Symmetric Mean Research Articles

A novel hybrid model for crude oil price forecasting based on MEEMD and Mix-KELM

It is of vital importance for governments, enterprises, and investors to forecast crude oil prices accurately, while this task is beset with difficulties and challenges due to the complex patterns in oil prices. This paper aims to propose a novel hybrid method to model and forecast the crude oil price by integrating median ensemble empirical mode decomposition (MEEMD) and mix-kernel extreme learning machine (Mix-KELM). Firstly, the emerging MEEMD is employed to decompose the crude oil price into several simple subseries. Secondly, a novel mix-kernel is developed for extreme learning machine (ELM) by combining the advantage of the local kernel (i.e., Radial Basis Function in learning ability) and global kernel (i.e., Sigmoid in generalization ability), with weights of the kernels optimized through genetic algorithm. Thirdly, the proposed Mix-KELM is applied to forecast the subseries of crude oil price, and the sub-forecasting results are integrated to generate the final results. The empirical results show that our proposed MEEMD-Mix-KELM model with different forecasting horizons significantly outperforms the benchmarks in terms of forecasting accuracy and robustness test. Taking one-step-ahead forecasting as an example, the proposed model exhibits the lowest prediction errors in terms of mean absolute error, symmetric mean absolute percentage error, and root mean squared error with values of 1.1767, 0.0135, and 1.5717, respectively.

Using meta-learning to recommend an appropriate time-series forecasting model

BackgroundThere are various forecasting algorithms available for univariate time series, ranging from simple to sophisticated and computational. In practice, selecting the most appropriate algorithm can be difficult, because there are too many algorithms. Although expert knowledge is required to make an informed decision, sometimes it is not feasible due to the lack of such resources as time, money, and manpower.MethodsIn this study, we used coronavirus disease 2019 (COVID-19) data, including the absolute numbers of confirmed, death and recovered cases per day in 187 countries from February 20, 2020, to May 25, 2021. Two popular forecasting models, including Auto-Regressive Integrated Moving Average (ARIMA) and exponential smoothing state-space model with Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend, and Seasonal components (TBATS) were used to forecast the data. Moreover, the data were evaluated by the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE) criteria to label time series. The various characteristics of each time series based on the univariate time series structure were extracted as meta-features. After that, three machine-learning classification algorithms, including support vector machine (SVM), decision tree (DT), random forest (RF), and artificial neural network (ANN) were used as meta-learners to recommend an appropriate forecasting model.ResultsThe finding of the study showed that the DT model had a better performance in the classification of time series. The accuracy of DT in the training and testing phases was 87.50% and 82.50%, respectively. The sensitivity of the DT algorithm in the training phase was 86.58% and its specificity was 88.46%. Moreover, the sensitivity and specificity of the DT algorithm in the testing phase were 73.33% and 88%, respectively.ConclusionIn general, the meta-learning approach was able to predict the appropriate forecasting model (ARIMA and TBATS) based on some time series features. Considering some characteristics of the desired COVID-19 time series, the ARIMA or TBATS forecasting model might be recommended to forecast the death, confirmed, and recovered trend cases of COVID-19 by the DT model.

Comparison of multiple linear regression and machine learning methods in predicting cognitive function in older Chinese type 2 diabetes patients.

