Abstract: A rule-based system is a special type of expert system, which typically consists of a set of if–then rules. Such rules can be used in the real world for both academic and practical purposes. In general, rule-based systems are involved in knowledge discovery tasks for both purposes and predictive modeling tasks for the latter purpose. In the context of granular computing, each of the rules that make up a rule-based system can be seen as a granule. This is due to the fact that granulation in general means decomposition of a whole into several parts. Similarly, each rule consists of a number of rule terms. From this point of view, each rule term can also be seen as a granule. As mentioned above, rule-based systems can be used for the purpose of knowledge discovery, which means to extract information or knowledge discovered from data. Therefore, rules and rule terms that make up a rule-based system are considered as information granules. This paper positions the research of rule-based systems in the granular computing context, which explores ways of achieving advances in the former area through the novel use of theories and techniques in the latter area. In particular, this paper gives a certain perspective on how to use set theory for management of information granules for rules/rule terms and different types of computational logic for reduction of learning bias. The effectiveness is critically analyzed and discussed. Further directions of this research area are recommended towards achieving advances in rule-based systems through the use of granular computing theories and techniques.

Abstract: We concentrated on the dental X-ray image segmentation problem.A new framework combining Otsu, FCM and semi-supervised fuzzy clustering was shown.It was tested on real datasets from Hanoi Medical University in terms of accuracy.The new framework has better performance than the relevant methods.Suggestions on means and variances of the criteria of the new framework were made. Dental X-ray image segmentation (DXIS) is an indispensable process in practical dentistry for diagnosis of periodontitis diseases from an X-ray image. It has been said that DXIS is one of the most important and necessary steps to analyze dental images in order to get valuable information for medical diagnosis support systems and other recognition tools. Specialized data mining methods for DXIS have been investigated to achieve high accuracy of segmentation. However, traditional image processing and clustering algorithms often meet challenges in determining parameters or common boundaries of teeth samples. It was shown that performance of a clustering algorithm is enhanced when additional information provided by users is attached to inputs of the algorithm. In this paper, we propose a new cooperative scheme that applies semi-supervised fuzzy clustering algorithms to DXIS. Specifically, the Otsu method is used to remove the Background area from an X-ray dental image. Then, the FCM algorithm is chosen to remove the Dental Structure area from the results of the previous steps. Finally, Semi-supervised Entropy regularized Fuzzy Clustering algorithm (eSFCM) is opted to clarify and improve the results based on the optimal result from the previous clustering method. The proposed framework is evaluated on a real collection of dental X-ray image datasets from Hanoi Medical University, Vietnam. Experimental results have revealed that clustering quality of the cooperative framework is better than those of the relevant ones. The findings of this paper have great impact and significance to researches in the fields of medical science and expert systems. It has been the fact that medical diagnosis is often an experienced and case-based process which requests long time practicing in real patients. In many situations, young clinicians do not have chance for such the practice so that it is necessary to utilize a computerized medical diagnosis system which could simulate medical processes from previous real evidences. By learning from those cases, clinicians would improve their experience and responses for later ones. In the view of expert systems, this paper made uses of knowledge-based algorithms for a practical application. This shows the advantages of such the algorithm in the conjunction domain between expert systems and medical informatics. The findings also suggested the most appropriate configuration of the algorithm and parameters for this problem that could be reused by other researchers in similar applications. The usefulness and significance of this research are clearly demonstrated within the extent of real-life applications.

Abstract: Thank you very much for reading fuzzy sets decision making and expert systems. As you may know, people have look hundreds times for their favorite readings like this fuzzy sets decision making and expert systems, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some malicious bugs inside their laptop. fuzzy sets decision making and expert systems is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the fuzzy sets decision making and expert systems is universally compatible with any devices to read.

