Papers by Mounir Ben Ayed
Mobile Cloud Computing in Healthcare System
Lecture Notes in Computer Science, 2015
A quality model for the evaluation of decision support systems based on a knowledge discovery from data process
Journal of Decision Systems, 2016
Adaptive security for Cloud data warehouse as a service
2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS), 2015
Cloud computing and mobile devices based system for healthcare application
2015 IEEE International Symposium on Technology and Society (ISTAS), 2015
Multi-agent Architecture for Visual Intelligent Remote Healthcare Monitoring System
Advances in Intelligent Systems and Computing, 2015
Combination of cognitive and HCI modeling for the design of KDD-based DSS used in dynamic situations
Decision Support Systems, 2015
Context Aware Criteria for the Evaluation of Mobile Decision Support Systems
ABSTRACT
Towards a quality model for the evaluation of Mobile Decision Support Systems
2015 4th International Conference on Advanced Logistics and Transport (ICALT), 2015
A Multi Agent System for Hospital Organization
International Journal of Machine Learning and Computing, 2015
Évaluation Des Algorithmes D'Apprentissage De Structure Pour Les Réseaux Bayésiens Dynamiques
The propagation and convergence of wireless communications, Internet, and mobile devices has give... more The propagation and convergence of wireless communications, Internet, and mobile devices has given rise to new types of decision support utilities commonly referred to as Mobile Decision Support Systems (MDSS). Drawing on technology acceptance and decision-making theories, this study explores critical factors in the use and the performance of MDSS. Our goal is to develop a methodological study in the field of mobile decision support systems evaluation. This domain is divided to several fields such as Human-Computer Interaction, mobile application, Knowledge Discovery from Data process and quality of information systems.
An Adaptive Multi-Agent System for Ontology Co-evolution
Proceedings of the International Conference on Agents and Artificial Intelligence, 2015
ABSTRACT A dynamic ontology evolution reflects the ontology adaptation, to a set of changes and t... more ABSTRACT A dynamic ontology evolution reflects the ontology adaptation, to a set of changes and their propagation to the other dependent components, to ensure its consistency. This process needs a frequent involvment of the user (ontologist), which is a complex and time consuming task. As a solution, in this paper we present an extension of an ontology evolution tool called DYNAMO MAS based on an adaptive multi-agent system (AMAS). We improve agents by adding new behaviour to adapt to ontologist actions in order to improve the proposals already made and to propose others.
Towards a dynamic knowledge base based on ontology for clinical decision support system
2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR), 2014
ABSTRACT Currently, due to the complexity of problems, several researchs are interested in develo... more ABSTRACT Currently, due to the complexity of problems, several researchs are interested in developing solutions for making decision strategies. The evolution of decision support systems (DSS) showed that there is an evolution of DSS from a classical one to an intelligent DSS or knowledge based DSS. Some of these knowledge can be generated using the Knowledge Discovery from Data process (KDD). Our research focus on clinical DSS based on KDD. KDD based DSS was developed to allow, doctors in the Intensive Care Unit, predicting and reventing nosocomial infections. The last phase of KDD process is to manage the extracted knowledge and choose the appropriate decision. Since KDD is an iterative process, results can be refined by repeating the various stages many times under the control of an expert. Thus, there is a need to preserve these knowledge, in a dynamic ontology, with the suggested solutions in order to automate decision making operation with the updated knowledge and consequently obtain a DSS totally based on KDD.
An agent-based Knowledge Discovery from Databases applied in healthcare domain
2013 International Conference on Advanced Logistics and Transport, 2013
ABSTRACT Knowledge Discovery from Databases (KDD) process is complex, iterative and interactive. ... more ABSTRACT Knowledge Discovery from Databases (KDD) process is complex, iterative and interactive. It takes place several phases. For its implementation, several modules should be developed (module for data storage, module for processing data, data mining module, evaluation module, knowledge management module). The objective of this study is to propose an approach which assimilates every module to an agent. These agents have to communicate and cooperate to help the user to make the most appropriate decision. Thus, The process of KDD can be likened to a Multi-Agent System (MAS). To validate our approach, we have applied a process of KDD for the fight against nosocomial infections within an intensive care unit (ICU) of a University hospital. On a technical level, we have developed a software tool for decision-making support in Java/XML through the agent platform ”Madkit”.
