Published in Journal of Big Data
Anomaly-based Intrusion Detection System (IDS) has been a hot research topic because of its ability to detect new threats rather than only memorized signatures threats of signature-based IDS. Especially after the availability of advanced technologies that increase the number of hacking tools and increase the risk impact of an attack. The problem of...
Published in Journal of Big Data
With the growing use of information technology in all life domains, hacking has become more negatively effective than ever before. Also with developing technologies, attacks numbers are growing exponentially every few months and become more sophisticated so that traditional IDS becomes inefficient detecting them. This paper proposes a solution to d...
Published in Journal of Big Data
We capture the public sentiment towards candidates in the 2020 US Presidential Elections, by analyzing 7.6 million tweets sent out between October 31st and November 9th, 2020. We apply a novel approach to first identify tweets and user accounts in our database that were later deleted or suspended from Twitter. This approach allows us to observe the...
Published in Journal of Big Data
This study develops a pragmatic scheme that facilitates insight development from the collective voice of target users in Twitter, which has not been considered in the existing literature. While relying on a wide range of existing approaches to Twitter user profiling, this study provides a novel and generic procedure that enables researchers to iden...
Published in Journal of Big Data
Coral reefs are very important ecosystem which are the foundation of all life on this earth, but now they are under threat. Coral bleaching are happening now at a serious rate and the ultimate goal of conservation effort toward this issue is behaviour change. One of the most important parts of conservation effort is monitoring. However, monitoring ...
Published in Journal of Big Data
This paper investigates car parking users’ behaviors from social media perspective using social network based analysis of online communities revealed by mining the associated hashtags in Twitter. We propose a new interpretable community detection approach for mapping user’s car parking behavior by combining Clique, K-core and Girvan–Newman communit...
Published in Journal of Big Data
During the coronavirus pandemic, the number of depression cases has dramatically increased. Several depression sufferers disclose their actual feeling via social media. Thus, big data analytics on social networks for real-time depression detection is proposed. This research work detected the depression by analyzing both demographic characteristics ...
Published in Journal of Big Data
With the proliferation of social media platforms that provide anonymity, easy access, online community development, and online debate, detecting and tracking hate speech has become a major concern for society, individuals, policymakers, and researchers. Combating hate speech and fake news are the most pressing societal issues. It is difficult to ex...
Published in Journal of Big Data
Twitter has been responsible for some major stock market news in the recent past, from rogue CEOs damaging their company to very active world leaders asking for brand boycotts, but despite its impact Twitter has still not been as impactful on markets as traditional news sources. In this paper we examine whether daily news sentiment of several compa...
Published in Journal of Big Data
CNN originates from image processing and is not commonly known as a forecasting technique in time-series analysis which depends on the quality of input data. One of the methods to improve the quality is by smoothing the data. This study introduces a novel hybrid exponential smoothing using CNN called Smoothed-CNN (S-CNN). The method of combining ta...
