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Influenza-Like Illness (ILI) constitutes a significant global health concern characterized by its high infection rates and widespread distribution worldwide. While influenza viruses, primarily types A and B, are primary contributors to ILI cases, other respiratory viruses also play a role in its prevalence. Jakarta, Indonesia's largest and densely populated city, has consistently reported a notable weekly number of ILI cases from 2016 to mid-2022. Intriguingly, this pattern of cases is irregular and does not exhibit a direct association with seasonal climate fluctuations. In response to this complex scenario, we have developed a SLIR mathematical model featuring a cumulative generating operator in the form of a multiple-terms sigmoid function, obtained from weekly cumulative data to derive model solutions. A total of 12 terms within the sigmoid function yielded a decent fit to the actual data spanning 339 weeks. Our correlation analysis unveiled distinct temporal relationships within the model, revealing an 8-week time lag between the dynamics of the infection rate and the latent compartment, along with a 2-week lag marking the incubation period between the latent and infected compartments. Furthermore, the effective reproduction number displayed recurrent fluctuations around a threshold of 1, indicating the endemic characteristics where infection persists within the population. This in-depth comprehension of ILI transmission dynamics and effective reproduction numbers plays a significant role in devising control measures and informed policy-making decisions. [ABSTRACT FROM AUTHOR]

The salp swarm algorithm (SSA) is a well-known optimization algorithm that is increasingly being utilized to solve many sorts of optimization problems. However, SSA may converge to sub-optimal solutions when it is applied to discrete problems such as the feature selection (FS) problem. This paper presents the enhanced opposition-based learning salp swarm algorithm (EOSSA), which is an improved SSA algorithm for solving the FS problem in intrusion detection systems (IDS). EOSSA incorporates four improvements into the original SSA algorithm. Firstly, the opposition-based learning (OBL) method is used in the initialization step of SSA to boost its population diversity. Secondly, the Elite opposition-based learning (EOBL) is used in the improvement loop of SSA to improve its exploration ability. Third, a variable neighborhood search (VNS) method is used in the improvement loop of SSA to improve its exploration mechanism to improve the local search space. Lastly, the Sigmoid binary transform function is used to convert the continuous candidate solutions produced by SSA into discrete binary solutions. EOSSA was evaluated against eighteen popular optimization algorithms (e.g., improved salp swarm algorithm based on opposition-based learning (ISSA), SSA, particle swarm algorithm (PSO), cuckoo search (CS), bat algorithm (BA), and Harris Hawk Optimization (HHO)) using eleven popular intrusion detection datasets (CICIDS2017, CSE-CIC-IDS2018, CICDDOS2019, CIRA-CIC-DoH, Intrusion detection 2018, UNSW-NB15, NSL-KDD, Phishing Legitimate, Malmem2022, IoT, and LUFlow Network) to Detect IoT Botnet Attacks. Moreover, EOSSA was compared with four machine learning algorithms (Decision Tree (DT), logistic regression (LR), Naive Bayes (NB), and K-Nearest Neighborhood (KNN)). The overall simulation results suggested that the proposed method is superior to the other algorithms in terms of the accuracy and number of selected features. The statistical analysis of the simulation results using the Friedman and Wilcoxon signed-rank test confirms the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The density peak clustering (DPC) is one of the most popular algorithms for segmenting images due to its simplicity and efficiency. Since DPC and its variants are not specifically designed for image segmentation, their segmentation results do not necessarily meet both subjective and objective metrics. We propose an expanded relative density-based clustering algorithm as a solution to the above problems, which can automatically determine the cluster number and make the image segmentation results more consistent with subjective criteria. First, the image is pre-segmented into superpixels using the simple linear iterative clustering algorithm, and the superpixels are represented by feature vectors containing color and texture information. Secondly, the expanded relative density of the data point is obtained by comparing the tightness of a mini-cluster with its neighboring mini-clusters. The Sigmoid function is then applied to the data point with small density but large relative distance to further adjust its relative distance so that the distribution of cluster centers matches the characteristics of the image. Next, the optimal cluster number is determined by the rate of change of the sum of squared errors. Finally, the cluster center pairs with smaller distances are merged using the cluster center merging algorithm. The experiments conducted on synthetic and real datasets demonstrate that the performance of the proposed algorithm outperforms the compared algorithms. [ABSTRACT FROM AUTHOR]

Monoclonal antibodies (mAbs), crucial in medicine and biopharmaceuticals, require optimized large-scale production to meet high clinical dosage demands. Most of the processes for industrial mAb production rely on fed-batch operations, resulting in significant downtime. Transitioning to fully continuous and integrated processes holds the potential to boost product yield, enhance quality, and reduce storage costs for intermediate products. The integrated continuous mAb production process can be divided into the upstream and downstream processes. One crucial aspect that ensures the continuity of the integrated process is the switching of the capture columns, which are typically chromatography columns operated in a fed-batch manner downstream. Due to the discrete nature of the switching operation, advanced process control algorithms such as economic MPC (EMPC) are computationally difficult to implement. This is because an integer nonlinear program (INLP) needs to be solved online at each sampling time. This paper introduces two computationally-efficient approaches for EMPC implementation, namely, a sigmoid function approximation approach and a rectified linear unit (ReLU) approximation approach. It also explores the application of deep reinforcement learning (DRL). These three methods are compared to the traditional switching approach which is based on a 1 % product breakthrough rule and which involves no optimization. • An optimal control formulation for the switching operation in mAb production. • Two approximation methods for solving the optimal control problem. • Extensive simulation results showing the properties of the solving methods. [ABSTRACT FROM AUTHOR]

