@article {779, title = {Knowledge-Intensive Decision Support System for Manufacturing Equipment Maintenance}, journal = {Journal of Digital Information Management}, volume = {18}, year = {2020}, month = {06/2020}, pages = {85-98}, type = {Research}, chapter = {85}, abstract = {

To ensure continuous production in industrial plants, the high valued manufacturing eqipments should be kept in good working conditions. This brings plants to search for means to control and reduce equipment failures. When faults emerge in plants, appropriate actions for fault diagnosis and reparation must be executed promptly and effectively to prevent large costs due to breakdowns. To provide reliable and effective maintenance support, the aid of advanced decision support technology utilizing previous repair experience is of crucial importance for the expert operators as it provides them valuable troubleshooting clues for new faults. Artificial intelligence (AI) technology, particularly, knowledge-based approach is promising for this domain. It captures efficiency of problem solving expertise from the domain experts; guides the expert operators in rapid fault detection and maintenance. This paper focuses on the design and development of a Knowledge-Intensive Decision Support System (KI-DSS) for Manufacturing Equipment Maintenance in industrial plants to support better maintenance decision and improve maintenance efficiency. With integration of case based Reasoning and ontology, the Ki-DSS not only carries out data matching retrieval, but also performs semantic associated data access which is important for intelligent knowledge retrieval in decision support system. A case is executed to illustrate the use of the proposed KI-DSS to show the feasibility of our approach and the benefit of the ontology support.

}, doi = {https://doi.org/10.6025/jdim/2020/18/3/85-98}, url = {http://dline.info/fpaper/jdim/v18i3/jdimv18i3_1.pdf}, author = {Djamila Bouhalouan and Bakhta Nachet and Abdelkader Adla} } @article {784, title = { Real Estate Loan Knowledge-Based Recommender System-}, journal = {Journal of Digital Information Management}, volume = {18}, year = {2020}, month = {04/2020}, pages = {65-77}, type = {Research}, chapter = {65}, abstract = {

\ In decision making, the decision-makers frequently employ and perform routine tasks. These processes normally are time-intensive, complex, and in most cases occur regularly. To address this challenge decision makers reuse the already successful decisions. During difficult times, such actions may lead to save time, energy and man-hours, and also result in effective decision making. Memory building depends on how we successfully store earlier knowledge. We through this work introduce a recommender system which is names as BLKBRS which utilized the earlier successful models. In this work we use a case of bank loan and experimented using a semi-structured multiple attribute recommendation environment, and equate the RL-KBRS with a conventional case based reasoning system. RL-KBRS will compensate for lack of experience of young bank consultants, which permits the spread of knowledge distribution to other banks.

}, doi = {https://doi.org/10.6025/jdim/2020/18/2/65-77}, url = {http://dline.info/fpaper/jdim/v18i2/jdimv18i2_3.pdf}, author = {Abdelkader Adla} } @article {807, title = {A CBR Decision Support System for Loan Evaluation-}, journal = {Journal of Digital Information Management}, volume = {17}, year = {2019}, month = {04/2019}, pages = {75-86}, type = {Research}, chapter = {75}, abstract = {

Group decision making is time-consuming, complex, critical and recurring in nature. In group decision making, the decision-makers often perform periodic and repetitive activities. This prompts the decision makers to store their knowledge and their experience invoked in prior problem solving sessions. The development of this group knowledge clearly assists in similar later decision-making activities and reduces the time required to come to a decision, particularly, in a critical situation. This shared knowledge constitutes the group memory. In this paper, we develop a Case-Based Reasoning Decision Support System called CBR-DSS that makes use of the rules, policies and standard procedures of prior groups embedded in the group memory. The CBR process is evolved by using the Analytic Hierarchy Process (AHP). In an effort to enhance utility and show the feasibility, we experiment our approach in a bank loan analysis domain, a semi-structured multiple attributes decision-making environment, and compare CBR-DSS with a conventional case based reasoning system. CBR-DSS will compensate for lack of experience of young bank consultants, and allow disseminating and distributing available experience to different bank sites.

}, doi = {https://doi.org/10.6025/jdim/2019/17/2/75-86}, url = {http://dline.info/fpaper/jdim/v17i2/jdimv17i2_3.pdf}, author = {Abdelkader Adla and Noureddine Benmessaoud} } @article {712, title = {A CBR Decision Support System for Loan Evaluation}, journal = {Journal of Digital Information Management}, volume = {17}, year = {2019}, month = {04/2019}, pages = {75-86}, type = {Research}, chapter = {75}, abstract = {

Group decision making is time-consuming, complex, critical and recurring in nature. In group decision making, the decision-makers often perform periodic and repetitive activities. This prompts the decision makers to store their knowledge and their experience invoked in prior problem solving sessions. The development of this group knowledge clearly assists in similar later decision-making activities and reduces the time required to come to a decision, particularly, in a critical situation. This shared knowledge constitutes the group memory. In this paper, we develop a Case-Based Reasoning Decision Support System called CBR-DSS that makes use of the rules, policies and standard procedures of prior groups embedded in the group memory. The CBR process is evolved by using the Analytic Hierarchy Process (AHP). In an effort to enhance utility and show the feasibility, we experiment our approach in a bank loan analysis domain, a semi-structured multiple attributes decision-making environment, and compare CBR-DSS with a conventional case based reasoning system. CBR-DSS will compensate for lack of experience of young bank consultants, and allow disseminating and distributing available experience to different bank sites.

}, keywords = {Case-based Reasoning, Group Decision Making, Group Memory, Information retrieval, Recommender system}, url = {https://doi.org/10.6025/jdim/2019/17/2/75-86}, author = {Abdelkader Adla and Noureddine Benmessaoud} } @article {829, title = {Intelligent Semantic Case Based Reasoning System for Fault Diagnosis {\textendash}}, journal = {Journal of Digital Information Management}, volume = {16}, year = {2018}, month = {04/2018}, pages = {53-63}, type = {Research}, chapter = {53}, abstract = {

Case-Based Reasoning (CBR) often shows significant promise for improving the effectiveness of decision support systems. Previous research in decision making has proposed the use of a CBR approach to accumulate, organize, preserve, link and share diverse knowledge coming from past experiences. However, existing CBR systems lack semantic understanding, which is important for intelligent knowledge retrieval in decision support systems. In order to overcome the limitation of existing CBR and develop an intelligent CBR system which can not only carry out data matching retrieval, but also perform semantic associated data access, and improve the traditional keyword-based search, this paper integrates ontology technology into a CBR system and proposes to combine semantic retrieval method and numerical measurement in case retrieval. Ontology technology is an ideal selection for realizing our system thanks to the good semantic understanding offered by ontology. The resulting ontology based CBR system is applied in fault diagnosis of industrial machines, a semi-structured decision-making environment involving multiple attributes. The case illustrates the use of proposed semantic CBR system, and shows the feasibility of our approach and the benefit of the ontology support.

}, url = {http://dline.info/fpaper/jdim/v16i2/jdimv16i2_1.pdf}, author = {Abdelkader Adla and Ahmed Ben Bella} } @article {651, title = {Intelligent Semantic Case Based Reasoning System for Fault Diagnosis}, journal = {Journal of Digital Information Management}, volume = {16}, year = {2018}, month = {042018}, pages = {53-63}, type = {Research}, author = {Abdelkader Adla and Ahmed Ben Bella} }