Open Access
ARTICLE
Amit Pimpalkar1,*, Kapil N. Vhatkar2, Rachna K. Somkunwar3, Shweta Koparde4, Dalia H. Elkamchouchi5, Ateeq Ur Rehman6,*, Pooja Verma7, Salil Bharany8
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.084384
Abstract Plants are fundamental to global food security; however, plant diseases significantly reduce agricultural productivity, making early and accurate detection essential. Traditional inspection approaches rely heavily on manual observation, which is labor-intensive, subjective, difficult to scale, and susceptible to human error. In contrast, artificial intelligence (AI) combined with computer vision (CV) offers an effective solution for early-stage disease detection, minimizing yield losses while overcoming the limitations of manual monitoring systems. In this study, a novel deep learning architecture, the Swin Transformer with Harmonic Densely Connected Network (STHarDNet), is proposed. The framework integrates a Swin Transformer (ST)… More >
Open Access
ARTICLE
Zhihao Dong1,2, Huakui Sun1,2,*, Yueyue Tao2, Daosen Zhai2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.084301
（This article belongs to the Special Issue: Deep Reinforcement Learning for Space-Air-Ground Integrated Edge Computing: Architectures, Algorithms, and Applications)
Abstract High-mobility Unmanned Aerial Vehicle (UAV) swarm networks suffer from fast-varying connectivity and interference, and therefore routing decisions must jointly account for link instability and topology changes. By leveraging mobile edge computing (MEC) capabilities, each UAV can perform online routing decisions locally without relying on centralized controllers. This paper develops a Predictive-Q learning framework for dynamic routing under interference and mobility, where the Q-value is trained by a multi-factor reward that explicitly models retransmission costs, predicts link lifetime from relative motion, and anticipates forward connectivity and neighbor redundancy. To further enhance reliability under harsh interference, we More >
Open Access
ARTICLE
Mohammad Ebrahimishadman, Alireza Souri*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.084246
Abstract Automatic Number Plate Recognition (ANPR) is widely used in Intelligent Transportation Systems (ITS) and smart parking applications, but running deep learning-based ANPR directly on low-power edge devices remains difficult because of computation time, memory, and latency limitations. In this study, we develop an edge-oriented ANPR pipeline for an Internet of Things (IoT)-based sensor-triggered stop-and-go smart parking platform, targeting deployment on a resource-constrained edge device. The pipeline combines YOLOv8 for license plate detection, PaddleOCR for text recognition, and a rule-based normalization stage to reduce Optical Character Recognition (OCR) errors caused by spacing inconsistencies and plate-format variations.… More >
Open Access
ARTICLE
Tianqi Wang, Yang Li, Zhisong Pan*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.083923
Abstract Existing knowledge distillation methods for object detection struggle to bridge the teacher-student capacity gap and overlook the inherent differences between classification and regression subtasks. To address these issues, we propose a Bridging Multi-dimensional Gaps Knowledge Distillation (BMGKD) method, which comprises two core modules: a feature difference distillation module and a response difference distillation module. The feature difference distillation module achieves global feature structural alignment via improved centered kernel alignment and performs local key feature alignment using joint spatial and channel-wise cosine similarity masks. The response difference distillation module constructs a dynamic classification mask and a… More >
Open Access
ARTICLE
Wenchang Yu1, Xiaoqin Ma1,2, Zheqing Zhang1, Kezhong Lu1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.083713
Abstract Feature selection grounded in neighborhood rough sets has attracted sustained research attention owing to its principled treatment of classification uncertainty. However, existing forward greedy algorithms typically evaluate uncertainty over the entire object universe at each iteration, resulting in prohibitive computational complexity on large-scale datasets. To address this inefficiency, we introduce a new uncertainty index built upon Boundary Object Sets (BOS). BOS are defined as objects whose neighborhood granules intersect with multiple decision classes, thereby capturing intrinsic classification ambiguity. The proposed measure quantifies the proportion of these boundary objects relative to the total universe size. Grounded More >
Open Access
ARTICLE
Muhammad Asim1,#, Muhammad Amin Shahid1,#, Abdullah Khan1, Muhammad Ishaq1, Jawad Khan2,*, Syed Qamrun Nisa3, Muhammad Amir Khan4, Ines Hilali Jaghdam5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.083596
（This article belongs to the Special Issue: Advances in Object Detection and Recognition)
Abstract Indoor object detection presents unique challenges such as occlusions, varying lighting conditions, and cluttered environments. While several object detection frameworks, including RetinaNet, Faster R-CNN, SSD, and EfficientDet, have been proposed, they often suffer from high computational cost, reduced inference speed, and limited accuracy in terms of mean Average Precision (mAP), particularly in real-time scenarios. In this study, lightweight YOLO variants, namely YOLOv7, YOLOv8s, YOLOv9s, and a fine-tuned YOLOv9s which considers the optimized training strategy based on albumentations. All the models are evaluated for indoor object detection using the RGB TUT Indoor dataset. The models are… More >
Open Access
ARTICLE
Na Li1, Yashu Zhang1, Fengpu Lin1, Liutao Zhao2,*, Zhongshan Zhu3, Chen Tom4, Tengfei Tu5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.083412
Abstract Accurate mapping of video imagery to physical space coordinates represents a fundamental challenge in dynamic target tracking and intelligent video analysis systems. Traditional methods struggle to maintain stable coordinate mapping in real-time video streams due to imaging distortion variations and changing environmental conditions. This paper presents a real-time coordinate mapping approach that integrates geometric constraints with online distortion correction to achieve stable pixel-to-target coordinate transformation for video target tracking applications. The proposed method introduces a planar geometric consistency constraint and an online distortion parameter update mechanism within a unified optimization framework, enabling adaptive adjustment of… More >
Open Access
ARTICLE
Abdulaziz A. Alsulami1, Qasem Abu Al-Haija2,*, Rayed Alakhtar3, Ahmad J. Tayeb3, Badraddin Alturki3, Huda Alsobhi4, Rayan A. Alsemmeari3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.083321
（This article belongs to the Special Issue: Intelligent and Privacy-Preserving Malware Detection: Advances in Deep Learning, Memory Forensics, and Federated Security)
Abstract The rapid growth of the Internet of Things (IoT) devices has increased the attack area of modern networks, which makes effective intrusion detection systems (IDSs) essential to detect attacks that target IoT infrastructures. Federated learning is a promising approach for collaborative model training in the absence of centralized raw data. Conventional federated approaches rely on fixed client participation and static training configurations, which ensure symmetric treatment of clients despite heterogeneous local data distributions. This can limit convergence and degrade detection performance in non-IID conditions. This paper proposes an Adaptive Action-Based Federated Learning (AA-FL) framework for… More >
Open Access
ARTICLE
Mohammed Abdullah Alsuwaiket*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082979
