In this tutorial, we take a hands-on approach to building an advanced convolutional neural network for DNA sequence classification. We focus on simulating real biological tasks, such as promoter ...

This is a general purpose aimbot, which uses a neural network for enemy/target detection. The aimbot doesn't read/write memory from/to the target process. It is essentially a "pixel bot", designed ...

College of Integrated Circuits and Micro-Nano Electronics, School of Microelectronics, State Key Laboratory of Integrated Chip and System, Fudan University, Shanghai 200433, China ...

Researchers in China have created a dataset of various PV faults and normalized it to accommodate different array sizes and typologies. After testing the new approach in combination with the 1D-CNN ...

Abstract: Convolutional Neural Network (CNN) is a popular model in computer vision and has the advantage of making good use of the correlation information of data. However, CNN is challenging to learn ...

Background: Early detection is clinically crucial for the strategic handling of sarcopenia, yet the screening process, which includes assessments of muscle mass, strength, and function, remains ...

Advances in deep learning methods have demonstrated remarkable progress in wheelset fault diagnosis. However, current deep neural networks suffer from design flaws, including low accuracy, high ...

Hefei National Research Center for Physical Sciences at the Microscale, Department of Physics, and CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology ...

Scientists have created a novel probabilistic model for 5-minutes ahead PV power forecasting. The method combines a convolutional neural network with bidirectional long short-term memory, attention ...

Quantum convolutional neural networks (QCNNs) represent a promising approach in quantum machine learning, paving new directions for both quantum and classical data analysis. This approach is ...