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Journal articles on the topic 'Expert systems (Computer science) Case-based reasoning'

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Published: 4 June 2021

Last updated: 1 February 2022

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1

Zhou, Jingkai, Calvin G. Messersmith, and Janet D. Harrington. "HIDES: A Computer-Based Herbicide Injury Diagnostic Expert System." Weed Technology 19, no. 2 (June 2005): 486–91. http://dx.doi.org/10.1614/wt-04-185r1.

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Diagnosis of herbicide injury can be complex because of the large number and interaction of factors leading to herbicide injury. Computer-based expert systems have great potential to assist users, particularly nonexperts, in accurate diagnosis of herbicide injury. Rule-based and case-based reasoning are the most widely used forms of expert systems, and each system has strengths and limitations. Approaches that integrate rule-based and case-based reasoning may augment the positive aspects of the two reasoning methods and simultaneously minimize their negative aspects. The Herbicide Injury Diagnostic Expert System (HIDES) integrates rule-based and case-based reasoning and uses field-specific information, injury symptoms, herbicide use history, and herbicide information to diagnose crop injury from herbicides. The HIDES program uses a set of rules to identify suspect herbicide(s) that is the candidate for causing the observed injury and possible sources of the suspect herbicide(s). Case-based reasoning is used to propose a probable cause of injury by making an analogy to previously solved cases. A four-step procedure is followed when using HIDES: information collection, suspect herbicide identification, suspect herbicide source determination, injury reason suggestion, and knowledge accumulation.

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Vargas, Juan E., and Savita Raj. "Developing maintainable expert systems using case-based reasoning." Expert Systems 10, no. 4 (November 1993): 219–25. http://dx.doi.org/10.1111/j.1468-0394.1993.tb00101.x.

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Yang, Jie, Chenzhou Ye, and Xiaoli Zhang. "An Expert System Shell for Fault Diagnosis." Robotica 19, no. 6 (September 2001): 669–74. http://dx.doi.org/10.1017/s0263574701003460.

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Traditional expert systems for fault diagnosis have a bottleneck in knowledge acquisition, and have limitations in knowledge representation and reasoning. A new expert system shell for fault diagnosis is presented in this paper to develop multiple knowledge models (object model, rules, neural network, case-base and diagnose models) hierarchically based on multiple knowledge. The structure of the expert system shell and the knowledge representation of multiple models are described. Diagnostic algorithms are presented for automatic modeling and hierarchical reasoning. It will be shown that the expert system shell is very effective in building diagnostic expert systems.

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Rich, Steven H., and V. Venkatasubramanian. "Model-based reasoning in diagnostic expert systems for chemical process plants." Computers & Chemical Engineering 11, no. 2 (January 1987): 111–22. http://dx.doi.org/10.1016/0098-1354(87)80012-1.

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Yoshino, Hajime, and Katsumi Nitta. "Special Issue on AI and Law." Journal of Advanced Computational Intelligence and Intelligent Informatics 1, no. 2 (December 20, 1997): 81–82. http://dx.doi.org/10.20965/jaciii.1997.p0081.

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Lawyers use a reasoning process known as legal reasoning to solve legal problems. Legal expert systems could potentially help lawyers solve legal problems more quick and adequately, enable students to study law at school or at home more easily, and help legal scholars and professionals analyze the law and legal systems more clearly and precisely.In 1992, Hajime Yoshino of Meiji Gakuin University started a “Legal Expert Systems” project. This “Legal Expert” project is funded by the Japanese Ministry of Education, Science and Culture and is scheduled to run from May 1992 to March 1998. Yoshino organized over 30 lawyers and computer scientists to clarify legal knowledge and develop legal expert systems.This project covers a wide range of technologies such as the analysis of legal knowledge, the analysis of legal rules on international trade (United Nations Convention on Contracts for International Sale of Goods (CISG)), legal knowledge representation, legal inference models, utility programs to develop legal knowledge bases, and user interfaces. This project, which ends in March 1998, will focus on developing comprehensive legal expert systems as the final product. In this issue, we present 12 papers written by “Legal Expert” project members.In this number, Hajime Yoshino gives are overview of the legal expert systems project, explaining its aims, objectives, and organization. Six papers that follow his introduction include three on case-based reasoning. Legal rules are given by ambiguous predicates, making it difficult sometimes to determine whether conditions for rules are satisfied by the facts given of an event. In such cases, lawyers often refer to old cases and generate hypotheses through analogical reasoning.Kaoru Hirota, Hajime Yoshino and Ming Qiang Xu apply fuzzy theory to case-based reasoning. A number of related systems have been developed, but most focus on qualitative similarities between old cases and the current case, and cannot measure quantitative similarities. Hirota et al. treat quantitative similarity by applying fuzzy theory, explaining their method using CISG examples.Ken Satoh developed a way to compute an interpretation of undefined propositions in a legal rule using adversarial case-based reasoning. He translated old cases giving possible interpretations for a proposition into clauses in abductive logic programming and introduced abducibles to reason dynamically about important factors in an old case to the interpretation suiting the user’s purpose.Yoshiaki Okubo and Makoto Haraguchi formalized a way of attacking legal argument. Assume that an opponent has constructed a legal argument by applying a statute with an analogical interpretation. From the viewpoint of legal stability, the same statue for similar cases should be applied with the same interpretation. We thereby create a hypothetical case similar to the case in question and examine whether the statue can be interpreted analogically. Such a hypothetically similar case is created with the help of a goal-dependent abstraction framework. If a precedent in which a statue has been applied to a case with a different interpretation – particularly complete interpretation – can be found, the opponent’s argument is attacked by pointing out the incoherence of its interpretation of the statue.Takashi Kanai and Susumu Kunifuji proposed a legal reasoning system using abductive logic programming that deals with ambiguities in described facts and exceptions not described in articles. They examined the problems to be solved to develop legal knowledge bases through abductive logic programming, e.g., how to select ambiguities to be treated in abductive reasoning, how to describe time relationships, and how to describe an exception in terms of the application of abductive logic programming to legal reasoning.Toshiko Wakaki, Ken Satoh, and Katsumi Nitta presented an approach of reasoning about dynamic preferences in the framework of circumscription based on logic programming. To treat dynamic preferences correctly is required in legal reasoning to handle metarules such as lex posterior. This has become a hotly discussed topic in legal reasoning and more general nonmonotic reasoning. Comparisons of their method, Brewka’s approach, and Prakken and Sartor’s approach are discussed.Hiroyuki Matsumoto proposed a general legal reasoning model and a way of describing legal knowledge systematically. He applied his method to Japanese Maritime Traffic Law.Six more papers are to be presented in the next number