Clinical Dynamic Decision Support System based on temporal association rules
2nd Middle East Conference on Biomedical Engineering, 2014
ABSTRACT Nosocomial Infections (NI) have been the major causes of morbidity and mortality of pati... more ABSTRACT Nosocomial Infections (NI) have been the major causes of morbidity and mortality of patients in intensive care units (ICUs) particularly in developing countries. Intensive surveillance and preventive measures is an effective element to fight against NI. Based on the temporal data recorded daily in the intensive care unit (ICU) and the help of some physicians, we plan to develop a Clinical Dynamic Decision Support System (CDDSS) based on knowledge discovery in databases (KDD) to help Physicians to predict and prevent NI. The CDDSS aims to the daily estimation of the NI occurrence probability, in the ICU patient hospitalization. The goal is to be able to anticipate if the association of some factors will support the appearance of the infections on the basis of patient histories. We propose to develop an algorithm for mining temporal association rules to extract temporal information. The discovery of temporal pattern would help them to take measures at time.
Multi-data source fusion agent based method for ECG classification
2013 International Conference on Computer Applications Technology (ICCAT), 2013
ABSTRACT In this paper, we purpose to develop a system to aid in the diagnosis of anomalies cardi... more ABSTRACT In this paper, we purpose to develop a system to aid in the diagnosis of anomalies cardiac signals (ECG). This system is based on data fusion and architected by using the multi-agents system for ECG classification. Therefore, the proposed system helps doctors to quickly and precisely diagnose a heart disease by examining only the class of the ECG beats. This system is tested on a MIT-BIH arrhythmia database.
Swarm intelligence and multi agent system in healthcare
2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR), 2014
ABSTRACT The domain of Healthcare is characterized by difficulty, dynamism and variety. In the 21... more ABSTRACT The domain of Healthcare is characterized by difficulty, dynamism and variety. In the 21st century healthcare represents different challenges (the increasing cost of care and the growing of populations). For that, Agent Technology can provide better healthcare than the traditional medical system. In the hospital, several types of medical problems can be solved by agents. As examples of problems, which emerge in the hospital, we mention: collaboration between hospital wards, elaborations of diagnostics, the collection of information about patients etc. The adaptation of cooperative Multi Agent System (MAS) can solve these problems. In this regard, this study proposes a general architecture that integrates Swarm Intelligence into Multi Agent healthcare System in order to make care as efficient as possible.
Perspective Wall Technique for Visualizing and Interpreting Medical Data
International Journal of Knowledge Discovery in Bioinformatics, 2012
ABSTRACT Increasing the improvement of confidence and comprehensibility of medical data as well a... more ABSTRACT Increasing the improvement of confidence and comprehensibility of medical data as well as the possibility of using the human capacities in medical pattern recognition is a significant interest for the coming years. In this context, we have created a visual knowledge discovery from databases application. It has been developed to efficiently and accurately understand a large collection of fixed and temporal patients' data in the Intensive Care Unit in order to prevent the nosocomial infection occurrence. It is based on data visualization technique which is the perspective wall. Its application is a good example of the usefulness of data visualization techniques in the medical domain.
Approach for the evaluation of a KDD based DSS visual representations
2nd Middle East Conference on Biomedical Engineering, 2014
ABSTRACT Due to the progressing growth of temporal data, data analysis and exploration become mor... more ABSTRACT Due to the progressing growth of temporal data, data analysis and exploration become more and more difficult task; particularly in the medical domain. For this reason, it seems important to use Decision Support System (DSS) based on the decisional tool Knowledge Discovery in Data (KDD) and the visualization techniques for assisting user to get and understand information. Our research is based on the visual representations of an existing KDD based DSS for the fight against nosocomial infections. In this paper, we are interested in proposing a method, focusing on the user evaluation of this system.
Using dynamic Bayesian networks for the prediction of mental deficiency in children with down syndrome
2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR), 2014
ABSTRACT This work is located in the domain of the Knowledge Discovery from Data (KDD). The purpo... more ABSTRACT This work is located in the domain of the Knowledge Discovery from Data (KDD). The purpose of the KDD is the extraction of knowledge or of a knowledge starting from great number of data which evolve in a dynamic way. In this work we propose an approach for the temporal KDD. The Bayesian Network (BN) is one of the techniques used in KDD. Our objective comes back to fix the best algorithm of incremental learning of structure extracted by the Dynamic Bayesian Network (DBN) and using it in the decision making in a dynamic way. Our scope of application is the case of Down Syndrome (DS) also known as trisomy 21, the data are provided by the medical genetics and Child Psychiatry units of the university hospital Hedi Chaker Sfax, Tunisia.
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Papers by Mounir Ben Ayed