FPT-ThaiTuan

Detect-Yoga-Poses-And-Correction-In-Real-Time-Using-Machine-Learning-Algorithms

Compare SVM mode yoga movement classification accuracy with Linear kernel, Polynomial kernel, RBF (Radial Basis Function) kernel, LSTM with accuracy up to 98%. In addition, it also supports adjusting the practitioner's movements according to standard movements.

Image-Processing-with-Python

Open source resources provide examples and sample source code for performing image processing using the Python programming language and OpenCV library. Reliable and accessible resources for learning and practicing image processing with Python.

Lung-Cancer-Classification-using-Support-Vector-Machine

Predicting-Stock-Prices-with-LSTM-and-GRU

Traffic-sign-classification-using-transfer-learning-with-ResNet152V2

Utilizing ResNet152V2 for Traffic Sign Classification: Achieve High Accuracy in Identifying 52 Sign Types with 99% Precision

Transfer-Learning-Use-Inception-v3-For-Image-Classification

Transfer Learning uses Inception v3 to classify human and horse images with 99.39% accuracy.

Use-Google-Play-Scraper-to-crawl-the-Google-Play-Store-for-Python

Use google-play-scraper 1.2.6 to collect app reviews.

Using-deep-learning-to-classify-fruits-using-the-VGG16-model

Classify 8 types of fruit using the VGG16 model with accuracy up to 90%

Using-Support-Vector-Machine-To-Automatically-Take-Class-Attendance

Use the SVM model to automatically take attendance in class with high accuracy up to 80%

Using-Word-Embeddings-for-Twitter-Sentiment-Analysis

The project researches sentiment analysis on Twitter, with the goal of evaluating the positivity, negativity or neutrality of comments. Using Word Embeddings, an advanced method in natural language processing, our model achieved a high accuracy of 96.61%. The model was trained on Twitter data and tested on a data comment dataset from Binance.

FPT-ThaiTuan/Use-Google-Play-Scraper-to-crawl-the-Google-Play-Store-for-Python

Use google-play-scraper 1.2.6 to collect app reviews.

FPT-ThaiTuan/Using-Word-Embeddings-for-Twitter-Sentiment-Analysis

The project researches sentiment analysis on Twitter, with the goal of evaluating the positivity, negativity or neutrality of comments. Using Word Embeddings, an advanced method in natural language processing, our model achieved a high accuracy of 96.61%. The model was trained on Twitter data and tested on a data comment dataset from Binance.

FPT-ThaiTuan/Traffic-sign-classification-using-transfer-learning-with-ResNet152V2

Utilizing ResNet152V2 for Traffic Sign Classification: Achieve High Accuracy in Identifying 52 Sign Types with 99% Precision

FPT-ThaiTuan/Using-deep-learning-to-classify-fruits-using-the-VGG16-model

Classify 8 types of fruit using the VGG16 model with accuracy up to 90%

FPT-ThaiTuan/Using-Support-Vector-Machine-To-Automatically-Take-Class-Attendance

Use the SVM model to automatically take attendance in class with high accuracy up to 80%

FPT-ThaiTuan/Detect-Yoga-Poses-And-Correction-In-Real-Time-Using-Machine-Learning-Algorithms

Compare SVM mode yoga movement classification accuracy with Linear kernel, Polynomial kernel, RBF (Radial Basis Function) kernel, LSTM with accuracy up to 98%. In addition, it also supports adjusting the practitioner's movements according to standard movements.

FPT-ThaiTuan/Image-Processing-with-Python

Open source resources provide examples and sample source code for performing image processing using the Python programming language and OpenCV library. Reliable and accessible resources for learning and practicing image processing with Python.

FPT-ThaiTuan/Lung-Cancer-Classification-using-Support-Vector-Machine

FPT-ThaiTuan/Predicting-Stock-Prices-with-LSTM-and-GRU

FPT-ThaiTuan/Transfer-Learning-Use-Inception-v3-For-Image-Classification

Transfer Learning uses Inception v3 to classify human and horse images with 99.39% accuracy.

FPT-ThaiTuan/Deep-Learning-Solutions-for-Hearing-Impaired-Emergency-Assistance

Revolutionize emergency response for the hearing-impaired with our innovative deep learning solution, providing real-time assistance and support during critical situations, ensuring safety and accessibility for all.