{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:14:01Z","timestamp":1760116441105,"version":"build-2065373602"},"reference-count":59,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Naif Arab University for Security Sciences","award":["NAUSS-23-R15"],"award-info":[{"award-number":["NAUSS-23-R15"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>There have been several catastrophic events that have impacted multiple economies and resulted in thousands of fatalities, and violence has generated a severe political and financial crisis. Multiple studies have been centered around the artificial intelligence (AI) and machine learning (ML) approaches that are most widely used in practice to detect or forecast violent activities. However, machine learning algorithms become less accurate in identifying and forecasting violent activity as data volume and complexity increase. For the prediction of future events, we propose a hybrid deep learning (DL)-based model that is composed of a convolutional neural network (CNN), long short-term memory (LSTM), and an attention layer to learn temporal features from the benchmark the Global Terrorism Database (GTD). The GTD is an internationally recognized database that includes around 190,000 violent events and occurrences worldwide from 1970 to 2020. We took into account two factors for this experimental work: the type of event and the type of object used. The LSTM model takes these complex feature extractions from the CNN first to determine the chronological link between data points, whereas the attention model is used for the time series prediction of an event. The results show that the proposed model achieved good accuracies for both cases\u2014type of event and type of object\u2014compared to benchmark studies using the same dataset (98.1% and 97.6%, respectively).<\/jats:p>","DOI":"10.3390\/info15110701","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T10:57:20Z","timestamp":1730717840000},"page":"701","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Efficient Deep Learning Framework for Optimized Event Forecasting"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9727-382X","authenticated-orcid":false,"given":"Emad Ul Haq","family":"Qazi","sequence":"first","affiliation":[{"name":"Center of Excellence in Cybercrimes and Digital Forensics (CoECDF), Naif Arab University for Security Sciences (NAUSS), Riyadh 11452, Saudi Arabia"}]},{"given":"Muhammad Hamza","family":"Faheem","sequence":"additional","affiliation":[{"name":"Center of Excellence in Cybercrimes and Digital Forensics (CoECDF), Naif Arab University for Security Sciences (NAUSS), Riyadh 11452, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3802-5687","authenticated-orcid":false,"given":"Tanveer","family":"Zia","sequence":"additional","affiliation":[{"name":"Center of Excellence in Cybercrimes and Digital Forensics (CoECDF), Naif Arab University for Security Sciences (NAUSS), Riyadh 11452, Saudi Arabia"},{"name":"School of Arts and Sciences, University of Notre Dame, Sydney, NSW 2007, Australia"}]},{"given":"Muhammad","family":"Imran","sequence":"additional","affiliation":[{"name":"Institute of Innovation, Science, and Sustainability, Federation University, Berwick, Melbourne, VC 3978, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3719-2387","authenticated-orcid":false,"given":"Iftikhar","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6307","DOI":"10.1007\/s40747-023-01037-z","article-title":"Attention-Based Spatial\u2013Temporal Multi-Graph Convolutional Networks for Casualty Prediction of Terrorist Attacks","volume":"9","author":"Hou","year":"2023","journal-title":"Complex Intell. 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