Imprecise Data Research Articles

Unlocking Energy Efficiency: Harnessing Fuzzy Logic Control for Lighting Systems

Energy efficiency is a critical concern for businesses and homeowners alike. Lighting systems account for a significant portion of energy usage, and optimizing these systems can lead to significant cost savings. One way to improve lighting system efficiency is by implementing fuzzy logic control (FLC) techniques. FLC is a type of control that uses imprecise data to make decisions, making it well-suited to the uncertain and dynamic environment of lighting systems. By using FLC, lighting systems can adjust brightness levels and schedules in response to occupancy and daylight levels, reducing energy consumption while maintaining a comfortable level of illumination. However, implementing FLC in lighting systems can be challenging, and requires careful design and tuning to ensure optimal performance. This article explores the benefits and challenges of using FLC in lighting systems and provides examples of successful implementations. Overall, the article highlights the potential of FLC as a valuable tool in achieving energy efficiency in lighting systems.

Aggregation Operators for Interval-Valued Pythagorean Fuzzy Hypersoft Set with Their Application to Solve MCDM Problem

Experts use Pythagorean fuzzy hypersoft sets (PFHSS) in their investigations to resolve the indeterminate and imprecise information in the decision-making process. Aggregation operators (AOs) perform a leading role in perceptivity among two circulations of prospect and pull out concerns from that perception. In this paper, we extend the concept of PFHSS to interval-valued PFHSS (IVPFHSS), which is the generalized form of interval-valued intuitionistic fuzzy soft set. The IVPFHSS competently deals with uncertain and ambagious information compared to the existing interval-valued Pythagorean fuzzy soft set. It is the most potent method for amplifying fuzzy data in the decision-making (DM) practice. Some operational laws for IVPFHSS have been proposed. Based on offered operational laws, two inventive AOs have been established: interval-valued Pythagorean fuzzy hypersoft weighted average (IVPFHSWA) and interval-valued Pythagorean fuzzy hypersoft weighted geometric (IVPFHSWG) operators with their essential properties. Multi-criteria group decision-making (MCGDM) shows an active part in contracts with the difficulties in industrial enterprise for material selection. But, the prevalent MCGDM approaches consistently carry irreconcilable consequences. Based on the anticipated AOs, a robust MCGDM technique is deliberate for material selection in industrial enterprises to accommodate this shortcoming. A real-world application of the projected MCGDM method for material selection (MS) of cryogenic storing vessels is presented. The impacts show that the intended model is more effective and reliable in handling imprecise data based on IVPFHSS.

Evaluation of groups using cooperative game with fuzzy data envelopment analysis

<abstract><p>Data Envelopment Analysis (DEA) is a prominent technique for evaluating the performance and ranking of a set of decision-making units (DMUs) that transform multiple inputs into multiple outputs. However, one of the challenges of the primary DEA models is facing imprecise data in real practical problems. To address this issue, fuzzy DEA have been proposed, which have been successfully applied in many real fields. On the other hand, in some real-world DEA applications, the primary objective of performance evaluation is the ranking of a group that consists of several DMUs that are typically under the control of a centralized management. In this paper, we try to use the theory of cooperative games and Shapley value method as a fair method to solve such games in order to rank groups in DEA. In this way, the resulting rank for groups is based on the average marginal shares of groups in different coalitions that are formed based on the theory of cooperative games. We applied the proposed method to rank groups of airlines considering fuzzy data. To the best of authors' knowledge, so far, no method has been presented in DEA literature for ranking groups in fuzzy environment and using game theory techniques.</p></abstract>

A DEA-WEI method for ranking universities in the presence of imprecise data

A DEA-WEI method for ranking universities in the presence of imprecise data

A Fuzzy Random Survival Forest for Predicting Lapses in Insurance Portfolios Containing Imprecise Data

We propose a fuzzy random survival forest (FRSF) to model lapse rates in a life insurance portfolio containing imprecise or incomplete data such as missing, outlier, or noisy values. Following the random forest methodology, the FRSF is proposed as a new machine learning technique for solving time-to-event data using an ensemble of multiple fuzzy survival trees. In the learning process, the combination of methods such as the c-index, fuzzy sets theory, and the ensemble of multiple trees enable the automatic handling of imprecise data. We analyse the results of several experiments and test them statistically; they show the FRSF’s robustness, verifying that its generalisation capacity is not reduced when modelling imprecise data. Furthermore, the results obtained using a real portfolio of a life insurance company demonstrate that the FRSF has a better performance in comparison with other state-of-the-art algorithms such as the traditional Cox model and other tree-based machine learning techniques such as the random survival forest.

