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(1) Background: Feature selection is the biggest challenge in feature-rich sentiment analysis to select the best (relevant) feature set, offer information about the relationships between features (informative), and be noise-free from high-dimensional datasets to improve classifier performance. This study aims to propose a binary version of a metaheuristic optimization algorithm based on Swarm Intelligence, namely the Salp Swarm Algorithm (SSA), as feature selection in sentiment analysis. (2) Methods: Significant feature subsets were selected using the SSA. Transfer functions with various types of the form S-TF, V-TF, X-TF, U-TF, Z-TF, and the new type V-TF with a simpler mathematical formula are used as a binary version approach to enable search agents to move in the search space. The stages of the study include data pre-processing, feature selection using SSA-TF and other conventional feature selection methods, modelling using K-Nearest Neighbor (KNN), Support Vector Machine, and Naïve Bayes, and model evaluation. (3) Results: The results showed an increase of 31.55% to the best accuracy of 80.95% for the KNN model using SSA-based New V-TF. (4) Conclusions: We have found that SSA-New V3-TF is a feature selection method with the highest accuracy and less runtime compared to other algorithms in sentiment analysis.

Pedo‐transfer functions (PTFs) relate soil/landscape static properties to a wide range of model inputs (e.g., soil hydraulic parameters) that are essential to soil hydrological modeling. Combining PTFs and hydrological models is a powerful strategy allowing the use of soil/landscape static properties for the generalization of large‐scale modeling. However, since the spatial scales of soil hydraulic parameters required for model inputs and soil/landscape static properties are often not identical, cross‐scale transfer is required, which can be a significant source of errors. Here, we investigate uncertainties in cross‐scale transfer and develop an approach that avoids them. The proposed method uses the convolutional neural network (CNN) as a cross‐scale transfer approach to directly map soil/landscape static properties to soil hydraulic parameters across different spatial scales. The proposed CNN approach is applied under two different estimation strategies to invert the hydraulic parameters of a soil‐water balance model and subsequently the quality of the parameters is assessed. Both synthetical and real‐world results around the conterminous United States indicate that in general the employed end‐to‐end strategy is superior to the two‐step strategy. The CNN‐based integrated model successfully reduces potential errors in cross‐scale transfer and can be applied to other areas lacking information on hydraulic parameters or observations. The proposed method can be extended to improve parameter estimation in earth system models and enhance our understanding of key hydrological processes. Key Points Uncertainties in scale conversion and pedo‐transfer function from cross‐scale transfer are systematically investigated A convolutional neural network that integrates scale conversion and pedo‐transfer functions is proposed Our methods effectively incorporate static properties at different scales and reduce errors in the cross‐scale transfer

Harris hawk optimization (HHO) is one of the recently proposed metaheuristic algorithms that has proven to be work more effectively in several challenging optimization tasks. However, the original HHO is developed to solve the continuous optimization problems, but not to the problems with binary variables. This paper proposes the binary version of HHO (BHHO) to solve the feature selection problem in classification tasks. The proposed BHHO is equipped with an S-shaped or V-shaped transfer function to convert the continuous variable into a binary one. Moreover, another variant of HHO, namely quadratic binary Harris hawk optimization (QBHHO), is proposed to enhance the performance of BHHO. In this study, twenty-two datasets collected from the UCI machine learning repository are used to validate the performance of proposed algorithms. A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). The experimental results show the superiority of the proposed QBHHO in terms of classification performance, feature size, and fitness values compared to other algorithms.

