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New Call for Papers
New Call for Papers
in conjunction with the
in conjunction with the
April 22-24, Patras, Greece
April 22-24, Patras, Greece
Aims and Scope
United Nation (UN) has defined a number of Sustainable Development Goals (SDGs), all related to global challenges. Research on these multi-disciplinary and multi-sector challenges are of significant value and importance. The 8th International Workshop on Artificial Intelligence for Disaster Management (AIDM-2025) is dedicated to the critical infrastructures, secure societies and sustainability challenges and in particular, to the dissemination of completed or works in progress that are original contributions and related to the emerging computational and collaborative technologies for the purpose of managing natural and man-made disasters and technological accidents. There are various advanced and emerging computational paradigms that could be applied as a means to mitigate and prepare for, respond to and recover from continuously growing occurrences of such disasters. Stakeholders in disaster management settings often find the effective and efficient utilization of supporting technologies quite a challenging process but very frequently a critical computational inclusion to the intelligence that it is required in the decision-making for protecting lives, organizations, property, environment and critical infrastructures. AIDM-2025 aims to prompt relevant discussion and highlight issues related to the stakeholders’ needs and the available technologies, which could be applied to support the operation and functioning during the aforementioned stages.
AIDM-2025 will highlight both technical and less technical challenges. Softer issues are less technical and relate to various stakeholders’ perspectives including technology acceptance, civil protection, fire and rescue services, ambulance and health services, humanitarian bodies and, technical infrastructure managers and administrators. AIDM-2025 aims also to discuss technical issues and specifically, advanced ICTs which could further stakeholders’ operations. Thus, advances of applicable technologies including data science, data analytics and Artificial Intelligence (AI) could be used to capture, store and analyze data from smart spaces need to be discussed. The scope of AIDM-2025 is to demonstrate the increased applicability and impact of computational intelligence in satisfying the disaster’s management domain challenging requirements. Finally, AIDM-2010 aims to provide a forum for original discussion and prompt future directions in the emerging area.
Topics:
Papers should be focused on past, current and emerging methods and/or use of technologies with a particular focus to the computational intelligence in the decision-making required for managing disasters.
The main topic areas include, but are not limited to:
o Critical reviews on disaster management stages: mitigation, preparedness, response and recovery
o Computational decision, risk assessment and management, business continuity and recovery
o Threat detection, prevention, monitoring, management and recovery (including network recovery)
o Needs analysis, contingency planning, policies, public awareness, training, resilience
o Risk identification, monitoring and assessment, safety, disturbance, pandemics, sustainable livelihood
o Security, trust, service reliability, identity management and privacy
o Ad-hoc networks, (sensor) network topologies & dynamics, network graphs, social networking analysis
o Artificial intelligence (AI), self-adaptive agents evolutionary computing, multi-objective optimization
o Machine and deep learning, data/IoT analysis, mining, visual computing, visual analytics
o Critical infrastructures, early warning and alerting systems, remote sensing, wireless communications, geographical information systems, satellite imagery, fault tolerance
o System architecture, languages, components, protocols, programs, portals, applications, dashboards
o Future Concepts and Frameworks in various disaster management settings
Organizing Committee
Workshop Co-chairs:
Eleana Asimakopoulou, Independent Scientist, Greece
eleana.asimakopoulou at googlemail.com
Nik Bessis, Edge Hill University, UK
nik.bessis at edgehill.ac.uk
Workshop Programme Committee Members:
Kevin Curran, Ulster University, UK
Alfredo Cuzzocrea, University of Calabria, Italy
Christian C Esposito, University of Salerno, Italy
Stavros Kolios, National Kapodistrian University of Athens, Greece
Sunitha Kuppuswamy, Anna University Chennai, India
Vaios Lappas, National Kapodistrian University of Athens, Greece
Francesco Palmieri, University of Salerno, Italy
Gianluca A Rizzo, Hesso Network (Valai), Switzerland
Stelios Sotiriadis, Birbeck College (University of London), UK
Sergio Toral Marn, University of Seville, Spain
Marcello Trovatti, Edge Hill University, UK
Submission
Paper Format
The submitted paper must be formatted according to the guidelines of Procedia Computer Science, MS Word Template, Latex, Template Generic, Elsevier.
Paper Length
Submitted technical papers must be no longer than 8 pages including all figures, tables and references.
Paper Submission
Authors are requested to submit their papers electronically using the online conference management system in PDF format before the deadline (see Important Dates). The submission processes will be managed by easychair.org. If you have used this system before, you can use the same username and password. If this is your first time using EasyChair, you will need to register for an account by clicking "I have no EasyChair account" button. Upon completion of registration, you will get a notification email from the system and you are ready for submitting your paper. You can upload and re-upload the paper to the system by the submission due date.
