Empowering decentralized identity systems for Web 3.0 in complex spherical fuzzy knowledge.

The Web 3.0 network system, the next generation of the world wide web, incorporates new technologies and algorithms to enhance accessibility, decentralization, and security, mimicking human comprehension and enabling more personalized user interactions. The key component of this environment is decentralized identity management (DIM), embracing an identity and access management strategy that empowers computing devices and individuals to manage their digital personas. Aggregation operators (AOs) are valuable techniques that facilitate combining and summarizing a finite set of imprecise data. It is imperative to employ such operators to effectively address multicriteria decision-making (MCDM) issues. Yager operators have a significant extent of adaptability in managing operational environments and exhibit excellent effectiveness in addressing decision-making (DM) uncertainties. The complex spherical fuzzy (CSF) model is more effective in capturing and reflecting the known unpredictability in a DM application. This research endeavors to enhance the DM scenario of the Web 3.0 environment using Yager aggregation operators within the CSF environment. We present two innovative aggregation operators, namely complex spherical fuzzy Yager-ordered weighted averaging (CSFYOWA) and complex spherical fuzzy Yager-ordered weighted geometric (CSFYOWG) operators. We elucidate some structural characteristics of these operators and come up with an updated score function to rectify the drawbacks of the existing score function in the CSF framework. By utilizing newly proposed operators under CSF knowledge, we develop an algorithm for MCDM problems. In addition, we adeptly employ these strategies to handle the MCDM scenario, aiming to identify the optimal approach for ensuring the privacy of digital identity or data in the evolving landscape of the Web 3.0 era. Moreover, we undertake a comparative study to highlight the veracity and proficiency of the proposed techniques compared to the previously designed approaches.

Utilizing aggregation operators based on q-rung orthopair neutrosophic soft sets and their applications in multi-attributes decision making problems.

Neutrosophic sets provide greater versatility in dealing with a variety of uncertainties, including independent, partially independent, and entirely dependent scenarios, which q-ROF soft sets cannot handle. Indeterminacy, on the other hand, is ignored completely or partially by q-ROF soft sets. To address this issue, this study offers a unique novel concept as known as q-RONSS, which combines neutrosophic set with q-ROF soft set. This technique addresses vagueness using a set of truth, indeterminacy, and false membership degrees associated with the parametrization tool, with the condition that the sum of the qth power of the truth, indeterminacy, and false membership degrees be less than or equal to one. In addition, this study outlines operational laws for the suggested structure. The main purpose this article is to define some averaging and geometric operators based on the q-rung orthopair neutrosophic soft set. Furthermore, this article provides a step-by-step method and a mathematical model for the suggested techniques. To solve a MADM issue, this research article proposes a numerical example of people selection for a specific position in a real estate business based on a variety of criteria. Finally, to demonstrate the proposed model's superiority and authenticity, this article performs several analyses, including sensitivity analysis, to address the reliability and influence of various parameter "q" values on the alternatives and the ultimate ranking outcomes using the averaging and geometric operators. A comparison of the proposed operators to current operators demonstrates the validity of the proposed structure. Furthermore, a comparison of the proposed structure to current theories demonstrates its superiority by overcoming their limits and offering a more flexible and adaptable framework. Finally, this study reviews the findings and consequences of our research.

Selection of appropriate location based on signed distance based ranking approach with interval type-2 trapezoidal fuzzy preference relations.

In real life situation, it is often difficult to judge the relative importance of different parameters being considered for evaluating some alternatives. In the context of fuzzy sets, it is a situation where it is difficult to define precise membership grades for attribute values. Here we require more generalized type of fuzzy sets which have a greater representational power than ordinary fuzzy sets. For this purpose we use "interval type-2 trapezoidal fuzzy preference relations (IT2TrFPRs)" in this article as a generalization of fuzzy preference relations and consider the environment discussed above, where there is no information on priority weights. A collective decision matrix will be constructed on the basis of hybrid averages using weighted averaging and signed distance based OWA operation. Then a least deviation model will be employed in order to determine the priority weight vectors. Finally, the alternatives will be ranked on the basis of weighted normalized signed distance of each alternative from the ideal solution. Moreover, a real life example of location selection is illustrated to elaborate the effectiveness of the proposed scheme.

