Expert System For Prediction Research Articles

Development of an expert system for technological support of required roughness when machining hardened steel parts on numerically controlled machines

This work addresses the problem of improving the surface quality of parts of rotation bodies, which are made of hardened steels, in the course of their machining on lathes with numerical program control. The research object was the surface roughness of parts. The methodology involved system analysis and synthesis, artificial neural networks, fuzzy logic, experiment, and processing of experimental results. A decomposition scheme for the expert system structure was developed, which can be used as the basis for formulating requirements to a future expert system for monitoring and prediction of roughness parameters. It was established that the models currently applied to describe the relationship between surface quality and the technological regimes used to ensure the technological level of roughness give a high error of over 20%. The possibility of using models that apply artificial intelligence and contain neural network blocks and decision-making devices based on fuzzy logic is substantiated. It is shown that such a combination makes it possible to customize the system for processing parts of a certain production range, as well as a more correct assessment of the onset of catastrophic wear of cutting tools. The neuro-fuzzy model was confirmed to have an error of less than 10%, which is significantly lower than when using spectral or correlation models. According to the testing results, the proposed expert system for monitoring and prediction of roughness parameters enables a 2.5-fold reduction in the scatter of roughness parameters under an increase in tool wear, compared to without its application. Thus, the proposed system makes it possible to assess the level of cutting tool wear more correctly and determine the onset of its limit state.

Design of a precise ensemble expert system for crop yield prediction using machine learning analytics

AbstractAgriculture is facing significant challenges in the development of crop yield forecasts, which are important aspects of decision‐making at the international, regional, and local levels. The area of agriculture is attracting growing attention because of increasing the demand for food supplies. To ensure future food supplies, crop yield prediction (CYP) provides the best decision‐making to assist farmers in agricultural yield forecasting efficiently. Nevertheless, CYP is a difficult endeavor because of the intricacy of the underlying mechanisms and the effect of numerous factors, including weather patterns, soil characteristics, and crop management techniques. In today's era, ensemble learning (EL) approaches have recently demonstrated significant promise for enhancing the reliability and accuracy of CYP. The success of the EL techniques depends on several facts, including how the base learner models are trained and how these are combined. This study provides important insights into the EL techniques for CYP. This paper proposes an expert system model named precise ensemble expert system for crop yield prediction (PEESCYP) to predict the best crop for agricultural land. The proposed PEESCYP model employs multiple imputation by chained equation (MICE) data imputation technique to treat the missing values of the collected dataset, the isolation forest (IF) technique for outlier detection, the ant colony optimization (ACO) technique to perform feature selection, robust scaling (RS) technique to perform data normalization, and the extra tree (ET) is used for classification to overcome the variance and overfitting problem of the single classifiers. The measurements of the proposed PEESCYP model have been collected by means of accuracy, precision, recall, and F‐1 score using a prepared dataset, which is collected from International Crops Research Institute for the Semi‐Arid Tropics (ICRISAT), and the proposed model is compared with different single‐classifier based ML models, EL models, and various existing models available in the literature. The results of this experiment underline that the proposed PEESCYP model outperforms the others.

A Systematic Review of Medical Expert Systems for Cardiac Arrest Prediction

Background:: Predicting cardiac arrest is crucial for timely intervention and improved patient outcomes. Machine learning has yielded astounding results by offering tailored prediction analyses on complex data. Despite advancements in medical expert systems, there remains a need for a comprehensive analysis of their effectiveness and limitations in cardiac arrest prediction. This need arises because there are not enough existing studies that thoroughly cover the topic. Objective:: The systematic review aims to analyze the existing literature on medical expert systems for cardiac arrest prediction, filling the gaps in knowledge and identifying key challenges. Methods:: This paper adopts the PRISMA methodology to conduct a systematic review of 37 publications obtained from PubMed, Springer, ScienceDirect, and IEEE, published within the last decade. Careful inclusion and exclusion criteria were applied during the selection process, resulting in a comprehensive analysis that utilizes five integrated layers- research objectives, data collection, feature set generation, model training and validation employing various machine learning techniques. Results and Conclusion:: The findings indicate that current studies frequently use ensemble and deep learning methods to improve machine learning predictions’ accuracy. However, they lack adequate implementation of proper pre-processing techniques. Further research is needed to address challenges related to external validation, implementation, and adoption of machine learning models in real clinical settings, as well as integrating machine learning with AI technologies like NLP. This review aims to be a valuable resource for both novice and experienced researchers, offering insights into current methods and potential future recommendations.

