The effective use of machine learning techniques such as artificial neural networks (ANN) for small data is a challenge because of poor accuracy of models. This paper presents a method of...

May 5, 2023 · We’ll follow the step-by-step process to build the ANN model for the image classification: Load the dataset; Preprocess the dataset; Define the ANN architecture; Train the model

Present paper discusses about artificial neural network algorithm (ANN) and its variants and their use in classification. ANN has many advantages but it has some hindrances like long training time, high computational cost, and adjustment of weight.

In this work, hybrid classification optimization methods such as Genetic Algorithm (GA), Particle Swam Optimization (PSO), and Fireworks Algorithm (... Histopathology image analysis is widely...

This work presents methodology for an automated design of analog circuits using global Artificial Neural Network (ANN) for an optimised dataset. The optimised dataset is generated using simulation based gm/Id technique, which reduces the dataset size

Flow chart of the ANN algorithm. Classified vehicle volumes are important inputs for traffic operation, pavement design, and transportation planning. However, such data are not...

Oct 17, 2019 · Event-driven data from SMs are collected in the DC and then fed to the UOC for being used as inputs for the novel ANN-based fault classification and location identification algorithm. On the basis of the data received, the algorithm can classify the fault type and locate it with high accuracy.

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classification problems. ANNs are based on representations of neural activity in the brain. The most popular design for ANNs is the so-called multilayer feed-forward network. Such networks have an input layer, an output layer, and one or more hidden layers. The following “architectural” diagram represents a 3-2-1 prediction ANN. The

Artificial Neural Network (ANN) model used for classification tasks. ANN models are a type of machine learning algorithm inspired by the biological neural networks of animal brains. They consist of interconnected nodes (neurons) organized in layers, including an input layer, one or more hidden layers, and an output layer.