Fuzzy Expert System Model Research Articles

Optimizing cost of quality in commercial projects using fuzzy expert system

ABSTRACT Previous studies on the Cost of Quality (COQ) have focused on direct costs, employing prevention, appraisal, and failure costs (PAF). However, the advent of ISO 9001–2015] edition underscores the importance of the risk approach in quality management, necessitating the inclusion of project risks in COQ calculation. This paper introduces a prototype Fuzzy Expert System (FES) model tailored for determining optimal COQ in commercial projects while accounting for project risks. Using the means of the Prevention - Appraisal - Failure model and utilizing data gathered from prior research and validated through official questionnaires and interviews, this study assigns weights to quality risks and allocates them to respective COQ components. Risks such as inadequate planning, coordination issues, construction errors, and delays are identified as influential factors affecting COQ. Our findings suggest that COQ in commercial projects typically amounts to approximately five percent of the project budget. Validation of the prototype Fuzzy Expert System achieved through application to five actual cases (including two for system familiarization and three for validation), demonstrates an accuracy rate of 95%. The prototype Fuzzy Expert System emerges as a valuable tool for COQ evaluation, serving as a reliable checkpoint for individuals with varying levels of expertise in the field.

Green Supplier Assessment with Fuzzy Expert System Approach

Today, reasons such as increasing pollution on a global extent and limited raw material resources are increasing the interest in green supply chain management (GSCM). GSCM includes the processes starting from the very beginning of the production process of a product, completing the production, delivering the product to the customer, and recycling the product at the end of its useful life. Its main purpose is to eliminate or minimize the damages caused to the environment in all of these processes. In order to achieve this goal, it has great importance to assess the suppliers, which are one of the most important components of the production process, in terms of becoming a green supplier.
 
 In this study, a fuzzy expert system model has been developed to assess the green suppliers based on green production technology, environmental management system, pollution control, product cost, quality, and lead time criteria. Mean Square Error (MSE) and coefficient of determination (R2) have been calculated to measure the performance of the developed model. These calculated values show that the green supplier assessment performance of the developed model is quite high.

Proposal of neural network model for neurocognitive rehabilitation and its comparison with fuzzy expert system model

This article focuses on the development of algorithms for a smart neurorehabilitation system, whose core is made up of artificial neural networks. The authors of the article have proposed a completely unique transfer of ACE-R results to the CHC model. This unique approach allows for the saturation of the CHC model domains according to modified ACE-R factor analysis. The outputs of the proposed algorithm thus enable the automatic creation of a personalized and optimized neurorehabilitation plan for individual patients to train their cognitive functions. A set of tasks in 6 levels of difficulty (level 1 to level 6) was designed for each of the nine CHC model domains. For each patient, the results of the ACE-R screening helped deter-mine the specific CHC domains to be rehabilitated, as well as the initial gaming level for rehabilitation in each domain. The proposed artificial neural network algorithm was adapted to real data from 703 patients. Experimental outputs were compared to the outputs of the initially designed fuzzy expert system, which was trained on the same real data, and all outputs from both systems were statistically evaluated against expert conclusions that were available. It is evident from the conducted experimental study that the smart neurorehabilitation system using artificial neural networks achieved significantly better results than the neurorehabilitation system whose core is a fuzzy expert system. Both algorithms are implemented into a comprehensive neurorehabilitation portal (Eddie), which was supported by a research project from the Technology Agency of the Czech Republic.

FUZZY EXPERT SYSTEM TO ESTIMATE THE COST OF QUALITY IN RESIDENTIAL CONSTRUCTION PROJECTS

Although the concept of the cost of quality (CoQ) is not new, earlier research focused on the direct costs of quality management, such as those associated with prevention, appraisal, and failure (PAF approach). The new ISO 9001-2015 edition has presented a new idea based on the risk approach rather than the process approach, which means that project risks have a crucial role in quality management and hence in the calculation of the CoQ. A prototype fuzzy expert system model is introduced in this study to calculate the optimum CoQ while taking project risks into account. The weights assigned to each CoQ component as well as the quality risks were gathered from previous studies and verified through formal surveys and interviews. The risks affecting CoQ include inadequate planning, communication and coordination failures, construction errors/omissions, and delays. The CoQ was found to be about 3% of the project budget. The prototype fuzzy expert system was validated with an accuracy of 96%.