A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction.

To explore a robust tool for advancing digital breeding practices through an artificial intelligence-driven phenotype prediction expert system, we undertook a thorough analysis of 11 non-linear regression models. Our investigation specifically emphasized the significance of Support Vector Regression (SVR) and SHapley Additive exPlanations (SHAP) in predicting soybean branching. By using branching data (phenotype) of 1918 soybean accessions and 42k SNP (Single Nucleotide Polymorphism) polymorphic data (genotype), this study systematically compared 11 non-linear regression AI models, including four deep learning models (DBN (deep belief network) regression, ANN (artificial neural network) regression, Autoencoders regression, and MLP (multilayer perceptron) regression) and seven machine learning models (e.g., SVR (support vector regression), XGBoost (eXtreme Gradient Boosting) regression, Random Forest regression, LightGBM regression, GPs (Gaussian processes) regression, Decision Tree regression, and Polynomial regression). After being evaluated by four valuation metrics: R2 (R-squared), MAE (Mean Absolute Error), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error), it was found that the SVR, Polynomial Regression, DBN, and Autoencoder outperformed other models and could obtain a better prediction accuracy when they were used for phenotype prediction. In the assessment of deep learning approaches, we exemplified the SVR model, conducting analyses on feature importance and gene ontology (GO) enrichment to provide comprehensive support. After comprehensively comparing four feature importance algorithms, no notable distinction was observed in the feature importance ranking scores across the four algorithms, namely Variable Ranking, Permutation, SHAP, and Correlation Matrix, but the SHAP value could provide rich information on genes with negative contributions, and SHAP importance was chosen for feature selection. The results of this study offer valuable insights into AI-mediated plant breeding, addressing challenges faced by traditional breeding programs. The method developed has broad applicability in phenotype prediction, minor QTL (quantitative trait loci) mining, and plant smart-breeding systems, contributing significantly to the advancement of AI-based breeding practices and transitioning from experience-based to data-based breeding.

APDF: An active preference-based deep forest expert system for overall survival prediction in gastric cancer

Accurate survival prediction is crucial for doctors to choose appropriate treatment options and make prognoses for gastric cancer patients. While machine learning algorithms have shown promise for improving prediction accuracy, the lack of doctor involvement and guidance can reduce the credibility of prediction results in practical clinical applications. To address this issue, we propose a survival prediction expert system integrating deep forest and active preference learning to incorporate doctors’ empirical knowledge into the prediction process. Our expert system first reduces feature dimensionality through feature selection, with doctor input to ensure clinical relevance. We then train estimators to simulate clinical prediction, and doctors compare these estimators in pairs and give preferences based on their experience. A recursive function based on the radial basis function is used to learn the preferences and assign weights to all the estimators. We apply our approach to two publicly available datasets and one private dataset. Our system achieves an AUC of 0.87 and a C-index of 0.75 for predictive classification and regression analysis tasks. Importantly, our system outperforms the clinical gold standard method (Nomogram) and other state-of-the-art machine learning methods with a maximum improvement of 12% and 10%, respectively, demonstrating superior performance. In conclusion, our expert system effectively incorporates doctors’ empirical knowledge into the survival prediction process, enhancing the credibility of prediction results in practical clinical applications. Our approach has significant potential for improving treatment decisions and prognoses for gastric cancer patients.

