Renewable energy forecasting is crucially important because of its fluctuation and stochastic characteristics. In this paper, a hybrid model for wind speed and power forecasting using neuro wavelet ...

By applying category theory, the research captures the constraints used in Geometric Deep Learning (GDL) and extends beyond to a wider array of neural network architectures. The proposed framework’s ...

Researchers from Alibaba Group and Peking University have open-sourced Kernel Neural Architecture Search (KNAS), an efficient automated machine learning (AutoML) algorithm that can evaluate proposed a ...

To tackle this issue, an ensemble architecture of machine learning and deep learning is proposed to detect click fraud in online advertisement campaigns. The proposed ensemble architecture consists of ...

A new computing architecture enables advanced machine-learning computations to be performed on a low-power, memory-constrained edge device. The technique may enable self-driving cars to make ...

Deep neural networks have a huge advantage: They replace “feature engineering”—a difficult and arduous part of the classic machine learning cycle—with an end-to-end process that ...

In this project, a deep learning-based approach is used for lane detection on semi-urban roads. The proposed model consists of two main components: a CNN architecture, ResNet101, for semantic ...

Analytics Deep Learning Machine Learning Neural architecture search promises to speed up the process of finding neural network architectures that will yield good models for a given dataset.

A Multi-Model Deep Learning Architecture for Diagnosing Multi-Class Skin Diseases Skin diseases pose a significant global public health concern, affecting 21–85% of the world’s population (1.26–5.1 ...

This proposed architecture identifies the brain image into either Glioma or healthy brain image. ... Manivannan K. A deep learning model integrating convolution neural network and multiple kernel K ...