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6

Lee, Newton S. "A computational paradigm that integrates rule-based and model-based reasoning in expert systems." International Journal of Intelligent Systems 5, no. 2 (June 1990): 135–51. http://dx.doi.org/10.1002/int.4550050202.

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Moselhi, Osama, Tarek Hegazy, and Paul Fazio. "Potential applications of neural networks in construction." Canadian Journal of Civil Engineering 19, no. 3 (June 1, 1992): 521–29. http://dx.doi.org/10.1139/l92-061.

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During the past decade, several engineering disciplines, including construction, have embarked on developing “intelligent” decision support systems based on artificial intelligence (AI) techniques, including expert systems, symbolic knowledge representation, and logic programming. These systems attempt to capture the domain experts' intelligent behaviour and reasoning process utilized in decision-making, without regard to the underlying mechanisms producing that behaviour. This approach involves describing behaviours, usually with rules and symbols. In contrast, neural networks (NN), another AI-based technique that has been pursued on a large scale during the past few years, does not describe behaviours but rather imitate them. Neural networks are particularly superior to traditional expert systems in providing timely solutions based primarily on analogy with previous experience, rather than reasoning or computation. As such, neural networks have a great potential to work either as a supplement or as a complement to algorithmic and (or) other AI-based systems, providing more suitable tools for solving the industry ill-structured problems.This paper describes several characteristics of neural networks and outlines the advantages and limitations of commonly used NN paradigms. Potential applications of each paradigm in construction are identified. Two example applications are provided to demonstrate the problem-solving capabilities of neural networks: (i) estimation of hourly production rate of an excavation equipment; and (ii) estimation of productivity level for a construction trade. Future possibilities of integrating neural networks with other problem-solving techniques are described. Key words: construction, management techniques, neural networks, expert systems, pattern recognition, computer applications.

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Kwong, C. K., and G. F. Smith. "A computational system for process design of injection moulding: Combining blackboard-based expert system and case-based reasoning approach." International Journal of Advanced Manufacturing Technology 14, no. 4 (April 1998): 239–46. http://dx.doi.org/10.1007/bf01199878.

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Sokolov, I. A. "Theory and practice in artificial intelligence." Вестник Российской академии наук 89, no. 4 (April 24, 2019): 365–70. http://dx.doi.org/10.31857/s0869-5873894365-370.

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Artificial Intelligence is an interdisciplinary field, and formed about 60 years ago as an interaction between mathematical methods, computer science, psychology, and linguistics. Artificial Intelligence is an experimental science and today features a number of internally designed theoretical methods: knowledge representation, modeling of reasoning and behavior, textual analysis, and data mining. Within the framework of Artificial Intelligence, novel scientific domains have arisen: non-monotonic logic, description logic, heuristic programming, expert systems, and knowledge-based software engineering. Increasing interest in Artificial Intelligence in recent years is related to the development of promising new technologies based on specific methods like knowledge discovery (or machine learning), natural language processing, autonomous unmanned intelligent systems, and hybrid human-machine intelligence.

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Baaz, Matthias, and Gerald Quirchmayr. "Logic-based models of analogical reasoning: A fundamental basis for the design of legal expert systems." Expert Systems with Applications 4, no. 4 (January 1992): 369–78. http://dx.doi.org/10.1016/0957-4174(92)90130-k.

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Lu, Pengzhen, Shengyong Chen, and Yujun Zheng. "Artificial Intelligence in Civil Engineering." Mathematical Problems in Engineering 2012 (2012): 1–22. http://dx.doi.org/10.1155/2012/145974.

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Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering.

