activity
20182022
most citedSupervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review

183 citations · 221 across the 8 of their papers we have counts for

collaborators

10 papers

eess.SP20226 cited

Digital Signal Analysis based on Convolutional Neural Networks for Active Target Time Projection Chambers

G. F. Fortino, J. C. Zamora, L. E. Tamayose +2

An algorithm for digital signal analysis using convolutional neural networks (CNN) was developed in this work. The main objective of this algorithm is to make the analysis of exper…

cs.LG20211 cited

FallDeF5: A Fall Detection Framework Using 5G-based Deep Gated Recurrent Unit Networks

Mabrook S. Al-Rakhami, Abdu Gumaei1, Meteb Altaf +4

Fall prevalence is high among elderly people, which is challenging due to the severe consequences of falling. This is why rapid assistance is a critical task. Ambient assisted livi…

cs.CV2021

Cloud based Scalable Object Recognition from Video Streams using Orientation Fusion and Convolutional Neural Networks

Muhammad Usman Yaseen, Ashiq Anjum, Giancarlo Fortino +2

Object recognition from live video streams comes with numerous challenges such as the variation in illumination conditions and poses. Convolutional neural networks (CNNs) have been…

cs.CR2021183 cited

Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review

Mario Di Mauro, Giovanni Galatro, Giancarlo Fortino +1

Machine Learning (ML) techniques are becoming an invaluable support for network intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats. Typica…

physics.ins-det202012 cited

Tracking Algorithms for TPCs using Consensus-Based Robust Estimators

J. C. Zamora, G. F. Fortino

A tracking algorithm based on consensus-robust estimators was implemented for the analysis of experiments with time-projection chambers. In this work, few algorithms beyond RANSAC…

cs.LG2020

Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review

L. Erhan, M. Ndubuaku, M. Di Mauro +5

Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behaviour. This is an important research problem, due to its broad set of ap…