Fuzzy Gaussian Research Articles

Long-Term Multivariate Time-Series Forecasting Model Based on Gaussian Fuzzy Information Granules

Long-Term Multivariate Time-Series Forecasting Model Based on Gaussian Fuzzy Information Granules

Point estimation of descriptive univariate process capability indices with allowable limits and uncertain quality measurement method

ABSTRACT The main purpose of this paper is to use fuzzy logic to provide a systematic approach for designing fuzzy control charts and compare the average run length type 2 of these charts in three different modes: Gaussian fuzzy, triangular fuzzy, and crisp cases. The process of solving and presenting the research results is by calculating the fuzzy control chart conditions and fuzzy descriptive capability indices through theoretical calculations and a project-based organizational case study and comparing the average run length type 2, which represents the expected interval between out-of-control events. Average run length type 2 illustrates that Gaussian and triangular quality control charts are able to detect an out-of-control signal more quickly. The main contributions of this paper are to use Gaussian fuzzy numbers in order to provide a systematic approach for designing fuzzy control charts, estimating descriptive capability indices, and analyzing the results of their estimation under fuzzy logic.

A fuzzy Gaussian process regression function approach for forecasting problem

A fuzzy regression function approach is a fuzzy inference system method whose rules cannot be determined based on expert opinion, unlike a classical fuzzy inference system. In a fuzzy regression function approach, an input matrix consists of memberships obtained by the fuzzy clustering method and lagged variables of the time series. In the fuzzy regression function approach, the output vector corresponding to this input matrix is also created and the parameter estimation for the method is carried out with the ordinary least square method. As it is known, the ordinary least square method assumes that the data are linear. In addition, although it is very useful to include a priori information describing the formation of the data in the model, in most cases this information is not available. It is also inappropriate to use a model that does not accurately characterize the data. However, it is not appropriate to estimate parameters for nonlinear data using the ordinary least square method. One of the methods to be used in such a situation is the Gaussian process regression method. While the parameters of a selected basis function are fitted in the ordinary least squares regression method, how all measured data are related is determined in the Gaussian process regression. Besides, Gaussian process regression is a Bayesian approach, it can provide uncertainty measurements on forecasts. In this study, a fuzzy Gaussian process regression function is proposed. The contribution of this paper is to propose a new fuzzy inference system that can be used to solve nonlinear data by proposing a fuzzy Gaussian process regression function. The performance of the newly proposed method is evaluated based on the closing values of the Bitcoin and Crude oil time series. The performance comparison of the proposed method is evaluated with many different forecasting methods and it is concluded that the proposed method has superior forecasting performance.

Estimating IBNR claim reserves using Gaussian Fuzzy Numbers

The study aimed to propose a new approach to the chain-ladder method using for that Gaussian Fuzzy Numbers. For that, the text introduces concepts and establishes new perspectives that allow not just the use of this kind of fuzzy number in the claim estimation context but also in other areas of knowledge, corroborating, in that way, to the expansion of the adoption of fuzzy logics in a general sense. The results indicates that the adoption of the proposed method offers huge benefits when compared to the traditional approach and previous works exploring other kind of fuzzy numbers.
 Keywords: claim reserves; fuzzy logic; fuzzy numbers; IBNR.

Fuzzy Gaussian GARCH and Fuzzy Gaussian EGARCH Models: Foreign Exchange Market Forecast

This article discusses a comparison of the GARCH and EGARCH conditional variance methods, with respect to the Fuzzy Gaussian GARCH and Fuzzy Gaussian EGARCH. The returns of four exchange rates were forecasted at daily periodicity from January 2015 to November 2022 and out-of-sample, January 2019, and December 2022. The results indicate that the Fuzzy GARCH and Fuzzy EGARCH models better estimate the volatility behaviour of the exchange market series compared to traditional techniques. Therefore, the recommendation is to estimate other high volatility variables to verify the efficiency of the fuzzy techniques, however, the main limitation is that it is not possible to apply traditional econometric tests for fuzzy techniques because the parameters are not estimated with the logarithm of maximum likelihood. The estimation of the parameters of GARCH and EGARCH models with fuzzy theory is the originality proposal. In conclusion, fuzzy methodologies have less error in forecasting the volatility of in-sample and out-of-sample exchange rates.

