Neuro-fuzzy Expert System Research Articles

The development of a method for increasing the reliability of the assessment of the state of the object

The object of the research is assessment of object with different degrees of embeddedness. The subject of the research is the process of assessing the state of objects using the apparatus of neuro-fuzzy expert systems, the apparatus of relational analysis and bio-inspired algorithms. The problem that is solved in the research is to increase the reliability of the assessment of the objects state, regardless of the number of attachments. The originality of the research is that: – possibility of increasing the reliability of the object state assessment due to the parallel use of two bio-inspired algorithms; – taking into account the degree of awareness of the object state, due to the application of correction coefficients for the degree of awareness; – construction of both object and relational models, which allows to increase the reliability of assessment of the objects state; – possibility of combining the results of the work of bio-inspired algorithms, which makes it possible to mutually verify the correctness of the work of each of the algorithms; – universality of solving the task of assessing the state of objects with different degrees due to the hierarchical nature of their description; – possibility of simultaneously searching for a solution in different directions; – adequacy of the obtained results. An example of the use of the proposed method is presented on the example of solving the task of determining the composition of an operational group of troops (forces) and elements of its operational construction. The specified example showed an increase in the reliability of the assessment of the objects state by an average of 20 % due to the use of additional improved procedures. It is advisable to use the proposed method to solve the problems of assessing the state of multidimensional objects in conditions of uncertainty and risks, which are characterized by high requirements for the reliability of the information obtained

Speed Control of Induction Motors using Neuro Fuzzy Expert Systems

Induction motors and their speed control is critical aspect in several real life applications. Often, the proportional integral derivative (PID) controlling mechanism is found in several applications in industries where speed of motors or induction motors is to be controlled. The PID controller is specifically useful since it tries to minimize the steady state error as well as increase the response or speed of the system, thereby incorporating the benefits of proportional derivative and proportional integral control. However, the real time operation of PID controllers is challenging due to its tuning. The controlling mechanism is critically important for the application of the PID. Previously, manual tuning was used which needed experts and was also prone to errors. With the advent of sophisticated optimization tools, automatic tuning has gained momentum. In this work, a combination of neural networks and fuzzy logic often called neuro fuzzy expert systems has been proposed for the speed control of induction motors. It has been shown that proposed system is capable to attain better results compared to conventional techniques. The system has been designed on Matlab/Simulink. Key Words: Induction Motors, Speed Control, PID Controllers, Fuzzy Logic, Neuro Fuzzy Expert Systems.

A Rule Based Intelligent Software to Predict Length of Stay and the Mortality Rate in Trauma Patients in the Intensive Care Unit.

Intensive Care Unit (ICU) has the highest mortality rate in the world. ICU has special equipment that leads to the hospital's most costly parts. The length of stay in the ICU is a special issue, and reducing this time is a practical approach. We aimed to use artificial intelligence to help early and timely diagnosis of the disease to help with health. We designed a rule-based intelligent system to predict the length of stay and the mortality rate of trauma patients in ICU. A neuro-Fuzzy and eight machine learning models were used to predict the mortality rate in trauma patients in ICU. The performances of these techniques were evaluated with accuracy, sensitivity, specificity, and area under the ROC curve. Decision-Table was used to predict the length of stay in trauma patients in ICU. For comparison, eight machine learning models were used. The method is compared based on Mean absolute error and relative absolute error (%). Neuro-Fuzzy expert system and Decision-Table showed better results than other techniques. Accuracy, sensitivity, specificity, and ROC Area of Nero-Fuzzy are 83.6735, 0.9744, 0.3000, 0.8379, and 1, respectively. The mean absolute error and Relative absolute error (%) of the Decision-Table model are 4.5426 and 65.4391, respectively. Neuro-Fuzzy expert system with the highest level of accuracy and a Decision-Table with the lowest Mean absolute error, which are rule-based models, are the best models. Therefore, these models are recommended as a valuable tool for prediction parameters of ICU as well as medical decision-making.

