scholarly journals A Survey on Machine Learning-Based Mobile Big Data Analysis: Challenges and Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Jiyang Xie ◽  
Zeyu Song ◽  
Yupeng Li ◽  
Yanting Zhang ◽  
Hong Yu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
State Of The Art ◽  
Channel Modeling ◽  
Big Data Analysis ◽  
Wireless Channel ◽  
Internet Of Vehicles ◽  
Mobile Big Data ◽  
Data Analysis Methods

This paper attempts to identify the requirement and the development of machine learning-based mobile big data (MBD) analysis through discussing the insights of challenges in the mobile big data. Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently applied data analysis methods are reviewed. Three typical applications of MBD analysis, namely, wireless channel modeling, human online and offline behavior analysis, and speech recognition in the Internet of Vehicles, are introduced, respectively. Finally, we summarize the main challenges and future development directions of mobile big data analysis.

scholarly journals Big Data Analysis Methods Based on Machine Learning to Ensure Information Security

2021 ◽  
Vol 192 ◽  
pp. 2633-2640
Author(s):  
Veselska Olga ◽  
Ziubina Ruslana ◽  
Fіnenko Yuriy ◽  
Nikodem Joanna
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Information Security ◽  
Big Data Analysis ◽  
Analysis Methods ◽  
Data Analysis Methods

The Rain Prediction in Australia Based Big Data Analysis and Machine Learning Technology

2021 ◽  
Author(s):  
Bohdan Polishchuk ◽  
Andrii Berko ◽  
Lyubomyr Chyrun ◽  
Myroslava Bublyk ◽  
Vadim Schuchmann
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Learning Technology

scholarly journals An Automated Analytics Engine for College Program Selection using Machine Learning and Big Data Analysis

2021 ◽  
Author(s):  
Jinhui Yu ◽  
Xinyu Luan ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Web Pages ◽  
Python Language ◽  
Program Selection ◽  
Front End

Because of the differences in the structure and content of each website, it is often difficult for international applicants to obtain the application information of each school in time. They need to spend a lot of time manually collecting and sorting information. Especially when the information of the school may be constantly updated, the information may become very inaccurate for international applicants. we designed a tool including three main steps to solve the problem: crawling links, processing web pages, and building my pages. In compiling languages, we mainly use Python and store the crawled data in JSON format [4]. In the process of crawling links, we mainly used beautiful soup to parse HTML and designed crawler. In this paper, we use Python language to design a system. First, we use the crawler method to fetch all the links related to the admission information on the school's official website. Then we traverse these links, and use the noise_remove [5] method to process their corresponding page contents, so as to further narrow the scope of effective information and save these processed contents in the JSON files. Finally, we use the Flask framework to integrate these contents into my front-end page conveniently and efficiently, so that it has the complete function of integrating and displaying information.

Techniques and Methods That Help to Make Big Data the Simplest Recipe for Success

2019 ◽  
pp. 161-194
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Data Analytics ◽  
Big Data Analysis ◽  
Customer Segmentation ◽  
Learning Context ◽  
Feature Vectors

Data analytics has grown in a machine learning context. Whatever the reason data is used or exploited, customer segmentation or marketing targeting, it must be processed first and represented on feature vectors. Many algorithms, such as clustering, regression, classification, and others, need to be represented and clarified in order to facilitate processing and statistical analysis. If we have seen, through the previous chapters, the importance of big data analysis (the Why?), as with every major innovation, the biggest confusion lies in the exact scope (What?) and its implementation (How?). In this chapter, we will take a look at the different algorithms and techniques analytics that we can use in order to exploit the large amounts of data.

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