Enhanced fuzzy Gaussian networks for sputum image based Mycobacterium detection

ABSTRACT The primary step involved in the diagnosis of Mycobacterium tuberculosis is the Sputum Smear Microscopy. The limitations of manual detection can be avoided with an automated technique which is carried out in this study. This paper uses the Enhanced Fuzzy Gaussian Networks (EFGN) for the detection of TB, by integrating the Gaussian model with the fuzzy and the neural network. In EFGN, the classified output obtained from the enhanced fuzzy and neural network is combined together depending on the Gaussian mixture model. The sputum smear microscopic image acts as the input, to which the process of thresholding is imposed to get the segmented result. Local Gradient Pattern (LGP), length, density, area, and histogram features are utilized to classify and count the bacilli objects using EFGN classifier. The performance is estimated based on the factors, such as Segmentation Accuracy (SA), and Mean Squared Error (MSE).

STIF: Intuitionistic fuzzy Gaussian membership function with statistical transformation weight of evidence and information value for private information preservation.

Data sharing to the multiple organizations are essential for analysis in many situations. The shared data contains the individual's private and sensitive information and results in privacy breach. To overcome the privacy challenges, privacy preserving data mining (PPDM) has progressed as a solution. This work addresses the problem of PPDM by proposing statistical transformation with intuitionistic fuzzy (STIF) algorithm for data perturbation. The STIF algorithm contains statistical methods weight of evidence, information value and intuitionistic fuzzy Gaussian membership function. The STIF algorithm is applied on three benchmark datasets adult income, bank marketing and lung cancer. The classifier models decision tree, random forest, extreme gradient boost and support vector machines are used for accuracy and performance analysis. The results show that the STIF algorithm achieves 99% of accuracy for adult income dataset and 100% accuracy for both bank marketing and lung cancer datasets. Further, the results highlights that the STIF algorithm outperforms in data perturbation capacity and privacy preserving capacity than the state-of-art algorithms without any information loss on both numerical and categorical data.

An incremental approach to forecasting and classification of taxi demand based on evolving fuzzy systems

Private transport has become a viable and increasingly popular alternative to urban transportation. However, with this growth, an old and recurring problem becomes more latent: the relationship between passenger demands and taxi supply. This problem suggests the creation and use of techniques which make it possible to reduce the gap between the demand for taxi passengers and the effective contingent of vehicles needed to meet this demand. This work introduces a new approach to forecasting and classifying taxi passengers’ demands. The proposed approach uses historical data from taxi rides and meteorological data. The Kruskal-Wallis method identifies the most relevant variables, and an evolving fuzzy system performs demand forecasting/classification. Five evolving systems are evaluated with our approach: Autonomous Learning Multi-Model (ALMMo), evolving Multivariable Gaussian Fuzzy System (eMG), evolving Fuzzy with Multivariable Gaussian Participatory Learning and Recursive Maximum Correntropy (eFCE), evolving Fuzzy with Multivariable Gaussian Participatory Learning and Multi-Innovations Recursive Weighted Least Squares (eFMI), and evolving Neo-Fuzzy Neuron (eNFN). In addition, computational experiments using real-world data were conducted to evaluate and compare the performance of the proposed approach. The results revealed that it obtained performance superior or comparable to state-of-the-art ones. Therefore, the experimental results suggest that the proposed approach is promising as an alternative for forecasting and classifying taxi passenger demand.

Two Stage Multi Modal Deep Learning Kannada Character Recognition Model Adaptive to Discriminative Patterns of Kannada Characters

