Fuzzy Reasoning System Research Articles

Evolutionary optimized fuzzy reasoning with mined diagnostic patterns for classification of breast tumors in ultrasound

Computer-aided-diagnostic (CAD) techniques are of great help in facilitating the diagnosis of breast ultrasound (BUS) images. Conventional CAD approaches segment the region of interest (ROI), extract image features, and finally train a classifier for outputting the final diagnostic decision. Although some reported CAD systems have achieved high accuracy, their performance heavily depended on the source of training data. Meanwhile, the classification procedures formed by low-level features are hardly explicable. A novel CAD approach for BUS data with human-in-the-loop is therefore proposed in this paper. Differing from conventional CAD systems, user participation is involved to manually give the scores for selected Breast Imaging Reporting and Data System (BI-RADS) features. Three experienced radiologists obtained 1062 breast ultrasound data samples with BI-RADS feature scores, which formed a quantified score dataset for mining diagnostic patterns based on a constant column biclustering method. The discovered diagnostic patterns (i.e., the biclusters) are input into a fuzzy reasoning system to infer the final diagnostic decision (i.e., benign or malignant). In addition, a particle swarm optimization (PSO) algorithm is employed to optimize the parameters of the membership function and judgment threshold for the fuzzy reasoning. The proposed system skips the image processing by utilizing the artificial scoring datasets, and the BUS images from different sources can be classified. Meanwhile, more understandable and acceptable results can be given by the fuzzy reasoning. In the experiment, 10-fold cross-validation was used to evaluate system performance. The average accuracy reached 96.81%, specificity 95.45%, sensitivity 97.72%, positive predictive value (PPV) 97.03%, and negative predictive value (NPV) 96.57%, outperforming previously reported methods.

A New Approach Applying Multi-objective Optimization using a Taguchi Fuzzy-based for Tourist Satisfaction Management

The paper describes the usage of the fuzzy Mamdani analysis and Taguchi method to optimize the tourism satisfaction in Thailand. The fuzzy reasoning system is applied to pursue the relationships among the options of a tour company in order to be used in Taguchi experiments as the responses. In this research, tourism satisfaction is carried out using L18 Taguchi orthogonal arrays on parameters such as budget, duration, hotel-choices, travel-options inside the country and theme of the travel are analyzed for one output objective as satisfaction. The output of the fuzzy reasoning system is used as an input in the response of each experiment in Taguchi method. But, the improvement is used for the mean de-fuzzified output in the same experiment. The result is estimated using Taguchi-Fuzzy application and if companies focus on the selected options, it is most probable to achieve more than 90 percent of satisfaction.

An assessment method of operator’s situation awareness reliability based on fuzzy logic-AHP

In digital control rooms, situation awareness (SA) reliability has become an important element affecting operator's reliability. In order to establish a more reasonable assessment method of SA reliability under the condition of very lack of data, based on the established influential factor model of SA reliability considering the causality relationship of performance shaping factors (PSFs) in this paper, a fuzzy logic and analytic hierarchy process (AHP)-based method is established to more objectively assess SA reliability. The weight of PSFs is identified using AHP, and a fuzzy logic method is used to simulate the fuzzy assessment and reasoning process of operator's SA reliability, and a standardized method is built to determine the fuzzy rule base of fuzzy reasoning system for SA reliability assessment for reducing the subjectivity and uncertainty of expert judgment. Finally, an example is provided to illustrate the specific application of the proposed method. The results show that the established method takes account of the weight of PSFs and their causal influencing relationship, and the fuzzy logic method used to assess SA reliability can overcome the subjectivity and uncertainty of expert judgment, which makes the assessment results more objective and realistic. Furthermore, the method can be used to get more SA error data and have a wide range of application value.

ПРИНЯТИЕ РЕШЕНИЙ ПО ИНВЕСТИРОВАНИЮ ИТ-ИННОВАЦИЙ НА ОСНОВЕ НЕЧЕТКОЙ ЭКСПЕРТНОЙ ИНФОРМАЦИИ

The subject of the research is the process of making managerial decisions for innovative IT projects investing. The paper focuses on the new approach to decision making on investing innovative IT projects using expert survey in a fuzzy reasoning system. As input information, expert estimates of projects have been aggregated into six indicators having a linguistic description of the individual characteristics of the project type "high", "medium", and "low". The task of decision making investing has been formalized and the term-set of the output variable Des has been defined: to invest 50-75% of the project cost; to invest 20-50% of the project cost; to invest 10-20% of the project cost; to send the project for revision; to turn down investing project. The fuzzy product model of making investment management decisions has been developed; it adequately describes the process of investment management. The expediency of using constructed production model on a practical example is shown.