Abstract Conventional deep learning networks impose prohibitive energy requirements on continuously operational network intelligence applications such as anomaly detection, traffic classification, and adaptive Quality-of-Service (QoS) control. This paper proposes NeuroPulse, a spiking-transformer hybrid neural architecture that combines the temporal sparsity of spiking neural networks (SNNs) with the representational power of sparse self-attention, enabling efficient deployment on neuromorphic network processors (NNPs). We propose a Rate-Coded Cross-Attention (RCCA) module, which converts population-coded spike-trains into attention queries, allowing long-range dependency modeling within sub-milliwatt (sub-mW) power budgets. NeuroPulse also supports catastrophe-free continual learning on non-stationary network traffic distributions via a More >
Open Access
ARTICLE
Maurice Kyla Octaviano, Jin-Taek Seong*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082842
Abstract Integrating high-frequency sequential signals with low-frequency contextual descriptors into a unified deep encoder is a recurring challenge in computational modelling, exemplified by cross-sectional stock ranking where price dynamics must be jointly modelled with quarterly accounting fundamentals. Existing approaches use late concatenation, where the contextual signal influences only the final prediction head and cannot shape upstream feature extraction. We propose Feature-wise Linear Modulation (FiLM) as an intermediate conditioning mechanism: fundamentals generate per-channel scaling (gamma) and shifting (beta) parameters that affinely transform the encoder’s intermediate representations before aggregation. The same price sequence thus yields different temporal features… More >
Open Access
ARTICLE
Pardis Sadatian Moghaddam1, Mahyar Mahmoudi2, Nuria Serrano3, Francisco Hernando-Gallego4, Diego Martín3,*, José Vicente Álvarez-Bravo3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081311
（This article belongs to the Special Issue: Secure and Intelligent Intrusion Detection for IoT and Cloud-Integrated Environments)
Abstract The rapid proliferation of the Internet of Things (IoT) and cyber-physical systems (CPS) within critical infrastructure sectors has significantly expanded the attack surface for advanced and stealthy cyber threats. Since these systems increasingly rely on real-time data exchange and autonomous control, developing intelligent, scalable, and adaptive anomaly detection mechanisms has become a pressing requirement. This paper proposes a novel hybrid framework, evolutionary-transformer-long short-term memory (Evo-Transformer-LSTM), that integrates the temporal modeling capability of LSTM networks, the global attention mechanism of Transformer encoders, and the optimization power of the improved chimp optimization algorithm (IChOA) for hyper-parameter tuning.… More >
Open Access
ARTICLE
Ashish Phogat1, Akash Ahlawat1, Virendra Singh2, Deepak Chhabra1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080735
Abstract Polyether ether ketone (PEEK) is a radical filament with excellent strength equivalent to cortical bone and high thermal-mechanical properties. PEEK’s acquisition is acceptable in the fabrication of cranio-maxillofacial implants because of its exceptional strength-to-weight ratio and biocompatibility. However, its implementation in fused filament fabrication (FFF) is impeded by the lack of a cohesive optimisation framework that involves varying vital parameters: layer height, infill density and two post-process parameters: annealing temperature, annealing time, which affect its mechanical performance. This research work introduces a comprehensive methodology that integrates experimental design, hybrid Genetic Algorithm Artificial Neural Network (GA-ANN)… More >
Open Access
ARTICLE
Mujahid Ali*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.084555
Abstract Understanding the determinants of travel mode choice (TMC) in urban contexts is essential for effective transport planning and policy development. Past studies predominantly employed traditional discrete choice models because of their simplicity, diversity, and high interpretability; however, they rely on restrictive assumptions. Although machine learning (ML) techniques have shown promising predictive capabilities, comparative assessments of traditional and ML approaches, particularly considering hyperparameter optimisation, remain limited. This study addresses this gap by comparing a traditional model with four ML algorithms: decision tree (DT), random forest (RF), support vector machine (SVM), and k-nearest neighbour (KNN). In addition,… More >
Open Access
ARTICLE
Khanh Nguyen-Trong1,*, Tan Nguyen-Thi-Thanh2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.083365
Abstract Automated wood surface defect detection is difficult to evaluate reliably because defects are often small, low-contrast, and visually confounded by natural wood texture, while reported performance can vary substantially with benchmark design and domain shift. To address this issue, we conduct a comparative study across three practically relevant settings: a curated seven-class benchmark, a broader in-domain seven-class protocol derived from the same source dataset, and supervised adaptation to a low-resource Vietnamese target domain. We compare lightweight two-stage detectors based on Faster Region-based Convolutional Neural Network (Faster R-CNN) with MobileNetV3-FPN against a compact You Only Look… More >
Open Access
ARTICLE
Laura Baitenova1, Gulnar Mukhamejanova2, Gauhar Munaitbas3,*, Saken Mambetov1, Zhanna Mukanova1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081004
Abstract Morphological parsing is a fundamental task in natural language processing, particularly for morphologically rich languages where words encode complex grammatical and semantic information. This paper proposes a multi-branch Transformer-enhanced neural framework for joint morphological representation learning, designed to improve segmentation and classification accuracy by integrating complementary feature extraction mechanisms. The proposed architecture combines convolutional layers for capturing local morphological patterns, recurrent layers for modeling sequential dependencies, and Transformer-based self-attention for learning global contextual relationships. This hybrid design enables the model to generate robust and context-aware representations that enhance morphological understanding. The framework is trained using… More >
Open Access
CORRECTION
Hamza Murad Khan1, Shakila Basheer2, Mohammad Tabrez Quasim3, Raja`a Al-Naimi4, Vijaykumar Varadarajan5, Anwar Khan1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.087222
Abstract This article has no abstract. More >
Open Access
ARTICLE
Saeed Ullah1, Junsheng Wu1,*, Mian Muhammad Kamal2,*, Mohammed K. Alzaylaee3, Heba G. Mohamed4,5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.084409
Abstract The proliferation of Internet of Things (IoT) devices has introduced unprecedented security challenges, necessitating efficient intrusion detection systems (IDS) capable of operating under severe resource constraints. This research presents a hardware-informed empirical study of quantized neural-network-based intrusion detection for resource-constrained IoT platforms, using an ARM Cortex-M4 deployment target as a reference. We evaluate FP32, FP16, and INT8 TensorFlow Lite model variants derived from a lightweight 1D-CNN and assess their trade-offs in clean-data accuracy, model size, estimated inference latency, estimated energy consumption, and adversarial robustness. INT8-quantized model achieves 99.10% accuracy on clean data while maintaining 97.50%… More >
Open Access
ARTICLE
Chien-Hao Tseng1, Min-Yu Chen1, Meng-Wei Lin1, Jyh-Horng Wu1, Chung-I Huang2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.083337