Objectives: Designing optical character recognition systems for Kannada character is challenging due to higher self-similarity in characters and higher number of character classes. This work addresses the two major problems of reduced accuracy and higher false positives due to higher self-similarity in characters. Methods: This work proposes a two stage multi modal deep learning technique to handle the complexity in Kannada character recognition. The characters are first grouped based on morphological and structural similarity. A novel morphological/structural difference maximization convolution kernel based deep learning modal is trained for each of character group to recognize characters in that group. This divide and conquer strategy reduce the complexity of deep learning model in learning discriminative features for Kannada character recognition. Findings: The proposed two stage multi modal deep learning provides 89% recognition accuracy which is at least 6% higher compared to existing works. The false positives in proposed solution are at least10% lower compared to existing works. Novelty: A novel sector intensity distribution feature specific to curve structure of Kannada characters for deciding the number of groups of characters. Classifier is designed for each group of characters. Classification is done using convolutional neural network with a novel morphological/structural difference maximization convolution kernel to solve the structural similarity problem in Kannada character recognition. Keywords: Handwritten character Recognition; Deep Learning; Machine Learning; Pattern Recognition; Fuzzy Gaussian

Comprehensive Risk Assessment Analysis of Accidental Falls in Shipyards Using the Gaussian Fuzzy AHP Model

Comprehensive Risk Assessment Analysis of Accidental Falls in Shipyards Using the Gaussian Fuzzy AHP Model

Research on a U-Net Bridge Crack Identification and Feature-Calculation Methods Based on a CBAM Attention Mechanism

Crack detection on bridges is an important part of assessing whether a bridge is safe for service. The methods using manual inspection and bridge-inspection vehicles have disadvantages, such as low efficiency and affecting road traffic. We have conducted an in-depth study of bridge-crack detection methods and have proposed a bridge crack identification algorithm for Unet, called the CBAM-Unet algorithm. CBAM (Convolutional Block Attention Module) is a lightweight convolutional attention module that combines a channel attention module (CAM) and a spatial attention module (SAM), which use an attention mechanism on a channel and spatially, respectively. CBAM takes into account the characteristics of bridge cracks. When the attention mechanism is used, the ability to express shallow feature information is enhanced, making the identified cracks more complete and accurate. Experimental results show that the algorithm can achieve an accuracy of 92.66% for crack identification. We used Gaussian fuzzy, Otsu and medial skeletonization algorithms to realise the post-processing of an image and obtain a medial skeleton map. A crack feature measurement algorithm based on the skeletonised image is proposed, which completes the measurement of the maximum width and length of the crack with errors of 1–6% and 1–8%, respectively, meeting the detection standard. The bridge crack feature extraction algorithm we present, CBAM-Unet, can effectively complete the crack-identification task, and the obtained image segmentation accuracy and parameter calculation meet the standards and requirements. This method greatly improves detection efficiency and accuracy, reduces detection costs and improves detection efficiency.

Fuzzy Gaussian mixture optimisation of the newsvendor problem: mixing fuzzy perception and randomness of customer demand

Motivated by the increasing exposition of decision makers to both statistical and judgemental based sources of demand information, we develop in this paper a fuzzy Gaussian Mixture Model (GMM) for the newsvendor permitting to mix probabilistic inputs with a subjective weight modelled as a fuzzy number. The developed framework can model for instance situations where sales are impacted by customers sensitive to online review feedback or expert opinions. It can also model situations where a marketing campaign leads to different stochastic alternatives for the demand with a fuzzy weight. Thanks to a tractable mathematical application of the fuzzy machinery on the newsvendor problem, we derived the optimal ordering strategy taking into account both probabilistic and fuzzy components of the demand. We show that the fuzzy GMM can be rewritten as a classical newsvendor problem with an associated density function involving these stochastic and fuzzy components of the demand. The developed model enables to relax the single modality of the demand distribution usually used in the newsvendor literature and to encode the risk attitude of the decision maker.

Optimum harnessing of solar energy with proper selection of phase changing material using integrated fuzzy-COPRAS Model

ABSTRACT Thermal Energy Storage (TES) devices can assist in storing the energy during the sunlight hours which can be used later on non-availability of solar power. Implementation of Phase Changing Material (PCM) can enhance efficiency of TES devices. Selection of resourceful and efficient PCM plays an essential role such that it can be successfully implanted nearly with all solar thermal devices. Assortment of the material involves selection of certain characteristics and properties associated with the performance of material as well as their physical, mechanical, economic and environmental properties. This study deals with a methodological analysis by integrating techniques for finding optimum PCM among various selected materials. Hybridization involves integrating Triangular Fuzzy Number (TFN) with Complex Proportional Assessment (COPRAS) method. In general, Fuzzy set comprises of three or more numbers, denoting it as TFN, Gaussian Fuzzy numbers, Trapezoidal Fuzzy Numbers, and so on. Although alternatives are many to design a fuzzy set depending on elements count, the TFNs are the ones considered as actual representations of a Fuzzy number. COPRAS is a well-known robust MCDM technique chosen to carry out the work of selection. Eight PCM have been compared on the parameters like Latent Heat, Cost, Melting points and others. Calcium Chloride Hexahydrate is the optimum material found as PCM. The result of this study would help planners and designers for an optimum selection of PCM in efficient harnessing of solar energy.

