A survey of machine learning algorithms for big data analytics

Athmaja, S.; Hanumanthappa, M.; Kavitha, V.

doi:10.1109/iciiecs.2017.8276028

Cited by 65 publications

(41 citation statements)

References 12 publications

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“…IoT: Internet of things; PB: petabytes; ZB: zettabyte; EB: exabyte; IDC: international data corporation; AI: artificial intelligence; ML: machine learning; NLP: natural language processing; CI: computational intelligence; FSVM: fuzzy support vector machines; SVM: support vector machines; POS: part-of-speech; ICA: IBM content analytics; EAs: evolutionary algorithms; ANN: artificial neural networks. [65,66], Deep learning [15,63], Fuzzy sets [67], Feature selection [9,60,61] Learning from unlabeled data Active learning [65,66] Scalability Distributed learning [12,63] Deep learning [56] Natural language processing Keyword search Fuzzy, Bayesian [68,70,71] Ambiguity of words in POS ICA [73], LIBLINEAR and MNB algorithm [68] Classification (simplifying language assumption)…”

Section: Discussionmentioning

confidence: 99%

“…Transfer learning is the ability to apply knowledge learned in one context to new contexts, effectively improving a learner from one domain by transferring information from a related domain [64]. Active learning refers to algorithms that employ adaptive data collection [65] (i.e., processes that automatically adjust parameters to collect the most useful data as quickly as possible) in order to accelerate ML activities and overcome labeling problems. The uncertainty challenges of ML techniques can be mainly attributed to learning from data with low veracity (i.e., uncertain and incomplete data) and data with low value (i.e., unrelated to the current problem).…”

Section: Machine Learning and Big Datamentioning

confidence: 99%

“…Manually labeling large data collections can be an expensive and strenuous task, yet learning from unlabeled data is very difficult as classifying data with unclear guidelines yields unclear results. Active learning has solved this issue by selecting a subset of the most important instances for labeling [65,66]. Deep learning is another learning method that can handle incompleteness and inconsistency issues in the classification procedure [15].…”

Section: Machine Learning and Big Datamentioning

confidence: 99%

See 2 more Smart Citations

Uncertainty in big data analytics: survey, opportunities, and challenges

2019

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According to the National Security Agency, the Internet processes 1826 petabytes (PB) of data per day [1]. In 2018, the amount of data produced every day was 2.5 quintillion bytes [2]. Previously, the International Data Corporation (IDC) estimated that the amount of generated data will double every 2 years [3], however 90% of all data in the world was generated over the last 2 years, and moreover Google now processes more than 40,000 searches every second or 3.5 billion searches per day [2]. Facebook users upload 300 million photos, 510,000 comments, and 293,000 status updates per day [2, 4]. Needless to say, the amount of data generated on a daily basis is staggering. As a result, techniques are required to analyze and understand this massive amount of data, as it is a great source from which to derive useful information.

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Section: Discussionmentioning

confidence: 99%

Section: Machine Learning and Big Datamentioning

confidence: 99%

Section: Machine Learning and Big Datamentioning

confidence: 99%

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Uncertainty in big data analytics: survey, opportunities, and challenges

2019

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“…The rate of fast increase of data is what we characterize as the Velocity of Data. This feature has helped researchers immensely considering the fact that data acquisition now isnt as tedious as it used to be some years ago [1].…”

Section: Big Datamentioning

confidence: 99%

Big Data, Machine Learning and the BlockChain Technology: An Overview

Acheampong¹

2018

IJCA

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The importance of big data in machine learning cannot be overemphasized in recent times. Through the evolution of big data, most scientific technologies that relied heavily on enormous data in solving complex issues in human lives gained grounds; machine learning is an instance of these technologies. Various machine learning models that yield groundbreaking throughputs with high efficiency rates in predicting, detecting, classifying, discovering and acquiring in-depth knowledge about events that would otherwise be very difficult to ascertain have been made possible due to big data. Although big data has undoubtedly helped in the field of machine learning research ,over the years, its mode of acquisition has posed great challenge in industries,education and other agencies that obtained them for various purposes. This is because these large quantities of data cannot be stored on personal computers with limited storage capabicity but required the use of high storage capacity servers for effective storage. These servers may be owned by a group of companies or individuals who had the singular priviledge to modify the data in their possession as and when deemed relevant thus the creation of a centralized data storage environment. These were mostly refered to as the Third Parties (TP) in the data acquisition process. For the services they rendered, these trusted parties priced data in their possession expensively. The adverse effect is a limitation on various researches that could help solve a number of problems in human lives. It is worth mentioning that the security of these data being purchased expensively cannot be even assured limiting various researches that thrive on secured data. In order to curb these occurrences and have better machine learning models, the incorporation of Blockchain Technology databases into machine learning. This paper discusses the concept of big data, Machine Learning and Blockchains. It further discusses how Big data has impacted the Machine learning Community, the significance of Machine Learning and how the BlockChain Technology could be used similarly impact the Machine Learning Community. The aim of this paper is to encourge further research in incoporating the BlockChain Technology into Machine Learning.

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“…Medical Sciences is no exclusion to this relentless evolution. Internet, robots/ AI, and telemedicine have been very important in science when it is about adopting the medical science and profession with this trend; yet it is often argued that when it comes to critical thinking, no AI can beat the instincts of an experienced Doctor [1]. Insofar as "preparing for the future" is concerned, Data Analytics have proven a highly reliable source of information in a plethora of sciences, and since "all data is equal" for the AI, prognosis of health conditions and eventual epidemics using Big Data is particularly attainable, and immensely important.…”

Section: Introductionmentioning

confidence: 99%

Review of Prediction of Disease Trends using Big Data Analytics

Nagavci¹,

Hamiti²,

Selimi³

2018

ijacsa

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Abstract-Big Data technologies promise to have a transformative impact in healthcare, public health, and medical research, among other application areas. Several intelligent machine learning techniques were designed and used to provide big data predictive analytics solutions for different illness. Nevertheless, there is no published research for prediction of allergy and respiratory system diseases. However, the impact of research and the finding of different cases is conducive to progress and further development of this. One of the goals of this paper is to devise a systematic mapping study, to explore and analyze existing research about disease prediction in healthcare information. According to the realized investigation of published research from 2012 up to today, we are focusing our research on studies that have been published around big data analytics. With this high number of secondary studies, it is important to conduct a review and provide an overview of the research situation and current developments in this area.

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A survey of machine learning algorithms for big data analytics

Cited by 65 publications

References 12 publications

Uncertainty in big data analytics: survey, opportunities, and challenges

Uncertainty in big data analytics: survey, opportunities, and challenges

Big Data, Machine Learning and the BlockChain Technology: An Overview

Review of Prediction of Disease Trends using Big Data Analytics

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