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• Machine Learning Meets the Semantic Web

  Konstantinos Ilias Kotis, Konstantina Zachila, Evaggelos Paparidis

  63-70

  
  Article ID: 3178
  1501  (Abstract) 413  (Download)
  

  Abstract: Remarkable progress in research has shown the efficiency of Knowledge Graphs (KGs) in extracting valuable external knowledge in various domains. A Knowledge Graph (KG) can illustrate high-order relations that connect two objects with one or multiple related attributes. The emerging Graph Neural Networks (GNN) can extract both object characteristics and relations from KGs. This paper... More

  2021-06-03

• To Perform Road Signs Recognition for Autonomous Vehicles Using Cascaded Deep Learning Pipeline

  Riadh Ayachi, Yahia ElFahem Said, Mohamed Atri

  1-10

  
  Article ID: 569
  1567  (Abstract) 394  (Download)
  

  Abstract: Autonomous vehicle is a vehicle that can guide itself without human conduction. It is capable of sensing its environment and moving with little or no human input. This kind of vehicle has become a concrete reality and may pave the way for future systems where computers take over the art of driving. Advanced artificial... More

  2019-07-19

• A Novel Fingerprint Recognition Framework with Attention Mechanism Based on Domain Adaptation for Improving Applicability in Overpressured Situations

  Jiahuai Ma, Alan Wilson

  56–65

  
  Article ID: 8128
  499  (Abstract) 95  (Download)
  

  Abstract: Fingerprint recognition is a widely adopted biometric technology, valued for its reliability and precision in identifying individuals. However, traditional recognition methods relying on handcrafted features struggle under challenging scenarios such as overpressured fingerprints, where excessive pressure distorts ridge patterns, significantly affecting performance. To address these challenges, this study proposes a novel framework combining domain adaptation... More

  2024-10-25

• DNN-Based AI-Driven H2/H∞ Filter Design of Nonlinear Stochastic Systems via Two-Coupled HJIEs-Supervised Adam Learning Algorithm

  Bor-Sen Chen, Jui-Ming Ma, Ruei-Syuan Wu

  34–55

  
  Article ID: 7261
  166  (Abstract) 104  (Download)
  

  Abstract: This study introduces a new approach using supervised learning deep neural networks (DNNs) to develop an AI-driven filter for nonlinear stochastic signal systems with external disturbance and measurement noise. The filter aims to achieve a balanced design between and norm of the state estimation error to achieve both optimal and robust filtering design of nonlinear... More

  2024-10-20

• Domain Adaptation-Based Deep Learning Framework for Android Malware Detection Across Diverse Distributions

  Shuguang Xiong, Xiaoyang Chen, Huitao Zhang, Meng Wang

  13-24

  
  Article ID: 6718
  545  (Abstract) 311  (Download)
  

  Abstract: This study addresses the challenge of Android malware detection, a critical issue due to the pervasive threats affecting mobile devices. As Android malware evolves, conventional detection methods struggle with novel or polymorphic malware that bypasses traditional defenses. This research leverages machine learning (ML) and deep learning (DL) techniques to overcome these limitations by adopting domain... More

  2024-06-29

• A Novel Domain Adaptation-based Framework for Face Recognition Under Darkened and Overexposed Situations

  Jiahuai Ma, Alan Wilson

  63–71

  
  Article ID: 8691
  219  (Abstract) 66  (Download)
  

  Abstract: Face recognition has become a cornerstone technology in various domains, including security, healthcare, and personalized applications. While traditional methods relied on handcrafted features and classical machine learning, advancements in deep learning have significantly improved face recognition's accuracy and robustness. However, challenges such as environmental variations—darkened or overexposed images—create domain shifts that compromise the generalization of... More

  2023-12-15