A Fuzzy Rule Based Approach to Predict Risk Level of Heart Disease

Health care domain systems globally face lots of difficulties because of the high amount of risk factors of heart diseases in peoples (WHO, 2013). To reduce risk, improved knowledge based expert systems played an important role and has a contribution towards the development of the healthcare system for cardiovascular disease. To make use of benefits of knowledge based system, it is necessary for health organizations and users; must need to know the fuzzy rule based expert system2019;s integrity, efficiency, and deployments, which are the open challenges of current fuzzy logic based medical systems. In our proposed system, we have designed a fuzzy rule based expert system and also by using data mining technique we have reduced the total number of attributes. Our system mainly focuses on cardiovascular disease diagnosis, and the dataset taken from UCI (Machine Learning Repository). We explored in the existing work. The majority of the researcher2019;s experimentation was made on 14 attributes out of 76. While, in our system we took advantage of 6 attributes for system design. In the preliminary stage UCI, data participated in suggested system that will get outcomes. The performance of the system matched with Neural Network and J48 Decision Tree Algorithm

The development of in-process surface roughness prediction systems in turning operation using accelerometer

Three in-process surface roughness prediction (ISRP) systems using linear multiple regression, fuzzy logic, and fuzzy nets algorisms, respectively, were developed to allow the prediction of real time surface roughness of a work piece on a turning operation. The surface roughness is predicted from feed rate, spindle speed, depth of cut, and machining vibration that is detected and collected by an accelerometer.;Two groups of data were collected for two cutters with nose radii of 0.016 and 0.031 inches, respective. A total of 162 training data sets and 54 testing data sets for each cutter were applied to train and test the system. While the multiple-regression-based system applied the linear relationships of the dependent variables and the dependent variable for the prediction, the fuzzy-logic-based and the fuzzy-nets-based systems relied on fuzzy theory for the prediction. The fuzzy rule banks employed in the fuzzy-logic-based system was generated with expert\u27s experiences as well as observation results from the experiments. Whereas, the rule banks employed in the fuzz-nets-system were rule banks self-extracted from the training data by the fuzzy-nets self-learning algorithm.;The predicted surface roughness values were compared with corresponding measured values. The average prediction accuracy with the three algorithms, linear multiple regression, fuzzy logic, and fuzzy nets algorisms, was 92.78%, 89.06%, and 95.70%, respectively. The use of the accelerometer was found valuable in increasing the prediction The Fuzzy-nets-based In-process Surface Roughness Prediction System was considered the best among the three tested systems. This conclusion relies on not only the best average prediction accuracy achieved, but also the self-learning ability of the fuzzy nets algorism

Curvature-based sparse rule base generation for fuzzy rule interpolation

Fuzzy logic has been successfully widely utilised in many real-world applications. The most common application of fuzzy logic is the rule-based fuzzy inference system, which is composed of mainly two parts including an inference engine and a fuzzy rule base. Conventional fuzzy inference systems always require a rule base that fully covers the entire problem domain (i.e., a dense rule base). Fuzzy rule interpolation (FRI) makes inference possible with sparse rule bases which may not cover some parts of the problem domain (i.e., a sparse rule base). In addition to extending the applicability of fuzzy inference systems, fuzzy interpolation can also be used to reduce system complexity for over-complex fuzzy inference systems. There are typically two methods to generate fuzzy rule bases, i.e., the knowledge driven and data-driven approaches. Almost all of these approaches only target dense rule bases for conventional fuzzy inference systems. The knowledge-driven methods may be negatively affected by the limited availability of expert knowledge and expert knowledge may be subjective, whilst redundancy often exists in fuzzy rule-based models that are acquired from numerical data. Note that various rule base reduction approaches have been proposed, but they are all based on certain similarity measures and are likely to cause performance deterioration along with the size reduction. This project, for the first time, innovatively applies curvature values to distinguish important features and instances in a dataset, to support the construction of a neat and concise sparse rule base for fuzzy rule interpolation. In addition to working in a three-dimensional problem space, the work also extends the natural three-dimensional curvature calculation to problems with high dimensions, which greatly broadens the applicability of the proposed approach. As a result, the proposed approach alleviates the ‘curse of dimensionality’ and helps to reduce the computational cost for fuzzy inference systems. The proposed approach has been validated and evaluated by three real-world applications. The experimental results demonstrate that the proposed approach is able to generate sparse rule bases with less rules but resulting in better performance, which confirms the power of the proposed system. In addition to fuzzy rule interpolation, the proposed curvature-based approach can also be readily used as a general feature selection tool to work with other machine learning approaches, such as classifiers

Expert System as Tools for Efficient Teaching and Learning Process in Educational System in Nigeria, First Step

