Fuzzy expert system design for operating room air-condition control systems

Background: Bank loan approval is one of the important pillars of the banking system; it is the process of approving or denying a loan to companies or individual customers by the bank. The approval process has a lot of parameters to be taken into consideration, which is ambiguous in nature; hence, bank loan approval required special knowledge to be executed. Aim: Due to the ambiguity of the approval process, we proposed the use of a fuzzy expert system which proved to handle such ambiguous problems to help banks easily and accurately make decisions on bank credit approval. This proposed fuzzy expert system will help banks in making accurate decisions easily even in the absence of the domain expert on credit approval based on the knowledge of an expert in the field. Method: The proposed fuzzy expert system was developed using a fuzzy tool in MATLAB software and it has two stages, where the first stage decides on three output parameters which are repayment, ability manage, and risk. Total asset, credit repayment, 18% earning, business stability, credit missed, asset/debt ratio, bond rating, and dollar to Naira ratio are the input parameters for the first stage of the system. The second stage of the system used the output parameters values of the first stage as its input parameters to make the final decision on whether to approve the credit or not. Results: Using 0.938, 0.583, 0.715, 0.88, 0.104, 0.897, 0.842, and 0.856 membership degree for total asset, credit request (loan amount), 18% earning, business stability, asset/debt ratio, bond rating, and dollar to naira ratio respectively as an input to the first stage of the system, the resultant output were 0.625, 0.367, and 0.25 for repayment, ability manage, and risk respectively, and those were feed to the second stage and result in 0.656 loan membership degree which means the loan can be approve to the customer

This paper deal with structural deformation of one rectangular, closed, thin walled, steel made, thermally loaded channel and influence of this deformation on the air flow through the channel, recognised as coupled fluid-structure interaction problem. The geometrically non-linear deformation of thermally loaded channel was solved using commercial finite-element analysis software ANSYS. The unsteady Navier-Stokes equations in their conservation form defining fluid flow are solved using commercial computer fluid dynamics software ANSYS CFX. A fuzzy logic-based expert system has been developed to predict some results of structural and fluid flow numerical simulation instead of directly evaluating it by a time-consuming software coupling of finite-element and computer fluid dynamics numerical analysis. Three input parameters, namely temperature of channel inner walls, difference in temperature of inner and outer surface of channel wall, and factor of mesh size, are varied while solving the fluid-structure interaction problem. A pressure drop downstream the flow was considered as a computer fluid dynamics output parameter of importance, while Von Misses maximum stresses were an finite-element analysis output parameter observed. The developed FLES can predict the results of finite-element and computer fluid dynamics analysis, a pressure drop and Von Misses maximum stresses respectively, within a reasonable accuracy limit and at lower computation cost compared to the finite-element, and CFD computer fluid dynamics packages combined into series of multi-physics analyses.

End-users conduct various on-line activities. Quite often they re-visit websites and use services to perform repeated activities, such as on-line shopping. The end-users are required to enter the same information into various web services to accomplish such repeated tasks. Typing redundant information repetitively into such services negatively impacts the user experience. In this study, we propose an approach to prevent end-users from such unnecessary interruption. Our approach propagates user inputs across services by linking similar input and output parameters. Our approach also pre-fills values to the input parameters which could not be filled by the values from other input or output parameters. We propose a meta-data model for storing user inputs and an Input Parameter Context Model for identifying similar input or output parameters. We have implemented our approach and evaluated it on the real world services through an empirical study. Our overall approach can reduce on average 37% of input parameters through the execution of composed services.

Limitations on the representation of knowledge, and hence the inference methods, naturally limit the domains of the first-generation expert systems. In response to these limitations, fuzzy expert systems or the second-generation of expert systems provide two essential and unique advantages in the design, development, and implementation of expert systems: (1) fuzzy knowledge representation and (2) fuzzy inference methods. It is suggested that second-generation expert systems be designed and implemented with the expressive powers of representation and improved inference methods based on fuzzy logic. A number of research topics and application areas are identified. >

In this paper, a fuzzy expert system is described for decision-making in geographical information systems (GIS), because fuzzy logic and expert system technology are available methods for approximating human reasoning and enhancing the level of intelligence in GIS. This system computes a potentiality map for problem solution with a problem specification provided by the user and expert knowledge (by means of production rules). It is a fuzzy expert system using a generalised modus ponens based on the fuzzy implication of Brouwer-Godel which is implemented by a fast algorithm. Several kind of rules are accepted and especially gradual rules. We have experimented our system on "Palni Hills", a mountainous region of meridional India to locate areas where the profitability of agriculture could be generally the best. The system is explicitly designed to provide the user with a decision-making environment for the cartographic problem solution to be carried out in a flexible manner.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this article, we present the development of a fuzzy expert system (FES) for fuzzy modeling of the performance of counterflow Ranque-Hilsch vortex tubes for different geometric constructions. Experimental values were obtained from a detailed experimental investigation. With these experimental values, FES models of the Ranque-Hilsch vortex tube behavior were designed using the MATLAB 6.5 fuzzy logic toolbox in Windows XP running on an Intel 3.0-Ghz PC. For this process P, N, ξ, and L/D were chosen as input and ΔT h , ΔT c , ΔT as output parameters. FES results agree well with experimental data. It was found that the coefficient of multiple determination (R 2 value) between the actual and fuzzy predicted data is ΔT h = 0.9801, the R 2 value for ΔT c values is 0.9841, and the R 2 value for ΔT values is 0.9748.

