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Leyu Zheng¹, Mingming Xiao^1,*, Yi Ren², Ke Li¹, Chang Sun¹
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072036 - 12 January 2026
（This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)
Abstract In a wide range of engineering applications, complex constrained multi-objective optimization problems (CMOPs) present significant challenges, as the complexity of constraints often hampers algorithmic convergence and reduces population diversity. To address these challenges, we propose a novel algorithm named Constraint Intensity-Driven Evolutionary Multitasking (CIDEMT), which employs a two-stage, tri-task framework to dynamically integrates problem structure and knowledge transfer. In the first stage, three cooperative tasks are designed to explore the Constrained Pareto Front (CPF), the Unconstrained Pareto Front (UPF), and the ε-relaxed constraint boundary, respectively. A CPF-UPF relationship classifier is employed to construct a problem-type-aware… More >

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Yanan Liu^1,*, Suhao Wang^1,*, Lei Cao¹, Pengfei Wang¹, Zheng Zhang², Shuo Qiu¹, Ruchan Dong¹
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072710 - 12 January 2026
Abstract Unmanned Aerial Vehicles (UAVs) in Flying Ad-Hoc Networks (FANETs) are widely used in both civilian and military fields, but they face severe security, trust, and privacy vulnerabilities due to their high mobility, dynamic topology, and open wireless channels. Existing security protocols for Mobile Ad-Hoc Networks (MANETs) cannot be directly applied to FANETs, as FANETs require lightweight, high real-time performance, and strong anonymity. The current FANETs security protocol cannot simultaneously meet the requirements of strong anonymity, high security, and low overhead in high dynamic and resource-constrained scenarios. To address these challenges, this paper proposes an Anonymous Authentication… More >

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Chee Chin Lim^1,2,*, Hui Wen Tiu¹, Qi Wei Oung^1,3, Chiew Chea Lau⁴, Xiao Jian Tan^2,5
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070468 - 12 January 2026
Abstract Breast cancer remains one of the leading causes of cancer mortality world-wide, with accurate molecular subtyping is critical for guiding treatment and improving patient outcomes. Traditional molecular subtyping via immuno-histochemistry (IHC) test is invasive, time-consuming, and may not fully represent tumor heterogeneity. This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes. Four pretrained models, including two Convolutional Neural Networks (MobileNet_V3_Large and VGG-16) and two Vision Transformers (ViT_B_16 and ViT_Base_Patch16_Clip_224) were fine-tuned to classify images into HER2-enriched, Luminal, Normal-like, and Triple Negative subtypes. Hyperparameter tuning,… More >

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268

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Hanaa Nafea¹, Awais Qasim², Sana Abdul Sattar², Adeel Munawar³, Muhammad Nadeem Ali⁴, Byung-Seo Kim^4,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072281 - 12 January 2026
Abstract The increased connectivity and reliance on digital technologies have exposed smart transportation systems to various cyber threats, making intrusion detection a critical aspect of ensuring their secure operation. Traditional intrusion detection systems have limitations in terms of centralized architecture, lack of transparency, and vulnerability to single points of failure. This is where the integration of blockchain technology with signature-based intrusion detection can provide a robust and decentralized solution for securing smart transportation systems. This study tackles the issue of database manipulation attacks in smart transportation networks by proposing a signature-based intrusion detection system. The introduced More >

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470

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Yewei Xiao, Xin Du^*, Wei Zeng
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072145 - 12 January 2026
Abstract Audio-visual speech recognition (AVSR), which integrates audio and visual modalities to improve recognition performance and robustness in noisy or adverse acoustic conditions, has attracted significant research interest. However, Conformer-based architectures remain computational expensive due to the quadratic increase in the spatial and temporal complexity of their softmax-based attention mechanisms with sequence length. In addition, Conformer-based architectures may not provide sufficient flexibility for modeling local dependencies at different granularities. To mitigate these limitations, this study introduces a novel AVSR framework based on a ReLU-based Sparse and Grouped Conformer (RSG-Conformer) architecture. Specifically, we propose a Global-enhanced Sparse… More >

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495

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Sai Xu^1,*, Jun Liu^1,*, Shengyu Huang¹, Zhi Li²
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071865 - 12 January 2026
（This article belongs to the Special Issue: Intelligent Computation and Large Machine Learning Models for Edge Intelligence in industrial Internet of Things)
Abstract In scenarios where ground-based cloud computing infrastructure is unavailable, unmanned aerial vehicles (UAVs) act as mobile edge computing (MEC) servers to provide on-demand computation services for ground terminals. To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs, this paper presents PER-MATD3, a multi-agent deep reinforcement learning algorithm with prioritized experience replay (PER) into the Centralized Training with Decentralized Execution (CTDE) framework. Specifically, PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution, while leveraging a shared replay buffer with More >

