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  • Open Access

    ARTICLE

    Unsupervised Low-Light Image Enhancement Based on Explicit Denoising and Knowledge Distillation

    Wenkai Zhang1,2, Hao Zhang1,2, Xianming Liu1, Xiaoyu Guo1,2, Xinzhe Wang1, Shuiwang Li1,2,*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2537-2554, 2025, DOI:10.32604/cmc.2024.059000 - 17 February 2025

    Abstract Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images. Supervised methods, which utilize paired high-low light images as training sets, can enhance the PSNR to around 20 dB, significantly improving image quality. However, such data is challenging to obtain. In recent years, unsupervised low-light image enhancement (LIE) methods based on the Retinex framework have been proposed, but they generally lag behind supervised methods by 5–10 dB in performance. In this paper, we introduce the Denoising-Distilled… More >

  • Open Access

    ARTICLE

    External Knowledge-Enhanced Cross-Attention Fusion Model for Tobacco Sentiment Analysis

    Lihua Xie1, Ni Tang1, Qing Chen1,*, Jun Li2,*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3381-3397, 2025, DOI:10.32604/cmc.2024.058950 - 17 February 2025

    Abstract In the age of information explosion and artificial intelligence, sentiment analysis tailored for the tobacco industry has emerged as a pivotal avenue for cigarette manufacturers to enhance their tobacco products. Existing solutions have primarily focused on intrinsic features within consumer reviews and achieved significant progress through deep feature extraction models. However, they still face these two key limitations: (1) neglecting the influence of fundamental tobacco information on analyzing the sentiment inclination of consumer reviews, resulting in a lack of consistent sentiment assessment criteria across thousands of tobacco brands; (2) overlooking the syntactic dependencies between Chinese… More >

  • Open Access

    ARTICLE

    An Efficient Anti-Quantum Blind Signature with Forward Security for Blockchain-Enabled Internet of Medical Things

    Gang Xu1,2,6, Xinyu Fan1, Xiu-Bo Chen2, Xin Liu4, Zongpeng Li5, Yanhui Mao6,7, Kejia Zhang3,*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2293-2309, 2025, DOI:10.32604/cmc.2024.057882 - 17 February 2025

    Abstract Blockchain-enabled Internet of Medical Things (BIoMT) has attracted significant attention from academia and healthcare organizations. However, the large amount of medical data involved in BIoMT has also raised concerns about data security and personal privacy protection. To alleviate these concerns, blind signature technology has emerged as an effective method to solve blindness and unforgeability. Unfortunately, most existing blind signature schemes suffer from the security risk of key leakage. In addition, traditional blind signature schemes are also vulnerable to quantum computing attacks. Therefore, it remains a crucial and ongoing challenge to explore the construction of key-secure,… More >

  • Open Access

    ARTICLE

    End-To-End Encryption Enabled Lightweight Mutual Authentication Scheme for Resource Constrained IoT Network

    Shafi Ullah1,*, Haidawati Muhammad Nasir2, Kushsairy Kadir3,*, Akbar Khan1, Ahsanullah Memon4, Shanila Azhar1, Ilyas Khan5, Muhammad Ashraf1

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3223-3249, 2025, DOI:10.32604/cmc.2024.054676 - 17 February 2025

    Abstract Machine-to-machine (M2M) communication networks consist of resource-constrained autonomous devices, also known as autonomous Internet of things (IoTs) or machine-type communication devices (MTCDs) which act as a backbone for Industrial IoT, smart cities, and other autonomous systems. Due to the limited computing and memory capacity, these devices cannot maintain strong security if conventional security methods are applied such as heavy encryption. This article proposed a novel lightweight mutual authentication scheme including elliptic curve cryptography (ECC) driven end-to-end encryption through curve25519 such as (i): efficient end-to-end encrypted communication with pre-calculation strategy using curve25519; and (ii): elliptic curve More >

  • Open Access

    ARTICLE

    GPU-Enabled Isogometric Topology Optimization with Bėzier Element Stiffness Mapping

    Xuesong Li1, Shuting Wang1,2, Nianmeng Luo1,*, Aodi Yang1, Xing Yuan1, Xianda Xie2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1481-1514, 2025, DOI:10.32604/cmes.2024.058798 - 27 January 2025

    Abstract Due to the high-order B-spline basis functions utilized in isogeometric analysis (IGA) and the repeatedly updating global stiffness matrix of topology optimization, Isogeometric topology optimization (ITO) intrinsically suffers from the computationally demanding process. In this work, we address the efficiency problem existing in the assembling stiffness matrix and sensitivity analysis using Bėzier element stiffness mapping. The Element-wise and Interaction-wise parallel computing frameworks for updating the global stiffness matrix are proposed for ITO with Bėzier element stiffness mapping, which differs from these ones with the traditional Gaussian integrals utilized. Since the explicit stiffness computation formula derived… More >