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Gu, Dongxiao, Changyong Liang, Kyung-Sun Kim, Changhui Yang, Wenjuan Cheng, and Jun Wang. "Which is More Reliable, Expert Experience or Information Itself? Weight Scheme of Complex Cases for Health Management Decision Making." International Journal of Information Technology & Decision Making 14, no. 03 (May 2015): 597–620. http://dx.doi.org/10.1142/s0219622014500424.

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How to obtain valuable knowledge more effectively from historical cases and satisfy the requirements of supporting diagnosis or management decision making is one of the important and challenging issues in the research field of modern historical information management and intelligent decision-making science. In this study, we develop a novel case-based reasoning (CBR) method which is based on information entropy and improved gray systems theory for knowledge acquisition of historical diagnosis decision-making cases. Specially, information entropy for weight determination is introduced into the CBR, as well as a gray system theory combined to support the diagnosis decision making of breast cancer. Based on two different real-world data sets, we conduct experimental studies to compare the performance of the Delphi method and information entropy. We also investigate which combination is best among different weight determination methods and retrieval algorithms. The results suggest that: generally, information entropy is a better approach to weight derivation and better matching effect can be obtained if it is integrated into the retrieval algorithm based on gray system theory rather than Euclidean distance algorithm. Our study can provide a novel approach to obtain weight values of cases, as well as an effective tool to mine valuable decision knowledge from historical cases in public hospitals.

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Kourtz, Peter. "Artificial intelligence: a new tool for forest management." Canadian Journal of Forest Research 20, no. 4 (April 1, 1990): 428–37. http://dx.doi.org/10.1139/x90-060.

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Articicial intelligence is a new science that deals with the representation, automatic acquisition, and use of knowledge. Artificial intelligence programs attempt to emulate human thought processes such as deduction, inference, language, and visual recognition. The goal of artificial intelligence is to make computers more useful for reasoning, planning, acting, and communicating with humans. Development of artificial intelligence applications involves the integration of advanced computer science, psychology, and sometimes robotics. Of the subfields that artificial intelligence can be broken into, the one of most immediate interest to forest management is expert systems. Expert systems involve encoding knowledge usually derived from an expert in a narrow subject area and using this knowledge to mimic his decision making. The knowledge is represented usually in the form of facts and rules, involving symbols such as English words. At the core of these systems is a mechanism that automatically searches for and pieces together the facts and rules necessary to solve a specific problem. Small expert systems can be developed on common microcomputers using existing low-cost commercial expert shells. Shells are general expert systems empty of knowledge. The user merely defines the solution structure and adds the desired knowledge. Larger systems usually require integration with existing forestry data bases and models. Their development requires either the relatively expensive expert system development tool kits or the use of one of the artificial intelligence development languages such as lisp or PROLOG. Large systems are expensive to develop, require a high degree of skill in knowledge engineering and computer science, and can require years of testing and modification before they become operational. Expert systems have a major role in all aspects of Canadian forestry. They can be used in conjunction with conventional process models to add currently lacking expert knowledge or as pure knowledge-based systems to solve problems never before tackled. They can preserve and accumulate forestry knowledge by encoding it. Expert systems allow us to package our forestry knowlege into a transportable and saleable product. They are a means to ensure consistent application of policies and operational procedures. There is a sense of urgency associated with the integration of artificial intelligence tools into Canadian forestry. Canada must awaken to the potential of this technology. Such systems are essential to improve industrial efficiency. A possible spin-off will be a resource knowledge business that can market our forestry knowledge worldwide. If we act decisively, we can easily compete with other countries such as Japan to fill this niche. A consortium of resource companies, provincial resource agencies, universities, and federal government laboratories is required to advance this goal.

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SIMOUDIS, EVANGELOS, and MARK ADLER. "INTEGRATING DISTRIBUTED EXPERTISE." International Journal of Cooperative Information Systems 01, no. 03n04 (December 1992): 393–410. http://dx.doi.org/10.1142/s0218215792000106.

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Over the past ten years a myriad of knowledge-based expert systems have been developed and deployed. These systems have a narrow scope and usually operate in stand-alone mode. They also follow different implementation philosophies and use a variety of reasoning methods. To address problems of wider scope, researchers have developed systems that utilize either centralized or distributed computational models. Each of these systems is homogeneous, and due to the way developed, prohibitively expensive for real-world settings. In this paper we present OMNI, a framework for integrating existing knowledge-based systems in a way that they can cooperate during problem-solving while they remain distributed over a computing environment.

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Kwong, C. K., and G. F. Smith. "A computational system for process design of injection moulding: Combining a blackboard-based expert system and a case-based reasoning approach." International Journal of Advanced Manufacturing Technology 14, no. 5 (May 1998): 350–57. http://dx.doi.org/10.1007/bf01178914.

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Chevallier, Nicola, and Alan D. Russell. "Automated schedule generation." Canadian Journal of Civil Engineering 25, no. 6 (December 1, 1998): 1059–77. http://dx.doi.org/10.1139/l98-029.

