Average Rule Research Articles

Combining Gabor energy with equilibrium optimizer algorithm for multi-modality medical image fusion

Medical image fusion is a technique of extracting information from multiple image modalities and combining them to create a single image with the aim of improving the image content and preserving information. Until now, many approaches have been introduced to enhance efficiency in medical image fusion. Nevertheless, there are still limitations to the fusion of the low and high-frequency coefficients of several current methods to our current knowledge. The first limitation is that the output images often lose detailed information because high-frequency coefficients can be fused by ineffective rules. The second limitation is the deterioration of the resulting image luminance when the low-frequency coefficients are fused by the average rule. In this study, we propose a novel method to overcome the limitations mentioned above, and this approach is described by several steps as follows. Firstly, the discrete stationary wavelet transform (DSWT) method is used to convert input images into high and low-frequency components. Secondly, a rule based on maximum Gabor energy (MGE) is introduced to fusing high-frequency coefficients, which allows important information to be preserved in the resulting image. Thirdly, low-frequency coefficients are fused by optimal parameters based on the equilibrium optimizer algorithm (EOA). This fusion rule ensures the resulting image has good quality. In order to verify our approach's effectiveness, we have used six image quality indexes and the latest five medical image fusion methods for comparison. The experimental results show that the proposed approach has overcome the disadvantages of some current methods.

A novel approach based on Three-scale image decomposition and Marine predators algorithm for multi-modal medical image fusion

Multi-modal medical image fusion not only creates an image that preserves important information from the input images but also significantly improves in quality. This work contributes significantly to improving the ability of the physician to diagnose. So far, there have been many proposed approaches to improve efficiency for medical image fusion. However, some existing approaches still have certain drawbacks. The first drawback is that some vital information such as edges may be lost in the output image because of the high-frequency component fusion rules’ inefficiency. The second drawback is that the fused images often have low contrast because they have applied an average rule for the low-frequency components. In this study, a novel approach is introduced to overcome the aforementioned drawbacks, and it includes the following main steps. Firstly, the three-scale decomposition (TSD) technique is introduced to obtain the base and detail components. Secondly, a rule base on local energy function using the Kirsch compass operator is applied to fusing detail layers, which helps the output image preserve important information. Thirdly, the Marine predators algorithm (MPA) is utilized to fuse base layers by optimal parameters, allowing the output image to have good quality. To verify the proposed approach's effectiveness, we have utilized five state-of-the-art medical image fusion approaches and six image quality metrics for comparison. Experimental results show that the proposed approach significantly enhanced the fused image’s quality and preserved edge information.

Multi-modal medical image fusion based on equilibrium optimizer algorithm and local energy functions

Multi-modal medical image fusion brings many benefits to clinical diagnosis and analysis because it creates favorable conditions for diagnostic imaging practitioners to make a more accurate diagnosis. According to our current knowledge, there are still some disadvantages to current image fusion approaches. The first one is that the fused images often have low contrast. The reason for this is several approaches use a weighted average rule for fusing low-frequency components. The second drawback is that the loss of detailed information in the fused image. This can be explained by the fact that the high-frequency components synthesized by the rules are not really effective. In this paper, two novel algorithms are proposed to tackle the above two disadvantages. The first algorithm is based on the Equilibrium optimizer algorithm (EOA) to find optimal parameters to fuse low-frequency components. This allows the fused image to have good contrast. The second algorithm is based on the sum of local energy functions using the Prewitt compass operator to create an efficient rule for the fusion of high-frequency components. This allows the fused image to significantly preserve details transferred from input images. Experimental results show that the proposed approach not only effective in significantly enhancing the quality of the fusion image but also preserving edge information carried from input images.