OPTIMIZATION OF CLOSE RANGE PHOTOGRAMMETRY NETWORK DESIGN APPLYING FUZZY COMPUTATION

Abstract. Measuring object 3D coordinates with optimum accuracy is one of the most important issues in close range photogrammetry. In this context, network design plays an important role in determination of optimum position of imaging stations. This is, however, not a trivial task due to various geometric and radiometric constraints affecting the quality of the measurement network. As a result, most camera stations in the network are defined on a try and error basis based on the user's experience and generic network concept. In this paper, we propose a post-processing task to investigate the quality of camera positions right after image capturing to achieve the best result. To do this, a new fuzzy reasoning approach is adopted, in which the constraints affecting the network design are all modeled. As a result, the position of all camera locations is defined based on fuzzy rules and inappropriate stations are determined. The experiments carried out show that after determination and elimination of the inappropriate images using the proposed fuzzy reasoning system, the accuracy of measurements is improved and enhanced about 17% for the latter network.

Fuzzy reasoning spiking neural P systems revisited: A formalization

Research interest within membrane computing is becoming increasingly interdisciplinary. In particular, one of the latest applications is fault diagnosis. The underlying mechanism was conceived by bridging spiking neural P systems with fuzzy rule-based reasoning systems. Despite having a number of publications associated with it, this research line still lacks a proper formalization of the foundations.

A fuzzy inference and big data analysis algorithm for the prediction of forest fire based on rechargeable wireless sensor networks

High incidence and destructiveness of forest fire determine the importance of forest fire prediction or early detection. we find that the observation of local weather and human behaviors are the most correlated to forest fire important factors. In this paper, a fuzzy inference and big data analysis algorithm is proposed to assess fire risk and calculated the quantitative potential fire risk. These factors converted to triangular fuzzy numbers are calculated by fuzzification and output fire rating level. The continuous 24-hour weather information, which can reflect the high accurate status of forest environment, is collected by the rechargeable wireless sensor network. The fuzzy reasoning system was evaluated for Nanjing City region, the capital of Jiangsu Province, China. The high possibility of potential forest fires risk can be measured by this algorithm, therefore we need to pay more attention to prevent forest fire condition.

A Comparative Study for Position Regulation and Anti-Swing Control of Highly Non-Linear Double Inverted Pendulum (DIP) System Using Different Soft Com

This paper presents a comparative analysis for stabilization and control of highly non-linear, complex and multi-variable Double Inverted Pendulum on cart. A Matlab-Simulink model of DIP has been built using governing mathematical equations. The objective is to control both the pendulums at vertical position while cart is free to move in horizontal direction. The control of DIP was achieved using three different soft-computing techniques namely Fuzzy logic reasoning, Neural networks (NN's) and Adaptive neuro fuzzy inference system (ANFIS). The results show that the ANFIS controller is more effective as compared to other two controllers in terms of settling time (sec), maximum overshoot (degree) and steady state error. The regression (R) and mean square error (MSE) values obtained after training of Neural network were adequate and the training error obtained in ANFIS was also optimum. All the three controllers were able to stabilize the DIP system but ANFIS control provides better results as illustrated with the help of graphs and tables.

Model Deteksi Autis Secara Dini Berdasarkan Pendekatan Logika Fuzzy Inference System Metode Mamdani

ABSTRACT - Autism is a disorder in children who have speech and a different way of communicating with other children his age were caused by developmental abnormalities of the nervous system. The term autism was first introduced by Leo Kanner in 1943. It used to autism is considered to be a lifelong disorder that can not be handled, but today many are aware that the handling of the symptoms of autism as early as possible can bring remarkable changes impact on children's development who have it. To check whether a child has autism or not, to use international standards on autism. ICD-10 (International Classification of Diseases) in 1993 and DSM-IV (Diagnostic and Statistical Manual) in 1994 to formulate criteria for the diagnosis of Autism Infantil with the same content, which is currently used throughout the world. Fuzzy logic can be used in diagnosing autis early childhood. Fuzzy Inference System (FIS) is a model Mamdani Mamdani fuzzy reasoning system that can be applied in the process of diagnosing autis in children of various criteria for patient characteristics. Using a data processing application logic while Mamdani FIS decision support systems using MATLAB toolbox R2011b. The result of this study is to diagnose autis decision support early childhood Mamdani FIS approach is more precise and efficient. Keywords : Diagnoses Of Autism, Fuzzy Inference System MethodMamdani. ABSTRACT - Autism is a disorder in children who have speech and a different way of developmental abnormalities of the nervous system. The term autism was first introduced by Leo Kanner in 1943. It used to be autonomous as it is possible to bring a remarkable change impact on children's development who have it. To check whether a child has autism or not, to use international standards on autism. ICD-10 (International Classification of Diseases) in 1993 and DSM-IV (Diagnostic and Statistical Manual) in 1994 to formulate criteria for the diagnosis of Autism Infantil with the same content, which is currently used throughout the world. Fuzzy logic can be used in diagnosis autis early childhood. Fuzzy Inference System (FIS) is a model Mamdani Mamdani fuzzy reasoning system that can be applied in the process of diagnosing autism in children of various criteria for patient characteristics. Using a data processing application logic while MIS FIS decision support systems using MATLAB toolbox R2011b. The result of this study is to diagnose autism decision support early childhood Mamdani FIS approach is more precise and efficient. Keywords: Diagnoses Of Autism, Fuzzy Inference System MethodMamdani.

