A survey of open source tools for machine learning with big data in the Hadoop ecosystem
With an ever-increasing amount of options, the task of selecting machine learning tools for
big data can be difficult. The available tools have advantages and drawbacks, and many …
big data can be difficult. The available tools have advantages and drawbacks, and many …
Higgs boson discovery using machine learning methods with pyspark
Higgs Boson is an elementary particle that gives the mass to everything in the natural world.
The discovery of the Higgs Boson is a major challenge for particle physics. This paper …
The discovery of the Higgs Boson is a major challenge for particle physics. This paper …
[HTML][HTML] Predicting preeclampsia and related risk factors using data mining approaches: A cross-sectional study
Z Manoochehri, S Manoochehri, F Soltani… - International Journal …, 2021 - ncbi.nlm.nih.gov
Background Preeclampsia is a type of pregnancy hypertension disorder that has adverse
effects on both the mother and the fetus. Despite recent advances in the etiology of …
effects on both the mother and the fetus. Despite recent advances in the etiology of …
Scalable proximal methods for cause-specific hazard modeling with time-varying coefficients
Survival modeling with time-varying coefficients has proven useful in analyzing time-to-event
data with one or more distinct failure types. When studying the cause-specific etiology of …
data with one or more distinct failure types. When studying the cause-specific etiology of …
Performance evaluation of data-driven intelligent algorithms for big data ecosystem
Artificial intelligence, specifically machine learning, has been applied in a variety of methods
by the research group to transform several data sources into valuable facts and …
by the research group to transform several data sources into valuable facts and …
A scalable approach for sentiment analysis of Turkish tweets and linking tweets to news
We present a framework for sentiment analysis on tweets related to news items. Given a set
of tweets and news items, our framework classifies tweets as positive or negative and links …
of tweets and news items, our framework classifies tweets as positive or negative and links …
Attribute dependency data analysis for massive datasets by fuzzy transforms
We present a numerical attribute dependency method for massive datasets based on the
concepts of direct and inverse fuzzy transform. In a previous work, we used these concepts …
concepts of direct and inverse fuzzy transform. In a previous work, we used these concepts …
Architectures of big data analytics: scaling out data mining algorithms using Hadoop–MapReduce and Spark
S Kamaruddin, V Ravi - 2021 - IET
Many statistical and machine learning (ML) techniques have been successfully applied to
small-sized datasets during the past one and half decades. However, in today's world …
small-sized datasets during the past one and half decades. However, in today's world …
A Human-in-the-Loop Anomaly Detection Architecture for Big Traffic Data of Cellular Network
S Liu, Y **a, D Wang - IEEE Access, 2024 - ieeexplore.ieee.org
In the era of mobile big data, smart mobile devices have become an integral part of our daily
life, which brings many benefits to the digital society. However, their popularity and relatively …
life, which brings many benefits to the digital society. However, their popularity and relatively …
An implementation of GPU accelerated mapreduce: using hadoop with openCL for breast cancer detection and compute-intensive jobs
H Ouhakki, A Elmoufidi - International Journal of Information Technology, 2024 - Springer
Abstract-In the realm of distributed computing for large-scale data processing, MapReduce
stands out for its efficiency. However, as tasks become increasingly compute-intensive, it …
stands out for its efficiency. However, as tasks become increasingly compute-intensive, it …