Special Issues
Table of Content

Data and Image Processing in Intelligent Information Systems

Submission Deadline: 15 November 2024 View: 197 Submit to Special Issue

Guest Editors

Prof. Manuel J. Cabral S. Reis, University of Trás-os-Montes e Alto Douro, Portugal
Prof. Carlos Manuel José Alves Serôdio, University of Trás-os-Montes e Alto Douro, Portugal
Prof. Frederico Augusto Dos Santos Branco, University of Trás-os-Montes e Alto Douro, Portugal
Dr. Nishu Gupta, Norwegian University of Science and Technology (NTNU), Norway

Summary

The rapid advancement of information technology has enabled a vast and ever-growing number of data and image processing applications in real daily life scenarios. The Special Issue "Data and Image Processing in Intelligent Information Systems" invites researchers to submit original research articles exploring the cutting-edge advancements and applications of data and image processing techniques within intelligent information systems. This issue aims to provide a comprehensive overview of how these techniques are transforming the landscape of information systems, from big data analytics to computer vision, and their integration into various domains such as healthcare, security, industry 4.0, smart cities, and more. Topics of interest include, but are not limited to:

· Advanced data processing techniques;

· Intelligent data analysis;

· Big data analytics for intelligent systems;

· Image recognition and classification;

· Medical imaging and health informatics;

· Privacy-preserving methods;

· Machine learning in data and image processing;

· Smart environments and smart cities.


Keywords

data and image processing; information systems; big data; computer vision; smart cities

Published Papers


  • Open Access

    ARTICLE

    Optimized Binary Neural Networks for Road Anomaly Detection: A TinyML Approach on Edge Devices

    Amna Khatoon, Weixing Wang, Asad Ullah, Limin Li, Mengfei Wang
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.051147
    (This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
    Abstract Integrating Tiny Machine Learning (TinyML) with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level. Constrained devices efficiently implement a Binary Neural Network (BNN) for road feature extraction, utilizing quantization and compression through a pruning strategy. The modifications resulted in a 28-fold decrease in memory usage and a 25% enhancement in inference speed while only experiencing a 2.5% decrease in accuracy. It showcases its superiority over conventional detection algorithms in different road image scenarios. Although constrained by computer resources and training datasets, our results indicate opportunities for More >

Share Link