Abstract Context-aware driving assistance must do more than detect objects: it has to identify the cues that materially affect risk, separate observable evidence from inference, and produce recommendations that humans can audit. This paper presents a grounded multi-agent multimodal large language model (MLLM) framework for interpretable risk assessment in driving scenes. The framework decomposes reasoning into four stages—context relevance evaluation, visual interpretation, factual verification with anomaly extraction, and risk assessment with action recommendation—so that the final advisory is generated only from a verified intermediate representation rather than directly from a free-form scene description. We evaluate the… More >
Open Access
ARTICLE
Qiya Wang1,2, Jia Liu1,2,*, Yuwei Lu1,2, Yujie Liu1,2, Peng Luo1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081589
Abstract Implicit Neural Representation (INR) is a technique that models continuous signals using neural networks, replacing traditional discrete grid representations with a coordinate-to-value mapping function. As a data carrier, INR is gradually being adopted as the target for steganographic processing. However, existing INR-based steganographic schemes typically require modifying network structures (e.g., weights, nodes) and retraining to obtain stego INRs, leading to high time consumption and the need for re-training when replacing cover images. To address this issue, this paper proposes StegaMIR (Steganography via Modulated Implicit Representations), an image steganographic scheme based on modulated implicit representations. It… More >
Open Access
ARTICLE
Xing Fang1, Yuanfang Chen1,2,*, Qiang Lin3, Kun Yang2,4, Gyu Myoung Lee5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.084208
Abstract The deployment of supervised anomaly detection is typically limited by the high cost of annotation, privacy constraints, and the scarcity of anomalous samples. These constraints have motivated the use of vision-language pre-trained models for zero-shot anomaly detection. However, existing CLIP-based methods still face three limitations: a shared set of prompts is applied across feature layers, anomaly maps are fused by fixed strategies, and image-level anomaly scores are determined solely by global image-text similarity. These limitations reduce the accuracy of pixel-level localization and weaken the reliability of image-level anomaly prediction. To overcome these limitations, LaRP-CLIP is More >
Open Access
ARTICLE
Osama Al-Haj Hassan1,*, Ammar Odeh1, Abdullah Aref 2, Ghassan Samara3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.083861
Abstract Mashups are among the key web technologies that provide end-users with customizable and personalized tools. Most mashup platforms are based on centralized architectures or do not employ fully decentralized architectures; therefore, in this paper, we propose a decentralized architecture for mashups that combines the strengths of structured and unstructured peer-to-peer networks. For the structured part, we rely on the Chord lookup protocol, and for the unstructured part, we build groups of nodes via two flavors of network flooding, namely, sequence number flooding and reverse path flooding. Brokers in the unstructured part would be responsible for More >
Open Access
ARTICLE
Reen-Cheng Wang1, Hong-Sheng Wang2, Kuo-Chun Tseng2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.083124
（This article belongs to the Special Issue: Next-Generation Optimization: Quantum and Hybrid Classical Computing for Real-World Applications)
Abstract Network microsegmentation has become a key mechanism for enforcing zero-trust architecture in enterprise environments, yet its effectiveness remains closely tied to initialization quality. This study formulates network microsegmentation as a state-dependent combinatorial optimization problem in which optimization behavior depends on the availability of structural guidance. A comparative analysis is conducted across four representative optimization paradigms, including genetic algorithms (GA), differential evolution (DE), particle swarm optimization (PSO), and amplitude-ensemble quantum-inspired tabu search (AE-QTS), under both structured and unstructured conditions. Experiments are conducted on a representative brownfield enterprise network using 30 independent runs per configuration. In addition… More >
Open Access
ARTICLE
Jeonghyun Park, Hwanhee Lee*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081336
Abstract Given a multi-turn conversational context and a raw user query, the goal of Conversational Query Reformulation (CQR) is to transform the query into a de-contextualized form that maximizes retrieval effectiveness for a downstream passage retriever. Conversational search seeks to retrieve relevant passages for the given questions in a conversational question answering system. Conversational Query Reformulation (CQR) improves conversational search by refining the original queries into de-contextualized forms to address issues such as omissions and coreferences. Previous CQR methods focus on imitating human-written queries, which may not always yield meaningful search results for the retriever. In… More >
Open Access
ARTICLE
Jackson Diaz-Gorrin1,*, Candido Caballero-Gil1, Pino Caballero-Gil1, Joanna Kolodziej2,3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082207
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications, 2nd Edition)
Abstract A lightweight flow-based intrusion detection system is proposed for identifying Mirai-based distributed denial-of-service attacks in Internet of Things (IoT) environments. Efficient intrusion detection at the network edge is essential for resource-constrained IoT deployments, where devices operate with limited processing, memory, and energy resources, making centralized or computationally intensive solutions impractical in real-world scenarios. Network traffic is represented using statistical and temporal features extracted from unidirectional flows constructed from the TII-SSRC-23 dataset. A balanced subset of 10,000 samples is used for training and evaluation, ensuring balanced data distribution and improving generalization across different traffic conditions. Three… More >
Open Access
ARTICLE
Ziyi Ju1, Ping Yu1, Rui Song1, Tonglin Chen1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082060
Abstract In modern high-performance chip design, achieving timing closure is essential to design success. With the increasing scale and complexity of modern chips, timing-driven placement has become increasingly important. Traditional placement methods primarily focus on minimizing wirelength, but lack timing optimization, making it difficult to meet the strict timing closure requirements of modern designs. Therefore, developing an efficient timing-driven placement method has become a critical challenge in modern chip design. This paper presents a novel timing-driven placement framework that integrates a variational graph autoencoder (VGAE) with a nonlinear mixed-size placement optimizer. The framework identifies timing-violation paths More >
Open Access
ARTICLE
Mubariz Khan1, Hafeez Ur Rehman Siddiqui2, Adil Ali Saleem2, Muhammad Amjad Raza2,3, Lázaro Javier Hernández Rodríguez4,5,6,7, Pablo Herrero García4,8,9, Isabel de la Torre Díez10,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.084269
Abstract Accurate forecasting of cryptocurrency prices remains an open challenge because classical statistical models cannot capture the non-linear, sentiment-driven dynamics of these markets. This study compares three hybrid deep learning architectures—VAR-LSTM, XGBoost-LSTM, and CNN-LSTM—to determine which best forecasts Bitcoin (BTC), Ethereum (ETH), and Dogecoin (DOGE) closing prices, and to quantify the marginal predictive value of Twitter sentiment integration. Six years of hourly OHLCV data (2017–2023) are augmented with VADER-scored Twitter sentiment polarity. Each model is formulated mathematically, implemented with documented hyperparameters (epochs, dropout, units;), and trained for one-step-ahead next-hour price prediction. Performance is measured by RMSE,… More >
