A Hybrid Method Based on Quantum-enhanced RNN and Data Integration for the Prediction of COVID-19 Outbreak

Due to the continuous spread of COVID-19 worldwide, it is urgent, especially in the data science era, to develop accurate data driven decision-aided method to early detect its outbreak. Currently, Deep Learning and especially Recurrent Neural Networks (RNN) are one of the promising methods to accurately predict COVID-19 outbreak. However, designing an accurate RNN is always a challenging task because RNN often require big data and computational cost. To overcome these challenges, we propose in this paper a novel method to predict daily COVID-19 positive cases that consists of two steps: 1) data integration where medical data and weather data are integrated to improve both data quantity and quality especially when we deal with countries with less facilities of collecting data and 2) quantum improvement where quantum and classical RNN are integrated to provide super-calculator for the prediction. Experiments on six countries from Africa (Tunisia, Algeria, Senegal, Cameron, Niger, and Nigeria) indicate two main results. First, through data integration, a correlation between medical and weather data is detected where we note a real impact of the weather on COVID-19 outbreak. Second, compared with classical RNN, quantum-enhanced RNN trained on augmented data achieved the best results in terms of accuracy as well as root mean square error (RMSE) and it required the lowest time for training. Thus, our main contributions are i) to enrich medical data by weather data to improve data quality and quantity and ii) to enhance RNN by quantum layers to accurately and speedily forecast COVID-19 outbreak. All implementations and datasets are available online to the scientific community at https://github.com/nasriAhmed/Master_Covid.git

Shape It Better than Skip It: Mapping the Territory of Quantum Computing and Its Transformative Potential

Quantum Computing (QC) is an emerging and fast-growing research field that combines computer science with quantum mechanics such as quantum superposition and quantum entanglement. In order to contribute to a clarification of this field, the objective of this paper is twofold. Firstly, it aims to map the territory in which most relevant QC researches, scientific communities and related domains are stated and its relationship with classical computing. Secondly, it aims to examine the future research agenda according to different perspectives. We will do so by conducting a systematic literature review (SLR) based on the most important databases from 2010 to 2022. Our findings demonstrate that there is still room for understanding QC and how it transforms business, society and learning

Systematic Literature Review on Sociotechnical Systems Resilience Assessment in a Holistic View

Sociotechnical systems (STS) refer to complex and large-scale systems that encompass interactions between society\u27s complex infrastructures and information technology. The purpose of this paper is to identify, study and compare research works related to the assessment of the resilience of these systems from an information systems perspective. We are interested in evaluating sociotechnical systems (STS) in general but we focus especially on their evolution during time and how to assess it. To this end, we conducted a systematic literature review (SLR) that has as output a list of models, approaches and methods of sociotechnical systems assessment. To compare these works, we used the systemic view criteria that aims to represent a system in a holistic point of view. Our main contribution is a detailed classification of the selected studies according to several criteria such as: the resilience as-assessment quantification, the considered systemic aspects in the assessment process and the studied domains. Our findings showed that the most assessment studies are qualitative and none of the studies assess an STS in a holistic view

Information Systems for Crisis Response and Management in Mediterranean Countries : Second International Conference, ISCRAM-med 2015, Tunis, Tunisia, October 28-30, 2015, Proceedings

International audienceno abstrac

Shape it better than skip it: mapping the territory of quantum computing and its transformative potential

International audienceQuantum Computing (QC) is an emerging and fast-growing research field that combines computer science with quantum mechanics such as quantum superposition and quantum entanglement. In order to contribute to a clarification of this field, the objective of this paper is twofold. Firstly, it aims to map the territory in which most relevant QC researches, scientific communities and related domains are stated and its relationship with classical computing. Secondly, it aims to examine the future research agenda according to different perspectives. We will do so by conducting a systematic literature review (SLR) based on the most important databases from 2010 to 2022. Our findings demonstrate that there is still room for understanding QC and how it transforms business, society and learning

A Meta-model for context-aware adaptive Business Process as a Service in collaborative cloud environment

International audienc

Information Systems for Crisis Response and Management in Mediterranean Countries : Third International Conference, ISCRAM-med 2016, Madrid, Spain, October 26-28, 2016, Proceedings

International audienceThis book constitutes the refereed proceedings of the Third International Conference on Information Systems for Crisis Response and Management in Mediterranean Countries, ISCRAM-med 2016, held in Madrid, Spain, in October 2016. Information systems and technologies can play a key role in crisis management in order to support preparation, response, mitigation and recovery processes. Yet technology is not enough to guarantee a better management process, and therefore the conference does not only focus on engineering technologies, but also on their application and practical experiences. The 12 full and 8 short papers presented in this volume were carefully reviewed and selected from 36 submissions. They are organized in topical sections on mobile apps for citizens, modeling and simulation, development of information systems, information and knowledge management, collaboration and coordination, social computing, and issues in humanitarian crisis

Towards Contextualizing Community Detection in Dynamic Social Networks

International audienceWith the growing number of users and the huge amount of information in dynamic social networks, contextualizing community detection has been a challenging task. Thus, modeling these social networks is a key issue for the process of contextualized community detection. In this work, we propose a temporal multiplex information graph-based model to represent dynamic social networks: we consider simultaneously the social network dynamicity, its structure (different social connections) and various members’ profiles so as to calculate similarities between “nodes” in each specific context. Finally a comparative study on a real social network shows the efficiency of our approach and illustrates practical uses