The prevalence of type 2 diabetes (T2D) has increased dramatically in recent decades, and there are increasing indications that dementia is related to T2D. Previous attempts to analyze such relationships principally relied on traditional multiple linear regression (MLR). However, recently developed machine learning methods (Mach-L) outperform MLR in capturing non-linear relationships. The present study applied four different Mach-L methods to analyze the relationships between risk factors and cognitive function in older T2D patients, seeking to compare the accuracy between MLR and Mach-L in predicting cognitive function and to rank the importance of risks factors for impaired cognitive function in T2D. We recruited older T2D between 60-95years old without other major comorbidities. Demographic factors and biochemistry data were used as independent variables and cognitive function assessment (CFA) was conducted using the Montreal Cognitive Assessment as an independent variable. In addition to traditional MLR, we applied random forest (RF), stochastic gradient boosting (SGB), Naïve Byer's classifier (NB) and eXtreme gradient boosting (XGBoost). Totally, the test cohort consisted of 197 T2D (98 men and 99 women). Results showed that all ML methods outperformed MLR, with symmetric mean absolute percentage errors for MLR, RF, SGB, NB and XGBoost respectively of 0.61, 0.599, 0.606, 0.599 and 0.2139. Education level, age, frailty score, fasting plasma glucose and body mass index were identified as key factors in descending order of importance. In conclusion, our study demonstrated that RF, SGB, NB and XGBoost are more accurate than MLR for predicting CFA score, and identify education level, age, frailty score, fasting plasma glucose, body fat and body mass index as important risk factors in an older Chinese T2D cohort.

Evaluation of Propulsion Technologies for Sustainable Road Freight Distribution Using a Dual Probabilistic Linguistic Group Decision-Making Approach

For the first time, this study addresses the problem of evaluating propulsion technologies for sustainable road freight distribution from the multicriteria group decision-making perspective. It aims to help logistics companies involved in global supply chains significantly reduce GHG emissions from freight distribution activities. To evaluate sustainable propulsion technologies, this article introduces a novel group decision-making approach with DPL term sets, which takes into account the simultaneous occurrence of stochastic and nonstochastic uncertainty. Since the existing methods do not consider the dependency among multiple numbers of criteria, the evaluation results become insignificant. To overcome this, we employ the Archimedean operations on DPL elements to develop DPL weighted Archimedean Maclaurin symmetric mean operator. For measuring the weights of criteria, an optimization model using a cross-entropy measure of DPL elements is developed. A case study for one of the major logistics companies in Serbia is presented to exhibit the relevance of the suggested approach, as well as offer decision-making guidelines in real-world settings. The research findings show that hybrid electric trucks are the most advantageous propulsion technology solution to reduce GHG emissions from distribution activities. The superiority of the proposed approach is demonstrated through the comparative investigation.

Unraveling the Multifaceted Nature of Breast Cancer: Harnessing the Power of Machine Learning and Statistical Models for In-depth Investigation, Prognosis, and Treatment Optimization

The most prevalent form of cancer in all of human history. Women typically have a shorter lifespan than men. The death toll from breast cancer is higher than that of any other type of cancer. After reaching puberty, women all around the world are at risk for developing breast cancer, however, the disease is more prevalent in elderly individuals. The death rate from breast cancer did not significantly change from the 1930s through the 1970s. In the 1980s, countries that were healthcare conscious and had early detection systems, as well as a variety of treatment modalities to eliminate invasive diseases, began to see an increase in their overall survival rates. This article employed decision tree models and time series models to anticipate breast cancer using time series data from 1930 to 2019. The breast cancer cases that occurred in the United States of America served as the basis for this study. The most precise prediction models will be produced as a result of this data analysis. In order to evaluate how well the predictions were made, we determined how accurate they were by calculating the symmetric mean absolute percentage errors (sMAPE), measured mean absolute percentage errors (MASE), and mean absolute percentage errors (MAPE).

Ekstrapolacija vertikalne brzine vetra korišćenjem statističkih pristupa

The wind power industry has experienced a significant increase and popularity in recent times, and the latest statistics indicate that this sector is still thriving. However, one of the essential steps in developing wind energy projects is finding suitable sites for wind farms, which involves understanding the nature of wind speed, wind direction, terrain, and environmental impacts. To predict the wind energy production over the expected lifespan of a wind farm, vertical wind speed extrapolation to the hub height of the wind turbine is necessary. Therefore, this study presents a comprehensive evaluation of seven statistical approaches for vertical wind speed extrapolation, including Generalized Linear Models (GLM), Linear Regression (LR), Support Vector Machines (SVM), Generalized Additive Models (GAM), Gaussian Process Regression (GPR), Regression Tree (RT), and Ensemble Regression (ER). The accuracy of these methods is assessed using performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Normalized RMSE (NRMSE), Normalized MSE (NMSE), Mean Bias Error (MBE), Mean Absolute Error (MAE), Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE), and R-squared (R2). The study concludes that, on average, GLM performs the best out of all seven statistical methods.