Open Access
ARTICLE
Mohammed Hassan Alnemari1,2,*, Abdelrahman Osman Elfaki3, Anas Bushnag1, Mohamed Hussien Mohamed Nerma1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082976
Abstract Sparse finite impulse response (FIR) filters reduce computational cost on resource-constrained devices, but selecting the sparsification threshold
Open Access
ARTICLE
Jiahua Kou1, Chengbo Guo1,*, Weiyue Xing1, Zheng Yang1, Jiaxuan Cao1, Shufa Sun1, Yanling Guo2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081445
（This article belongs to the Special Issue: Vision, LiDAR, and Sensor Fusion-Based SLAM for Autonomous Navigation)
Abstract Dynamic objects in LiDAR SLAM often introduce ghosting artifacts that degrade map quality. While offline methods can successfully clean these maps, they lack real-time capabilities. Conversely, online methods often suffer from state oscillation (where moving objects are misclassified as static when they temporarily stop) and incomplete point cloud removal. To address these challenges, we propose DGMSE, a real-time framework for removing dynamic point clouds in complex urban environments. Our approach consists of three sequential steps. First, the PointPillars 3D detection network quickly isolates potential dynamic objects, significantly reducing computational overhead. Second, to mitigate state oscillation, More >
Open Access
ARTICLE
Thossapon Kaewrakmuk, Jakkree Srinonchat*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081437
Abstract Robotic systems require reliable tactile perception to evaluate object stiffness during physical interaction. This study proposes a lightweight dual-branch architecture, named Hybrid-CNN-ResVgg, designed to improve hardness recognition using data from a low-cost piezoresistive tactile sensor. The model combines a one-dimensional convolutional neural network (1D-CNN) based on a ResNet8-Lite architecture for learning temporal signal patterns and a two-dimensional convolutional neural network (2D-CNN) based on a VGG6-Lite architecture for learning spatial representations derived from Gramian Angular Difference Fields (GADF). A cross-architecture fusion mechanism is introduced to integrate temporal and spatial features while reducing redundant representation learning. Experiments… More >
Open Access
ARTICLE
Rashid Jahangir1,*, Muhammad Asif Nauman2, Oumaima Saidani3, Faisal Ramzan2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080025
（This article belongs to the Special Issue: Deep Learning for Emotion Recognition)
Abstract Speech Emotion Recognition (SER) is a critical component of affective computing with broad applications in human–computer interaction, mental health monitoring, and intelligent multimedia systems. However, SER remains challenging due to the emotional ambiguity, lack of labeled data, class imbalance, and speaker variability. This study presents an effective SER framework that integrates contrastive representation learning, optimized spectrogram-based data augmentation, and selective synthetic data generation by using TimeGAN to enhance emotion classification performance. Contrastive learning enables the model to better discriminate acoustically similar emotions while Optuna automatically tunes augmentation strategies such as noise injection, time shifting, and More >
Open Access
ARTICLE
Nusrat Yasmin Nadia1, Md Habibul Arif2, Habibor Rahman Rabby3, Md Iftekhar Monzur Tanvir1, Md Jakir Hossen4,*, M. F. Mridha5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.074236
Abstract Supply chain resilience and efficiency are vital in industries characterized by volatile demand and uncertain supply, such as textiles and personal protective equipment (PPE). Traditional forecasting and optimization approaches often operate in isolation, limiting their real-world effectiveness. This paper proposes a Hybrid AI Framework for Demand–Supply Forecasting and Optimization (HAF-DS), which integrates a Long Short-Term Memory (LSTM)–based demand forecasting module with a mixed-integer linear programming (MILP) optimization layer. The LSTM captures temporal and contextual demand dependencies, while the optimization layer prescribes cost-efficient replenishment and allocation decisions. The framework jointly minimizes forecasting error and operational cost More >
Open Access
ARTICLE
Yixiang Wan, Wenqiu Zhu*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.084474
Abstract Addressing the two critical challenges of missed detection of distant small targets and difficulty in identifying occluded targets under complex road conditions, this paper proposes YOLO-PBE, an improved high-precision vehicle detection model based on YOLOv11n. First, to tackle the fine-grained feature loss caused by conventional strided convolutions during downsampling, we add a high-resolution P2 detection layer and introduce SPD-Conv, a lossless spatial-to-depth feature transformation technique, for feature extraction. By preserving complete pixel-level information, the model's perception accuracy for distant small vehicles is enhanced. For feature fusion, we design an improved BiFPN incorporating a Ghost module.… More >
Open Access
ARTICLE
Pei Xie1, Xiaoying Yang1,*, Bo Li1, Zhijie Pei1, Fenghai Yang2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.083100
Abstract To address the critical requirements for collaborative delivery of multiple tasks within each order in personalized mass customization, this paper develops a dynamic parallel machine scheduling model that accounts for stochastic machine failures and order priorities, thereby more accurately reflecting the uncertainties and complexities of real-world production environments. A dual-objective optimization framework is adopted to minimize both the makespan (maximum task completion time) and the variance of task completion times, aiming to improve the coordination and reliability of intra-order task delivery. An adaptive weighted reward function is designed to balance overall scheduling efficiency with consistency… More >
Open Access
ARTICLE
Yasemin Onal*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082593
Abstract Photovoltaic (PV) power generation exhibits considerable sensitivity to both weather variability and fluctuations in solar irradiance. Consequently, precise forecasting of PV power is crucial for ensuring grid reliability, load balancing, and the effective functioning of energy markets within a grid-connected solar plant. Conventional forecasting methodologies frequently prove inadequate in accurately capturing the nonlinear and intricate temporal patterns present within PV datasets. To address these shortcomings, this research presents a hybrid short-term PV power forecasting model. This model integrates Neighborhood Component Analysis (NCA) for dimensionality reduction with a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) framework.… More >
Open Access
REVIEW
Qiwu Wu1, Tao Yang2,*, Yunchen Su2, Lingzhi Jiang3, Tao Tong2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082757
Abstract Deep reinforcement learning (DRL) has become an important method in Unmanned Aerial Vehicle(UAV) path planning, but the field still lacks a dedicated bibliometric review that summarizes its publication patterns, intellectual structure, and thematic evolution. This study analyzes 1402 Web of Science publications from 2010 to 2025 using CiteSpace, VOSviewer, and the Bibliometrix R package. Three main findings are reported. First, the bibliometric evidence suggests a four-phase evolution of the field—foundational exploration (2015–2016), continuous-control breakthrough (2017–2019), multi-agent collaborative coordination (2020–2022), and complex-scenario integration (2023–2025)—as reflected in publication trends, keyword bursts, and co-citation clusters. Second, co-citation and keyword More >