Interval Type-2 Fuzzy Passive Filtering for Nonlinear Singularly Perturbed PDT-Switched Systems and Its Application

The problem of designing a passive filter for nonlinear switched singularly perturbed systems with parameter uncertainties is explored in this paper. Firstly, the multiple-time-scale phenomenon is settled effectively by introducing a singular perturbation parameter in the plant. Secondly, the interval type-2 fuzzy set theory is employed where parameter uncertainties are expressed in membership functions rather than the system matrices. It is worth noting that interval type-2 fuzzy sets of the devised filter are different from the plant, which makes the design of the filter more flexible. Thirdly, the persistent dwell-time switching rule, as a kind of time-dependent switching rules, is used to manage the switchings among nonlinear singularly perturbed subsystems, and this rule is more general than dwell-time and average dwell-time switching rules. Next, sufficient conditions are provided for guaranteeing that the filtering error system is globally uniformly exponentially stable with a passive performance. Furthermore, on the basis of the linear matrix inequalities, the explicit expression of the designed filter can be obtained. Finally, a tunnel diode electronic circuit is rendered as an example to confirm the correctness and the validity of the developed filter.

A novel approach based on Grasshopper optimization algorithm for medical image fusion

The fusion of multi-modal medical images makes a significant contribution to clinical diagnosis and analysis because it allows diagnostic imaging practitioners to make a more accurate diagnosis. According to our current knowledge, there are some limitations of current medical image fusion approaches. The first limitation is that the use of a weighted average rule for fusing low-frequency components. This limitation leads to a decrease in the intensity of the brightness of the fused image. The second limitation is that the utilizing of fusion rules for high-frequency components is not really optimal. This is likely to result in the loss of detailed information in the fused image. In this paper, a novel approach, including two algorithms, is proposed to address the above-mentioned limitations. The first algorithm is based on the Grasshopper optimization algorithm (GOA) to find optimal parameters with the aim of fusing low-frequency components. This allows the fused image to have good contrast. The second algorithm is based on the Kirsch compass operator to create an efficient rule for the fusion of high-frequency components. This allows the fused image to significantly preserve details transferred from input images. Experimental results show that the proposed approach not only effective in enhancing significantly the contrast of the fused image but also preserving edge information carried from input images to the composite image.

Diversifying Estimation Errors: An Efficient Averaging Rule for Portfolio Optimization

We propose an averaging rule that combines established minimum-variance strategies to minimize the expected out-of-sample variance. Our rule overcomes the problem of selecting the “best” strategy ex-ante and diversifies remaining estimation errors of the single strategies included in the averaging. Extensive simulations show that the contributions of estimation errors to the out-of-sample variances are uncorrelated between the considered strategies. This implies that averaging over multiple strategies o˙ers sizable diversification benefits. Our rule leverages these benefits and compares favorably to eleven strategies in terms of out-of-sample variance on both simulated and empirical data sets. The Sharpe ratio is across all data sets at least 25% higher than for the 1/N portfolio.

Multi-Modality Medical Image Fusion Using Cross-Bilateral Filter and Neuro-Fuzzy Approach.

Context:The proposed technique uses the edge-preserving capabilities of cross-bilateral filter (CBF) and artificial intelligence technique adaptive neuro-fuzzy inference system (ANFIS) to fuse multi-modality medical images.Aims:The aim is to present the unlike information onto a single image as each modality of medical image contains the unalike domain of information.Settings and Design:First, the multi-modality medical images are decomposed using CBF by tuning its parameters: radiometric and geometric sigma producing CBF component and detail component. This detail is fed to ANFIS for fusion. On the other hand, the sub-bands obtained from DWT are fused using average rule. Reconstruction method gives final image.Subjects and Methods:ANFIS is used to train the Sugeno systems using neuro-adaptive learning. The fuzzy inference system in the ANFIS is used to define fuzzy rules for fusion. On the other hand, bior2.2 is used to decompose the source images.Statistical Analysis Used:The performance is verified on the Harvard database with five cases, and the results are equated with conventional metrics, objective metrics as well as visual inspection. The statistics of the metrics values is visualized in the form of column chart.Results:In Case 1, better results are obtained for all conventional metrics except for average gradient (AG) and spatial frequency (SF). It also achieved preferred objective metric values. In Case 2, all metrics except AG, mutual information, fusion symmetry, and SF are better values among all methods. In Cases 3, 4, and 5, all the metrics have achieved desired values.Conclusions:Experiments conclude that conventional, objective, visual evaluation shows best results for Cases 1, 3, 4, and 5.