A two-stage credit scoring model based on random forest: Evidence from Chinese small firms

Small firms are major contributors to most economies, often supported by government policies. However, the credit scoring of small firms is complicated and costly, making it a challenging field of research. Using loan data from 3045 small firms in China, we design a two-stage expert system for default prediction that quantifies the variables and thresholds that have a key impact. Firstly, we use SMOTE to deal with the imbalanced data and secondly, we employ random forest to build predictive credit features. Dominance analysis shows that, when making default assessments on Chinese small firms, it is important to consider not only financial factors, but also non-financial and macroeconomic factors. In particular, the net cash profit, the firm's legal disputes and the per capita disposable income of urban residents are key factors in credit scoring. Robustness tests show that our proposed methodology performs better than other machine learning models, and this result is robust with observations from other countries.

An expert-based system to predict population survival rate from health data.

Timely detection and understanding of causes for population decline are essential for effective wildlife management and conservation. Assessing trends in population size has been the standard approach, but we propose that monitoring population health could prove more effective. We collated data from 7 bottlenose dolphin (Tursiops truncatus) populations in the southeastern United States to develop a method for estimating survival probability based on a suite of health measures identified by experts as indices for inflammatory, metabolic, pulmonary, and neuroendocrine systems. We used logistic regression to implement the veterinary expert system for outcome prediction (VESOP) within a Bayesian analysis framework. We fitted parameters with records from 5 of the sites that had a robust network of responders to marine mammal strandings and frequent photographic identification surveys that documented definitive survival outcomes. We also conducted capture-mark-recapture (CMR) analyses of photographic identification data to obtain separate estimates of population survival rates for comparison with VESOP survival estimates. The VESOP analyses showed that multiple measures of health, particularly markers of inflammation, were predictive of 1- and 2-year individual survival. The highest mortality risk 1year following health assessment related to low alkaline phosphatase (odds ratio [OR]=10.2 [95% CI: 3.41-26.8]), whereas 2-year mortality was most influenced by elevated globulin (OR=9.60 [95% CI: 3.88-22.4]); both are markers of inflammation. The VESOP model predicted population-level survival rates that correlated with estimated survival rates from CMR analyses for the same populations (1-year Pearson's r=0.99, p=1.52×10-5 ; 2-year r=0.94, p=0.001). Although our proposed approach will not detect acute mortality threats that are largely independent of animal health, such as harmful algal blooms, it can be used to detect chronic health conditions that increase mortality risk. Random sampling of the population is important and advancement in remote sampling methods could facilitate more random selection of subjects, obtainment of larger sample sizes, and extension of the approach to other wildlife species.

Expert system for managing data on the competencies of a modern manager

In the modern economy, digitalization has become one of the key components of the Russian Federation regions socio-economic development. Enterprises of various industries are faced with the need to process large amounts of data, which greatly complicates data management, and therefore the relevance of the analysis of artificial intelligence technologies increases. Training employees for industrial processes is a major challenge in any industry. Effective human resource management requires an accurate assessment and presentation of available competencies, as well as an effective mapping of the required competencies for specific positions. Competences enable the company to achieve high production and economic results. The aim of the study is to develop a structural model of a predictive expert system for managing data on the competencies of a modern manager by combining artificial and human intelligence, which can serve as a decision support tool for managers in real conditions to improve the efficiency of a particular enterprise. The study of the demand for managers and requirements for candidates in the Russian Federation and the Republic of Tatarstan was conducted on the data of the largest Russian Internet recruitment company HeadHunter. To develop a structural model of the proposed expert system, information from specialized scientific publications published in the Russian and foreign scientific literature of the Web of science and Scopus databases was used. The expert system will allow the manager to find the best options for using employees, predict the development of the enterprise as a whole and its individual divisions, which will significantly increase the key performance indicators of any company.

A Robust Design-Based Expert System for Feature Selection and COVID-19 Pandemic Prediction in Japan.