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Even for the most well-intentioned companies, the commitment required for the initial drafting of a plan is a real barrier to developing a useful schedule for a construction project. This paper explores how artificial intelligence and expert systems have and can be used for automating the generation of plans and schedules. Findings from a thorough literature review and observations about the general characteristics of projects provide a backdrop to explain the reasoning that has led to the authors' approach to developing a module capable of generating draft plans and schedules. This approach combines expert systems with large building blocks of predefined project scoping, sequencing, and scaling knowledge contained in standard templates and rules. It relies on templates to store much of the reasoning needed to produce a project plan and schedule, and on the use of generalized planning structures as activities. A draft schedule can then be automatically generated by way of a relatively simple rule set that assigns activity property values dealing with physical locations and production rates, as well as other project scale related values. The role of a physical model or view of a project is emphasised.Key words: construction, scheduling, project management, expert system, template, automated schedule generation, rule based, computer integration.

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Petit, Maxime, Grégoire Pointeau, and Peter Ford Dominey. "Reasoning based on consolidated real world experience acquired by a humanoid robot." Interaction Studies 17, no. 2 (December 14, 2016): 248–78. http://dx.doi.org/10.1075/is.17.2.04pet.

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Abstract The development of reasoning systems exploiting expert knowledge from interactions with humans is a non-trivial problem, particularly when considering how the information can be coded in the knowledge representation. For example, in human development, the acquisition of knowledge at one level requires the consolidation of knowledge from lower levels. How is the accumulated experience structured to allow the individual to apply knowledge to new situations, allowing reasoning and adaptation? We investigate how this can be done automatically by an iCub that interacts with humans to acquire knowledge via demonstration. Once consolidated, this knowledge is used in further acquisitions of experience concerning preconditions and consequences of actions. Finally, this knowledge is translated into rules that allow reasoning and planning for novel problem solving, including a Tower of Hanoi scenario. We thus demonstrate proof of concept for an interaction system that uses knowledge acquired from human interactions to reason about new situations.

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Tayefeh Mahmoudi, Maryam, Kambiz Badie, and Mahmood Kharrat. "Text organization via projection from researcher‐space onto text‐space." Kybernetes 37, no. 8 (September 17, 2008): 1151–64. http://dx.doi.org/10.1108/03684920810884955.

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PurposeThe purpose of this paper is to propose a new approach for organizing texts for researchers based on projection from researcher space (consisting of reasoning ability and research ability) onto text space (consisting of text features).Design/methodology/approachThe projection from researcher space onto text space is performed on the grounds of the differences between the sets of essential text features which are consistent, respectively, for the reasoning ability (as researcher's existing ability) and the research ability (as researcher's desired ability) using the concept of a dependency graph.FindingsThrough the projection from researcher space onto text space, one can expect to find an effective text which can help a person with certain reasoning ability acquire a certain research ability in the related domain.Research limitations/implicationsAcquisition of reasoning ability (ies) may call for a comprehensive questionnaire or test protocol whose validation by the expert may not necessarily be convenient. Appropriate questionnaires/test protocols, as well as knowledge‐based models for the text features at different layers (to assure derivation of more reliable features), is suggested as future work.Practical implicationsA salient benefit of the proposed approach is its flexibility in responding to a wide range of users with different models. It thus can be used as an efficient tool for online e‐learning and e‐research purposes in cyber‐learning environment.Originality/valueThe originality of the paper mostly lies in the concept of projection from researcher space onto text space as an approach for provision of appropriate text features; and the application of dependency graph as a potential means for identifying those text features whose prerequisites exist in the research abilities of the researcher.

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Maniamma, Jisha, and Hiroaki Wagatsuma. "A Semantic Web-based Representation of Human-logical Inference for Solving Bongard Problems." JUCS - Journal of Universal Computer Science 26, no. 10 (October 28, 2020): 1343–63. http://dx.doi.org/10.3897/jucs.2020.070.

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Bongard Problems (BPs) are a set of 100 visual puzzles introduced by M. M. Bongard in the mid-1960s. BPs have been established as benchmark puzzles for understanding the human context-based learning abilities to solve ill- posed problems. The puzzle requires the logical explanation as the answer to distinct two classes of figures from redundant options, which can be obtained by a thinking process to alternatively change the target frame (hierarchical level of analogy) of thinking from a wide range concept networks as D. R. Hofstadter suggested. Some minor research results to solve a limited set of BPs have reported based a single architecture accompanied with probabilistic approaches; however the central problem on BP's difficulties is the requirement of flexible changes of the target frame, therefore non-hierarchical cluster analyses does not provide the essential solution and hierarchical probabilistic models needs to include unnecessary levels for learning from the beginning to prevent a prompt decision making. We hypothesized that logical reasoning process with limited numbers of meta-data descriptions realizes the sophisticated and prompt decision-making and the performance is validated by using BPs. In this study, a semantic web-based hierarchical model to solve BPs was proposed as the minimum and transparent system to mimic human-logical inference process in solving of BPs by using the Description Logic (DL) with assertions on concepts (TBox) and individuals (ABox). Our results demonstrated that the proposed model not only provided individual solutions as a BP solver, but also proved the correctness of Hofstadter's idea as the flexible frame with concept networks for BPs in our actual implementation, which no one has ever achieved. This fact will open the new horizon for theories for designing of logical reasoning systems especially for critical judgments and serious decision-making as expert humans do in a transparent and descriptive way of why they judged in that manner.