CNN-based Multimodal Touchless Biometric Recognition System using Gait and Speech

Person identification using biometric features is an effective method for recognizing and authenticating the identity of a person. Multimodal biometric systems combine different biometric modalities in order to make better predictions as well as for achieving increased robustness. This paper proposes a touchless multimodal person identification model using deep learning techniques by combining the gait and speech modalities. Separate pipelines for both the modalities were developed using Convolutional Neural Networks. The paper also explores various fusion strategies for combining the two pipelines and shows how various metrics get affected with different fusion strategies. Results show that weighted average and product fusion rules work best for the data used in the experiments.

Type I error of t-tests from the simple moving average technical trading rules

Listed: Ren, Louie (University of Houston-Victoria, TX, USA) Ren, Peter (University of Houston-Downtown, Houston, TX, USA) Registered:

Some Results of Stock Trading Strategy Based on Fuzzy System

In order to better study the stock trend and master the basic rules of stock trading, this paper uses the fuzzy system theory to transform the technology trading strategy in stock into the membership function in fuzzy mathematics, so as to study the trend change of technology trading rules in dynamic stock price with mathematical language. It can be seen from the experimental results that when only the moving average rule is adopted, the stock price trend is relatively flat, and the specific changes of the price cannot be accurately obtained. However, when the bull and bear rule is added, the stock price jumps greatly, so that the changes of the price can be easily captured, so as to make corresponding strategic adjustments to maximize the income.

A Novel Structure of Highly Interpretable Fuzzy Rules Extraction

Introduction: Extracting effective rules from medical data with two indicators of accuracy and high interpretability is essential to increase the accuracy and speed of diagnosis by specialists. As a result, the production of medical assistant systems that are able to detect the rules governing the data plays a vital role in early detection of the disease and thus increase the chances of treatment, disease control and maintaining the quality of life of patients.Material and Methods: In this paper, a system of automatic extraction of rules from medical data by a new hybrid method based on fuzzy logic and genetic algorithm is presented. Genetic algorithms are used to automatically generate these rules. The Parkinson UCI dataset including 195 records and 23 variables was used to evaluate the proposed method based on the criteria of interpretability, accuracy, sensitivity and specificity.Results: The evaluation of the proposed model on the Parkinson's dataset was the accuracy of 84.62%. This accuracy is supported by 4 fuzzy rules with an average rule length of 2 and using 7 linguistic terms extremely low, very low, low, normal, high, very high and extremely high. All fuzzy membership functions that represent each term have the same width.Conclusion: The proposed method, based on the three criteria of low number of rules, short rule length and symmetric membership functions with equal width for all variables, is quite suitable for automatic production of accurate and compact rules with high interpretability in medical data. . A 90% dimensionality reduction in the experimental evaluation showed that this model could be used to implement real-time systems.

Effectiveness of Moving Average Rules During COVID-19 Pandemic: Evidence from Malaysian Stock Market

Effectiveness of Moving Average Rules During COVID-19 Pandemic: Evidence from Malaysian Stock Market

Technical Analysis and Malaysian Banking Sector during COVID-19 Pandemic

The announcements of Movement Control Order and Loan Moratorium caused a significant impact on the stock prices of Malaysian banks during the COVID-19 pandemic. This study aims to investigate the effectiveness of technical analysis in predicting the stock price movement and the ability of the technical analysis in generating returns. In doing so, six moving average rules used as the proxy of technical analysis and tested in this study. Majority of the MA rules shown positive returns before the various announcements dates. Specifically, this study revealed that MA rules of (2,5) and (2,10) were among the best performing MA rules during the COVID-19 pandemic. This study also recommends the investors to use the signals emitted by the technical indicator as the reference for their investment decision in the banks’ stock.