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725

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Woo Hyun Park^*, Dong Ryeol Shin
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069949 - 12 January 2026
Abstract With the recent increase in data volume and diversity, traditional text representation techniques are struggling to capture context, particularly in environments with sparse data. To address these challenges, this study proposes a new model, the Masked Joint Representation Model (MJRM). MJRM approximates the original hypothesis by leveraging multiple elements in a limited context. It dynamically adapts to changes in characteristics based on data distribution through three main components. First, masking-based representation learning, termed selective dynamic masking, integrates topic modeling and sentiment clustering to generate and train multiple instances across different data subsets, whose predictions are… More >

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452

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Qian Yu^1,2, Gui Zhang^2,*, Ying Wang¹, Xin Wu², Jiangshu Xiao², Wenbing Kuang¹, Juan Zhang²
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072172 - 12 January 2026
Abstract Detecting small forest fire targets in unmanned aerial vehicle (UAV) images is difficult, as flames typically cover only a very limited portion of the visual scene. This study proposes Context-guided Compact Lightweight Network (CCLNet), an end-to-end lightweight model designed to detect small forest fire targets while ensuring efficient inference on devices with constrained computational resources. CCLNet employs a three-stage network architecture. Its key components include three modules. C3F-Convolutional Gated Linear Unit (C3F-CGLU) performs selective local feature extraction while preserving fine-grained high-frequency flame details. Context-Guided Feature Fusion Module (CGFM) replaces plain concatenation with triplet-attention interactions to… More >

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283

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ARTICLE

Lifu He¹, Zhongchu Huang¹, Haidong Shao^2,*, Zhangbo Hu¹, Yuting Wang¹, Jie Mei¹, Xiaofei Zhang³
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073227 - 12 January 2026
（This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems-2nd Edition)
Abstract Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction. However, several challenges remain. First, signal noise collected during blade operation masks fault features, severely impairing the fault diagnosis performance of deep learning models. Second, current blade fault diagnosis often relies on single-sensor data, resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states. To address these issues, a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed. Specifically, a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to More >

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385

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Sophort Siet¹, Sony Peng², Ilkhomjon Sadriddinov³, Kyuwon Park^4,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071192 - 12 January 2026
Abstract Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories. The collaborative filtering (CF) model, which depends exclusively on user-item interactions, commonly encounters challenges, including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior. This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking (BPR) optimization to address these limitations. With the strong support of Long Short-Term Memory (LSTM) networks, we apply it to identify sequential dependencies of user behavior and then incorporate… More >

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360

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ARTICLE

Chao Li^1,3,#, Jianing Wang^1,3,#, Caichang Ding^2,*, Zhiwei Ye^1,3
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071242 - 12 January 2026
Abstract Underwater images frequently suffer from chromatic distortion, blurred details, and low contrast, posing significant challenges for enhancement. This paper introduces AquaTree, a novel underwater image enhancement (UIE) method that reformulates the task as a Markov Decision Process (MDP) through the integration of Monte Carlo Tree Search (MCTS) and deep reinforcement learning (DRL). The framework employs an action space of 25 enhancement operators, strategically grouped for basic attribute adjustment, color component balance, correction, and deblurring. Exploration within MCTS is guided by a dual-branch convolutional network, enabling intelligent sequential operator selection. Our core contributions include: (1) a More >

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268

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ARTICLE

Afnan Alhindi^*, Saad Al-Ahmadi, Mohamed Maher Ben Ismail
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072625 - 12 January 2026
（This article belongs to the Special Issue: Integrating Split Learning with Tiny Models for Advanced Edge Computing Applications in the Internet of Vehicles)
Abstract Split Learning (SL) has been promoted as a promising collaborative machine learning technique designed to address data privacy and resource efficiency. Specifically, neural networks are divided into client and server sub-networks in order to mitigate the exposure of sensitive data and reduce the overhead on client devices, thereby making SL particularly suitable for resource-constrained devices. Although SL prevents the direct transmission of raw data, it does not alleviate entirely the risk of privacy breaches. In fact, the data intermediately transmitted to the server sub-model may include patterns or information that could reveal sensitive data. Moreover,… More >