Under α stable distribution impulse noise environment, the least mean p-power algorithm (LMP) cannot handle the instability of the algorithm well, due to the large amplitude of the input signal and the large number of useless small weight coefficients in the sparse channel, which delay the convergence speed of the algorithm. In this paper, we develop a new adaptive filtering algorithm, named the proportional normalization least mean p-power (PNLMP) adaptive filtering algorithm. We first introduce a step size control matrix to improve the overall convergence speed and convergence accuracy of the algorithm. Next, to reduce the influence of a large impulse response on the LMP algorithm, a high-order tongue-line function about error is introduced in the normalization processing. Finally, we extend the tongue-line function to ensure that the cost function can also switch freely between V-shaped and M-shaped. This new algorithm overcomes the problem that the traditional LMP algorithm is only applicable to a specific environment and solves the problem of slow convergence speed and low convergence accuracy. Under α stable distribution impulse noise environment, the simulations show that the PNLMP algorithm can improve the convergence speed and stronger system tracking capability compared with existing algorithms, overcoming the problems of slow overall convergence and low convergence accuracy in the traditional algorithm. [ABSTRACT FROM AUTHOR]

To address the problems of uneven transition between foreground and sky regions, inaccurate transmission rate fusion, and unnatural color restoration in image dehazing, an image dehazing algorithm using weighted fusion with the Sigmoid function is proposed. This algorithm uses different attenuation constants in the RGB three channels, and uses Sigmoid as the weight function to fuse the estimated transmission rates of the sky region based on the color attenuation prior and the estimated transmission rates of the foreground region based on the dark channel prior. Finally, the haze-free image is restored through the atmospheric scattering model. Experimental results show that the dehazing effect of this algorithm is better for hazy images containing sky regions, and the restored images are clear and natural in color, improving the subjective visual effect of the images. Objective comparisons also confirm the effectiveness and applicability of this algorithm. [ABSTRACT FROM AUTHOR]

Planning an achievable trajectory for a mobile robot usually consists of two steps: (i) finding a path in the form of a sequence of discrete waypoints and (ii) transforming this sequence into a continuous and smooth curve. To solve the second problem, this paper proposes algorithms for automatic dynamic smoothing of the primary path using a tracking differentiator with sigmoid corrective actions. Algorithms for setting the gains of the differentiator are developed, considering a set of design constraints on velocity, acceleration, and jerk for various mobile robots. When tracking a non-smooth primary path, the output variables of the differentiator generate smooth trajectories implemented by a mechanical plant. It is shown that the tracking differentiator with a different number of blocks also generates derivatives of the smoothed trajectory of any required order, taking into account the given constraints. Unlike standard analytical methods of polynomial smoothing, the proposed algorithm has a low computational load. It is easily implemented in real time on the on-board computer. In addition, simple methods for modeling a safety corridor are proposed, taking into account the dimensions of the vehicle when planning a polygon with stationary obstacles. Confirming results of numerical simulation of the developed algorithms are presented. [ABSTRACT FROM AUTHOR]

The point features of low-texture images are insufficient and unreliable, so it is difficult to achieve good alignment and easy to damage the image structure. To solve these problems, in this paper, we propose a new image stitching method by using the sigmoid function to create perception mask. Firstly, the point features and line features are used to improve the accuracy of image registration and the naturalness of distortion. Secondly, an energy function is used to optimize the alignment model. Finally, we propose to use sigmoid function to create perception mask image to reduce artifacts and retain image structure. The gradient domain fusion algorithm is combined to achieve image fusion. Experimental results are provided to demonstrate that the proposed method is superior to some previous methods in reducing artifacts and maintaining image structure. [ABSTRACT FROM AUTHOR]

In numerous geometric and physical applications of complex analysis, estimating the sharp bounds of coefficient-related problems of univalent functions is very important due to the fact that these coefficients describe the core inherent properties of conformal maps. The primary goal of this paper was to calculate the sharp estimates of the initial coefficients and some of their combinations (the Hankel determinants, Zalcman's functional, etc.) for the class of symmetric starlike functions linked with the sigmoid function. Moreover, we also determined the bounds of second-order Hankel determinants containing coefficients of logarithmic and inverse functions of the same class. [ABSTRACT FROM AUTHOR]

In this paper, a modified distributed Nash equilibrium seeking problem with input saturation has been investigated. The payoff function of each player is related to the communication topology, which is closer to the actual scenes. The sigmoid function is utilized to limit the range of the input. Through the network communication between players and the gradient of the players' payoff functions, the finite-time and fixed-time distributed Nash equilibrium seeking protocols with input saturation are given. Using the Lyapunov stability analysis, it is determined that the action of each player converges to the Nash equilibrium if all the players update their action according to the proposed algorithms. The numerical simulations are also provided to testify the algorithms. [ABSTRACT FROM AUTHOR]

Gegenbauer polynomials constitute a numerical tool that has attracted the interest of many function theorists in recent times mainly due to their real-life applications in many areas of the sciences and engineering. Their applications in geometric function theory (GFT) have also been considered by many researchers. In this paper, this powerful tool is associated with the prolific concepts of convolution and subordination. The main purpose of the research contained in this paper is to introduce and study a new subclass of analytic functions. This subclass is presented using an operator defined as the convolution of the generalized distribution and the error function and applying the principle of subordination. Investigations into this subclass are considered in connection to Carathéodory functions, the modified sigmoid function and Bell numbers to obtain coefficient estimates for the contained functions. [ABSTRACT FROM AUTHOR]

Endocardial and epicardial border identification has been of extensive interest in cardiac Magnetic Resonance Images (MRIs). It is a difficult job to segment the epicardium and endocardium accurately and automatically from cardiac MRI owing to the cardiac tissues’ complexity even though the prevailing Deep Learning (DL) methodologies had attained significant success in medical imaging segmentation. Hence, by employing effectual ResNeXT-50-centric Inverse Edge Indicator Level Set (IEILS) and anisotropic sigmoid diffusion algorithms, this system has proposed cardiac MRI segmentation. The work has endured some function for an effectual partition of epicardium and endocardium. Initially, by employing the Truncated Kernel Function (TK)-Trilateral Filter, the noise removal function is executed on the input cardiac MRI. Next, by wielding the ResNeXT-50 IEILS, the Left and Right Ventricular (LV/RV) regions are segmented. The epicardium and endocardium are segmented by the ASD algorithm once the LV/RV is separated from the Left Ventricle (LV) region. Here, the openly accessible Sunnybrook and the Right Ventricle (RV) datasets are wielded. Then, the prevailing state-of-art algorithms are analogized to the outcomes achieved by the proposed framework. Regarding accuracy, sensitivity, and specificity, the proposed methodology executed the cardiac MRI segmentation process precisely along with the other surpassed state-of-the-art methodologies. [ABSTRACT FROM AUTHOR]

