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2023Salah Ghodhbani, Sabeur Elkosantini
ADL based Framework For Multimodal Data Fusion in Traffic Jam prediction
In this paper, we propose new Hybrid method based on Deep Learning combine two independent model such as CNN, LSTM models to fuse multimodal and spatial temporal data. The proposed model uses Extended Kalman Filter (EKF) to combine result of the proposed, 2023
Abstract
Recently, intelligent transportation system (ITS) is considered as one of the most important issues in smart city applications. Its supports urban and regional development and promotes economic growth, social development, and enhances human well-being. ITS integrates new technologies of information and communication including sensors, social media IoT devices which can generate a massive amount of heterogeneous and multimodal data known as big data term. In this context, Data Fusion techniques (DF) seem promising and have emerged from transportation applications and hold a promising opportunity to deal with imperfect raw data for capturing reliable, valuable and accurate information. In literature many DF techniques based on machine learning remarkably renovates fusion techniques by offering the strong ability of computing and predicting. In this paper, we propose new Hybrid method based on Deep Learning combine two independent model such as CNN, LSTM models to fuse multimodal and spatial temporal data. The proposed model uses Extended Kalman Filter (EKF) to combine result of the proposed DL classifiers. In the other side, the proposed approach uses CBOA algorithm for feature selection in order to provide effective exploration of significant features with faster convergence
, , ,Effective healthcare service recommendation with network representation learning: A recursive neural network approach
Data & Knowledge Engineering, 2023
Abstract
Recently, recommender systems have been combined with healthcare systems to recommend needed healthcare items for both patients and medical staff. By monitoring the patients’ states, healthcare services and their consumed smart medical objects can be recommended to a medical team according to the patient’s critical situation and requirements. However, a common drawback of the few existing solutions lies in the limited modeling of the healthcare information network. In addition, current solutions do not consider the typed nature of healthcare items. Moreover, existing healthcare recommender systems lack flexibility, and none of them offers re-configurable healthcare workflows to medical staff. In this paper, we take advantage of collaborative filtering and representation learning principles, by proposing a method for the recommendation of healthcare services. These latter follow a predefined execution pattern, i.e. treatment/medication workflow, that is determined by our framework depending on the patient’s state. To achieve this goal, we model the healthcare information network as a knowledge graph. This latter, based on an incremental learning method, is then transformed into a cuboid space to facilitate its processing. That is by learning latent representations of its content (e.g., smart objects, healthcare services, patients symptoms, etc.). Finally, a collaborative recommendation method is defined to select the high-quality healthcare services that will be composed and executed according to a determined workflow model. Experimental results have proven the efficiency of our solution in terms of recommended services’ quality.
, , , , ,An Evidence Theory Based Embedding Model for the Management of Smart Water Environments
Sensors, 2023
Abstract
Having access to safe water and using it properly is crucial for human well-being, sustainable development, and environmental conservation. Nonetheless, the increasing disparity between human demands and natural freshwater resources is causing water scarcity, negatively impacting agricultural and industrial efficiency, and giving rise to numerous social and economic issues. Understanding and managing the causes of water scarcity and water quality degradation are essential steps toward more sustainable water management and use. In this context, continuous Internet of Things (IoT)-based water measurements are becoming increasingly crucial in environmental monitoring. However, these measurements are plagued by uncertainty issues that, if not handled correctly, can introduce bias and inaccuracy into our analysis, decision-making processes, and results. To cope with uncertainty issues related to sensed water data, we propose combining network representation learning with uncertainty handling methods to ensure rigorous and efficient modeling management of water resources. The proposed approach involves accounting for uncertainties in the water information system by leveraging probabilistic techniques and network representation learning. It creates a probabilistic embedding of the network, enabling the classification of uncertain representations of water information entities, and applies evidence theory to enable decision making that is aware of uncertainties, ultimately choosing appropriate management strategies for affected water areas.
Web service adaptation: A decade’s overview
Computer Science Review, 2023
Abstract
With the exponential growth of communication and information technologies, adaptation has gained a significant attention as it becomes a key feature of service-based systems, allowing them to operate and evolve in highly dynamic and uncertain environments. Although several Web service standards and frameworks have been proposed and extended, existing solutions do not provide a suitable architecture, in which all aspects of monitoring and adaptation (e.g., proactive, cross-layer, and autonomic adaptation) can be expressed. In addition, the emergence of new computing environments to host and execute various types of services (Web/cloud services, big data-intensive services, mobile services, microservices, etc.) raises the need for more efficient monitoring and adaptation systems. This survey aims to bring a synthesis and a road-map to the adaptation of service-based systems. We also discuss adaptation solutions in emerging service models, such as cloud services and big services. Based on an adaptation taxonomy which we extracted from the surveyed approaches, and by identifying the main requirements and goals of service adaptation in Web, cloud and big data environments, detailed analysis and discussions, as well as the open issues, are provided.
, Atef DridiAn Improved Tabu Search Algorithm for the Green Vehicle Routing Problem
In 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 938-943). IEEE., 2023
Abstract
This paper addresses the Green Vehicle Routing Problem (GVRP) which is a variant of the VRP that uses alternative fuel vehicles (AFVs) to perform routes. Since AFVs have limited fuel tank capacity, refueling among the routes at alternative fuel stations (AFSs) is considered. To solve the problem, we propose an improved version of the tabu search metaheuristic. The proposed algorithm relies on two main components: a local search component and a perturbation component. Computational results show that our approach is highly effective in terms of solution quality and CPU time.