Fuzzy logic expert system with multi-objective optimization of carbon nanotube infused nanopaint surface characteristics analysis using IRB 1410 robot

In this investigative study, the Taguchi design (L9) of experiments with TOPSIS multi-objective optimization is used for the robot nanopainting. The optimization objective is to maximize the film glue individually and minimize the surface blemishes and film thickness by varying the IRB 1410 robot painting parameters, such as the robot speed, pressure, and distance. The multi-wall carbon nanotube is infused into the paint materials using the ultrasonication process, and the mechanical properties are analysed with a comparison to regular paint materials. The virtual robot cycle time analysis is carried out for three different robot pathway patterns, namely linear, zig-zag, and circular. It is then compared with real-time experimental robot nanopaint on cold rolled close annealing steel materials. The obtained results show that surface roughness and thickness are minimized by 54% and 33.4%, respectively, using the robot nano spray coating when compared with regular spray coating. The film adhesive of robot Nano spray coating is maximized by 2.8% more than normal spray coating. A fuzzy logic expert system model is used to evaluate the surface finish characteristics of the nanopaint surface with low prediction error and with 89.2% confidence level. Heat transfer analysis of the robot nano-coated substrate is compared with that of the robot normal-coated substrate.

Fuzzy expert systems for prediction of ICU admission in patients with COVID-19

The pandemic COVID-19 disease has had a dramatic impact on almost all countries around the world so that many hospitals have been overwhelmed with COVID-19 cases. As medical resources are limited, deciding on the proper allocation of these resources is a very crucial issue. Besides, uncertainty is a major factor that can affect decisions, especially in medical fields. To cope with these issues, we use fuzzy logic (FL) as one of the most suitable methods in modeling systems with high uncertainty and complexity. We intend to make use of the advantages of FL in decisions on cases that need to treat in ICU. In this study, an interval type-2 fuzzy expert system is proposed for the prediction of ICU admission in COVID-19 patients. For this prediction task, we also developed an adaptive neuro-fuzzy inference system (ANFIS). Finally, the results of these fuzzy systems are compared to some well-known classification methods such as Naive Bayes (NB), Case-Based Reasoning (CBR), Decision Tree (DT), and K Nearest Neighbor (KNN). The results show that the type-2 fuzzy expert system and ANFIS models perform competitively in terms of accuracy and F-measure compared to the other diagnosis systems.

Neuro-fuzzy algorithm for clustering multidimensional objects in conditions of incomplete data

The paper is considered development of fuzzy expert system model for identifying faults in complex systems using data mining methods based on searching for hidden patterns in databases. The use of neural network technologies makes it possible to detect nonlinear dependencies of input and output data, improve the quality of an objective assessment of the state of complex technical objects, which ultimately will reduce the number of emergency situations during operation. A method is proposed for identifying the optimal number of fuzzy clusters in the space of training examples and determining, on their basis, the parameters of the membership functions for the input variables and inference results. Considered a neuro-fuzzy algorithm for clustering multidimensional objects in conditions of incompleteness and fuzzy initial information.

Fuzzy-Expert System for Indicator and Quality Evaluation of Teaching and Learning Processes Online Study Programs

The quality of education is essential so that it needs to be improved. This study aims to design and realize an information system for monitoring and evaluating the quality of the online teaching and learning process using the fuzzy-expert system algorithm. By using the fuzzy-expert system model, the Tsukamoto method can monitor the online teaching and learning process. The tsukamoto method is used because it is very flexible. Fuzzy-expert system can process a lot of data and get better results than using the excel application. The assessment parameters in this study were the presence of lecturers and students, the suitability of the lecture material, the development of an academic atmosphere, the assessment of student skills, the suitability of learning support facilities and infrastructure, the performance of education personnel in the learning process, assessment of student attitudes, student knowledge, lecturers’ role models, and lecturer performance in teaching. The fuzzy stages used in the system are the process of fuzzification, rules, and defuzzification. This study concludes that fuzzy assessment can be carried out by producing good evaluations for the value range of 80-100, enough for the range of values of 54-79, and less in the range of values 0-55.