Expert systems are frequently used to make predictions in various areas. However, the practical robustness of expert systems is not as good as expected, mainly due to the fact that finding an ideal system configuration from a specific dataset is a challenging task. Therefore, how to optimize an expert system has become an important issue of research. In this paper, a new method called the robust design-based expert system is proposed to bridge this gap. The technical process of this system consists of data initialization, configuration generation, a genetic algorithm (GA) framework for feature selection, and a robust mechanism that helps the system find a configuration with the highest robustness. The system will finally obtain a set of features, which can be used to predict a pandemic based on given data. The robust mechanism can increase the efficiency of the system. The configuration for training is optimized by means of a genetic algorithm (GA) and the Taguchi method. The effectiveness of the proposed system in predicting epidemic trends is examined using a real COVID-19 dataset from Japan. For this dataset, the average prediction accuracy was 60%. Additionally, 10 representative features were also selected, resulting in a selection rate of 67% with a reduction rate of 33%. The critical features for predicting the epidemic trend of COVID-19 were also obtained, including new confirmed cases, ICU patients, people vaccinated, population, population density, hospital beds per thousand, middle age, aged 70 or older, and GDP per capital. The main contribution of this paper is two-fold: Firstly, this paper has bridged the gap between the pandemic research and expert systems with robust predictive performance. Secondly, this paper proposes a feature selection method for extracting representative variables and predicting the epidemic trend of a pandemic disease. The prediction results indicate that the system is valuable to healthcare authorities and can help governments get hold of the epidemic trend and strategize their use of healthcare resources.

Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage.

COVID-19 has imposed many challenges and barriers on traditional healthcare systems due to the high risk of being infected by the coronavirus. Modern electronic devices like smartphones with information technology can play an essential role in handling the current pandemic by contributing to different telemedical services. This study has focused on determining the presence of this virus by employing smartphone technology, as it is available to a large number of people. A publicly available COVID-19 dataset consisting of 33 features has been utilized to develop the aimed model, which can be collected from an in-house facility. The chosen dataset has positive and negative samples, demonstrating a high imbalance of class populations. The Adaptive Synthetic (ADASYN) has been applied to overcome the class imbalance problem with imbalanced data. Ten optimal features are chosen from the given 33 features, employing two different feature selection algorithms, such as K Best and recursive feature elimination methods. Mainly, three classification schemes, Random Forest (RF), eXtreme Gradient Boosting (XGB), and Support Vector Machine (SVM), have been applied for the ablation studies, where the accuracy from the XGB, RF, and SVM classifiers achieved , , and , respectively. As the XGB algorithm confers the best results, it has been implemented in designing the Android operating system base and web applications. By analyzing 10 users’ questionnaires, the developed expert system can predict the presence of COVID-19 in the human body of the primary suspect. The preprocessed data and codes are available on the GitHub repository.

Modeling the Performance of Fuzzy Expert System for Prediction of Combustion, Engine Performance, and Exhaust Emission Parameters of a Spark Ignition Engine Fueled With Waste Bread Bioethanol-Gasoline Blends

Abstract This article focuses on the use of a rule-based Mamdani-type fuzzy expert system for the prediction of Pmax, HRRmax, ID, and CD as combustion parameters, BTE and BSFC as engine performance parameters, and CO, CO2, HC, and NOx as exhaust emission parameters of fuel blends formed by blending waste bread bioethanol with gasoline in different proportions. For modeling of 55 test conditions created by being operated test engine with 11 different test fuels under five different engine loads. As a result of the study, while combustion parameters were predicted with correlation coefficients in the range of 0.948–0.973% for waste bread bioethanol-gasoline blends, correlation coefficients for engine performance and exhaust emission parameters were in the range of 0.968–0.977% and 0.955–0.991% respectively. Similarly, the ranges of correlation coefficients obtained for sugar beet bioethanol-gasoline blends with fuzzy expert system were as follows: 0.967–0.971% for engine performance parameters, 0.955–0.978% for exhaust emission parameters, and 0.951–0.964% for combustion parameters. These results prove that costly and labor-intensive engine tests can be predicted with minimum effort and high accuracy with the developed model.

