Search results for "UML"

Infantile Hemangioma Detection using Deep Learning

2020

Infantile hemangiomas are the most common type of benign tumor which appear in the first weeks of life. As currently there is no robust protocol to monitor and assess the hemangioma status, this study proposes a preliminary method to detect the lesion. Therefore, in this paper we describe a hemangiomas classifier based on a linear convolutional neural network architecture. The challenge was to achieve a good classification using a relatively small internal database of 240 images from 40 different patients. The results are promising as the CNN performance evaluation showed a level of accuracy on the test set of 93.84%. Five metrics were calculated to assess the proposed model performances: a…

Modular approach to microswimming

2018

The field of active matter in general and microswimming in particular has experienced a rapid and ongoing expansion over the last decade. A particular interesting aspect is provided by artificial autonomous microswimmers constructed from individual active and inactive functional components into self-propelling complexes. Such modular microswimmers may exhibit directed motion not seen for each individual component. In this review, we focus on the establishment and recent developments in the modular approach to microswimming. We introduce the bound and dynamic prototypes, show mechanisms and types of modular swimming and discuss approaches to control the direction and speed of modular microsw…

A one class classifier for Signal identification: a biological case study

2008

The paper describes an application of a one-class KNN to identify different signal patterns embedded in a noise structured background. The problem become harder whenever only one pattern is well represented in the signal, in such cases one class classifier techniques are more indicated. The classification phase is applied after a preprocessing phase based on a Multi Layer Model (MLM) that provides a preliminary signal segmentation in an interval feature space. The one-class KNN has been tested on synthetic data that simulate microarray data for the identification of nucleosomes and linker regions across DNA. Results have shown a good recognition rate on synthetic data for nucleosome and lin…

A Sentiment Enhanced Deep Collaborative Filtering Recommender System

2021

Recommender systems use advanced analytic and learning techniques to select relevant information from massive data and inform users’ smart decision-making on their daily needs. Numerous works exploiting user’s sentiments on products to enhance recommendations have been introduced. However, there has been relatively less work exploring higher-order user-item features interactions for sentiment enhanced recommender system. In this paper, a novel Sentiment Enhanced Deep Collaborative Filtering Recommender System (SE-DCF) is developed. The architecture is based on a Neural Attention network component aggregated with the output predictions of a Convolution Neural Network (CNN) recommender. Speci…

Evaluating Classifiers for Mobile-Masquerader Detection

2006

As a result of the impersonation of a user of a mobile terminal, sensitive information kept locally or accessible over the network can be abused. The means of masquerader detection are therefore needed to detect the cases of impersonation. In this paper, the problem of mobile-masquerader detection is considered as a problem of classifying the user behaviour as originating from the legitimate user or someone else. Different behavioural characteristics are analysed by designated one-class classifiers whose classifications are combined. The paper focuses on selecting the classifiers for mobile-masquerader detection. The selection process is conducted in two phases. First, the classification ac…

Generating App Product Lines in a Model-Driven Cross-Platform Development Approach

2016

Within software product lines (SPL) similar software products are created based on common features. We applied this versatile approach to cross-platform app development by extending the domain-specific language (DSL) of an established model-driven development framework. The goal was to support the formulation of coherent building blocks of business use cases, referred to as workflow elements. While the former implementation already abstracted from technical details and provided the possibility to reuse low level features, it now enables to build business apps by combining coherent, self-contained workflow elements. Providing this support on the language level facilitates reusable component-…

A Framework for Component Reuse in a Metamodelling-Based Software Development

2001

<title>Expanding context against weighted voting of classifiers</title>

2000

In the paper we propose a new method to integrate the predictions of multiple classifiers for Data Mining and Machine Learning tasks. The method assumes that each classifier stands in it's own context, and the contexts are partially ordered. The order is defined by monotonous quality function that maps each context to the value from the interval [0,1]. The classifier that has the context with better quality is supposed to predict better than the classifier from worse quality. The objective is to generate the opinion of `virtual' classifier that stands in the context with quality equal to 1. This virtual classifier must have the best accuracy of predictions due to the best context. To do thi…

Towards interpretable classifiers with blind signal separation

2012

Blind signal separation (BSS) is a powerful tool to open-up complex signals into component sources that are often interpretable. However, BSS methods are generally unsupervised, therefore the assignment of class membership from the elements of the mixing matrix may be sub-optimal. This paper proposes a three-stage approach using Fisher information metric to define a natural metric for the data, from which a Euclidean approximation can then be used to drive BSS. Results with synthetic data models of real-world high-dimensional data show that the classification accuracy of the method is good for challenging problems, while retaining interpretability.

Fiberless, Multi-Channel fNIRS-EEG System Based on Silicon Photomultipliers: Towards Sensitive and Ecological Mapping of Brain Activity and Neurovasc…

2020

Portable neuroimaging technologies can be employed for long-term monitoring of neurophysiological and neuropathological states. Functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) are highly suited for such a purpose. Their multimodal integration allows the evaluation of hemodynamic and electrical brain activity together with neurovascular coupling. An innovative fNIRS-EEG system is here presented. The system integrated a novel continuous-wave fNIRS component and a modified commercial EEG device. fNIRS probing relied on fiberless technology based on light emitting diodes and silicon photomultipliers (SiPMs). SiPMs are sensitive semiconductor detectors, whose large…