Performance prediction of DMUs using integrated DEA-SVR approach with imprecise data: application on Indian banks

Performance prediction of DMUs using integrated DEA-SVR approach with imprecise data: application on Indian banks

Autonomous learning for fuzzy systems: a review

As one of the three pillars in computational intelligence, fuzzy systems are a powerful mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy systems take the form of linguistic IF-THEN fuzzy rules that are easy to understand for human. In this sense, fuzzy inference mechanisms have been developed to mimic human reasoning and decision-making. From a data analytic perspective, fuzzy systems provide an effective solution to build precise predictive models from imprecise data with great transparency and interpretability, thus facilitating a wide range of real-world applications. This paper presents a systematic review of modern methods for autonomously learning fuzzy systems from data, with an emphasis on the structure and parameter learning schemes of mainstream evolving, evolutionary, reinforcement learning-based fuzzy systems. The main purpose of this paper is to introduce the underlying concepts, underpinning methodologies, as well as outstanding performances of the state-of-the-art methods. It serves as a one-stop guide for readers learning the representative methodologies and foundations of fuzzy systems or who desire to apply fuzzy-based autonomous learning in other scientific disciplines and applied fields.

An optimized fuzzy deep learning model for data classification based on NSGA-II

As a powerful paradigm, deep learning (DL) models have been used in many applications for classification tasks in images, text, and audio. Through DL models, we can learn task-driven features from big data. However, DL models are fully deterministic and cannot handle uncertain and imprecise data. DL models are often sensitive to noise in data and do not operate well in areas where data are vague. Moreover, when there is a large feature set or high-dimensional data with irrelevant and redundant features, the DL models’ performance decreases in classification due to training with irrelevant features. To gain reliable results in such high-dimensional problems, DL models require a large amount of data which usually grows exponentially concerning the number of features. Data uncertainty problems and irrelevant features in DL models cause low performance in classification tasks. This paper proposes an optimized fuzzy deep learning (OFDL) model for data classification based on Non-Dominated Sorting Genetic Algorithm II (NSGA-II). OFDL utilizes optimization in the composition of DL and fuzzy learning via the NSGA-II in multi-modal learning. To achieve effective classification, OFDL first considers intelligent feature selection by finding the best trade-offs between two conflicting objective functions, minimizing the number of features, and maximizing the accuracy (maximizing weights of selected features). Next, to reach optimized backpropagation and fuzzy membership functions, OFDL utilizes Pareto optimal solutions for multi-objective optimization using NSGA-II based on their objective functions. Furthermore, the fusion layer in OFDL fused optimized views of DL and fuzzy learning that provides a high-level representation of inputs and optimum features for classification tasks where the data contain high uncertainties and noises. This functionality gives valuable attributes during classification since identifying and selecting appropriate features ensures prompt and correct class. Also, it provides deep insight into tackling the effect of ambiguous data and each feature’s uncertainty in the classification tasks. The examination of OFDL reveals good performance in terms of F-measure, accuracy, recall, precision, and True Positive Rate (TPR) compared to fuzzy classifiers. Furthermore, OFDL has higher accuracy in classification tasks than earlier fuzzy DNN models.