Selecting optimal celestial object for space observation in the realm of complex spherical fuzzy systems.

The sensible selection of celestial objects for observation by the James Web Space Telescope (JWST) is pivotal for the precise decision-making (DM) process, aligning with scientific priorities and instrument capabilities to maximize valuable data acquisition to address key astronomical questions within the constraints of limited observation time. Aggregation operators are valuable models for condensing and summarizing a finite set of data of imprecise nature. Utilization of these operators is critical when addressing multi-attribute decision-making (MCDM) challenges. The complex spherical fuzzy (CSF) framework effectively captures and represents the uncertainty that arises in a DM problem with more precision. This paper presents two novel aggregation operators, namely the complex spherical fuzzy Yager weighted averaging (CSFYWA) operator and the complex spherical fuzzy Yager weighted geometric (CSFYWG) operator. Many fundamental structural properties of these operators are delineated, and thereby an improved score function is suggested that addresses the limitations of the existing score function within the CSF system. The newly defined operators are applied to formulate an algorithm for MADM problems to tackle the challenges of ambiguous data in the selection process. Moreover, these strategies are effectively applied to handle the MADM problem of selecting the optimal astronomical object for space observation within the CSF context. Additionally, a comparative analysis is also performed to demonstrate the validity and superiority of the proposed techniques compared to the existing strategies.

Cardiovascular disease detection using a novel stack-based ensemble classifier with aggregation layer, DOWA operator, and feature transformation.

Due to their widespread prevalence and impact on quality of life, cardiovascular diseases (CVD) pose a considerable global health burden. Early detection and intervention can reduce the incidence, severity, and progression of CVD and prevent premature death. The application of machine learning (ML) techniques to early CVD detection is therefore a valuable approach. In this paper, A stack-based ensemble classifier with an aggregation layer and the dependent ordered weighted averaging (DOWA) operator is proposed for detecting cardiovascular diseases. We propose transforming features using the Johnson transformation technique and normalizing feature distributions. Three diverse first-level classifiers are selected based on their accuracy, and predictions are combined using the aggregation layer and DOWA. A linear support vector machine (SVM) meta-classifier makes the final classification. Adding the aggregation layer to the stacking classifier improves classification accuracy significantly, according to the study. The accuracy is enhanced by 5%, resulting in an impressive overall accuracy of 94.05%. Moreover, the proposed system significantly increases the area under the receiver operating characteristic (ROC) curve compared to recent studies, reaching 97.14%. It further reinforces the classifier's reliability and effectiveness in classifying cardiovascular disease by distinguishing between positive and negative instances. With improved accuracy and a high area under the curve (AUC), the proposed classifier exhibits robustness and superior performance in the detection of cardiovascular diseases.

A fuzzy parametric model for decision making involving F-OWA operator with unknown weights environment.

Weight determining of attributes is an important factor in decision support systems since it corresponds to the relative importance of each criteria which is necessary to be determined since all the attributes aren't equally important. The aim of this paper is to put forward a method for multi Criteria decision making (MCDM) problems based on three trapezoidal fuzzy numbers under completely unknown weights environment. Based on the idea that the attribute with a larger deviation value among alternatives should be assigned a larger weight, an optimization model based on maximizing deviation method is established. F-OWA is considered to be vastly superior from the existing operators which usually take into account only the relative significance of decision makers. F-OWA operator considers not only the ratings of attribute values but also their ordered position that is it not only signifies the decision makers but also values the individual assessments. We utilize fuzzy ordered weighted averaging (F-OWA) operator to compute the collective overall preference value of each alternative and select the most desirable one according to their expected score values. The presented method is more generalized since we have used TTFNs, which are more effective in capturing uncertainty than IT2FS, just like triangular fuzzy numbers have a better representational power than simple interval numbers. Moreover, an illustrative example is given for the justification of the proposed technique.

Order Space-Based Morphology for Color Image Processing.