T-Spherical Fuzzy Information and Shweizer-Sklar Operations Based Maclaurin Symmetric Mean Operator and Their Applications

T-spherical fuzzy set (TSFS) is the generalization of the fuzzy set (FS) which extracts the information from the real-life scenario with certainty. Aside from the remarkable advantage of being able to account for the connections among the multi-input considerations, such as multi-attributes or multi-experts in the multi-attribute group decision-making (MAGDM), the Maclaurin symmetric mean operator (MSMO) is also the generalization of several different existing operators. Moreover, one important class of T-norms (TN) and T-conorms (TC) is the Schweizer-Sklar TN (SSTN) and TC (SSTC). In this article, the operational laws for TSFS based on SSTN and SSTC are introduced first. Then the introduced operations are used to develop a class of aggregation operators (AOs) to aggregate the information in the form of the T-spherical fuzzy values (TSFVs). The introduced operators in this article are the T-spherical fuzzy Schweizer-Sklar MSMO (TSFSSMSMO) and the T-spherical fuzzy Schweizer-Sklar weighted MSMO (TSFSSWMSMO). Further, the TSFSSMSMO and TSFWMSMO are applied to a specific multi-attribute group decision-making (MAGDM) problem to show the significance of the developed operators.

COMPARATIVE PERFORMANCE OF ARIMA, SARIMA AND GARCH MODELS IN MODELLING AND FORECASTING UNEMPLOYMENT AMONG ASEAN-5 COUNTRIES

Unemployment, especially after the COVID-19 pandemic, is a critical issue for any country as it has economic and social ramifications. Consequently, forecasting unemployment becomes an essential task as it can guide government policy. Time series data are frequently influenced by outliers (unexpected events), and some outliers may exist with extreme observation to reduce the forecasting effectiveness of robust estimators. This study compared the performance of Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models in modelling and forecasting unemployment rates during the COVID-19 pandemic among the ASEAN-5 countries. These countries include Malaysia, Singapore, Thailand, the Philippines and Indonesia. The monthly unemployment data from January 2010 to December 2021 were applied for all cases, except Thailand, until December 2020. Each adequate model from both forecasting mechanisms underwent forecasting. Their performance was compared based on root mean squared error (RMSE), mean absolute error (MAE), Theil inequality coefficient and symmetric mean absolute percentage error (SMAPE). Static forecasting from the ARIMA and SARIMA models was found to perform better than the GARCH model in modelling and forecasting the unemployment rate among ASEAN-5 countries during the pandemic period.

Evaluating data-driven and hybrid modeling of terrestrial actual evapotranspiration based on an automatic machine learning approach