Abstract: Agriculture sector is evolving with the advent of the information and communication technology. Efforts are being made to enhance the productivity and reduce losses by using the state of the art technology and equipment. As most of the farmers are unaware of the technology and latest practices, many expert systems have been developed in the world to facilitate the farmers. However, these expert systems rely on the stored knowledge base. We propose an expert system based on the Internet of Things (IoT) that will use the input data collected in real time. It will help to take proactive and preventive actions to minimize the losses due to diseases and insects/pests.

Abstract: Infants and children suffer from a lot of mouth problems with a wide range of severity, and usually physicians have difficulties dealing with these problems due to their similarities. In this paper an expert system was designed to help users to correctly diagnose mouth problems in infants and children(teething, gingivitis, impetigo, inflamed papillae, mucocele, oral thrush, allergic reaction, chickenpox, hand-foot-mouth disease, strep throat, cold sores, canker sores, gingivostomatitis) with some information about the disease and how to treat it. SL5 Object expert system language was used to design and implement this expert system. Keywords— Artificial Intelligence; Expert Systems; SL5 Object; Mouth problems; infants

Abstract: The truth of every human being is the end his life with death, and this leads to leaving assets and funds for those after him and can lead to hate between the heirs, it has made a point of Islamic law on all aspects of life, including the subject of the inheritance of the deceased. The main problem is how to get the knowledge of the basics of inheritance. This paper reviews work done in the use of expert system software to calculate inheritance in Islam. A proposed expert system was designed and developed using CLIPS language to calculate the inheritance in Islam.

Abstract: A lot of infants have significant food-related problems, as well as spitting up, rejecting new foods, or not accepting to eat at specific times. These issues are frequently ordinary and are not a sign that the baby is unwell. According to the National Institutes of Health, 25% of generally developing infants and 35% of babies with neurodevelopmental disabilities are tormented by some sort of feeding problem. Some, for example rejecting to eat specific foods or being overly finicky, are momentary and don’t cause any health dangers. This paper proposes an expert system that can be used to successfully diagnose Feeding problems in infants and children. The suggested systems were found to be beneficial approach in addition to existing impartial ones. So far as the authors are aware, this is the initial effort of using an expert system in attaining good performance in a real world application. This expert system was designed and implemented to help parents diagnose these problems and get a recommendation of how to deal with infants and children.

Abstract: Intelligent Tutoring Systems incorporate Artificial Intelligence techniques, in order to imitate a human tutor. These expert systems are able to assess student’s proficiency, to provide solved examples and exercises for practice in each topic, as well as to provide immediate and personalized feedback to learners. The present study is a systematic review that evaluates the contribution of the Intelligent Tutoring Systems developed so far, to Mathematics Education, representing some of the most representative studies of the last decade.

Abstract: Currently FOREX (foreign exchange market) is the largest financial market over the world. Usually the Forex market analysis is based on the Forex time series prediction. Nevertheless, trading expert systems based on such predictions do not usually provide satisfactory results. On the other hand, stock trading expert systems called also “mechanical trading systems”, which are based on the technical analysis, are very popular and may provide good profits. Therefore, in this paper we propose a Forex trading expert system based on some new technical analysis indicators and a new approach to the rule-base evidential reasoning (RBER) (the synthesis of fuzzy logic and the Dempster–Shafer theory of evidence). We have found that the traditional fuzzy logic rules lose an important information, when dealing with the intersecting fuzzy classes, e.g., such as Low and Medium and we have shown that this property may lead to the controversial results in practice. In the framework of the proposed in the current paper new approach, an information of the values of all membership functions representing the intersecting (competing) fuzzy classes is preserved and used in the fuzzy logic rules. The advantages of the proposed approach are demonstrated using the developed expert system optimized and tested on the real data from the Forex market for the four currency pairs and the time frames 15 m, 30 m, 1 h and 4 h.

