Nayeemul Islam Nayeem¹, Shirin Mahbuba¹, Sanjida Islam Disha¹, Md Rifat Hossain Buiyan¹, Shakila Rahman^1,*, M. Abdullah-Al-Wadud², Jia Uddin^3,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1541-1557, 2025, DOI:10.32604/cmc.2025.065061 - 29 August 2025

Abstract Human activity recognition is a significant area of research in artificial intelligence for surveillance, healthcare, sports, and human-computer interaction applications. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The dataset consists of 14,186 images across 19 activity classes, from dynamic activities such as running and swimming to static activities such as sitting and sleeping. Preprocessing included resizing all images to 512 512 pixels, annotating them… More >

Bowei Go, Xianli An^*

Journal of Psychology in Africa, Vol.35, No.4, pp. 523-533, 2025, DOI:10.32604/jpa.2025.070123 - 17 August 2025

Abstract This study investigated the impact of problematic mobile phone use (PMPU) on emotion recognition. The PMPU levels of 150 participants were measured using the standardized SAS-SV scale. Based on the SAS-SV cutoff scores, participants were divided into PMPU and Control groups. These participants completed two emotion recognition experiments involving facial emotion stimuli that had been manipulated to varying emotional intensities using Morph software. Experiment 1 (n = 75) assessed differences in facial emotion detection accuracy. Experiment 2 (n = 75), based on signal detection theory, examined differences in hit and false alarm rates across emotional expressions. More >

Yazeed Alkhrijah^1,2, Shehzad Khalid³, Syed Muhammad Usman^4,*, Amina Jameel³, Danish Hamid⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1113-1141, 2025, DOI:10.32604/cmes.2025.068726 - 31 July 2025

Abstract Arabic Sign Language (ArSL) recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing (DHH) community. Researchers have proposed multiple methods for automated recognition of ArSL; however, these methods face multiple challenges that include high gesture variability, occlusions, limited signer diversity, and the scarcity of large annotated datasets. Existing methods, often relying solely on either skeletal data or video-based features, struggle with generalization and robustness, especially in dynamic and real-world conditions. This paper proposes a novel multimodal ensemble classification framework that integrates geometric features derived from 3D skeletal joint… More >

Yasir Khan Jadoon¹, Yasir Noman Khalid¹, Muhammad Attique Khan², Jungpil Shin^3,*, Fatimah Alhayan⁴, Hee-Chan Cho⁵, Byoungchol Chang^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1143-1164, 2025, DOI:10.32604/cmes.2025.066984 - 31 July 2025

Abstract Real-time surveillance is attributed to recognizing the variety of actions performed by humans. Human Action Recognition (HAR) is a technique that recognizes human actions from a video stream. A range of variations in human actions makes it difficult to recognize with considerable accuracy. This paper presents a novel deep neural network architecture called Attention RB-Net for HAR using video frames. The input is provided to the model in the form of video frames. The proposed deep architecture is based on the unique structuring of residual blocks with several filter sizes. Features are extracted from each… More >

Hussain Dawood¹, Marriam Nawaz², Tahira Nazir³, Ali Javed², Abdul Khader Jilani Saudagar^4,*, Hatoon S. AlSagri⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 429-459, 2025, DOI:10.32604/cmes.2025.066415 - 31 July 2025

Abstract Reliable human action recognition (HAR) in video sequences is critical for a wide range of applications, such as security surveillance, healthcare monitoring, and human-computer interaction. Several automated systems have been designed for this purpose; however, existing methods often struggle to effectively integrate spatial and temporal information from input samples such as 2-stream networks or 3D convolutional neural networks (CNNs), which limits their accuracy in discriminating numerous human actions. Therefore, this study introduces a novel deep-learning framework called the ARNet, designed for robust HAR. ARNet consists of two main modules, namely, a refined InceptionResNet-V2-based CNN and… More >

Chongyang Wang¹, Qiongyan Li¹, Shu Liu², Pengle Cheng^1,*, Ying Huang³

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5157-5176, 2025, DOI:10.32604/cmc.2025.067367 - 30 July 2025