A robust optimization approach to risk-averse routing of marine crude oil tankers

Marine accidents involving crude oil tankers are rare though such incidents have led to catastrophic socioeconomic and environmental consequences. This low probability – high consequence nature of marine oil spills renders the existing expected consequence technique ineffective and calls for the development of a risk-averse approach. We propose a conditional value-at-risk (CVaR) based methodology for selecting the appropriate size of crude oil tankers and then routing them over the intercontinental network such that the weighted sum of transport cost and CVaR based transport risk is minimized. In addition, we also develop a robust formulation of the mixed-integer routing problem, which not only facilitates tiding over imprecision in the risk data but also prepares risk-averse shipment plan. The proposed methodology was used to analyze the realistic marine network of a major oil supplier and to demonstrate its benefits, and to underscore the value of incorporating robustness.

Regular string token fuzzy Petri nets

Fuzzy Petri nets are a type of classical Petri nets designed to deal with imprecise and ambiguous data, that have been widely used to represent fuzzy production rules and fuzzy rule-based reasoning. In this paper, we introduce a new model called string token fuzzy Petri nets to generate fuzzy regular languages. Also, we study the equivalences of fuzzy regular grammar and regular string token fuzzy Petri net and establish some closure properties such as union, catenation, kleene closure, reversal, homomorphism and inverse homomorphism of the languages generated by the regular string token fuzzy Petri nets.

Uncertain Threshold Autoregressive Model via LASSO Procedure

Uncertain time series models have been proposed to forecast future data based on imprecise known data. As an uncertain time series model, the uncertain threshold autoregressive model was proposed to deal with nonlinear data. The previous study estimated the unknown parameters in the uncertain threshold autoregressive model with the least-squares estimation. However, for nonlinear time series models, the least-squares estimation may cause overfitting of the model. To deal with this problem, this paper adds the least absolute shrinkage and selection operator penalty to the uncertain threshold autoregressive model to alleviate the level of overfitting. In addition, this paper uses the sum of square errors criterion to select the optimum order of the model. Finally, a real stock price example is provided to compare two different parameter estimation methods.

Machine learning-based automatic focusing for high magnification systems

Precise focus plays a critical role in the quality of astronomical observations, significantly affecting scientific research. Accordingly, the automatic focusing of a high magnification telescope system is desirable. The auto-focusing level is measured using a focus measure operator. The principal challenge is to find a robust operator that can accurately estimate the focus level for high magnification astronomical images that suffer from high blur. In addition, images can suffer from aberrations as the distance from the image center increases, along with dramatic effects associated with dynamic observation conditions such as temperature, clouds, humidity, and seeing. In this study, we first propose a focus measure relying on fuzzy logic that can handle imprecise data. Second, we optimize the parameters of the fuzzy membership functions using multivariate particle swarm optimization (PSO). We apply the proposed method to five sequences of different star clusters acquired using the 74-inch telescope at the Kottamia astronomical observatory (KAO) during good seeing conditions. We then compare our proposed method with other traditional focus operators and rank them according to evaluation criteria. The results show that the proposed operator generally outperforms the others, and that the optimized operator provides further improvement.

Application of a Model Based on Rough Set Theory (RST) to Estimate the Energy Efficiency of Public Buildings

The study was carried out on a group of 85 public buildings, which differed in type of use, construction technology and heating systems. From the collected data, a set of qualitative and quantitative variables characterizing them in terms of heat demand was extracted. In this paper, the authors undertook to test the suitability of a model based on rough set theory (RST), which allows the analysis of imprecise, general and uncertain data. To obtain input data for the RST model in quantitative form, the authors used an alternative approach, which is a method based on the thermal properties of buildings. The quality of the predictive model was evaluated based on the following indicators, such as the coefficient of determination (R2), the mean bias error (MBE), the coefficient of variance of the root mean square error (CV RMSE) and the mean absolute percentage error (MAPE), which are accepted as statistical calibration standards by ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers). A quality-acceptable predictive model must meet the calibration conditions: MBE ±5%, CV RMSE < 15% and R2 > 0.75. For the analyzed RST model, the following values of evaluation indicators were obtained: MBE = −1.1%, CV RMSE = 11.8% and R2 = 0.91. The evaluation results obtained gave rise to the conclusion that the method used, which is based on a limited amount of data describing buildings, gives good results in estimating the unit rate of energy demand for heating.