Fusion of Infrared and Visible Images Using Fast Global Smoothing Decomposition and Target-Enhanced Parallel Gaussian Fuzzy Logic.

As a powerful technique to merge complementary information of original images, infrared (IR) and visible image fusion approaches are widely used in surveillance, target detecting, tracking, and biological recognition, etc. In this paper, an efficient IR and visible image fusion method is proposed to simultaneously enhance the significant targets/regions in all source images and preserve rich background details in visible images. The multi-scale representation based on the fast global smoother is firstly used to decompose source images into the base and detail layers, aiming to extract the salient structure information and suppress the halos around the edges. Then, a target-enhanced parallel Gaussian fuzzy logic-based fusion rule is proposed to merge the base layers, which can avoid the brightness loss and highlight significant targets/regions. In addition, the visual saliency map-based fusion rule is designed to merge the detail layers with the purpose of obtaining rich details. Finally, the fused image is reconstructed. Extensive experiments are conducted on 21 image pairs and a Nato-camp sequence (32 image pairs) to verify the effectiveness and superiority of the proposed method. Compared with several state-of-the-art methods, experimental results demonstrate that the proposed method can achieve more competitive or superior performances according to both the visual results and objective evaluation.

Variances in knowledge-based interval type 2 Gaussian fuzzy on linear regression models

Fuzzy logic is a branch of artificial intelligence that has been used extensively in developing Fuzzy systems and models. These systems usually offer artificial intelligence based on the predictive mathematical models used; in this case linear regression mathematical model. Interval type 2 Gaussian fuzzy logic is a fuzzy logic that utilizes Gaussian upper membership function and the lower membership function, with a footprint of uncertainty in between the Gaussian membership functions. The artificial intelligence solutions predicted by these interval type 2 fuzzy systems depends on the training and the resultant linear regression mathematical model developed, which usually extract their training data from the expert knowledge stored in their knowledge bases. The variances in the expert knowledge stored in these knowledge-bases usually affect the overall accuracy of the linear regression predictive models of these systems, due to the variances in the training data. This research therefore establishes the extent that these variances in knowledge bases affect the predictive accuracy of these models, with a case study on knowledge bases used to predict learners’ knowledge level abilities. The calculated linear regression predictive models show that for every variance in the knowledge base, there occurs a change in linear regression predictive model with an intercept value factor commensurate to the variances and their respective weights in the knowledge bases.

Similarity Measure of Gaussian Fuzzy Numbers and Its Application

Similarity Measure of Gaussian Fuzzy Numbers and Its Application

Gaussian-based hybrid approach to Entropy for analyzing energy efficiency of emerging economies

This study aims to evaluate energy efficiency in emerging economies. Within this context, a hybrid fuzzy multi-criteria decision-making (MCDM) model is generated. In this model, firstly 7 different criteria that can affect energy efficiency are identified with the help of a comprehensive literature review. After that, these criteria are weighted by considering Gaussian fuzzy Entropy methodology. In the next stage, emerging 7 (E7) countries are ranked regarding the performance of energy efficiency with Gaussian fuzzy the interactive multi-criteria decision-making (TODIM) approach. In the final stage, the criteria have also been evaluated by using fuzzy decision-making trial and evaluation laboratory (DEMATEL) method for robustness check. Similarly, the alternatives are also ranked by using technique for order preference by similarity to ideal solution (TOPSIS) to evaluate the coherency of the analysis results. Finally, the calculations are also made with triangular and trapezoidal fuzzy numbers to control the reliability of the proposed model. The findings indicate that economic development and technological competency are the main factors that influence energy efficiency. Moreover, it is also stated that Russia and Brazil are the most successful countries in energy efficiency whereas India and Turkey are in the last places. Therefore, these issues can be helpful to minimize the costs of energy usage for these countries

