BITS Faculty Publications

Permanent URI for this communityhttp://localhost:4000/handle/123456789/1867

Browse

Subject: search.filters.subject.Data analytics

Search Results

Now showing 1 - 7 of 7
  • No Thumbnail Available
    Item
    Determining disaster severity through social media analysis: testing the methodology with South East Queensland Flood tweets
    (Elsevier, 2020-01) Goonetilleke, Ashantha
    Social media was underutilised in disaster management practices, as it was not seen as a real-time ground level information harvesting tool during a disaster. In recent years, with the increasing popularity and use of social media, people have started to express their views, experiences, images, and video evidences through different social media platforms. Consequently, harnessing such crowdsourced information has become an opportunity for authorities to obtain enhanced situation awareness data for efficient disaster management practices. Nonetheless, the current disaster-related Twitter analytics methods are not versatile enough to define disaster impacts levels as interpreted by the local communities. This paper contributes to the existing knowledge by applying and extending a well-established data analysis framework, and identifying highly impacted disaster areas as perceived by the local communities. For this, the study used real-time Twitter data posted during the 2010–2011 South East Queensland Floods. The findings reveal that: (a) Utilising Twitter is a promising approach to reflect citizen knowledge; (b) Tweets could be used to identify the fluctuations of disaster severity over time; (c) The spatial analysis of tweets validates the applicability of geo-located messages to demarcate highly impacted disaster zones.
  • No Thumbnail Available
    Item
    How engaging are disaster management related social media channels? the case of Australian state emergency organisations
    (Elsevier, 2020-09) Goonetilleke, Ashantha
    Social media is increasingly becoming a formal tool of communication across the world. For example, state emergency organisations maintain social media channels to share information with millions of people. While community engagement through social media has become a trend across the world, measuring community engagement levels of such social media channels is a highly time demanding, and also an understudied, but important, area of research. This paper, through an empirical investigation, addresses the question of how engaging disaster management related social media channels are. The study adopted five indices—i.e., popularity, commitment, virality, engagement, and utilisation—in order to evaluate the levels of community engagement by various social media channels. As the case study, official Facebook and Twitter pages maintained by the state emergency organisations of three Australian states, namely New South Wales, Victoria, and Queensland, were scrutinised. The results revealed that: (a) Social media acts as a promising vehicle to capture dispersed community knowledge on disaster management, but it still needs more utilisation; (b) Publishing social media posts with images and animated maps increases community engagement levels, and; (c) Social media posts, with an aim to increase the situation awareness, receive higher community attention than the other posts.
  • No Thumbnail Available
    Item
    A data analytic-based logistics modelling framework for E-commerce enterprise
    (Taylor & Francis, 2022-01) Verma, Abhishek
    Data-driven approaches have noteworthy significance in managing and improving logistics in E-commerce enterprises. This study focuses on the development of an integrated framework to analyse the Brazilian E-Commerce enterprise public dataset. From the analysis, it is found that sellers of Ibitinga city of SP state had the most count of late deliveries where 42 sellers are under-performing in terms of estimated delivery time. Locations of customers and sellers were spotted on a map to get a geographical representation. The proposed framework may help E-Commerce enterprise owners and retail merchants to make better decisions related to sales and E-Commerce enterprise logistics.
  • No Thumbnail Available
    Item
    Business intelligence and data analytics: Proceedings of BIDA 2024
    (Springer, 2025) Verma, Abhishek
    This book is a collection of the high-quality research articles presented at the International Conference on Business Intelligence and Data Analytics (BIDA 2024), organized by RV Institute of Management (RVIM), Bengaluru, India, during April 2024. The book covers state-of-the-art research articles from the researchers and practitioners working in the field of business intelligence, data analytics, decision support systems, data warehousing and data mining, big data analytics, predictive and prescriptive analytics, and machine learning for business applications and their real-world applications.
  • No Thumbnail Available
    Item
    Business intelligence and data analytics
    (Springer, 2025) Verma, Abhishek
    This book is a collection of the high-quality research articles presented at the International Conference on Business Intelligence and Data Analytics (BIDA 2024), organized by RV Institute of Management (RVIM), Bengaluru, India, during April 2024. The book covers state-of-the-art research articles from the researchers and practitioners working in the field of business intelligence, data analytics, decision support systems, data warehousing and data mining, big data analytics, predictive and prescriptive analytics, and machine learning for business applications and their real-world applications.
  • No Thumbnail Available
    Item
    Security solutions against attacks in mobile ad hoc networks and their verification using BAN logic
    (IEEE, 2017) Dua, Amit
    In the last few years, there has been tremendous interest in ad hoc wireless networks as they have massive military and commercial potential. An ad hoc wireless network serves as an independent network that comprises of mobile devices that utilize wireless transmission for communication, having no fixed infrastructure. These networks eliminate the complexity of infrastructure setup and hence can be deployed in quick time. However, on the negative side, such networks are very vulnerable to attacks against availability, service integrity, security, privacy and several other possible threats. To overcome these attacks, several security mechanisms have been proposed to ensure the reliability of ad hoc networks.
  • No Thumbnail Available
    Item
    Decision tree and SVM-based data analytics for theft detection in smart grid
    (IEEE, 2016-03) Dua, Amit
    Nontechnical losses, particularly due to electrical theft, have been a major concern in power system industries for a long time. Large-scale consumption of electricity in a fraudulent manner may imbalance the demand-supply gap to a great extent. Thus, there arises the need to develop a scheme that can detect these thefts precisely in the complex power networks. So, keeping focus on these points, this paper proposes a comprehensive top-down scheme based on decision tree (DT) and support vector machine (SVM). Unlike existing schemes, the proposed scheme is capable enough to precisely detect and locate real-time electricity theft at every level in power transmission and distribution (T&D). The proposed scheme is based on the combination of DT and SVM classifiers for rigorous analysis of gathered electricity consumption data. In other words, the proposed scheme can be viewed as a two-level data processing and analysis approach, since the data processed by DT are fed as an input to the SVM classifier. Furthermore, the obtained results indicate that the proposed scheme reduces false positives to a great extent and is practical enough to be implemented in real-time scenarios.