Combination of anti-PD-L1 and radiotherapy in hepatocellular carcinoma: a mathematical model with uncertain parameters

Blockade of programmed death-ligand-1 (PD-L1) as a new method of immunotherapy for cancers has shown limited efficacy in hepatocellular carcinoma (HCC). The combination of anti-PD-L1 and radiotherapy (RT) enhances the antitumor effect in HCC cancer. The efficacy and interactions of these treatments can be addressed by a mathematical model. We developed a mathematical model using a set of ordinary differential equations (ODEs). The variables include cancer cells, cytotoxic T lymphocytes (CTLs), programmed cell death-1 (PD-1), PD-L1, anti-PD-L1, and ionizing radiation. The model is parameterized with imprecise data set of murine HCC model and the effect of parametric uncertainty is assessed by the fuzzy theorem. The global sensitivity analysis (GSA) is performed to assess model robustness against perturbation in parameters and to identify the most influential parameters on the dynamics of cells and proteins. In silico predictions are consistent with experimental data. The model simulation shows that anti-PD-L1 and RT have a synergistic effect. In silico assessment of treatments’ efficacy in the fuzzy setting of parameters revealed that anti-PD-L1 therapy, RT, and combination treatment caused the uncertainty band of tumor cells to lead to lower populations. This model as a validated rigorous simulation framework can be used to deepen our understanding of tumor and immune cell interactions and helps clinicians to investigate the efficacy of different time schedules of anti-PD-L1, RT, and combination therapy. The fuzzy theorem in conjunction with the classical ODE model that is parameterized by imprecise data was used to predict reliable outcomes of treatment.

Estimating Latent Linear Correlations from Fuzzy Frequency Tables

This research concerns the estimation of latent linear or polychoric correlations from fuzzy frequency tables. Fuzzy counts are of particular interest to many disciplines including social and behavioral sciences and are especially relevant when observed data are classified using fuzzy categories—as for socioeconomic studies, clinical evaluations, content analysis, inter-rater reliability analysis—or when imprecise observations are classified into either precise or imprecise categories—as for the analysis of ratings data or fuzzy-coded variables. In these cases, the space of count matrices is no longer defined over naturals and, consequently, the polychoric estimator cannot be used to accurately estimate latent linear correlations. The aim of this contribution is twofold. First, we illustrate a computational procedure based on generalized natural numbers for computing fuzzy frequencies. Second, we reformulate the problem of estimating latent linear correlations from fuzzy counts in the context of expectation–maximization-based maximum likelihood estimation. A simulation study and two applications are used to investigate the characteristics of the proposed method. Overall, the results show that the fuzzy EM-based polychoric estimator is more efficient to deal with imprecise count data as opposed to standard polychoric estimators that may be used in this context.

Decision Rule Prediction for Assessment of Rotor Spun Cotton Yarn Strength Using Rough Set

ABSTRACT Being an agro-based natural product, cotton always poses vagueness in the parameters. Hence, formulation of decision rules for prediction of cotton yarn quality from the imprecise cotton fiber properties is an intricate problem. Yarn tenacity is considered an imperative quality parameter in the cotton textile industry. Modelling of yarn tenacity has remained as the cynosure of research for textile engineers. In recent years, rough set theory has evolved as one of the most important techniques used for handling imprecise data. One of the cardinal uses of rough set theory is its application to rule generation. Our approach focuses on the elimination of the redundant data set in order to generate effective decision rules which retain the accuracy of the original data set. In this work rough set theory is employed to generate decision rules to predict yarn tenacity from six input parameters. The validation results prove that the generated 45 decision rules accurately predicted 14 out of 16 unknown test data. Thus, in the present competitive market, this model is potent for getting recognition from the textile industry.

