Wael A. Altabey^*

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.079495 - 24 August 2026

Abstract The goal of this paper is to improve the monitoring of civil structures when we pair unmanned aerial vehicles (UAVs) technology with the current proposed algorithm, particularly to identify cracks in concrete structures. Typically, the current UAV methods are more about creating state maps of these structures, but they struggle with the impact of the drone’s movement on crack detection accuracy. This presents challenges for using intelligent systems for concrete crack detection. The current approach combines advanced technologies with a network of high-definition cameras mounted on inspection UAV systems and distributed in different parts of… More >

Jiyeong Park¹, Sercan Yeşilköy¹, Doyeon Lim¹, Huiryeong Park¹, Eunseo Lee¹, Mohsen Ali Alawami^1,*, Ki-Woong Park^2,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.081091 - 13 August 2026

Abstract The recent increase in deepfake content has significantly increased cyber threats. Although numerous deepfake detection technologies have achieved high accuracy, there are limits to clarifying the rationale behind their detection decisions. To bridge the gap, in our study, we leverage the combination of Explainable Artificial Intelligence (XAI) and Large Language Models (LLMs) to deliver clear, consistent, and understandable interpretations of deepfake detection outcomes. To do that, we integrate XAI and LLMs to visually represent detection rationales and automatically generate coherent natural-language explanations. During the implementation of our method, we developed a multi-task learning framework based… More >

Bindu Vadlamudi¹, Subhojit Dawn^1,*, Ishwarya Devarakonda¹, Sri Hari Priya Lanka¹, Sujan Turaka¹, Taha Selim Ustun^2,*

Energy Engineering, Vol.123, No.9, 2026, DOI:10.32604/ee.2026.083555 - 06 August 2026

Abstract Agricultural irrigation consumes a large share of electricity in rural areas, creating predictable peak conditions on distribution systems that can lead to grid instability and unreliability. Classic load-forecasting and scheduling methods are time-consuming and unable to respond rapidly to fluctuating irrigation demand. Additionally, most traditional methods require a stable internet connection to function and therefore cannot readily adapt to seasonal changes or crop-specific irrigation requirements. This creates inefficiencies in energy consumption and inconsistencies in water delivery to consumers. To reduce these drawbacks, this research proposes a framework for irrigation forecasting and dynamic scheduling for agricultural… More >

Jiawang Zou, Annan Jiang^*, Xinzhi Wang, Hao Huang

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082647 - 27 July 2026

Abstract Accurate forecasting of settlement in buildings adjacent to shield tunnels remains a critical challenge in underground engineering due to complex spatiotemporal interactions and nonlinear relationships among multi-source monitoring data and construction parameters. To address this issue, a Sparrow Search Algorithm (SSA)-optimized Convolutional Neural Network–Long Short-Term Memory–Transformer (CNN-LSTM-Transformer) hybrid framework is proposed, explicitly incorporating the relative spatial relationship between the shield excavation face and adjacent structures. In this framework, the Convolutional Neural Network (CNN) module extracts spatial features from monitoring data and tunneling parameters, capturing interdependencies among different construction indicators and reflecting local spatial heterogeneity of… More > Graphic Abstract

Shing-Tai Pan^*, Yi-Zhen Huang, Zhi-Qing Chen

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081460 - 23 July 2026

Abstract This paper proposes a multimodal fusion framework that integrates speech and visual features to enhance the accuracy of emotion recognition. The principal contribution lies in extending the visual component from single-image to multi-image emotion recognition. Specifically, the proposed framework employs an InceptionV3 Convolutional Neural Network (CNN)-based architecture to extract features from multiple facial images representing the speaker’s expressions throughout an utterance. These features are concatenated into a single vector and subsequently processed by Long Short-Term Memory (LSTM) or Hidden Markov Model (HMM) for temporal modeling. For the speech modality, Mel-Frequency Cepstral Coefficients (MFCC) or filter… More >

Thossapon Kaewrakmuk, Jakkree Srinonchat^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081437 - 23 July 2026

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 >

Rashid Jahangir^1,*, Muhammad Asif Nauman², Oumaima Saidani³, Faisal Ramzan²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080025 - 23 July 2026

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 >

Jaewan Lee¹, Seonghwan Park², Junghwan Kook^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080930 - 30 June 2026

Abstract In bearing fault diagnosis for Prognostics and Health Management (PHM), the overall performance of data-driven models is strongly influenced by the coupled effects of preprocessing, model configuration, and decision fusion. However, these components are often optimized independently, resulting in fragmented workflows that limit global optimality, reproducibility, and computational efficiency of the model. This study presents a computationally unified three-stage sequential optimization framework that systematically coordinates the preprocessing selection, model hyperparameter optimization, and decision-level fusion within a consistent surrogate-based optimization architecture. In the first stage, candidate preprocessing schemes reflecting physical fault mechanisms—outer race, inner race, rolling… More > Graphic Abstract

Sami Ullah^1,2, Najmul Hassan², Naeem Bhatti², Asad Saleem^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082467 - 15 June 2026

Abstract Underwater imaging facilitates the exploration of the underwater environment. However, irregular optical absorption and light scattering in water, ranging from clear to highly turbid conditions, often result in low visibility, color distortion, and blurriness in underwater images (UWIs). Conventional UWI enhancement methods are limited by inefficient physical modeling, while deep learning-based approaches are constrained by the scarcity of paired training datasets. In this work, we propose a hybrid learning framework for UWI enhancement that leverages the usefulness of both conventional and deep learning-based techniques. At first, we preprocess the UWIs using a revised underwater physical… More >

Sun-Kyoung Kang¹, Yeonwoo Lee^2,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081553 - 15 June 2026

Abstract Black ice formation on road surfaces presents a serious hazard due to its low visibility and high slipperiness, underscoring the critical need for timely and accurate detection in intelligent transportation systems. In this paper, we propose AdaMsDCNet, an adaptive multi-scale dilated convolution network designed for real-time black-ice semantic segmentation on resource-constrained edge platforms, applying a Convolutional Neural Network (CNN) with an adaptive Multi-Scale Dilated Convolution (MsDC) feature fusion encoder-decoder architecture. The key concept of AdaMsDCNet is to employ an encoder-decoder architecture with parallel multi-scale dilated convolutional paths that adjust dilation rates at different encoder depths… More >