A Machine Learning Algorithm for Classification, Analyzation and Prediction of Multimedia Messages in Social Networks

2020 ◽  
pp. 485-499
Author(s):  
U. Tanuja ◽  
H. L. Gururaj ◽  
V. Janhavi
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

Traffic Analysis Based on Short Texts from Social Media

2016 ◽  
Vol 7 (1) ◽  
pp. 63-79 ◽  
Author(s):  
Ana Maria Magdalena Saldana-Perez ◽  
Marco Moreno-Ibarra
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Mexico City ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Economic Issues ◽  
Social Events ◽  
Social Environmental ◽  
Real Time Information ◽  
Time Information

Social networks provide information about activities of humans and social events. Thus, with the help of social networks, we can extract the traffic events that occur in a city. In the context of an urban area, this kind of data allows to obtaining contextual real-time information shared among citizens that will be useful to address social, environmental and economic issues. In this paper, the authors describe a methodology to obtain information related to traffic events such as accidents or congestion, from Twitter messages and RSS services. A text mining process is applied on the messages to acquire the relevant data, then data are classified by using a machine learning algorithm. The events are geocoded and transformed into geometric points to be represented on a map. The final repository lets data to be available for further works related to the traffic events on the study area. As a case of study we consider Mexico City.

scholarly journals Identification of Fake Accounts for Making Trust on Network using Machine Learning

2019 ◽  
Vol 8 (2S3) ◽  
pp. 796-800
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
The Novel ◽  
Related Information ◽  
Legitimate User ◽  
The People ◽  
Novel Approach ◽  
The Social

Social Networks are gradually influencing the people to communicate with each other and share the personal, public related information. Different social networks have different target people. In particular Facebook used to establish the friendship, LinkedIn to find new Job, these Rabid growth of social networks, people tend to misuse the social networks to spoil others reputation or to steal the others information’s. Fake profiles are dangerous in social networks platform. It is essential to identify the fake users from social networks. This work presents the novel approach to predict and differentiate the fake user and legitimate user from social networks by using Machine Learning algorithm and we achieved significant results.

Traffic Analysis Based on Short Texts From Social Media

Crowdsourcing ◽  
2019 ◽  
pp. 819-837
Author(s):  
Ana Maria Magdalena Saldana-Perez ◽  
Marco Moreno-Ibarra
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Mexico City ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Economic Issues ◽  
Social Events ◽  
Social Environmental ◽  
Real Time Information ◽  
Time Information

Social networks provide information about activities of humans and social events. Thus, with the help of social networks, we can extract the traffic events that occur in a city. In the context of an urban area, this kind of data allows to obtaining contextual real-time information shared among citizens that will be useful to address social, environmental and economic issues. In this paper, the authors describe a methodology to obtain information related to traffic events such as accidents or congestion, from Twitter messages and RSS services. A text mining process is applied on the messages to acquire the relevant data, then data are classified by using a machine learning algorithm. The events are geocoded and transformed into geometric points to be represented on a map. The final repository lets data to be available for further works related to the traffic events on the study area. As a case of study we consider Mexico City.

Classification of Traffic Events Notified in Social Networks' Texts

2018 ◽  
pp. 6973-6984 ◽  
Author(s):  
Ana Maria Magdalena Saldana-Perez ◽  
Marco Antonio Moreno-Ibarra ◽  
Miguel Jesus Torres-Ruiz
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Learning Algorithm ◽  
Relevant Information ◽  
User Generated Content ◽  
Machine Learning Algorithm ◽  
Information Processes ◽  
The Social

It is interesting to exploit the user generated content (UGC), and to use it with a view to infer new data; volunteered geographic information (VGI) is a concept derived from UGC, which main importance lies in its continuously updated data. The present approach tries to explode the use of VGI, by collecting data from a social network and a RSS service; the short texts collected from the social network are written in Spanish language; a text mining and a recovery information processes are applied over the data, in order to remove special characters on text, and to extract relevant information about the traffic events on the study area, then data are geocoded. The texts are classified by using a machine learning algorithm into five classes, each of them represents a specific traffic event or situation.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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