Open Access
ARTICLE
Hongzhen Liu1, Liang Xie1, Zhiqiang Ru2,*, Yuan Wan1, Zhe Zhang1, Xi Fang1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082755
Abstract Federated learning is a privacy-preserving machine learning framework that facilitates model training directly on decentralized data that, due to privacy concerns or transmission costs, cannot be centralized on a server for traditional model training. To prevent adversaries from reconstructing the original data via parameters transmitted during the process, homomorphic encryption is a commonly adopted method. However, it introduces significant communication and computation costs and risks total security failure if any secret key is compromised. This paper proposes a privacy-preserving aggregation mechanism that enables each client to independently generate partial keys for encryption while allowing decryption… More >
Open Access
ARTICLE
Suchang Yang, Hongtao Yu*, Ruiyang Huang, Huansha Wang, Ran Li, Junzheng Li
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082651
（This article belongs to the Special Issue: Dynamics, Control and Optimization in Complex Networks)
Abstract Modeling dynamic graphs in continuous time is critical for applications such as user behavior prediction and recommendation systems. These models can effectively capture fine-grained and long-term temporal dependencies. However, existing approaches often suffer from high computational costs and optimization difficulties, especially when handling time-sorted neighborhood sequences over long horizons. In this work, we propose DyG-Hyena, a novel continuous-time dynamic graph learning framework that combines conditional variational autoencoder (CVAE)-assisted temporal modeling with efficient feature fusion. Our approach has two main innovations: (i) Efficient temporal fusion—we replace the Transformer with an improved, lightweight Hyena module to model More >
Open Access
ARTICLE
Longhu Huang, Sheng Zheng*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082622
Abstract In the field of online automated defect inspection for small-size liquid crystal display modules (LCMs), the accuracy of module loading is crucial for the subsequent lighting inspection. However, due to the physical characteristics of the module’s flexible ribbon cable, the ribbon often exhibits varying degrees of curling, causing conventional monocular vision systems to frequently encounter local underexposure or overexposure when positioning the workpiece, resulting in loss of local details and significantly affecting subsequent positioning and loading. To address the problem of local image degradation caused by abnormal exposure, this study proposes a regional image generation… More >
Open Access
ARTICLE
Olzhas Olzhayev1, Bakhytzhan Kulambayev2,*, Azizah Suliman3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082618
Abstract Automated road damage detection is a critical component of intelligent transportation systems, enabling efficient infrastructure maintenance and improved traffic safety. However, existing approaches often suffer from limited contextual understanding, insufficient segmentation accuracy, and suboptimal real-time performance. This study presents TCR-RoadNet, a transformer-enhanced multi-task deep learning architecture designed for simultaneous road damage detection and segmentation in real-world driving environments. The proposed framework integrates a multi-scale convolutional backbone with a Transformer Context Refinement (TCR) module to capture both fine-grained structural details and long-range spatial dependencies across feature scales. To further enhance performance, a Decoupled Detection Head (DDH)… More >
Open Access
ARTICLE
Qiuhao Xu1,2, Chen Wang1,3,*, Xi Wen1, Lurong Jiang1, Wenying Zheng4,*, Zhengkui Chen1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082310
（This article belongs to the Special Issue: GenAI/AI in Biometric Recognition: Theoretical Foundations, Applications, and Emerging Challenges)
Abstract The convergence of Generative Artificial Intelligence and biometric recognition is reshaping modern healthcare. It enables more adaptive and intelligent human–machine interactions. Epilepsy, a common neurological disorder affecting millions worldwide, relies heavily on electroencephalography (EEG) signals for diagnosis and monitoring. Wearable consumer devices with EEG sensors support continuous physiological data collection. However, transmitting sensitive biometric data to centralized servers introduces serious privacy and security risks. Federated learning (FL) provides a distributed training framework that keeps raw data on local devices. Despite this advantage, existing FL methods remain vulnerable to gradient leakage attacks, where adversaries may infer More >
Open Access
ARTICLE
Hangyu Hu1, Liangrui Zhang1, Xiaowei Huang1, Xingmiao Yao1,2,*, Youyang Qu3, Xia Wu1, Guangmin Hu1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081752
Abstract With the evolution of information technology toward more advanced intelligence and automation, Security Orchestration, Automation, and Response (SOAR) has become a critical foundation for security incident handling, owing to its intelligent orchestration capabilities. Security playbooks, as the core mechanism for automated response in SOAR, require well-designed workflows and precise action matching to ensure efficient and accurate alert handling. However, with the rising sophistication of attacks and the expanding scale of security alerts, traditional expert-driven playbook recommendation approaches often degrade in recommendation quality or completely fail when existing playbook repositories cannot adequately cover unknown or novel… More >
Open Access
ARTICLE
Junchen Huo1, Song Wang1,*, Enqing Chen1, Yingqiang Ding1, Shouyi Yang2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081658
Abstract Multi-modal 3D object detection, which leverages the complementary strengths of LiDAR point clouds and camera RGB images, has emerged as a critical component of 3D perception in autonomous driving. As a critical challenge in multi-modal learning, modality alignment aims to establish accurate semantic correspondences across distinct modalities. However, existing methods encounter significant difficulties in achieving robust alignment when data from one modality is obscured, such as in the presence of object occlusion or adverse environmental conditions, including illumination variations and inclement weather. To alleviate this issue, we present CG-MAE, a dual-branch Bird’s-Eye-View (BEV) masked autoencoder… More >
Open Access
ARTICLE
Atef Ibrahim1,*, Fayez Gebali2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081651
Abstract The ongoing expansion of the Internet of Things (IoT) fundamentally alters industrial and economic paradigms by integrating intelligent nodes throughout operational frameworks. Nonetheless, vulnerabilities surrounding system integrity and data confidentiality present major bottlenecks to widespread adoption, a dilemma severely intensified by impending quantum computing capabilities. Defending these networks demands the integration of post-quantum cryptographic primitives; yet, the severe hardware constraints characterizing peripheral IoT components complicate practical deployment. Quantum-resistant lattice cryptography offers a highly promising pathway to overcome these limitations, largely because the foundational security and throughput of these protocols hinge on polynomial multiplication performance. Consequently,… More >
Open Access
ARTICLE
Shasha Tian1,2, Zhengyang Chen1,3, Kai Ren1,2, Na Li1,2, Chongwei Ruan4, Zhijia Cui1,3, Mian Wu4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081556
（This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications, 2nd Edition)