The performances of physics-based, data-driven, and hybrid models for estimating terrestrial actual evapotranspiration (ETa) is currently under debate, which requires thorough evaluations of those models particularly with recent developments in automatic machine learning (AML) techniques. In this study, six AML-based models were first constructed using the H2O-AML platform, from which an optimal (AML-OP) model was selected for estimating daily ETa at ecosystem scales. In addition, hybrid models were developed by combining the six AML models with surface conductance (Gs) inverted from the Penman-Monteith equation and an optimal (PM-OP) model was also selected. With 15 predictor variables as model inputs that were compiled from various data sources, the performances of those models for estimating daily ETa were evaluated using observed data from the FLUXNET2015 dataset. The results revealed that no models showed consistently low noise levels across different ecosystem types, making it necessary to use AML techniques for selecting ecosystem-specific models. Interestingly, the AML-OP models (root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE) were 0.16–0.31 mm d-1 and 9 %–36 % respectively) showed slightly better predictive results than the PM-OP models (RMSE and SMAPE were 0.23–0.36 mm d-1 and 15 %–68 % respectively), likely owing to model parameter uncertainties and tight constraints of physical models on application condition. Secondly, as ETa nonlinearly responds to environmental variables, model predictability under extreme weather (drought and heatwave) conditions was examined. The results showed that the prediction of the AML-OP and PM-OP models expectedly worsened (RMSE and SMAPE increased by 0.06–0.77 mm d-1 and −19 % to 79 %, respectively); however, the AML-OP model still outperformed the PM-OP model in most ecosystems, further underscoring the need to understand ETa regulation mechanisms under varying climatic conditions. Finally, the PM-OP models developed here provided better daily ETa estimates compared to other recently proposed hybrid models (RMSE reduced by 0.98–1.80 mm d-1). Both models can be better applied to wetlands that have been less frequently evaluated previously (RMSE reduction of 0.22 mm d-1 and 0.18 mm d-1 for the AML-OP and PM-OP models).

Dual-environment feature fusion-based method for estimating building-scale population distributions

ABSTRACTInformation on the population distribution at the building scale can help governments make supplemental decisions to address complex urban management issues. However, the discontinuity and strong spatial heterogeneity of research units at the building scale make it challenging to fuse multi-source geographic data, which causes significant errors in population estimation. To address this problem, this study proposes a method for population estimation at the building scale based on Dual-Environment Feature Fusion (DEFF). The dual environments of buildings were constructed by splitting the physical boundaries and extracting features suitable for the dual-environment scale from multi-source geographic data to describe the complex environmental features of buildings. Meanwhile, Data Quality Weighting based Technique for Order of Preference by Similarity to Ideal Solution (DQW-TOPSIS) method was proposed to assign appropriate weights to the features of the external environment for better feature fusion. Finally, a regression model was established using dual-environment features for building-scale population estimation. The experimental areas chosen for this study were Jianghan and Wuchang Districts, both located in Wuhan City, China. The estimated results of the DEFF were compared with those of the ablation experiments, as well as three publicly accessible population datasets, specifically LandScan, WorldPop, and GHS-POP, at the community scale. The evaluation results showed that DEFF had an R2 of approximately 0.8, Mean Absolute Error (MAE) of approximately 1200, Root Mean Square Error (RMSE) of approximately 1700, and both Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) of approximately 26%, indicating an improved performance and verifying the validity of the proposed method for fine-scale population estimation.

Sustainability Assessment of Regional Transportation: An Innovative Fuzzy Group Decision-Making Model

In this paper, an innovative fuzzy group decision-making model is designed for assessing regional transportation sustainability, focusing on the correlation between various attributes of the evaluation system. The focus of this model is the partitioned Maclaurin symmetric mean operator because of its better applicability when considering attribute correlation and attribute grouping. The modified spherical fuzzy partitioned Maclaurin symmetric mean operator is proposed, which has superior application scope. Its weighted form and special cases are discussed. Then, the extended statistical variance method and the evidence-based Bayes approximation method are used to obtain weight vectors of attributes and experts. In addition, a fuzzy assessment model of sustainable transportation is developed. Finally, a numerical example of regional transportation sustainability assessment and a comparison with previous studies are presented to illustrate the feasibility and universality of this method.

