Yu-Xuan Zhou¹, Kai-Qing Zhou^1,*, Wei-Lin Chen¹, Zhou-Hua Liao¹, Di-Wen Kang^1,2

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075099 - 10 February 2026

Abstract The Pigeon-Inspired Optimization (PIO) algorithm constitutes a metaheuristic method derived from the homing behaviour of pigeons. Initially formulated for three-dimensional path planning in unmanned aerial vehicles (UAVs), the algorithm has attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation, coupled with advantages in real-time performance and robustness. Nevertheless, as applications have diversified, limitations in convergence precision and a tendency toward premature convergence have become increasingly evident, highlighting a need for improvement. This review systematically outlines the developmental trajectory of the PIO algorithm, with a particular focus on its core… More >

Le Thi Hong Van^1,*, Le Duc Thuan¹, Pham Van Huong¹, Nguyen Hieu Minh²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075027 - 10 February 2026

Abstract Optimizing convolutional neural networks (CNNs) for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy. This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection. Unlike conventional single-objective approaches, the proposed method formulates a global multi-objective fitness function that integrates accuracy, precision, recall, and model size (speed/model complexity penalty) with adjustable weights. This design enables both single-objective and weighted-sum multi-objective optimization, allowing adaptive selection of optimal CNN configurations for diverse deployment… More >

Lishu Qin^*, Yu Gao, Xinyuan Lu

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074739 - 10 February 2026

Abstract With the rapid development of intelligent navigation technology, efficient and safe path planning for mobile robots has become a core requirement. To address the challenges of complex dynamic environments, this paper proposes an intelligent path planning framework based on grid map modeling. First, an improved Safe and Smooth A* (SSA*) algorithm is employed for global path planning. By incorporating obstacle expansion and corner-point optimization, the proposed SSA* enhances the safety and smoothness of the planned path. Then, a Partitioned Dynamic Window Approach (PDWA) is integrated for local planning, which is triggered when dynamic or sudden… More >

Zhendong Du^*, Hanliu Wang, Kenji Hashimoto

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074520 - 10 February 2026

Abstract Planning in lexical-prior-free environments presents a fundamental challenge for evaluating whether large language models (LLMs) possess genuine structural reasoning capabilities beyond lexical memorization. When predicates and action names are replaced with semantically irrelevant random symbols while preserving logical structures, existing direct generation approaches exhibit severe performance degradation. This paper proposes a symbol-agnostic closed-loop planning pipeline that enables models to construct executable plans through systematic validation and iterative refinement. The system implements a complete generate-verify-repair cycle through six core processing components: semantic comprehension extracts structural constraints, language planner generates text plans, symbol translator performs structure-preserving mapping,… More >

Junnian Wang¹, Xiaoxia Wang¹, Zexin Luo¹, Qixiang Ouyang¹, Chao Zhou¹, Huanyu Wang^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074473 - 10 February 2026

Abstract Internet of Things (IoTs) devices are bringing about a revolutionary change our society by enabling connectivity regardless of time and location. However, The extensive deployment of these devices also makes them attractive victims for the malicious actions of adversaries. Within the spectrum of existing threats, Side-Channel Attacks (SCAs) have established themselves as an effective way to compromise cryptographic implementations. These attacks exploit unintended, unintended physical leakage that occurs during the cryptographic execution of devices, bypassing the theoretical strength of the crypto design. In recent times, the advancement of deep learning has provided SCAs with a… 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 >

Argho Dey¹, Yunfei Yin^1,2,*, Zheng Yuan¹, Zhiwen Zeng¹, Xianjian Bao³, Md Minhazul Islam¹

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074122 - 10 February 2026

Abstract Autonomous vehicles rely heavily on accurate and efficient scene segmentation for safe navigation and efficient operations. Traditional Bird’s Eye View (BEV) methods on semantic scene segmentation, which leverage multimodal sensor fusion, often struggle with noisy data and demand high-performance GPUs, leading to sensor misalignment and performance degradation. This paper introduces an Enhanced Channel Attention BEV (ECABEV), a novel approach designed to address the challenges under insufficient GPU memory conditions. ECABEV integrates camera and radar data through a de-noise enhanced channel attention mechanism, which utilizes global average and max pooling to effectively filter out noise while… More >

Jian Huang¹, Mingwei Li¹, Yulong Tian¹, Yi Yao², Hao Han^1,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073201 - 10 February 2026

Abstract With the popularization of new technologies, telephone fraud has become the main means of stealing money and personal identity information. Taking inspiration from the website authentication mechanism, we propose an end-to-end data modem scheme that transmits the caller’s digital certificates through a voice channel for the recipient to verify the caller’s identity. Encoding useful information through voice channels is very difficult without the assistance of telecommunications providers. For example, speech activity detection may quickly classify encoded signals as non-speech signals and reject input waveforms. To address this issue, we propose a novel modulation method based… More >

Jianing Wu¹, Yufeng Chen^1,*, Li Yin¹, Huajun He², Panshuan Jin²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072937 - 10 February 2026

Abstract To conduct marine surveys, multiple unmanned surface vessels (Multi-USV) with different capabilities perform collaborative mapping in multiple designated areas. This paper proposes a task allocation algorithm based on integer linear programming (ILP) with flow balance constraints, ensuring the fair and efficient distribution of sub-areas among USVs and maintaining strong connectivity of assigned regions. In the established grid map, a search-based path planning algorithm is performed on the sub-areas according to the allocation scheme. It uses the greedy algorithm and the A* algorithm to achieve complete coverage of the barrier-free area and obtain an efficient trajectory More >

Roshni Khedgaonkar¹, Pravinkumar Sonsare², Kavita Singh¹, Ayman Altameem³, Hameed R. Farhan⁴, Salil Bharany⁵, Ateeq Ur Rehman^6,*, Ahmad Almogren^7,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072651 - 10 February 2026

Abstract Recent studies indicate that millions of individuals suffer from renal diseases, with renal carcinoma, a type of kidney cancer, emerging as both a chronic illness and a significant cause of mortality. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have become essential tools for diagnosing and assessing kidney disorders. However, accurate analysis of these medical images is critical for detecting and evaluating tumor severity. This study introduces an integrated hybrid framework that combines three complementary deep learning models for kidney tumor segmentation from MRI images. The proposed framework fuses a customized U-Net and Mask R-CNN… More >