Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. They belong to a family of generalized linear classifiers. In other terms, SVM is a classification and regression prediction tool that uses machine learning theory to maximize predictive accuracy. In this article, the discussion about linear and non-linear SVM classifiers with their functions and parameters is investigated. Due to the equality type of constraints in the formulation, the solution follows from solving a set of linear equations. Besides this, if the under-consideration problem is in the form of a non-linear case, then the problem must convert into linear separable form with the help of kernel trick and solve it according to the methods. Some important algorithms related to sentimental work are also presented in this paper. Generalization of the formulation of linear and non-linear SVMs is also open in this article. In the final section of this paper, the different modified sections of SVM are discussed which are modified by different research for different purposes.

Energy-efficient management and highly reliable communication and transmission mechanisms are major issues in Underwater Wireless Sensor Networks (UWSN) due to the limited battery power of UWSN nodes within an harsh underwater environment. In this paper, we integrate the three main techniques that have been used for managing Transmission Power-based Sparsity-conscious Energy-Efficient Clustering (CTP-SEEC) in UWSNs. These incorporate the adaptive power control mechanism that converts to a suitable Transmission Power Level (TPL), and deploys collaboration mobile sinks or Autonomous Underwater Vehicles (AUVs) to gather information locally to achieve energy and data management efficiency (Security) in the WSN. The proposed protocol is rigorously evaluated through extensive simulations and is validated by comparing it with state-of-the-art UWSN protocols. The simulation results are based on the static environmental condition, which shows that the proposed protocol performs well in terms of network lifetime, packet delivery, and throughput.

Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. They belong to a family of generalized linear classifiers. In other terms, SVM is a classification and regression prediction tool that uses machine learning theory to maximize predictive accuracy. In this article, the discussion about linear and non-linear SVM classifiers with their functions and parameters is investigated. Due to the equality type of constraints in the formulation, the solution follows from solving a set of linear equations. Besides this, if the under-consideration problem is in the form of a non-linear case, then the problem must convert into linear separable form with the help of kernel trick and solve it according to the methods. Some important algorithms related to sentimental work are also presented in this paper. Generalization of the formulation of linear and non-linear SVMs is also open in this article. In the final section of this paper, the different modified sections of SVM are discussed which are modified by different research for different purposes.

The current investigations present the novel structure of Morlet wavelet neural network (MWNN) for the numerical solutions of Painlevé equation-II arising in nonlinear optics. An error based fitness function is constructed using the sense of differential model and its initial or boundary conditions, which is further optimized through the hybrid computing terminologies of the global search genetic algorithm (GA) and local search interior-point algorithm (IPA), i.e., GA-IPA. The precision of the process is observed through the overlapping of the proposed and reference results, and negligible absolute error. Moreover, the statistical analysis using the multiple independent runs is performed by different tests including Theil’s inequality coefficient, variance account for and semi inter-quartile range, which shows the dependability of the proposed MWNN-GA-IPA in order to Painlevé equation-II arising in nonlinear optics. The proposed MWNN-GA-IPA optics is discussed for the first time for solving the Painlevé equation-II arising in nonlinear.

A design of Gudermannian neural network (GNN) is executed for the numerical results of the Painlevé model-II in the context of nonlinear optics (PM-II-NO). One of the forms of artificial neural networks is GNN, which uses the Gudermannian function (GF) as a merit function. This function has a nonlinearity that performs a complicated association in inputs and outputs. The design of the merit function is performed based on the differential PM-II-NO and boundary conditions, which is optimized further using the hybrid of the global search particle swarm optimization (PSO) and local search sequential quadratic programming (SQP), i.e., PSO-SQP. The algorithm’s accuracy is perceived via matching of obtained and reference database solutions, and insignificant performance of absolute error. Furthermore, the statistical investigations based on multiple independent executions are performed in order to check the reliability of the scheme by various tests, e.g., mean square error, semi inter-quartile range, and Theil inequality coefficient in order to present the significance and reliability of the designed GNN-PSO-SQP for the PM-II-NO. The comparison of the proposed results taking 5, 15, and 45 numbers of neurons and literature results is also presented to perform the neuron analysis of this study. This designed neural network is mainly valuable to solve differential systems, such as PM-II-NO, which can competently estimate the solutions by applying the GF properties.