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Kwon, Ohbyung, Ghi Paul Im, and Kun Chang Lee. "Corrigendum to “MACE-SCM: A multi-agent and case-based reasoning collaboration mechanisms for supply chain management under supply and demand uncertainties” [Expert Systems with Applications 33 (3) (2007) 690–705]." Expert Systems with Applications 34, no. 3 (April 2008): 2229. http://dx.doi.org/10.1016/j.eswa.2007.10.002.

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Zekri, Firas, Afef Samet Ellouze, and Rafik Bouaziz. "A Fuzzy-Based Customisation of Healthcare Knowledge to Support Clinical Domestic Decisions for Chronically Ill Patients." Journal of Information & Knowledge Management 19, no. 04 (November 27, 2020): 2050029. http://dx.doi.org/10.1142/s021964922050029x.

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The development of customised healthcare systems is becoming an important issue in the healthcare industry due to the rapid increase in the number of chronically ill patients. These systems aim to deliver effective care to patients having chronic diseases through customised services. However, knowledge bases need also to be customised since systems are confronted with huge amount of personalised and imprecise medical knowledge. Therefore, we propose in this paper a new system to customise medical knowledge according to progressive disease phases and pathological cases. A rule management process first customises rules according to the specificities of every disease phase, and then matches a private knowledge base with each enrolled patient. This base contains only the patient’s customised knowledge. After reasoning, another customisation process is carried out by the component, Result Manager, which ensures the validation of the system outcomes by the pathological case experts, before being recommended. This will better ensure the recommendation of the generated results to the non-professional users. In addition, Result Manager offers fuzzy semantic queries to the experts. In conclusion, our new decision support system makes medical aid decisions not only addressed to physicians, but also to chronically ill patients and persons regarded as caregivers.

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Cholewa, Wojciech. "Mechanical Analogy of Statement Networks." International Journal of Applied Mathematics and Computer Science 18, no. 4 (December 1, 2008): 477–86. http://dx.doi.org/10.2478/v10006-008-0042-7.

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Mechanical Analogy of Statement NetworksThe paper demonstrates briefly the reasoning capabilities in condition monitoring offered by systems based on statement networks. The usefulness of the networks considered results among others from possibilities of their optimization related to the minimization of contradictions between rules acquired from different knowledge sources. A mechanical analogy of such networks introduces an interpretation of statements as material points that are able to move. Dependencies between statements are considered as approximate necessary and approximate sufficient conditions, which are represented by unilateral constraints imposed on the introduced material points. A model of a dynamic statement network can be obtained out of the network consisting of statements represented by material points with assigned masses, where the inertia of statements may be taken into account. The paper introduces a measure of conditional contradictions of statements, which can be used for monitoring knowledge bases in running expert systems.

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Alharbe, Nawaf, and Anthony S. Atkins. "Transforming to a smart hospital system." International Journal of Pervasive Computing and Communications 12, no. 4 (November 7, 2016): 503–22. http://dx.doi.org/10.1108/ijpcc-07-2016-0037.

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Purpose Expert knowledge is an important organisational resource, and organisations need to retain the knowledge learned by experience, which can be shared as part of inter-professional learning. In a healthcare context, radio-frequency identification (RFID) and ZigBee technologies can be used together, to provide real-time information for decision support and to create a secure and reliable smart hospital management information system (SHMIS) that allows the dynamic control of objects and transforms operational processes, while minimising any potential risks to patients and staff. Currently, the RFID technology in Saudi Arabia is being solely used for the monitoring of newborn infants, and some difficulties have been encountered during the different stages of tagging and monitoring. The current system in Medina Maternity and Children’s Hospital (MMCH) uses battery-powered active RFID tags, which are expensive and require routine maintenance. This study aims to discuss the way in which the MMCH in Medina, Saudi Arabia, could be transformed into an SHMIS. Design/methodology/approach The extraordinary growth of RFID and ZigBee technologies has made it possible to identify, locate and track objects in various environments in real time. The RFID technology is a non-contact identification technology that is cheap and reliable but has limited range in the case of passive tags. ZigBee has greater range and lower power consumption, giving more precise location of the object’s movements (0.6 m). Passive RFID when combined with ZigBee technology can be used to improve services provided by healthcare organisations through continuous data collection and supporting real-time decision-making, by applying expert knowledge of domain experts to data produced by communication from electronic and sensor technologies. Findings A prototype object-tracking system using RFID and ZigBee was developed to support the knowledge transformation for knowledge reasoning for decision support (KRDS), and the outcome of this research was validated with domain experts in hospitals in Saudi Arabia. Two feasibility case studies were conducted at MMCH in Saudi Arabia, to evaluate the proposed system. A survey was also conducted to address the requirements at MMCH, and the researcher adopted a range of strategy techniques, including interviews and meetings with staff, and the setting up of communities of practise (CoPs) at the target hospital. Research limitations/implications This paper has investigated the transformation process of an automatic healthcare tracking and monitoring systems for the purpose of developing a smart system in Saudi hospitals. For the scope of the project, the prototype implementation was restricted to a laboratory environment, to demonstrate the proposed proof of concept. The next phase will include conducting a scale up of the system, with implementation and testing done in a real hospital environment. Originality/value This paper proposes a prototype application of an (SHMIS that allows the dynamic control of objects and transforms operational processes, while minimising any potential risks to patients and staff.

