Fuzzy regression for perceptual image quality assessment

The current rate of success in launching satellites and advances in onboard and ground image processing have led to a dramatic increase in the scale of remote sensing image data. This has resulted in considerable research on how to provide the best quality of experience to end users. However, subjective image quality assessment (IQA) is time-consuming, cumbersome, expensive and cannot be implemented automatically using computers. Thus, subjective IQA may not be suitable for application requirements. In this paper, we design and construct a usability-based subjective remote sensing IQA database. A corresponding no-reference IQA method is also proposed. The new IQA method uses scale-invariant feature transforms to form a dictionary and then a support vector machine to obtain an IQA model. The experimental results show that the new subjective IQA database is highly suited to the task, and the quality predictions of the new IQA method correlate well with human subjective scores in the new database.

Fuzzy regression analysis is an important regression analysis method to predict uncertain information in the real world. In this paper, the input data are crisp with randomness; the output data are trapezoid fuzzy number, and three different risk preferences and chaos optimization algorithm are introduced to establish fuzzy regression model. On the basis of the principle of the minimum total spread between the observed and the estimated values, risk-neutral, risk-averse, and risk-seeking fuzzy regression model are developed to obtain the parameters of fuzzy linear regression model. Chaos optimization algorithm is used to determine the digital characteristic of random variables. The mean absolute percentage error and variance of errors are adopted to compare the modeling results. A stock rating case is used to evaluate the fuzzy regression models. The comparisons with five existing methods show that our proposed method has satisfactory performance.

The characteristic of the fuzzy regression model is to enwrap all the given samples. An interval of fuzzy regression model is created by considering how far a sample is from the central values. That means when samples are widely scattered the size of an interval of the fuzzy model is widened. That is, the fuzziness of the fuzzy regression model is decided by the range of sample distribution. Therefore, many research results on a fuzzy regression model in order to describe the possibility of the target system have been reported. We have proposed two fuzzy robust regression models which remove influences of improper data such as unusual data and outliers. In this paper, we describe the model building of our fuzzy robust regressions by removing influences of improper data.

Fuzzy QSARs for predicting log Koc of persistent organic pollutants

Artificial Intelligence (AI) is the enhancement and method of computer system that handles tasks which requires human like intelligence such as recognition, language translation and visual interpretation. Subjective image quality assessment (IQA) is difficult to be implemented in real-time systems, methodology for enhancing the involvement in producing IQA model is to improve the quality of image by significant evaluation. Intuitively, human eyes are not sensitive to the distortion and damage from the area with lesser visual saliency (VS), VS is closely related to IQA. With this consideration, an effective IQA was proposed, which involved two processes. The local quality map of a distorted image was computed using the structural similarity function of its feature attributes, such as brightness, chrominance and gradient. Second, the local quality map was weighted with visual saliency (VS) to get the objective evaluation of image quality. The VS was modeled by extracting the saliency of low-level features of the image, wiping off the molestation information from these saliency based on an apriori threshold, and combining the effective information to construct the saliency map. Image processing using fuzzy is gathering features and segments as fuzzy set while processing images. The experiments on the two largest database for six classical IQA metrics demonstrate that performance of weighted-VS IQA metrics is superior to the performance of no weighted-VS IQA metrics, and the proposed IQA method has higher computational accuracy than the other IQA metrics under a moderate computational complexity, especially for two types of distortion images, such as local block-wise (Block) and fast-fading (FTF).

Inflation targeting is defined as the determination of an acceptable inflation rate for a specific period of time by considering the general variables and data of inflation and also as the implementation of monetary policies in such a way that they can reach predetermined rates. Accurate inflation estimates are needed to reach the targets. Acquiring the estimations accurately leads to correct decisions. Fuzzy regression method is a method that gives successful results when classical regression estimates cannot be obtained. During the periods when Turkey undergoes economical crisis, it is difficult to make accurate inflation estimates. More reliable estimates can be made with the help of fuzzy regression models. In this study, inflation estimates were obtained through fuzzy regression model estimation and the results were interpreted.

Error measures for fuzzy linear regression: Monte Carlo simulation approach

Applications of Soft Computing to Human-centered Information Systems

This study presents an integrated fuzzy regression and time-series technique to estimate and predict electricity demand. Furthermore, it is difficult to model uncertain behaviour of energy consumption with only conventional time-series and fuzzy regression, which could be an ideal substitute for such cases. After reviewing various fuzzy regression models and studying their advantages and shortcomings, the best model is selected. Also, the impact of data preprocessing and post-processing on the fuzzy regression performance is to study and to show that this method does not contribute to the efficiency of the model. In addition, another unique feature of this study is utilisation of autocorrelation function to define input variables versus trial and error method. At last, the comparison of actual data with fuzzy regression and ARIMA model, using Granger?Newbold test, is achieved. Monthly electricity consumption of Iran from 1995 to 2005 is considered as the case of this study.