SETApp: A machine learning and image analysis based application to automate the sea urchin embryo test

Since countless xenobiotic compounds are being found in the environment, ecotoxicology faces an astounding challenge in identifying toxicants. The combination of high-throughput in vivo/in vitro bioassays with high-resolution chemical analysis is an effective way to elucidate the cause-effect relationship. However, these combined strategies imply an enormous workload that can hinder their implementation in routine analysis. The purpose of this study was to develop a new high throughput screening method that could be used as a predictive expert system that automatically quantifies the size increase and malformation of the larvae and, thus, eases the application of the sea urchin embryo test in complex toxicant identification pipelines such as effect-directed analysis. For this task, a training set of 242 images was used to calibrate the size-increase and malformation level of the larvae. Two classification models based on partial least squares discriminant analysis (PLS-DA) were built and compared. Moreover, Hierarchical PLS-DA shows a high proficiency in classifying the larvae, achieving a prediction accuracy of 84 % in validation. The scripts built along the work were compiled in a user-friendly standalone app (SETApp) freely accessible at https://github.com/UPV-EHU-IBeA/SETApp. The SETApp was tested in a real case scenario to fulfill the tedious requirements of a WWTP effect-directed analysis.

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.

Fuzzy logic based expert system for prediction of tensile strength in Fused Filament Fabrication (FFF) process

Abstract Fused Filament Fabrication (FFF) is a widely used extrusion based Additive Manufacturing (AM) technique. Despite having many advantages, parts manufactured through this technique falls short in strength criteria. Strength of the parts depend on the selection of process parameters i.e. layer thickness, part orientation and extrusion temperature. The present paper provides a methodology for prediction in additive manufacturing technique using hybrid approach. A fuzzy logic based system is designed to predict the tensile strength. Through experiments, higher strength is obtained at lower orientation, higher extrusion temperature and higher layer thickness. The experimental work is done by Taguchi method and comparison between fuzzy value and experimental value is studied. In fuzzy system, the numerical and computational fuzzy values are compared. The mean error between numerical and computational fuzzy value is 1.2%. Also the mean error between experimental and fuzzy value is 0.5%. The current proposed study demonstrate a greater agreement between computational and numerical fuzzy value with experimental value of tensile strength.

An Intuitionistic Fuzzy Logic based Hybrid Expert System for Breast Cancer Prediction

An Intuitionistic Fuzzy Logic based Hybrid Expert System for Breast Cancer Prediction

Research on equipment fault prediction expert system based on big data dimension reduction

Due to the complex structure and massive volume of large equipment in petrochemical enterprises, it was very difficult to carry out inspection and maintenance work. The traditional expert system used a single knowledge base and reasoning machine, so the processing efficiency and prediction accuracy were low. In this paper, the dimensionality reduction which is used to process a large amount of physical information collected by the sensors, remove redundant components and extract main characteristic parameters. Meanwhile, combined with the powerful pattern recognition and judgment ability of fuzzy neural network, the forward reasoning mechanism and error back propagation keeping continuous training correction until meeting the accuracy requirements, the expert system gave the equipment fault prediction conclusion. Finally, taking main fan oil station circulating oil pump and motor of heavy oil catalytic cracking unit and nine stages of bearing damage as monitoring objects, the fault predictions was implemented by the following steps: data collection, feature extraction, dimension reduction of big data and expert system with the fusion of fuzzy neural network, and it is proved that the expert system with fuzzy neural network based on dimension reduction can greatly improve the convergence speed.

A deep learning approach for hydrological time-series prediction: A case study of Gilgit river basin

