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2023Sami 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.
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2022, Saoussen Bel Haj Kacem,
Compositional Rule of Inference with a Complex Rule Using Lukasiewicz t-Norm
New Mathematics and Natural Computation, 2022, vol. 18, no 02, p. 525-544., 2022
Abstract
Inference systems are intelligent software performed generally to help people take appropriate decisions and solve problems in specific domains. Fuzzy inference systems are a kind of these systems that are based on fuzzy knowledge. To handle the fuzziness in the inference, the compositional rule of inference is used, which has two parameters: a t-norm and an implication operator. However, most of the combinations of t-norm/implication do not give an adequate inference result that coincides with human intuitions. This was the motivation for several works to study these combinations and to identify those that are compatible, in order to guarantee a performance close to that of humans. We are interested in this paper to a more general form of rules, which is complex rules, whose premise is a conjunction of propositions. To obtain the consequence in a fuzzy inference system using the compositional rule of inference with a complex rule, we study, in this work, Lukasiewicz t-norm which was not investigated before in this context. We combine it with known implications, and we verify the satisfaction of some criteria that model human intuitions.
Houyem Ben Hassen, Jihene Tounsi, , Sabeur ElkosantiniCase-based reasoning for home health care planning considering unexpected events
IFAC-PapersOnLine, 55(10), 1171-1176, 2022
Abstract
In recent years, Home Health Care (HHC) has gained popularity in different countries around the world (e.g. France, US, Germany, etc.). The HHC consists in providing medical services to patients at home. During the HHC service, caregivers’ planning may be disrupted by some unexpected events (e.g. urgent request, caregiver absence, traffic congestion, etc.), which makes HHC activities infeasible. This paper addresses the daily HHC routing and scheduling problem by considering unpredicted events. To solve this problem, we propose a Case-Based Reasoning (CBR) methodology. Our purpose is to create the HHC case base which contains the knowledge about the perturbation.
Mouna Karaja, Abir Chaabani, Ameni Azzouz, Lamjed Ben SaidEfficient bi-level multi objective approach for budget-constrained dynamic Bag-of-Tasks scheduling problem in heterogeneous multi-cloud environment.
Appl Intell 53, 9009–9037 (2023), 2022
Abstract
Bag-of-Tasks is a well-known model that processes big-data applications supporting embarrassingly parallel jobs with independent tasks. Scheduling Bag-of-Tasks in a dynamic multi-cloud environment is an NP-hard problem that has attracted a lot of attention in the last years. Such a problem can be modeled using a bi-level optimization framework due to its hierarchical scheme. Motivated by this issue, in this paper, an efficient bi-level multi-follower algorithm, based on hybrid metaheuristics, is proposed to solve the multi-objective budget-constrained dynamic Bag-of-Tasks scheduling problem in a heterogeneous multi-cloud environment. In our proposed model, the objective function differs depending on the scheduling level: The upper level aims to minimize the makespan of the whole Bag-of-Tasks under budget constraints; while each follower aims to minimize the makespan and the execution cost of tasks belonging to the Bag-of-Tasks. Since multiple conflicting objectives exist in the lower level, we propose an improved variant of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) called Efficient NSGA-II (E-NSGA-II), applying a recently proposed quick non-dominated sorting algorithm (QNDSA) with quasi-linear average time complexity. By performing experiments on proposed synthetic datasets, our algorithm demonstrates high performance in terms of makespan and execution cost while respecting budget constraints. Statistical analysis validates the outperformance of our proposal regarding the considering metrics.
Sofian Boutaib, Maha Elarbi, Slim Bechikh, , Lamjed Ben SaidA bi-level evolutionary approach for the multi-label detection of smelly classes
Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO), 2022
Abstract
This paper presents a new evolutionary method and tool called BMLDS (Bi-level Multi-Label Detection of Smells) that optimizes a population of classifier chains for the multi-label detection of smells. As the chain is sensitive to the labels’ (i.e., smell types) order, the chains induction task is framed as a bi-level optimization problem, where the upper-level role is to search for the optimal order of each considered chain while the lower-level one is to generate the chains. This allows taking into consideration the interactions between smells in the multi-label detection process. The statistical analysis of the experimental results reveals the merits of our proposal with respect to several existing works.
, Nadia Ben Azzouna, Rihab Abidi, , Lamjed Ben SaidSP-TRUST: a trust management model for speed trust in vehicular networks
International Journal of Computers and Applications, 44(11), 1065-1073., 2022
Abstract
Information security mechanisms are crucial for Vehicular Ad-hoc NETworks (VANET) applications in order to preserve their robustness. The efficiency of these applications relies on the reliability of the used information, especially the vehicle-related information such as location and speed. Trust management models are crucial to evaluate the quality of the used information. Accordingly, we focus in this paper on the assessment of speed information to detect the malicious vehicles using a fuzzy-based model (SP-TRUST). The proposed model relies on two fuzzy inference systems. The first one evaluates the speed trust based on traffic rules (inter-vehicle distance) while considering the road topology (angle deviation) and the traffic state (density). The correlation between these parameters and the speed value is assessed. The second inference system assesses the speed trust based on the behavior of neighbor vehicles using the median speed of vehicles. Simulations were carried out to evaluate the robustness and the scalability of SP-TRUST model. The results of the experimental studies proved that the model performs well in detecting different behaviors of malicious vehicles in different scenarios, especially when the percentage of malicious vehicles is lower than 50%.
Zahra Kodia, Lamjed Ben SaidStock market prediction of Nifty 50 index applying machine learning techniques
Applied Artificial Intelligence 36:1, 2022
Abstract
The stock market is viewed as an unpredictable, volatile, and competitive market. The prediction of stock prices has been a challenging task for many years. In fact, many analysts are highly interested in the research area of stock price prediction. Various forecasting methods can be categorized into linear and non-linear algorithms. In this paper, we offer an overview of the use of deep learning networks for the Indian National Stock Exchange time series analysis and prediction. The networks used are Recurrent Neural Network, Long Short-Term Memory Network, and Convolutional Neural Network to predict future trends of NIFTY 50 stock prices. Comparative analysis is done using different evaluation metrics. These analysis led us to identify the impact of feature selection process and hyper-parameter optimization on prediction quality and metrics used in the prediction of stock market performance and prices. The performance of the models was quantified using MSE metric. These errors in the LSTM model are found to be lower compared to RNN and CNN models.