Abstract To address the issues of low exploration efficiency and “geometric myopia” caused by the lack of high-level environmental structure modeling for mobile robots in complex indoor environments, this paper proposes an active SLAM object navigation method based on Situational Semantic Augmented Graph (SSAG). Unlike methods that learn policies solely on pixel-level semantic maps or exploit only object-level relations for implicit association, this work elevates local observations online into a room-level topological graph and performs explicit semantic reasoning over unobserved regions. First, an online room segmentation algorithm is employed to transform unstructured sensory data into a… More >
Open Access
REVIEW
Ferenc Erdős1,*, Vijayakumar Varadarajan2,3,4, Viorel-Costin Banţa5, Stephen Afrifa6,7
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081005
Abstract Standard retrieval-augmented generation (RAG) can perform poorly in AI for IT Operations (AIOps) settings because it is topology-blind. Basic RAG retrieves isolated, flat text snippets without enforcing structural or causal constraints, causing large language models to generate explanations that contradict the running system’s actual dependency structure. To address this gap, we conducted a systematic review following PRISMA 2020, searching Scopus, IEEE Xplore, Web of Science, and Google Scholar (last searched 31 January 2026). We included empirical or systems-oriented studies applying graph-based retrieval to ground a generative model in an IT, cloud, or software-operations setting, and… More >
Open Access
ARTICLE
Rashid Jahangir1,*, Nazik Alturki2, Muhammad Zubair Khan1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080593
（This article belongs to the Special Issue: Modern Challenges in Cryptography and Cybersecurity)
Abstract Deepfake audio created with sophisticated speech synthesis and voice cloning technologies is a threat to the credibility of digital communication. Its realism has raised serious concerns in different applications such as digital forensics, cybersecurity, media authentication and voice-based security systems. However, deepfake audio detection still remains difficult. Synthetic speech tends to have subtle artifacts that can mimic the natural vocal pattern very closely. Variations in speakers, recording conditions and background noise make the task more complex. In addition, dataset imbalance and low diversity in training samples could lead to low robustness in the model. To… More >
Open Access
ARTICLE
Jialing Tao, Song Huang*, Changyou Zheng*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080119
Abstract Background: Jailbreak attacks, which use crafted prompts to bypass safety alignments of Large Language Models (LLMs) and generate harmful content, pose a significant security threat. Existing methods often optimize for a single objective (e.g., attack success rate), neglecting critical factors like query efficiency, which limits their practicality and generalization. Methods: We propose a Componentized Multi-Objective Optimization Framework (CMOOF), which introduces a paradigm shift: it searches for generalizable and query-efficient attack strategy templates within a structured, component-based strategy space. CMOOF leverages the NSGA-II algorithm to explicitly co-optimize two first-class objectives: Attack Success Rate (ASR) and Query More >
Open Access
ARTICLE
Mohammad Q. Al-Jamal1, Mahmoud Al Jamal2, Bashar S. Khassawneh3,*, Ayoub Alsarhan4,5, Amina Salhi6, Tahani Alsubait7
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079984
Abstract Energy sustainability and secure operation are persistent challenges in Internet-of-Things (IoT) wireless sensor networks (WSNs), where limited battery capacity, heterogeneous traffic, and security procedures jointly drive premature node depletion and service degradation. This paper proposes an uncertainty-aware bilevel co-optimization framework that unifies residual-energy prediction with robust, energy-aware scheduling for clustered IoT-WSNs. At the lower level, a lightweight temporal predictor (TCN + LSTM with stochastic sampling) learns short-horizon residual-energy evolution from multivariate, dataset-aligned windows capturing sensing/communication activity, proximity-to-cluster-head effects, and security overhead (authentication latency, key exchange, and rekeying), and produces both point forecasts and uncertainty estimates to… More >
Open Access
ARTICLE
Chin Soon Ku1,*, Hui Yi Lim2, Ana Nabilah Binti Sa’uadi2, Siew Cheng Lai1, Jit Theam Lim3, Pei Xuan Ku4, Zeng-Wei Hong5, Lip Yee Por6
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082586
Abstract In the expanding Internet of Things (IoT) ecosystem, billions of interconnected devices exchange sensitive data, making secure and usable authentication critical. IoT devices in public or shared environments are vulnerable to shoulder-surfing and video recorded observation attacks. Traditional passwords and static graphical schemes remain susceptible due to predictable patterns and direct credential entry. This study presents a novel recognition-based graphical authentication scheme that combines pass-image selection with compass direction substitution and rotation logic to resist observation-based attacks. A prototype was evaluated with 58 participants over three days. Usability metrics included registration time, login time, success… More >
Open Access
ARTICLE
Hongliang Tian, Xiaoke Liu*, Bolin Song, Chenying Pei
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082430
（This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies, 2nd Edition)
Abstract In the intelligent inspection of power systems, the detection of equipment defects is confronted with problems such as low background discrimination, multi-scale morphological differences, and the difficulty in identifying small targets and fine-grained defects, which makes it hard for existing models to balance detection accuracy and computational efficiency. To address this, this study proposes an improved lightweight detection framework, GRID-YOLO. This framework enhances the semantic discrimination ability of the backbone network for complex defects by introducing a cross-stage hierarchical multi-cognitive spatial attention module (C2MSA), designs an enhanced multi-scale bidirectional feature pyramid network (EMFPN) to achieve… More >
Open Access
ARTICLE
Betül Şenyayla1, Aytuğ Onan2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081260
Abstract Large Language Models (LLMs) have become a cornerstone of modern natural language processing, achieving strong performance across diverse tasks. Despite these advances, their tendency to generate hallucinated or factually unsupported content remains a critical challenge for reliable deployment. Existing evaluation approaches predominantly rely on single-task settings and aggregate performance metrics, implicitly assuming that hallucination behavior is uniform across tasks. However, this assumption is fundamentally flawed, as hallucination characteristics vary significantly depending on task formulation, linguistic context, and evaluation criteria. To address these limitations, this paper proposes HalluBench, a task-aware multi-LLM benchmarking framework designed for systematic… More >
Open Access
ARTICLE
Xiaoyun Yuan1,2,3,*, Zhengge Yi1,2, Jingxi Zhang1,2, Hairui Zhang3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.072275
（This article belongs to the Special Issue: Cyberspace Mapping and Anti-Mapping Techniques)
Abstract Website Fingerprinting (WF) has emerged as a promising technique for identifying user access patterns to Hidden Services (HS). Despite growing interest in WF for HS, the absence of well-established foundations and systematic guidelines for feature selection undercuts the robustness of WF techniques in the face of concept drift. To address this gap, we present an empirical study focusing on feature resilience under concept drift in WF for HS. Specifically, we categorize features into network-specific and network-agnostic groups and quantify their information leakage potential via mutual information. We further assess each feature’s resilience to concept drift More >
