Anomaly Detection Based on Indicators Aggregation

Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the source of the problem that produced the anomaly is also essential. This is particularly the case in aircraft engine health monitoring where detecting early signs of failure (anomalies) and helping the engine owner to implement efficiently the adapted maintenance operations (fixing the source of the anomaly) are of crucial importance to reduce the costs attached to unscheduled maintenance. This paper introduces a general methodology that aims at classifying monitoring signals into normal ones and several classes of abnormal ones. The main idea is to leverage expert knowledge by generating a very large number of binary indicators. Each indicator corresponds to a fully parametrized anomaly detector built from parametric anomaly scores designed by experts. A feature selection method is used to keep only the most discriminant indicators which are used at inputs of a Naive Bayes classifier. This give an interpretable classifier based on interpretable anomaly detectors whose parameters have been optimized indirectly by the selection process. The proposed methodology is evaluated on simulated data designed to reproduce some of the anomaly types observed in real world engines.Comment: International Joint Conference on Neural Networks (IJCNN 2014), Beijing : China (2014). arXiv admin note: substantial text overlap with arXiv:1407.088

COVID-19 Imposes Rethinking of Conferencing -- Environmental Impact Assessment of Artificial Intelligence Conferences

It has been noticed that through COVID-19 greenhouse gas emissions had a sudden reduction. Based on this significant observation, we decided to conduct a research to quantify the impact of scientific conferences' air-travelling, explore and suggest alternative ways for greener conferences to re-duce the global carbon footprint. Specifically, we focused on the most popular conferences for the Artificial Intelligence community based on their scientific impact factor, their scale, and the well-organized proceedings towards measuring the impact of air travelling participation. This is the first time that systematic quantification of a state-of-the-art subject like Artificial Intelligence takes place to define its conferencing footprint in the broader frames of environmental awareness. Our findings highlight that the virtual way is the first on the list of green conferences' conduction although there are serious concerns about it. Alternatives to optimal conferences' location selection have demonstrated savings on air-travelling CO2 emissions of up to 63.9%.Comment: 18 pages, 5 figure

Neuron - synapse level problem decomposition method for cooperative neuro - evolution of feedforward networks for time series prediction

A major concern in cooperative coevolution for neuro-evolution is the appropriate problem decomposition method that takes into account the architectural properties of the neural network. Decomposition to the synapse and neuron level has been proposed in the past that have their own strengths and limitations depending on the application problem. In this paper, a new problem decomposition method that combines neuron and synapse level is proposed for feedfoward networks and applied to time series prediction. The results show that the proposed approach has improved the results in selected benchmark data sets when compared to related methods. It also has promising performance when compared to other computational intelligence methods from the literature