  • Open Access

    ARTICLE

    Optimizing BERT for Bengali Emotion Classification: Evaluating Knowledge Distillation, Pruning, and Quantization

    Md Hasibur Rahman, Mohammed Arif Uddin, Zinnat Fowzia Ria, Rashedur M. Rahman*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1637-1666, 2025, DOI:10.32604/cmes.2024.058329 - 27 January 2025

    Abstract The rapid growth of digital data necessitates advanced natural language processing (NLP) models like BERT (Bidirectional Encoder Representations from Transformers), known for its superior performance in text classification. However, BERT’s size and computational demands limit its practicality, especially in resource-constrained settings. This research compresses the BERT base model for Bengali emotion classification through knowledge distillation (KD), pruning, and quantization techniques. Despite Bengali being the sixth most spoken language globally, NLP research in this area is limited. Our approach addresses this gap by creating an efficient BERT-based model for Bengali text. We have explored 20 combinations… More > Graphic Abstract

    Optimizing BERT for Bengali Emotion Classification: Evaluating Knowledge Distillation, Pruning, and Quantization

  • Open Access

    ARTICLE

    Estimated Ultimate Recovery and Productivity of Deep Shale Gas Horizontal Wells

    Haijie Zhang1, Haifeng Zhao2, Ming Jiang3,*, Junwei Pu1, Yuanping Luo1, Weiming Chen1, Tongtong Luo1,4, Zhiqiang Li5, Xinan Yu6

    FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.1, pp. 221-232, 2025, DOI:10.32604/fdmp.2024.053496 - 24 January 2025

    Abstract Pressure control in deep shale gas horizontal wells can reduce the stress sensitivity of hydraulic fractures and improve the estimated ultimate recovery (EUR). In this study, a hydraulic fracture stress sensitivity model is proposed to characterize the effect of pressure drop rate on fracture permeability. Furthermore, a production prediction model is introduced accounting for a non-uniform hydraulic fracture conductivity distribution. The results reveal that increasing the fracture conductivity leads to a rapid daily production increase in the early stages. However, above 0.50 D·cm, a further increase in the fracture conductivity has a limited effect on More >

  • Open Access

    ARTICLE

    Experimental and Numerical Study of Bonding Capacity of Interface between Ultra-High Performance Concrete and Steel Tube

    Ruikun Xu1, Jiu Li1, Wenjie Li1, Wei Zhang2,*

    Structural Durability & Health Monitoring, Vol.19, No.2, pp. 285-305, 2025, DOI:10.32604/sdhm.2024.057513 - 15 January 2025

    Abstract This study investigates the bond performance at the interfacial region shared by Ultra-High Performance Concrete (UHPC) and steel tubes through push-out tests. This study examines how changes in steel fiber volumetric ratio and thickness of steel tube influence the bond strength characteristics. The results show that as the enhancement of the steel tube wall thickness, the ultimate bond strength at the interface improves significantly, whereas the initial bond strength exhibits only slight variations. The influence of steel fiber volumetric ratio presents a nonlinear trend, with initial bond strength decreasing at low fiber content and increasing More >

  • Open Access

    ARTICLE

    Internet of Things Software Engineering Model Validation Using Knowledge-Based Semantic Learning

    Mahmood Alsaadi, Mohammed E. Seno*, Mohammed I. Khalaf

    Intelligent Automation & Soft Computing, Vol.40, pp. 29-52, 2025, DOI:10.32604/iasc.2024.060390 - 10 January 2025

    Abstract The agility of Internet of Things (IoT) software engineering is benchmarked based on its systematic insights for wide application support infrastructure developments. Such developments are focused on reducing the interfacing complexity with heterogeneous devices through applications. To handle the interfacing complexity problem, this article introduces a Semantic Interfacing Obscuration Model (SIOM) for IoT software-engineered platforms. The interfacing obscuration between heterogeneous devices and application interfaces from the testing to real-time validations is accounted for in this model. Based on the level of obscuration between the infrastructure hardware to the end-user software, the modifications through device replacement, More >

  • Open Access

    ARTICLE

    Enhancing Vehicle Overtaking System via LoRa-Enabled Vehicular Communication Approach

    Kwang Chee Seng, Siti Fatimah Abdul Razak*, Sumendra Yogarayan

    Computer Systems Science and Engineering, Vol.49, pp. 239-258, 2025, DOI:10.32604/csse.2024.056582 - 10 January 2025

    Abstract Vehicle overtaking poses significant risks and leads to injuries and losses on Malaysia’s roads. In most scenarios, insufficient and untimely information available to drivers for accessing road conditions and their surrounding environment is the primary factor that causes these incidents. To address these issues, a comprehensive system is required to provide real-time assistance to drivers. Building upon our previous research on a LoRa-based lane change decision-aid system, this study proposes an enhanced Vehicle Overtaking System (VOS). This system utilizes long-range (LoRa) communication for reliable real-time data exchange between vehicles (V2V) and the cloud (V2C). By More >

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