Shi-Min Chen, Li-Li Wang^*

International Journal of Mental Health Promotion, Vol.28, No.6, 2026, DOI:10.32604/ijmhp.2026.077617 - 23 June 2026

Abstract Backgrounds: The factors influencing emotion regulation choice (ERC) are numerous, raising the question of how to classify them systematically. Methods: This study proposed a framework of four first-order factors—the emotion to be regulated, emotion regulation goals, emotion regulation resources, and psychosocial context—by integrating several key theories of ERC, including the Action Control Theory of Emotion Regulation, the Extended Process Model of Emotion Regulation, the Process-specific Timing Framework Theory, the Selection, Optimization, and Compensation model of Emotion Regulation, and the Emotion as Social Information Theory. Results: This research also provided a detailed examination of the effects of multiple More >

K. Mithra^1,*, Prem Kumar Santhanam²

Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 99-108, 2026, DOI:10.32604/jimh.2026.083110 - 18 June 2026

Abstract Objective: To develop a low-cost automated cataract severity classification system operating on standard consumer-grade colour photographs of the eye, without specialised ophthalmic hardware. Methods: A hybrid framework was designed that fuses deep features from a Convolutional Neural Network (CNN) with five handcrafted Grey-Level Co-occurrence Matrix (GLCM) and intensity descriptors—mean intensity, uniformity, standard deviation, contrast, and energy—extracted from a Hough-circle-localised pupil Region of Interest (ROI). A multi-class Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel classifies each image into one of four severity grades: normal, immature, mature, or hypermature cataract. Results: The proposed fused system More >

Jesús Águila-León¹, Carlos Vargas-Salgado^2,*, Dácil Díaz-Bello², Fabián Lara-Vargas³

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.078087 - 18 June 2026

Abstract Integrating renewable energy sources presents technical challenges due to their variable nature, particularly in predicting and managing microgrid operational modes. Accurate identification of grid states—interconnected or islanded—is essential for maintaining stability and optimizing performance under fluctuating environmental conditions to meet energy demand. This work proposes a bio-inspired, optimized binary classification model based on Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN), with the architecture and hyperparameters tuned using the novel Mosquito Mating Swarm Optimization (MMSO) algorithm, inspired by mosquito mating behavior and swarm dynamics. The model employs an MLP-ANN with a variable number of hidden layers and… More > Graphic Abstract

Jiangyong Yu, Chuanping Hu^*, Runnan Wang

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082810 - 15 June 2026

Abstract With the proliferation of Internet of Things (IoT) devices, accurate device fingerprinting of highly encrypted traffic has emerged as a critical challenge for ensuring network security. Existing deep learning models are either difficult to deploy in real-time due to excessive computational complexity (e.g., Transformers) or are limited in performance because their structure does not match the inherent hierarchy of traffic data (e.g., flattened state space models). Furthermore, a general lack of transparency in their decision-making processes restricts their trustworthiness in security-critical scenarios. To address these challenges, this paper proposes a Hierarchical Mamba with Gated Attribution More >

HyeSun Chang, Sangjun Lee^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082718 - 15 June 2026

Abstract Accurate identification of heart murmurs from auscultation recordings is essential for early cardiovascular screening and diagnosis. While deep learning offers strong potential for automated heart murmur classification, existing models often exhibit overconfident, incorrect predictions and limited generalization due to dataset bias and class imbalance. To address these challenges, this study proposes a two-stage confidence-regulated learning framework that jointly optimizes feature representation and decision reliability. Rather than focusing solely on improving classification performance, this work emphasizes enhancing prediction reliability through confidence-aware decision-making. The proposed framework integrates supervised contrastive learning (SCL) to strengthen the discriminative structure of… More >

Taghreed Alkhodaidi^1,*, Wadee Alhalabi¹, Miada Almasre²

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081665 - 15 June 2026

Abstract The rapid growth of the IoMT has resulted in critical security threats to healthcare infrastructure, which require highly sophisticated IDSs that can detect a wide range of and unbalanced attack patterns. This study has addressed a critical challenge faced by network security data, which is class imbalance, by presenting a comprehensive evaluation of data balancing techniques on both a real-world standard data set, CICIoMT2024, and a synthetic data set, SynIoMT2026, which we generated to mimic the characteristics of the standard data set for developing a highly controlled data set. Three data balancing techniques, ADASYN, Sample… More >

Xinshan Huang¹, Zirui Pei¹, Chaohong Tan², Zuqiang Meng^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081626 - 15 June 2026

Abstract Multimodal Sentiment Analysis (MSA) plays an important role in understanding social media content; however, existing methods often struggle with the heterogeneity and complex interactions between images and text. These challenges include inter-modal information asymmetry, insufficient feature fusion, and noise interference, which collectively limit robustness and accuracy. To address these issues, we propose a multimodal sentiment classification model termed Multi-Branch Cross-Modal Cross-Attention Gating (MB-CMCAG). The model first incorporates a Transformer-based image caption generation module to convert raw images into semantically rich auxiliary textual descriptions, which complement the original text and form paired textual inputs with enhanced… More >

Naeem Ullah^1,*, Javed Ali Khan², Michelina Ruocco³, Antonio Della Cioppa⁴, Ivanoe De Falco⁵, Giovanna Sannino⁵

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081477 - 15 June 2026

Abstract Fruit diseases significantly impact agricultural productivity, yet automated detection systems often fail to provide interpretable predictions and are sensitive to background variations in images, particularly in orange fruit disease datasets. Current deep learning approaches are prone to background bias, which reduces explainability and generalization. To address this, we propose a deep learning framework that explicitly reduces background noise and bias in orange fruit disease image classification while providing interpretable, pixel-level predictions. The framework integrates existing architectural components, including grouped convolutions with channel shuffling, Leaky ReLU and clipped ReLU activations, and attention-based feature extraction, within a… More >

Munthir Qasaimeh¹, Mostafa Ali¹, Qasem Abu Al-Haija^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080864 - 27 May 2026

Abstract Vision Transformers (ViTs) have recently achieved high performance in retinal Optical Coherence Tomography (OCT) classification studies. However, ViT models continue to face significant challenges, including high computational cost, vulnerability to adversarial attacks, and pronounced sensitivity to preprocessing techniques. This study introduces GreenShield, a unified framework designed to produce an efficient and robust ViT model, referred to as GreenShield-ViT, which outperforms existing lightweight ViT variants in terms of adversarial robustness for retinal OCT classification. The framework integrates a gradient-based block-importance pruning strategy to compress the ViT/B-16 architecture, and adversarial training with proper ImageNet normalization and anti-saturation… More >

Amjad Hussain^1,*, Ayesha Saadia^2,*, Chihhsiong Shih³, Nazish Nawaz², Amir H. Gandomi^4,*, Khursheed Aurangzeb⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078451 - 27 May 2026

Abstract Malware has evolved from the early Creeper virus into highly sophisticated and organized cyber threats. Over time, it grew in sophistication, adopting advanced techniques, stealth tactics, and autonomous propagation. Modern malware leverages encryption, obfuscation, zero-day exploits, and AI-assisted techniques to conduct stealthy and persistent attacks. Classification of its exact family is the end goal to defend and mitigate the latest attacks. Researchers have contributed significantly and introduced many techniques to tackle malware threats. Binary detection is performed at a large scale, but very little in multi-class classification. In this research, a hybrid technique is proposed… More >