On the example of a control system for an unmanned aerial vehicle, we consider the problems of filtering, smoothing and restoring derivatives of reference action signals. These signals determine the desired spatial path of the plant at the first approximation. As a rule, researchers have considered these problems separately and have used different methods to solve each of them. The paper aims to develop a unified approach that provides a comprehensive solution to mentioned problems. We propose a dynamic admissible path generator. It is constructed as a copy of the canonical control plant model with smooth and bounded sigmoid corrective actions. For the deterministic case, a synthesis procedure has been developed, which ensures that the output variables of the generator track a non-smooth reference signal. Moreover, it considers the constraints on the velocity and acceleration of the plant. As a result, the generator variables produce a naturally smoothed spatial curve and its derivatives, which are realizable reference actions for the plant. The construction of the generator does not require exact knowledge of the plant parameters. Its dynamic order is less than that of the standard differentiators. We confirm the effectiveness of the approach by the results of numerical simulation. [ABSTRACT FROM AUTHOR]

In this study, we present a continuous-valued logical approach to generalize the sigmoid function. Our starting point is the kappa function, which is known as a unary operator in continuous-valued logic. First we extend the kappa function to the (a , b) interval, and then we interpret the generalized sigmoid function as the limit of the extended kappa function when a and b tend to the negative and positive infinity, respectively. Since the extended kappa function is induced by an additive generator of a strict t-norm or strict t-conorm, the generalized sigmoid function is operator dependent. Based on the properties of this new function, we show that it can be viewed as the generalization of the classical sigmoid function. Also, we demonstrate that the classical sigmoid function is a special case of the generalized sigmoid function. Next, we provide a sufficient condition for the equality of two generalized sigmoid functions. It is well known that the classical sigmoid function can be utilized in logistic regression and in preference modeling. Here, we demonstrate how the logistic regression can be generalized using the generator function-based sigmoid function. Also, we show that the generalized sigmoid function can be viewed as a preference measure. [ABSTRACT FROM AUTHOR]

This study deals with analytic functions with bounded turnings, defined in the disk O d = z : z < 1 . These functions are subordinated by sigmoid function 2 1 + e − z and their class is denoted by BT Sg . Sharp coefficient inequalities, including the third Hankel determinant for class BT Sg , were investigated here. The same was also included for the logarithmic coefficients related to functions of the class BT Sg . [ABSTRACT FROM AUTHOR]

Aim: To predict the loan of the person using a Novel Decision Tree (DT) over the Logistic Regression algorithm (LR). Materials and Methods: The existing model uses a Logistic Regression algorithm and in the proposed Novel Decision Tree. The 20 sample values are used to find out the mean. Std. deviation, std. error means. The sigmoid function is calculated using Levene's Test for Equality of Variances both assumed and non-assumed. Decision trees use different algorithms to decide to split a node into two or more sub-nodes. The purity of the node depends upon the target variable. The prediction of the loan can be done only after knowing all the factors. The sample size was measured as 40 for both groups using G power (80%). Results: The graph explains the comparison of the mean accuracy value with algorithms Novel Decision Tree and Logistic Regression where the mean accuracy is about 88% and 70%. The statistical significant value obtained is 0.78 (p>0.05), it shows insignificance between the groups based on an independent sample T-Test. Conclusion: The mean accuracy rate of the Novel Decision tree algorithm has been improved to 88% compared to Logistic Regression which is having around 70% mean accuracy. [ABSTRACT FROM AUTHOR]

The sigmoid function is a widely used nonlinear activation function in neural networks. In this article, we present a modular approximation methodology for efficient fixed-point hardware implementation of the sigmoid function. Our design consists of three modules: piecewise linear (PWL) approximation as the initial solution, Taylor series approximation of the exponential function, and Newton–Raphson method-based approximation as the final solution. Its modularity enables the designer to flexibly choose the most appropriate approximation method for each module separately. Performance evaluation results indicate that our work strikes an appropriate balance among the objectives of approximation accuracy, hardware resource utilization, and performance. [ABSTRACT FROM AUTHOR]

In our present study, two subclasses of starlike functions which are symmetric about the origin are considered. These two classes are defined with the use of the sigmoid function and the trigonometric function, respectively. We estimate the first four initial logarithmic coefficients, the Zalcman functional, the Fekete–Szegö functional, and the bounds of second-order Hankel determinants with logarithmic coefficients for the first class S seg * and improve the obtained estimate of the existing second-order Hankel determinant of logarithmic coefficients for the second class S sin * . All the bounds that we obtain in this article are proven to be sharp. [ABSTRACT FROM AUTHOR]

Since the torque characteristic of a surface permanent magnet synchronous motor (SPMSM) is strongly dependent on its magnetization distribution (MD), the prior estimation of MD is essential for its practical design. An effective method for estimating MD involves minimizing the squared function of the difference between the measured magnetic flux density and the flux density derived from the numerical analysis of the region surrounding the permanent magnet. However, as there are countless MDs for reconstructing the specified magnetic flux density, it is important to estimate the realistic MD. In this article, we proposed an estimation method to derive the realistic MD based on the Biot–Savart law incorporated with mathematical programming (steepest descent method and quasi-Newton method). In this method, MD was formulated in the spherical coordinate system. Since the constraints regarding magnetization intensity and angle were defined by the sigmoid function, both quantities were definitely distributed within the constraints. Consequently, the generation of nonphysical MD was efficiently suppressed. Furthermore, the validity of the proposed method was illustrated in comparison with other estimation methods, such as the least-square method, Tikhonov regularization, and the truncated singular value decomposition (TSVD) method. [ABSTRACT FROM AUTHOR]

