36 citations · 103 across the 13 of their papers we have counts for
13 papers
Exploring LLM Capabilities in Extracting DCAT-Compatible Metadata for Data Cataloging
Lennart Busch, Daniel Tebernum, Gissel Velarde
Efficient data exploration is crucial as data becomes increasingly important for accelerating processes, improving forecasts and developing new business models. Data consumers ofte…
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets
Mateo Lopez-Ledezma, Gissel Velarde
Cybersecurity has become essential worldwide and at all levels, concerning individuals, institutions, and governments. A basic principle in cybersecurity is to be always alert. The…
An approach to melodic segmentation and classification based on filtering with the Haar-wavelet
Gissel Velarde, Tillman Weyde, David Meredith
We present a novel method of classification and segmentation of melodies in symbolic representation. The method is based on filtering pitch as a signal over time with the Haar-wave…
Wavelet-Filtering of Symbolic Music Representations for Folk Tune Segmentation and Classification
Gissel Velarde, Tillman Weyde, David Meredith
The aim of this study is to evaluate a machine-learning method in which symbolic representations of folk songs are segmented and classified into tune families with Haar-wavelet fil…
Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security Applications
Markus Haug, Gissel Velarde
This work empirically evaluates machine learning models on two imbalanced public datasets (KDDCUP99 and Credit Card Fraud 2013). The method includes data preparation, model trainin…
A Machine Learning Approach For Bitcoin Forecasting
Stefano Sossi-Rojas, Gissel Velarde, Damian Zieba
Bitcoin is one of the cryptocurrencies that is gaining more popularity in recent years. Previous studies have shown that closing price alone is not enough to forecast stock market…
An Open-Source and Reproducible Implementation of LSTM and GRU Networks for Time Series Forecasting
Gissel Velarde, Pedro Branez, Alejandro Bueno +2
This paper introduces an open-source and reproducible implementation of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) Networks for time series forecasting. We evalua…
Tree Boosting Methods for Balanced andImbalanced Classification and their Robustness Over Time in Risk Assessment
Gissel Velarde, Michael Weichert, Anuj Deshmunkh +4
Most real-world classification problems deal with imbalanced datasets, posing a challenge for Artificial Intelligence (AI), i.e., machine learning algorithms, because the minority…
Evaluating XGBoost for Balanced and Imbalanced Data: Application to Fraud Detection
Gissel Velarde, Anindya Sudhir, Sanjay Deshmane +3
This paper evaluates XGboost's performance given different dataset sizes and class distributions, from perfectly balanced to highly imbalanced. XGBoost has been selected for evalua…
Forecasting with Deep Learning
Gissel Velarde
This paper presents a method for time series forecasting with deep learning and its assessment on two datasets. The method starts with data preparation, followed by model training…
Benchmarking Algorithms for Automatic License Plate Recognition
Marcel Del Castillo Velarde, Gissel Velarde
We evaluated a lightweight Convolutional Neural Network (CNN) called LPRNet [1] for automatic License Plate Recognition (LPR). We evaluated the algorithm on two datasets, one compo…
HEAR: Holistic Evaluation of Audio Representations
Joseph Turian, Jordie Shier, Humair Raj Khan +20
What audio embedding approach generalizes best to a wide range of downstream tasks across a variety of everyday domains without fine-tuning? The aim of the HEAR benchmark is to dev…
Artificial Intelligence and its impact on the Fourth Industrial Revolution: A Review
Gissel Velarde
Artificial Intelligence may revolutionize everything during the so-called fourth industrial revolution, which carries several emerging technologies and could progress without prece…