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

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.

Unsupervised anomaly detection in vibration signal using PyCaret vs BiLSTM

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

Personalised fitness recommendation by multi-level deep learning approach.

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

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.

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

Deep Learning project October 2023

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

🚀 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. 📊🌟

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.

NLP on Depression text dataset from Kaggle

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

This project aimed to build a customer support intent detection system using a bidirectional LSTM model, trained on customer support chat logs and their corresponding intents. The model accurately classified new logs into 27 intent categories, showing effectiveness in deep learning for natural language processing.