This paper aims to present a novel hybrid algorithm named SPSOA to address problems of low search capability and easy to fall into local optimization of seagull optimization algorithm. Firstly, the Sobol sequence in the low-discrepancy sequences is used to initialize the seagull population to enhance the population's diversity and ergodicity. Then, inspired by the sigmoid function, a new parameter is designed to strengthen the ability of the algorithm to coordinate early exploration and late development. Finally, the particle swarm optimization learning strategy is introduced into the seagull position updating method to improve the ability of the algorithm to jump out of local optimization. Through the simulation comparison with other algorithms on 12 benchmark test functions from different angles, the experimental results show that SPSOA is superior to other algorithms in stability, convergence accuracy, and speed. In engineering applications, SPSOA is applied to blind source separation of mixed images. The experimental results show that SPSOA can successfully realize the blind source separation of noisy mixed images and achieve higher separation performance than the compared algorithms. [ABSTRACT FROM AUTHOR]

In this paper, two new algorithms are proposed using modified sigmoid activation function as the cost function and combining with affine projection (AP) method, which are sigmoid affine projection sign algorithm (SAPSA) and sigmoid affine projection algorithm (SAPA). Conventional AP-type algorithms converge fast but are susceptible to impulsive interference. The sigmoid function has an upper bound, thus reducing the effect of impulse interference. The proposed algorithms combine the advantages of fast convergence speed of the affine projection method and strong noise resistance of the sigmoid cost function. Meanwhile the L 2 -norm constraint is used to adjust the updating formula of the weight vector, which further improves noise resistance. Several simulation experiments show that SAPSA and SAPA are superior to other AP-type algorithms under impulse interference. • A new cost function based on sigmoidal activation function is proposed. • The sigmoid affine projection sign algorithm (SAPSA) and sigmoid affine projection algorithm (SAPA) are proposed. • A new L 2 -norm constraint method is proposed. [ABSTRACT FROM AUTHOR]

The purpose of this article is to introduce a new subclass of analytic and bi-univalent functions, in associated with sigmoid function and to investigate the upper bounds for |a2| and |a3|, where a2, a3 are the initial Taylor-Maclaurin coefficients. Further, we obtain the Fekete-Szegö inequalities for this subclass of the bi-univalent function class S. We also give several illustrative examples of the bi-univalent function class which we introduce here. [ABSTRACT FROM AUTHOR]

During the flight of quadrotor unmanned aerial vehicle manipulator, external wind and model uncertainties will significantly affect the accuracy and stability of the controller. This study investigates the problem of high-precision attitude control for the quadrotor manipulator that is equipped with a 2-DOF robotic arm in presence of several disturbances based on a new sliding mode observer and a corresponding sliding mode controller. As for the proposed sliding mode observer, to reduce its estimation chattering phenomenon, a sigmoid function is exploited to replace the discontinuous signum function. Moreover, to adapt to the estimation of disturbances with different upper bounds of the first derivative, a fuzzy logic system algorithm is used to adaptively update the observer gains and slope parameters of the sigmoid function. Furthermore, a generalized super-twisting algorithm is incorporated into the proposed sliding mode observer. Similarly, the sliding mode controller is constructed by using the generalized super-twisting algorithm and a sigmoid function in addition to the sliding mode observer-based feedforward disturbance compensation. In addition, to further relieve the influence of system sensor noise, a tracking differentiator is exploited to incorporate with both proposed sliding mode observer and sliding mode controller. Finally, to demonstrate the effectiveness of the proposed method, several simulations and experiments on the quadrotor unmanned aerial vehicle manipulator system are conducted under varying external disturbances. [ABSTRACT FROM AUTHOR]

Many practical problems, such as computer science, communications network, product design, system control, statistics and finance, etc.,can be formulated as a probabilistic constrained optimization problem (PCOP) which is challenging to solve since it is usually nonconvex and nonsmooth. Effective methods for the probabilistic constrained optimization problem mostly focus on approximation techniques, such as convex approximation, D.C. (difference of two convex functions) approximation, and so on. This paper aims at studying a smooth approximation approach. A smooth approximation to the probabilistic constraint function based on a sigmoid function is analyzed. Equivalence of PCOP and the corresponding approximation problem are shown under some appropriate assumptions. Sequential convex approximation (SCA) algorithm is implemented to solve the smooth approximation problem. Numerical results suggest that the smooth approximation approach proposed is effective for optimization problems with probabilistic constraints. [ABSTRACT FROM AUTHOR]

Speckle noise reduces the image contrast significantly making the highly scattering structures boundaries difficult to distinguish. This has limited the usage of optical coherence tomography (OCT) images in clinical routine and hindered its potential by depriving clinicians from assessing useful information that are needed in disease monitoring, treatment, progression, and decision making. To overcome this limitation, we propose a fast and robust OCT image enhancement framework using non-linear statistical parametric technique. In the proposed framework, we utilize prior statistical information to model the image to follow Gaussian distribution. After which, a newly designed dual-parametric sigmoid function (DPSF) is utilized to control the dynamic range and contrast level of the image. To balance the intensity range and contrast level, both linear and non-linear normalization operations are performed, then followed by a mapping operation to obtain the enhanced image. Experimentation results on the three OCT vendors show that the proposed method obtained high values in EME, PSNR, SSIM, ρ, and low value in MSE of 36.72, 38.87, 0.87, 0.98, and 25.12 for Cirrus; 40.77, 41.84, 0.89, 0.98, and 22.15 for Spectralis; and 30.81, 32.10, 0.81, 0.96, and 28.55 for Topcon OCT devices, respectively. The proposed DPSF framework performs better than the state-of-the-art methods and improves the interpretability and perception of the OCT images, which can provide clinicians and computer vision program with good quantitative and qualitative information. [ABSTRACT FROM AUTHOR]

