IEEE

SXAD: Shapely eXplainable AI-Based Anomaly Detection Using Log Data

Abstract: Artificial Intelligence (AI) has made tremendous progress in anomaly detection. However, AI models work as a black-box, making it challenging to provide reasoning behind their judgments in a ...
GitHub

Arduino library to implement float16ext data type.

This experimental library defines the float16ext (2 byte) data type, including conversion function to and from float32 type. It is an extension to the float16 library ...
GitHub

LogLLM: Log-based Anomaly Detection Using Large Language Models

Experimental Results on HDFS, BGL, Liberty, and Thunderbird datasets. The best results are indicated using bold typeface.
IEEE

Meta-Learning and Transformer Model through Geophysical Well Logging Data Processed via Weighted Wavelet Thresholding Denoising Algorithm for Lithology Identification

Abstract: This study enhances lithology identification in petroleum exploration by combining weighted wavelet threshold denoising (WWTD) with advanced machine learning. WWTD first reduces noise in ...