Muhammad Adil¹, Nadeem Javaid^1,*, Imran Ahmed², Abrar Ahmed³, Nabil Alrajeh^4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071215 - 10 November 2025

Abstract Heart disease remains a leading cause of mortality worldwide, emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention. However, existing Deep Learning (DL) approaches often face several limitations, including inefficient feature extraction, class imbalance, suboptimal classification performance, and limited interpretability, which collectively hinder their deployment in clinical settings. To address these challenges, we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture. The preprocessing stage involves label encoding and feature scaling. To address the issue of… More >

Binglei Yue, Aili Jiang, Chun Yang, Junwei Lei, Heng Liu, Yin Zhang^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-28, 2026, DOI:10.32604/cmc.2025.071047 - 10 November 2025

Abstract With the growing advancement of wireless communication technologies, WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution. Among the available signal types, Channel State Information (CSI) offers fine-grained temporal, frequency, and spatial insights into multipath propagation, making it a crucial data source for human-centric sensing. Recently, the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments. This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI. We first outline mainstream CSI acquisition tools and their hardware specifications, More >

Seyong Jin¹, Muhammad Fayaz², L. Minh Dang³, Hyoung-Kyu Song³, Hyeonjoon Moon^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.070667 - 10 November 2025

Abstract Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics. While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information, existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors. In order to address these challenges and maximize the performance of brain tumor segmentation, this research introduces a novel SwinUNETR-based model by integrating a new decoder block, the Hierarchical Channel-wise Attention Decoder (HCAD), into a powerful SwinUNETR encoder. The HCAD… More >

Ali S. Alzaharani, Abid Iqbal^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-27, 2026, DOI:10.32604/cmc.2025.070410 - 10 November 2025

Abstract In this study, an automated multimodal system for detecting, classifying, and dating fruit was developed using a two-stage YOLOv11 pipeline. In the first stage, the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them. These bounding boxes are subsequently passed to a YOLOv11 classification model, which analyzes cropped images and assigns class labels. An additional counting module automatically tallies the detected fruits, offering a near-instantaneous estimation of quantity. The experimental results suggest high precision and recall for detection, high classification accuracy (across 15 classes), and near-perfect counting in More >

Thierry Mugenzi, Cahit Perkgoz^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.070381 - 10 November 2025

Abstract Missing data presents a crucial challenge in data analysis, especially in high-dimensional datasets, where missing data often leads to biased conclusions and degraded model performance. In this study, we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision. The proposed loss combines (i) a guided, masked mean squared error focusing on missing entries; (ii) a noise-aware regularization term to improve resilience against data corruption; and (iii) a variance penalty to encourage expressive yet stable reconstructions. We evaluate the proposed model across four missingness mechanisms, such as Missing… More >

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 >