Large-scale machine learning based on functional networks for biomedical big data with high performance computing platforms

Elsebakhi, Emad; Lee, Frank; Schendel, Eric R.; Haque, Anwar; Kathireason, Nagarajan; Pathare, Tushar; Syed, Najeeb; Al‐Ali, Rashid

doi:10.1016/j.jocs.2015.09.008

Cited by 60 publications

(31 citation statements)

References 8 publications

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“…Specifically, "Data pre-processing studies" section contains the description for those techniques Data pre-processing [41][42][43][44][45][46][47][48] Cost-sensitive learning [41,[49][50][51] Applications on imbalanced Big Data [52][53][54] related to data pre-processing. "Cost-sensitive learning studies" section those approaches that carry out an algorithmic modification by means of a cost-sensitive learning.…”

Section: Addressing Imbalanced Classification In Big Data Problems: Cmentioning

confidence: 99%

See 1 more Smart Citation

An insight into imbalanced Big Data classification: outcomes and challenges

Fernández

Río

Chawla

et al. 2017

Complex Intell. Syst.

171

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Big Data applications are emerging during the last years, and researchers from many disciplines are aware of the high advantages related to the knowledge extraction from this type of problem. However, traditional learning approaches cannot be directly applied due to scalability issues. To overcome this issue, the MapReduce framework has arisen as a "de facto" solution. Basically, it carries out a "divide-andconquer" distributed procedure in a fault-tolerant way to adapt for commodity hardware. Being still a recent discipline, few research has been conducted on imbalanced classification for Big Data. The reasons behind this are mainly the difficulties in adapting standard techniques to the MapReduce programming style. Additionally, inner problems of imbalanced data, namely lack of data and small disjuncts, are accentuated during the data partitioning to fit the MapReduce programming style. This paper is designed under three main pillars. First, to present the first outcomes for imbalanced classification in Big Data problems, introducing the current

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Section: Addressing Imbalanced Classification In Big Data Problems: Cmentioning

confidence: 99%

“…A large-scale machine learning classifier based on functional networks was used in [54] for the classification of biomedical data. In their methodology, an iterative process is carried out to sample the data in a MapReduce sampling strategy prior to the learning stage of the model until the accuracy reaches a stable value.…”

Section: Biomedical Datamentioning

confidence: 99%

An insight into imbalanced Big Data classification: outcomes and challenges

Fernández

Río

Chawla

et al. 2017

Complex Intell. Syst.

171

View full text Add to dashboard Cite

show abstract

“…Enormous Scale Machine Learning dependent on Functional Networks for Biomedical Big Data with High Performance Computing Platforms [3] by Elsebakhi, E proposed a system utilitarian systems dependent on penchant score and Newton Raphson-most extreme probability enhancements as another huge scale AI classifier. The athor proposed technique which spares the computational time and have dependable exhibitions alongside future road for augmentation to manage cutting edge sequencing information on superior registering stages.…”

Section: A Enormous Data Classificationmentioning

confidence: 99%

“…For our situation, we are investigating the apache flash as the huge information examination apparatus for security examination [21]. MapReduce and Spark are huge scale stages supporting a few calculations, for example, hereditary calculations and molecule swarm improvement, among others [3] [13]. MapReduce is a proficient parallel programming model for the information escalated applications for handling enormous informational indexes.…”

Section: Introductionmentioning

confidence: 99%

The Deep Learning and Apache Spark Enabled Architecture for Improving the Performance of Big Data Classification

Brahmane¹,

Krishna²

2019

IJITEE

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At present the Big Data applications, for example, informal communication, therapeutic human services, horticulture, banking, financial exchange, instruction, Facebook and so forth are producing the information with extremely rapid. Volume and Velocity of the Big information assumes a significant job in the presentation of Big information applications. Execution of the Big information application can be influenced by different parameters. Expediently search, proficiency and precision are the a portion of the overwhelming parameters which influence the general execution of any Big information applications. Due the immediate and aberrant inclusion of the qualities of 7Vs of Big information, each Big Data administrations anticipate the elite. Elite is the greatest test in the present evolving situation. In this paper we propose the Big Data characterization way to deal with speedup the Big Data applications. This paper is the review paper, we allude different Big information advancements and the related work in the field of Big Data Classification. In the wake of learning and understanding the writing we discover the holes in existing work and techniques. Finally we propose the novel methodology of Big Data characterization. Our methodology relies on the Deep Learning and Apache Spark engineering. In the proposed work two stages are appeared; first stage is include choice and second stage is Big Data Classification. Apache Spark is the most reasonable and predominant innovation to execute this proposed work. Apache Spark is having two hubs; introductory hubs and last hubs. The element choice will be occur in introductory hubs and Big Data Classification will happen in definite hubs of Apache Spark.

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“…This method is useful in a distributed heterogeneous data environment and is beneficial for healthcare support service providers to access large ranges of data sources in the mobile application environment. In 2015, Elsebakshi et al [51] proposed a function network based optimization method for large scale medical data.…”

Section: Cloud Computing In the Field Of Bioinformaticsmentioning

confidence: 99%

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

Sandhu

Gill

Sood

2016

Journal of Computational Science

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a b s t r a c t H1N1 is an infectious virus which, when spread affects a large volume of the population. It is an airborne disease that spreads easily and has a high death rate. Development of healthcare support systems using cloud computing is emerging as an effective solution with the benefits of better quality of service, reduced costs and flexibility. In this paper, an effective cloud computing architecture is proposed which predicts H1N1 infected patients and provides preventions to control infection rate. It consists of four processing components along with secure cloud storage medical database. The random decision tree is used to initially assess the infection in any patient depending on his/her symptoms. Social Network Analysis (SNA) is used to present the state of the outbreak. The proposed architecture is tested on synthetic data generated for two million users. The system provided 94% accuracy for the classification and around 81% of the resource utilization on Amazon EC2 cloud. The key point of the paper is the use of SNA graphs to calculate role of an infected user in spreading the outbreak known as Outbreak Role Index (ORI). It will help government agencies and healthcare departments to present, analyze and prevent outbreak effectively.

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Large-scale machine learning based on functional networks for biomedical big data with high performance computing platforms

Cited by 60 publications

References 8 publications

An insight into imbalanced Big Data classification: outcomes and challenges

An insight into imbalanced Big Data classification: outcomes and challenges

The Deep Learning and Apache Spark Enabled Architecture for Improving the Performance of Big Data Classification

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

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