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359

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ARTICLE

Qing Long¹, Bing Yi², Haiqiao Liu^3,*, Zhiling Peng¹, Xiang Liu¹
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072058 - 12 January 2026
（This article belongs to the Special Issue: Attention Mechanism-based Complex System Pattern Intelligent Recognition and Accurate Prediction)
Abstract Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring. However, conventional detection approaches are highly susceptible to noise, illumination variations, and complex environmental conditions, which often reduce detection accuracy and real-time performance. To address these limitations, we propose Lightweight and Precise YOLO (LP-YOLO), a high-precision detection framework that integrates a self-attention mechanism with a feature pyramid, built upon YOLOv8. First, to overcome the restricted receptive field and parameter redundancy of conventional Convolutional Neural Networks (CNNs), we design an enhanced backbone based on Wavelet Convolutions (WTConv), which expands the… More >

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880

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343

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Ishaani Priyadarshini^*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072343 - 12 January 2026
（This article belongs to the Special Issue: Nature-Inspired Optimization & Applications in Computer Science: From Particle Swarms to Hybrid Metaheuristics)
Abstract Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes. This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) architecture, optimized using the Firefly Optimization algorithm (FO). The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data, capturing both local patterns and sequential dependencies in diagnostic features, while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance. The approach is evaluated on two benchmark biomedical datasets: one comprising diagnostic data… More >

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Ruidong Chen^1,#, Guopeng Wang^2,#, Jingyuan Yang¹, Ziyu Wang¹, Fang Zou¹, Jia Sun¹, Xingpeng Tang¹, Ting Chen^1,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.068878 - 12 January 2026
Abstract The performance of data restore is one of the key indicators of user experience for backup storage systems. Compared to the traditional offline restore process, online restore reduces downtime during backup restoration, allowing users to operate on already restored files while other files are still being restored. This approach improves availability during restoration tasks but suffers from a critical limitation: inconsistencies between the access sequence and the restore sequence. In many cases, the file a user needs to access at a given moment may not yet be restored, resulting in significant delays and poor user… More >

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248

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ARTICLE

Ahmad Zia¹, Nazia Azim², Bekarystankyzy Akbayan³, Khalid J. Alzahrani⁴, Ateeq Ur Rehman^5,*, Faheem Ullah Khan⁶, Nouf Al-Kahtani⁷, Hend Khalid Alkahtani^8,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073818 - 12 January 2026
Abstract The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks. Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay. In this network, the task processed at fog nodes reduces transmission delay. Still, it increases energy consumption, while routing tasks to the cloud server saves energy at the cost of higher communication delay. Moreover, the… More >

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ARTICLE

Shuai Zhao, Zhiwei Zhang^*, Junkai Wang, Ye Yuan, Guoren Wang
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073315 - 12 January 2026
（This article belongs to the Special Issue: Recent Advances in Blockchain Technology and Applications)
Abstract With the development of sharded blockchains, high cross-shard rates and load imbalance have emerged as major challenges. Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates. Account partitioning based on historical transaction graphs is effective in reducing cross-shard rates but suffers from load imbalance and limited adaptability to dynamic workloads. Meanwhile, because of the coupling between consensus and execution, a target shard must receive both the partitioned transactions and the partitioned accounts before initiating consensus and execution. However, we observe that transaction partitioning and subsequent consensus do not require… More >

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357

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Shuangqing Song¹, Yuan Chen², Xuguang Hu¹, Juwei Zhang^1,3,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072392 - 12 January 2026
（This article belongs to the Special Issue: Enhancing AI Applications through NLP and LLM Integration)
Abstract In multi-domain neural machine translation tasks, the disparity in data distribution between domains poses significant challenges in distinguishing domain features and sharing parameters across domains. This paper proposes a Transformer-based multi-domain-aware mixture of experts model. To address the problem of domain feature differentiation, a mixture of experts (MoE) is introduced into attention to enhance the domain perception ability of the model, thereby improving the domain feature differentiation. To address the trade-off between domain feature distinction and cross-domain parameter sharing, we propose a domain-aware mixture of experts (DMoE). A domain-aware gating mechanism is introduced within the… More >

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333

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Fei Tang^1,*, Ping Wang¹, Jiang Yu¹, Huihui Zhu¹, Mengxue Qin¹, Ling Yang²
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073423 - 12 January 2026
（This article belongs to the Special Issue: Privacy-Enhancing Technologies for Secure Data Cooperation and Circulation)
Abstract Attribute-Based Encryption (ABE) has emerged as a fundamental access control mechanism in data sharing, enabling data owners to define flexible access policies. A critical aspect of ABE is key revocation, which plays a pivotal role in maintaining security. However, existing key revocation mechanisms face two major challenges: (1) High overhead due to ciphertext and key updates, primarily stemming from the reliance on revocation lists during attribute revocation, which increases computation and communication costs. (2) Limited universality, as many attribute revocation mechanisms are tailored to specific ABE constructions, restricting their broader applicability. To address these challenges,… More >