Fuzzy Case-Based Reasoning System

In this paper, we propose a fuzzy case-based reasoning system, using a case-based reasoning (CBR) system that learns from experience to solve problems. Different from a traditional case-based reasoning system that uses crisp cases, our system works with fuzzy ones. Specifically, we change a crisp case into a fuzzy one by fuzzifying each crisp case element (feature), according to the maximum degree principle. Thus, we add the “vague” concept into a case-based reasoning system. It is these somewhat vague inputs that make the outcomes of the prediction more meaningful and accurate, which illustrates that it is not necessarily helpful when we always create accurate predictive relations through crisp cases. Finally, we prove this and apply this model to practical weather forecasting, and experiments show that using fuzzy cases can make some prediction results more accurate than using crisp cases.

Rough-fuzzy rule interpolation

Fuzzy rule interpolation forms an important approach for performing inference with systems comprising sparse rule bases. Even when a given observation has no overlap with the antecedent values of any existing rules, fuzzy rule interpolation may still derive a useful conclusion. Unfortunately, very little of the existing work on fuzzy rule interpolation can conjunctively handle more than one form of uncertainty in the rules or observations. In particular, the difficulty in defining the required precise-valued membership functions for the fuzzy sets that are used in conventional fuzzy rule interpolation techniques significantly restricts their application. In this paper, a novel rough-fuzzy approach is proposed in an attempt to address such difficulties. The proposed approach allows the representation, handling and utilisation of different levels of uncertainty in knowledge. This allows transformation-based fuzzy rule interpolation techniques to model and harness additional uncertain information in order to implement an effective fuzzy interpolative reasoning system. Final conclusions are derived by performing rough-fuzzy interpolation over this representation. The effectiveness of the approach is illustrated by a practical application to the prediction of diarrhoeal disease rates in remote villages. It is further evaluated against a range of other benchmark case studies. The experimental results confirm the efficacy of the proposed work.

Designing and Implementation of Fuzzy Case-based Reasoning System on Android Platform Using Electronic Discharge Summary of Patients with Chronic Kidney Diseases.

Introduction:Case-based reasoning (CBR) systems are one of the effective methods to find the nearest solution to the current problems. These systems are used in various spheres as well as industry, business, and economy. The medical field is not an exception in this regard, and these systems are nowadays used in the various aspects of diagnosis and treatment.Methodology:In this study, the effective parameters were first extracted from the structured discharge summary prepared for patients with chronic kidney diseases based on data mining method. Then, through holding a meeting with experts in nephrology and using data mining methods, the weights of the parameters were extracted. Finally, fuzzy system has been employed in order to compare the similarities of current case and previous cases, and the system was implemented on the Android platform.Discussion:The data on electronic discharge records of patients with chronic kidney diseases were entered into the system. The measure of similarity was assessed using the algorithm provided in the system, and then compared with other known methods in CBR systems.Conclusion:Developing Clinical fuzzy CBR system used in Knowledge management framework for registering specific therapeutic methods, Knowledge sharing environment for experts in a specific domain and Powerful tools at the point of care.

A fuzzy reasoning system for evaluating the efficiency of cabin baggage screening at airports

The growing threat of unlawful interference and terrorist acts has led to widespread implementation of screening systems for checking people and baggage at airports. Introducing limits regarding objects permitted to be transported and screening procedures themselves have decreased the comfort of travelling and reduced the capacity of terminals. It is therefore important to examine the efficiency of screening, whether carried out under regular circumstances or in a situation where threat level is high. The purpose of this study is to develop an effective method and calculation tool making it possible to quickly and exactly determine the effectiveness of cabin baggage screening, depending on the equipment available, the choice of screening staff, and the organisational solutions applied. What is more, the human factor is of great significance as far as cabin baggage screening is concerned. It introduces a certain amount of subjectivity, imprecision, and incompleteness of description. Due to this, fuzzy reasoning solutions have been employed. The results indicate that it is possible for the efficiency of cabin baggage screening to vary significantly at various screening checkpoints (SC), even within one airport. It is also demonstrated that it is possible to actually manage the level of screening efficiency, also in a situation where the risk of an attack is greater than usual. One should avoid taking global decisions and, instead, focus on assessing screening at particular SCs and take steps on the basis of the results of such an assessment. Results obtained with the use of a computer tool under the name of COBAFAS demonstrate that it is then possible to improve the efficiency of screening without hindering the capacity of the airport at the same time.