Abstract: This paper describes the design of a desktop based intelligent tutoring system for teaching diabetes to the student to overcome the difficulties they face. Intelligent Tutoring Systems purposed to provide immediate and customized instruction or feedback to learners. One of a teacher jobs is preparing materials to the students then explaining it, this system will save time for teachers and students, and they can reach it when and where they want to, so it will help individualized learning. This system supports the concept of recent health strategy, skilled patient who has developed a high level of knowledge and expertise to enable them to manage and control their own conditions. The researchers designed and developed the system using clinical medicine books, doctors, and questioners. The system helps students to deeply understand diabetes and diagnose it by explaining its types and shows the reasoning for each one. An initial study was done to measure the effect and performance of using intelligent tutoring system on the students. Evaluation of the system has shown pretty satisfactory results and positive effects as far as its learning capabilities and usability are concerned. Suheir H. Almurshidi, Samy S. Abu NaserDesign and Development of Diabetes Intelligent Tutoring System EUROPEAN ACADEMIC RESEARCH Vol. IV, Issue 9 / December 2016 8118

Abstract: In the decision making process of new product development, companies need to understand consumer preference for newly developed products. A recently developed belief rule based (BRB) inference methodology is used to formulate the relationship between consumer preference and product attributes. However, when the number of product attributes is large, the methodology encounters the challenge of dealing with an oversized rule base. To overcome the challenge, the paper incorporates factor analysis into the BRB methodology and develops a BRB expert system for predicting consumer preference of a new product. Firstly, a small number of factors are extracted from product attributes by conducting both exploratory and confirmatory factor analysis. Secondly, a belief rule base is constructed to model the causal relationships between the characteristic factors and consumer preference for products using experts' knowledge. Furthermore, a BRB expert system is developed for predicting consumer preference in new product development, where the factor values transformed from product attributes are taken as inputs. Relevant rules in the system are activated by the input data, and then the activated rules are aggregated using the evidential reasoning (ER) approach to generate the predicted consumer preference for each product. Finally, the BRB expert system is illustrated using the data collected from 100 consumers of several tea stores through a market survey. The results show that the prototype of the BRB expert system has superior fitting capability on training data and high prediction accuracy on testing data, and it has great potential to be applied to consumer preference prediction in new product development.

Abstract: This chapter presents a short history of artificial intelligence, and we discuss the Turing Test, which is a test of machine intelligence. We discuss strong and weak AI, where strong AI considers an AI programmed computer to be essentially a mind, whereas weak AI considers a computer to simulate thought without real understanding. We discuss Searle’s Chinese room, which is a rebuttal of strong AI, and we discuss philosophical issues in AI and Weizenbaum’s views on the ethics of AI. There are many subfields in AI and we discuss logic, neural networks and expert systems.

Abstract: Recently there is an increasing technological development in intelligent tutoring systems. This field has become interesting to many researchers. In this paper, we present an intelligent tutoring system for teaching information security. This intelligent tutoring systems target the students enrolled in Advanced Topics in Information Security in the faculty of Engineering and Information Technology at Al-Azhar University in Gaza. Through which the student will be able to study the course and solve related problems. An evaluation of the intelligent tutoring systems was carried out and the results

Abstract: Fuzzy Petri nets (FPNs) are an important modeling tool for knowledge representation and reasoning, which have been extensively used in a lot of fields. However, the conventional FPN models have been criticized as having many shortcomings in the literature. Many different models have been suggested to enhance the performance of FPNs, but deficiencies still exist in these models. First, various types of uncertain knowledge information provided by domain experts are very hard to be modeled by the existing FPN models. Second, the traditional FPNs determine the results of knowledge reasoning using the min, max, and product operators, which may not work well in many practical applications. In this paper, we propose a new type of FPN model based on intuitionistic fuzzy sets and ordered weighted averaging operators to deal with the problems and improve the effectiveness of the conventional FPNs. Moreover, a max-algebra-based reasoning algorithm is developed in order to implement the intuitionistic fuzzy reasoning formally and automatically. Finally, a case study concerning fault diagnosis of aircraft generator is presented to demonstrate the proposed intuitionistic FPN model. Numerical experiments show that the new FPN model is feasible and quite effective for knowledge representation and reasoning of intuitionistic fuzzy expert systems.