In educational field, many of the expert systems application are embedded inside the Intelligent Tuttoring System (ITS) by using techniques from adaptive hypertext and hypermedia. Most of the systems usually will assist student in their learning by using adaptation techniques to personalize with the environment, prior of student and students ability to learn in terms of technology, expert system in education has expanded very consistently from micro computer to web based (Woodin, 2001) and agent based expert system, it can provide an excellent alternative to private tutoring at anytime from any place (Markham, 2001) where internet is provided. Also agent based expert system surely will help users by finding materials from the web based on users profile. Supposedly, agent expert system should have capability to diagnose the users and giving the results according to the problems. Besides the use of expert system in technology, it also had tremendous changes in the applying of methods and techniques. Starting from a simple rule based system, currently expert system techniques had adapted a fuzzy logic (Starek, Tomer, Bhaskar, and Garcia, 2001) and hybrid based technique (Pretzas, Hatzilygeroudis, and Koutsojannis, 2001)

Learning Fuzzy Reactive Behaviors in Autonomous Robots

This paper is concerned with the learning of basic behaviors in autonomous robots. In this way, we present a method for the adaptation of basic reactive behaviors implemented as fuzzy controllers applying a genetic algorithm to the evolution of the fuzzy rule system. In this sense, we show our experiments in the evolution of control rules based on symbolic concepts represented as linguistic labels. The rules will be formulated in a fuzzy way and in order to test the rules obtained in each generation of the genetic algorithm a real robot has been used. The individual with the best performance is chosen to generate a new population: the elite strategy. All the new individuals were tested in the same real environment. In conclusion, the individuals of the last generation offer a set of rules that provides better performance than the ones designed by a non-expert designe

A Clustering and SVM Regression Learning-Based Spatiotemporal Fuzzy Logic Controller with Interpretable Structure for Spatially Distributed Systems

Many industrial processes and physical systems are spatially distributed systems. Recently, a novel 3-D FLC was developed for such systems. The previous study on the 3-D FLC was concentrated on an expert knowledge-based approach. However, in most of situations, we may lack the expert knowledge, while input-output data sets hidden with effective control laws are usually available. Under such circumstance, a data-driven approach could be a very effective way to design the 3-D FLC. In this study, we aim at developing a new 3-D FLC design methodology based on clustering and support vector machine (SVM) regression. The design consists of three parts: initial rule generation, rule-base simplification, and parameter learning. Firstly, the initial rules are extracted by a nearest neighborhood clustering algorithm with Frobenius norm as a distance. Secondly, the initial rule-base is simplified by merging similar 3-D fuzzy sets and similar 3-D fuzzy rules based on similarity measure technique. Thirdly, the consequent parameters are learned by a linear SVM regression algorithm. Additionally, the universal approximation capability of the proposed 3-D fuzzy system is discussed. Finally, the control of a catalytic packed-bed reactor is taken as an application to demonstrate the effectiveness of the proposed 3-D FLC design

Intelligent adaptive testing system

Modern learning is impossible without automated knowledge testing systems. At present, the most progressive are adaptive testing models in which the complexity of tasks varies depending on the correctness of the patient’s answers. This article describes the development of an intelligent adaptive testing system using a fuzzy mathematics device. An intelligent adaptive testing system has been developed; the module that implements the expert system uses the production base of the rules. The input parameters of testing are the percentage of correct responses, the degree of correctness of the response, the duration of the response, and the number of attempts. The output is a change in the current level of training of the student on the basis of which test questions of related complexity are selected. As a method of logical inference, the Mamdani method is used which consists of six operational actions: phazification — conversion of exact values of input variables into values of linguistic variables through belonging functions, this served as the basis for designing a fuzzy base of rules of the expert system; aggregation of sub-conditions — determination of the truth of conditions for each linguistic rule of the fuzzy inference system; activating sub-conclusions — finding the degree of truth of each of the sub-conclusions in the linguistic rule; accumulation of conclusions — finding the belonging function for each of the output linguistic variables; defazzification — finding a numerical value for each of the output linguistic variables. A developed intelligent adaptive testing system (ISAT) is presented that allows, based on the analysis of test results, to determine the current level of training of students, to adapt the material to the level of their training. This system allows you to dynamically present questions of appropriate complexity in real time. When using the developed intelligent adaptive testing system, students will be provided with questions of the appropriate level of complexity, this will allow building an individual learning trajectory. The introduction of a predefined system will ensure the implementation of a personalized approach for organizing the learning process; will increase the accuracy of assessing students’ knowledge. The use of the technology of fuzzy expert systems allows for automated, intelligent control of students’ knowledge