In high-speed CNC (compute numerical control) machining, the feedrate scheduling has played an important role to ensure machining quality and machining efficiency. In this paper, a novel feedrate scheduling method is proposed for generating smooth feedrate profile conveniently with the consideration of both chord error and kinematic error. First, a relationship between feedrate value and chord error is applied to determine the feedrate curve. Then, breaking points, which can split whole curve into several blocks, can be found out using proposed two step screening method. For every block, the feedrate profile based on Sigmoid function is generated. With the consideration of kinematic limitation and machining efficiency, a time-optimal feedrate adjustment algorithm is proposed to further adjust feedrate value at breaking points. After achieving feedrate profile for each block, all blocks' feedrate profile will be connected smoothly. The resulting feedrate profile is more concise compared with the polynomial profile and more efficient compared with the trigonometric profile. Finally, simulations with two free-form NURBS curves are conducted and comparison with the sine-curve method is carried out to verify the feasibility and applicability of the proposed method. In order to further validate the feasibility of proposed method, machining simulation experiments are also conducted using Unigraphics NX. [ABSTRACT FROM AUTHOR]

Injection molding has been broadly used in the mass production of plastic parts and must meet the requirements of efficiency and quality consistency. Machine learning can effectively predict the quality of injection-molded part. However, the performance of machine learning models largely depends on the accuracy of the training. Hyperparameters such as activation functions, momentum, and learning rate are crucial to the accuracy and efficiency of model training. This research aims to analyze the influence of hyperparameters on testing accuracy, explore the corresponding optimal learning rate, and provide the optimal training model for predicting the quality of injection-molded parts. In this study, stochastic gradient descent (SGD) and stochastic gradient descent with momentum (SGDM) are used to optimize the artificial neural network model. Through optimization of these training model hyperparameters, the width testing accuracy of the injection product is improved. The experimental results indicate that in the absence of momentum effects, all five activation functions can achieve more than 90% of the training accuracy with a learning rate of 0.1. Moreover, when optimized with the SGD, the learning rate of the Sigmoid activation function is 0.1, and the testing accuracy reaches 95.8%. Although momentum has the least influence on accuracy, it affects the convergence speed of the Sigmoid function, which reduces the number of required learning iterations (82.4% reduction rate). In summary, optimizing hyperparameter settings can improve the accuracy of model testing and markedly reduce training time. [ABSTRACT FROM AUTHOR]

Aiming at the problems in the iterative process of seagull optimization algorithm ( SOA), such as local optimization, slow convergence speed and low optimization accuracy, this paper proposed a golden sine guide and sigmoid continuous seagull optimization algorithm( GSCSOA) . In the seagull migration stage, the algorithm used the sigmoid function as a nonlinear convergence factor to guide the seagull search process, so that the algorithm maintained a stronger global optimization ability in the early stage and converged faster in the later stage. In the seagull rushing stage, it introduced the idea of Taboo search, so that the seagulls always moved to the area with higher confidence, and the optimal position continued to change in one iteration, which improved the optimization accuracy. After that, it used the golden sine mechanism to guide the population position to update, which narrowed the search range, and improved the local optimization ability. Finally, this paper used 12 test functions and CEC2014 function set to test the improved algorithm, and the results prove that the improved seagull algorithm has better convergence speed and accuracy than original algorithm and other algorithms. [ABSTRACT FROM AUTHOR]

In order to improve the global exploration and local development performance of the arithmetic optimization algorithm ( AOA), this paper put forward a multi-head inverse series arithmetic optimization algorithm ( SFG-AOA) with activation mechanism . The activation mechanism strategy was a probabilistic evolution mechanism based on the sigmoid function based on the operator position update level. The activation factor was order to ensure that the calculation was inherited from the parent's position information while dynamically adjusting the operator optimization step length to expand new solutions and global exploration performance. It introduced the backward learning and gray wolf information feedback mechanism based on fitness optimization to improve the algorithm development stage, and improved the optimization accuracy. It appropriately modified the nonlinear convergence curve coefficient MOP to construct a random adjustment strategy. Finally, numerical experiments show that the differences in the improvement effects of different strategies. [ABSTRACT FROM AUTHOR]

In the present paper, for a sandwich plate with homogeneous core, the nonlinear dynamic response is analyzed in which material properties of functionally graded facesheets are varied in accordance with sigmoid law. The sandwich plate of various configurations is presumed to be built on Pasternak foundation and subjected to harmonic load. Equilibrium and compatibility equations are derived by taking into consideration the geometrical non-linearity in von Karman sense. The Airy's stress function and Galerkin method are used to solve geometric nonlinearity and implement the different edge conditions, respectively, for sandwich sigmoid function plate. A few numerical examples validate the accuracy of the present formulation with the results of available theory for different geometric parameters, elastic foundation, and plate configuration. A convergence study is performed to determine the number of terms in order to converge the solution, and it is deduced that considering the first four terms provides sufficient accuracy with average error less than 0.22% which is acceptable for any computation. The present paper is the first proposal to analyze the nonlinear dynamic characteristics of sandwich sigmoid function plate with homogeneous core and functionally graded material face sheets in ambient conditions. This article is also new in its assessment of the plate behavior in terms of predicting the periodicity such as periodic, quasi-periodic, chaotic, etc. and analyzing the frequency-amplitude relation. The aforementioned prediction aids researchers in active control of plates and shells under dynamic loading. It is found that core thickness in the sandwich plate reduces the effect of the nonlinearity. Also, the plate configurations 1-0-1 and 1-8-1 show the sensitive features in the dynamic response with varying aspect factors and span-to-thickness ratios. [ABSTRACT FROM AUTHOR]