Mathematical morphology is a fundamental tool based on order statistics for image processing, such as noise reduction, image enhancement and feature extraction, and is well-established for binary and grayscale images, whose pixels can be sorted by their pixel values, i.e., each pixel has a single number. On the other hand, each pixel in a color image has three numbers corresponding to three color channels, e.g., red (R), green (G) and blue (B) channels in an RGB color image. Therefore, it is difficult to sort color pixels uniquely. In this paper, we propose a method for unifying the orders of pixels sorted in each color channel separately, where we consider that a pixel exists in a three-dimensional space called order space, and derive a single order by a monotonically nondecreasing function defined on the order space. We also fuzzify the proposed order space-based morphological operations, and demonstrate the effectiveness of the proposed method by comparing with a state-of-the-art method based on hypergraph theory. The proposed method treats three orders of pixels sorted in respective color channels equally. Therefore, the proposed method is consistent with the conventional morphological operations for binary and grayscale images.

Adaptive coherent weighted averaging algorithm for enhancement of photoacoustic tomography images of brain.

One common method to improve the low signal-to-noise ratio of the photoacoustic (PA) signal generated from weak absorbers or absorbers located in deep tissue is to acquire signal multiple times from the same region and perform averaging. However, pulse-to-pulse laser fluctuations together with differences in the beam profile of the pulses create undeterministic multiple scattering processes in the tissue. This phenomenon consequently induces a spatiotemporal displacement in the PA signal samples which in turn deteriorates the effectiveness of signal averaging. Here, we present an adaptive coherent weighted averaging algorithm to adjust the locations and values of PA signal samples for more efficient signal averaging. The proposed method is evaluated in a linear array-based PA imaging setup of ex vivo sheep brain.

Development of national stressor-specific genus sensitivity values.

Effective water quality management is based on associations between at least two pieces of information: a stressor and a response. However, assessments are hindered by the lack of pre-developed stressor-response associations. To remedy this, I developed genus stressor-specific sensitivity values (SVs) for up to 704 genera to estimate a sensitive genera ratio (SGR) metric for as many as 34 common stream stressors. The SVs were estimated from a large, paired macroinvertebrate and environmental data set for the contiguous United States. Environmental variables measuring potential stressors were selected that were generally uncorrelated and usually had several thousand station observations. I calculated relative abundance weighted averages (WA) for each genus and environmental variable meeting data requirements in a calibration data set. Each environmental variable was split into 10 intervals along each stressor gradient. Genera were assigned an SV from 1 to 10 based on the interval consistent with the WA for each environmental parameter. Using the calibration derived SVs, SGRs were calculated for the calibration and a validation subsets. SGRs are the number of genera with SV ≤ 5 divided by the total number of genera in a sample. In general, as stress increased, the SGR (range: 0-1) decreased for many environmental variables, but for five environmental variables, the decrease was not consistent. The 95 % confidence intervals of the mean of the SGRs were greater for least disturbed stations compared to all other stations for 23 of the remaining 29 environmental variables. Regional performance of SGRs was evaluated by subdividing the calibration data set into West, Central, and East subsets and recalculating SVs. SGR mean absolute errors were smallest in the East and Central regions. These stressor-specific SVs expand the available tools for assessing stream biological impairments from commonly encountered environmental stressors.

Siting MPAs for multiple protecting purposes by co-consideration of ecological importance and anthropogenic impacts.

The global marine ecosystem has been significantly altered by the combined effects of multiple anthropogenic impacts. Systematic planning of marine protected areas (MPAs) is of paramount importance in alleviating conflicts between humans and the sea. Existing approaches, however, merely integrate both ecological and anthropogenic factors for multiple conservation purposes. By combining the three main anthropogenic impact factors with two main ecological importance factors, this study used a GIS-based AHP-OWA method to identify different levels of priority protection for MPAs in Zhejiang, China. Our results proved that: 1) the multi-objective MPA siting issues can be addressed by the GIS-based AHP-OWA method through scenario simulation; 2) the best locations for MPAs are in the northeast, central, and southern marine areas of Zhejiang; 3) considering the trade-off degree, spatial conservation efficiency, and spatial heterogeneity, an optimized MPA siting scheme can be developed for decision-makers. The proposed MPA siting method and case study may provide an effective technical reference for solving regional marine spatial planning (MSP) issues in the future.

Multi-attribute decision-making for electronic waste recycling using interval-valued Fermatean fuzzy Hamacher aggregation operators.