Exposure of Botnets in Cloud Environment by Expending Trust Model with CANFES Classification Approach

Many cloud service providers offer access to versatile, dependable processing assets following a compensation as-you-go display. Investigation into the security of the cloud focusses basically on shielding genuine clients of cloud administrations from assaults by outer, vindictive clients. Little consideration is given to restrict malicious clients from utilizing the cloud to dispatch assaults, for example, those as of now done by botnets. These assaults incorporate propelling a DDoS attack, sending spam and executing click extortion. Bots’ detection in the cloud environment is a complex process. The purpose of this study was to create a multi-layered architecture that could detect a variety of existing and emerging botnets. The goal is to be able to detect a larger range of bots and botnets by relying on several techniques called trust model. On this work, the port access verification in trust model is achieved by a Heuristic factorizing algorithm which verifies the port accessibility between client-end-user and client server. Further, back-off features are extracted from the particular node and all these structures are trained and categorized with a Co-Active Neuro Fuzzy Expert System (CANFES) classifier. The performance of the proposed bot detection system in the internet environment is analyzed latency, detection rate, packet delivery ration, energy availability and precision.

A joint neuro-fuzzy malaria diagnosis system

Diagnosis takes a definitive role in the course of determining about clarifying patients as either having or not having the disorder. This method is relatively sluggish and tedious. Various fact-finding and data-mining methods are part of the approach of this article. In the development of the Collaborative Neuro-Fuzzy Expert System diagnosis platform, Neural Networks and Fuzzy Logic, which are artificial intelligence methods, have been merged together. Oral interviews were conducted with medical professionals whose experience was caught in the Expertise Developed Fuzzy Proficient Scheme. With Microsoft Visual C # (C Sharp) Programming Language and Microsoft SQL (Structured Query Language) Server 2012 to handle the database, the Neuro-Fuzzy Expert Framework diagnostic software was introduced. To capture the predominant signs, questionnaires were administered to the patients and filled out by the doctors on behalf of the patients.

On the Development of Neuro-Fuzzy Expert System for Detection of Leghemoglobin (NFESDL) in Legumes

The regular supply of affordable complete meals most especially protein from animals has been threatened. Protein sourced from animals carry too many health risks. Obesity, cancer, diabetes, etc., have been traced to the consumption of meats, most especially beef. Medical experts claim that some ailments are as a result of the chemically processed feeds given to raise animals. Therefore, an alternative to meat from plants is imperative. This led to the development of a neuro-fuzzy expert system for detection of leghemoglobin in legumes. This work utilized production rule-base technique and forward-chaining mechanisms with linguistic antecedent conditions to detect the presence of leghemoglobin in plants. To further remove clumsiness and ambiguity in the identification process, metrics/weights were obtained and attached to each morphological feature. MATLAB platform was employed for the development of the system. Class and objects were used to model the information elicited. The result is a system that detects the presence of leghemoglobin in plants. Keywords: Expert system, inference system, neuro-fuzzy, dataset, leghemoglobin

Developing and Evaluating a Neuro-Fuzzy Expert System for Improved Food and Nutrition in Nigeria

Protein is a class of food needed daily by humans and even animals. Protein is rated as 20% to 30% of daily food requirements making it a very significant part of daily needs in compliance with the International Labour Organization (ILO) order on food. This class of food from animals has been threatened and carries a lot of health risks unlike protein from plant sources. The need for an alternative to plant protein led to this work. A neuro-fuzzy expert system for detection of leghemoglobin in legumes was developed and evaluated. Knowledge acquisition was done by oral interview of prominent biochemists and botanists that provided key technical facts on leghemoglobin and visits to botanical gardens of Society for Underutilized Legumes (SUL) in Nigeria. Production rule-base technique and forward-chaining mechanisms with linguistic antecedent conditions were used. MATLAB platform was employed for the development of the system. Confusion matrix was employed for the performance evaluation of the developed system. The result is a neuro-fuzzy expert system with gaussian membership functions with accuracy of 100% as against 99.56% for triangular, trapezoidal and gaussian combination functions, precision of 100% for all the membership functions evaluated and recall of 100% for gaussian membership functions and 99.53% for triangular, trapezoidal and Gaussian combination functions.

