Special Issue "Powering the Future Intelligence - Ambient Social Media Analytics"

Submission Deadline: 15 January 2021 (closed)
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Guest Editors
Prof. Ansar Yasar, Hasselt University, Belgium.
Dr. Haroon Malik, Marshall University, USA.
Prof. Stephane Galland, UTBM, France.

Summary

Information and communication technology has changed rapidly over the past 20 years with a key development being the emergence of social media and the Internet of Things (IoT). The development allows people and object to sense, analyze and send data and disseminate information in large social communities. Across the globe, mobile devices, social networking platforms such as Twitter, YouTube, Facebook, Tumbler, Instagram and hundreds of microblogging sites in conjunctions with 9 billion Internet of Things devices (exist as of today to a projected 200 billion by 2020) are generating truly mind-boggling data every day. There are 2.5 quintillion bytes of data created each day at our current pace, but that pace is only accelerating. Despite social media, fueled by IoT devices, is now a critical part of the information eco-system, extracting information and intelligence from the traces of IoT devices and social data to aid the decision making process is still

There is a pressing need to advance the state-of-the-art of Ambient Social Media Analytics. Ambient Social Data is generated by wearable devices, smartphones, pervasive IoT devices, and shared/posted on social media platforms. Ambient Social Media Analytic is concerned with developing and evaluating informatics tools and frameworks to collect, monitor, analyze, summarize, and visualize the ambient data; an amalgamation of sensory data coupled with social traces. Ambient analytics research serves several purposes:

• Facilitating conversations and interaction between ubiquitous online communities and

• Extracting useful patterns and intelligence to serve entities that include, but are not limited to, active contributors in ongoing dialogues.

To date, ambient social media analytics face several challenges. First, the ambient social media contains a rich set of data not only from regular posts on the social media platforms, indeed the data originates for wearable devices, smartphones, and IoT devices. Such amalgamation of data has yet not been coherently and systemically addressed in the mining and analytics literature. A few examples include establishing tractability links being ambient devices and actors producing social data traces tagging free from the text by taking in contextual and pervasive context. Second, scarcity of the tools, methodology, approaches, and frameworks to can derive the “wisdom of the crowd” in a context-rich application setting. Third, ambient assisted social meida analytics requires concrete performance measures to support decision making for a wide variety and spectrum of application. There do not yet exist large sets of quantative measures to stratify the needs of a plethora of IoT and social media application. Lastly, ambient social media data are crescendos streams of data, with increasing volume, high velocity, veracity, and variety. Thus, pose a significant challenge to computing in general and semantic computing in particular. 


Keywords
Future Intelligence, Ambient Networks, Pervasive Computing

Published Papers
  • Methodology for Detecting Strabismus through Video Analysis and Intelligent Mining Techniques
  • Abstract Strabismus is a medical condition that is defined as the lack of coordination between the eyes. When Strabismus is detected at an early age, the chances of curing it are higher. The methods used to detect strabismus and measure its degree of deviation are complex and time-consuming, and they always require the presence of a physician. In this paper, we present a method of detecting strabismus and measuring its degree of deviation using videos of the patient’s eye region under a cover test. Our method involves extracting features from a set of training videos (training corpora) and using them to… More
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