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Ahmet Ezgi¹, Aytuğ Onan^2,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071301 - 12 January 2026
（This article belongs to the Special Issue: Contrastive Representation Learning for Next-Generation LLMs: Methods and Applications in NLP)
Abstract Knowledge distillation has become a standard technique for compressing large language models into efficient student models, but existing methods often struggle to balance prediction accuracy with explanation quality. Recent approaches such as Distilling Step-by-Step (DSbS) introduce explanation supervision, yet they apply it in a uniform manner that may not fully exploit the different learning dynamics of prediction and explanation. In this work, we propose a task-structured curriculum learning (TSCL) framework that structures training into three sequential phases: (i) prediction-only, to establish stable feature representations; (ii) joint prediction–explanation, to align task outputs with rationale generation; and (iii)… More >

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Yang Liu, Qi Lu, Junjie Wu, Huaichang Yin, Shiwei Cheng^*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070273 - 12 January 2026
Abstract The development of brain-computer interfaces (BCI) based on motor imagery (MI) has greatly improved patients’ quality of life with movement disorders. The classification of upper limb MI has been widely studied and applied in many fields, including rehabilitation. However, the physiological representations of left and right lower limb movements are too close and activated deep in the cerebral cortex, making it difficult to distinguish their features. Therefore, classifying lower limbs motor imagery is more challenging. In this study, we propose a feature extraction method based on functional connectivity, which utilizes phase-locked values to construct a… More >

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744

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Hang Wen^1,2, Kai Zeng^1,2,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072909 - 12 January 2026
（This article belongs to the Special Issue: Omnipresent AI in the Cloud Era Reshaping Distributed Computation and Adaptive Systems for Modern Applications)
Abstract Federated learning often experiences slow and unstable convergence due to edge-side data heterogeneity. This problem becomes more severe when edge participation rate is low, as the information collected from different edge devices varies significantly. As a result, communication overhead increases, which further slows down the convergence process. To address this challenge, we propose a simple yet effective federated learning framework that improves consistency among edge devices. The core idea is clusters the lookahead gradients collected from edge devices on the cloud server to obtain personalized momentum for steering local updates. In parallel, a global momentum… More >

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Jinfeng Ji¹, Geunseok Yang^2,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071733 - 12 January 2026
（This article belongs to the Special Issue: Attention Mechanism-based Complex System Pattern Intelligent Recognition and Accurate Prediction)
Abstract Automated Program Repair (APR) techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects. Despite considerable progress in APR methodologies, existing approaches frequently lack contextual awareness of runtime behaviors and structural intricacies inherent in buggy source code. In this paper, we propose a novel APR approach that integrates attention mechanisms within an autoencoder-based framework, explicitly utilizing structural code affinity and execution context correlation derived from stack trace analysis. Our approach begins with an innovative preprocessing pipeline, where code segments and stack traces are… More >

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358

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Anis Zarrad¹, Thomas Armstrong², Jaber Jemai^3,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072768 - 12 January 2026
（This article belongs to the Special Issue: AI-Powered Software Engineering)
Abstract Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability. While prioritization selects the most relevant test cases for optimal coverage, ranking further refines their execution order to detect critical faults earlier. This study investigates machine learning techniques to enhance both prioritization and ranking, contributing to more effective and efficient testing processes. We first employ advanced feature engineering alongside ensemble models, including Gradient Boosted, Support Vector Machines, Random Forests, and Naive Bayes classifiers to optimize test case prioritization, achieving an accuracy score of 0.98847… More >

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Nghia Dinh¹, Vinh Truong Hoang^1,*, Viet-Tuan Le¹, Kiet Tran-Trung¹, Ha Duong Thi Hong¹, Bay Nguyen Van¹, Hau Nguyen Trung¹, Thien Ho Huong¹, Kittikhun Meethongjan^2,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069587 - 12 January 2026
（This article belongs to the Special Issue: Emerging Machine Learning Methods and Applications)
Abstract Ensuring the reliability of power transmission networks depends heavily on the early detection of faults in key components such as insulators, which serve both mechanical and electrical functions. Even a single defective insulator can lead to equipment breakdown, costly service interruptions, and increased maintenance demands. While unmanned aerial vehicles (UAVs) enable rapid and cost-effective collection of high-resolution imagery, accurate defect identification remains challenging due to cluttered backgrounds, variable lighting, and the diverse appearance of faults. To address these issues, we introduce a real-time inspection framework that integrates an enhanced YOLOv10 detector with a Hybrid Quantum-Enhanced More >

