neural network

PERFORMANCE ANALYSIS OF CNN-ENHANCED GENETIC ALGORITHM FOR TOPOLOGICAL OPTIMIZATION IN METAMATERIAL DESIGN

The Combination of Convolutional Neural Networks (CNN) and Genetic Algorithms (GA) provides a promising approach for topological optimization of complex lattice structures. Lattice structures are commonly used as base in the design of high-performance metamaterials. This paper presents a review of the effectiveness and efficiency of the CNN-GA method. We will examine the ability of the method to generate optimal complex structures while minimizing material usage. CNN is utilized mainly as an analysis instrument.

Comparative Analysis of Maximum Power Point Tracking Algorithms for Photovoltaic Panels

The growing demand for electricity and the need for environmentally friendly energy sources are driving the active development of renewable technologies, with solar energy playing a leading role. Photovoltaic (PV) systems are capable of converting solar radiation into electrical energy; however, their efficiency depends on the ability to adapt to changing external conditions, such as solar irradiance and ambient temperature.

Optimization of the Algorithm Flow Graph Width in Neural Networks to Reduce the Use of Processor Elements on Single-board Computers

The article presents a method for optimizing the algorithm flow graph of a deep neural network to reduce the number of processor elements (PE) required for executing the algorithm on single-board computers. The proposed approach is based on the use of a structural matrix to optimize the neural network architecture without loss of performance. The research demonstrated that by reducing the width of the graph, the number of processor elements was reduced from 3 to 2, while maintaining network performance at 75% efficiency.

Intelligent Fake News Prediction System Based on NLP and Machine Learning Technologies

The article describes a study of identification of fake news based on natural language processing, big data analysis and deep learning technology. The developed system automatically checks the news for signs of fake news, such as the use of manipulative language, unverified sources and unreliable information. Data visualization is implemented on the basis of a friendly user interface that displays the results of news analysis in a convenient and understandable format.

Evaluation of Classification Accuracy Using Feedforward Neural Network for Dynamic Objects

This paper investigates the impact of the number of hidden layers, the number of neurons in these layers, and the types of activation functions on the accuracy of classifying projectiles of six types (A – (artillery); A/M – (artillery/missile); A/R – (armor-piercing); A/RC – (armor-piercing- incendiary); M – (missile); R – (armor-piercing shells)) using a multi-layer neural network, evaluated by a confusion matrix.