Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Filters

2022 - 20252

Settings

Has files: NoSubject: search.filters.subject.Disaster management

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    A System Engineering Approach to Disaster Resilience—An Introduction
    (Springer Science and Business Media Deutschland GmbH, 2022) Ghosh, C.; Kolathayar, S.
    In this dynamic earth, each and every place is affected from natural, technological, biological, environmental hazards and/or related impacts. Depending on the extent of resilience measures in place and socioeconomic status of the countries, their infrastructures and environmental sensitiveness, the damage, and loss patterns are exposed. So to ensure basic security and quality of life against all, impending hazards have been the key issues for the academia and industries vis-a-vis administrative setup. Therefore, disaster resilience has become a systemic challenge for the mankind, and eventually, responding to disasters has been into the mainframe of all concerned governance from the time that natural resources are being extracted and used for the exploiting more and more from the mother nature. But in recent times as we are making lots of infrastructural growth, it is more so critical with the onset of deadly infectious disease outbreaks, acts of terrorism, social unrest, and fluctuation in the share market leading to financial disasters. From perspective of system engineering approaches, this chapter explains various facets of disaster resilience paradigm with particular motivation to the infrastructure growth and sustenance. Additionally, a summary of the 38 selected papers categorized into six sub-themes about the necessary approaches to elevate resilience to disasters is presented. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
  • No Thumbnail Available
    Item
    Disaster Classification Using Multimodality Techniques by Integrating Images and Text
    (Springer Science and Business Media Deutschland GmbH, 2025) Medapati, B.M.R.; Pais, S.M.; Bhattacharjee, S.
    In today’s digital era, the abundance of information shared through social media platforms in times of disaster has emerged as a crucial asset for enhancing disaster response operations. This research initiative was specifically dedicated to enhance disaster categorization by integrating image and tweet text data. The devised model comprises two distinct modules aimed at optimizing the classification process. The primary module focuses on extracting insights from text and images independently using VGG-16 for images, and Bidirectional Long Short-Term Memory and Convolutional Neural Network for texts, subsequently executing the classification task. Conversely, the secondary module is designed to learn the interconnectedness between textual content and images using Contrastive Learning Image Pretraining (CLIP). After this late fusion is used to combine the outcomes of these modules and later softmax classification is used for the classification of the incident into one of seven humanitarian categories thereby enhancing the precision and efficacy of disaster classification. The developed model gives an accuracy of 75% with no data and image augmentations and the result was improved to 93% with different combinations of augmentations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.