David Hason Rudd^1,*, Cesar Sanin², Md Rafiqul Islam³, Xianzhi Wang¹, Huan Huo¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084015 - 13 August 2026

Abstract The accurate classification of human emotions from speech remains a formidable challenge due to the dynamic, non-stationary properties of audio signals and pervasive background noise. Traditional single-domain extraction methods frequently fail to capture overlapping acoustic phenomena, resulting in high misclassification rates among acoustically similar emotions. To overcome this, we propose the Dual-Decomposition Ensemble (DDE-SER), an architecture that synergizes 1D adaptive frequency filtering with 2D spatial spectrogram separation. The framework operates through two distinct pipelines: an adaptive time-domain branch that leverages VGG-optiVMD to autonomously extract Intrinsic Mode Functions (IMFs), and a structural spectrogram branch that applies… More >

Hafiz Khizer bin Talib¹, Yanlong Cao², Muhammad Zaman^3,*, Sharifah Sakinah Syed Ahmad⁴, Nikola Ivkovic⁵, Mario Konecki⁵, Adnan Akhunzada⁶

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.078779 - 13 August 2026

Abstract Micro-expression recognition (MER) is a demanding problem in affective computing because micro-expressions are brief, low-amplitude, involuntary facial movements that often reveal concealed affective states. Their recognition is complicated by weak muscle activation, short temporal duration, inter-subject variability, class imbalance, illumination changes, and the limited scale of publicly available MER datasets. To address these constraints, this paper introduces BroadAttNet, an attention-driven convolutional framework that embeds a Broadbent-inspired selective attention layer into a compact CNN backbone. The proposed layer learns to assign higher importance to discriminative facial regions while suppressing spatially redundant or noisy responses, thereby improving… More >

Jordán Pascual Espada¹, Lucía Alonso Virgós^2,*, Juan Luis Carús³, Miguel Ángel Fernández Fernández³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.077447 - 13 August 2026

Abstract This research focuses on multi-object detection for interrelated industrial components, such as substation parts. Traditional vision models like YOLO rely primarily on visual features, which limits their ability to: (1) disambiguate visually similar objects using contextual cues like relative position or size, and (2) reinforce low-confidence detections that are spatially plausible given neighboring elements. For instance, a visually similar but semantically incorrect object may be misclassified, or a valid but partially occluded component may be discarded due to a low appearance-based score. Our approach addresses these issues by integrating formalized rules based on relative positions… More >

Kewen Li, Xiaoyong Yu, Shifeng Ou^*, Jueming Pan

Energy Engineering, Vol.123, No.9, 2026, DOI:10.32604/ee.2025.073825 - 06 August 2026

Abstract The increasing integration of renewable energy sources (e.g., wind and solar power) into distribution grids and the development of new, source–grid–load–storage coordinated power systems have led to a substantial expansion in the volume of situational awareness data in the distribution networks. Moreover, the transmission of low-voltage distribution measurement data via a power line carrier (PLC) is often susceptible to packet loss and, consequently, data gaps. To address these issues, this paper proposes a data completion method using a conditional generative adversarial network (CGAN) integrated with a three-dimensional convolutional neural network (3D-CNN). This approach leverages the… 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

Amit Pimpalkar^1,*, Kapil N. Vhatkar², Rachna K. Somkunwar³, Shweta Koparde⁴, Dalia H. Elkamchouchi⁵, Ateeq Ur Rehman^6,*, Pooja Verma⁷, Salil Bharany⁸

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084384 - 23 July 2026

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 >

Mubariz Khan¹, Hafeez Ur Rehman Siddiqui², Adil Ali Saleem², Muhammad Amjad Raza^2,3, Lázaro Javier Hernández Rodríguez^4,5,6,7, Pablo Herrero García^4,8,9, Isabel de la Torre Díez^10,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084269 - 23 July 2026

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 >

Phil Steadman^*, Paul Jenkins, Rajkumar Singh Rathore^*, Chaminda Hewage

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083998 - 23 July 2026

Abstract Modern malware is increasingly employing polymorphism, packing, and metamorphism to evade traditional signature-based detection. Because of this, there is an urgency to have more reliable classification systems. Visual malware analysis, where binaries are converted into grayscale images, has demonstrated potential in revealing structural patterns of malware family classification. However, recent methods mostly rely on single-stream, lightweight Convolutional Neural Networks (CNNs). These models have a major blind spot. The visual representation textures can be heavily obscured without changing the underlying malicious code, causing severe performance drops on newer or even rare malware classes. This paper presents… More >

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 >

Chuan Lin^*, Weixian Chen, Guangtao Hao^*

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.080605 - 12 July 2026

Abstract Accurate building electricity load forecasting (BELF) can provide a regulatory basis for building energy management systems and promote the transition of buildings toward low-carbon and intelligent operation modes. However, building electricity load is influenced by historical loads, as well as outside environmental conditions such as humidity and temperature, which reduces the prediction accuracy of models. To tackle these challenges, this study presents a BELF model, which consists of a modal component grouping approach, grouped feature attention mechanism, and multi-scale residual depthwise convolution memory module. First, the modal component grouping method analyzes building electricity load in… More >