Automatic Intelligent Korean Character Semantic Recognition and Analysis Framework based on Machine Learning

2020 ◽  
Author(s):  
Yunpeng Dou
Keyword(s):  
Machine Learning ◽  
Analysis Framework

scholarly journals Machine Learning Based Automotive Forensic Analysis for Mobile Applications Using Data Mining

2015 ◽  
Vol 16 (2) ◽  
pp. 350
Author(s):  
MD. Hussain Khan ◽  
G. Pradeepini
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Mobile Apps ◽  
Forensic Analysis ◽  
Evaluation Framework ◽  
Security Evaluation ◽  
Analysis Framework ◽  
Security Issues ◽  
The People ◽  
Using Data

<p>Phone is a device which provides communication between the people through voice, text, video etc. Now a day’s people may leave without food but not without using phones. No of operating systems are working with various versions and various security issues are working. Security is very important task in Mobiles and mobile apps. To improve the security status of mobiles, existing methodology is using cloud computing and data mining. Out traditional method is named as MobSafe to identify the mobile apps antagonism or graciousness. In the proposed system, we adopt Android Security Evaluation Framework (ASEF) and Static Android Analysis Framework (SAAF).In this paper, our proposed system works on machine learning to conduct automotive forensic analysis of mobile apps based on the generated multifaceted data in this stage.</p>

scholarly journals The use of Machine Learning Techniques in a Web-Based Learning Diagnosis System Program

2020 ◽  
Vol 8 (6) ◽  
pp. 4861-4865
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Analysis Framework ◽  
Web Based ◽  
Web Based Learning ◽  
Learning Techniques ◽  
Insightful Analysis ◽  
System Program

This work proposes a canny learning finding framework that bolsters a Web-based topical learning model, which expects to develop students' capacity of information incorporation by giving the students the chances to choose the learning themes that they are intrigued, and gain information on the particular subjects by surfing on the Internet to look through related adapting course-product and examining what they have realized with their associates. In view of the log documents that record the students' past web-based learning conduct, an insightful analysis framework is utilized to give fitting learning direction to help the students in improving their investigation practices and grade online class interest for the teacher. The accomplishment of the students' last reports can likewise be anticipated by the conclusion framework precisely. Our trial results uncover that the proposed learning finding framework can proficiently assist students with expanding their insight while surfing in the internet Web-based "topic based learning" model.

A Sentiment Analysis Framework on COVID-19 in Major Cities of Malaysia based on Tweets using Machine Learning Classification Model

2021 ◽  
Author(s):  
Raihah Aminuddin ◽  
Muhammad Akmal Bistamam ◽  
Shafaf Ibrahim ◽  
Nur Nabilah Abu Mangshor ◽  
Siti Feirusz Ahmad Fesol ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Classification Model ◽  
Analysis Framework ◽  
Machine Learning Classification

scholarly journals Grounded reality meets machine learning: A deep-narrative analysis framework for energy policy research

2020 ◽  
Vol 69 ◽  
pp. 101704 ◽  
Author(s):  
Ramit Debnath ◽  
Sarah Darby ◽  
Ronita Bardhan ◽  
Kamiar Mohaddes ◽  
Minna Sunikka-Blank
Keyword(s):  
Machine Learning ◽  
Energy Policy ◽  
Narrative Analysis ◽  
Policy Research ◽  
Analysis Framework

scholarly journals Machine learning based distributed big data analysis framework for next generation web in IoT

2021 ◽  
Vol 18 (2) ◽  
pp. 597-618
Author(s):  
Sushil Singh ◽  
Jeonghun Cha ◽  
Tae Kim ◽  
Jong Park
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Cloud Layer ◽  
Analysis Framework ◽  
Next Generation ◽  
Distributed Framework ◽  
Data Scaling

For the advancement of the Internet of Things (IoT) and Next Generation Web, various applications have emerged to process structured or unstructured data. Latency, accuracy, load balancing, centralization, and others are issues on the cloud layer of transferring the IoT data. Machine learning is an emerging technology for big data analytics in IoT applications. Traditional data analyzing and processing techniques have several limitations, such as centralization and load managing in a massive amount of data. This paper introduces a Machine Learning Based Distributed Big Data Analysis Framework for Next Generation Web in IoT. We are utilizing feature extraction and data scaling at the edge layer paradigm for processing the data. Extreme Learning Machine (ELM) is adopting in the cloud layer for classification and big data analysis in IoT. The experimental evaluation demonstrates that the proposed distributed framework has a more reliable performance than the traditional framework.

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