Factors associated with induction of labour and pregnancy outcomes in 14 healthcare facilities in Sri Lanka

Induction of Labour (IOL) is an important practice that is carried out commonly in modern day obstetrics. In medium to large healthcare facilities in Sri Lanka, it is estimated that approximately 35.5% of all deliveries involve IOL. This research attempts to identify the factors that affect IOL and to assess the association between IOL and the pregnancy outcome. In this study, we considered 18309 women who were admitted to 14 healthcare facilities for delivery in 3 randomly selected provinces in Sri Lanka (Western, Southern and Eastern provinces), during July to October 2011. Multinomial Logistic Regression model (MLR) and Fuzzy Expert System (FES) were used to identify the factors that lead to IOL. The MLR model predicts IOL with a classification rate of 65.5% and the FES predicts IOL with an accuracy of 55.10%. Maternal age, number of previous caesarian sections, number of previous births, estimated gestational age, Pre-Eclampsia, number of previous pregnancies, Placenta Preavia, Abruption Placenta, total number of neonates delivered, birth weight and Maternal Severity Index (MSI) were identified as factors associated with IOL. Neonatal status after seven days of life can also be predicted using the developed FES. FES is predictive of IOL and birth outcome, where if the FES score is between 0.8570 and 0.8854, the patient will belong to the induced group and the baby would be alive after seven days of birth. This study concludes that, MLR and FES models can be used to predict IOL outcomes. These findings can be informative to healthcare providers when counselling women for labour induction and develop evidence-based protocols on IOL.

An intelligent approach for thermal-hydraulic studies on safety and efficiency of nuclear power plant

An effective identification of breakdowns is of premier importance for the safe and reliable operation of Nuclear Power Plants (NPP). The potential consequences of several terrorized activities may harm the society using nuclear and its associated facilities by using faulty components. Subsequently, rigorous nuclear reactor thermal-hydraulic experimental events support design improvement which are important vectors to be considered in nuclear safety measures. To challenge this problem, a number of linear and nonlinear pattern recognition methods have been utilized. Therefore, the aim of the present work is to study the application of fuzzy expert system (FES) model on a complex system such as nuclear reactor thermal-hydraulic system and their adaptation for the early forecast of irregular behaviour. The purpose of this study is to examine the relationship between reactor design parameters, in particular safety function areas of heat removal capability, conductivity of cladding, convective heat transfer characteristics of coolant and their impact of severity, and to illustrate how fuzzy expert system might play an important role in assessment of risk. Several simulation cases are conducted for evaluating the performance of the present diagnosis system. The simulation results show the good performance of the present intelligent approach in thermal-hydraulic studies on safety and efficiency of NPP.

Landslide susceptibility mapping in and around Mussoorie Township using fuzzy set procedure, MamLand and improved fuzzy expert system-A comparative study

A landslide susceptibility map (LSM) is an imperative element in the planning of sustainable development practices and geo-environmental conservations in mountainous terrains. In recent times, approaches that couple soft computing techniques and Geographic Information System (GIS) has emerged as better-suited models that can diminish the flaws and limitations of heuristic, probabilistic and distribution approaches in landslide susceptibility mapping. This paper presents an improved fuzzy expert system (FES) model, a fusion of Mamdani fuzzy inference system (Mamdani-FIS) and frequency ratio method for GIS-based landslide susceptibility mapping. The improved FES model has been applied for mesoscale (1:15,000) landslide susceptibility mapping of Mussoorie Township, Uttarakhand, India, along with conventional fuzzy set procedure (FSP) and an existing FES model, MamLand. The LSMs generated through different procedures have been validated and compared by means of spatial distribution of susceptibility zones and statistical analysis with the help of landslide inventory. The validation and comparative analysis have indicated the significantly better performance of the improved FES model over FSP and MamLand.

