information. The new approach shows promising results in real world classification problems.
Yasmine Amor, Lilia Rejeb, , , , Lamjed Ben SaidRule-based Recommendation System for Traffic Congestion Measures
KES STS 2023, 2023
Abstract
Traffic congestion has become a serious concern in both developed and developing countries. Increasing demand for urban transport has led to plenty of issues including longer travel times, higher fuel consumption and greater vehicular crash rates and therefore to a deterioration in the quality of life. On grounds of the wide range of problems that traffic congestion can cause, the study of traffic congestion measures and their implementation is a crucial step that should be considered in analyzing traffic. However, these measures might vary per country. They are context-sensitive. Therefore, the purpose of this study is to develop a recommendation system able to generate the congestion measures in accordance with the context under study. The goal of this research is to assist researchers and traffic operators to choose the most suitable congestion measures to the studied area.