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Yongmei Zhang^*, Tianxin Zhang, Linghua Tian
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073159 - 12 January 2026
（This article belongs to the Special Issue: Advances in Time Series Analysis, Modelling and Forecasting)
Abstract Marine forecasting is critical for navigation safety and disaster prevention. However, traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial features. To address the limitations, the paper proposes a TimeXer-based numerical forecast correction model optimized by an exogenous-variable attention mechanism. The model treats target forecast values as internal variables, and incorporates historical temporal-spatial data and seven-day numerical forecast results from traditional models as external variables based on the embedding strategy of TimeXer. Using a self-attention structure, the model captures correlations between exogenous variables and target sequences, explores intrinsic More >

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Ahmed Awad Mohamed¹, Eslam Abdelhakim Seyam^2,*, Ahmed R. Elsaeed³, Laith Abualigah⁴, Aseel Smerat^5,6, Ahmed M. AbdelMouty⁷, Hosam E. Refaat⁸
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073171 - 12 January 2026
Abstract In recent years, fog computing has become an important environment for dealing with the Internet of Things. Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing. Task scheduling is crucial for efficiently handling IoT user requests, thereby improving system performance, cost, and energy consumption across nodes in cloud computing. With the large amount of data and user requests, achieving the optimal solution to the task scheduling problem is challenging, particularly in terms of cost and energy efficiency. In this paper, we develop novel strategies to save energy consumption across… More >

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269

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Tao Geng¹, Shuaibing Li^1,*, Yunyun Yun¹, Yongqiang Kang¹, Hongwei Li², Junmin Zhu²
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071644 - 12 January 2026
（This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems-2nd Edition)
Abstract In order to address the challenges posed by complex background interference, high miss-detection rates of micro-scale defects, and limited model deployment efficiency in photovoltaic (PV) module defect detection, this paper proposes an efficient detection framework based on an improved YOLOv11 architecture. First, a Re-parameterized Convolution (RepConv) module is integrated into the backbone to enhance the model’s sensitivity to fine-grained defects—such as micro-cracks and hot spots—while maintaining high inference efficiency. Second, a Multi-Scale Feature Fusion Convolutional Block Attention Mechanism (MSFF-CBAM) is designed to guide the network toward critical defect regions by jointly modeling channel-wise and spatial… More >

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Yonghua Huo^1,2, Ying Liu^1,*, Anni Jiang³, Yang Yang³
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072681 - 12 January 2026
Abstract With the advent of sixth-generation mobile communications (6G), space–air–ground integrated networks have become mainstream. This paper focuses on collaborative scheduling for mobile edge computing (MEC) under a three-tier heterogeneous architecture composed of mobile devices, unmanned aerial vehicles (UAVs), and macro base stations (BSs). This scenario typically faces fast channel fading, dynamic computational loads, and energy constraints, whereas classical queuing-theoretic or convex-optimization approaches struggle to yield robust solutions in highly dynamic settings. To address this issue, we formulate a multi-agent Markov decision process (MDP) for an air–ground-fused MEC system, unify link selection, bandwidth/power allocation, and task… More >

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Wang Zhang^1,#, Haozhuo Cao^2,#, Qiangqiang Yao^1,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072948 - 12 January 2026
（This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)
Abstract Recent advances in deep learning have significantly improved image deblurring; however, existing approaches still suffer from limited global context modeling, inadequate detail restoration, and poor texture or edge perception, especially under complex dynamic blur. To address these challenges, we propose the Multi-Resolution Fusion Network (MRFNet), a blind multi-scale deblurring framework that integrates progressive residual connectivity for hierarchical feature fusion. The network employs a three-stage design: (1) TransformerBlocks capture long-range dependencies and reconstruct coarse global structures; (2) Nonlinear Activation Free Blocks (NAFBlocks) enhance local detail representation and mid-level feature fusion; and (3) an optimized residual subnetwork… More >

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Abdul Rahaman Wahab Sait^1,*, Yazeed Alkhurayyif²
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072481 - 12 January 2026
Abstract Malware poses a significant threat to the Internet of Things (IoT). It enables unauthorized access to devices in the IoT environment. The lack of unique architectural standards causes challenges in developing robust malware detection (MD) models. The existing models demand substantial computational resources. This study intends to build a lightweight MD model to detect anomalies in IoT networks. The authors develop a transformation technique, converting the malware binaries into images. MobileNet V2 is fine-tuned using improved grey wolf optimization (IGWO) to extract crucial features of malicious and benign samples. The ResNeXt model is combined with… More >

