Analogue engineering still relies heavily on manual intervention, but that is changing with the growing use of AI/ML.

Quantum-inspired Leaky Integrate-and-Fire (QLIF) neurons for PyTorch, adaptive thresholds, dynamic spike probabilities, synaptic plasticity, neuromodulation, and optional qubit-based spike decisions.

Abstract: In the era of artificial intelligence, the complexity and diversity of data have posed unprecedented challenges for prediction tasks. Fuzzy information granules (FIGs) have emerged as a ...

An evnet driven model that uses financial time series data with New York Times information to form a LSTM recurrent neural network. There are 3 models. The first 2 models are based on price and volume ...

The Heisenberg uncertainty principle puts a limit on how precisely we can measure certain properties of quantum objects. But researchers may have found a way to bypass this limitation using a quantum ...

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in ...

Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are two widely used neuroimaging techniques, with complementary strengths and weaknesses. Predicting fMRI activity from ...

State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China ...

With the accelerating pace of urbanization, the issue of air pollution has become increasingly severe. Notably, carbon monoxide (CO), as a prevalent harmful gas, poses potential threats to both human ...