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 (ML...

Machine Learning algorithms reach great performances on different Natural Language Processing tasks. Among these methods, Neural Networks (NN or MLP) recently received a great interest from researchers due to their capability to represent complex internal structures. However, MLPs employ basic word level or thème-based features and, therefore, reveal little in way of document statistical struct...

The Global Positioning System (GPS) is vulnerable to various deliberate and unintentional interferences. Therefore, identifying and coping with various interferences in this system is essential. This paper analyzes a method of reducing the dimensions of Cross Ambiguity Function (CAF) images in improving the identification of spoofing interference at the GPS using Multi-Layer Perceptron Neural N...

This paper presents the application of different Neural Network (NN) models for classifying the power system states as secure/insecure. Traditional method of security evaluation involves performing load flow and transient stability analysis for each contingency, making it infeasible for real time application. Pattern Recognition (PR) approach is recognized as an alternative tool. The NN models ...

Lung diseases are one of the most common diseases that affect the human community worldwide. When the diseases are not diagnosed they may lead to serious problems and may even lead to transience. As an outcome to assist the medical community this study helps in detecting some of the lung diseases specifically bronchitis, pneumonia and normal lung images. In this paper, to detect the lung diseas...

Lung diseases are one of the most common diseases that affect the human community worldwide. When the diseases are not diagnosed they may lead to serious problems and may even lead to transience. As an outcome to assist the medical community this study helps in detecting some of the lung diseases specifically bronchitis, pneumonia and normal lung images. In this paper, to detect the lung diseas...

We propose a multistage recognition method built as a cascade of a multi-layer perceptron (MLP) and a k-nearest neighbor (k-NN) classiier. MLP, being a distributed method, generalizes to learn a \rule" and the k-NN, being a local method, learns the localized \exceptions" rejected by the \rule." Because the rule-learner handles a large percentage of the examples using a simple and general rule, ...

Diabetic retinopathy (DR) is an important cause of visual impairment in developed countries. Automatic recognition of DR lesions in fundus images can contribute to the diagnosis of the disease. The aim of this study is to automatically detect one of these lesions, hard exudates (EXs), in order to help ophthalmologists in the diagnosis and follow-up of the disease. We propose an algorithm which ...

In this paper, we present a computationally efficient neural network (NN) for equalization of nonlinear communication channels with 4-QAM signal constellation. The functional link NN (FLANN) for nonlinear channel equalization which we had proposed earlier, offers faster mean square error (MSE) convergence and better bit error rate (BER) performance compared to multilayer perceptron (MLP). Here,...