Inferring the structures and the dynamics of the complex networked systems based on time series data is a challenging problem. The existing reconstruction methods often rely on the knowledge of the dynamics on networks. In many cases, a prior knowledge of the dynamics is unknown, so it is natural to ask: is it possible to reconstruct network and estimate the dynamical processes on complex networks only rely on the observed data? In this article, we develop a framework to reconstruct the structures of networks with binary-state dynamics, in which the knowledge of the original dynamical processes is unknown. Within the reconstruction framework, the transition probabilities of binary dynamical processes are described by the Sigmoid function in logistic regression, we then apply the mean-field approximation to enable maximum likelihood estimation (MLE), which gives rise to that the network structure can be inferred by solving the linear system of equations. Meanwhile, the original dynamical processes can be simulated by estimating the parameters in the Sigmoid function. Our framework has been validated by a variety of binary dynamical processes on synthetic and empirical networks, indicating that our method can not only reveal the network structures but also estimate the dynamical processes. Moreover, the high accuracy of our method is highlighted by comparing it with the existing methods. [ABSTRACT FROM AUTHOR]

Multicriteria decision analysis evaluates multiple conflicting criteria in decision making, but conflicting criteria are typical in evaluating options. As the existing ordering operations involved in multicriteria decision making cannot easily be implemented with intervals, we assume that scalar representative values with intervals can effectively avoid this issue. To deal with interval-valued criteria, we propose a generalized golden rule representative value approach, which involves the sigmoid function of backpropagation neural networks to tune parameters. Our approach considers the uncertainties and side effects of the interval variables to improve individual scalar representative values. Based on numerical examples, we address the effectiveness of the proposed approach, and we provide a specific application concerning multicriteria decision making with interval criteria satisfaction. [ABSTRACT FROM AUTHOR]

In this work, a certain class Cα is defined and its coefficient bounds established. Moreover, initial coefficients of the class Cα related to sigmoid functions and its Fekete-Szego functional are obtained. We also find an upper bound for the second Hankel determinant |H2(1)| for this class. [ABSTRACT FROM AUTHOR]

Estimation of noise often has a major impact on the quality of enhanced signal, especially when it comes in speech enhancement applications. The non-stationary noise statistics vary with time, making decision of speech active/inactive frame is however difficult. Further, since there is no prior information of noise distribution, the estimators use the recursive averaging with a fixed smoothing coefficient ranging from 0.70 to 0.99. This fixed smoothing coefficient actually correlates the previous frames of noise statistics. Unfortunately, using fixed smoothing coefficient, the estimator treats both speech active/inactive frames equally which may cause the leakage of speech/noise power and results in loss of speech intelligibility. To address this problem and to increase the noise estimation accuracy, this paper proposes a posteriori SNR and frequency dependent adaptive smoothing coefficient. Further, this paper investigates the performance of proposed weighted sigmoid function (WSIG) noise estimator. From both objective and subjective quality assessments, it is clearly evident that the proposed noise estimator yields considerably better tracking of noise spectral variations compared to the existing state of the art methods. [ABSTRACT FROM AUTHOR]

During the big data era, there is a continuous occurrence of developing the learning of imbalanced data gives a pathway for the research field along with data mining and machine learning concepts. In recent years, Big Data and Big Data Analytics having high eminence due to data exploration by many of the applications in real-time. Using machine learning will be a greater solution to solve the difficulties that occur when we learn the imbalanced data. Many real-world applications have to predict the solutions for highly imbalanced datasets with the imbalanced target variable. In most of the cases, the target variable assigns or having the least occurrences of the target values due to the sort of imbalances associated with things or events strongly applicable for the users who avail the solutions (for example, results of stock changes, fraud finding, network security, etc.). The expansion of the availability of data due to the rise of big data from the network systems such as security, internet transactions, finance manipulations, surveillance of CCTV or other devices makes the chance to the critical study of insufficient knowledge from the imbalance data when supporting the decision making processes. The data imbalance occurrence is a challenge to the research field. In recent trends, there is more data level and an algorithm level method is being upgraded constantly and leads to develop a new hybrid framework to solve this problem in classification. Classifying the imbalanced data is a challenging task in the field of big data analytics. This study mainly concentrates on the problem existing in most cases of real-world applications as an imbalance occurs in the data. This difficulty present due to the data distribution with skewed nature. We have analyses the data imbalance and find the solution. This paper concentrates mainly on finding a better solution to this nature of the problem to be solved with the proposed framework using a hybrid ensemble classifier based on the Binary Cross-Entropy method as loss function along with the Gradient Boost Algorithm. [ABSTRACT FROM AUTHOR]

The grey wolf optimizer (GWO) is an optimization algorithm that draws inspiration from nature. It is an optimization algorithm based on population that iteratively searches for the optimal solution by simulating the social behavior and hunting behavior of grey wolves. It has recently been shown that GWO can be improved by the introduction of initializing, movement, selecting and updating. In this paper, we extend an improved grey wolf optimizer with weighting functions (IGWO-WFs), which include multi-modal adaptive function, sigmoid function and autoregressive function. The IGWO-WFs has 74% improved to the conventional algorithms. It can be resolved the instability and convergence issues of GWO and investigate the effectiveness of the methods through numerical simulations and the path planning of Unmanned Aerial Vehicles (UAVs). • The output range of sigmoid function can be thresholded to improve algorithmic convergence. • The Multi-modal function has multiple extrema and a strong global search capability. • The Autoregressive function automatically adjusts its parameters to optimize the performance. • Improve the efficiency of DLH strategy. [Display omitted] [ABSTRACT FROM AUTHOR]

Domain Gene rate Algorithm (DGA) was frequently used by malicious services to evade domain detection.In view of the strong concealment of DGA domain, the slow detection speed and the poor real time pe rformance in existing detection methods, a DGA domain detection method based on Deep Independ ently Recurrent Neural Network (Deep-IndRNN) was proposed by using deep learning.In this method,do main was firstly vectorized by using the Bag-of-Words (Bo W) model. A lso, the inter-character features of domain were extracted by using Deep-IndRNN. Furthermore, domain was classified and exported by using Sigmoid function.As the main characteristics of the method, a multi-sequence input of Deep-IndRNN was stitched into a single vect or input, and cyclic processing was replaced by single-step p rocessing. Mean while, combined with the characteristics of Deep-IndRNN which could save longer memory, it not only effectively released system resources such as GPU and CPU occupied by deep learning, but also greatly improved training and detection speed under the premise of ensuring higher accuracy and precision.Experimental re sults show that the DGA domain det ection method based on Deep-IndRNN has high accuracy and precision in the det ection task.Compared with similar detection methods, such as DNN, CNN, LSTM, Bi LSTM and CNN-LSTM-Concat, the method proposed in this paper can significantly improve the training and detection speed,and is effective and feasible in practice. [ABSTRACT FROM AUTHOR]