Understanding the challenges of determining thermal comfort in vernacular dwellings: A meta-analysis

• Challenges in evaluating thermal comfort and modelling vernacular dwellings intertwine. • These entail inadequate current standards, lack of framework, and faulty modelling. • Inaccurate modelling linked to inconsistent validation, imprecise data, and software limitations. • Current comfort models must predict the possibility of adjusting their thresholds. • Creating a hygrothermal model calibration standard is key for validating vernacular heritage. The thermal comfort assessment of vernacular dwellings, as well as their inherently linked thermal dynamic modelling process, present specific challenges which remain unaddressed in the literature. The overarching purpose of this review is to identify and analyse the main methods and challenges in thermal comfort evaluation and modelling of vernacular dwellings and recommend pathways for future improvement. The main challenges found regarding thermal comfort evaluation intertwine with those of modelling vernacular dwellings. These are: i. the inadequacy of current standards; ii. the use of steady-state approaches despite evidence of their inadequacy; iii. the lack of a clear monitoring framework and insufficient occupant surveying; iv. increased uncertainty from imprecise or unfeasible in situ monitoring; v. inaccurate modelling, due to: a lack of consistent methodology and guidelines for hygrothermal model calibration; imprecise input data and; inherent software limitations in modelling vernacular elements. The main recommendations identified through this analysis include the improvement of current comfort standards and models based on further long-term field studies and the possibility of adjusting their thresholds, the development of a common monitoring framework and which parameters to focus on, and the creation of a standard on hygrothermal model calibration entailing the most adequate indexes and variables.

Optimizing Mineralization of Bioprinted Bone Utilizing Type-2 Fuzzy Systems

Bioprinting is an emerging tissue engineering method used to generate cell-laden scaffolds with high spatial resolution. Bioprinting parameters, such as pressure, nozzle size, and speed, highly influence the quality of the bioprinted construct. Moreover, cell suspension density and other critical biological parameters directly impact the biological function. Therefore, an approximation model that can be used to find the optimal bioprinting parameter settings for bioprinted constructs is highly desirable. Here, we propose a type-2 fuzzy model to handle the uncertainty and imprecision in the approximation model. Specifically, we focus on the biological parameters, such as the culture period, that can be used to maximize the output value (mineralization volume 21.8 mm3 with the same culture period of 21 days). We have also implemented a type-1 fuzzy model and compared the results with the proposed type-2 fuzzy model using two levels of uncertainty. We hypothesize that the type-2 fuzzy model may be preferred in biological systems due to the inherent vagueness and imprecision of the input data. Our numerical results confirm this hypothesis. More specifically, the type-2 fuzzy model with a high uncertainty boundary (30%) is superior to type-1 and type-2 fuzzy systems with low uncertainty boundaries in the overall output approximation error for bone bioprinting inputs.

Not so fast: Identifying and remediating slow and imprecise cryptocurrency exchange data

Using raw tick-level trade data from 17 major cryptocurrency exchanges, we show that heterogeneity in matching engines can affect the computation of various liquidity and trading metrics. Using simple analytical techniques, we generate an algorithm to identify exchanges with slow matching engines or imprecise timestamps. Having identified problematic exchanges, we propose tractable techniques which can remediate the bias in metrics generated by problematic exchanges. Our techniques and exchange classifications are useful for academic and industry-based users of cryptocurrency exchange data to identify and remediate problematic trade-level data.

A Robust Possibilistic Bi-Objective Mixed Integer Model for Green Biofuel Supply Chain Design under Uncertain Conditions

In recent years, concerns regarding issues such as climate change, greenhouse gas emissions, fossil reserve dependency, and petroleum price fluctuation have led countries to focus on renewable energies. Meanwhile, in developing countries, designing an appropriate biofuel supply chain network regarding environmental competencies is an important problem. This paper presents a new bi-objective mixed integer mathematical model aiming to minimize CO2 emission and total costs in the process of the biofuel supply chain, creating a suitable green supply chain network. In this respect, CO2 emission and biofuel demand are regarded as uncertain data to address the real complex cases. Moreover, the SAUGMECON approach was implemented to construct a single objective model, and the obtained Pareto optimal points were depicted and analyzed. Thereby, a robust possibilistic programming approach was implemented to the proposed model to handle existing imprecise data. Furthermore, the applicability and performance of the proposed model were demonstrated based on an experimental example. In this respect, the obtained results from the proposed robust possibilistic programming model were compared with its crisp form to show the robustness and reliability of the proposed uncertain mathematical model.