A comparison analysis between classical and quantum-classical (or hybrid) neural network and the impact effectiveness of a compound adversarial attack.

A quantum-classical (or hybrid) neural network and the use of a adversarial attack mechanism. The core libraries employed are Quantinuum pytket and pytket-qiskit. torchattacks is used for the white-box, targetted, compounded adversarial attacks.

Hybrid neural network is protected against adversarial attacks using various defense techniques, including input transformation, randomization, and adversarial training.

Hybrid neural network model is protected against adversarial attacks using either adversarial training or randomization defense techniques

Implementation of DENFIS for predicting time-series data.

hCNN: Hybrid Neural Network (Hybrid-NN), a MATLAB NN toolbox that supports complex valued data and insertion of Signal Processing Modules.

Twitter Sentiment Analysis using Deep learning models.

(ICCV 2021) BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search

A short-term predictive traffic model for a locality Oxford, UK was developed using a Hybrid Temporal Graph Convolutional Neural Network.

A custom lightweight neural network that incorporates a Bag Of Visual Words model alongside a custom shallow CNN to estimate the apparent age of a face.

Sentiment Analysis of Tweets using Neural Networks with Pytorch

Absolutely incredible and fresh-pressed Google Colab Notebook to train and generate Music with the SOTA GGA-MG (AR-CNN) Hybrid Neural Network (AI).

A Hybrid Nerual Network Classifier with Oversample Minority Class.

Kickstarter project success or failure prediction. Using Word2Vec to train embedding file.

Building multi-input CNN for predicting the age of bones from X-Ray and gender using PyTorch.