{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:51:34Z","timestamp":1781110294158,"version":"3.54.1"},"reference-count":14,"publisher":"IGI Global Scientific Publishing","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:p>This paper presents a rule structure called Concept Based Censor Production Rule (CBCPR) that deals with real time cases. CBCPR is an extension of a rule structure called Censored Production Rule (CPR). CPR is a standard rule structure with UNLESS slot, which contains various censor conditions that might rarely happen and prevent the action of the rule to be taken. The more time one has, the more censor conditions one can check. The major extension of CPR is by concentrating on what is called concept. The concept is what about the user needs the decision. Each rule will have a certain concept title that specifies its job. In addition, in every CBCPR structure, at least one slot related to UNLESS part in the rule is existing, where each UNLESS slot is related to a certain category having censor conditions concerned with the concept. The structure will help the system to give more certain answers within the given time for the real-time systems instead of keep checking unnecessary censor conditions for the same concept of different UNLESS categories.<\/jats:p>","DOI":"10.4018\/ijdsst.2018010104","type":"journal-article","created":{"date-parts":[[2017,10,17]],"date-time":"2017-10-17T13:27:58Z","timestamp":1508246878000},"page":"59-67","source":"Crossref","is-referenced-by-count":2,"title":["Concept Based Censor Production Rules"],"prefix":"10.4018","volume":"10","author":[{"given":"Nabil M.","family":"Hewahi","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Bahrain, Alsakheer, Bahrain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJDSST.2018010104-0","doi-asserted-by":"publisher","DOI":"10.1016\/0169-023X(92)90003-T"},{"key":"IJDSST.2018010104-1","doi-asserted-by":"crossref","unstructured":"Bharadwaj, K., Hewahi, N., & Brando, M. (1996), Adaptive hierarchical censored production rule-based system: A genetic algorithm approach. In Advances in Artificial Intelligence, LNAI (Vol. 1159, pp. 81-90). Springer-Verlag.","DOI":"10.1007\/3-540-61859-7_9"},{"key":"IJDSST.2018010104-2","doi-asserted-by":"crossref","unstructured":"Bharadwaj, K., & Silva, J. (1998). Towards integrating hierarchical censored production rule (HCPR) based system and neural networks. In F. Oliveira (Ed.), Advances in Artificial Intelligence, LNAI (Vol. 1515, pp. 121-130).","DOI":"10.1007\/10692710_13"},{"key":"IJDSST.2018010104-3","doi-asserted-by":"publisher","DOI":"10.1016\/0950-5849(95)98041-D"},{"key":"IJDSST.2018010104-4","unstructured":"Chuandry, D., & Jain, N. (2013). Live EHCPRs system. In Proceedings of the Conference on Advances in Communication and Control Systems (CAC2S \u201913)."},{"key":"IJDSST.2018010104-5","doi-asserted-by":"crossref","unstructured":"Compton, P., & Richards, D. (1998). 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