The complexity and high dimensions of big data sonar, as well the unavoidable presence unwanted signals such noise, clutter, reverberation in environment sonar propagation, have made classification one most interesting applicable topics for active researchers this field. This paper proposes use Grasshopper Optimization Algorithm (GOA) to train Multilayer Perceptron Artificial Neural Network (MLP-NN) also select optimal features (called GMLP-GOA). GMLP-GOA hybrid classifier first extracts...

In this paper, a radial basis function neural network (RBF-NN) automatic sonar target recognition system is proposed. For the RBF-NN training phase, whale optimisation algorithm (WOA) developed with fuzzy has been used (which called FWOA). The reason for using lack of correct identification boundary between two stages exploration and exploitation. Thus, tuning effective parameters WOA left to Mamdani type. was trained by chimp (ChOA), genetic (GA), Evolution Strategy (ES), league...

Currently, the use of intelligent systems for automatic recognition targets in fields defence and military has increased significantly. The primary advantage these is that they do not need human participation target processes. This paper uses particle swarm optimization (PSO) algorithm to select optimal features micro-Doppler signature sonar targets. effect referred amplitude/phase modulation on received signal by rotating parts a such as propellers. Since different targets' geometric...

Artificial intelligence (AI) techniques have been considered effective technologies in diagnosing and breaking the transmission chain of COVID-19 disease. Recent research uses deep convolution neural network (DCNN) as discoverer or classifier X-ray images. The most challenging part networks is subject their training. Descent-based (GDB) algorithms long used to train fullymconnected layer (FCL) at DCNN. Despite ability GDBs run converge quickly some applications, disadvantage manual...

The existence of various sounds from different natural and unnatural sources in the deep sea has caused classification identification marine mammals intending to identify endangered species become one topics interest for researchers activist fields. In this paper, first, an experimental data set was created using a designed scenario. whale optimization algorithm (WOA) is then used train multilayer perceptron neural network (MLP-NN). However, due large size data, not determined clear boundary...

Abstract The existence of various sounds from different natural and unnatural sources in the deep sea has caused classification identification marine mammals intending to identify endangered species become one topics interest for researchers activists field. In this paper, first, an experimental data set was created using a designed scenario. Whale Optimization Algorithm (WOA) is then used train multilayer perceptron neural network (MLP-NN). However, due large size data, algorithm not...

In this paper, we propose using a propeller modulation on the transmitted signal (called sonar micro-Doppler) and different support vector machine (SVM) kernels for automatic recognition of moving targets.In general, main challenge researchers craftsmen working in field target is lack access to valid comprehensive database.Therefore, mathematical model simulate received from can respond challenge.The used paper simulates return targets well.The resulting signals have unique properties are...

This paper proposes a method for underwater target recognition based on micro-Doppler effects (called STR_MD) using majority voting ensemble classifier weighted with particle swarm optimization (PSO) MV-PSO). The effect refers to amplitude/phase modulation of the received signal by rotating parts such as propellers. Since different targets’ geometric and physical properties differ, their signature is different. inconsistency can be considered an effective issue (especially in frequency...