279 research outputs found
An Ensemble Semi-Supervised Adaptive Resonance Theory Model with Explanation Capability for Pattern Classification
Most semi-supervised learning (SSL) models entail complex structures and
iterative training processes as well as face difficulties in interpreting their
predictions to users. To address these issues, this paper proposes a new
interpretable SSL model using the supervised and unsupervised Adaptive
Resonance Theory (ART) family of networks, which is denoted as SSL-ART.
Firstly, SSL-ART adopts an unsupervised fuzzy ART network to create a number of
prototype nodes using unlabeled samples. Then, it leverages a supervised fuzzy
ARTMAP structure to map the established prototype nodes to the target classes
using labeled samples. Specifically, a one-to-many (OtM) mapping scheme is
devised to associate a prototype node with more than one class label. The main
advantages of SSL-ART include the capability of: (i) performing online
learning, (ii) reducing the number of redundant prototype nodes through the OtM
mapping scheme and minimizing the effects of noisy samples, and (iii) providing
an explanation facility for users to interpret the predicted outcomes. In
addition, a weighted voting strategy is introduced to form an ensemble SSL-ART
model, which is denoted as WESSL-ART. Every ensemble member, i.e., SSL-ART,
assigns {\color{black}a different weight} to each class based on its
performance pertaining to the corresponding class. The aim is to mitigate the
effects of training data sequences on all SSL-ART members and improve the
overall performance of WESSL-ART. The experimental results on eighteen
benchmark data sets, three artificially generated data sets, and a real-world
case study indicate the benefits of the proposed SSL-ART and WESSL-ART models
for tackling pattern classification problems.Comment: 13 pages, 8 figure
Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods
Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques.
The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns.
The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other.
The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques.
The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy
Artificial Intelligence in Process Engineering
In recent years, the field of Artificial Intelligence (AI) is experiencing a boom, caused by recent breakthroughs in computing power, AI techniques, and software architectures. Among the many fields being impacted by this paradigm shift, process engineering has experienced the benefits caused by AI. However, the published methods and applications in process engineering are diverse, and there is still much unexploited potential. Herein, the goal of providing a systematic overview of the current state of AI and its applications in process engineering is discussed. Current applications are described and classified according to a broader systematic. Current techniques, types of AI as well as pre- and postprocessing will be examined similarly and assigned to the previously discussed applications. Given the importance of mechanistic models in process engineering as opposed to the pure black box nature of most of AI, reverse engineering strategies as well as hybrid modeling will be highlighted. Furthermore, a holistic strategy will be formulated for the application of the current state of AI in process engineering
Signal processing and graph-based semi-supervised learning-based fault diagnosis for direct online induction motors
In this thesis, fault diagnosis approaches for direct online induction motors are proposed using signal processing and graph-based semi-supervised learning (GSSL). These approaches are developed using experimental data obtained in the lab for two identical 0.25 HP three-phase squirrel-cage induction motors. Various electrical and mechanical single- and multi-faults are applied to each motor during experiments. Three-phase stator currents and three-dimensional vibration signals are recorded simultaneously in each experiment. In this thesis, Power Spectral Density (PSD)-based stator current amplitude spectrum analysis and one-dimensional Complex Continuous Wavelet Transform (CWT)-based stator current time-scale spectrum analysis are employed to detect broken rotor bar (BRB) faults. An effective single- and multi-fault diagnosis approach is developed using GSSL, where discrete wavelet transform (DWT) is applied to extract features from experimental stator current and vibration data. Three GSSL algorithms (Local and global consistency (LGC), Gaussian field and harmonic functions (GFHF), and greedy-gradient max-cut (GGMC)) are adopted and compared in this study. To enable machine learning for untested motor operating conditions, mathematical equations to calculate features for untested conditions are developed using curve fitting and features obtained from experimental data of tested conditions
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