Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras

Network Intrusion Detection based on various machine learning and deep learning algorithms using UNSW-NB15 Dataset

Interactive Visual Machine Learning Demos.

A Novel Statistical Analysis and Autoencoder Driven Intelligent Intrusion Detection Approach

Multi-Class Text Classification for products based on their description with Machine Learning algorithms and Neural Networks (MLP, CNN, Distilbert).

Detects DDOS attacks using ML

This repository is MLP implementation of classifier on MNIST dataset with PyTorch

Classifying Audio to Emotion

This is a binary classification problem related with Autistic Spectrum Disorder (ASD) screening in Adult individual. Given some attributes of a person, my model can predict whether the person would have a possibility to get ASD using different Supervised Learning Techniques and Multi-Layer Perceptron.

In this project we use RAVDESS Dataset to classify Speech Emotion using Multi Layer Perceptron Classifier

Multilayer Perceptron Neural network for binary classification between two type of breast cancer ("benign" and "malignant" )using Wisconsin Breast Cancer Database

Machine Learning algorithms Implementation from Scratch

Ensemble PhoBERT with FastText Embedding to improve performance on Vietnamese Sentiment Analysis tasks.

This repository contains supervised fake news detection on LIAR dataset. Check out the analysis details for more details.

🔍 | 📈 | Life / Health Insurance Fraud Detection | 📋 | (Codeshahstra Round 1 Hackathon)

Micro neural network with multi-dimensional layers, multi-shaped data, fully or locally meshing, conv2D, unconv2D, Qlearning, ... for test!

Desktop App & Web App for Cricket Outcome Prediction.

Speech Emotion Recognition using MLP Classifier.

Insurance claim fraud detection using machine learning algorithms.

In this study, we aimed to detect fraudulent activities in the supply chain through the use of neural networks. The study focused on building two machine learning models using the MLPClassifier algorithm from the scikit-learn library and a custom neural network using the Keras library in Python.