Audio Source Separation using the Non Negative Matrix Multiplication
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Updated
Apr 17, 2023 - Jupyter Notebook
Audio Source Separation using the Non Negative Matrix Multiplication
Building a Collaborative Filtering based Recommender system using e-commerce data.
Semi-Supervise cellular deconvolution of Bulk RnaSeq using NMF and CiberSortx, DCQ or others
M.S. and EPGP Assignment - Machine learning model using NLP topic modeling to automatically classify customer complaints based on products and services for improved customer service in the financial industry.
Detailed sentiment analysis (overall and aspect based sentiment analysis) on major Singapore attractions.
Survey Insights Engine
Unsupervised classification of products based on their text description (NLP) or image (computer vision)
Code and associated data relating to Kowalski, MacGregor, Long, Bell, and Cronin, "Automated Library Generation and Serendipity Quantification enables Diverse Discovery in Coordination Chemistry", JACS, 2022
A Project on Topic Modeling using alogoriths like LSA/LSI, LDA, NMF on RACE dataset
Codebase and Data for the work on Network diffusion-based approach for survival prediction and identification of biomarkers using multi-omics data of Papillary Renal Cell Carcinoma
This project aims to identify key topics in Quora questions related to popular applications. We'll use Non-Negative Matrix Factorization (NMF) to extract meaningful themes and patterns from this data.
2023 NCKU Image Processing Homework Code
Pipeline de PLN do projeto "Mood Hound" (6º DSM - 2023, FATEC Profº Jessen Vidal - SJC)
Statistical machine learning techniques
Topic Modelling von Tagesschau-Nachrichtenmeldungen mit NMF
A movie recommendation website powered by an unsupervised learning non-negative matrix factorization algorithm. This project provides users with personalized movie recommendations based on their viewing history and preferences.
Repository of LS Insight, a program that used NMF and dependency analysis for augmented biblical exegesis.
This repository classifies Goodreads Fantasy book reviews into subgenres using advanced topic modeling techniques like NMF, LDA, and BERTopic. A dataset of 2M English-language reviews is analyzed, with topics compared to predefined subgenres using cosine similarity. Heatmaps and summaries visualize the results.
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