We present a supervised learning classification method for model-free fault detection and diagnosis, aiming to improve the maintenance quality of motor pumps installed on oil rigs. We investigate our generic fault diagnosis method on 2000 examples of real-world vibrational signals obtained from operational faulty industrial machines. The diagnostic system detects each considered fault in an inp...

As cyber-attacks become remarkably sophisticated, effective Intrusion Detection Systems (IDSs) are needed to monitor computer resources and provide alerts regarding unusual or suspicious behavior. Despite using several machine learning (ML) data mining methods achieve high effectiveness, these systems have not proven ideal. Current intrusion detection algorithms suffer from dimensionality, redu...

Several studies have used machine learning algorithms to develop intrusion systems (IDS), which differentiate anomalous behaviours from the normal activities of network systems. Due ease automated data collection and subsequently an increased size collected on traffic activities, complexity analysis is increasing exponentially. A particular issue, due statistical computation limitations, a sing...

Discovering disease biomarkers from gene expression data has been greatly advanced by feature selection (FS) methods, especially using ensemble FS (EFS) strategies with perturbation at the ( i.e., homogeneous EFS) or method level heterogeneous EFS). Here, we proposed a hybrid EFS design that explores both types of to disrupt associations good performance single dataset, algorithm, specific comb...

Nowadays, as information systems are more open to the Internet, the importance of secure networks is tremendously increased. New intelligent Intrusion Detection Systems (IDSs) which are based on sophisticated algorithms rather than current signature-base detections are in demand. In this paper, we propose a new data-mining based technique for intrusion detection using an ensemble of binary clas...

The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applicatio...

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This me...

We present a generic procedure for diagnosing faults using features extracted from noninvasive machine signals, based on supervised learning techniques to build the fault classifiers. An important novelty of our research is the use of 2000 examples of vibration signals obtained from operating faulty motor pumps, acquired from 25 oil platforms off the Brazilian coast during five years. Several f...