How a Plastic Injection Molding Manufacturer Reduced Scrap Rates by 25%
ProcessMiner Delivers 25% Reduction in Scrap Rates
Artificial Intelligence Platform Achieves Unprecedented Autonomous Chemistry Control for Tissue Mill
The goal of this project was to autonomously control part of a tissue mill’s continuous manufacturing process using artificial intelligence and predictive analytics to reduce raw material consumption while maintaining the product quality with specification limit.
LSTM Autoencoder for Extreme Rare Event Classification in Keras
Here we will learn the details of data preparation for LSTM models, and build an LSTM Autoencoder for rare-event classification. This post is a continuation of my previous post-Extreme Rare Event Classification using Autoencoders. In the previous post, we talked about the challenges in an extremely rare event data with less than 1% positively labeled data.
Extreme Rare Event Classification using Autoencoders in Keras
In a rare-event problem, we have an unbalanced dataset. Meaning, we have fewer positively labeled samples than negative. In a typical rare-event problem, the positively labeled data are around 5–10% of the total. In an extremely rare event problem, we have less than 1% positively labeled data.
Estimating Non-Linear Correlation in R
Correlation estimations are commonly used in various data mining applications. In my experience, nonlinear correlations are quite common in various processes. Due to this, nonlinear models, such as SVM, are employed for regression, classification, etc.
Sequence Embedding for Clustering and Classification
Here we will learn an approach to get vector embeddings for string sequences. These embeddings can be used for Clustering and Classification. Sequence modeling has been a challenge. This is because of the inherent un-structuredness of sequence data. Just like texts in Natural Language Processing (NLP), sequences are arbitrary strings.