In this present paper, we introduce the new subclass of analytic univalent functions associated with quasi-subordination in the field of sigmoid functions. We obtained the coefficient bounds and Fekete-Szego inequality belongs to the defined class. Also, we extracted the new subclasses from the dened class of analytic functions. [ABSTRACT FROM AUTHOR]

The most important structural characteristics in the study of large-scale real-world complex networks in pattern analysis are degree distribution. Empirical observations on the pattern of the real-world networks have led to the claim that their degree distributions follow, in general, a single power law. However, a closer observation, while fitting, shows that the single power-law distribution is often inadequate to meet the data characteristics properly. Since the degree distribution in the log–log scale actually displays, under inspection, two different slopes unlike what happens while fitting with the single power law. These two slopes with a transition in between closely resemble the pattern of the sigmoid function. This motivates us to derive a novel double power-law distribution for accurately modeling the real-world networks based on the sigmoid function. The proposed modeling approach further leads to the identification of a transition degree which, it has been demonstrated, may have a significant implication in analyzing the complex networks. The applicability, as well as effectiveness of the proposed methodology, is shown using rigorous experiments and also validated using statistical tests. [ABSTRACT FROM AUTHOR]

In this paper, the authors investigate the initial coefficient bounds for a new generalized subclass of analytic functions related to Sigmoid functions. Also, the relevant connections with the famous classical Fekete-Szegö inequality for these classes are discussed. [ABSTRACT FROM AUTHOR]

Let S SG ∗ = { f ∈ A : z f ′ (z) / f (z) ≺ 2 / (1 + e - z) } . For this class, several radius estimates and coefficient bounds are obtained as well as structural formula, growth theorem, distortion theorem and inclusion relations are established. Further, let p be an analytic function such that p (0) = 1 . Sharp bounds on β ∈ R are determined for various first-order differential subordinations such as 1 + β z p ′ (z) / p k (z) , p (z) + β z p ′ (z) / p k (z) ≺ 2 / (1 + e - z) to imply that p (z) ≺ (1 + A z) / (1 + B z) , where - 1 ≤ B < A ≤ 1 or 1 + z and also when the position of dominants is interchanged. Moreover, these results are extended by considering β to be a complex number. [ABSTRACT FROM AUTHOR]

Using biomarkers to model disease course effectively and make early prediction is a challenging but critical path to improving diagnostic accuracy and designing preventive trials for neurological disorders. Leveraging the domain knowledge that certain neuroimaging biomarkers may reflect the disease pathology, we propose a model inspired by the neural mass model from cognitive neuroscience to jointly model nonlinear dynamic trajectories of the biomarkers. Under a nonlinear mixed‐effects model framework, we introduce subject‐ and biomarker‐specific random inflection points to characterize the critical time of underlying disease progression as reflected in the biomarkers. A latent liability score is shared across biomarkers to pool information. Our model allows assessing how the underlying disease progression will affect the trajectories of the biomarkers, and, thus, is potentially useful for individual disease management or preventive therapeutics. We propose an EM algorithm for maximum likelihood estimation, where in the E step, a normal approximation is used to facilitate numerical integration. We perform extensive simulation studies and apply the method to analyze data from a large multisite natural history study of Huntington's Disease (HD). The results show that some neuroimaging biomarker inflection points are early signs of the HD onset. Finally, we develop an online tool to provide the individual prediction of the biomarker trajectories given the medical history and baseline measurements. [ABSTRACT FROM AUTHOR]

This paper deals with a multi-objective topology optimization problem in an asymmetrically heated channel, based on both pressure drop minimization and heat transfer maximization. The problem is modeled by assuming steady-state laminar natural convection flow. The incompressible Navier-Stokes equations coupled with the convection-diffusion equation, under the Boussinesq approximation, are employed and are solved with the finite volume method. In this paper, we discuss some limits of classical pressure drop cost function for buoyancy-driven flow and, we then propose two new expressions of objective functions: the first one takes into account work of pressure forces and contributes to the loss of mechanical power while the second one is related to thermal power and is linked to the maximization of heat exchanges. We use the adjoint method to compute the gradient of the cost functions. The topology optimization problem is first solved for a Richardson (Ri) number and Reynolds number (Re) set respectively to Ri ∈{100,200,400} and Re = 400. All these configurations are investigated next in order to demonstrate the efficiency of the new expressions of cost functions. We compare two types of interpolation functions for both the design variable field and the effective diffusivity. Both interpolation techniques have pros and cons and give slightly the same results. We notice that we obtain less isolated solid elements with the sigmoid-type interpolation functions. Then, we choose to work with the sigmoid and solve the topology optimization problem in case of pure natural convection, by setting Rayleigh number to {3 × 103,4 × 104,5 × 105}. In all considered cases, our algorithm succeeds to enhance one of the phenomenon modeled by the proposed cost functions without deteriorating the other one. The optimized design obtained suppresses any reversal flow at the exit of the channel. We also show that the thermal exchanges are improved by computing the Nusselt numbers and bulk temperature. We conclude that the new expressions of objective functions are well suited to deal with natural convection optimization problem in a vertical channel. [ABSTRACT FROM AUTHOR]