Multilayer artificial neural networks (ANN) are often used for the solution of classification problems or for the time series forecasting. An appropriate number of learning and testing patterns must be available for the ANN training. Each training pattern is composed of n input parameters and m output parameters. Number m is usually given by the problem formulation, but the number n may be often selected from a greater set of input parameters. An optimal selection of input parameters is a very important task especially in a situation when the number of usable input parameters is great and the analytical relations between the input and output parameters are not known. The number of neurons in all ANN layers must be generally kept as small as possible because of the optimal generalisation ability. In this paper we present a possible way for the selection of significant input parameters (the so called markers), which are the most important ones from the point of view of influence on the output parameters. These parameters are later used for the training of ANN. A statistical approach is usually used for this reason [5]. After some experience in the ANN application we recognised that the approach based on mathematical logic, i. e. the GUHA method (General Unary Hypotheses Automaton) is also suitable for the determination of markers. Besides the minimisation of the number of elements in the input layer of ANN, also the number of neurons in hidden layers must be optimised. For this reason standard methods of pruning can be used, described e. g. in [1]. We have used this method in the following applications: - Optimisation of the intervals between the major overhaul of plane engines by the analysis of tribodiagnostic data. Only selected types of chemical pollution in oil can be taken into account. - Prediction of bleeding of patients with chronic lymphoblastic leukemia. Only a part of parameters about the patient is important from this point of view (see [2]). - Optimisation of the quality and reliability prediction of artificial resin production in chemical factory. Only a part of the production parameters (times of production phases, temperatures, percentage of components etc.) have straight influence on the product. - Optimisation of the prosody control in the text-to-speech synthesis. This application is described in the paper.

A fuzzy logic expert system to estimate intrinsic extinction vulnerabilities of marine fishes to fishing

Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine

Identification of a Moss Growth System using an Artificial Neural Network Model

The steel plant’s workplace environmental hazard parameters in Ilorin, Nigeria was evaluated using response surface methodology (RSM). Three environmental parameters (illumination, temperature and noise level) were measured. The data obtained were compared with the Occupational Safety and Health (OSHA) standard for the workplace environment. Based on the preliminary analysis of the workplace environment, five variables (No. of lightings, no. of windows, no. of machines, no. of workers and age of machines) were considered as input parameters. RSM was used to perform the modelling and optimization to identify functional relationships between the input and output parameters. Three (3) model equations one for each of the output parameters were developed and checked for adequacy and validity. All developed model equations were found to present functional relationships between input and output parameters. Hence, all developed model equations can be used as reliable tools for estimating, predicting, and conducting analysis for workplace environmental hazard. Best optimized results were selected based on desirability (0–1). Illumination, temperature and noise level got desirability rate of 0.921, 1.000 and 0.983 respectively. The outcome of this study suggested that the environmental parameters studied within the workplace do not conform with the OSHA standard and as a result may constitute long-term health risks to the workers.

In this study, chronic intestine illness symptoms such as sedimentation and prostate specific antigen are used for the design of fuzzy expert system to determine the drug (salazopyrine) dose. Suitable drug dose for patients is obtained by using data of ten patients. The results of some patients are compared with the doses recommended to them by the physician. As a result, it has been seen that proposed system is helped to shorten the treatment duration and minimize or remove the negative effects of determination of drug dose for helping physicians.

The fuzzy-associative metaheuristic approach addresses the urgent task of developing a marketing decision support system based on a fuzzy trained associative rules expert system, aimed at improving the accuracy and efficiency of consumer preference analysis. The proposed system combines the interpretability of fuzzy logic with data-driven learning via associative rules and parameter identification using an adaptive multi-agent optimization method. To achieve this goal, associative rule learning techniques (Apriori and FP-Growth) were used to extract frequent consumer behavior patterns. A fuzzy expert system was developed, in which the parameters of membership functions are optimized by the Adaptive Vibrating Particle System (AVPS) metaheuristic. Unlike traditional vibrating particle systems, AVPS integrates iteration-dependent control of particle positions, enabling global search in early iterations and local refinement at later stages, thus improving convergence speed and solution precision. The architecture was implemented using Python-based tools (TensorFlow, Keras, Pandas, mlxtend, Scikit-Fuzzy), and validated on the “Consumer Behavior and Shopping Habits” dataset. The fuzzy expert system achieved an accuracy of 0.98, outperforming human experts (0.80), traditional VPS optimization (0.93), and backpropagation-based training (0.90). The system also reduces reliance on manually tuned parameters and increases robustness to data incompleteness and noise. Scientific novelty lies in combining a fuzzy associative rule-learning framework with AVPS-based optimization, offering a scalable and interpretable decision-making mechanism. The developed system contributes to the advancement of intelligent recommendation engines, personalized marketing tools, and decision support systems in consumer-oriented analytics.

