Intrusion Detection Systems for IoT: opportunities and challenges offered by Edge Computing and Machine Learning
P Spadaccino, F Cuomo - arXiv preprint arXiv:2012.01174, 2020 - arxiv.org
Key components of current cybersecurity methods are the Intrusion Detection Systems
(IDSs) were different techniques and architectures are applied to detect intrusions. IDSs can …
(IDSs) were different techniques and architectures are applied to detect intrusions. IDSs can …
A deep autoencoder approach for detection of brain tumor images
Brain tumor detection received much attention due to its clinical significance for early
treatment. Accurate diagnosis and classification of brain tumors are still challenging despite …
treatment. Accurate diagnosis and classification of brain tumors are still challenging despite …
Low-power detection and classification for in-sensor predictive maintenance based on vibration monitoring
In this work, a new custom design of an anomaly detection and classification system is
proposed. It is composed of a convolutional Auto-Encoder (AE) hardware design to perform …
proposed. It is composed of a convolutional Auto-Encoder (AE) hardware design to perform …
Deep-compact-clustering based anomaly detection applied to electromechanical industrial systems
F Arellano-Espitia, M Delgado-Prieto… - Sensors, 2021 - mdpi.com
The rapid growth in the industrial sector has required the development of more productive
and reliable machinery, and therefore, leads to complex systems. In this regard, the …
and reliable machinery, and therefore, leads to complex systems. In this regard, the …
EPYNET: Efficient pyramidal network for clothing segmentation
ADS Inacio, HS Lopes - Ieee Access, 2020 - ieeexplore.ieee.org
Soft biometrics traits extracted from a human body, including the type of clothes, hair color,
and accessories, are useful information used for people tracking and identification. Semantic …
and accessories, are useful information used for people tracking and identification. Semantic …
Solar panel identification via deep semi-supervised learning and deep one-class classification
As residential photovoltaic (PV) system installations continue to increase rapidly, utilities
need to identify the locations of these new components to manage the unconventional two …
need to identify the locations of these new components to manage the unconventional two …
Deep neural network for compressive sensing and application to massive MIMO channel estimation
Z Mohades, V Tabataba Vakili - Circuits, Systems, and Signal Processing, 2021 - Springer
In this paper, we consider the problem of sparse signal recovery using a learned dictionary
in multiple measurement vectors (MMVs) case. Employing deep neural networks, we …
in multiple measurement vectors (MMVs) case. Employing deep neural networks, we …
Identification of abnormal states in videos of ants undergoing social phase change
Biology is both an important application area and a source of motivation for development of
advanced machine learning techniques. Although much attention has been paid to large …
advanced machine learning techniques. Although much attention has been paid to large …
On the Generalized Likelihood Ratio Test and One-Class Classifiers
F Ardizzon, S Tomasin - arXiv preprint arXiv:2210.12494, 2022 - arxiv.org
One-class classification (OCC) is the problem of deciding whether an observed sample
belongs to a target class. We consider the problem of learning an OCC model that performs …
belongs to a target class. We consider the problem of learning an OCC model that performs …
Detection and classification of brain abnormality by a novel hybrid EfficientNet-deep autoencoder (EF-DA) CNN model from MRI brain images in smart health …
This paper presents the novel smart hybrid EfficientNet-deep autoencoder (EF-DA) Deep
Neural Network model to classify brain images. This is the succession of modified …
Neural Network model to classify brain images. This is the succession of modified …