02140nas a2200157 4500008004100000245008800041210006900129260001200198300001000210490000700220520163500227100002401862700001901886700002101905856005601926 2020 eng d00aKnowledge-Intensive Decision Support System for Manufacturing Equipment Maintenance0 aKnowledgeIntensive Decision Support System for Manufacturing Equ c06/2020 a85-980 v183 a

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.

1 aBouhalouan, Djamila1 aNachet, Bakhta1 aAdla, Abdelkader uhttp://dline.info/fpaper/jdim/v18i3/jdimv18i3_1.pdf01334nas a2200133 4500008004100000245005900041210005500100260001200155300001000167490000700177520093900184100002101123856005601144 2020 eng d00a Real Estate Loan Knowledge-Based Recommender System-0 aReal Estate Loan KnowledgeBased Recommender System c04/2020 a65-770 v183 a

 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.

1 aAdla, Abdelkader uhttp://dline.info/fpaper/jdim/v18i2/jdimv18i2_3.pdf01671nas a2200145 4500008004100000245005500041210005200096260001200148300001000160490000700170520124300177100002101420700002801441856005601469 2019 eng d00aA CBR Decision Support System for Loan Evaluation-0 aCBR Decision Support System for Loan Evaluation c04/2019 a75-860 v173 a

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.

1 aAdla, Abdelkader1 aBenmessaoud, Noureddine uhttp://dline.info/fpaper/jdim/v17i2/jdimv17i2_3.pdf01840nas a2200205 4500008004100000245005400041210005200095260001200147300001000159490000700169520124300176653002501419653002601444653001701470653002601487653002301513100002101536700002801557856004901585 2019 eng d00aA CBR Decision Support System for Loan Evaluation0 aCBR Decision Support System for Loan Evaluation c04/2019 a75-860 v173 a

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.

10aCase-based Reasoning10aGroup Decision Making10aGroup Memory10aInformation retrieval10aRecommender system1 aAdla, Abdelkader1 aBenmessaoud, Noureddine uhttps://doi.org/10.6025/jdim/2019/17/2/75-8601755nas a2200145 4500008004100000245007700041210006900118260001200187300001000199490000700209520129500216100002101511700002101532856005601553 2018 eng d00aIntelligent Semantic Case Based Reasoning System for Fault Diagnosis –0 aIntelligent Semantic Case Based Reasoning System for Fault Diagn c04/2018 a53-630 v163 a

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.

1 aAdla, Abdelkader1 aBen Bella, Ahmed uhttp://dline.info/fpaper/jdim/v16i2/jdimv16i2_1.pdf00424nas a2200133 4500008004100000245007300041210006900114260001100183300001000194490000700204100002100211700002100232856003700253 2018 eng d00aIntelligent Semantic Case Based Reasoning System for Fault Diagnosis0 aIntelligent Semantic Case Based Reasoning System for Fault Diagn c042018 a53-630 v161 aAdla, Abdelkader1 aBen Bella, Ahmed uhttps://dline.info/newl/node/651