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Chapter and Conference Paper
Impact of Autoencoder Latent Space on IoT CoAP Attack Categorization
The Internet of Things (IoT) encompasses a vast network of interconnected devices and systems, usually with limited computational power, memory, and energy resources. These characteristics make IoT ecosystems ...
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Chapter and Conference Paper
Machine Learning Based System for Detecting Battery State-of-Health
In different fields, such as electric mobility, the increasing use of small consumer electronics devices, and the development of renewable energy alternatives, energy storage systems have become crucial to tec...
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Chapter and Conference Paper
Beta Hebbian Learning for Intrusion Detection in Networks of IoT Devices
This research paper is focused on security in IoT devices network, by providing a visual tool based on Beta Hebbian Learning (BHL) to easily identify attacks in the network to human experts. Contrary to Artifi...
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Chapter and Conference Paper
Detection of Denial of Service Attacks in an MQTT Environment Using a One-Class Approach
Nowadays, Internet of things (IoT) systems add connectivity to physical and common objects offering new possibilities, this systems have special features such as the low capacity of the devices and behaviour o...
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Chapter and Conference Paper
Comparative Analysis of Clustering Techniques for a Hybrid Model Implementation
This research is oriented to compare the performance of two clustering methods in order to know what is the best one for archiving high quality hybrid models. For testing purposes, a real dataset has been obta...
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Chapter and Conference Paper
Comparative of Clustering Techniques for Academic Advice and Performance Measurement
This article presents an innovative proposal for improving personalized student performance counselling. The methodology implemented applies clustering techniques in order to obtain group profiles of students ...
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Chapter and Conference Paper
Analyzing IoT-Based Botnet Malware Activity with Distributed Low Interaction Honeypots
The increasing number of Internet of Things devices, and their limited built-in security, has led to a scenario where many of the most powerful and dangerous botnets nowadays are comprised of these type of com...
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Chapter and Conference Paper
Assessing Feature Selection Techniques for a Colorectal Cancer Prediction Model
Risk prediction models for colorectal cancer play an important role to identify people at higher risk of developing this disease as well as the risk factors associated with it. Feature selection techniques hel...
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Article
A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques
In this paper, we present a new approach to categorize the wear of cutting tools used in edge profile milling processes. It is based on machine learning and computer vision techniques, specifically using B-ORC...
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Article
Open AccessAdaptive local binary pattern with oriented standard deviation (ALBPS) for texture classification
A new method to describe texture images using a hybrid combination of local and global texture descriptors is proposed in this paper. In this regard, a new adaptive local binary pattern (ALBP) descriptor is pr...