The paper reports on the development of unique standard gas consumption profiles for the end gas consumers and the preparation of a method for the implementation of the developed profiles for forecasting and preliminary gas allocation. Four years of gas consumption and temperature measurements were used to develop eight types of consumption profiles for 17 gas consumer groups, which were grouped according to their professional activity. As an alternative to the exponential, Gompertz or logistic model functions, frequently used in gas consumption model developments, the sigmoid model function is implemented and model constants for the eight types of profiles are developed based on the knowledge of the temperature independent portion of the gas consumption and separate treatment of workdays/weekends. Based on these profiles, a method was developed for the preliminary allocation of the gas consumption. The developed profiles and the gas consumption allocation method were validated on the available set of gas consumption data for the Slovenian gas market, proving the sigmoid model function based gas consumption allocation as an accurate and viable means of gas consumption forecasting. • Unique standard gas consumption profiles are developed. • A method for usage of the profiles for preliminary allocation is developed. • We provide a way to fairly and in a standard way allocate gas consumption. • The data needed to set up the consumption profiles is attached. [ABSTRACT FROM AUTHOR]

• A method for obtaining smooth and time-optimal S-curve trajectory is presented. • The modified sigmoid function is utilized to establish a continuous jerk profile. • The generated motion profiles are adjustable and infinitely differentiable. • Full actuation capability is exploited through improved synchronization strategy. • The results show the validity of the method in yielding multi-axis motions. In this paper, a smooth and time-optimal S-curve trajectory planning method is proposed to meet the requirements of high-speed and ultra-precision operation for robotic manipulators in modern industrial applications. This method utilizes a piecewise sigmoid function to establish a jerk profile with suitably chosen phase durations such that the generated trajectories are infinitely continuously differentiable under the given constraints on velocity, acceleration and jerk. All the trajectory parameters are derived with an analytical algorithm to ensure an acceptable computational cost. This S-curve model achieves a greater efficiency than the trigonometric models, while avoiding the high complexity presented by conventional high order polynomial models. The trade-off between efficiency and smoothness can be modulated by the limit value of the snap (the derivative of jerk), this feature is advantageous for adapting to different task requirements. Furthermore, a synchronization strategy is suggested to coordinate multi-axis motions, enabling the full capabilities of the actuators to be exploited. The feasibility and practicality of the proposed approach is evaluated by the simulation and experimental studies in comparison with other benchmark techniques in the literature. [ABSTRACT FROM AUTHOR]

In the present investigation we discuss the neighborhoods of analytic functions defined by using modified sigmoid function. Further, we also give some applications of Jack's lemma. [ABSTRACT FROM AUTHOR]

In the present study, stress-function Galerkin (SFG) method is employed to investigate the dynamic characteristics of a new sigmoid law based sandwich functionally graded plate (S-FGM) plates resting of Pasternak elastic foundation in the thermal environment. For modified sigmoid law, a new temperature profile is derived considering 1D steady state heat conduction equation. The Hamiltonian formulation is done to derive governing equations and nonlinearity, due to Von- Karman strains, is worked out using Airy's function in conjunction with Galerkin's method. The time and frequency domain analysis is then performed using a numerical integration scheme and harmonic balance method, respectively. The nonlinear rise in temperature is considered across the thickness due to the temperature difference between the top and the bottom surface of the simply supported plate with immovable edges. Wide-Ranging parametric studies for, linear and nonlinear, frequency and time domain analysis have been performed by taking into consideration the effect of thickness ratio, inhomogeneity parameter, thermal load, and foundation parameter for various configurations of the sandwich plates. Poincaré maps, phase-plane plots and time responses are demonstrated to study the nonlinear dynamics behavior of sandwich S-FGM plate under harmonic excitation. The variation of aspect ratios shows the route to chaos. With the Winkler foundation, the response is chaotic but becomes weakly chaotic with the introduction of the Pasternak type foundation. The dynamic response clearly shows the route to chaos with the varying thermal load from ΔT = 0–600 K. It is observed that the periodicity of the plate behavior is primarily affected by considering different configurations of the sandwich S-FGM plate. The computed results and observations can be utilized as a validation study for future examination for sandwich S-FGM plates. • The governing equations are formulated using Galerkin approach in conjunction with stress function method. • The distribution of temperature across the thickness of sandwich S-FGM plate is derived in accordance with the polynomial series method for steady state heat conduction equation. • A nonlinear complex behaviour of the plate is studied using time displacement response, phase-plane plot and Poincaré map. • The dynamic response shows the route to chaos with the varying thermal load. • The variation of aspect ratio shows the route to chaotic nature of the system as with Winkler foundation the response is chaotic and become weak chaotic with the introduction of Pasternak type foundation. [ABSTRACT FROM AUTHOR]

Due to a lack of a gold standard objective marker, the current practice for diagnosing a neurological disorder is mostly based on clinical symptoms, which may occur in the late stage of the disease. Clinical diagnosis is also subject to high variance due to between- and within-subject variability of patient symptomatology and between-clinician variability. Effectively modeling disease course and making early prediction using biomarkers and subtle clinical signs are critical and challenging both for improving diagnostic accuracy and designing preventive clinical trials for neurological disorders. Leveraging the domain knowledge that certain biological characteristics (ie, causal genetic mutation) is part of the disease mechanism, and certain markers (eg, neuroimaging measures, motor and cognitive ability measures) reflect pathological process, we propose a nonlinear model with random inflection points depending on subject-specific characteristics to jointly estimate the changing trajectories of the markers in the same disease domain. The model scales different markers into comparable progression curves with a temporal order based on the mean inflection point and establishes the relationship between the progression of markers with the underlying disease mechanism. The model also assesses how subject-specific characteristics affect the dynamic trajectory of different markers, which offers information on designing preventive therapeutics and personalized disease management strategy. We perform extensive simulation studies and apply our method to markers in neuroimaging, cognitive, and motor domains of Huntington's disease using the data collected from a large multisite natural history study of Huntington's disease, where we assess the temporal ordering of disease impairment between domains. We show that atrophy from certain brain area occurs first, followed by motor and cognitive domain, and show that an average patient has already experienced substantial regional brain atrophy when reaching clinical diagnosis age. [ABSTRACT FROM AUTHOR]