The utilization of electrical and electronics equipments in waste recycling has become a paramount for various countries. The waste electrical and electronics equipment (WEEE) recyclers own a crucial position in the environmental growth of a country as they help to minimize the carbon emissions during the recycling of WEEE in the most eco-friendly way. Therefore, the selection and assessment of an appropriate WEEE recycling partner has become a most important part of DM (decision-making) applications. The collusion of numerous quantitative and qualitative factors makes the recycling partner selection problem, a multifaced and significant decision for the managerial experts. The main objective of this work is to propose MADM (multi-attribute decision-making) techniques to evaluate the WEEE recycling partners under interval-valued Fermatean fuzzy (IVFF) information. In this regard, certain Hamacher AOs (aggregation operators) are proposed to develop the required DM method. These AOs include Hamacher weighted averaging, ordered weighted averaging, weighted geometric, ordered weighted geometric, generalized Einstein weighted averaging, generalized Einstein ordered weighted averaging, generalized Einstein weighted geometric, etc. Then, these averaging operators are utilized to come up with a MADM techniques under IVFF environment. Furthermore, the constructed technique is applied to a case study in China to incorporate with the e-waste recycling partner selection problem. Moreover, a brief comparison of the proposed with is presented with various existing techniques to manifest the productivity and coherence of the proposed model. Finally, the accuracy and consistency of results shows that the proposed technique is fully compatible and applicable to handle any MADM problem.

Deep Ensemble Learning for the Automatic Detection of Pneumoconiosis in Coal Worker's Chest X-ray Radiography.

Globally, coal remains one of the natural resources that provide power to the world. Thousands of people are involved in coal collection, processing, and transportation. Particulate coal dust is produced during these processes, which can crush the lung structure of workers and cause pneumoconiosis. There is no automated system for detecting and monitoring diseases in coal miners, except for specialist radiologists. This paper proposes ensemble learning techniques for detecting pneumoconiosis disease in chest X-ray radiographs (CXRs) using multiple deep learning models. Three ensemble learning techniques (simple averaging, multi-weighted averaging, and majority voting (MVOT)) were proposed to investigate performances using randomised cross-folds and leave-one-out cross-validations datasets. Five statistical measurements were used to compare the outcomes of the three investigations on the proposed integrated approach with state-of-the-art approaches from the literature for the same dataset. In the second investigation, the statistical combination was marginally enhanced in the ensemble of multi-weighted averaging on a robust model, CheXNet. However, in the third investigation, the same model elevated accuracies from 87.80 to 90.2%. The investigated results helped us identify a robust deep learning model and ensemble framework that outperformed others, achieving an accuracy of 91.50% in the automated detection of pneumoconiosis.

A hybrid GIS-MCDM approach for multi-level risk assessment and corresponding effective criteria in optimal solar power plant.

This study aims to propose a hybrid method for suitability assessment with different risk levels to construct solar power plants (CSPPs) in southern Iran. The fuzzy-analytic hierarchy process (AHP) and fuzzy were applied to forecast and determine the suitable location for CSPPs. To extract suitable location maps with different risk levels for CSPPs, ordered weighted averaging (OWA) was implemented. In addition, the best subset regression method was used to determine the most effective factors in CSPPs. Based on the results of the fuzzy-AHP method, 42% of the southern regions of the area was suitable for CSPPs. Based on the results of the OWA method, the most suitable areas were located in the north and south in all of the risk areas with increasing values. The results demonstrated that the FCM and sub-clustering approaches can accurately predict land suitability classes (LSCs) for CSPPs. Moreover, the best subsets regression (BSR) results showed that distance to power transmission line (PTL) and temperature exhibited the strongest correlation. Finally, receiver operating characteristics (ROC) were used to determine the accuracy of these methods. The results showed that the area under the curve (AUC) values were highly accurate (AUCFuzzy-AHP = 85.0%, AUCOWA = 83.0%).

Multi-scenario simulation of ecological risk assessment based on ecosystem service values in the Beijing-Tianjin-Hebei region.

