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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.

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 optical measurements of areal surface topography, the instrument transfer function (ITF) quantifies height response as a function of the lateral spatial frequency content of the surface. The ITF is used widely for optical full-field instruments such as Fizeau interferometers, confocal microscopes, interference microscopes, and fringe projection systems as a more complete way to characterize lateral resolving power than a single number such as the Abbe limit. This paper is a comprehensive review of the ITF, including standardized definitions, ITF prediction using theoretical simulations, common uses, limitations, and evaluation techniques using material measures.

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.

Sound localization is important for all eared animals and the spatio‐spectral cues for localization are described through the head‐related transfer function (HRTF). Current state‐of‐the‐art for obtaining the HRTF involves either direct measurement with a microphone at the eardrum, or a μCT scan to create a 3D model of the head for acoustic modelling. Both methods usually involve dead animals. We developed a low cost, portable, and adaptable 3D photogrammetry approach to create scaled 3D models of different sized animals with sufficient detail to simulate the HRTF using the boundary element method and tested it on two species of small, echolocating bats (Myotis daubentonii and M. nattereri) and a domestic pig (Sus domesticus). We directly compare the mesh models generated by our photogrammetry method to μCT scans as well as the simulated HRTFs from both with measurements using an in‐ear microphone. Furthermore, we designed a set‐up of 28 cameras to obtain 3D models and HRTF from live awake animals and tested it on a M. daubentonii. The geometries of the mesh models of bats match well between photogrammetry and μCT, but with increasing errors where line‐of‐sight is compromised for photogrammetry. The resulting HRTFs are in good agreement when comparing μCT and in‐ear measurements to photogrammetry for both bats and pig. The 3D model and simulated HRTF of the live and awake bat likewise aligns well to the results from the deceased animals. Photogrammetry is a viable alternative to μCT scans for the generation of surface models of animals for the purpose of acoustic modelling. These models allow numerical modelling of HRTFs at biologically relevant frequencies, shown for animals ranging from small bats to large domestic pigs and applicable to even larger animals. Moreover, photogrammetry allows for model generation and subsequent HRTF simulation of live, awake animals, abolishing the need for euthanasia and anaesthesia. It paves the way for large‐scale acquisition of 3D models for various purposes including modelling of animal hearing.

We develop cohomological interpretations for several types of automorphic forms for Hecke triangle groups of infinite covolume. We then use these interpretations to establish explicit isomorphisms between spaces of automorphic forms, cohomology spaces and spaces of eigenfunctions of transfer operators. These results show a deep relation between spectral entities of Hecke surfaces of infinite volume and the dynamics of their geodesic flows.

Salp swarm algorithm (SSA) is one of the recent meta-heuristic algorithms that imitate the behaviors of salps during the navigating and foraging in oceans to perform global optimization. However, the original study of this algorithm was proposed to solve continuous problems, and it cannot be applied to binary problems directly. In this paper, a new binary version of the SSA named BSSA is proposed based on a modified Arctan transformation. This modification has two features regarding the transfer function, namely multiplicity and mobility. By this modification, the exploration and exploitation capabilities can be enhanced. The proposed BSSA is compared among four variants of transfer functions for solving global optimization problems. Also, a comparative study with different binary algorithms including binary particle swarm, binary bat algorithm and binary sine–cosine algorithm on twenty-four benchmark problems is conducted. Furthermore, the nonparametric statistical test based on Wilcoxon’s rank-sum is carried out at 5% significance level to judge statistically the significant of the obtained results among the different algorithms. The results affirm the superior performance of the modified BSSA variant over the other variants as well as the existing approaches regarding solution quality.

In machine learning, searching for the optimal feature subset from the original datasets is a very challenging and prominent task. The metaheuristic algorithms are used in finding out the relevant, important features, that enhance the classification accuracy and save the resource time. Most of the algorithms have shown excellent performance in solving feature selection problems. A recently developed metaheuristic algorithm, gaining-sharing knowledge-based optimization algorithm (GSK), is considered for finding out the optimal feature subset. GSK algorithm was proposed over continuous search space; therefore, a total of eight S-shaped and V-shaped transfer functions are employed to solve the problems into binary search space. Additionally, a population reduction scheme is also employed with the transfer functions to enhance the performance of proposed approaches. It explores the search space efficiently and deletes the worst solutions from the search space, due to the updation of population size in every iteration. The proposed approaches are tested over twenty-one benchmark datasets from UCI repository. The obtained results are compared with state-of-the-art metaheuristic algorithms including binary differential evolution algorithm, binary particle swarm optimization, binary bat algorithm, binary grey wolf optimizer, binary ant lion optimizer, binary dragonfly algorithm, binary salp swarm algorithm. Among eight transfer functions, V4 transfer function with population reduction on binary GSK algorithm outperforms other optimizers in terms of accuracy, fitness values and the minimal number of features. To investigate the results statistically, two non-parametric statistical tests are conducted that concludes the superiority of the proposed approach.

In this work we study strong spectral properties of Ruelle transfer operators related to a large family of Gibbs measures for contact Anosov flows. The ultimate aim is to establish exponential decay of correlations for Hölder observables with respect to a very general class of Gibbs measures. The approach invented in 1997 by Dolgopyat in “On decay of correlations in Anosov flows” and further developed in Stoyanov (2011) is substantially refined here, allowing to deal with much more general situations than before, although we still restrict ourselves to the uniformly hyperbolic case. A rather general procedure is established which produces the desired estimates whenever the Gibbs measure admits a Pesin set with exponentially small tails, that is a Pesin set whose preimages along the flow have measures decaying exponentially fast. We call such Gibbs measures regular. Recent results in Gouëzel and Stoyanov (2019) prove existence of such Pesin sets for hyperbolic diffeomorphisms and flows for a large variety of Gibbs measures determined by Hölder continuous potentials. The strong spectral estimates for Ruelle operators and well-established techniques lead to exponential decay of correlations for Hölder continuous observables, as well as to some other consequences such as: (a) existence of a non-zero analytic continuation of the Ruelle zeta function with a pole at the entropy in a vertical strip containing the entropy in its interior; (b) a Prime Orbit Theorem with an exponentially small error.

A head-related transfer function (HRTF) describes an acoustic transfer function between a point sound source in the free-field and a defined position in the listener’s ear canal, and plays an essential role in creating immersive virtual acoustic environments (VAEs) reproduced over headphones or loudspeakers. HRTFs are highly individual, and depend on directions and distances (near-field HRTFs). However, the measurement of high-density HRTF datasets is usually time-consuming, especially for human subjects. Over the years, various novel measurement setups and methods have been proposed for the fast acquisition of individual HRTFs while maintaining high measurement accuracy. This review paper provides an overview of various HRTF measurement systems and some insights into trends in individual HRTF measurements.