Machine Learning in Big Data

Wang, Lidong

doi:10.11591/ijaas.v4.i4.pp117-123

Cited by 14 publications

(9 citation statements)

References 7 publications

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“…These features may also be combined with clinical features to develop prediction models. Various machine algorithms have been used to develop a prediction model depending upon the need i.e., regression algorithms, Linear and Logistic regression, K-Nearest Neighbor, decision trees algorithms, i.e., Random Forest, Support Vector Machine, Bayesian Network, and deep learning algorithms, i.e., Convolutional Neural Networks, Recurrent Neural Networks and Artificial Neural Networks [22][23][24].…”

Section: Prediction Model Developmentmentioning

confidence: 99%

Radiomics: a quantitative imaging biomarker in precision oncology

Jha

Mithun

Purandare

et al. 2022

Nuclear Medicine Communications

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Cancer treatment is heading towards precision medicine driven by genetic and biochemical markers. Various genetic and biochemical markers are utilized to render personalized treatment in cancer. In the last decade, noninvasive imaging biomarkers have also been developed to assist personalized decision support systems in oncology. The imaging biomarkers i.e., radiomics is being researched to develop specific digital phenotype of tumor in cancer. Radiomics is a process to extract high throughput data from medical images by using advanced mathematical and statistical algorithms. The radiomics process involves various steps i.e., image generation, segmentation of region of interest (e.g. a tumor), image preprocessing, radiomic feature extraction, feature analysis and selection and finally prediction model development. Radiomics process explores the heterogeneity, irregularity and size parameters of the tumor to calculate thousands of advanced features. Our study investigates the role of radiomics in precision oncology. Radiomics research has witnessed a rapid growth in the last decade with several studies published that show the potential of radiomics in diagnosis and treatment outcome prediction in oncology. Several radiomics based prediction models have been developed and reported in the literature to predict various prediction endpoints i.e., overall survival, progression-free survival and recurrence in various cancer i.e., brain tumor, head and neck cancer, lung cancer and several other cancer types. Radiomics based digital phenotypes have shown promising results in diagnosis and treatment outcome prediction in oncology. In the coming years, radiomics is going to play a significant role in precision oncology.

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Section: Prediction Model Developmentmentioning

confidence: 99%

Radiomics: a quantitative imaging biomarker in precision oncology

Jha

Mithun

Purandare

et al. 2022

Nuclear Medicine Communications

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“…The deep learning technique is part of a set of machine learning methods based on artificial neural networks (LeCun et al, 2015). According to Wang and Alexander (2016), one can use machine learning to process large volumes of structured data and unstructured data that are difficult to process using traditional database and software techniques.…”

Section: Deep Learningmentioning

confidence: 99%

Automatic analysis of magnetograms for identification and classification of active regions using Deep Learning

Oliveira¹,

Gradvohl²

2020

RBCA

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Some phenomena that occur in the Sun have consequences on Earth. Among these phenomena, solar flares release large amounts of radiation and energy that impact on Earth's life and technological systems. These flares usually come from sunspots, which derive from solar magnetic activities. One strategy to predict solar flares is to identify active regions, i. e., a group of sunspots with a high potential to cause solar flares. This paper reports the use of the deep learning technique to identify and classify active regions from magnetogram analysis. To achieve these tasks, we created a dataset with magnetograms and performed tests to choose the best deep learning models for the identification and classification of active regions. The results of the best models reached accuracies higher than 80% for both the identification and classification tasks. Based on these results, we implemented a system in Python to automate the complete identification and classification process, also reported in this paper.

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“…Machine learning is the use of computers to intelligently discover knowledge for the purpose of making decisions [2]. It is grouped into two main types, namely supervised learning and unsupervised learning.…”

Section: Machine Learningmentioning

confidence: 99%

A Survey of Machine Learning’s Electricity Consumption Models

Farouk¹,

Asante²,

Ben³

2018

CAE

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Electricity is a very important commodity used for both domestic and industrial purposes. It is generated from many sources which include the thermal, coal, nuclear and hydro. Its demand is increasing on regular basis as result of the ever increasing world population coupled with other socioeconomic factors. This therefore requires effective predictions of the future needed electricity to sustain it demand. However, predicting the exact amount of electricity for all times is a challenge. Over predictions can lead to wasteful investment whiles under predictions can lead to inadequate electricity supply with eventual blackouts, social unrest and low economic growth. The aim of this paper is to present the various electricity consumption predictions models indicating the machine learning algorithm and the variables used in the modeling

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Machine Learning in Big Data

Cited by 14 publications

References 7 publications

Radiomics: a quantitative imaging biomarker in precision oncology

Radiomics: a quantitative imaging biomarker in precision oncology

Automatic analysis of magnetograms for identification and classification of active regions using Deep Learning

A Survey of Machine Learning’s Electricity Consumption Models

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