Development of a complex method for finding a solution for neuro-fuzzy expert systems

Development of a complex method for finding a solution for neuro-fuzzy expert systems

Performance Analysis of Glioma Brain Tumor Segmentation Using Ridgelet Transform and Co-Active Adaptive Neuro Fuzzy Expert System Methodology

Objective: The Glioma brain tumor detection and segmentation methods are proposed in this paper using machine learning approaches. The primary objective of this paper is to provide high level of tumor region segmentation using optimization and machine learning techniques. Methods: The boundary edge pixels are detected using Kirsch's edge detectors and then contrast adaptive histogram equalization method is applied on the edge detected pixels. Then, Ridgelet transform is applied on this enhanced brain image in order to obtain the Ridgelet multi resolution coefficients. Further, features are derived from the Ridgelet transformed coefficients and the features are optimized using Principal Component Analysis (PCA) method and these optimized features are classified into Glioma or non-Glioma brain images using Co-Active Adaptive Neuro Fuzzy Expert System (CANFES) classifier. Results: The proposed method with PCA and CANFES classification approach obtains 97.6% of sensitivity (Se), 98.56% of Specificity (sp), 98.73% of Accuracy (Acc), 98.85% of Precision (Pr), 98.11% of False Positive Rate (FPR) and 98.185 of False Negative Rate (FNR), then the proposed Glioma brain tumor detection method using CANFES classification approach only.

Development of an improved method for finding a solution for neuro-fuzzy expert systems

Nowadays, artificial intelligence has entered into all spheres of human activity. However, there are some problems in the analysis of objects, for example, there is a priori uncertainty about the state of objects and the analysis takes place in a difficult situation against the background of intentional (natural) interference and uncertainty. The best solution in this situation is to integrate with the data analysis of information systems and artificial neural networks. This paper develops an improved method for finding solutions for neuro-fuzzy expert systems. The proposed method allows increasing the efficiency and reliability of making decisions about the state of the object. Increased efficiency is achieved through the use of evolving neuro-fuzzy artificial neural networks, as well as an improved procedure for their training. Training of evolving neuro-fuzzy artificial neural networks is due to learning their architecture, synaptic weights, type and parameters of the membership function, as well as the application of the procedure of reducing the dimensionality of the feature space. The analysis of objects also takes into account the degree of uncertainty about their condition. In the proposed method, when searching for a solution, the same conditions are calculated once, which speeds up the rule revision cycle and instead of the same conditions of the rules, references to them are used. This reduces the computational complexity of decision-making and does not accumulate errors in the training of artificial neural networks as a result of processing the information coming to the input of artificial neural networks. The use of the proposed method was tested on the example of assessing the state of the radio-electronic environment. This example showed an increase in the efficiency of assessment at the level of 20–25 % by the efficiency of information processing

DEVELOPMENT OF AN ADVANCED METHOD OF FINDING SOLUTIONS FOR NEURO-FUZZY EXPERT SYSTEMS OF ANALYSIS OF THE RADIOELECTRONIC SITUATION

Nowadays, artificial intelligence has entered into all spheres of our life. The system of analysis of the electronic environment is not an exception. However, there are a number of problems in the analysis of the electronic environment, namely the signals. They are analyzed in a complex electronic environment against the background of intentional and natural interference. Also, the input signals do not match the standards due to the influence of different types of interference. Interpretation of signals depends on the experience of the operator, the completeness of additional information on a specific condition of uncertainty. The best solution in this situation is to integrate with the data of the information system analysis of the electronic environment and artificial neural networks. Their advantage is also the ability to work in real time and quick adaptation to specific situations. These circumstances cause uncertainty in the conditions of the task of signal recognition and fuzzy statements in their interpretation, when the additional involved information may be incomplete and the operator makes decisions based on their experience. That is why, in this article, an improved method for finding solutions for neuro-fuzzy expert systems of analysis of the electronic environment is developed. Improving the efficiency of information processing (reducing the error) of evaluation is achieved through the use of neuro-fuzzy artificial neural networks that are evolving and learning not only the synaptic weights of the artificial neural network, but also the type and parameters of the membership function. High efficiency of information processing is also achieved through training in the architecture of artificial neural networks by taking into account the type of uncertainty of the information that has to be assessed and work with clear and fuzzy products. This reduces the computational complexity of decision-making and absence of accumulation of an error of training of artificial neural networks as a result of processing of the arriving information on an input of artificial neural networks. The use of the proposed method was tested on the example of assessing the state of the electronic environment. This example showed an increase in the efficiency of assessment at the level of 20–25 % on the efficiency of the processing information

