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Chen Xia, Christian Eduardo Verdonk Gallego, Adrián Fabio Bracero, Víctor Fernando Gómez Comendador and Rosa María Arnaldo Valdés
This paper investigates the impact of trajectory predictor performance on the encounter probability generated by an adaptive conflict detection tool and examines the flexibility of the tool dependent on its adjustable thresholds, using historical radar t...
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Zitong Wang, Enrang Zheng, Jianguo Liu and Tuo Guo
Traditional methods of orthogonal basis function decomposition have been extensively used to detect magnetic anomaly signals. However, the determination of the relative velocity between the detection platform and the magnetic target remains elusive in pr...
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Yuchen Dong, Heng Zhou, Chengyang Li, Junjie Xie, Yongqiang Xie and Zhongbo Li
Camouflaged object detection (COD) is an arduous challenge due to the striking resemblance of camouflaged objects to their surroundings. The abundance of similar background information can significantly impede the efficiency of camouflaged object detecti...
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Yu Yao and Quan Qian
We develop the online process parameter design (OPPD) framework for efficiently handling streaming data collected from industrial automation equipment. This framework integrates online machine learning, concept drift detection and Bayesian optimization t...
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Mazen Gazzan and Frederick T. Sheldon
Ransomware attacks have emerged as a significant threat to critical data and systems, extending beyond traditional computers to mobile and IoT/Cyber?Physical Systems. This study addresses the need to detect early ransomware behavior when only limited dat...
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Juan Luis Pérez-Ruiz, Yu Tang, Igor Loboda and Luis Angel Miró-Zárate
In the field of aircraft engine diagnostics, many advanced algorithms have been proposed over the last few years. However, there is still wide room for improvement, especially in the development of more integrated and complete engine health management sy...
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Aristeidis Karras, Anastasios Giannaros, Christos Karras, Leonidas Theodorakopoulos, Constantinos S. Mammassis, George A. Krimpas and Spyros Sioutas
In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces ...
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Abdul Rehman Khalid, Nsikak Owoh, Omair Uthmani, Moses Ashawa, Jude Osamor and John Adejoh
In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble met...
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Morhaf Aljber, Han Soo Lee, Jae-Soon Jeong and Jonathan Salar Cabrera
In tsunami studies, understanding the intricate dynamics in the swash area, characterised by the shoaling effect, remains a challenge. In this study, we employed the adaptive mesh refinement (AMR) method to model tsunami inundation and propagation in the...
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Qasem Abu Al-Haija and Ahmed Al-Tamimi
Automatic dependent surveillance-broadcast (ADS-B) is the future of aviation surveillance and traffic control, allowing different aircraft types to exchange information periodically. Despite this protocol?s advantages, it is vulnerable to flooding, denia...
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