Abstract With rapid urbanization, fires pose significant challenges in urban governance. Traditional fire detection methods often struggle to detect smoke in complex urban scenes due to environmental interferences and variations in viewing angles. This study proposes a novel multimodal smoke detection method that fuses infrared and visible imagery using a transformer-based deep learning model. By capturing both thermal and visual cues, our approach significantly enhances the accuracy and robustness of smoke detection in business parks scenes. We first established a dual-view dataset comprising infrared and visible light videos, implemented an innovative image feature fusion strategy, and More >

Yun Zhang, Zhenhua Yu^*, Xufei Hu, Xuya Cong, Ou Ye

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5403-5426, 2025, DOI:10.32604/cmc.2025.067120 - 30 July 2025

Abstract Devices in Industrial Internet of Things are vulnerable to voice adversarial attacks. Studying adversarial speech samples is crucial for enhancing the security of automatic speech recognition systems in Industrial Internet of Things devices. Current black-box attack methods often face challenges such as complex search processes and excessive perturbation generation. To address these issues, this paper proposes a black-box voice adversarial attack method based on enhanced neural predictors. This method searches for minimal perturbations in the perturbation space, employing an optimization process guided by a self-attention neural predictor to identify the optimal perturbation direction. This direction… More >

Selime Tuba Pesen, Mehmet Ali Altuncu^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5597-5611, 2025, DOI:10.32604/cmc.2025.066702 - 30 July 2025

Abstract EEG signals are widely used in emotion recognition due to their ability to reflect involuntary physiological responses. However, the high dimensionality of EEG signals and their continuous variability in the time-frequency plane make their analysis challenging. Therefore, advanced deep learning methods are needed to extract meaningful features and improve classification performance. This study proposes a hybrid model that integrates the Swin Transformer and Temporal Convolutional Network (TCN) mechanisms for EEG-based emotion recognition. EEG signals are first converted into scalogram images using Continuous Wavelet Transform (CWT), and classification is performed on these images. Swin Transformer is… More >

Asaad Algarni¹, Iqra Aijaz Abro², Mohammed Alshehri³, Yahya AlQahtani⁴, Abdulmonem Alshahrani⁴, Hui Liu^5,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5879-5896, 2025, DOI:10.32604/cmc.2025.064601 - 30 July 2025

Abstract Inertial Sensor-based Daily Activity Recognition (IS-DAR) requires adaptable, data-efficient methods for effective multi-sensor use. This study presents an advanced detection system using body-worn sensors to accurately recognize activities. A structured pipeline enhances IS-DAR by applying signal preprocessing, feature extraction and optimization, followed by classification. Before segmentation, a Chebyshev filter removes noise, and Blackman windowing improves signal representation. Discriminative features—Gaussian Mixture Model (GMM) with Mel-Frequency Cepstral Coefficients (MFCC), spectral entropy, quaternion-based features, and Gammatone Cepstral Coefficients (GCC)—are fused to expand the feature space. Unlike existing approaches, the proposed IS-DAR system uniquely integrates diverse handcrafted features using… More >

Jonathan Ponmile Oguntoye¹, Sunday Adeola Ajagbe^2,3,*, Oluyinka Titilayo Adedeji¹, Olufemi Olayanju Awodoye¹, Abigail Bola Adetunji¹, Elijah Olusayo Omidiora¹, Matthew Olusegun Adigun²

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5713-5732, 2025, DOI:10.32604/cmc.2025.062440 - 30 July 2025

Abstract This study proposes a system for biometric access control utilising the improved Cultural Chicken Swarm Optimization (CCSO) technique. This approach mitigates the limitations of conventional Chicken Swarm Optimization (CSO), especially in dealing with larger dimensions due to diversity loss during solution space exploration. Our experimentation involved 600 sample images encompassing facial, iris, and fingerprint data, collected from 200 students at Ladoke Akintola University of Technology (LAUTECH), Ogbomoso. The results demonstrate the remarkable effectiveness of CCSO, yielding accuracy rates of 90.42%, 91.67%, and 91.25% within 54.77, 27.35, and 113.92 s for facial, fingerprint, and iris biometrics,… More >