Integrating Agents into a Collaborative Knowledge-based System for Business Rules Consistency Management

Capitalization and reuse of expert knowledge are very important for the survival of an enterprise. This paper presents a collaborative approach that utilizes domain ontology and agents. Thanks to our knowledge formalizing process, we give to domain expert an opportunity to store different forms of retrieved knowledge from experiences, design rules, business rules, decision processes, etc. The ontology is built to support business rules management. The global architecture is mainly composed of agents such as Expert agent, Evaluator agent, Translator agent, Security agent and Supervisor agent. The Evaluator agent is at the heart of our functional architecture, its role is to detect the problems that may arise in the consistency management module and provides a solution to these problems in order to validate the accuracy of business rules. In addition, a Security agent is defined to handle both security aspects in rules modeling and multi-agent system. The proposed approach is different from the others in terms of the number of rule’s inconsistencies which are detected and treated like contradiction, redundancy, invalid rules, domain violation and rules never applicable, the collaboration that is initiated among business experts and the guarantee of security of the business rules and all the agents which constitute our system. The developed collaborative system is applied in an industrial case study.

An Effective Tool for the Experts' Recommendation Based on PROMETHEE II and Negotiation: Application to the Industrial Maintenance

In this article, we propose an expert recommendation tool that relies on the skills of experts and their interventions in collaboration. This tool provides us with a list of the most appropriate (effective) experts to solve business problems in the field of industrial maintenance. The proposed system recommends experts using an unsupervised classification algorithm that takes into account the competences of the experts, their preferences and the stored information in previous collaborative sessions. We have tested the performance of the system with K-means and C-means algorithms. To fix the inconsistencies detected in business rules, the PROMETHEE II multi-criteria decision support method is integrated into the extended CNP negotiation protocol in order to classify the experts from best to worst. The study is supported by the well known petroleum company in Algeria namely SONATRACH where the experimentations are operated on maintenance domain. Experiments results show the effectiveness of our approach, obtaining a recall of 86%, precision of 92% and F-measure of 89%. Also, the proposed approach offers very high results and improvement, in terms of response time (154.28 ms), space memory (9843912 bytes) and negotiation rounds

Integrating Agents into a Collaborative Knowledge-based System for Business Rules Consistency Management

Capitalization and reuse of expert knowledge are very important for the survival of an enterprise. This paper presents a collaborative approach that utilizes domain ontology and agents. Thanks to our knowledge formalizing process, we give to domain expert an opportunity to store different forms of retrieved knowledge from experiences, design rules, business rules, decision processes, etc. The ontology is built to support business rules management. The global architecture is mainly composed of agents such as Expert agent, Evaluator agent, Translator agent, Security agent and Supervisor agent. The Evaluator agent is at the heart of our functional architecture, its role is to detect the problems that may arise in the consistency management module and provides a solution to these problems in order to validate the accuracy of business rules. In addition, a Security agent is defined to handle both security aspects in rules modeling and multi-agent system. The proposed approach is different from the others in terms of the number of rule’s inconsistencies which are detected and treated like contradiction, redundancy, invalid rules, domain violation and rules never applicable, the collaboration that is initiated among business experts and the guarantee of security of the business rules and all the agents which constitute our system. The developed collaborative system is applied in an industrial case study.

Automated Diabetes Disease Prediction System based on Risk Factors Assessment: Taking Charge of Your Health

Diabetes is one of the most common diseases worldwide, and its prevalence rate continues to rise. This increase is due to factors related to nutrition and lifestyle on the one hand, and to genetic factors on the other hand, thus creating a real public health problem. Therefore, it is crucial to identify diabetes early in order to allow rapid treatment, capable of slowing down the progression of the disease. The objective of this work is to propose an automatic diabetes prediction system based on the following machine learning techniques: SVM, KNN, Decision Tree and Logistic Regression. Using risk factors specific to the Algerian environment, we constructed a new dataset that includes 823 patients, with 418 being diabetic and 405 being non-diabetic. In order to choose the relevant features and identify the most informative risk factors, we combined several feature extraction methods such as ANalysis Of Variance (ANOVA), Recursive Feature Elimination (RFE) and we used also the features proposed by the Pima Indian Diabetes Dataset (PIDD). The results of this study provided valuable information on the comparative performance of different machine learning models in the prediction of diabetes, as well as on the importance of the selected characteristics. [JJCIT 2023; 9(4.000): 377-394