In this study, a framework for ecological risk assessment based on ecosystem service values and risk probability was established. Remote sensing was used to estimate the value of ecosystem services at the regional scale. Considering the natural and anthropogenic factors and using the entropy weight method to assign weights, probability index was constructed. In addition, multiple scenarios based on the ordered weighted averaging (OWA) method were simulated to reduce subjective uncertainty in the assessment. The results showed that the ecosystem service values generated by the gas regulation value accounted for the largest proportion, with a ratio of 46% in the Beijing-Tianjin-Hebei region. From 2005 to 2015, the value of ecosystem services decreased, falling by 2.5 × 107 Yuan. The level of ecological risk was relatively high, with a corresponding area ratio of 32.89%. Spatially, the areas with high risk were concentrated in the southeastern areas, and areas with relatively low risk were distributed in the western and northern areas. This high risk was probably caused by urbanization which was characterized by reduction of farmland and increase in impervious surface. Multi-scenario simulation showed that the areas of unstable ecological risk zones covered 30% and were mainly concentrated in the surroundings of developing cities. In areas of unstable risk distribution, the relationship between development and protection should be considered. This framework increases the reliability and practicability of ecological risk assessment results and has potential application value for regional risk control in the context of urbanization.

Development of the Karun macroinvertebrate tolerance index (KMTI) for semi-arid mountainous streams in Iran.

The most robust approach to ecological monitoring and assessment is the use of regionally calibrated indicators. These should be calculated based on collocated biological (response) and physicochemical (stressor) variables and an objective rating and scoring system. In developing countries, a frequent lack of financial and technical resources for monitoring has led to many environmental problems being overlooked, such as the degradation of streams, rivers, and watersheds. In this paper, we propose the Karun Macroinvertebrate Tolerance Index (KMTI) for application to rivers in the Karun River basin, which is the largest watershed in Iran, draining semi-arid mountainous regions. The KMTI is the first biological index specifically developed and calibrated for Iranian water resources. Benthic macroinvertebrates, physical habitat, hydromorphic, and water quality data were collected and measured at 54 sites across four seasons in 2018 and 2019. A total of 101 families of benthic macroinvertebrates belonging to eight classes and 21 orders were identified, and tolerance values were determined for 95 families. The KMTI was found to be most efficient in identifying ecological degradation when data were used from winter samples with a discrimination efficiency (DE) 90% and a four-season mean of 84.3%. Also, the best DE of the water quality classification table based on the KMTI index was equal to 86.9%.

A multi-criteria GIS-based model for wind farm site selection with the least impact on environmental pollution using the OWA-ANP method.

Wind energy is considered one of the most efficient and cost-effective ways to generate electricity, since it has a low environmental impact. So, it is essential to identify the best places to build wind farms that have the lowest impact on human health and the highest performance. In order to determine the appropriate locations for the construction of wind power plants, in the study first, the interpolation maps of the most important parameters for the construction of wind power plants were created. Then, using the analytic network process (ANP) method due to higher accuracy than other weighting methods (the two-by-two comparison of external and internal data), the weight of each criterion was determined by establishing the external and internal relationships between the criteria and sub-criteria. In this study, since the objective was to prepare land suitability maps with different levels of risk in order to further manage the area, the OWA method was used to prepare land suitability maps. Based on the results of the ANP method for weighing each parameter, wind speed and protected areas were the most and least important parameters to build the power plant. According to the results of the OWA method, 0.78 and 0.1% of the area were suitable for building power plants at high and low risk levels, respectively. The study also found that the number of wind turbines that can be built in the region at both high and low risk levels was 422 and 75, respectively. Using the buffer function, the number of turbines for the construction of high-risk power plants was reduced to 284 by using the appropriate distance from residential areas. The ANP and OWA methods were used to prepare several maps for the evaluation of land suitability with different levels of risk, one of which could be used for the construction of a power plant.

Effect of subject state on auditory brainstem response threshold using Kalman-weighted averaging.