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Patry, G. G., and M. W. Barnett. "Innovative Computing Techniques for Development of an Integrated Computer Control System." Water Science and Technology 26, no. 5-6 (September 1, 1992): 1365–74. http://dx.doi.org/10.2166/wst.1992.0579.

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Over the past decade there has been a shift in emphasis from design and construction of wastewater treatment facilities to operation. Poor plant performance, high costs and damage to the environment have resulted from operational problems. Wastewater treatment consists of a complex sequence of inter-dependent biological, physical and chemical processes subject to time-varying hydraulic and organic load conditions. Wastewater treatment process operation and control is a knowledge intensive task. Research on improving operation and control has centred on identifying important mechanisms responsible for observed behaviour and modelling both the process and optimum ways of operating the process. These models have served as useful tools for improving operation and control. Many different approaches have been used, including deterministic modelling, stochastic modelling and, more recently, linguistic modelling. Complex mathematical models of wastewater treatment processes consisting of large numbers of non-linear differential equations can be constructed using tools such as the General Purpose Simulator (GPS) and, given appropriate data, model parameters can be evaluated and updated using existing optimization routines. Object oriented programming (OOP) and a model based reasoning (MBR) approach provides a useful framework for development of deep-knowledge expert systems (ES). Data-driven modelling methods, including both time series analysis and artificial neural network (ANN) techniques, can also be employed to make maximum use of information contained in process data. Each of these model types is a necessary component of a computer system for operational control of wastewater treatment but, in isolation, none are sufficient for making the system robust. An integrated environment for combining these techniques has been developed for this purpose and the basis for its development is described.

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DODARO, CARMINE, and FRANCESCO RICCA. "The External Interface for Extending WASP." Theory and Practice of Logic Programming 20, no. 2 (December 10, 2018): 225–48. http://dx.doi.org/10.1017/s1471068418000558.

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Answer set programming (ASP) is a successful declarative formalism for knowledge representation and reasoning. The evaluation of ASP programs is nowadays based on the conflict-driven clause learning (CDCL) backtracking search algorithm. Recent work suggested that the performance of CDCL-based implementations can be considerably improved on specific benchmarks by extending their solving capabilities with custom heuristics and propagators. However, embedding such algorithms into existing systems requires expert knowledge of the internals of ASP implementations. The development of effective solver extensions can be made easier by providing suitable programming interfaces. In this paper, we present the interface for extending the CDCL-based ASP solver wasp. The interface is both general, that is, it can be used for providing either new branching heuristics or propagators, and external, that is, the implementation of new algorithms requires no internal modifications of wasp. Moreover, we review the applications of the interface witnessing it can be successfully used to extend wasp for solving effectively hard instances of both real-world and synthetic problems.

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Krynicki, Michał. "A Note on Rough Concepts Logic." Fundamenta Informaticae 13, no. 2 (April 1, 1990): 227–35. http://dx.doi.org/10.3233/fi-1990-13206.

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In papers [4,5] Pawlak introduced the notion of a rough set and approximation space. In [6] Pawlak formulated some concept of rough logic. The notion of the approximate truth was considered by many philosophers and logicians and in the last time by computer scientists. This was motivated by some research in artificial intelligence as for example expert systems, approximate reasoning methods and information system with imprecise information. The concept of rough logic introduced in [6] based on the notion of approximate truth determined by rough sets. Following these ideas Rasiowa and Skowron in [7] proposed the apropriate first order logic for concepts of rough definability. We denote this logic by LR. In [9] Szczerba proposed some logic with additional quantifier as rough concepts logic. We denote this logic by L(QR). The aim of this paper is a comparizing of these two logics with respect to their expressive power and giving some propositions of some modificated versions of rough concepts logics. We use more or less standard notation. By [a]R we denote the equivalence class of the element a with respect to the equivalence relation R. We write L ⩽ L ′ if expressive power of the logic L is weaker then t.he expressive power of the logic L ′ (i.e. each class of models definable by a sentence from L is also definable by a sentence from L ′ ). If L ⩽ L ′ and L ′ ⩽ L then we say that L and L ′ are equivalent and denote this by L ≡ L ′ . If L ⩽ L ′ but L ≢ L ′ then we write L < L ′ .

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Vo, Dinh Phuoc, and David Macchion. "A use of case-based reasoning technique in building expert systems." Future Generation Computer Systems 9, no. 4 (December 1993): 311–19. http://dx.doi.org/10.1016/0167-739x(93)90033-l.

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Marfalino, Hari, Dinda Djesmedi, and Afandi Iddil Filresi. "Diagnosis of Simplex Herpes by Case-Based Reasoning Method." SinkrOn 4, no. 1 (September 18, 2019): 58. http://dx.doi.org/10.33395/sinkron.v4i1.10151.