Objective. To explore whether the image noises and the metal artifacts could be further managed by the combined use of two technologies, the adaptive statistical iterative reconstruction (ASIR) and the monochromatic imaging generated by gemstone spectral imaging (GSI) dual-energy CT. Materials and Methods. Fifty-one patients with 318 spinal pedicle screws were prospectively scanned with dual energy CT by using fast kV-switching GSI between 80 and 140 kVp. The monochromatic GSI images at 110 keV were reconstructed either without ASIR or with ASIR of various levels (30%, 50%, 70% and 100%). For these five sets of images, both objective and subjective image quality assessments were performed to evaluate the image quality. Results. With objective image quality assessment, the metal artifacts (measured by an artifacts index) significantly decreased when increasing levels of ASIR was utilized (p < 0.001). Moreover, adding ASIR to GSI also decreased the image noise (p < 0.001) and improved the signal-to-noise ratio (SNR, p < 0.001). With subjective image quality analysis, the inter-reader agreements were good, with intra-class correlation coefficients (ICC) of 0.89 to 0.99. Meanwhile, the visualization of the peri-implant soft tissue was improved at higher ASIR levels (p < 0.001). Conclusion. Combined use of ASIR and GSI is shown to decrease the image noise and improve the image quality in post-spinal fusion CT scans. Optimal results were achieved with ASIR levels of over 70%.

The regression model is generally utilized in several fields of study because of its applications. Regression is an extremely incredible approach; it builds up a connection between dependent and independent variables. We have addressed a powerful computational model by utilizing dengue information joined with fuzzy multiple linear regression. Information is accumulated on dengue fever through the survey. This paper is centered on the comparison of the crisp method with fuzzy multiple linear regression, and then, the utilization of a fuzzy multiple regression method is explained after the comparison. We have used multiple regression and then converted the said technique into three fuzzy cases. The effectiveness of the fuzzy multiple regression model is measured by numerical computation and comparison of both techniques. 2020 Mathematics Subject Classification. Primary 30C45; 30C50; 30C80; Secondary 11B65, 47B38.

This study evaluates the image quality of postmortem photon-counting computed tomography (PMPCCT) compared to conventional postmortem energy-integrating computed tomography (PMCT) for a protocol adapted to post-mortem imaging. The focus lies on objective image quality parameters such as noise, signal-to-noise ratio, and contrast-to-noise ratio, supplemented by subjective image quality evaluations.A Chest Phantom N1 and one decedent were scanned using PMCT (Siemens SOMATOM® X-Cite; slice thicknesses of 1mm and 0.5mm; Br40, Br60) and PMPCCT (Siemens NAEOTOM Alpha.peak®; slice thicknesses of 1mm, 0.4mm, and 0.2mm; Br40, Br60). Image quality parameters were computed for regions of interest. In addition, two radiologists conducted subjective image quality evaluations (noise, overall image quality, sharpness, bone details, contour visibility, and artifact formation) using a modified Likert scale.The overall findings were mixed, with PMPCCT potentially demonstrating an advantage over PMCT in terms of SNR and CNR, particularly at moderate slice thicknesses of 1mm and 0.4mm. The results obtained from the phantom exhibit in some cases considerable differences compared to those from the decedent, so that model studies can hardly be transferred to reality without scrutiny. PMPCCT outperformed PMCT in subjective assessments of overall image quality, sharpness, and bone detail, particularly with Br40 reconstruction kernels.PMPCCT may demonstrate advantages in objective and subjective image quality, with improvements in soft tissue imaging, sharpness, and bone detail at moderate slice thicknesses. These results may suggest PMPCCTs promising potential in forensic imaging. Further studies are needed to investigate and optimize its utility in postmortem settings.

When we attempt to model a complex system including a human as an important component, it may be difficult to represent the system by a deterministic mathematical model. The main reason of this difficulty is that the system itself inherently has some fuzziness concerning subjective judgement of a human. In this paper, we propose a fuzzy nonlinear regression method with RFLN (RCE-based fuzzy learning network), which is capable of extracting knowledge of the experts automatically. RFLN is an extended RCE (restricted Coulomb energy) model, hence it needs few iterations in learning and its additional learning is easy. The proposed method has higher flexibility than fuzzy linear regression models. We propose learning algorithms to identify a nonlinear interval model which approximately includes all the given input-output data. The proposed method has characteristics of faster learning and of easier additional learning. The effectiveness of the method is shown by numerical experiments.

An integrated fuzzy regression-data envelopment analysis algorithm for optimum oil consumption estimation with ambiguous data

Tanaka suggested that the parameters of a linear regression model should be made fuzzy to bring about the swing of a system and created a fuzzy linear regression model. This model can be formulated in a linear programming problem that minimizes a span between the upper and lower limits under constraints that include all data. In recent years, all the attention has been focused on a fuzzy number that has an indifferent zone. A fuzzy number is defi ned by using a Type 2 membership function. This paper addresses the fact that a Type 2 membership function has the upper and lower limits and shows that a Type 2 membership function can be identifi ed by expanding a fuzzy linear regression model into a fuzzy linear polynomial regression model. Finally, after a proposed fuzzy polynomial model is identifi ed, a mathematical model will be created for a fuzzy decision-making method that has an indifferent zone.