A fuzzy expert system model for risks qualification in real estate projects

This paper presents a fuzzy expert system (FES) model to determine qualifications of real estate experts in field of risk management in developing countries. The model computes the importance weight factor for the experts who participate in risk qualification process, based on a number of attributes. FES model contributes to real estate industry by solving a fundamental problem for project teams who lack the capability of determining experts' qualifications impact on the quality of their risk assessments. FES model adds an essential component to risk management process by providing a tool that captures the knowledge of project management teams, in the form of expert rules, to compute a unique value that ranks the experts participated in the risk assessment process. A case study in Egypt is presented to test validity of the model.

A fuzzy expert system model for risks qualification in real estate projects

A fuzzy expert system model for risks qualification in real estate projects

A fuzzy logic-based prediction model for fracture force using low-power fiber laser beam welding

Laser beam welding is an advanced technique to join dissimilar materials together. Important laser joining parameters such as laser power, welding speed, and overlapping factor would determine the weld penetration of the joints. In the current study, a single-sided welding of a titanium alloy, Ti6Al4V, and a nickel-based alloy, Inconel 600, in a T-joint configuration was conducted using a continuous-wave, low-power fiber laser. The strengths of the welded joints were evaluated using pull tests. These results were used to build an intelligent fuzzy expert system model to predict the fracture force of the joint. Using MATLAB R2009b, the fuzzy logic development was made based on the Mamdani technique. Twenty-four real-time experiments were carried out and 18 numerical testing data were used to develop the fuzzy logic. The resulting viewer surfaces showed that the overlapping factor was the highest influencing laser welding parameter, followed by the welding speed and the laser power. The calculated relative error between the real and predicted results was 6.95% indicating acceptable results. This was supported by the goodness of fit value of 0.9849. The findings of this study have extended the knowledge of the fracture force prediction using the fuzzy expert system model.

A Fuzzy Logic-Based Prediction Model for Kerf Width in Laser Beam Machining

In laser beam machining, the main concern is the machining quality as kerf width of the end product. It is essential for industrial applications to cut the workpiece with minimum kerf width. However, it is difficult to develop a precise functional relationship between input and output variables in laser machining. Therefore, an effort has been conducted to build up an intelligent fuzzy expert system (FES) model to predict the kerf width in CO2 laser cutting. The employed input parameters were assisting gas pressure, laser power, cutting speed, and standoff distance. The fuzzy logic was performed on fuzzy toolbox in MATLAB R2009b by employing Mamdani technique. In total, 81 experiments were carried out and experimental results were used for training and testing of the developed fuzzy model. Relative error and goodness of fit were used to investigate the accuracy of the prediction ability and the values of 3.852% and 0.994, respectively, were found to be satisfactory. This paper will extend knowledge about the prediction of kerf width by using FES model.

Applications of Clustering Algorithms in Academic Performance Evaluation

In this paper, we explore the applicability of K-means and Fuzzy C-Means clustering algorithms to student allocation problem that allocates new students to homogenous groups of specified maximum capacity, and analyze effects of such allocations on the academic performance of students. The paper also presents a Fuzzy set and Regression analysis based Dynamic Fuzzy Expert System model which is capable of dealing with imprecision and missing data that is commonly inherited in the student academic performance evaluation. This model automatically converts crisp sets into fuzzy sets by using C-Means clustering algorithm method. The comparative performance analysis indicates that the student group formed by Fuzzy C-Means clustering algorithm performed better than groups formed by K-Means, classical fuzzy logic clustering algorithms and Bayesian classifications.