OBJECTIVES: This study aims to explore the impact of a subject's testing state on auditory brainstem response (ABR) thresholds using a novel ABR system (Vivosonic Integrity™), which incorporates Kalman-weighted averaging and bluetooth electrical isolation to address the limitation of conventional ABR limitation to obtain a stable result under non-sedated conditions, especially for infants and children. METHOD: Twenty-four adults (18-34 years old, 48 ears) with normal hearing were enrolled for ABR testing under three different states (lying quietly in the supine position or sleeping-lying; watching silent videos quietly in a seated position-sitting; and writing in a seated position-writing), which simulate the behaviors of young children most often encountered during non-sedated Kalman-weighted ABR testing in clinical practice. The click ABR (cABR) and tone-burst ABR (tbABR) thresholds (0.5, 1, 2, and 4 kHz) of each subject and the time taken to reach the monaural threshold for each kind of stimulus were recorded. RESULTS: (1) The cABR and tbABR thresholds were observed to increase in the following order: lying < sitting < writing. Significant threshold differences were found between any two states, except for between the sitting and lying states for the cABR and between sitting and writing for the 0.5 kHz tbABR. (2) The time required for cABR testing in the writing state was significantly longer than that in the lying and sitting states. The time required for 1 and 4 kHz tbABR testing in the lying state was significantly shorter than that in the sitting or writing state. For 2 KHz tbABR, only testing time under writing was significantly longer than that under lying. There were no significant differences in the time used for 0.5 kHz tbABR testing among different states. CONCLUSIONS: Different testing states have significant impacts on the thresholds of ABRs using Kalman-weighted averaging. A subject's state during ABR testing warrants consideration, and normal levels and correction values to estimate the hearing threshold from the ABR threshold should be determined for different testing states.

R.Graph: A new risk-based causal reasoning and its application to COVID-19 risk analysis.

Various unexpected, low-probability events can have short or long-term effects on organizations and the global economy. Hence there is a need for appropriate risk management practices within organizations to increase their readiness and resiliency, especially if an event may lead to a series of irreversible consequences. One of the main aspects of risk management is to analyze the levels of change and risk in critical variables which the organization's survival depends on. In these cases, an awareness of risks provides a practical plan for organizational managers to reduce/avoid them. Various risk analysis methods aim at analyzing the interactions of multiple risk factors within a specific problem. This paper develops a new method of variability and risk analysis, termed R.Graph, to examine the effects of a chain of possible risk factors on multiple variables. Additionally, different configurations of risk analysis are modeled, including acceptable risk, analysis of maximum and minimum risks, factor importance, and sensitivity analysis. This new method's effectiveness is evaluated via a practical analysis of the economic consequences of new Coronavirus in the electricity industry.

A Wide Dynamic Range Sigma-Delta Modulator for EEG Acquisition Using Randomized DWA and Dynamic-Modulated Scaling-Down Techniques.

This paper proposes a wide dynamic range (DR) and high-resolution discrete-time (DT) 2-order 4-bit sigma-delta modulator with a novel dynamic-modulated scaling-down (DM-SD) technology for non-invasive electroencephalogram (EEG) acquisition. The DM-SD technology can expand the input dynamic range and suppress large input offsets at the same time. The modulator was designed with 180nm CMOS technology with an area of 0.49 mm2. We achieve a 118.1 dB SNDR when the input signal is 437.5 Hz and the signal bandwidth is 1500 Hz. Due to the proposed DM-SD technology, the DR is expanded to 126 dB. The power consumption of the whole modulator is 1.6 mW and a 177.8 dB Schreier figure-of-merit (FoMs) is realized.

Medical Device Risk Assessment Based on Ordered Weighted Averaging Aggregation Operator.

BACKGROUND: A medical device is any instrument, apparatus, implement, machine, appliance, software, material, which is intended material, to be utilized, either alone or in combination, for medical purpose. These devices should work precisely and the maintenance program of them has also a key role to achieve this goal. Many of the maintenance programs have not considered important functional parameters such as equipment type, risk factors, and expert opinion. OBJECTIVE: The purpose of this study is to present a novel fuzzy method for medical device risk assessment. The obtained values for risk could be used to prioritize maintenance operations by considering allocation budget. MATERIAL AND METHODS: This experimental study aims to make a new application of Ordered-Weighted Average operator in aggregation of different parameters for calculating Risk Priority Number. This model is a fuzzy multi-criteria decision making approach based on risk maintenance framework for medical device prioritization. RESULTS: A limited budget is one of the barrier in medical centers. The suggested framework presents a simple and reliable method to choose the best maintenance strategy for each kind of medical device by considering budget limitation. Based on obtained results from numerical model, defibrillators and surgical suction have respectively the highest and the lowest priority in mentioned example. CONCLUSION: Risk prioritization of medical devices is valuable because the medical centers can prioritize maintenance operations and thereby to establish preference of maintenance strategy. Implementation of our proposed maintenance program has many effective results in medical center budgets.