An Performance Analysis of Thyroid Tumor Detection using CANFES Classification with Optimized Features

The human body metabolism is maintained by proper segregation of thyroid hormones from thyroid gland. The abnormal cells are formed in this thyroid gland due to improper food habits and genes from forefathers. These abnormal cells lead to the development of tumor regions in the thyroid gland. In this paper, a tumor region in thyroid ultra sound image is detected and segmented using Co-Active Adaptive Neuro Fuzzy Expert System (CANFES) classification framework with its improved performance. The proposed framework is split into three main modules as Enhancement, Gabor transform, CANFES classification trained by feature extraction process with tumor segmentation method. The extracted features are optimized using Genetic Algorithm (GA). The CANFES classification with GA improves approximately 2.5% of classification rate when compared with CANFES classification without GA This proposed system achieves 97.7% of sensitivity, 99.8% of specificity and 99.1% of accuracy.

Optimized Scheduling of Generating Unit for Economic Load Dispatch using Neuro-Fuzzy Expert Systems

Optimized Scheduling of Generating Unit for Economic Load Dispatch using Neuro-Fuzzy Expert Systems

Stages of Conceptualization and Formalization in the Design of the Model of the Neuro-Fuzzy Expert System of Professional Selection of Pupils

Buyak B.B., Tsidilo I.M., Repskiy V.Í. and Lyalyuk V.P. Stages of Conceptualization and Formalization in the Design of the Model of the Neuro-Fuzzy Expert System of Professional Selection of Pupils.
 The article describes the problem of designing a neuro-fuzzy expert system of professional selection at the stages of conceptualization and formalization, which involves the definition of concepts, relationships and management mechanisms necessary to describe the solution of problems in the chosen subject field. The structural model of the decision making system for determining the professional selection of students for training in IT specialties is substantiated. Three subsystems are proposed as structural components for studying: psychological peculiarities, personal qualities, factual knowledge, abilities and skills of students. The quality of the system’s operation is determined by the use of various techniques for acquiring knowledge on the basis of which the knowledge base of the neuro-fuzzy system and the combination of the use of fuzzy and stochastic data will be formed.

Human Motion Recognition Using E-textile Sensor and Adaptive Neuro-Fuzzy Inference System

The present paper is intended to introduce a new approach in order to classify human body movements by using textile sensor embedded fabrics. An intelligent processing model embedded in muscle activity pants has been developed based on adaptive neuro-fuzzy inference System (ANFIS) in order to recognize the types of several standard human motions. The processing circuit would digitize motion data from the fabric stretch sensor developed in previous research. Data were continuously flowed into the memory of microcontroller chip and processed in order to get important factors like as input variables of the classification model. The parameters chosen for developing the ANFIS system are the average of amplitude (AMP), the standard deviation of amplitude (STD), and the average cycle (CYC). The final decision on the types of the motions would be stored or transmitted to nearby monitoring devices. In this study, laboratory scale experiments were conducted for four different types of human motions such as walking, jumping, running, and sprinting in order to examine the feasibility of the ANFIS model developed. The accuracy of ANFIS model was compared with results of fuzzy inference system (FIS) model and artificial neural network (ANN) model. As expected, the results indicated that the adaptive neurofuzzy expert system developed could be used as one of the smart simulators in order to recognize human motions with robust and high accuracy classification rate. Based on the test statistics, ANFIS model has been proved to be superior to ANN and FIS in terms of classification rate.

The Development of a Neuro-Fuzzy Expert System for Wastewater Treatment Processes Monitoring

This paper presents the extension of the hardware-software experimental system SENFpHCTRL initially developed for wastewater pH control, for monitoring other parameters (total suspended solids, extractable, biochemical and chemical oxygen demand, phenols and chlorides) from other chemical processes of a treatment plant, such as: precipitation, coagulation and flocculation, oxidation and reduction (processes briefly presented at the begining of the paper). The process of expanding the SENFpHCTRL system involved the adding to the initial expert system of a set of heuristic knowledge for other parameters and the development of the system graphical interface. It is also presented the adapted form of the system hardware component at PC and microcontroller level and the results of the simulations made with the extended system.