This article, written by Technology Editor Dennis Denney, contains highlights of paper SPE 94357, "Treating Uncertainties in Reservoir-Performance Prediction With Neural Networks," by J.P. Lechner, SPE, OMV A.G., and G. Zangl, SPE, Schlumberger Information Solutions, prepared for the 2005 SPE Europec/EAGE Annual Conference, Madrid, Spain, 13–16 June. In development projects, reservoir parameters are known only within certain ranges, which results in various realizations of the subsurface. Because of the computational time involved, simulation models to obtain a probability distribution of possible outcomes cannot cover all possible parameter combinations. Creating a response surface based on a reduced number of simulation runs becomes necessary. Such a response surface can be used to approximate results for several variations of input parameters. An approach in which reservoir response is captured by an artificial neural network (ANN) has been investigated. The trained ANN model was used in Monte Carlo simulations to generate the probability distribution of possible outcomes. Introduction Often, simulation models are built to design field-development plans. Because of the low level of information during the early development stage, a range of uncertainty in the prediction scenarios must be considered. Such uncertainties can be handled with ANNs. Numerical models (simulation models) are detailed and powerful predictive tools that can answer many questions regarding subsurface engineering. However, especially in the early development phase, uncertainties are large, and prediction results will span a broad range. To take into account the whole range of possible outcomes of reservoir simulation, optimization routines would have to run the numerical models perhaps thousands of times. Although the most likely solution that respects subsurface uncertainties is of interest, the most profitable solution for reservoir management must be found. Because of the computational time involved, these methodologies often are not used. Building a response surface that can predict many possible outcomes of a numerical simulation by processing a large variety of input parameters could provide a solution to this problem. The response surface must cover the non-linear dependencies between input and output parameters. Experimental design and response-surface modeling has been used to delineate subsurface uncertainty. Common applications include determining the probability distribution of original oil in place from geological models, quantifying uncertainty in predevelopment or preredevelopment projects, predicting production performance from different realizations of a geological model, and optimizing the location of new wells to maximize net profit. Methodology Fig. 1 shows the proposed workflow. As a first step, a limited number of simulation runs is carried out to define the most sensitive input parameters. In these runs, parameters are altered one at a time, either to the minimum or to the maximum. After selecting the most influential input parameters, three values (minimum, best-estimate, and maximum) are defined, and an experimental design is set up. The experimental design aims at obtaining the maximum information with a minimum number of simulation runs. However, practice showed that a minimum number of training data sets are required by the ANN to be able to make sufficiently accurate predictions. With these data sets containing varying input parameters and the corresponding output, an ANN is trained and tested with several additional simulation runs. After defining the probability distribution of the input parameters, the trained ANN is used as a proxy function for a Monte Carlo simulation. Thereby, a probability distribution of the desired output parameter (e.g., cumulative production after a certain number of years) is gained. The quality of the ANN is measured by the root-mean-square error of the deviation between the predicted and the actual output.

Peptic ulcer disease causes abdominal discomfort and pain. Helicobacter pylori bacteria, infection and long-term use of anti-inflammatory drugs are the most common causes of peptic ulcers. Untreated ulcers can lead to other more serious health complications. Gastric cancer is the second commonest cause of death from malignant disease. Speed performance is always quietly essential in detecting and treating peptic ulcer. Combining artificial intelligence with medical knowledge provides a faster and more accurate diagnosis. The main purpose of this study is to design a novel, inexpensive, reliable and quick Fuzzy Expert System for diagnosis of peptic ulcer disease. A data set of 101 Male adult Wistar rats with a weight range of 200–250 g were obtained from the Pasteur institute. Peptic ulcer induced by Indomethacin (50 mg/kg, 2 ml). A computational approach based on a Fuzzy Inference System (FIS) is suggested in this study for the evaluation of peptic ulcer. The Fuzzy Inference System was produced with Fuzzy C-Means and tuned using the Adaptive Neuro-Fuzzy Inference System model (ANFIS). In order to compare the two methods, the performance of the FIS was evaluated with a ROC curve that prepares the FCM accuracy of 90% and ANFIS accuracy of 85%. In conclusion, the Fuzzy Expert System can potentially increase the accuracy and efficiency of medical practices for peptic ulcer diseases to move towards more precision medicine and treatment. This applicable Fuzzy system may impact hospitals and health systems in improving efficiency and productivity, while reducing the cost of care. It can also be considered in medicine to reduce manual tasks and human error.