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Changjun Zhou¹, Dongfang Chen¹, Chenyang Shi¹, Taiyong Li^2,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073662 - 12 January 2026
Abstract With the rapid development of smart manufacturing, intelligent safety monitoring in industrial workshops has become increasingly important. To address the challenges of complex backgrounds, target scale variation, and excessive model parameters in worker violation detection, this study proposes ADCP-YOLO, an enhanced lightweight model based on YOLOv8. Here, “ADCP” represents four key improvements: Alterable Kernel Convolution (AKConv), Dilated-Wise Residual (DWR) module, Channel Reconstruction Global Attention Mechanism (CRGAM), and Powerful-IoU loss. These components collaboratively enhance feature extraction, multi-scale perception, and localization accuracy while effectively reducing model complexity and computational cost. Experimental results show that ADCP-YOLO achieves a More >

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Juhwan Kim¹, Gwanghyun Jo², Dongsik Jo^1,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072664 - 12 January 2026
（This article belongs to the Special Issue: Extended Reality (XR) and Human-Computer Interaction)
Abstract In recent years, three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly, enabling remote collaboration among users in extended Reality (XR) environments. In addition, methods for deploying multiple cameras for motion capture of users (e.g., performers) are widely used in computer graphics. As the need to minimize and optimize the number of cameras grows to reduce costs, various technologies and research approaches focused on Optimal Camera Placement (OCP) are continually being proposed. However, as most existing studies assume homogeneous camera setups, there is a growing demand for studies on heterogeneous camera setups.… More >

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Weishan Gao^1,2, Ye Wang^1,2, Xiaoyin Wang^1,2, Xiaochuan Jing^1,2,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073850 - 12 January 2026
Abstract In the field of intelligent surveillance, weakly supervised video anomaly detection (WSVAD) has garnered widespread attention as a key technology that identifies anomalous events using only video-level labels. Although multiple instance learning (MIL) has dominated the WSVAD for a long time, its reliance solely on video-level labels without semantic grounding hinders a fine-grained understanding of visually similar yet semantically distinct events. In addition, insufficient temporal modeling obscures causal relationships between events, making anomaly decisions reactive rather than reasoning-based. To overcome the limitations above, this paper proposes an adaptive knowledge-based guidance method that integrates external structured… More >

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Georgia Garani^1,*, George Pramantiotis², Francisco Javier Moreno Arboleda³
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071509 - 12 January 2026
（This article belongs to the Special Issue: Big Data-Driven Intelligent Decision Systems)
Abstract Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation. Modern seismological research produces vast volumes of heterogeneous data from seismic networks, satellite observations, and geospatial repositories, creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making. Data warehousing technologies provide a robust foundation for this purpose; however, existing earthquake-oriented data warehouses remain limited, often relying on simplified schemas, domain-specific analytics, or cataloguing efforts. This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity. The framework integrates… More >

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Danping Niu¹, Yuan Ping^1,*, Chun Guo², Xiaojun Wang³, Bin Hao⁴
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073630 - 12 January 2026
Abstract With the increasing complexity of malware attack techniques, traditional detection methods face significant challenges, such as privacy preservation, data heterogeneity, and lacking category information. To address these issues, we propose Federated Dynamic Prototype Learning (FedDPL) for malware classification by integrating Federated Learning with a specifically designed K-means. Under the Federated Learning framework, model training occurs locally without data sharing, effectively protecting user data privacy and preventing the leakage of sensitive information. Furthermore, to tackle the challenges of data heterogeneity and the lack of category information, FedDPL introduces a dynamic prototype learning mechanism, which adaptively adjusts the More >

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Jun Li¹, Chunyan Liang^1,*, Zhiguo Liu¹, Fengpei Ge²
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071011 - 12 January 2026
Abstract To enhance speech emotion recognition capability, this study constructs a speech emotion recognition model integrating the adaptive acoustic mixup (AAM) and improved coordinate and shuffle attention (ICASA) methods. The AAM method optimizes data augmentation by combining a sample selection strategy and dynamic interpolation coefficients, thus enabling information fusion of speech data with different emotions at the acoustic level. The ICASA method enhances feature extraction capability through dynamic fusion of the improved coordinate attention (ICA) and shuffle attention (SA) techniques. The ICA technique reduces computational overhead by employing depth-separable convolution and an h-swish activation function and More >

