Hadoop based Feature Selection and Decision Making Models on Big Data

Bikku, Thulasi; Rao, N. Sambasiva; Akepogu, Ananda Rao

doi:10.17485/ijst/2016/v9i10/88905

Cited by 20 publications

(8 citation statements)

References 8 publications

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“…Reduce stage is a combination of shuffling and merging. All intermediate values associated with the same key are merged in reduce phase [8]. The following Figure 3 represents the internal working of MapReduce based C5.0 Classification.…”

Section: Mapreduce Based C50 Classificationmentioning

confidence: 99%

Classifying Agricultural Crop PestData Using Hadoop MapReduceBased C5.0 Algorithm

Revathy

Balamurali

Lawrance³

2019

JCSM

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Data mining is a methodology of exploring and processing large pre-existing databases in order to find the hidden information. In agriculture sector, data mining can help farmers to develop yield. Crops can be protected from vertebrate pests and diseases by predicting and enhancing crop cultivation through efficient data mining methods. The main aim of this research is to classify agricultural crop pests which are categorized by different colours. This research includes data cleaning, feature selection and execution of C5.0 algorithm using map reduce. Data cleaning has taken away the noisy data in crop pest data that offers improved accuracy. In feature selection, Relief filter is applied for selecting particular attributes of the crop pest data set instead of using full attribute set. It performs choosing attributes by calculating the attribute weights based upon distances. As the size of the pest dataset has attained terabyte range, typical data mining techniques cannot process the big data at logical time. Hadoop MapReduce programming model has been put into practice to compact with huge data set. It is a software framework for distributed processing of large amount of data. This research work proposed

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Section: Mapreduce Based C50 Classificationmentioning

confidence: 99%

Classifying Agricultural Crop PestData Using Hadoop MapReduceBased C5.0 Algorithm

Revathy

Balamurali

Lawrance³

2019

JCSM

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“…The real parts for Hadoop VIRTUALIZATION are like the equipment assets required for nonexclusive processing [10]. The foundation manages the processor the executives, memory the executives, fringe the board, arrange the executives and capacity the board [11]. The capacity the executives for virtual machines assumes the significant job for execution improvements.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hadoop Based Virtualization Automated Framework

Deol*,

Nagaraju

2019

IJITEE

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With the huge development in relocation to distributed computing for customary applications, the interest inHadoop VIRTUALIZATION is the new requirement for further research and improvements. The extreme interest for Hadoop VIRTUALIZATION in distributed computing is been tended to with the innovation improvement and regarding virtual machines. Significantly the equipment segments the executives of virtual machine is impressively achieving the pick of research with the ongoing headways by different organizations and open source explores like AlphaVM, Hyper-V, Integrity Virtual Machines, JPC (Virtual Machine), PowerVM, Sun xVM, VMware Workstation and z LPARs. Another most imperative segment of Hadoop VIRTUALIZATION and virtual machine picture stockpiling is been the bottleneck for the further headway. The impediments are being tended to by bunching and capacity territory organizing, anyway these current arrangements are no counterpart for the modern evaluation interest for the VM picture stockpiling necessities. Subsequently in this work we propose a cloud based virtual machine stockpiling structure intended for huge scale arrangement fulfilling alternate requirements for capacity highlights like replication and estimations of execution enhancements. This work additionally exhibits the headways in execution for Hadoop VIRTUALIZATION the board

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“…With an ever expanding universe of big data, ML needs to develop and progress with a specific end goal to change huge information into noteworthy insight. ML tends to the topic of how to manufacture a PC framework that enhances consequently through experience [1]. ML systems empower clients to reveal hidden structure and make forecasts from extensive datasets.…”

Section: Introductionmentioning

confidence: 99%

“…Infrequently in the wake of survey the information, we can't translate the example or concentrate data from the information. All things considered, we apply machine learning [1]. With the wealth of datasets accessible, the interest for machine learning is in rise.…”

Section: Introductionmentioning

confidence: 99%

A survey on big data analytics with deep learning in text using machine learning mechanisms

Anandan¹,

Bhyrapuneni²,

Kalaivani³

et al. 2018

IJET

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Big Data Analytics and Deep Learning are two immense purpose of meeting of data science. Big Data has ended up being major a tantamount number of affiliations both open and private have been gathering huge measures of room specific information, which can contain enduring information about issues, for instance, national cognizance, motorized security, coercion presentation, advancing, and healing informatics. Relationship, for instance, Microsoft and Google are researching wide volumes of data for business examination and decisions, influencing existing and future progression. Critical Learning figuring's isolate odd state, complex reflections as data outlines through another levelled learning practice. Complex reflections are learnt at a given level in setting of all around less asking for thoughts figured in the past level in the dynamic framework. An indispensable favoured perspective of Profound Learning is the examination and culture of beast measures of unconfirmed data, making it a fundamental contraption for Great Statistics Analytics where offensive data is, everything seen as, unlabelled and un-arranged. In the present examination, we investigate how Deep Learning can be used for keeping an eye out for some essential issues in Big Data Analytics, including removing complex cases from Big volumes of information, semantic asking for, information naming, smart data recovery, and streamlining discriminative errands .Deep learning using Machine Learning(ML) is continuously unleashing its power in a wide range of applications. It has been pushed to the front line as of late mostly attributable to the advert of huge information. ML counts have never been remarkable ensured while tried by gigantic data. Gigantic data engages ML counts to uncover more fine-grained cases and make more advantageous and correct gauges than whenever in late memory with deep learning; on the other hand, it exhibits genuine challenges to deep learning in ML, for instance, show adaptability and appropriated enlisting. In this paper, we introduce a framework of Deep learning in ML on big data (DLiMLBiD) to guide the discussion of its opportunities and challenges. In this paper, different machine learning algorithms have been talked about. These calculations are utilized for different purposes like information mining, picture handling, prescient examination, and so forth to give some examples. The fundamental favourable position of utilizing machine learning is that, once a calculation realizes what to do with information, it can do its work consequently. In this paper we are providing the review of different Deep learning in text using Machine Learning and Big data methods.

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Hadoop based Feature Selection and Decision Making Models on Big Data

Cited by 20 publications

References 8 publications

Classifying Agricultural Crop PestData Using Hadoop MapReduceBased C5.0 Algorithm

Classifying Agricultural Crop PestData Using Hadoop MapReduceBased C5.0 Algorithm

A Novel Hadoop Based Virtualization Automated Framework

A survey on big data analytics with deep learning in text using machine learning mechanisms

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