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Details

  • Name

    Elsa Ferreira Gomes
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st November 2016
Publications

2022

MigraR: An open-source, R-based application for analysis and quantification of cell migration parameters

Authors
Shaji, N; Nunes, F; Rocha, MI; Gomes, EF; Castro, H;

Publication
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

Abstract
Background and objective: Cell migration is essential for many biological phenomena with direct impact on human health and disease. One conventional approach to study cell migration involves the quantitative analysis of individual cell trajectories recorded by time-lapse video microscopy. Dedicated software tools exist to assist the automated or semi-automated tracking of cells and translate these into coordinate positions along time. However, cell biologists usually bump into the difficulty of plotting and computing these data sets into biologically meaningful figures and metrics. Methods: This report describes MigraR, an intuitive graphical user interface executed from the RStudio (TM) (via the R package Shiny), which greatly simplifies the task of translating coordinate positions of moving cells into measurable parameters of cell migration (velocity, straightness, and direction of movement), as well as of plotting cell trajectories and migration metrics. One innovative function of this interface is that it allows users to refine their data sets by setting limits based on time, velocity and straightness. Results: MigraR was tested on different data to assess its applicability. Intended users of MigraR are cell biologists with no prior knowledge of data analysis, seeking to accelerate the quantification and visualization of cell migration data sets delivered in the format of Excel files by available cell-tracking software. Conclusions: Through the graphics it provides, MigraR is an useful tool for the analysis of migration parameters and cellular trajectories. Since its source code is open, it can be subject of refinement by expert users to best suit the needs of other researchers. It is available at GitHub and can be easily reproduced.

2022

The Usage of Data Augmentation Strategies on the Detection of Murmur Waves in a PCG Signal

Authors
Torres, J; Oliveira, J; Gomes, EF;

Publication
BIOSIGNALS: PROCEEDINGS OF THE 15TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES - VOL 4: BIOSIGNALS

Abstract
Cardiac auscultation is a key screening tool used for cardiovascular evaluation. When used properly, it speeds up treatment and thus improving the patient's life quality. However, the analysis and interpretation of the heart sound signals is subjective and dependent of the physician's experience and domain knowledge. A computer assistant decision (CAD) system that automatically analyse heart sound signals, can not only support physicians in their clinical decisions but also release human resources to other tasks. In this paper, and to the best of our knowledge, for the first time a SMOTE strategy is used to boost a Convolutional Neural Network performance on the detection of murmur waves. Using the SMOTE strategy, a CNN achieved an overall of 88.43%.

2022

Approaches to manage and understand student engagement in programming

Authors
Tavares, PC; Gomes, EF; Henriques, PR; Vieira, DM;

Publication
Open Education Studies

Abstract
Computer Programming Learners usually fail to get approved in introductory courses because solving problems using computers is a complex task. The most important reason for that failure is concerned with motivation; motivation strongly impacts on the learning process. In this paper we discuss how techniques like program animation, and automatic evaluation can be combined to help the teacher in Computer Programming courses. In the article, PEP system will be introduced to explain how it supports teachers in classroom and how it engages students on study sessions outside the classroom. To support that work, students' motivation was studied; to complement that study, a survey involving students attending the first year of Algorithms and Programming course of an Engineering degree was done. It is also presented a tool to analyse surveys, using association rules. © 2022 Paula Correia Tavares et al., published by De Gruyter.

2022

Automatic Classification of Bird Sounds: Using MFCC and Mel Spectrogram Features with Deep Learning

Authors
Carvalho, S; Gomes, EF;

Publication
VIETNAM JOURNAL OF COMPUTER SCIENCE

Abstract
Bird species identification is a relevant and time-consuming task for ornithologists and ecologists. With growing amounts of audio-annotated data, automatic bird classification using machine learning techniques is an important trend in the scientific community. Analyzing bird behavior and population trends helps detect other organisms in the environment and is an important problem in ecology. Bird populations react quickly to environmental changes, which make their real-time counting and tracking challenging and very useful. A reliable methodology that automatically identifies bird species from audio would therefore be a valuable tool for the experts in different scientific and applicational domains. The goal of this work is to propose a methodology to identify bird sounds. In this paper, we explore deep learning techniques that are being used in this domain, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to classify the data. In deep learning, audio problems are commonly approached by converting them into images using audio feature extraction techniques such as Mel Spectrograms and Mel Frequency Cepstral Coefficients (MFCCs). We propose and test multiple deep learning and feature extraction combinations in order to find the most suitable approach to this problem.

2021

Automatic Identification of Bird Species from Audio

Authors
Carvalho, S; Gomes, EF;

Publication
INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2021

Abstract
Bird species identification is a relevant and time-consuming task for ornithologists and ecologists. With growing amounts of audio annotated data, automatic bird classification using machine learning techniques is an important trend in the scientific community. Analyzing bird behavior and population trends helps detect other organisms in the environment and is an important problem in ecology. Bird populations react quickly to environmental changes, which makes their real time counting and tracking challenging and very useful. A reliable methodology that automatically identifies bird species from audio would therefore be a valuable tool for the experts in different scientific and applicational domains. The goal of this work is to propose a methodology able to identify bird species by its chirp. In this paper we explore deep learning techniques that are being used in this domain, such as Convolutional Neural Networks and Recurrent Neural Networks to classify the data. In deep learning, audio problems are commonly approached by converting them into images using audio feature extraction techniques such as Mel Spectrograms and Mel Frequency Cepstral Coefficients. We propose and test multiple deep learning and feature extraction combinations in order to find the most suitable approach to this problem.

Supervised
thesis

2022

Deteção de patologia cardíaca usando machine learning

Author
JESSICA FELIZ DOS SANTOS

Institution
IPP-ISEP

2021

Deteção de patologia em sons cardíacos usando deep learning

Author
JOSÉ PEDRO INEZ DE MEIRA TORRES

Institution
IPP-ISEP

2021

Deep learning para classificação automática de sons usando o Audioset

Author
MIGUEL ÂNGELO MOREIRA ROCHA

Institution
IPP-ISEP

2020

Identificação automática de aves a partir de áudio

Author
SILVESTRE DANIEL DIAS CARVALHO

Institution
IPP-ISEP

2019

Análise e previsão de acidentes rodoviários usando data mining

Author
BRUNO MIGUEL FERREIRA TEIXEIRA

Institution
IPP-ISEP