Streamflow prediction is a significant undertaking for water resources planning and management. Accurate forecasting of streamflow always being a challenging task for the hydrologist due to its high stochasticity and dynamic patterns. Several traditional and the deep learning models have been applied to simulate the complex nature of the hydrological system. However, to develop and explore a better expert system for prediction is a continuous exertion for hydrological studies. In this study, a deep neural network, namely a one-dimensional convolutional neural network (1D-CNN) and extreme learning machine (ELM) are explored for one-step-ahead streamflow forecasting for three-time horizons (daily, weekly and monthly) in Gilgit River, Pakistan. The 1D-CNN model gained incredible popularity due to its state-of-the-art performance and nominal computational complexity; while ELM model performed superfast as compared to traditional/deep learning architecture, gives comparable performance with fast execution rate. A comparative analysis is presented to assess the performance of the 1D-CNN related to the ELM model. The performance measurement matrices defined as the correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE) computed between the observed and predicted streamflow to evaluate the 1D-CNN and ELM model efficacy. The results indicated that the ELM model performed relatively better than the 1D-CNN model based on predefined statistical measures in three-time scale. In numerical terms, the superiority of ELM over 1D-CNN model was demonstrated by R2 = 0.99, MAE = 18.8, RMSE = 50.14, and R2 = 0.97, MAE = 136.59, RMSE = 230.9, for daily streamflow (testing phase) respectively. Based on our findings, it can be concluded that the ELM model would be an alternative to the 1D-CNN model for highly accurate streamflow forecasting in mountainous regions of the world.

Clinical decision support system to predict chronic kidney disease: A fuzzy expert system approach

Background and objectivesDiagnosis and early intervention of chronic kidney disease are essential to prevent loss of kidney function and a large amount of financial resources. To this end, we developed a fuzzy logic-based expert system for diagnosis and prediction of chronic kidney disease and evaluate its robustness against noisy data. MethodsAt first, we identified the diagnostic parameters and risk factors through a literature review and a survey of 18 nephrologists. Depending on the features selected, a set of fuzzy rules for the prediction of chronic kidney disease was determined by reviewing the literature, guidelines and consulting with nephrologists. Fuzzy expert system was developed using MATLAB software and Mamdani Inference System. Finally, the fuzzy expert system was evaluated using data extracted from 216 randomly selected medical records of patients with and without chronic kidney disease. We added noisy data to our dataset and compare the performance of the system on original and noisy datasets. ResultsWe selected 16 parameters for the prediction of chronic kidney disease. The accuracy, sensitivity, and specificity of the final system were 92.13 %, 95.37 %, and 88.88 %, respectively. The area under the curve was 0.92 and the Kappa coefficient was 0.84, indicating a very high correlation between the system diagnosis and the final diagnosis recorded in the medical records. The performance of the system on noisy input variables indicated that in the worse scenario, the accuracy, sensitivity, and specificity of the system decreased only by 4.43 %, 7.48 %, and 5.41 %, respectively. ConclusionConsidering the desirable performance of the proposed expert system, the system can be useful in the prediction of chronic kidney disease.

Fuzzy Expert System for Prediction of Prostate Cancer

A fuzzy expert system (FES) for the prediction of prostate cancer (PC) is prescribed in this paper. Age, prostate-specific antigen (PSA), prostate volume (PV) and [Formula: see text] Free PSA ([Formula: see text]FPSA) are fed as inputs into the FES and prostate cancer risk (PCR) is obtained as the output. Using knowledge-based rules in Mamdani type inference method the output is calculated. If PCR [Formula: see text], then the patient shall be advised to go for a biopsy test for confirmation. The efficacy of the designed FES is tested against a clinical dataset. The true prediction for all the patients turns out to be [Formula: see text] whereas only for positive biopsy cases it rises to [Formula: see text]. This simple yet effective FES can be used as supportive tool for decision-making in medical diagnosis.

Expert Systems and knowledge management for failure prediction to onshore pipelines: issue to Industry 4.0 implementation

Abstract: The paper aims to propose an Expert System to predict the failure of onshore pipelines. Knowledge Management supports expertise sharing throughout the organization. The Expert System Prototype Model proposed is classified as Empirical Descriptive Research, and may support maintenance management. The findings evidence that Expert System proposed grounded in employee knowledge may be considered a promising solution to support Industry 4.0 implementation. The Expert System facilitates the decision-making of the experts so that the employees’ expertise can be better used in the implementation of Industry 4.0 and to face the new challenges related to the daily work in the organization. In this context, the Expert System can be considered as an innovative approach to manage maintenance processes and supporting reliable, consistent decisions during I.4 implementation.