Publication
All accepted papers will be scheduled for oral presentations and will be included in the conference proceedings published by Elsevier Science in the open-access Procedia Computer Science series on-line.
Proceedings
The ANT-2025 proceedings will be published by Elsevier Science in the open-access Procedia Computer Science series on-line. All accepted submissions including AIDM workshop papers will be included in the proceedings of the conference. All accepted papers must be accompanied by a full paid registration by at least one of the authors in order to be included in the proceedings.
Important Dates
Submission Deadline: November 20, 2024
Notification of Acceptance: January 15, 2024
Final Manuscript: February 9, 2024
Author Registration: tba
Conference Dates: April 22-24, 2025
New Book Releases
New Book Releases
Eleana Asimakopoulou, Nik Bessis
Release Date: April, 2021|Copyright: © 2021 |Pages: 255
DOI: 10.4018/978-1-7998-6736-4
ISBN13: 9781799867364|ISBN10: 1799867366|ISBN13 Softcover: 9781799867371|EISBN13: 9781799867388
Pandemics are disruptive. Thus, there is a need to prepare and plan actions in advance for identifying, assessing, and responding to such events to manage uncertainty and support sustainable livelihood and wellbeing. A detailed assessment of a continuously evolving situation needs to take place, and several aspects must be brought together and examined before the declaration of a pandemic even happens. Various health organizations; crisis management bodies; and authorities at local, national, and international levels are involved in the management of pandemics. There is no better time to revisit current approaches to cope with these new and unforeseen threats. As countries must strike a fine balance between protecting health, minimizing economic and social disruption, and respecting human rights, there has been an emerging interest in lessons learned and specifically in revisiting past and current pandemic approaches. Such approaches involve strategies and practices from several disciplines and fields including healthcare, management, IT, mathematical modeling, and data science. Using data science to advance in-situ practices and prompt future directions could help alleviate or even prevent human, financial, and environmental compromise, and loss and social interruption via state-of-the-art technologies and frameworks.
Data Science Advancements in Pandemic and Outbreak Management demonstrates how strategies and state-of-the-art IT have and/or could be applied to serve as the vehicle to advance pandemic and outbreak management. The chapters will introduce both technical and non-technical details of management strategies and advanced IT, data science, and mathematical modelling and demonstrate their applications and their potential utilization within the identification and management of pandemics and outbreaks. It also prompts revisiting and critically reviewing past and current approaches, identifying good and bad practices, and further developing the area for future adaptation. This book is ideal for data scientists, data analysts, infectious disease experts, researchers studying pandemics and outbreaks, IT, crisis and disaster management, academics, practitioners, government officials, and students interested in applicable theories and practices in data science to mitigate, prepare for, respond to, and recover from future pandemics and outbreaks.
Coverage:
The many academic areas covered in this publication include, but are not limited to:
Artificial Intelligence; Big Data; Data Analytics; Data Science; E-Healthcare; Emergency Medicine; Epidemic Modellings; Internet of Things; Machine Learning; Mobile Sensors; Pandemic and Outbreak Management; Pandemic Misinformation; Social Media Analytics
Eleana Asimakopoulou, Nik Bessis
Indexed In: SCOPUS
Release Date: June, 2010|Copyright: © 2010 |Pages: 370
DOI: 10.4018/978-1-61520-987-3
ISBN13: 9781615209873|ISBN10: 1615209875|EISBN13: 9781615209880
Disaster management is a dynamic and fluid area, which requires the involvement of expertise from different authorities and organisations. There is a need to prepare and plan in advance actions in response to disaster related events in order to support sustainable livelihood by protecting lives, property and the environment.
Advanced ICTs for Disaster Management and Threat Detection: Collaborative and Distributed Frameworks demonstrates how strategies and state-of-the-art ICT have and/or could be applied to serve as a vehicle to advance disaster management approaches, decisions and practices. This book provides both a conceptual and practical guidance to disaster management while also identifying and developing effective and efficient approaches, mechanisms, and systems using emerging technologies to support an effective operation. This state-of-the-art reference collection attempts to prompt the future direction for disaster managers to identify applicable theories and practices in order to mitigate, prepare for, respond to and recover from various foreseen and/or unforeseen disasters.
Coverage:
The many academic areas covered in this publication include, but are not limited to:
Disaster management training; Early Warning Systems; Forest Fire Evacuation Data GPS; ICT deployment in disaster management; ICTs in disaster management; Mathematical models for decision support systems; Media and disaster communication; Medical information systems; Multi-criteria decision support systems; Natural Disaster Management; Sensor and computing infrastructure; Social media and crisis information; Web 2.0 for decision support
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