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Artificial Intelligence (Artificial Intelligence or AI) is defined as the intelligence shown by an artificial entity. Such systems are generally considered computers. Intelligence is created and incorporated into a machine (computer) so that it can do work as humans can. Artificial Intelligence (Artificial Intelligence or AI) is defined as intelligence shown by an artificial entity. Such systems are generally considered computers. Intelligence is created and incorporated into a machine (computer) so that it can do work like humans can. CBR method is a method used to solve a problem by using previous experience to solve a new problem. This research will discuss about Herpes Simplex, a venereal disease. In implementing this expert system, it is used by families of patients with Herpes Simplex. The goal is that the expert system can assist the patient's family in conducting the initial diagnosis process to determine the symptoms felt by sufferers of Herpes Simplex. And with the existence of this expert system can carry out appropriate treatment by sufferers of Herpes Simplex.

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Mehdi Owrang O., M. "Case Discovery in Case-Based Reasoning Systems." Information Systems Management 15, no. 1 (January 1998): 74–78. http://dx.doi.org/10.1201/1078/43183.15.1.19980101/31107.12.

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Im, Kwang Hyuk, and Sang Chan Park. "Case-based reasoning and neural network based expert system for personalization." Expert Systems with Applications 32, no. 1 (January 2007): 77–85. http://dx.doi.org/10.1016/j.eswa.2005.11.020.

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Zhao, Jinsong, Lin Cui, Lihua Zhao, Tong Qiu, and Bingzhen Chen. "Learning HAZOP expert system by case-based reasoning and ontology." Computers & Chemical Engineering 33, no. 1 (January 2009): 371–78. http://dx.doi.org/10.1016/j.compchemeng.2008.10.006.

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Grupe, Fritz. "CASE-BASED REASONING." Information Systems Management 10, no. 2 (1993): 77–80. http://dx.doi.org/10.1080/10580539308906934.

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Majeed, Roza Fuad, Soran AB. M. Saeed, Dana Abdulmajeed Abdilkarim, and Haval Mohammed Sidqi. "Skin Tumors Diagnosis Utilizing Case Based Reasoning and The Expert System." Kurdistan Journal of Applied Research 5, no. 1 (June 8, 2020): 96–114. http://dx.doi.org/10.24017/science.2020.1.10.

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Skin cancer is considered as the most type of cancer that happens in humans. Three basic types of cancer occur which are basal cell carcinoma (BCC), Squamous cell carcinoma (SCC). Skin cancer leads to death if it is not diagnosed in an early stage. Fortunately, early diagnosis of skin cancer raises the survival rate of victims. Computer-aided has a great role to detect skin cancer which leads to saving human life. Based on that, this study proposes a computer-aided diagnosis (CAD) system that detects skin cancer using digital images, techniques of image processing, by using the case-based reasoning and expert system. The main goal for designing this system is to create a cheap, easy-to-use, and relatively accurate system for detecting skin cancer in an early stage to save human life, raises the survival rate, and decreases the cost of the dermoscopy test.

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Dona, Dona, Hendri Maradona, and Masdewi Masdewi. "SISTEM PAKAR DIAGNOSA PENYAKIT JANTUNG DENGAN METODE CASE BASED REASONING (CBR)." ZONAsi: Jurnal Sistem Informasi 3, no. 1 (April 19, 2021): 1–12. http://dx.doi.org/10.31849/zn.v3i1.6442.

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Heart disease is a disease that is very dangerous for human survival, therefore it must be addressed early on from the appearance of symptoms. Advances in expert systems can overcome this problem by designing a web-based computer system that uses databases and programming languages such as PHP-MySQL so that it can help heart patients to diagnose the disease. The purpose of this research is to build a web-based expert system for diagnosing heart disease. Expert system application in this decision making uses the Case Based Reasoning (CBR) method, namely the method of making decisions by comparing new cases with old cases through four processes of retrieve, reuse, revise, and retain. Analysis and system design used are Context Diagram, Data FlowDiagram, Entity Relationship Diagram and Flowchart. In this expert system, the system will giving orders in the form of selecting the symptoms experienced, namely what symptoms are experienced. Then the patient selects the symptoms experienced by checking the symptoms experienced. The system will provide diagnostic results based on the symptoms experienced by the patient. Result diagnosis in the form of the type of heart disease experienced and the treatment solution and how much the percent chance the patient has the disease.

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Vedayoko, Lucky Gagah, Endang Sugiharti, and Much Aziz Muslim. "Expert System Diagnosis of Bowel Disease Using Case Based Reasoning with Nearest Neighbor Algorithm." Scientific Journal of Informatics 4, no. 2 (November 10, 2017): 134–42. http://dx.doi.org/10.15294/sji.v4i2.11770.

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Expert System is a computer system that has been entered the base of knowledge and set of rules to solve problems like an expert. One method in the expert system is Case Based Reasoning. To strengthen the retrieve stage of this method, the Nearest Neighbor algorithm is used. Bowel is one of the digestive organs susceptible to disease. The purpose of this study is to implement expert systems using Case Based Reasoning with Nearest Neighbor algorithm in diagnosing bowel disease and determine the accuracy of the system. Data used in this research are 60 data, obtained from medical record RSUD dr. Soetrasno Rembang. Variables used are general symptoms and types of diseases. The level of system accuracy resulting from scenario are 40 data as source case, and 20 data as target case that is equal to 95%.

