Deep Learning project October 2023

Predict genre of movie from title and description using a Bidirectional LSTM.

Sentiment Analysis on Amazon Reviews using Machine learning models and one deep learning model.

Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC) 2020

Code and specs for CS-Embed's entry for SemEval-2020 Task-9. We present code-switched embeddings, code for code-switched bilstm sentiment classifier, and code for CS tweet collection.

NLP Named Entity Recognition dalam bidang Biomedis, mendeteksi teks dan membuat klasifikasi apakah teks tersebut mempunyai entitas plant atau disease, memberi label pada teks, menguji hubungan entitas plant dan disease, menilai kecocokan antara kedua entitas, membandingkan hasil uji dengan menggunakan models BERT-BILSTM-CRF

This project aims to develop a system for automatically extracting emotion-cause pairs from conversational data, enhancing dialogue systems and mental health support through deeper understanding of emotional triggers.

Forecasting monthly sales witNh LSTM, Forecasting with RNN, Vector-autoregressive model

NLP on Depression text dataset from Kaggle

Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available. Data Set Characteristics: Multivariate, Time-Series. This database have 2,075,259 rows and 7 columns.

This is the minor-project that is to be submitted to my University, during the 7th semester. We build a BiLSTM model and train the model on textual data - Twitter data.

🚀 Deciphering Customer Sentiments: Harnessing the power of deep learning models such as LSTM and hybrid CNN-LSTM, we've crafted a dynamic Streamlit web app. It turns customer text reviews from Amazon datasets into actionable insights, helping you gauge product quality effortlessly. 📊🌟

Predicting sentiment polarity of COVID-19 news articles using Machine Learning and Deep Learning models

Personalised fitness recommendation by multi-level deep learning approach.

The task was to detect fake news regarding COVID-19.

Unsupervised anomaly detection in vibration signal using PyCaret vs BiLSTM