Kompetensi Kemahiran Berbahasa Mahasiswa Program Studi Pendidikan Bahasa dan Sastra Indonsia Universitas Islam Riau

This study aimed to analyze the literacy competencies of students' language proficiency in the aspects of listening, responding to the rules, reading and writing. Literacy competence is a competency that must be considered by educators in honing language proficiency which is an important issue, especially in the development of language skills in students. This research used a quantitative-descriptive method. The data obtained from this study were the results of the Indonesian language proficiency test (UKBI) of students in the Indonesian Language and Literature Education study program. The population used in this study were students of the Indonesian Language and Literature Education study program, while the sample who had taken the UKBI test was 24 students. The results of this study indicated that the students' language proficiency literacy competence in the listening aspect obtains an average of 538, responds to the average rule of 464, reads an average of 519, and writes an average of 596. The overall average of language proficiency literacy competencies was 529. Twenty-five was in the middle-ranking, indicating that students have adequate proficiency in communicating using Indonesian, both oral and written. With these skills, the student concerned can understand factual information, properly capture and re-reveal information.

RETRACTED: Design of hospital IoT smart system and nucleoside drugs for treatment of hepatitis and liver cirrhosis

Abstract Cirrhosis of the liver, as well as of chronic hepatitis B infection, is in the advanced form. Alcoholism, autoimmune disease, C-type hepatitis, and other conditions can cause cirrhosis of the liver. In the case of hepatitis B, it can gradually lead to scar tissue formation, and the virus attacks the liver. Antiviral treatment of hepatitis B is one of the most quickly creating territories in current medication. Rules for the Management of persistent hepatitis B (CH-B) have been proposed, has been fixed by a few scholarly gatherings and gathering. Proposals for the nucleoside or nucleotide simple from the average flow rules treatment, to one another, will be contrasted with the past rules. Many of the other nucleoside analogs there are currently in clinical trials in early and developed countries. A certain percentage of shares lamivudine but have had a severe problem; many seems to have significant antiviral effects than existing drugs. Some of these new antiviral agents, if approved for use in the treatment of chronic hepatitis B, realizing the potential for long-term inhibition of improving the replication and stabilization or liver disease hepatitis B virus can be, fortunately, such entecavir and tenofovir new, such as more effective arrival, has a very low resistivity, has a substantial and long-term viral suppression. The motivation behind this survey, the most recent advancement on the therapy of constant hepatitis B, is presented the component of antiviral action of various specialists in detail, the viability of viral concealment, the aftereffect of the treatment endpoint, the specialist improvement of resilience, and the medication of this utilization the ideal system.

Heterogeneous Labor Market Effects of Monetary Policy

This paper analyzes the heterogeneous effects of monetary policy on workers with different levels of labor force attachment. Exploiting variation in labor market tightness across metropolitan areas, we show that the employment of populations with lower labor force attachment--Blacks, high school dropouts, and women--is more responsive to expansionary monetary policy in tighter labor markets. We develop a New Keynesian model with heterogeneous workers that explains these results. The model shows that expansionary monetary shocks lead to larger and more persistent increases in the employment of low attachment populations when the central bank follows an average inflation targeting rule and when the Phillips curve is flatter. These findings suggest that the Federal Reserve's recent move from a strict to an average inflation targeting framework will especially benefit workers with lower labor force attachment.

