Shu-Yin Chiang^*, Shin-En Huang

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074528 - 10 February 2026

Abstract This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping (SLAM), real-time object recognition, and dynamic obstacle avoidance. The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping, fusing geometric and visual data to build a high-fidelity 2D semantic map. This map enables the robot to identify and project object information for improved situational awareness. Experimental results show that object recognition reached 95.4% mAP@0.5. Semantic completeness increased from 68.7% (single view) to 94.1% (multi-view) with an More >

Chang Xu^1,*, Xianbo Yin², Zhiyong Zhou¹, Bomin Liu¹

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.074417 - 10 February 2026

Abstract This study presents a hybrid CNN-Transformer model for real-time recognition of affective tactile biosignals. The proposed framework combines convolutional neural networks (CNNs) to extract spatial and local temporal features with the Transformer encoder that captures long-range dependencies in time-series data through multi-head attention. Model performance was evaluated on two widely used tactile biosignal datasets, HAART and CoST, which contain diverse affective touch gestures recorded from pressure sensor arrays. The CNN-Transformer model achieved recognition rates of 93.33% on HAART and 80.89% on CoST, outperforming existing methods on both benchmarks. By incorporating temporal windowing, the model enables More >

Nouman Ahmad^1,*, Changsheng Zhang¹, Uroosa Sehar^2,3,4

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073029 - 10 February 2026

Abstract Although Named Entity Recognition (NER) in cybersecurity has historically concentrated on threat intelligence, vital security data can be found in a variety of sources, such as open-source intelligence and unprocessed tool outputs. When dealing with technical language, the coexistence of structured and unstructured data poses serious issues for traditional BERT-based techniques. We introduce a three-phase approach for improved NER in multi-source cybersecurity data that makes use of large language models (LLMs). To ensure thorough entity coverage, our method starts with an identification module that uses dynamic prompting techniques. To lessen hallucinations, the extraction module uses… More >

Fawad Salam Khan^1,*, Noman Hasany², Muzammil Ahmad Khan³, Shayan Abbas⁴, Sajjad Ahmed⁵, Muhammad Zorain⁶, Wai Yie Leong^7,*, Susama Bagchi⁸, Sanjoy Kumar Debnath⁸

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.072893 - 10 February 2026

Abstract The initial noise present in the depth images obtained with RGB-D sensors is a combination of hardware limitations in addition to the environmental factors, due to the limited capabilities of sensors, which also produce poor computer vision results. The common image denoising techniques tend to remove significant image details and also remove noise, provided they are based on space and frequency filtering. The updated framework presented in this paper is a novel denoising model that makes use of Boruta-driven feature selection using a Long Short-Term Memory Autoencoder (LSTMAE). The Boruta algorithm identifies the most useful… More >

Xiaorui Zhang^1,2,*, Yingying Wang², Wei Sun³, Shiyu Zhou², Haoming Zhang⁴, Pengpai Wang¹

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072626 - 10 February 2026

Abstract Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition. However, existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds, small target objects, and limited training data, leading to poor recognition. Fine-grained images exhibit “small inter-class differences,” and while second-order feature encoding enhances discrimination, it often requires dual Convolutional Neural Networks (CNN), increasing training time and complexity. This study proposes a model integrating discriminative region localization and efficient second-order feature encoding. By ranking feature map channels via a fully connected layer, it selects high-importance channels to generate an More >

Aman Aman Ullah^1,2,#, Yanfeng Wu^1,#, Shaheryar Najam³, Nouf Abdullah Almujally⁴, Ahmad Jalal^5,6,*, Hui Liu^1,7,8,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072508 - 10 February 2026

Abstract Human object detection and recognition is essential for elderly monitoring and assisted living however, models relying solely on pose or scene context often struggle in cluttered or visually ambiguous settings. To address this, we present SCENET-3D, a transformer-driven multimodal framework that unifies human-centric skeleton features with scene-object semantics for intelligent robotic vision through a three-stage pipeline. In the first stage, scene analysis, rich geometric and texture descriptors are extracted from RGB frames, including surface-normal histograms, angles between neighboring normals, Zernike moments, directional standard deviation, and Gabor-filter responses. In the second stage, scene-object analysis, non-human objects… More >

Nouf Abdullah Almujally¹, Tanvir Fatima Naik Bukht², Shuaa S. Alharbi³, Asaad Algarni⁴, Ahmad Jalal^2,5, Jeongmin Park^6,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072243 - 10 February 2026

Abstract Recognising human-object interactions (HOI) is a challenging task for traditional machine learning models, including convolutional neural networks (CNNs). Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI. The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity. HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability. This work proposes a Novel quantum gate-enabled hybrid CNN (QEH-CNN) for effective HOI recognition. The model enhances CNN performance by integrating quantum computing components. The framework begins with bilateral image filtering,… More >

Muhammad Hamdan Azhar^1,2,#, Yanfeng Wu^1,#, Nouf Abdullah Almujally³, Shuaa S. Alharbi⁴, Asaad Algarni⁵, Ahmad Jalal^2,6, Hui Liu^1,7,8,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.071988 - 10 February 2026

Abstract Recognizing human interactions in RGB videos is a critical task in computer vision, with applications in video surveillance. Existing deep learning-based architectures have achieved strong results, but are computationally intensive, sensitive to video resolution changes and often fail in crowded scenes. We propose a novel hybrid system that is computationally efficient, robust to degraded video quality and able to filter out irrelevant individuals, making it suitable for real-life use. The system leverages multi-modal handcrafted features for interaction representation and a deep learning classifier for capturing complex dependencies. Using Mask R-CNN and YOLO11-Pose, we extract grayscale… More >

Mohammed Al-Mahbashi^1,2,*, Ali Ahmed³, Abdolraheem Khader^4,*, Shakeel Ahmad³, Mohamed A. Damos⁵, Ahmed Abdu⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075909 - 29 January 2026

Abstract Reliable detection of traffic signs and lights (TSLs) at long range and under varying illumination is essential for improving the perception and safety of autonomous driving systems (ADS). Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions. To overcome these limitations, this research presents FED-YOLOv10s, an improved and lightweight object detection framework based on You Only look Once v10 (YOLOv10). The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations, an Efficient Multiscale Attention (EMA) mechanism to More >

Yewei Xiao, Xin Du^*, Wei Zeng

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072145 - 12 January 2026

Abstract Audio-visual speech recognition (AVSR), which integrates audio and visual modalities to improve recognition performance and robustness in noisy or adverse acoustic conditions, has attracted significant research interest. However, Conformer-based architectures remain computational expensive due to the quadratic increase in the spatial and temporal complexity of their softmax-based attention mechanisms with sequence length. In addition, Conformer-based architectures may not provide sufficient flexibility for modeling local dependencies at different granularities. To mitigate these limitations, this study introduces a novel AVSR framework based on a ReLU-based Sparse and Grouped Conformer (RSG-Conformer) architecture. Specifically, we propose a Global-enhanced Sparse… More >