CBSN: Comparative measures of normalization techniques for brain tumor segmentation using SRCNet

The segmentation of tumors in the brain MRI scans is a difficult job for doctors and radiologists. The segmentation done by different medical experts may also have differences in their opinion for the segmented region, which is popularly known as regions of interest (ROIs). To date, researchers and academicians have proposed several approaches and frameworks for semi- and full-automatic segmentation techniques to identify ROIs accurately. It is prevalent that automatic segmentation gives comparable or even better results compared to human experts for several publicly known and privately collected datasets. Additionally, these are beneficial in those areas where doctors and radiologists’ availability is either uneven or scarce because of geographical dispersion. The convolutional neural networks (CNN) are considered for segmentation for ROIs due to their wide popularity. They have outperformed humans over tasks like object identification and image classification. The publicly available datasets or those collected from different medical institutions may have different statistics, resolution, and properties. Therefore, pre-processing has an essential role in achieving better and accurate delineation and segmentation of tumors. In the proposed work, CBSN, we consider well-known normalization techniques such as Gaussian Mixture Models (GMM), Fuzzy C-Means (FCM), and Z-score normalization for pre-processing the BraTS (Brain Tumor Segmentation Challenge) 2018 dataset. We utilized three variants of U-Net architecture, convolutional block attention module (CBAM), squeeze and excitation module (SEM), and refinement module (RM) for the segmentation of the ROIs. Utilizing Z-score performs better than other normalization techniques for tumor core (TC) and whole tumor (WT) segmentation. In contrast, FCM performs superior to the other two normalization techniques on enhancement tumor (ET) segmentation.

Fuzzy modeling of vegetable straw cover crop productivity at different nitrogen doses

The Direct Planting System (DPS), also referred to as direct seeding or no-tillage farming, is distinguished by the permanent occupation of the agricultural land with plant crops besides the maintenance of the straw under the soil surface. However, for its effectiveness, it is necessary to adopt suitable cover crops for each region. Sorghum has a high tolerance to the dry season and a high volume of dry matter production, which are important characteristics for adoption as a cover crop. Millet, besides having a high resistance to periods of water stress, has also a high concentration of biomass production. In this context, the present work aimed to analyze, through the use of mathematical models, the effects of nitrogen in sorghum and millet crops, adopted as vegetation cover crops, under different levels of nitrogen in relation to the dry matter production variables. The comparison of the fuzzy (Gaussian and triangular) and polynomial regression models was performed using statistical tests and verification of the determination coefficient. Analytically, the best statistical parameters for triangular fuzzy modeling were verified in comparison to the Gaussian fuzzy model and polynomial regression of degree two. However, in relation to the behavior of the mathematical modeling curve, the Gaussian model proved to be best in keeping with the reality of the agronomic phenomena, mainly due to the smoothing of the curve over the data set.

In the search of oil and gas incident causal factors: The Gaussian Fuzzy Analytic Hierarchy Process approach

Learning from events is a crucial measure to reduce the number of catastrophic incidents on board the oil and gas industry. It is critical to have a better understanding of the root causes behind the incidents. A better understanding of the incident causal factors can be accomplished by investigating thoroughly previous incident reports and determine the most notable factors in contributing to the incidents. This paper mainly elaborates and analyzes incident reports produced by the International Oil and Gas Producer Association’s (IOGP’s) reports from 2010 until 2018 located in the worldwide operation. The most contributing factors lead to the oil and gas incidents are examined in this paper utilizing Gaussian fuzzy analytic hierarchy process, an improved methodology of fuzzy analytic hierarchy process (FAHP) by embedding Gaussian fuzzy number within the evaluation process. Gaussian fuzzy number is obtained by random simulation corresponding to the Gaussian probability density function. The result of this paper reveals the more accurate and realistic representation of incident causal factors determination. This point of view helps in better explaining oil and gas incidents causal factors, where precaution measures should be directed and efficiently managed.