Critical analysis of the methodology for deriving equations of motion O. Reynolds for turbulent flows

Annotation The O. Reynolds equation is the only one in the scientific world that is derived from equations that are valid for a medium that has completely different surface forces than the surface forces in real turbulent flows. It is shown that by violating the formulated averaging rules and algebraic rules, additional terms were introduced into the inertial part of the Navier-Stokes equations while maintaining the surface forces that determine the nature of motion of a completely different medium, thereby turning the averaged equations into inequalities. Based on the analysis of all violations allowed during averaging, it is shown that it is impossible to obtain new equations of motion by averaging the Navier-Stokes equations, and the stresses introduced by O. Reynolds do not reflect the real stresses in a turbulent flow. When you eliminate violations of the averaging rules, all of them turn to zero. As an alternative solution to the problem, we derive equations based on equations of motion in stresses that are valid for any media, and simplify it to a form suitable for technical calculations.

A new representation of generalized averaged Gauss quadrature rules

Gauss quadrature rules associated with a nonnegative measure with support on (part of) the real axis find many applications in Scientific Computing. It is important to be able to estimate the quadrature error when replacing an integral by an ℓ-node Gauss quadrature rule in order to choose a suitable number of nodes. A classical approach to estimate this error is to evaluate the associated (2ℓ+1)-node Gauss–Kronrod rule. However, Gauss–Kronrod rules with 2ℓ+1 real nodes might not exist. The (2ℓ+1)-node generalized averaged Gauss formula associated with the ℓ-node Gauss rule described in Spalević (2007) [16] is guaranteed to exist and provides an attractive alternative to the (2ℓ+1)-node Gauss–Kronrod rule. This paper describes a new representation of generalized averaged Gauss formulas that is cheaper to evaluate than the available representation.

Intelligent multimodal medical image fusion with deep guided filtering

Medical image fusion is a synthesis of visual information present in any number of medical imaging inputs into a single fused image without any distortion or loss of detail. It enhances image quality by retaining specific features to improve the clinical applicability of medical imaging for treatment and evaluation of medical conditions. A big challenge in the processing of medical images is to incorporate the pathological features of the complement into one image. The fused image presents various challenges, such as existence of fusion artifacts, hardness of the base, comparison of medical image input, and computational cost. The techniques of hybrid multimodal medical image fusion (HMMIF) have been designed for pathologic studies, such as neurocysticercosis, degenerative and neoplastic diseases. Two domain algorithms based on HMMIF techniques have been developed in this research for various medical image fusion applications for MRI-SPECT, MRI-PET, and MRI-CT. NSCT is initially used in the proposed method to decompose the input images which give components of low and high frequency. The average fusion rule applies to NSCT components with low frequency. The NSCT high frequency components are fused by the law of full fusion. NSCTs high frequency is handled with directed image filtration scheme. The fused picture is obtained by taking inverse transformations from all frequency bands with the coefficients obtained from them. The methods suggested are contrasted with traditional approaches in the state of the art. Experimentation proves that the methods suggested are superior in terms of both qualitative and quantitative assessment. The fused images using proposed algorithms provide information useful for visualizing and understanding the diseases to the best of both sources’ modality.

Numerical Modeling of Transcritical and Supercritical Fuel Injections Using a Multi-Component Two-Phase Flow Model

In the present numerical study, implicit large eddy simulations (LES) of non-reacting multi-components mixing processes of cryogenic nitrogen and n-dodecane fuel injections under transcritical and supercritical conditions are carried out, using a modified reacting flow solver, originally available in the open source software OpenFOAM®. To this end, the Peng-Robinson (PR) cubic equation of state (EOS) is considered and the solver is modified to account for the real-fluid thermodynamics. At high pressure conditions, the variable transport properties such as dynamic viscosity and thermal conductivity are accurately computed using the Chung transport model. To deal with the multicomponent species mixing, molar averaged homogeneous classical mixing rules are used. For the velocity-pressure coupling, a PIMPLE based compressible algorithm is employed. For both cryogenic and non-cryogenic fuel injections, qualitative and quantitative analyses are performed, and the results show significant effects of the chamber pressure on the mixing processes and entrainment rates. The capability of the proposed numerical model to handle multicomponent species mixing with real-fluid thermophysical properties is demonstrated, in both supercritical and transcritical regimes.