Enhancing network intrusion detection using effective stacking of ensemble classifiers with multi-pronged feature selection technique
Information security depends on Network Intrusion Detection (NID), which properly identifies
network threats. This work explores simulating a NID system by stacking ensemble …
network threats. This work explores simulating a NID system by stacking ensemble …
Involution fused convolution for classifying eye-tracking patterns of children with Autism Spectrum Disorder
Abstract Autism Spectrum Disorder (ASD) is a neurological condition that is challenging to
diagnose. Numerous studies demonstrate that children diagnosed with autism struggle with …
diagnose. Numerous studies demonstrate that children diagnosed with autism struggle with …
Augmenting Aquaculture Efficiency through Involutional Neural Networks and Self-Attention for Oplegnathus Punctatus Feeding Intensity Classification from Log Mel …
Simple Summary Managing fish feeding well is important for both making fish farming better
and keeping aquatic environments healthy. By looking at the sounds fish make, this study …
and keeping aquatic environments healthy. By looking at the sounds fish make, this study …
A light-weight factorized convolutions based dual-input fuzzy-CNN for efficient motor bearing fault diagnosis
Efficient and timely identification of bearing faults is imperative to ensure operational
normalcy, reduced down-times and health hazards in motor fault tolerant control systems …
normalcy, reduced down-times and health hazards in motor fault tolerant control systems …
Soundscape Characterization Using Autoencoders and Unsupervised Learning
DA Nieto-Mora, MC Ferreira de Oliveira… - Sensors, 2024 - mdpi.com
Passive acoustic monitoring (PAM) through acoustic recorder units (ARUs) shows promise
in detecting early landscape changes linked to functional and structural patterns, including …
in detecting early landscape changes linked to functional and structural patterns, including …
A computationally efficient method for induction motor bearing fault detection based on parallel convolutions and semi-supervised GAN
Accurate and timely bearing fault detection is imperative for optimal system functioning and
the implementation of preventative maintenance measures. Deep learning models provide …
the implementation of preventative maintenance measures. Deep learning models provide …
A Fault Data Generation Method for Enhanced Fault Diagnosis Based on Conditional Recurrent Gan with Maximum Degenerate Trend Retention
Y Cheng, P Wang, H Gu, J Zeng, J Ma - Available at SSRN 4953401 - papers.ssrn.com
Optimal data conditions are crucial prerequisites for the effective implementation of data-
driven fault-diagnosis methods. Usually, a lack of the necessary training samples prevents …
driven fault-diagnosis methods. Usually, a lack of the necessary training samples prevents …