Hamacher Operations for Complex Cubic q-Rung Orthopair Fuzzy Sets and Their Application to Multiple-Attribute Group Decision Making

In this paper, based on the advantages of q-rung orthopair fuzzy sets (q-ROFSs), complex fuzzy sets (CFSs) and cubic sets (CSs), the concept of complex cubic q-rung orthopair fuzzy sets (CCuq-ROFSs) is introduced and their operation rules and properties are discussed. The objective of this paper was to develop some novel Maclaurin symmetric mean (MSM) operators for any complex cubic q-rung orthopair fuzzy numbers (CCuq-ROFNs) using Hamacher t-norm and t-conorm inspired arithmetic operations. The advantage of employing Hamacher t-norm and t-conorm based arithmetic operations with the MSM operator lies in their ability to take into account not only the interrelationships among multiple attributes but also to provide flexibility in the aggregation process due to the involvement of additional parameters. Also, the prominent characteristic of the MSM is that it can capture the interrelationship among the multi-input arguments and can provide more flexible and robust information fusion. Thus, based on the CCuq-ROF environment, we develop some new Hamacher operations for CCuq-ROFSs, such as the complex cubic q-rung orthopair fuzzy Hamacher average (CCuq-ROFHA) operator, the weighted complex cubic q-rung orthopair fuzzy Hamacher average (WCCuq-ROFHA) operator, the complex cubic q-rung orthopair fuzzy Hamacher Maclaurin symmetric mean (CCuq-ROFHMSM) operator and the weighted complex cubic q-rung orthopair fuzzy Hamacher Maclaurin symmetric mean (WCCuq-ROFHMSM) operator. Further, we develop a novel multi-attribute group decision-making (MAGDM) approach based on the proposed operators in a complex cubic q-rung orthopair fuzzy environment. Finally, a numerical example is provided to demonstrate the effectiveness and superiority of the proposed method through a detailed comparison with existing methods.

Partitioned dual Maclaurin symmetric mean operators based on picture fuzzy sets and their applications in multi-attribute decision-making problems

The partitioned Dual Maclaurin symmetric mean (PDMSM) operator has the supremacy that can justify the interrelationship of distinct characteristics and there are a lot of exploration consequences for it. However, it has not been employed to manage “multi-attribute decision-making” (MADM) problems represented by picture fuzzy numbers. The basic inspiration of this identification is to develop the novel theory of picture fuzzy PDMSM operator, and weighted picture fuzzy PDMSM operator and to identify their important results (Idempotency, Monotonicity, and Boundedness). Further, to identify the best decision, every expert realized that they needed the best way to find the beneficial optimal using the proper decision-making procedure, for this, we diagnosed the MADM tool in the consideration of deliberated approaches based on PF information. Finally, to drive the characteristics of the invented work, several examples are utilized to test the manifest of the comparative analysis with various more existing theories, which is a fascinating and meaningful technique to deeply explain the features and exhibited of the proposed approaches.

Spatial-temporal offshore wind speed characteristics prediction based on an improved purely 2D CNN approach in a large-scale perspective using reanalysis dataset

Accurate prediction of spatial–temporal wind speed with a large-scale perspective is vital for the wind farm dynamic regulation, the floating wind turbine deployment and its early warning. Machine-learning approaches have been applied for wind prediction to alleviate the computational challenges of traditional numerical models. Yet, most previous studies emphasized the forecasting accuracy employing location-specific dataset, such methodologies are site-specific and ignore the importance of spatial–temporal fidelity required by the 2D regional wind farm blueprint. Furtherly, the Recurrent Neural Networks based approach they employed exhibit low efficiency in terms of model convergence on the aspect of practical engineering purposes. This study firstly proposed an improved Unet-based machine learning method using residual learning to predict the spatial–temporal wind field in the North Atlantic Ocean with great potential wind-energy in a large spatial-scale perspective. Several state-of-the-art machine learning methods were established for a better performance comparison. A public reanalysis dataset with very high resolution (0.25° x 0.25°) was employed to verify the proposed method with multi-steps ahead predictions, the model performance was furtherly compared with the rest approaches under different criteria, consisting of Pearson correlation coefficient (PCC), Mean absolutely error (MAE), Root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE), all exhibiting considerable improvements. The overall forecasting RMSE for the proposed method dropped to 0.088 m/s and 0.087 m/s for wind component U and V correspondingly. Furtherly, comprehensive discussions were provided with both temporal and spatial perspective, revealing that the proposed model can offer an accurate 2D wind speed prediction along timeline (the PCC equals to 0.99). The proposed data-driven method presents promising potential to accurately preserve the spatial-resolved patterns which are variant in both spatial and temporal dimensions, and show superiority in terms of computational efficiency in terms of achieving large-scale wind pattern predictions.

