Search results for "Mach"
showing 10 items of 3360 documents
Ensemble Feature Selection Based on Contextual Merit and Correlation Heuristics
2001
Recent research has proven the benefits of using ensembles of classifiers for classification problems. Ensembles of diverse and accurate base classifiers are constructed by machine learning methods manipulating the training sets. One way to manipulate the training set is to use feature selection heuristics generating the base classifiers. In this paper we examine two of them: correlation-based and contextual merit -based heuristics. Both rely on quite similar assumptions concerning heterogeneous classification problems. Experiments are considered on several data sets from UCI Repository. We construct fixed number of base classifiers over selected feature subsets and refine the ensemble iter…
One-Class Classifiers : A Review and Analysis of Suitability in the Context of Mobile-Masquerader Detection
2007
One-class classifiers employing for training only the data from one class are justified when the data from other classes is difficult to obtain. In particular, their use is justified in mobile-masquerader detection, where user characteristics are classified as belonging to the legitimate user class or to the impostor class, and where collecting the data originated from impostors is problematic. This paper systematically reviews various one-class classification methods, and analyses their suitability in the context of mobile-masquerader detection. For each classification method, its sensitivity to the errors in the training set, computational requirements, and other characteristics are consi…
Learning Similarity Scores by Using a Family of Distance Functions in Multiple Feature Spaces
2017
There exist a large number of distance functions that allow one to measure similarity between feature vectors and thus can be used for ranking purposes. When multiple representations of the same object are available, distances in each representation space may be combined to produce a single similarity score. In this paper, we present a method to build such a similarity ranking out of a family of distance functions. Unlike other approaches that aim to select the best distance function for a particular context, we use several distances and combine them in a convenient way. To this end, we adopt a classical similarity learning approach and face the problem as a standard supervised machine lea…
How the purpose of the inter-firm relationships influences the choice of the governance form: evidence from the machine tool and the pharmaceutical i…
2013
In today's competitive landscape, the choice of the appropriate mode to govern an inter-firm relationship is a critical factor for companies. In the literature several theoretical strands have examined the impact of the purpose of inter-firm relationships on the forms of governance. Building on a robust literature review on the topic, this study focuses on a specific issue influencing the choice of the form of governance in inter-firm relationships, i.e. the purpose of inter-firm relationships with relation to partner's resources. We gather in a unique framework three typologies of partner's resources, i.e. production, R&D and marketing, and through two empirical analyses in two different I…
Helicobacter pylori gamma-glutamyl transpeptidase and vacuolating cytotoxin promote gastric persistence and immune tolerance
2013
Infection with the gastric bacterial pathogen Helicobacter pylori is typically contracted in early childhood and often persists for decades. The immunomodulatory properties of H. pylori that allow it to colonize humans persistently are believed to also account for H. pylori ’s protective effects against allergic and chronic inflammatory diseases. H. pylori infection efficiently reprograms dendritic cells (DCs) toward a tolerogenic phenotype and induces regulatory T cells (Tregs) with highly suppressive activity in models of allergen-induced asthma. We show here that two H. pylori virulence determinants, the γ-glutamyl transpeptidase GGT and the vacuolating cytotoxin VacA, contribute critic…
Advanced Monitoring of Rotor Broken Bar in Double Squirrel Cage Induction Machines Based on Wavelet Analysis
2012
The diagnosis of induction machine faults is commonly realized through Motor Current Signature Analysis (MCSA), i.e. by classical spectrum analysis of the input currents. In case of large or double cage induction motors, rotor broken bars fault detection based on sideband current components may fail due to the presence of inter bar currents that reduce the degree of rotor asymmetry, leading to reduced relevance of these spectral components. But inter bar currents produce core vibrations in the axial direction, which can be detected using vibration analysis techniques, in order to overcome the limits of the classical MCSA for these purposes. In this paper, an advanced use of the Discrete wav…
Design of a transverse flux machine for power generation from seawaves
2014
In this paper, we present a transverse flux linear generator. We investigate the possibility to use this generator to extract energy from seawaves. We propose an optimization procedure that allows us to obtain an optimized design of the generator. The optimized design of the converter shows a power generation capability index much higher than other renewable systems.
Use of Machine Learning and Artificial Intelligence to Drive Personalized Medicine Approaches for Spine Care
2020
Personalized medicine is a new paradigm of healthcare in which interventions are based on individual patient characteristics rather than on “one-size-fits-all” guidelines. As epidemiological datasets continue to burgeon in size and complexity, powerful methods such as statistical machine learning and artificial intelligence (AI) become necessary to interpret and develop prognostic models from underlying data. Through such analysis, machine learning can be used to facilitate personalized medicine via its precise predictions. Additionally, other AI tools, such as natural language processing and computer vision, can play an instrumental part in personalizing the care provided to patients with …
Normal and Abnormal Tissue Classification in Positron Emission Tomography Oncological Studies
2018
Positron Emission Tomography (PET) imaging is increasingly used in radiotherapy environment as well as for staging and assessing treatment response. The ability to classify PET tissues, as normal versus abnormal tissues, is crucial for medical analysis and interpretation. For this reason, a system for classifying PET area is implemented and validated. The proposed classification is carried out using k-nearest neighbor (KNN) method with the stratified K-Fold Cross-Validation strategy to enhance the classifier reliability. A dataset of eighty oncological patients are collected for system training and validation. For every patient, lesion (abnormal tissue) and background (normal tissue around …