Rizwan-Majeed / Sentiment-Analysis-from-Images-Using-Deep-LearningLinks
Convolutional Neural Network (CNN) was trained on 48x48 pixel grayscale images to predict 5 different emotions from images. Ten different models with different settings were trained to find the best model and The best model was able to predict 5 emotions from images with 88% training accuracy and 70% testing accuracy.
☆11Updated 3 years ago
Alternatives and similar repositories for Sentiment-Analysis-from-Images-Using-Deep-Learning
Users that are interested in Sentiment-Analysis-from-Images-Using-Deep-Learning are comparing it to the libraries listed below
Sorting:
- optimizing locations of electric vehicle charging stations in the city of Toronto☆33Updated 3 years ago
- Complementary Jupyter notebooks for load forecasting tutorial.☆12Updated 5 years ago
- ☆17Updated 4 years ago
- Forecasts next 24 hours of hourly energy demand with Keras, Prophet, and SARIMA (statsmodels)☆104Updated 2 years ago
- Graph theory to optimize the road transportation network of a retail company☆22Updated last month
- This repository contains all the code and data used in our article titled “Estimating international trade status of countries from global…☆10Updated 2 years ago
- Modeling methods of System Dynamics – Supply Chain Simulation using the Anylogic software☆10Updated last month
- Spatial-Economic Analysis for Optimal EV Charging Station Placement using Machine Learning.☆15Updated last year
- This project optimizes EV charging station placement and capacity using MATLAB. It calculates optimal locations based on voltage stabilit…☆16Updated last year
- Short-term load forecasting with machine learning☆10Updated 4 years ago
- Predicting the energy consumption of EVs using the RNN and LSTM. Competencies: Machine Learning, RNN, SUMO Simulation. Python Libraries: …☆20Updated 4 years ago
- Modelling marine traffic in the ice-covered Baltic Sea using AIS data☆79Updated 5 years ago
- Energy consumption prediction using LSTM/GRU networks in PyTorch☆69Updated 2 years ago
- Probabilistic Deep Learningfor Electric-Vehicle Energy-Use Prediction☆23Updated 4 years ago
- I predict air quality index of a city in China using a Long Short Term Memory (LSTM) neural network. for a year. Executed time series ana…☆29Updated 5 years ago
- OBD-II Data Based Driver Identification System Based on Deep-LSTM☆12Updated 5 years ago
- This is the accompanying repository to the paper: "Day-ahead net load forecasting with self-attention: dealing with meter and meta data u…☆10Updated last year
- Identification of road surfaces and 12 different classes like speed bumps, paved, unpaved, markings, water puddles, potholes, etc.☆15Updated 2 years ago
- Submission of an in-class NLP sentiment analysis competition held at Microsoft AI Singapore group. This submission entry explores the per…☆15Updated 3 years ago
- Using electric vehicle charging data, I explore when drivers are likely to plug in their cars, and how much additional electricity demand…☆16Updated 5 years ago
- Traffic Accident Analysis using python machine learning☆30Updated last year
- A code from paper "A Global Modeling Framework for Load Forecasting in Distribution Networks"☆12Updated 2 years ago
- Weather Forecasting report over the Jaipur Dataset for Rain Prediction☆29Updated 6 years ago
- How to use XGBoost for multi-step time series forecasting☆43Updated 3 years ago
- Tweet sentiment analysis using various deep learning algorithms ranging from MLP, CNN, RNN to Transformers☆16Updated 5 years ago
- Driving range prediction by looking at energy consumption rate of Electronic Vehicles using ML regression techniques.☆14Updated 4 years ago
- Multivariate Time series Analysis Using LSTM & ARIMA☆38Updated 6 years ago
- Multistep Traffic Forecasting by Dynamic Graph Convolution: Interpretations of Real-Time Spatial Correlations☆16Updated last year
- [Big Data Analytics]Analyzing relationship between changes in weather and its impact on the city taxis☆13Updated 8 years ago
- Optimize E-Commerce Last-Mile Delivery with Python☆33Updated last month