Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications

Carneiro, Tiago; Nóbrega, Raul Victor Medeiros da; Nepomuceno, Thiago; Bian, Gui‐Bin; Albuquerque, Victor Hugo C. de; Gupta, Deepak

doi:10.1109/access.2018.2874767

Cited by 453 publications

(233 citation statements)

References 15 publications

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“…This section presents and analyzes the results obtained through the proposed approach described in detail in Section 4 . All the proposed approach procedures have been executed on Google Colaboratory [48] and implemented using Python with Keras [49] . Keras is a high-level neural network API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.…”

Section: Experiments Resultsmentioning

confidence: 99%

An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization

Ezzat

Hassanien

Ella

2021

Applied Soft Computing

155

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In this paper, a novel approach called GSA-DenseNet121-COVID-19 based on a hybrid convolutional neural network (CNN) architecture is proposed using an optimization algorithm. The CNN architecture that was used is called DenseNet121, and the optimization algorithm that was used is called the gravitational search algorithm (GSA). The GSA is used to determine the best values for the hyperparameters of the DenseNet121 architecture. To help this architecture to achieve a high level of accuracy in diagnosing COVID-19 through chest x-ray images. The obtained results showed that the proposed approach could classify 98.38% of the test set correctly. To test the efficacy of the GSA in setting the optimum values for the hyperparameters of DenseNet121. The GSA was compared to another approach called SSD-DenseNet121, which depends on the DenseNet121 and the optimization algorithm called social ski driver (SSD). The comparison results demonstrated the efficacy of the proposed GSA-DenseNet121-COVID-19. As it was able to diagnose COVID-19 better than SSD-DenseNet121 as the second was able to diagnose only 94% of the test set. The proposed approach was also compared to another method based on a CNN architecture called Inception-v3 and manual search to quantify hyperparameter values. The comparison results showed that the GSA-DenseNet121-COVID-19 was able to beat the comparison method, as the second was able to classify only 95% of the test set samples. The proposed GSA-DenseNet121-COVID-19 was also compared with some related work. The comparison results showed that GSA-DenseNet121-COVID-19 is very competitive.

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Section: Experiments Resultsmentioning

confidence: 99%

An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization

Ezzat

Hassanien

Ella

2021

Applied Soft Computing

155

View full text Add to dashboard Cite

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“…Another advantage is that PyRosetta can be accessed through the Google Colaboratory online web browser, which requires no local computer installation and can quickly integrate opensource packages. In addition to its accessibility, Google Colaboratory provides students with free and powerful computational resources (45). These advantages address the current technological challenges with the current resources for PyRosetta.…”

Section: Discussionmentioning

confidence: 99%

“…Google Colaboratory is an online web environment for Jupyter Notebooks on a cloud-based virtual machine accessible with any browser. Google Colaboratory provides students with powerful computational resources, including 13 GB of RAM, 33 GB of disk space, and continuous sessions of up to 12 hours (45). While Jupyter Notebooks have been used for engineering education (52,53), Google Colaboratory offers a few advantages for studying biomolecular modeling, starting with the free in-the-cloud computing power.…”

Section: B Students Can Access the Multimedia Pyrosetta Workhops Onmentioning

confidence: 99%

PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design

Le¹,

Adolf-Bryfogle²,

Klima³

et al. 2020

Preprint

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Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of fifteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.

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“…Signal processing operations were performed in Matlab 2017 to obtain time-frequency scalograms for each subcarrier. Deep transfer learning was performed on the extracted features using Tensorflow [37] and Keras [38] on Google colab [39] with the help of K40 Nvidia graphics processing units. The training and testing procedure was based on hold out validation with 80% data for training and 20% for testing.…”

Section: Methodsmentioning

confidence: 99%

WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning

et al. 2019

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Freezing of Gait (FOG) is an episodic absence of forward movement in Parkinson's Disease (PD) patients and represents an onset of disabilities. FOG hinders daily activities and increases fall risk. There is high demand for automating the process of FOG detection due to its impact on health and well being of individuals. This work presents WiFreeze, a noninvasive, line of sight, and lighting agnostic WiFi-based sensing system, which exploits ambient 5G spectrum for detection and classification of FOG. The core idea is to utilize the amplitude variations of wireless Channel State Information (CSI) to differentiate between FOG and activities of daily life. A total of 225 events with 45 FOG cases are captured from 15 patients with the help of 30 subcarriers and classification is performed with a deep neural network. Multiresolution scalograms are proposed for time-frequency signatures of human activities, due to their ability to capture and detect transients in CSI signals caused by transitions in human movements. A very deep Convolutional Neural Network (CNN), VGG-8K, with 8K neurons each, in fully connected layers is engineered and proposed for transfer learning with multiresolution scalogram features for detection of FOG. The proposed WiFreeze system outperforms all existing wearable and vision-based systems as well as deep CNN architectures with the highest accuracy of 99.7% for FOG detection. Furthermore, the proposed system provides the highest classification accuracies of 94.3% for voluntary stop and 97.6% for walking slow activities, with improvements of 9% and 23%, respectively, over the best performing state-of-the-art deep CNN architecture.China [3]. Every 1 in 37 people in the United Kingdom are diagnosed with PD at some stage of their life [1]. PD is caused by the neuronal loss and damage in the motor area of the brain, particularly the centers of the limbic, visceromotor, and somatomotor systems [4]. Most PD patients experience difficulty in walking, which indicates an onset of gait, balance, and other disabilities [5]. Half of the PD patients experience Freezing of Gate (FOG) symptoms, which represent an episodic absence of forward feet movement, despite of the intention for forward progress [6]. The frequency as well as the duration of FOG episodes increases with the progression of PD. FOG and falls are both interconnected phenomena, and FOG is known to hinder daily activities and increase fall risk [7], resulting in severe consequences for older adults.Conventional clinical methods to validate FOG occurrences and their severity are based on self-report questionnaires [8] and doctors' reports based on direct observation. Due to the impact of FOG on population in general and falls in older adults in particular, there is a high demand for automating the process of detection and recognition of FOG events. Currently, various methods using wearable devices and vision-based systems have been exploited for FOG detection. A number of systems have been proposed with wearable sensors or cameras for FOG detectio...

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Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications

Cited by 453 publications

References 15 publications

An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization

An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization

PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design

WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning

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