Qualitative analysis of deep learning frameworks

Gutoski, Matheus; Hattori, Leandro Takeshi; Aquino, Nelson Marcelo Romero; Senefonte, Helen C. de Mattos; Ribeiro, Matilde; Lazzaretti, André E.; Lopes, Heitor Silvério

doi:10.21528/lnlm-vol15-no1-art3

Cited by 3 publications

(3 citation statements)

References 20 publications

(34 reference statements)

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“…The infrastructure used to carry out the simulations was Google Colab (Collaboratory), which offers 12GB of RAM and a Tesla K80 GPU. It provides pre-configured Python 2 and 3 runtimes with essential machine learning and artificial intelligence libraries, such as TensorFlow, Matplotlib, and Keras [31,32].…”

Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Convolutional Neural Networks Applied in the Detection of Pneumonia Through X-Ray Images of Children

Silva¹,

Araújo²,

Souza³

et al. 2021

Learn. Nonlin. Mod.

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Pneumonia is one of the most common medical problems in clinical practice and is the leading fatal infectious disease worldwide. According to the World Health Organization, pneumonia kills about 2 million children under the age of 5 and is constantly estimated to be the leading cause of infant mortality, killing more children than AIDS, malaria, and measles combined. A key element in the diagnosis is radiographic data, as chest x-rays are routinely obtained as a standard of care and can aid to differentiate the types of pneumonia. However, a rapid radiological interpretation of images is not always available, particularly in places with few resources, where childhood pneumonia has the highest incidence and mortality rates. As an alternative, the application of deep learning techniques for the classification of medical images has grown considerably in recent years. This study presents five implementations of convolutional neural networks (CNNs): ResNet50, VGG-16, InceptionV3, InceptionResNetV2, and ResNeXt50. To support the diagnosis of the disease, these CNNs were applied to solve the classification problem of medical radiographs from people with pneumonia. InceptionResNetV2 obtained the best recall and precision results for the Normal and Pneumonia classes, 93.95% and 97.52% respectively. ResNeXt50 achieved the best precision and f1-score results for the Normal class (94.62% and 94.25% respectively) and the recall and f1-score results for the Pneumonia class (97.80% and 97.65%, respectively).

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Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Convolutional Neural Networks Applied in the Detection of Pneumonia Through X-Ray Images of Children

Silva¹,

Araújo²,

Souza³

et al. 2021

Learn. Nonlin. Mod.

View full text Add to dashboard Cite

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“…A challenge to be overcome by this method is to decide the initial topology, weights and biases to start with. The work [12], provides a qualitative evaluation of four of the most popular Deep Learning frameworks, including: Caffe, Torch, Lasagne and TensorFlow. A printed character recognition task was used as case study and a Convolutional Neural Network was implemented for this purpose.…”

Section: Related Workmentioning

confidence: 99%

Evolving deep neural networks for Time Series Forecasting

Campos¹,

Pereira²,

Duarte³

et al. 2021

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The aim of this paper is to introduce a biologically inspired approach that can automatically generate Deep Neural networks with good prediction capacity, smaller error and large tolerance to noises. In order to do this, three biological paradigms were used: Genetic Algorithm (GA), Lindenmayer System and Neural Networks (DNNs). The final sections of the paper present some experiments aimed at investigating the possibilities of the method in the forecast the price of energy in the Brazilian market. The proposed model considers a multi-step ahead price prediction (12, 24, and 36 weeks ahead). The results for MLP and LSTM networks show a good ability to predict peaks and satisfactory accuracy according to error measures comparing with other methods.

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“…Developing an ML model involves several tasks from acquiring a (labeled) set of examples, selecting an appropriate learning algorithm and its parameters, training the model, and evaluating the model's performance (Lwakatare et al, 2019;Ramos et al, 2020). It requires an understanding of complex algorithms and working processes, as well as a constantly increasing zoo of architectures, frameworks, etc., which makes choosing a suitable one a difficult task for novices (Gillies, 2016;Gutosk, 2017;Sulmont et al, 2019) as well as requiring the user to have a certain level of programming skills (Xie et al, 2019). As a consequence, students typically face several difficulties when starting to learn ML, making the process of building ML models inaccessible to many people (Ramos et al, 2020;Sankaran et al, 2018;Tamilselvam et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Visual tools for teaching machine learning in K-12: A ten-year systematic mapping

et al. 2021

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Teaching Machine Learning in school helps students to be better prepared for a society rapidly changing due to the impact of Artificial Intelligence. This requires age-appropriate tools that allow students to develop a comprehensive understanding of Machine Learning in order to become creators of smart solutions. Following the trend of visual languages for introducing algorithms and programming in K-12, we present a ten-year systematic mapping of emerging visual tools that support the teaching of Machine Learning at this educational stage and analyze the tools concerning their educational characteristics, support for the development of ML models as well as their deployment and how the tools have been developed and evaluated. As a result, we encountered 16 tools targeting students mostly as part of short duration extracurricular activities. Tools mainly support the interactive development of ML models for image recognition tasks using supervised learning covering basic steps of the ML process. Being integrated into popular block-based programming languages (primarily Scratch and App Inventor), they also support the deployment of the created ML models as part of games or mobile applications. Findings indicate that the tools can effectively leverage students’ understanding of Machine Learning, however, further studies regarding the design of the tools concerning educational aspects are required to better guide their effective adoption in schools and their enhancement to support the learning process more comprehensively.

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Qualitative analysis of deep learning frameworks

Cited by 3 publications

References 20 publications

Comparative Analysis of Convolutional Neural Networks Applied in the Detection of Pneumonia Through X-Ray Images of Children

Comparative Analysis of Convolutional Neural Networks Applied in the Detection of Pneumonia Through X-Ray Images of Children

Evolving deep neural networks for Time Series Forecasting

Visual tools for teaching machine learning in K-12: A ten-year systematic mapping

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