Adel Saad Assiri^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069826 - 10 November 2025

Abstract Customer churn is the rate at which customers discontinue doing business with a company over a given time period. It is an essential measure for businesses to monitor high churn rates, as they often indicate underlying issues with services, products, or customer experience, resulting in considerable income loss. Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth. Traditional machine learning (ML) models often struggle to capture complex temporal dependencies in client behavior data. To address this, an optimized deep learning (DL) approach using a Regularized Bidirectional Long Short-Term… More >

Ameer Hamza, Robertas Damaševičius^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-41, 2026, DOI:10.32604/cmc.2025.069721 - 10 November 2025

Abstract This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities, focusing on recent trends from 2022 to 2025. The primary objective is to evaluate methodological advancements, model performance, dataset usage, and existing challenges in developing clinically robust AI systems. We included peer-reviewed journal articles and high-impact conference papers published between 2022 and 2025, written in English, that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification. Excluded were non-open-access publications, books, and non-English articles. A structured search was… More >

Arvind Mukundan^1,2, Riya Karmakar¹, Devansh Gupta³, Hsiang-Chen Wang^1,4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069646 - 10 November 2025

Abstract Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0. Manual inspection of products on assembly lines remains inefficient, prone to errors and lacks consistency, emphasizing the need for a reliable and automated inspection system. Leveraging both object detection and image segmentation approaches, this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning (DL) models. Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images… More >

Haihao He^1,2, Xianzhou Dong^1,*, Shuangshuang Wang¹, Chengzhang Zhu¹, Xiaotong Zhao^1,2

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069513 - 10 November 2025

Abstract Clock synchronization has important applications in multi-agent collaboration (such as drone light shows, intelligent transportation systems, and game AI), group decision-making, and emergency rescue operations. Synchronization method based on pulse-coupled oscillators (PCOs) provides an effective solution for clock synchronization in wireless networks. However, the existing clock synchronization algorithms in multi-agent ad hoc networks are difficult to meet the requirements of high precision and high stability of synchronization clock in group cooperation. Hence, this paper constructs a network model, named DAUNet (unsupervised neural network based on dual attention), to enhance clock synchronization accuracy in multi-agent wireless ad hoc… More >

Abeer Alhuzali^1,*, Qamar Al-Qahtani¹, Asmaa Niyazi¹, Lama Alshehri¹, Fatemah Alharbi²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.069491 - 10 November 2025

Abstract The surge in smishing attacks underscores the urgent need for robust, real-time detection systems powered by advanced deep learning models. This paper introduces PhishNet, a novel ensemble learning framework that integrates transformer-based models (RoBERTa) and large language models (LLMs) (GPT-OSS 120B, LLaMA3.3 70B, and Qwen3 32B) to enhance smishing detection performance significantly. To mitigate class imbalance, we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques. Our system employs a dual-layer voting mechanism: weighted majority voting among LLMs and a final ensemble vote to classify messages as ham, spam, or smishing. Experimental More >

Xuanhong Wang¹, Hongyu Guo¹, Jiazhen Li¹, Mingchen Wang¹, Xian Wang¹, Yijun Zhang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.069482 - 10 November 2025

Abstract Over the past decade, large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation. However, these models require enormous datasets and parameters, and their multi-step generation processes are often inefficient and difficult to control. To address these challenges, we propose CAFE-GAN, a CLIP-Projected GAN with Attention-Aware Generation and Multi-Scale Discrimination, which incorporates a pre-trained CLIP model along with several key architectural innovations. First, we embed a coordinate attention mechanism into the generator to capture long-range dependencies and enhance feature representation. Second, we introduce a trainable linear projection layer after the CLIP text… More >

Yongbo Jiang, Shengnan Huang, Tao Feng, Baofeng Duan^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-22, 2026, DOI:10.32604/cmc.2025.069423 - 10 November 2025

Abstract In the context of modern software development characterized by increasing complexity and compressed development cycles, traditional static vulnerability detection methods face prominent challenges including high false positive rates and missed detections of complex logic due to their over-reliance on rule templates. This paper proposes a Syntax-Aware Hierarchical Attention Network (SAHAN) model, which achieves high-precision vulnerability detection through grammar-rule-driven multi-granularity code slicing and hierarchical semantic fusion mechanisms. The SAHAN model first generates Syntax Independent Units (SIUs), which slices the code based on Abstract Syntax Tree (AST) and predefined grammar rules, retaining vulnerability-sensitive contexts. Following this, through More >

Hamza Murad Khan¹, Shakila Basheer², Mohammad Tabrez Quasim³, Raja`a Al-Naimi⁴, Vijaykumar Varadarajan⁵, Anwar Khan^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.069327 - 10 November 2025

Abstract With the increasing growth of online news, fake electronic news detection has become one of the most important paradigms of modern research. Traditional electronic news detection techniques are generally based on contextual understanding, sequential dependencies, and/or data imbalance. This makes distinction between genuine and fabricated news a challenging task. To address this problem, we propose a novel hybrid architecture, T5-SA-LSTM, which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attention-enhanced (SA) Long Short-Term Memory (LSTM). The LSTM is trained using the Adam optimizer, which provides faster and more stable convergence compared… More >

Moneerah Alotaibi^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069195 - 10 November 2025

Abstract Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods, which often demand extensive computational resources and struggle with diverse data acquisition techniques. This research presents a novel approach for vehicle classification and recognition in aerial image sequences, integrating multiple advanced techniques to enhance detection accuracy. The proposed model begins with preprocessing using Multiscale Retinex (MSR) to enhance image quality, followed by Expectation-Maximization (EM) Segmentation for precise foreground object identification. Vehicle detection is performed using the state-of-the-art YOLOv10 framework, while feature extraction incorporates Maximally Stable Extremal… More >

Qing Guo^1,2, Juwei Zhang^1,2,3,*, Bingyi Ren^1,2

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069053 - 10 November 2025

Abstract Traffic sign detection is an important part of autonomous driving, and its recognition accuracy and speed are directly related to road traffic safety. Although convolutional neural networks (CNNs) have made certain breakthroughs in this field, in the face of complex scenes, such as image blur and target occlusion, the traffic sign detection continues to exhibit limited accuracy, accompanied by false positives and missed detections. To address the above problems, a traffic sign detection algorithm, You Only Look Once-based Skip Dynamic Way (YOLO-SDW) based on You Only Look Once version 8 small (YOLOv8s), is proposed. Firstly,… More >