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Xu, Xu. "Improving the Reliability of Case-Based Reasoning Systems." International Journal of Computational Intelligence Systems 3, no. 3 (2010): 256. http://dx.doi.org/10.2991/ijcis.2010.3.3.1.

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Xu, Xu, Ke Wang, Weimin Ma, and Jie Lin. "Improving the Reliability of Case-Based Reasoning Systems." International Journal of Computational Intelligence Systems 3, no. 3 (September 2010): 256–65. http://dx.doi.org/10.1080/18756891.2010.9727696.

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Smith, J. C., Daphne Gelbart, and Deborah Graham. "Building expert systems in case-based law." Expert Systems with Applications 4, no. 4 (January 1992): 335–42. http://dx.doi.org/10.1016/0957-4174(92)90126-d.

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Varshavskii, P. R., and A. P. Eremeev. "Modeling of case-based reasoning in intelligent decision support systems." Scientific and Technical Information Processing 37, no. 5 (December 2010): 336–45. http://dx.doi.org/10.3103/s0147688210050096.

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Zhang, Daisheng. "VEHICLE-FAULT DIAGNOSIS EXPERT SYSTEM BASED ON INTEGRATION OF CASE-BASED AND RULE-BASED REASONING." Chinese Journal of Mechanical Engineering 38, no. 07 (2002): 91. http://dx.doi.org/10.3901/jme.2002.07.091.

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Smiti, Abir, and Zied Elouedi. "Dynamic maintenance case base using knowledge discovery techniques for case based reasoning systems." Theoretical Computer Science 817 (May 2020): 24–32. http://dx.doi.org/10.1016/j.tcs.2019.06.026.

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Sun, Bo, Li Xu, Xuemin Pei, and Huaizu Li. "Scenario-based knowledge representation in case-based reasoning systems." Expert Systems 20, no. 2 (May 2003): 92–99. http://dx.doi.org/10.1111/1468-0394.00230.

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Sadek, Adel, Spencer Morse, John Ivan, and Wael El-Dessouki. "Case-Based Reasoning for Assessing Intelligent Transportation Systems Benefits." Computer-Aided Civil and Infrastructure Engineering 18, no. 3 (May 2003): 173–83. http://dx.doi.org/10.1111/1467-8667.00308.

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Liu, Cheng-Hsiang, Long-Sheng Chen, and Chun-Chin Hsu. "An association-based case reduction technique for case-based reasoning." Information Sciences 178, no. 17 (September 2008): 3347–55. http://dx.doi.org/10.1016/j.ins.2008.05.006.

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45

Breese, J. S., and D. Heckerman. "Decision-theoretic case-based reasoning." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 26, no. 6 (1996): 838–42. http://dx.doi.org/10.1109/3468.541343.

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Hassan, Sa`adah. "Soft Systems Methodology in Environment-Aware Case-Based Reasoning System Analysis." Information Technology Journal 9, no. 3 (March 15, 2010): 467–73. http://dx.doi.org/10.3923/itj.2010.467.473.

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Chua, Hui Na, and S. M. F. D. Syed Mustapha. "Converged services composition with case-based reasoning." International Journal of Information and Communication Technology 13, no. 3 (2018): 257. http://dx.doi.org/10.1504/ijict.2018.094316.

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He, Wu, Feng-Kwei Wang, Tawnya Means, and Li Da Xu. "Insight into interface design of web-based case-based reasoning retrieval systems." Expert Systems with Applications 36, no. 3 (April 2009): 7280–87. http://dx.doi.org/10.1016/j.eswa.2008.09.043.

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Liu, Bingyu, Dezhi Hao, Xianwen Gao, and Dingsen Zhang. "Optimal-Setting for Ore and Water Feeding in Grinding Process Based on Improved Case-Based Reasoning." Applied Sciences 11, no. 13 (June 23, 2021): 5835. http://dx.doi.org/10.3390/app11135835.

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The grinding product particle size is the most crucial operational index of mineral grinding processes. The size and consistency of the product directly affects the subsequent dressing and sintering. In this paper, a novel expert system is proposed for guiding the operating variables to keep the product stable with the wildly varying ore properties. First, case-based reasoning (CBR) is introduced to describe the whole grinding process with the historical data and expert experience. Second, the generative adversarial network (GAN) is employed to extend the raw data to enhance the flexibility of CBR. Moreover, the weights of different features in CBR is optimized by improved non-dominated sorting genetic algorithm II (NSGA-II). Finally, the proposed method is validated by a set of actual data collected from a Chinese dressing plant. The experimental result demonstrates the effectiveness of the proposed method.

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Edais, Shadi M., and Ahmad F. Shubita. "A case-based reasoning system for software reuse." International Journal of Applied Systemic Studies 9, no. 1 (2020): 31. http://dx.doi.org/10.1504/ijass.2020.10030728.

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