Jagadesh Balasubramani¹, Surendran Rajendran^1,*, Mohammad Zakariah², Abeer Alnuaim²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2793-2807, 2025, DOI:10.32604/cmc.2025.062266 - 16 April 2025

Abstract Autism spectrum disorder (ASD) is a multifaceted neurological developmental condition that manifests in several ways. Nearly all autistic children remain undiagnosed before the age of three. Developmental problems affecting face features are often associated with fundamental brain disorders. The facial evolution of newborns with ASD is quite different from that of typically developing children. Early recognition is very significant to aid families and parents in superstition and denial. Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD. Presently, artificial intelligence (AI) significantly contributes to the emerging computer-aided… More >

Amjad A. Alsuwaylimi^1,*, Zaid S. Alenezi²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3165-3185, 2025, DOI:10.32604/cmc.2025.061674 - 16 April 2025

Abstract Cyberbullying is a remarkable issue in the Arabic-speaking world, affecting children, organizations, and businesses. Various efforts have been made to combat this problem through proposed models using machine learning (ML) and deep learning (DL) approaches utilizing natural language processing (NLP) methods and by proposing relevant datasets. However, most of these endeavors focused predominantly on the English language, leaving a substantial gap in addressing Arabic cyberbullying. Given the complexities of the Arabic language, transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained… More >

Meng Zhu^1,2, Xingyue Wang³, Honge Ren^3,4,*, Abeer Hakeem⁵, Linda Mohaisen^5,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2889-2905, 2025, DOI:10.32604/cmc.2025.061421 - 16 April 2025

Abstract Changing a person’s posture and low resolution are the key challenges for person re-identification (ReID) in various deep learning applications. In this paper, we introduce an innovative architecture using a dual attention network that includes an attention module and a joint measurement module of spatial-temporal information. The proposed approach can be classified into two main tasks. Firstly, the spatial attention feature map is formed by aggregating features in the spatial dimension. Additionally, the same operation is carried out on the channel dimension to form channel attention feature maps. Therefore, the receptive field size is adjusted… More >

Weitao Ha¹, Sheng Gang², Yahya D. Navaei³, Abubakar S. Gezawa⁴, Yaser A. Nanehkaran^2,5,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3025-3057, 2025, DOI:10.32604/cmc.2025.061343 - 16 April 2025

Abstract Music recommendation systems are essential due to the vast amount of music available on streaming platforms, which can overwhelm users trying to find new tracks that match their preferences. These systems analyze users’ emotional responses, listening habits, and personal preferences to provide personalized suggestions. A significant challenge they face is the “cold start” problem, where new users have no past interactions to guide recommendations. To improve user experience, these systems aim to effectively recommend music even to such users by considering their listening behavior and music popularity. This paper introduces a novel music recommendation system… More >

Zengyu Cai^1,*, Yuming Dai¹, Jianwei Zhang^2,3,*, Yuan Feng⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3335-3350, 2025, DOI:10.32604/cmc.2025.061206 - 16 April 2025

Abstract The rapid development and widespread adoption of Internet technology have significantly increased Internet traffic, highlighting the growing importance of network security. Intrusion Detection Systems (IDS) are essential for safeguarding network integrity. To address the low accuracy of existing intrusion detection models in identifying network attacks, this paper proposes an intrusion detection method based on the fusion of Spatial Attention mechanism and Residual Neural Network (SA-ResNet). Utilizing residual connections can effectively capture local features in the data; by introducing a spatial attention mechanism, the global dependency relationships of intrusion features can be extracted, enhancing the intrusion More >

Min Yao^1,*, Guofeng Liu², Yaozu Zhang³, Guangjie Hu¹

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2189-2205, 2025, DOI:10.32604/cmc.2025.060609 - 16 April 2025

Abstract Knowledge distillation (KD) is an emerging model compression technique for learning compact object detector models. Previous KD often focused solely on distilling from the logits layer or the feature intermediate layers, which may limit the comprehensive learning of the student network. Additionally, the imbalance between the foreground and background also affects the performance of the model. To address these issues, this paper employs feature-based distillation to enhance the detection performance of the bounding box localization part, and logit-based distillation to improve the detection performance of the category prediction part. Specifically, for the intermediate layer feature… More >

Jiming Lan¹, Bo Zeng^1,*, Suiqun Li¹, Weihan Zhang¹, Xinyi Shi²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2865-2888, 2025, DOI:10.32604/cmc.2025.060260 - 16 April 2025

Abstract The Quadric Error Metrics (QEM) algorithm is a widely used method for mesh simplification; however, it often struggles to preserve high-frequency geometric details, leading to the loss of salient features. To address this limitation, we propose the Salient Feature Sampling Points-based QEM (SFSP-QEM)—also referred to as the Deep Learning-Based Salient Feature-Preserving Algorithm for Mesh Simplification—which incorporates a Salient Feature-Preserving Point Sampler (SFSP). This module leverages deep learning techniques to prioritize the preservation of key geometric features during simplification. Experimental results demonstrate that SFSP-QEM significantly outperforms traditional QEM in preserving geometric details. Specifically, for general models… More >

Jian Ge¹, Qin Qin^1,*, Jinhua Jiang¹, Zhiwei Shen², Zimei Tu¹, Yahui Zhang¹

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3211-3226, 2025, DOI:10.32604/cmc.2025.059389 - 16 April 2025

Abstract Manufacturers must identify and classify various defects in automotive sealing rings to ensure product quality. Deep learning algorithms show promise in this field, but challenges remain, especially in detecting small-scale defects under harsh industrial conditions with multimodal data. This paper proposes an enhanced version of You Only Look Once (YOLO)v8 for improved defect detection in automotive sealing rings. We introduce the Multi-scale Adaptive Feature Extraction (MAFE) module, which integrates Deformable Convolutional Network (DCN) and Space-to-Depth (SPD) operations. This module effectively captures long-range dependencies, enhances spatial aggregation, and minimizes information loss of small objects during feature More >

Phan-Anh-Huy Nguyen^*, Luu-Luyen Than

Intelligent Automation & Soft Computing, Vol.40, pp. 221-234, 2025, DOI:10.32604/iasc.2025.058976 - 14 April 2025

Abstract The burgeoning e-commerce industry has made online customer reviews a crucial source of feedback for businesses. Sentiment analysis, a technique used to extract subjective information from text, has become essential for understanding consumer sentiment and preferences. However, traditional sentiment analysis methods often struggle with the nuances and context of natural language. To address these issues, this study proposes a comparison of deep learning models that figure out the optimal method to accurately analyze consumer reviews on women's clothing. CNNs excel at capturing local features and semantic information, while LSTMs are adept at handling long-range dependencies… More >

Mourad Benmalek^1,*,#,*, Abdessamed Seddiki^2,#, Kamel-Dine Haouam¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1157-1184, 2025, DOI:10.32604/cmes.2025.062841 - 11 April 2025

Abstract The Internet of Medical Things (IoMT) connects healthcare devices and sensors to the Internet, driving transformative advancements in healthcare delivery. However, expanding IoMT infrastructures face growing security threats, necessitating robust Intrusion Detection Systems (IDS). Maintaining the confidentiality of patient data is critical in AI-driven healthcare systems, especially when securing interconnected medical devices. This paper introduces SNN-IoMT (Stacked Neural Network Ensemble for IoMT Security), an AI-driven IDS framework designed to secure dynamic IoMT environments. Leveraging a stacked deep learning architecture combining Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), the model optimizes data management More >