Dental Expert System

The early 20th century brought along a better understanding of dental disease and prevention. However, advancement in computer technology has encouraged researchers to develop software for assisting doctors in making decision without consulting the specialists directly. Software development exploits the potential of human intelligence such as reasoning, making decision, learning by experience and many others. The software was not meant to replace the specialist or doctor, yet it was developed to assist general practitioners and specialist in diagnosing and predicting patient's condition from certain rules or experience. The goal of this paper is to demonstrate the practical applicability of Information and Communication Technology (ICT) for the diagnosis of dental ailments based on a set of symptoms. Expert system is a computer system that emulates the decision-making ability of a human expert. Expert system in medical applications reduces cost, time, human expertise and medical error. This paper on Dental Expert System is a desktop based application designed to replace the manual system used by most Medical organizations in treatment. This paper is aimed at emphasizing on the use of expert systems to diagnose mild dental problems. In this paper, the ED Expert System which is referred to as Electronic Dentist was developed based on the Coactive Neuro-Fuzzy Expert System Model and implemented by using C# programming language. The expert system is a simple and user friendly desktop application which could be used by anyone so as to complement the manual process of diagnosis. General Terms Symptoms, Artificial Intelligence, Neural Network.

Implementing a neuro fuzzy expert system for optimising the performance of chemical recovery boiler

In chemical recovery boilers of paper mills, main steam outlet temperature control cannot be solved by straight forward automation control. As prior knowledge of the mechanism to maximise steam generation without affecting steam main temperature is unknown, a backpropogation supervisory neural network has been designed which exhibits a good degree of reinforcement learning. Various parameters considered encompassing concentration, composition and firing load of black liquor solids may not have ideal fixed values. Hence, a type 2 fuzzy logic model has been designed which in turn monitors the parameters and predicts the results. Errors are fed back iteratively through the backpropogation network, until the network learns the model. Fuzzy C-means clustering technique has been used to find coherent clusters. Then sensitivity analysis has been done to identify the parameters playing a significant role in obtaining the results. As it can be observed that the behaviour is stochastic, particle swarm optimisation has been implemented to optimise the combined effect of all parameters. Through this tool connecting steam attemperation control and smart soot blowing, clean heating surface is ensured resulting in enhanced green energy output and availability.

Adaptive neuro-fuzzy expert systems for predicting specific energy consumption and energy stability margin in crab walking of six-legged robots

In this paper, adaptive neuro-fuzzy expert systems have been designed to predict specific energy consumption and normalized energy stability margin for crab walking of a six-legged robot. The application of this technique for crab gait generation of the six-legged robot is new, to the best of the authors' knowledge. Three approaches based on adaptive neuro-fuzzy inference system have been developed and their performances are compared with each other. Genetic algorithm-tuned multiple adaptive neuro-fuzzy inference systems are found to perform better than other approaches. This could be due to a more exhaustive search carried out by the genetic algorithm compared to back-propagation algorithm and the use of two separate adaptive neuro-fuzzy inference systems for two different outputs.

Neuro-Fuzzy Expert System for evaluating the performance of Distributed Software System Architecture

A Neuro-Fuzzy Performance Evaluation Model (NFPEM) proposed in Akinnuwesi, Uzoka, Olabiyisi, and Omidiora (2012) was reviewed in this work with the view of modifying it and thus making it flexible and scalable. The neuro-fuzzy expert system (NFES) reported in this paper is an enhancement to NFPEM with expert system components. NFES can be used to evaluate the performance of Distributed Software System Architecture (DSSA) with user-centric variables as parameters for performance measurement. The algorithm developed for NFES was implemented using Coldfusion programming language and MySQL relational database management system. The prototype of NFES was simulated using some life data and the performance results obtained point to the DSSA responsiveness to the users' requirements that are defined at the requirements definition phase of the software development process. Thus the performance value is a qualitative value representing DSSA (i.e. system) responsiveness.