A Fuzzy Rule-based Expert System for the Prognosis of the Risk of Development of the Breast Cancer

Soft computing techniques play an important role for decision-making applications with imprecise and uncertain knowledge. The application of fuzzy soft computing applications is rapidly emerging in the medical diagnosis and prognosis. A fuzzy expert system models knowledge as a set of explicit linguistic rules and performs reasoning with words. Although there are several technology-oriented studies reported for breast cancer diagnosis, few studies have been reported for the breast cancer prognosis. However, prognosis of breast cancer suffers from uncertainty and imprecision associated to imprecise input measures and incompleteness of knowledge as well as diagnosis. This research presents a fuzzy expert system for breast cancer prognosis. This approach is capable enough to capture ambiguity and imprecision prevalent in the characterization of the breast cancer. For this, the paper utilizes a Mamdani fuzzy inference model, which is more intuitive and has high interpretability for interacting with human experts during prognosis process. The main advantage of this work compared to other related studies, mostly presented for assessing the risk of the cancer development stage, is using unbiased input variables in the prognosis process; i.e., this model has the potential to predict the risk of developing breast cancer even in healthy females. Furthermore, the fuzzy expert system was evaluated on real dataset and the results of system were compared to an obstetrician decisions. The performance results on real dataset reveals superiority of the fuzzy expert system in the prognosis process with an average accuracy of 95%, compared to other related works. This approach is optimistic for prediction of breast cancer risk and early diagnosis of the cancer and can consequently improve survival rate.

Prediction of heat transfer performance of CuO/water nanofluids flow in spirally corrugated helically coiled heat exchanger using fuzzy logic technique

This paper presents the fuzzy logic expert system (FLES) for heat transfer performance investigation in helically coiled heat exchanger with spirally corrugated wall operated with water and CuO/water nanofluids. Compared with traditional logic model, fuzzy logic is more efficient in connecting the multiple units to a single output and is invaluable supplements to classical hard computing techniques. Hence, the main objective of this analysis is to investigate the relationship between heat exchanger working parameters and performance characteristics, and to determine how fuzzy logic expert system plays a significant role in prediction of heat transfer performance. Analytical values are taken in helically coiled heat exchanger with spirally corrugated wall operated with water and CuO/water nanofluids for investigation of heat transfer performance. The heat transfer coefficients of CuO/water nanofluids significantly increased about 5.90–14.24% with the increase of volume concentrations compared to water and while the values of the friction factor decreased with the increase in volume flow rate and volume concentration by using nanofluid instead of water. A fuzzy logic expert system model has developed for the prediction of heat transfer coefficient and friction factor. Verification of the developed fuzzy logic model was carried out through various numerical error criteria. For all parameters, the relative error of predicted values are found to be less than and/or slightly above the acceptable limit (5%). The goodnesses of fit of the prediction values from the fuzzy logic expert system model are found to be close to 1.0 as expected, and hence demonstrated the good performance of the developed system.

Measuring intellectual capital in the university sector using a fuzzy logic expert system

The main aim of this study is to find a method to measure the intellectual capital (IC) of an organization which is able to combine management and measurement views, to reflect the newest concepts regarding IC, and to take into consideration the ‘vague’ interactions between IC categories. We posit the idea that a fuzzy expert system model can address these issues, since it takes account of the qualitative nature of most IC indicators and the different IC subcategories. The main advantage of an IC score developed through a FES model is to provide a reliable IC index. The model presented in this article applied to data derived from the Austrian universities’ IC reports is a pilot model, sufficiently flexible for individual adaptations and adjustments. The main limitation of the study is that further tests can be carried out only in the presence of available and comparable IC data which are currently not available.

B3: Fuzzy-Based Data Center Load Optimization in Cloud Computing

Cloud computing started a new era in getting variety of information puddles through various internet connections by any connective devices. It provides pay and use method for grasping the services by the clients. Data center is a sophisticated high definition server, which runs applications virtually in cloud computing. It moves the application, services, and data to a large data center. Data center provides more service level, which covers maximum of users. In order to find the overall load efficiency, the utilization service in data center is a definite task. Hence, we propose a novel method to find the efficiency of the data center in cloud computing. The goal is to optimize date center utilization in terms of three big factors—Bandwidth, Memory, and Central Processing Unit (CPU) cycle. We constructed a fuzzy expert system model to obtain maximum Data Center Load Efficiency (DCLE) in cloud computing environments. The advantage of the proposed system lies in DCLE computing. While computing, it allows regular evaluation of services to any number of clients. This approach indicates that the current cloud needs an order of magnitude in data center management to be used in next generation computing.