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Moyi Zhang, Yixin Wang^*, Yu Cheng
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071021 - 12 January 2026
Abstract Modern intelligent systems, such as autonomous vehicles and face recognition, must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations. However, when neural networks learn new classes sequentially, they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes. This challenge, which lies at the core of class-incremental learning, severely limits the deployment of continual learning systems in real-world applications with streaming data. Existing approaches, including rehearsal-based methods and knowledge distillation techniques, have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features… More >

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Olanrewaju Lawrence Abraham^1,2,*, Md Asri Ngadi¹, Johan Bin Mohamad Sharif¹, Mohd Kufaisal Mohd Sidik¹
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072279 - 12 January 2026
Abstract Task scheduling in cloud computing is a multi-objective optimization problem, often involving conflicting objectives such as minimizing execution time, reducing operational cost, and maximizing resource utilization. However, traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems. To address this limitation, we introduce MDMOSA (Multi-objective Dwarf Mongoose Optimization with Simulated Annealing), a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service (IaaS) cloud environments. MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization (DMO) with the exploitation strengths of Simulated Annealing (SA), More >

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Xiang Luo¹, Yuxuan Peng², Renghong Xie¹, Peng Li³, Yuwen Qian^3,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073700 - 12 January 2026
Abstract Deep learning has made significant progress in the field of oriented object detection for remote sensing images. However, existing methods still face challenges when dealing with difficult tasks such as multi-scale targets, complex backgrounds, and small objects in remote sensing. Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot. Therefore, we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture, specifically optimized for the characteristics of large target scale variations, diverse orientations, and numerous small objects… More >

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328

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Zixuan Guo¹, Houbin Wang², Sameer Kumar^1,*, Yuanfang Chen³
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072064 - 12 January 2026
Abstract With the rapid development of digital culture, a large number of cultural texts are presented in the form of digital and network. These texts have significant characteristics such as sparsity, real-time and non-standard expression, which bring serious challenges to traditional classification methods. In order to cope with the above problems, this paper proposes a new ASSC (ALBERT, SVD, Self-Attention and Cross-Entropy)-TextRCNN digital cultural text classification model. Based on the framework of TextRCNN, the Albert pre-training language model is introduced to improve the depth and accuracy of semantic embedding. Combined with the dual attention mechanism, the… More >

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Jixin Wu^1,2, Mingtao Zhou^2,3, Di Wu^2,3, Wenqi Ren⁴, Jiatian Mei^2,3, Shu Zhang^1,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072964 - 12 January 2026
（This article belongs to the Special Issue: Advances in Action Recognition: Algorithms, Applications, and Emerging Trends)
Abstract End-to-end Temporal Action Detection (TAD) has achieved remarkable progress in recent years, driven by innovations in model architectures and the emergence of Video Foundation Models (VFMs). However, existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs, which become particularly pronounced when processing long video sequences. Moreover, the need for precise temporal boundary annotations makes data labeling extremely expensive. In low-resource settings where annotated samples are scarce, direct fine-tuning tends to cause overfitting. To address these challenges, we introduce Dynamic Low-Rank Adapter (DyLoRA), a lightweight fine-tuning framework tailored specifically… More >

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Atheer Aleran¹, Hanan Almukhalfi¹, Ayman Noor¹, Reyadh Alluhaibi², Abdulrahman Hafez³, Talal H. Noor^1,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070741 - 12 January 2026
Abstract Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs. Conventional maintenance methods, such as reactive maintenance (i.e., run to failure) or time-based preventive maintenance (i.e., scheduled servicing), prove ineffective for complex systems with many Internet of Things (IoT) devices and sensors because they fall short in detecting faults at early stages when it is most crucial. This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory (LSTM) Networks and Convolutional Neural Networks (CNNs). The framework… More >

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Bo Li, Jie Jia^*, Peiwen Tan, Xinyan Chen, Dongjin Li
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071186 - 12 January 2026
（This article belongs to the Special Issue: Deep Learning: Emerging Trends, Applications and Research Challenges for Image Recognition)
Abstract Medical image segmentation is of critical importance in the domain of contemporary medical imaging. However, U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual information. Although the subsequent U-KAN model enhances nonlinear representation capabilities, it still faces challenges such as gradient vanishing during deep network training and spatial detail loss during feature downsampling, resulting in insufficient segmentation accuracy for edge structures and minute lesions. To address these challenges, this paper proposes the RE-UKAN model, which innovatively improves upon U-KAN. Firstly, a residual network is introduced into the encoder to effectively… More >