Cognitive amalgam with a distributed fuzzy case-based reasoning system for an accurate cardiac arrhythmias diagnosis

The distributed case-based reasoning consists of distributing the reasoning through a set of agents and the cases through a set of case bases for improving the performance and for ensuring the maintainability of the reasoning system. The most important challenge of the medical applications resides in the precision of decisions; hence the novel decision support systems should introduce more aspects of reasoning for achieving this criterion. In this paper, we describe a cognitive amalgam with a distributed case-based reasoning enriched by an expert system which infer from an XML knowledge base extracted from the domain expertise. Each cognitive agent capitalises its knowledge from same class cases by using a machine learning algorithm enriched by a fuzzy similarity measures function. The aim of this proposition is to reach the optimal criteria of medical applications. The achieved experimental results applied in the diagnosis of complex type of data obtained from the electrocardiogram ECG signal improves our proposition and promotes more realisations in the medical domain.

인터넷 기반 멀티 전자침 구현

It is used the important method that Oriental doctor determines patient`s disease status observing patient`s state of tongue in Oriental medicine clinic. In this paper, it developed the how to use the pulse diagnosis and tongue diagnosis based on s mart based electronic acupuncture. It will do objective judgment without wrong diagnosis. In this paper, we developed the algorithm that it automatically determines patient health condition and smart electronic acupuncture kit using fuzzy logic and fuzzy reasoning system were completed. In this paper, Simulation results proved that acupuncture is effective than the traditional method of using electronic intelligence.

The NFE Model and its Convergence

A tightly-coupled NFE model combined with Neural network, Fuzzy reasoning and Expert system is constructed in this paper based on the neural network-fuzzy reasoning model by introducing expert system technology. The structure and modeling process of the generalized model are presented, and it is derived in theory that this model possesses convergence property and can be regarded as a category of complete information fusion model.

Adoption of Neural Network in Forecasting the Trends of Stock Market

The stock market is a very complicated nonlinear dynamic system, it has both the high income and high risk properties. So the forecast of stock market trend has been always paid attention to by stockholders and invest organization. Forecasting stock prices and their trends are important factors in achieving significant gains in financial markets. In this paper, a neural network-driven fuzzy reasoning system for stock price forecastis proposed on the basis of the trends of stock market.

Weighted fuzzy interpolative reasoning systems based on interval type-2 fuzzy sets

In this paper, we present a weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on interval type-2 fuzzy sets. We also apply the proposed weighted fuzzy interpolative reasoning method to deal with the truck backer-upper control problem. The proposed method satisfies the seven evaluation indices for fuzzy interpolative reasoning. The experimental results show that the proposed method outperforms the existing methods. It provides us with a useful way for dealing with fuzzy interpolative reasoning in sparse fuzzy rule-based systems.

Intuitionistic fuzzy system for fingerprints authentication

Using a fuzzy logic control system, we propose in this paper an optimized method to reduce the points number to be used in order to identify a person using fuzzy fingerprints. Two fingerprints are similar if n out of N points from the skin are identical. We discuss a criteria to choose these points. Our method compares two matching sets and selects the optimal set from these, using a fuzzy reasoning system based on intuitionistic fuzzy numbers. The advantage of our method with respect to the classical existing methods consists in a smaller number of calculations.

Fuzzy rule-based reasoning approach for event detection and annotation of broadcast soccer video

This paper presents an approach for event detection and annotation of broadcast soccer video. It benefits from the fact that occurrence of some audiovisual features demonstrates remarkable patterns for detection of semantic events. However, the goal of this paper is to propose a flexible system that can be able to be used with minimum reliance on predefined sequences of features and domain knowledge derivative structures. To achieve this goal, we design a fuzzy rule-based reasoning system as a classifier which adopts statistical information from a set of audiovisual features as its crisp input values and produces semantic concepts corresponding to the occurred events. A set of tuples is created by discretization and fuzzification of continuous feature vectors derived from the training data. We extract the hidden knowledge among the tuples and correlation between the features and related events by constructing a decision tree (DT). A set of fuzzy rules is generated by traversing each path from root toward leaf nodes of constructed DT. These rules are inserted in fuzzy rule base of designed fuzzy system and employed by fuzzy inference engine to perform decision-making process and predict the occurred events in input video. Experimental results conducted on a large set of broadcast soccer videos demonstrate the effectiveness of the proposed approach.