, Saoussen Bel Haj Kacem, Sabeur Elkosantini, ,On the Different Concepts and Taxonomies of eXplainable Artificial Intelligence
In : International Conference on Intelligent Systems and Pattern Recognition. Cham : Springer Nature, 2023, 75-85., 2023
Abstract
Presently, Artificial Intelligence (AI) has seen a significant shift in focus towards the design and development of interpretable or explainable intelligent systems. This shift was boosted by the fact that AI and especially the Machine Learning (ML) field models are, currently, more complex to understand due to the large amount of the treated data. However, the interchangeable misuse of XAI concepts mainly “interpretability” and “explainability” was a hindrance to the establishment of common grounds for them. Hence, given the importance of this domain, we present an overview on XAI, in this paper, in which we focus on clarifying its misused concepts. We also present the interpretability levels, some taxonomies of the literature on XAI techniques as well as some recent XAI applications.
Hamdi Ouechtati, Nadia Ben Azzouna, Lamjed Ben SaidA fuzzy logic-based model for filtering dishonest recommendations in the Social Internet of Things
Journal of Ambient Intelligence and Humanized Computing, 14(5), 6181-6200., 2023
Abstract
In the recent year, Internet of Things (IoT) has been adopted in several real-world applications such as smart transportation, smart city, retail, agriculture, smart factory, etc. to make human life more reliable. The integration of social networking concepts into the IoT led to the rise of a new paradigm: the Social Internet of Things (SIoT). In the SIoT environment, the objects are capable of establishing in an autonomous way many social relationships anywhere and anytime with other trusted objects. However, in such environment, objects may provide dishonest recommendations due to malicious reasons such as bad mouthing, ballot stuffing, random opinion, etc. In order to cater these challenges, we propose a new fuzzy logic-based model to filter dishonest recommendations and estimate their trust level based on (1) their values and sending time and the place coordinates and (2) the social relationship parameters of the recommenders. Results prove that our proposed approach is able to detect 100% of the fake Sybil attack and achieves 100% of Recognition Proportion, Sensitivity, Specificity, Accuracy and F1 score and gets 0% of False Negative and False Positive Proportions in presence of up to 90% dishonest recommendations.
, Lilia Rejeb,Tailoring project management practices for decision making: an in-depth comparative study
International Journal of Project Organisation and ManagementVol. 15, No. 2, pp 158-183. DOI: 10.1504/IJPOM.2023.131677, 2023
Abstract
The efforts to successfully complete projects lead to the development of various project management (PM) standards, best practices and guidelines, issued by different organisational bodies. These PM practices, when appropriately implemented, lead to a better project performance. However, studies on how to adopt and adapt such practices according to management needs, remain limited. In this paper, we are going to focus on how to map the project requirements with the suitable PM practices to support the PM decision making. To respond this question, we put forward an in-depth comparative study amongst the well-established PM practices considering a set of features to pick out their challenges, limits as well as their applicability. Through this comparison, we extend the discussion of PM practices features by contrasting them to three distinct categories of requirements which are technical, contextual and behavioural. This analysis allows us to map each category to its corresponding practice(s). Our finding provides comprehensive recommendation guidelines to both practitioners and researchers in order to improve their decision making in line with the project environment.
Abir Chaabani, Mouna Karaja, Lamjed Ben SaidAn Efficient Non-dominated Sorting Genetic Algorithm For Multi-objective Optimization.
9th International Conference on Control, Decision and Information Technologies, CoDIT 2023, Rome, Italy., 2023
Abstract
Multi-Objective Evolutionary Algorithms (MOEAs) is actually one of the most attractive and active research field in computer science. Significant research has been conducted in handling complex multi-objective optimization problems within this research area. The Non-Dominated Sorting Genetic Algorithm (NSGA-II) has garnered significant attention in various domains, emphasizing its specific popularity. However, the complexity of this algorithm is found to be O(MN2) with M objectives and N solutions, which is considered computationally demanding. In this paper, we are proposing a new variant of NSGA-II termed (Efficient-NSGA-II) based on our recently proposed quick non-dominated sorting algorithm with quasi-linear average time complexity; thereby making the NSGA-II algorithm efficient from a computational cost viewpoint. Experiments demonstrate that the improved version of the algorithm is indeed much faster than the previous one. Moreover, comparisons results against other multi-objective algorithms on a variety of benchmark problems show the effectiveness and the efficiency of this multi-objective version.
Samira Harrabi, Ines Ben Jaafar,Survey on IoV Routing Protocols
Wireless Personal Communications 128(1), 2023
Abstract
Internet of vehicles (IoV) can be considered as a superset of vehicular ad-hoc networks (VANETs). It extends VANET’s structure, applications and scale. Unlike, the traditional intelligent transportation system (ITS), IoV focus more on information interactions between vehicles, roadside units (RSU) and humans. The principal aim is to make people obtain road traffic information easily and in real-time, to ensure the travel convenience, and to increase the travel comfort. The goal behind the Internet of vehicles is essentially to be used in urban traffic environment to ensure network access for passengers and drivers. The environment of the IoV is the combination of different wireless network environment as well as road conditions. Despite its continuing expansion, the IOV contains different radio access technologies that lead to a heterogeneous network, and make it more crucial than the VANET. These drawbacks pose numerous challenges, especially the routing one. In IoV environment, the routing protocol must cope with events such as link failure and to find the best route to propagate the data toward the desired destination. In this paper, we mainly focus on surveying the IoV routing protocols, hence we present and compare unicast, multicast and broadcast protocols.