This article deals with the prediction of the upcoming solar activity cycle, Solar Cycle 25. We propose that astronomical ephemeris, specifically taken from the catalogs of aphelia of the four Jovian planets, could be drivers of variations in solar activity, represented by the series of sunspot numbers (SSN) from 1749 to 2020. We use singular spectrum analysis (SSA) to associate components with similar periods in the ephemeris and SSN. We determine the transfer function between the two data sets. We improve the match in successive steps: first with Jupiter only, then with the four Jovian planets and finally including commensurable periods of pairs and pairs of pairs of the Jovian planets (following Mörth and Schlamminger in Planetary Motion, Sunspots and Climate, Solar-Terrestrial Influences on Weather and Climate , 193, 1979 ). The transfer function can be applied to the ephemeris to predict future cycles. We test this with success using the “hindcast prediction” of Solar Cycles 21 to 24, using only data preceding these cycles, and by analyzing separately two 130 and 140 year-long halves of the original series. We conclude with a prediction of Solar Cycle 25 that can be compared to a dozen predictions by other authors: the maximum would occur in 2026.2 (± 1 yr) and reach an amplitude of 97.6 (± 7.8), similar to that of Solar Cycle 24, therefore sketching a new “Modern minimum”, following the Dalton and Gleissberg minima.

Cancer gene expression data has the characteristics of high-dimensional, multi-text and multi-classification. The problem of cancer subtype diagnosis can be solved by selecting the most representative and predictive genes from a large number of gene expression data. Feature selection technology can effectively reduce the dimension of data, which helps analyze the information on cancer gene expression data. A multi-strategy fusion binary sea-horse optimizer based on Gaussian transfer function (GOG-MBSHO) is proposed to solve the feature selection problem of cancer gene expression data. Firstly, the multi-strategy includes golden sine strategy, hippo escape strategy and multiple inertia weight strategies. The sea-horse optimizer with the golden sine strategy does not disrupt the structure of the original algorithm. Embedding the golden sine strategy within the spiral motion of the sea-horse optimizer enhances the movement of the algorithm and improves its global exploration and local exploitation capabilities. The hippo escape strategy is introduced for random selection, which avoids the algorithm from falling into local optima, increases the search diversity, and improves the optimization accuracy of the algorithm. The advantage of multiple inertial weight strategies is that dynamic exploitation and exploration can be carried out to accelerate the convergence speed and improve the performance of the algorithm. Then, the effectiveness of multi-strategy fusion was demonstrated by 15 UCI datasets. The simulation results show that the proposed Gaussian transfer function is better than the commonly used S-type and V-type transfer functions, which can improve the classification accuracy, effectively reduce the number of features, and obtain better fitness value. Finally, comparing with other binary swarm intelligent optimization algorithms on 15 cancer gene expression datasets, it is proved that the proposed GOG1-MBSHO has great advantages in the feature selection of cancer gene expression data.

Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are used to select effective features. However, most of them are not effective and scalable enough to select effective features from large medical datasets as well as small ones. Therefore, in this paper, a binary moth-flame optimization (B-MFO) is proposed to select effective features from small and large medical datasets. Three categories of B-MFO were developed using S-shaped, V-shaped, and U-shaped transfer functions to convert the canonical MFO from continuous to binary. These categories of B-MFO were evaluated on seven medical datasets and the results were compared with four well-known binary metaheuristic optimization algorithms: BPSO, bGWO, BDA, and BSSA. In addition, the convergence behavior of the B-MFO and comparative algorithms were assessed, and the results were statistically analyzed using the Friedman test. The experimental results demonstrate a superior performance of B-MFO in solving the feature selection problem for different medical datasets compared to other comparative algorithms.

For the common difficulties of noise control in a low frequency region, an adjustable parallel Helmholtz acoustic metamaterial (APH-AM) was developed to gain broad sound absorption band by introducing multiple resonant chambers to enlarge the absorption bandwidth and tuning length of rear cavity for each chamber. Based on the coupling analysis of double resonators, the generation mechanism of broad sound absorption by adjusting the structural parameters was analyzed, which provided a foundation for the development of APH-AM with tunable chambers. Different from other optimization designs by theoretical modeling or finite element simulation, the adjustment of sound absorption performance for the proposed APH-AM could be directly conducted in transfer function tube measurement by changing the length of rear cavity for each chamber. According to optimization process of APH-AM, The target for all sound absorption coefficients above 0.9 was achieved in 602–1287 Hz with normal incidence and that for all sound absorption coefficients above 0.85 was obtained in 618–1482 Hz. The distributions of sound pressure for peak absorption frequency points were obtained in the finite element simulation, which could exhibit its sound absorption mechanism. Meanwhile, the sound absorption performance of the APH-AM with larger length of the aperture and that with smaller diameter of the aperture were discussed by finite element simulation, which could further show the potential of APH-AM in the low-frequency sound absorption. The proposed APH-AM could improve efficiency and accuracy in adjusting sound absorption performance purposefully, which would promote its practical application in low-frequency noise control.

