An Empirical Evaluation of Intelligent Machine Learning Algorithms under Big Data Processing Systems

Suleiman, Dima; Al-Zewairi, Malek; Naymat, Ghazi Al

doi:10.1016/j.procs.2017.08.270

Cited by 9 publications

(10 citation statements)

References 3 publications

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“…The development of tools to help clinicians collect and process information is rapidly increasing and central to the innovation moving through perioperative medicine, including telemedicine. 1,2 Over the next decade, data tasks will be increasingly performed by computer-based systems, offering new, exciting pathways to improved care -if challenges that inevitably accompany could add value above their clinical decision-making skills [23][24][25] A recent work using EEG as the target to drive remifentanil and propofol titration during anesthetic cases argues that ML-based approaches could supplement our clinical decision trees. 26 However, there are areas of concern for the use of ML in anesthesiology and perioperative medicine due to its potential impact on patient safety and reliability, removing the autonomy of clinicians, and negative impact on clinical decision heuristics.…”

Section: Technology Innovations In Anesthesiologymentioning

confidence: 99%

Technology Innovations in Anesthesiology

Schnetz¹,

Kaynar²,

Esper³

et al. 2021

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Section: Technology Innovations In Anesthesiologymentioning

confidence: 99%

Technology Innovations in Anesthesiology

Schnetz¹,

Kaynar²,

Esper³

et al. 2021

ASA Monitor

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“…1). The region covers as area of 100.2 Km 2 that is located between 51°04′ 18″ and 51°11′ 04″ E, and between 35°25′ 48″ and 35°33′ 16" N. The selected region is almost flat with elevation varies between 1000 m and 1070 m above sea level (Sorkhabi 2017). As shown in Fig.…”

Section: Study Areamentioning

confidence: 99%

“…This study used a trial and error method to tune (adjust) the networks (Kalogirou 2009;Triyason et al 2015). To illustrate, we examined the performance of Gradient descent with momentum and adaptive learning rate backpropagation (GDX), Levenberg-Marquardt (LM) and Bayesian regularization (BR) to train the networks (Asadisaghandi & Tahmasebi 2011;Suleiman et al 2017). Briefly, the GDX algorithm uses backpropagation to calculate the derivatives of performance cost function regarding the weight and bias variables of the network.…”

Section: Spectral Range Determination and Band Selectionmentioning

confidence: 99%

Spectral enhancement of Landsat OLI images by using Hyperion data: a comparison between multilayer perceptron and radial basis function networks

2020

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The deactivation of Earth Observing-1 satellite has resulted in the termination of capturing Hyperion data as a unique source of hyperspectral satellite imagery. These images also were collected through an on-demand service and thereby are not available for the entire Earth's surface. The Operational Land Imager (OLI) sensor, on the other hand, provides a free source of multi-spectral images with global coverage. Recognized these facts, the aim of this paper is to enhance the spectral resolution of OLI images by using existing Hyperion imageries to generate a high spectral resolution image for a desired date and site. This was conducted through the artificial neural network (ANN). To find the suitable ANN, we compared the performance of multilayer perceptron (MLP) and radial basis function (RBF) networks for spectral enhancement.The research obtained two Hyperion and OLI images covering West Region of Tehran, Iran, on 4 January 2016. From 242 original Hyperion spectral bands, we selected 31 bands to reproduce from OLI spectral bands. These were determined through visual inspection, principal component analysis and Pearson's correlation test. The MLP and RBF networks were generated based on the OLI bands 1-7 and per 31 Hyperion bands as input and output layers respectively. The comparison between the spectral bands of spectra-enhanced image and original Hyperion data indicated a good agreement (0.884 > R 2 > 0.692). This study also found MLP network delivered higher accuracy against RBF network for spectral enhancement. The spectra-enhanced image can be used in studies with the need of images with continuous spectral bands.

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“…The dataset was published on the Kaggle website 2 as part of a public competition to come up with a recommendation system to predict-product(s) existing customers of the Santander Bank could buy. The dataset consists of forty-eight features and 13,647,310 instances; the first twenty-four features are personal data; while the last twenty-four features are financial products provided by the bank [21].…”

Section: Santander Bank Datasetmentioning

confidence: 99%

Empirical Evaluation of the Classification of Deep Learning under Big Data Processing Platforms

2020

IJATCSE

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A significant amount of data produced by industries must be processed by machine learning algorithms to facilitates the decision-making process. Traditional machine learning platforms cannot handle the data characterized by their volume, variety, and velocity. Several machine learning toolkits have recently been developed to manage big data. This paper examines the efficiency of H2O and SparklingWater machine learning platforms, dedicated to the processing of big data, in terms of training time and error metrics. Experiments are conducted on four datasets which are the Santander Bank, Credit Card Fraud Detection Dataset, p53 Mutants, and Poker Hand Datasets. The evaluation results show that the H2O platform significantly outperforms the SparklingWater platform in terms of model training time by almost fifty percent thus achieving convergent results.

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An Empirical Evaluation of Intelligent Machine Learning Algorithms under Big Data Processing Systems

Cited by 9 publications

References 3 publications

Technology Innovations in Anesthesiology

Technology Innovations in Anesthesiology

Spectral enhancement of Landsat OLI images by using Hyperion data: a comparison between multilayer perceptron and radial basis function networks

Empirical Evaluation of the Classification of Deep Learning under Big Data Processing Platforms

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