Abstract: Development of an entropy-based evaluation method to evaluate ontology strength.Evaluation an ontological semantic ontology using the evaluation method.Evaluation of the backbone of the UMLS with this method. In this paper we introduce a method to develop knowledge bases for medical decision support systems, with a focus on evaluating such knowledge bases. Departing from earlier efforts with concept maps, we developed an ontological-semantic knowledge base and evaluated its information content using the metrics we have developed, and then compared the results to the UMLS backbone knowledge base. The evaluation method developed uses information entropy of concepts, but in contrast to previous approaches normalizes it against the number of relations to evaluate the information density of knowledge bases of varying sizes. A detailed description of the knowledge base development and evaluation is discussed using the underlying algorithms, and the results of experimentation of the methods are explained. The main evaluation results show that the normalized metric provides a balanced method for assessment and that our knowledge base is strong, despite having fewer relationships, is more information-dense, and hence more useful. The key contributions in the area of developing expert systems detailed in this paper include: (a) introduction of a normalized entropy-based evaluation technique to evaluate knowledge bases using graph theory, (b) results of the experimentation of the use of this technique on existing knowledge bases.

Abstract: The belief rule-base (BRB) inference methodology, which uses the evidential reasoning (RIMER) approach, has been widely popular in recent years. As an expert-system methodology using the RIMER approach, BRB is used for storing various types of uncertain knowledge in the form of belief structure. Several structure-learning approaches have been proposed in recent years. However, these approaches are deficient in various aspects, do not have repeatability, hold incomplete data, and are constrained by the associated scale-utility value. Moreover, considering the influence of the number of rules for a BRB system, two scenarios are designed to reveal the relationship between structure feature and fewer/excessive rules. Excessive rules may lead to a BRB that is equipped with an over-complete structure, whereas significantly fewer rules may result in a BRB with an incomplete structure. To solve these problems, we initially proposed to develop an adjusted structure that is leading to the establishment of a complete structure instead of incomplete and over-complete structures. By scenario analysis and experimental verification through parameter learning of BRBs, we summarize several features of two scenarios, which can be used to reveal certain number of key BRB properties. Finally, density and error analyses are introduced to dynamically prune or add rules to construct the complete structure, particularly that of the BRB comprising multiple-antecedent attributes. We verify the effectiveness of the proposed approach by testing its use in a practical case study on oil pipeline-leak detection and demonstrate how the approach can be implemented.

Abstract: A model for assessment of passenger security control efficiency was created.Human factor and the technical factor were taken into account collectively.Hierarchical fuzzy inference system was used and implemented.Passenger security control efficiency was determined in real conditions.Method and software for airport management support was developed. Elements of air transport infrastructure as well as passengers and aircraft are constantly at risk of terrorist attack. One of the most important preventative methods is the security control of persons and baggage at airports. Managing this process requires finding a compromise between high capacity of the terminal and the high effectiveness of the security control. The purpose of this study is to show the applicability of an expert system, which assists security managers in deciding how to organise the security screening process. Due to the important role of the human factor, the need to use expert's opinions and the high uncertainty and imprecise nature of information, the developed model and computer tool FUPSCA (FUzzy Passenger Security Control Assessment) uses the fuzzy sets theory and a fuzzy inference system. It's use allows us to adjust the operating parameters of the security screening checkpoint, namely the WTMD sensitivity, number of employees and the frequency of manual controls, to the current level of terrorist threat. As a result of the study it was found that if we want to achieve higher security control effectiveness we should first increase the WTMD's sensitivity and only then increase the frequency of additional manual controls and not the other way round. Of course the FUPSCA system provides specific, quantitative answers. In the future it will be necessary to manage the operation of the passenger security control system using multi-criteria evaluations of: capacity, effectiveness, passenger comfort. FUPSCA will be able to effectively support this process.