Open Access
ARTICLE
Adnan Hnaif1,*, Hanadi Al-Shawabkah2, Ayman Alqafaan2, Mohammad Alia1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082998
Abstract Many fast pattern-matching mechanisms are used in NIDS (Network Intrusion Detection Systems) to filter higher volumes of network traffic prior to invoking expensive rule verification stages. This filtering phase in signature-based engines, such as Snort, needs to preserve exact matching semantics while being able to process at high throughput on commodity hardware. Here, we introduce a hybrid CPU–GPU architecture-aware framework for exact multi-pattern matching based on the Weighted Exact Matching Algorithm (WEMA). WEMA performs the most relevant matching based on deterministic ordered indexing of category units, which eliminates chaotic control flow (which occurs with automata… More >
Open Access
ARTICLE
Riya Sharma1,*, Ashima Singh1, Anju Bala1, Mukesh Singh2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082720
（This article belongs to the Special Issue: AI-Enabled Prognostics and Health Management: Advanced Methodologies, Intelligent Systems, and Field Applications)
Abstract State of health (SoH) prediction of lithium-ion batteries is a critical yet challenging task due to the complex, highly non-linear, and time-dependent nature of degradation processes under diverse operating conditions. Variability in usage patterns, environmental factors, and electrochemical dynamics further limits the robustness and generalisation capability of conventional estimation models. This study proposes a hybrid deep learning framework that combines Gated Recurrent Units (GRUs) with Kolmogorov–Arnold Networks (KANs) to address these challenges. GRUs are employed to effectively capture temporal dependencies in sequential battery data, while KANs enhance the model’s ability to learn complex non-linear functional… More >
Open Access
ARTICLE
MengDie Hu#, Na Wang*, XueHui Du#, BaiDong Huang#, KaiYuan Wang#
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082658
Abstract With the rapid development of the Internet of Things (IoT) and edge intelligence, the volume of data generated by edge devices has grown explosively. Federated learning (FL), characterized by the paradigm of “data remaining local while models are shared,” has emerged as a key approach for adapting to the distributed architecture of edge computing, breaking down data silos, and enabling privacy preservation. However, its practical deployment in edge computing environments still faces significant challenges, including limited device resources and pronounced data heterogeneity. Existing pruning strategies for federated learning are predominantly based on static and single-design… More >
Open Access
ARTICLE
Li Peng1,2, Xiangbing Li1,2, Kun Zou1, Yong Liu1,2,*, Haibo Huang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082629
（This article belongs to the Special Issue: Deep Learning for Next-Generation Cybersecurity: Architectures, Robustness and Applications)
Abstract In recent years, the transferability of adversarial examples has attracted significant attention. To improve the effectiveness of black-box attacks, a frequency-domain decay constraint is introduced, inspired by weight decay and regularization techniques commonly employed during model training. By treating adversarial perturbations as inputs in an optimization process, this constraint aims to mitigate the excessive reliance on low-frequency components during adversarial example generation, thereby enhancing transferability. Fourier heatmaps are utilized to analyze the sensitivity of input samples, enabling a decomposition of the frequency spectrum into low-frequency and high-frequency components. Based on this analysis, low-frequency attenuation is More >
Open Access
ARTICLE
Saken Tleuberdin1, Dina Satybaldina2,*, Raikhan Muratkhan3, Gulsipat Abisheva2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081906
Abstract We perform a cross-layer penetration testing on one of the most popular Wi-Fi smart locks (Tuya 902V). The methodology combines wireless traffic analysis using an Alfa AWUS036AXML adapter, forced re-association via deauthentication to make Wi-Fi Protected Access 2 (WPA2) 4-way Extensible Authentication Protocol over LAN (EAPOL) handshake visible with Airodump/Aireplay, offline dictionary attack with Aircrack-ng, Android app reverse engineering using Apktool, Jadx, and MobSF; denial-of-service experiment (DoS) executed by hping3; Near-Field Communications (NFC)/Radio-Frequency Identification (RFID) key-clone attempt by Flipper Zero. Handshake is empirically captured but no Wi-Fi passphrase found under 14M dictionary entries; DoS test… More >
Open Access
ARTICLE
Yuxuan Feng, Lili Liang*, Guanglu Sun, Yanrui Wei
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081876
Abstract In doped two-dimensional nanomaterials, magnetism is one of the important physical properties. By introducing foreign doping atoms or molecules, the electronic structure of the material can be effectively regulated, leading to changes in magnetic behavior. Currently, magnetic property prediction has achieved considerable results with the help of traditional CNNs, but there are still obvious limitations: (1) The feature extraction of dopant sites is constrained by fixed receptive fields, making it difficult to characterize local structural perturbations in the vicinity of dopant atoms and their spatial influence propagating to surrounding regions; (2) CNNs lack the capability… More >
Open Access
ARTICLE
Yuanzhen Wang1, Hongxin Zhang2,3,*, Shaofei Sun1, Yaqi Zhang2, Xing Fang4, Zhi Sun2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081308
Abstract The effectiveness of profiling deep learning side-channel attacks relies on the assumption that training and attack data follow the same distribution. However, when the profiling device differs from the target device, process-voltage-temperature (PVT) variations and clock jitter countermeasures cause distribution shifts in power traces, rendering models trained on the source device ineffective on the target. Existing domain adaptation methods typically rely on a single distributional constraint without jointly constraining kernel mean embeddings and covariance structure, thus limiting their effectiveness against strong defenses such as clock jitter. We propose Robust Feature Alignment for Side-Channel Analysis (RFA-SCA),… More >
Open Access
ARTICLE
Sara Tehsin1, Tallha Akram2,*, Syed Rameez Naqvi3, Meshal Alharbi4, Abdulrahman Alabduljabbar2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081304
Abstract Transformers have become the dominant architecture for sequence modeling in natural language processing; however, their effectiveness critically depends on how positional information is encoded. Conventional positional encodings, while effective, may have limited structural flexibility for capturing complex global sequence relationships. Recent quantum-inspired approaches have sought to address this limitation, yet many either oversimplify quantum principles or introduce substantial computational or hardware overhead. We introduce a novel Quantum Fourier Transform (QFT)-inspired positional encoding scheme for transformers, motivated by the structured frequency representation of the QFT. Unlike prior approaches that either emulate quantum operations superficially or require… More >
Open Access
ARTICLE
Yun Liu1, Jinghua Zhao1, Liang Ma1, Zijie Huang2,3,*, Lizhi Cai2,3, Jianxin Ge2,3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.084179
