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Conference

SOCO-CISIS-ICEUTE 

About: SOCO-CISIS-ICEUTE is an academic conference. The conference publishes majorly in the area(s): Artificial neural network & Support vector machine. Over the lifetime, 277 publications have been published by the conference receiving 1458 citations.

Papers published on a yearly basis

Papers
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Proceedings Article
01 Jan 2013
TL;DR: A methodology to detect and associate fake profiles on Twitter social network which are employed for defamatory activities to a real profile within the same network by analysing the content of comments generated by both profiles is presented.
Abstract: The use of new technologies along with the popularity of social networks has given the power of anonymity to the users. The ability to create an alter-ego with no relation to the actual user, creates a situation in which no one can certify the match between a profile and a real person. This problem generates situations, repeated daily, in which users with fake accounts, or at least not related to their real identity, publish news, reviews or multimedia material trying to discredit or attack other people who may or may not be aware of the attack. These acts can have great impact on the affected victims’ environment generating situations in which virtual attacks escalate into fatal consequences in real life. In this paper, we present a methodology to detect and associate fake profiles on Twitter social network which are employed for defamatory activities to a real profile within the same network by analysing the content of comments generated by both profiles. Accompanying this approach we also present a successful real life use case in which this methodology was applied to detect and stop a cyberbullying situation in a real elementary school.

147 citations

Book ChapterDOI
01 Jan 2014
TL;DR: This paper has used the text in the tweet and machine learning and compression algorithms to filter those undesired tweets and proposes a content-based approach to filter spam tweets.
Abstract: Twitter has become one of the most used social networks. And, as happens with every popular media, it is prone to misuse. In this context, spam in Twitter has emerged in the last years, becoming an important problem for the users. In the last years, several approaches have appeared that are able to determine whether an user is a spammer or not. However, these blacklisting systems cannot filter every spam message and a spammer may create another account and restart sending spam. In this paper, we propose a content-based approach to filter spam tweets. We have used the text in the tweet and machine learning and compression algorithms to filter those undesired tweets.

44 citations

Book ChapterDOI
01 Jan 2014
TL;DR: A new method based on anomaly detection that extracts the strings contained in application files in order to detect malware is proposed.
Abstract: The usage of mobile phones has increased in our lives because they offer nearly the same functionality as a personal computer. Specifically, Android is one of the most widespread mobile operating systems. Indeed, its app store is one of the most visited and the number of applications available for this platform has also increased. However, as it happens with any popular service, it is prone to misuse, and the number of malware samples has increased dramatically in the last months. Thus, we propose a new method based on anomaly detection that extracts the strings contained in application files in order to detect malware.

31 citations

Book ChapterDOI
01 Jan 2014
TL;DR: A structured resource would allow researches and industry professionals to write relatively simple queries to retrieve all the information regards transcriptions of any accident, instead of the thousands of abstracts provided by querying the unstructured corpus.
Abstract: The development of automatic methods to produce usable structured information from unstructured text sources is extremely valuable to the oil and gas industry. A structured resource would allow researches and industry professionals to write relatively simple queries to retrieve all the information regards transcriptions of any accident. Instead of the thousands of abstracts provided by querying the unstructured corpus, the queries on structured corpus would result in a few hundred well-formed results.

29 citations

Book ChapterDOI
01 Jan 2014
TL;DR: The objective of this work is to propose the k-nearest neighbor (kNN) regression as geo-imputation preprocessing step for pattern-label-based short-term wind prediction of spatio-temporal wind data sets and show that kNN regression is the most superior method for imputation.
Abstract: The shift from traditional energy systems to distributed systems of energy suppliers and consumers and the power volatileness in renewable energy imply the need for effective short-term prediction models. These machine learning models are based on measured sensor information. In practice, sensors might fail for several reasons. The prediction models cannot naturally cannot work properly with incomplete patterns. If the imputation method, which completes the missing data, is not appropriately chosen, a bias may be introduced. The objective of this work is to propose the k-nearest neighbor (kNN) regression as geo-imputation preprocessing step for pattern-label-based short-term wind prediction of spatio-temporal wind data sets. The approach is compared to three other methods. The evaluation is based on four turbines with neighbors of the NREL Western Wind Data Set and the values are missing uniformly distributed. The results show that kNN regression is the most superior method for imputation.

24 citations

Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
20191
201769
201677
2014124
20136