Henda Ben Ghézala

Safa Ben Atitallah, Maha Driss, Henda Ben Ghezala

Deep learning-based disease diagnosis applications are essential for accurate diagnosis at various disease stages. However, using personal data exposes traditional centralized learning systems to privacy concerns. On the other hand, by positioning processing resources closer to the device and enabling more effective data analyses, a distributed computing paradigm has the potential to revolutionize disease diagnosis. Scalable architectures for data analytics are also crucial in healthcare, where data analytics results must have low latency and high dependability and reliability. This study proposes a microservices-based approach for IoT data analytics systems to satisfy privacy and performance requirements by arranging entities into fine-grained, loosely connected, and reusable collections. Our approach relies on federated learning, which can increase disease diagnosis accuracy while protecting data privacy. Additionally, we employ transfer learning to obtain more efficient models. Using more than 5800 chest X-ray images for pneumonia detection from a publicly available dataset, we ran experiments to assess the effectiveness of our approach. Our experiments reveal that our approach performs better in identifying pneumonia than other cutting-edge technologies, demonstrating our approach's promising potential detection performance.

Safa Ben Atitallah, Maha Driss, Wadii Boulila et al.

By the start of 2020, the novel coronavirus disease (COVID-19) has been declared a worldwide pandemic. Because of the severity of this infectious disease, several kinds of research have focused on combatting its ongoing spread. One potential solution to detect COVID-19 is by analyzing the chest X-ray images using Deep Learning (DL) models. In this context, Convolutional Neural Networks (CNNs) are presented as efficient techniques for early diagnosis. In this study, we propose a novel randomly initialized CNN architecture for the recognition of COVID-19. This network consists of a set of different-sized hidden layers created from scratch. The performance of this network is evaluated through two public datasets, which are the COVIDx and the enhanced COVID-19 datasets. Both of these datasets consist of 3 different classes of images: COVID19, pneumonia, and normal chest X-ray images. The proposed CNN model yields encouraging results with 94% and 99% of accuracy for COVIDx and enhanced COVID-19 dataset, respectively.

Safa Ben Atitallah, Maha Driss, Wadii Boulila et al.

Recently, Convolutional Neural Networks (CNNs) have made a great performance for remote sensing image classification. Plant recognition using CNNs is one of the active deep learning research topics due to its added-value in different related fields, especially environmental conservation and natural areas preservation. Automatic recognition of plants in protected areas helps in the surveillance process of these zones and ensures the sustainability of their ecosystems. In this work, we propose an Enhanced Randomly Initialized Convolutional Neural Network (ERI-CNN) for the recognition of columnar cactus, which is an endemic plant that exists in the Tehuacán-Cuicatlán Valley in southeastern Mexico. We used a public dataset created by a group of researchers that consists of more than 20000 remote sensing images. The experimental results confirm the effectiveness of the proposed model compared to other models reported in the literature like InceptionV3 and the modified LeNet-5 CNN. Our ERI-CNN provides 98% of accuracy, 97% of precision, 97% of recall, 97.5% as f1-score, and 0.056 loss.

Ahmed Maalel, Henda Ben Ghézala

The coordination and cooperation of all the stakeholders involved is a decisive point for the control and the resolution of problems. In the insecurity events, the resolution should refer to a plan that defines a general framework of the procedures to be undertaken and the instructions to be complied with; also, a more precise process must be defined by the actors to deal with the case represented by the particular problem of the current situation. Indeed, this process has to cope with a dynamic, unstable and unpredictable environment, due to the heterogeneity and multiplicity of stakeholders, and finally due to their possible geographical distribution. In this article, we will present the first steps of validation of a collaborative decision-making approach in the context of crisis situations such as road accidents. This approach is based on ontologies and multi-agent systems.

Kamel Karoui, Fakher Ben Ftima, Henda Ben Ghezala

To supervise and guarantee a network security, the administrator uses different security components, such as firewalls, IDS and IPS. For a perfect interoperability between these components, they must be configured properly to avoid misconfiguration between them. Nevertheless, the existence of a set of anomalies between filtering rules and alerting rules, particularly in distributed multi-component architectures is very likely to degrade the network security. The main objective of this paper is to check if a set of security components are interoperable. A case study using a firewall and an IDS as examples will illustrate the usefulness of our approach.

Nesrine Ben Yahia, Narjès Bellamine, Henda Ben Ghezala

Decision making whenever and wherever it is happened is key to organizations success. In order to make correct decision, individuals, teams and organizations need both knowledge management (to manage content) and collaboration (to manage group processes) to make that more effective and efficient. In this paper, we explain the knowledge management and collaboration convergence. Then, we propose a formal description of mixed and multimodal decision making (MDM) process where decision may be made by three possible modes: individual, collective or hybrid. Finally, we explicit the MDM process based on UML-G profile.

Nesrine Ben yahia, Narjès Bellamine, Henda Ben Ghézala

Collaboration technology typically focuses on collaboration and group processes (cooperation, communication, coordination and coproduction). Knowledge Management (KM) technology typically focuses on content (creation, storage, sharing and use of data, information and knowledge). Yet, to achieve their common goals, teams and organizations need both KM and collaboration technology to make that more effective and efficient. This paper is interested in knowledge management and collaboration regarding their convergence and their integration. First, it contributes to a better understanding of the knowledge management and collaboration concepts. Second, it focuses on KM and collaboration convergence by presenting the different interpretation of this convergence. Third, this paper proposes a generic framework of collaborative knowledge management.