Abstract Automated library migration reduces refactoring costs but challenges traditional evolutionary algorithms, which often suffer from premature convergence and poor recall in sparse, complex API mapping spaces. To address this, we propose QIMIG, a multi-objective optimization framework integrating quantum-inspired encoding with quality-aware and greedy heuristic filtering. QIMIG utilizes a probabilistic Q-bit representation to maintain population diversity and avoid local optima. Simultaneously, its heuristic components leverage historical usage context to filter semantic noise and guide the search toward valid mappings. Evaluated on 9 real-world migration rules derived from 57,447 open-source projects, QIMIG statistically significantly outperforms state-of-the-art baselines More >
Open Access
ARTICLE
Ying Cao1, Penghui Zhao1, Xinyu Qiao1, Ningfan Zhan1, Xiaomei Zou2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.084057
Abstract Multimodal Sentiment Analysis (MSA) integrates diverse modalities to identify emotional states, yet performance often suffers in scenarios with missing data. In this situation, despite the promising results of recent methods, the failure of part methods to fully exploit the latent valid information contained in incomplete modalities may degrade predictive performance. Besides, to address the oversight of varying contributions across modalities to sentiment understanding, the score-based weighting schemes in the exhibited methods remain overly sensitive to data fluctuations, leading to unstable and unreliable predictions. To this end, we propose a novel method, Data Mining and Uncertainty-Aware… More >
Open Access
ARTICLE
Amir Ijaz*, Hashem Haghbayan, Abdul Malik, Ethiopia Nigussie, Juha Plosila
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.083797
Abstract The exponential growth of the Internet of Things (IoT) has led to an urgent need for highly energy-efficient communication strategies, especially for battery-powered or self-sustaining devices. In this work, we present a comprehensive framework for minimizing communication energy in IoT nodes operating in swarm robotic systems. We examine and integrate multiple low-power wireless technologies (BLE, LoRaWAN, MQTT, CoAP) with advanced Medium Access Control (MAC) protocols. We additionally propose adaptive scenarios leveraging both ambient energy harvesting and passive backscatter transmission. Our solution employs adaptive scheduling and dynamic transmission power management. Specifically, a Deep Q-Learning (DQL) agent More >
Open Access
ARTICLE
Linyu Dong1, Tao Li2, Hao Li2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.083042
Abstract While Transformer-based detectors excel in global modeling, their efficacy in unmanned aerial vehicle (UAV)-based tiny object detection is limited by information loss during aggressive downsampling and the lack of high-frequency structural cues. To bridge this gap, we propose HiFreq-DETR, a dedicated framework that optimizes the synergy between spatial fidelity and semantic discriminability. The core innovation lies in its hierarchical information preservation strategy, which employs a ResNeSt14d backbone coupled with an
Open Access
ARTICLE
Xiaorong Feng1,2, Ying Gao1,*, Leyu Shi2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082481
Abstract Open source software has become a fundamental component of modern software ecosystems, supporting a wide range of critical applications in operating systems, cloud services, embedded systems, and security-sensitive infrastructures. However, the rapid growth of open source projects also brings increasingly serious security challenges. Many widely used C/C++ components still contain hidden vulnerabilities, and attackers are no longer limited to exploiting traditional memory-related bugs such as buffer overflows or use-after-free errors. In recent years, non-memory logic flaws, including improper authentication, incorrect state transitions, flawed boundary checks, and insecure API usage, have become more prevalent and more… More >
Open Access
ARTICLE
Shaohuang Bian1,#, Qinxiu Gao1,#, Shan Su1, Weifeng Wang1, Feng Huang2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081818
Abstract Tomato, as a globally important crop, its freshness directly affects postharvest quality, market value, and consumer acceptance. Traditional tomato freshness evaluation mainly relies on manual inspection and experience-based judgment, which is time-consuming, labor-intensive, and inefficient. Meanwhile, plasma technology has shown promising potential in agricultural preservation due to its safety and effectiveness, making the evaluation of tomato freshness after plasma treatment particularly important. In recent years, with the rapid development of deep learning technology, non-destructive detection methods based on image analysis have become important tools for agricultural product quality assessment. This study proposes an improved YOLOv8n-based… More >
Open Access
ARTICLE
Israt Jahan1, Afsana Begum1, Bibhas Roy Chowdhury Piyas1,*, Fahmid Al Farid2,3,*, Fatama Jannat Tisha1, Shahrin Islam1, Abu Saleh Musa Miah4, Hezerul Abdul Karim3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081631
Abstract Transforming underlying cardiovascular risk into actionable clinical decisions remains a major challenge in contemporary healthcare. Despite advances in cardiology, early-stage cardiovascular disease often remains undetected, which hinders timely intervention and leads to preventable deaths. To overcome this problem, this study presents an explainable machine learning framework for the early diagnosis of cardiovascular disease (CVD). Initially, this study examined several data-balancing strategies, for example, SMOTE (Synthetic Minority Over-sampling Technique), SMOTETomek (Synthetic Minority Over-sampling Technique + Tomek Links), Tomek Links, ADASYN (Adaptive Synthetic Sampling), and SMOTE-ENN (Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors) within the data-preprocessing pipeline. We… More >
Open Access
ARTICLE
Akmalbek Abdusalomov1, Kudratjon Zohirov2, Azizbek Khojamurotov3, Furkat Safarov3,4, Alpamis Kutlimuratov5, Jasur Sevinov6,7, Zavqiddin Temirov8, Abror Buriboev5,9,10, Heung Seok Jeon11,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079237
Abstract Medical texts are often complex and difficult to understand for non-specialists, creating barriers to effective communication in the clinical and rehabilitation fields. Although recent advances in natural language processing (NLP) have enabled automated text simplification, existing approaches often struggle to maintain medical accuracy and frequently result in factual inconsistencies or distortions. To address these issues, we propose the Neuro-Semantic Clinical Filter (NSCF), a novel NLP-based framework designed for clinically accurate simplification of medical texts. The proposed method integrates a Medical Concept Graph Encoder (MCGE) to incorporate structured domain knowledge, a Neuro-Symbolic Transformer (NSTR) for supervised… More >
Open Access
ARTICLE
Spandana Saggurthi1, Anand Nayyar2, Sk Hasane Ahammad1, Sumendra Yogarayan3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080058
（This article belongs to the Special Issue: Nature-Inspired Optimization & Applications in Computer Science: From Particle Swarms to Hybrid Metaheuristics)
Abstract This work presents a multi-objective optimization framework for systematic design-space exploration of a 28 GHz single-stage cascode LNA (Low noise amplifier) in 22 nm FDSOI technology using NSGA-II and MOPSO algorithms. The objectives of the paper include simultaneous minimization of noise figure (NF) and power consumption while maximizing gain under matching and stability constraints. Using device parameters and circuit models that were developed for a 22 nm FDSOI process technology, an optimization framework was created in Python, with the passive components
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