In the area of machine learning performance analysis is the major task in order to get a better performance both in training and testing model. In addition, performance analysis of machine learning techniques helps to identify how the machine is performing on the given input and also to find any improvements needed to make on the learning model. Feed-forward neural network (FFNN) has different area of applications, but the epoch convergences of the network differs from the usage of transfer function. In this study, to build the model for classification and moisture prediction of soil, rectified linear units (ReLU), Sigmoid, hyperbolic tangent (Tanh) and Gaussian transfer function of feed-forward neural network had been analyzed to identify an appropriate transfer function. Color, texture, shape and brisk local feature descriptor are used as a feature vector of FFNN in the input layer and 4 hidden layers were considered in this study. In each hidden layer 26 neurons are used. From the experiment, Gaussian transfer function outperforms than ReLU, sigmoid and tanh transfer function. But the convergence rate of Gaussian transfer function took more epoch than ReLU, Sigmoid and tanh.

Feature selection, widely used in data preprocessing, is a challenging problem as it involves hard combinatorial optimization. So far some meta-heuristic algorithms have shown effectiveness in solving hard combinatorial optimization problems. As the arithmetic optimization algorithm only performs well in dealing with continuous optimization problems, multiple binary arithmetic optimization algorithms (BAOAs) utilizing different strategies are proposed to perform feature selection. First, six algorithms are formed based on six different transfer functions by converting the continuous search space to the discrete search space. Second, in order to enhance the speed of searching and the ability of escaping from the local optima, six other algorithms are further developed by integrating the transfer functions and Lévy flight. Based on 20 common University of California Irvine (UCI) datasets, the performance of our proposed algorithms in feature selection is evaluated, and the results demonstrate that BAOA_S1LF is the most superior among all the proposed algorithms. Moreover, the performance of BAOA_S1LF is compared with other meta-heuristic algorithms on 26 UCI datasets, and the corresponding results show the superiority of BAOA_S1LF in feature selection. Source codes of BAOA_S1LF are publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/124545-binary-arithmetic-optimization-algorithm

Pansharpening is the process of merging the spectral resolution of a multi-band remote-sensing image with the spatial resolution of a co-registered single-band panchromatic observation of the same scene. Conceived and contextualized over 30 years ago, panharpening methods have progressively become more and more sophisticated, but simultaneously they have started producing fewer and fewer reproducible results. Their recent proliferation is most likely due to the lack of standardized assessment procedures and especially to the use of non-reproducible results for benchmarking. In this paper, we focus on the reproducibility of results and propose a modified version of the popular additive wavelet luminance proportional (AWLP) method, which exhibits all the features necessary to become the ideal benchmark for pansharpening: high performance, fast algorithm, absence of any manual optimization, reproducible results for any dataset and landscape, thanks to: (i) spatial analysis filter matching the modulation transfer function (MTF) of the instrument; (ii) spectral transformation implicitly accounting for the spectral responsivity functions (SRF) of the multispectral scanner; (iii) multiplicative detail-injection model with correction of the path-radiance term introduced by the atmosphere. The revisited AWLP has been comparatively evaluated with some of the high performing methods in the literature, on three different datasets from different instruments, with both full-scale and reduced-scale assessments, and achieves the first place, on average, in the ranking of methods providing reproducible results.