Deep Learning k-Space-to-Image Reconstruction Facilitates High Spatial Resolution and Scan Time Reduction in Diffusion-Weighted Imaging Breast MRI.

For time-consuming diffusion-weighted imaging (DWI) of the breast, deep learning-based imaging acceleration appears particularly promising. To investigate a combined k-space-to-image reconstruction approach for scan time reduction and improved spatial resolution in breast DWI. Retrospective. 133 women (age 49.7 ± 12.1 years) underwent multiparametric breast MRI. 3.0T/T2 turbo spin echo, T1 3D gradient echo, DWI (800 and 1600 sec/mm2). DWI data were retrospectively processed using deep learning-based k-space-to-image reconstruction (DL-DWI) and an additional super-resolution algorithm (SRDL-DWI). In addition to signal-to-noise ratio and apparent diffusion coefficient (ADC) comparisons among standard, DL- and SRDL-DWI, a range of quantitative similarity (e.g., structural similarity index [SSIM]) and error metrics (e.g., normalized root mean square error [NRMSE], symmetric mean absolute percent error [SMAPE], log accuracy error [LOGAC]) was calculated to analyze structural variations. Subjective image evaluation was performed independently by three radiologists on a seven-point rating scale. Friedman's rank-based analysis of variance with Bonferroni-corrected pairwise post-hoc tests. P < 0.05 was considered significant. Both DL- and SRDL-DWI allowed for a 39% reduction in simulated scan time over standard DWI (5 vs. 3 minutes). The highest image quality ratings were assigned to SRDL-DWI with good interreader agreement (ICC 0.834; 95% confidence interval 0.818-0.848). Irrespective of b-value, both standard and DL-DWI produced superior SNR compared to SRDL-DWI. ADC values were slightly higher in SRDL-DWI (+0.5%) and DL-DWI (+3.4%) than in standard DWI. Structural similarity was excellent between DL-/SRDL-DWI and standard DWI for either b value (SSIM ≥ 0.86). Calculation of error metrics (NRMSE ≤ 0.05, SMAPE ≤ 0.02, and LOGAC ≤ 0.04) supported the assumption of low voxel-wise error. Deep learning-based k-space-to-image reconstruction reduces simulated scan time of breast DWI by 39% without influencing structural similarity. Additionally, super-resolution interpolation allows for substantial improvement of subjective image quality. 4 TECHNICAL EFFICACY: Stage 1.

Transformer based on the prediction of psoriasis severity treatment response

Psoriasis, a chronic inflammatory skin disease, must be continuously monitored to prevent recurrence and increase treatment effectiveness. However, time-series analysis studies considering the chronic characteristics of psoriasis are rare. The psoriasis area and severity index (PASI) score, a therapeutic decision-making index for psoriasis, has limitations in detecting disease variability during treatment. For successful treatment, an imaging-based approach that can intuitively observe lesion changes is required. We propose a novel deep learning-based evaluation method that identifies psoriasis severity characteristics and predicts changes in severity during treatment. The proposed method consists of two steps: extraction of deep features of psoriasis and prediction of disease severity in time series using the deep features. The proposed deep-feature model extracted global and deformable convolution layers V2-based local features using the hierarchical information of RegNetY-1.6G and fused the features to predict disease severity. After severity classification using fusion features, the f1-score was 0.92. Next, we constructed a short-term time-series disease dataset and extracted deep features using the proposed model. Among six time-series analysis models (LSTM, GRU, GRU-FCN, ResNet, InceptionTime, and Transformer), Transformer demonstrated the best prediction performance, with a root mean squared log error (RMSLE) of 0.44 and a symmetric mean absolute percentage error (SMAPE) of 0.34. From the Student’s t-test and regression analysis, the predictions for reducing disease severity for each patient and body part were similar to the actual values. Our study verified the possibility of tracking dynamic changes in the disease and personalized treatment using a psoriasis treatment response prediction method.

