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Lightweight Methods and Resource-efficient Computing Solutions

Submission Deadline: 15 March 2025 View: 393 Submit to Special Issue

Guest Editors

Prof. Muhammad Khurram Khan, King Saud University, Saudi Arabia
Prof. Vidyasagar Potdar, Curtin University, Australia
Prof. Yuan Tian, Nanjing Institute of Technology, China


Summary

Lightweight methods and resource-efficient computing are emerging topics that have attracted increasing attention. Lightweight methods aim to simplify or optimize the design and implementation of algorithms, models or architectures. In terms of resource consumption problems, resource-efficient computing leverages the available resources in a distributed system to support computationally intensive tasks.  


It is well-known that lightweight computing and resource-efficient computing are promising methods for many challenges in the fields of big data, Internet of Things and artificial intelligence. Further research is supposed to concentrate on resource management, data operation and other issues, by applying data compression, feature dimensionality reduction, lightweight encryption, to boost computing efficiency, save energy and cut down the computing cost of various applications, especially for resource-constrained devices. At the same time, hardware assistance and software development deserve attention, to accommodate different application scenarios and satisfy the QoS requirements of data services. In addition, investigating the innovation and optimization of lightweight analysis methods, lightweight information exchange methods, energy-efficient computing and green software, cybersecurity and privacy can enhance the performance and reliability of lightweight computing, and provide better data support and services for the future intelligent society to some extent.


This special issue aims to explore various research topics, share and exchange the latest advances and future visions of experts and scholars in these relevant fields, and foster further research on energy-efficient, green, sustainable hardware and software development as well as advanced algorithms, technologies, architectures, mechanisms and applications. We also hope to facilitate cross-disciplinary discussions and discover new opportunities, offering readers a comprehensive and in-depth perspective, and inspiring more innovation and collaboration. Topics of interest include, but are not limited to:

· Data compression and indexing

· Energy-efficient computing and green software

· Feature dimensionality reduction

· Hardware assisted/accelerated solutions

· Lightweight analysis methods

· Lightweight cryptography, cybersecurity and privacy

· Lightweight information delivery/exchange methods

· Resource management in IoT, fog, edge, and cloud computing



Published Papers


  • Open Access

    ARTICLE

    Faster AMEDA—A Hybrid Mesoscale Eddy Detection Algorithm

    Xinchang Zhang, Xiaokang Pan, Rongjie Zhu, Runda Guan, Zhongfeng Qiu, Biao Song
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1827-1846, 2024, DOI:10.32604/cmes.2024.054298
    (This article belongs to the Special Issue: Lightweight Methods and Resource-efficient Computing Solutions)
    Abstract Identification of ocean eddies from a large amount of ocean data provided by satellite measurements and numerical simulations is crucial, while the academia has invented many traditional physical methods with accurate detection capability, but their detection computational efficiency is low. In recent years, with the increasing application of deep learning in ocean feature detection, many deep learning-based eddy detection models have been developed for more effective eddy detection from ocean data. But it is difficult for them to precisely fit some physical features implicit in traditional methods, leading to inaccurate identification of ocean eddies. In… More >

  • Open Access

    ARTICLE

    Ensemble Filter-Wrapper Text Feature Selection Methods for Text Classification

    Oluwaseun Peter Ige, Keng Hoon Gan
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1847-1865, 2024, DOI:10.32604/cmes.2024.053373
    (This article belongs to the Special Issue: Lightweight Methods and Resource-efficient Computing Solutions)
    Abstract Feature selection is a crucial technique in text classification for improving the efficiency and effectiveness of classifiers or machine learning techniques by reducing the dataset’s dimensionality. This involves eliminating irrelevant, redundant, and noisy features to streamline the classification process. Various methods, from single feature selection techniques to ensemble filter-wrapper methods, have been used in the literature. Metaheuristic algorithms have become popular due to their ability to handle optimization complexity and the continuous influx of text documents. Feature selection is inherently multi-objective, balancing the enhancement of feature relevance, accuracy, and the reduction of redundant features. This… More >

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