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Found 8 papers, 1 papers with code
A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data
This paper proposes a trustable LSTM-Autoencoder Network for cyberbullying detection on social media using synthetic data.
Autism Disease Detection Using Transfer Learning Techniques: Performance Comparison Between Central Processing Unit vs Graphics Processing Unit Functions for Neural Networks
Moreover, the performance of the neural network models in terms of accuracy increased on GPU compared to CPU.
Feature Engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networks
We have conducted a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information.
Software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms
Our goal is to distinguish the performance between QNN and NN and to conduct the experiment, we develop two different models for QNN and NN by utilizing Pennylane for quantum and TensorFlow and Keras for traditional respectively.
Multi-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Network
Furthermore, we develop five different stacking models such as inceptionv3-inceptionv3, Densenet-mobilenet, inceptionv3-Xception, Resnet50-Vgg16, and stack-six for classifying the skin lesions and found that the stacking models perform poorly.
Handwritten Word Recognition using Deep Learning Approach: A Novel Way of Generating Handwritten Words
We have also demonstrated the process of recognizing handwritten documents using the deep learning model.
Deep Learning Approach for Classifying the Aggressive Comments on Social Media: Machine Translated Data Vs Real Life Data
This paper particularly worked on the Hindi, Bangla, and English datasets to detect aggressive comments and have shown a novel way of generating machine-translated data to resolve data unavailability issues.
Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection
The optimal use of DNN-based classifiers requires careful tuning of the hyper-parameters.