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Zonglin Li^1,#, Xu Zhao^2,#, Yan Cao^2,*, Yazhe Li³, Yihong Zhang¹
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071511 - 12 January 2026
Abstract With the rapid development of Internet technology, REST APIs (Representational State Transfer Application Programming Interfaces) have become the primary communication standard in modern microservice architectures, raising increasing concerns about their security. Existing fuzz testing methods include random or dictionary-based input generation, which often fail to ensure both syntactic and semantic correctness, and OpenAPI-based approaches, which offer better accuracy but typically lack detailed descriptions of endpoints, parameters, or data formats. To address these issues, this paper proposes the APIDocX fuzz testing framework. It introduces a crawler tailored for dynamic web pages that automatically simulates user interactions More >

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Jalees Ur Rahman¹, Muhammad Hanif¹, Usman Haider^2,*, Saeed Mian Qaisar^3,*, Sarra Ayouni⁴
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071251 - 12 January 2026
Abstract Deep neural networks have achieved excellent classification results on several computer vision benchmarks. This has led to the popularity of machine learning as a service, where trained algorithms are hosted on the cloud and inference can be obtained on real-world data. In most applications, it is important to compress the vision data due to the enormous bandwidth and memory requirements. Video codecs exploit spatial and temporal correlations to achieve high compression ratios, but they are computationally expensive. This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos. However, contrary… More >

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Tsu-Yang Wu¹, Chengyuan Yu¹, Yanan Zhao², Saru Kumari³, Chien-Ming Chen^1,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069139 - 12 January 2026
Abstract With the increasing complexity of logistics operations, traditional static vehicle routing models are no longer sufficient. In practice, customer demands often arise dynamically, and multi-depot systems are commonly used to improve efficiency. This paper first introduces a vehicle routing problem with the goal of minimizing operating costs in a multi-depot environment with dynamic demand. New customers appear in the delivery process at any time and are periodically optimized according to time slices. Then, we propose a scheduling system TS-DPU based on an improved ant colony algorithm TS-ACO to solve this problem. The classical ant colony More >

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Sarah M. Alhammad¹, Diaa Salama AbdElminaam^2,3,*, Asmaa Rizk Ibrahim⁴, Ahmed Taha²
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069263 - 12 January 2026
（This article belongs to the Special Issue: Nature-Inspired Optimization & Applications in Computer Science: From Particle Swarms to Hybrid Metaheuristics)
Abstract Accurate parameter extraction of photovoltaic (PV) models plays a critical role in enabling precise performance prediction, optimal system sizing, and effective operational control under diverse environmental conditions. While a wide range of metaheuristic optimisation techniques have been applied to this problem, many existing methods are hindered by slow convergence rates, susceptibility to premature stagnation, and reduced accuracy when applied to complex multi-diode PV configurations. These limitations can lead to suboptimal modelling, reducing the efficiency of PV system design and operation. In this work, we propose an enhanced hybrid optimisation approach, the modified Spider Wasp Optimization… More >

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Rayeesa Mehmood^*, Sergei Koltcov, Anton Surkov, Vera Ignatenko
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072735 - 12 January 2026
（This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)
Abstract Activation pruning reduces neural network complexity by eliminating low-importance neuron activations, yet identifying the critical pruning threshold—beyond which accuracy rapidly deteriorates—remains computationally expensive and typically requires exhaustive search. We introduce a thermodynamics-inspired framework that treats activation distributions as energy-filtered physical systems and employs the free energy of activations as a principled evaluation metric. Phase-transition–like phenomena in the free-energy profile—such as extrema, inflection points, and curvature changes—yield reliable estimates of the critical pruning threshold, providing a theoretically grounded means of predicting sharp accuracy degradation. To further enhance efficiency, we propose a renormalized free energy technique that More >

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Ziyu Li¹, Zhen Chen², Xuejing Fu², Tong Mo^1,*, Weiping Li¹
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071994 - 12 January 2026
Abstract Recommendation systems are key to boosting user engagement, satisfaction, and retention, particularly on media platforms where personalized content is vital. Sequential recommendation systems learn from user-item interactions to predict future items of interest. However, many current methods rely on unique user and item IDs, limiting their ability to represent users and items effectively, especially in zero-shot learning scenarios where training data is scarce. With the rapid development of Large Language Models (LLMs), researchers are exploring their potential to enhance recommendation systems. However, there is a semantic gap between the linguistic semantics of LLMs and the… More >

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