Multisynchrosqueezing short-time fractional Fourier transform and its application in rolling bearing instantaneous frequency estimation

Multisynchrosqueezing transform (MSST) enhances the time-frequency energy concentration by using iterative reassignment operations in time-frequency analysis (TFA). However, its effectiveness is limited for signals with rapidly changing instantaneous frequency. To address this issue, this paper presents a novel time-frequency representation (TFR) method called multisynchrosqueezing short-time fractional Fourier transform, which offers improved TF concentration for strongly frequency-modulated signals. Firstly, a high-resolution TFR of the signal is obtained by locally optimized short-time fractional Fourier transform (STFrFT). Secondly, iterative synchrosqueezing operations are introduced to further enhance the STFrFT energy concentration, with a termination strategy relying on Rényi entropy proposed to ascertain the optimal number of iterations. Finally, the ideal TFA with high energy concentration is achieved. The proposed method was validated using multi-scene simulated signals and variable-speed bearing signals. The results show that the proposed method exhibits superior time-frequency energy concentration and instantaneous frequency estimation accuracy. The estimation error of the method is consistently at least 40% lower than that of the compared short-time Fourier transform-based methods, as assessed through the evaluation criteria of maximum relative error, mean square error and symmetric mean absolute percentage error.

Multi-attribute decision-making based on q-rung dual hesitant power dual Maclaurin symmetric mean operator and a new ranking method

Multi-attribute decision-making based on q-rung dual hesitant power dual Maclaurin symmetric mean operator and a new ranking method

Symmetric mean curvature flow on the n-sphere

In this article, we generalize our previous results (Chen and Gaspar, 2023) joint with Pedro Gaspar to higher dimensions, prove the existence of (infinitely many) eternal weak mean curvature flows in Sn+1 (for all n≥2) connecting a Clifford hypersurface S1(1n)×Sn−1(n−1n) to the equatorial spheres Sn.

Online Product Decision Support Using Sentiment Analysis and Fuzzy Cloud-Based Multicriteria Model Through Multiple E-Commerce Platforms

The competitive landscape of multiple e-commerce platforms and the vast amount of product reviews associated with these platforms have supported both consumers' online shopping decision making and also served as a reference for product attribute performance improvement. This paper proposes a sentiment-driven fuzzy cloud multi-criteria model for online product ranking and performance to provide purchase recommendations. In this novel model, Bi-directional Long Short-Term Memory Network-Conditional Random Fields (BiLSTM-CRF), sentiment analysis, and K-means clustering are first integrated to mine product attributes and compute sentiment values based on reviews from various platforms. Next, considering the confidence of the sentiment value, the cloud model is combined with q-rung orthopair fuzzy sets to define the new concept of the q-rung orthopair fuzzy cloud (q-ROFC) and the interaction operational laws between q-ROFCs are given. The sentiment values of each product attribute from different platforms are cross-combined and transformed into a type of q-ROFC, while multiple interactive information matrices are established. To investigate the correlation among homogeneous attributes, the q-ROFC interaction weighted partitioned Maclaurin Symmetric mean operator is proposed. Finally, we provide real-world examples of online mobile phone ranking and attribute performance evaluation. The results show that our proposed method offers significant advantages in dealing with customer purchase decisions for online products and problems with performance direction identification. Managerial implications are discussed.