Zhengshun Fei^1,*, Qiao Sun¹, Chuang Yang², Siranee Nuchitprasitchai³, Yongping Zheng¹, Xinjian Xiang^1,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.078696 - 23 July 2026

Abstract Path planning is a critical component for enabling autonomous navigation in mobile robots. Sampling-based planners are widely adopted due to their strong generality, yet they rely heavily on uniform sampling, which often leads to unstable performance and high computational cost in complex environments. To address this issue, recent studies feed free-space point clouds into neural networks to infer a set of guidance states near the optimal path, thereby enabling non-uniform sampling; however, the accuracy of the guidance set becomes a key bottleneck for further improvement. In this paper, we propose an improved point-cloud neural RRT*… More >

Naimul Hasan Shadesh^*, Zannatul Ferdous, Bipasha Iasmin

Journal on Artificial Intelligence, Vol.8, pp. 335-357, 2026, DOI:10.32604/jai.2026.078589 - 22 July 2026

Abstract Sentiment analysis is a core task in Natural Language Processing (NLP) that aims to identify opinions and sentiment polarity expressed in textual data. This study presents a systematic empirical evaluation of classical machine learning–based sentiment analysis methods using a unified experimental framework. Several supervised classifiers, including Decision Trees, Logistic Regression, Support Vector Machines (SVM), Random Forests, Naïve Bayes, and K-Nearest Neighbors (KNN), are evaluated on labeled text datasets collected from multiple domains such as product reviews, customer feedback, hotel reviews, and social media content. The experimental pipeline includes standard NLP preprocessing steps—text normalization, tokenization, stopword More >

Rubina Castro^1,2, Bruno Silva^1,3, Luiz Guerreiro Lopes^1,4, Fábio Mendonça^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079979 - 15 June 2026

Abstract Path planning for autonomous underwater vehicles requires reliable and computationally efficient methods, particularly in cluttered environments. This work presents a comparative evaluation of representative approaches, including metaheuristic optimization methods (continuous genetic algorithm, particle swarm optimization, gray wolf optimizer, and Jaya), a sampling-based method (probabilistic roadmap with genetic refinement), a reactive strategy (artificial potential fields), and a control-based approach (model predictive control with control barrier functions). The algorithms are assessed in a controlled two-dimensional simulated workspace with randomly generated obstacles and systematically increasing obstacle density. Each configuration is evaluated across multiple independent trials using metrics such… More >

Abdullahi Abubakar Imam^1,*, Sahalu Balarabe Junaidu², Hussaini Mamman³, Ganesh Kumar³, Abdullateef Oluwagbemiga Balogun³, Sunder Ali Khowaja⁴, Shuib Basri³, Luiz Fernando Capretz⁵, Asmah Husaini⁶, Hanif Abdul Rahman⁶, Usman Ali¹, Fatoumatta Conteh¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.076187 - 30 March 2026

Abstract Artificial Intelligence (AI) in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease, which include hemoglobin A1c (HbA1c), oral glucose tolerance test (OGTT), and fasting plasma glucose (FPG) screening techniques, which are invasive and limited in scale. Machine learning (ML) and deep neural network (DNN) models that use large datasets to learn the complex, nonlinear feature interactions, but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy. Conversely, DNN models are more robust, though the ability to reach a high accuracy rate consistently on… More >

Long Li, Hengyang Wu^*, Na Wang

Journal on Artificial Intelligence, Vol.8, pp. 169-182, 2026, DOI:10.32604/jai.2026.077462 - 23 March 2026

Abstract Zero-shot learning as an emerging approach in image captioning techniques, has garnered significant attention from researchers in recent years due to its ability to accomplish tasks without requiring specific category training data. Existing zero-shot image captioning schemes largely rely on traditional language models, which exhibit low efficiency and suboptimal generation quality. To address this issue, this study proposes Hamiltonian Monte Carlo for Image Captioning (HMCIC). This method first models the image captioning task as a probabilistic sampling problem in parameter space, integrating semantic matching and syntactic coherence into an energy function to guide the generation… More >

Seunggyu Byeon¹, Jung-hun Lee², Jong-Deok Kim^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074033 - 12 March 2026

Abstract This paper introduces a fuzzy C-means-based pooling layer for convolutional neural networks that explicitly models local uncertainty and ambiguity. Conventional pooling operations, such as max and average, apply rigid aggregation and often discard fine-grained boundary information. In contrast, our method computes soft memberships within each receptive field and aggregates cluster-wise responses through membership-weighted pooling, thereby preserving informative structure while reducing dimensionality. Being differentiable, the proposed layer operates as standard two-dimensional pooling. We evaluate our approach across various CNN backbones and open datasets, including CIFAR-10/100, STL-10, LFW, and ImageNette, and further probe small training set restrictions More >

Jiaxi Wang¹, Wenbo Sun², Ziyuan Zhou¹, Shihua Wu¹, Jiang Xu¹, Shan Ji^3,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073237 - 10 February 2026

Abstract Lightweight nodes are crucial for blockchain scalability, but verifying the availability of complete block data puts significant strain on bandwidth and latency. Existing data availability sampling (DAS) schemes either require trusted setups or suffer from high communication overhead and low verification efficiency. This paper presents ISTIRDA, a DAS scheme that lets light clients certify availability by sampling small random codeword symbols. Built on ISTIR, an improved Reed–Solomon interactive oracle proof of proximity, ISTIRDA combines adaptive folding with dynamic code rate adjustment to preserve soundness while lowering communication. This paper formalizes opening consistency and prove security… More >

Wenfeng Zheng¹, Guangyu Xu², Siyu Lu³, Junmin Lyu⁴, Feng Bao^5,*, Lirong Yin^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075741 - 29 January 2026

Abstract Graph Neural Networks (GNNs), as a deep learning framework specifically designed for graph-structured data, have achieved deep representation learning of graph data through message passing mechanisms and have become a core technology in the field of graph analysis. However, current reviews on GNN models are mainly focused on smaller domains, and there is a lack of systematic reviews on the classification and applications of GNN models. This review systematically synthesizes the three canonical branches of GNN, Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Graph Sampling Aggregation Network (GraphSAGE), and analyzes their integration pathways More >

Tao Geng¹, Shuaibing Li^1,*, Yunyun Yun¹, Yongqiang Kang¹, Hongwei Li², Junmin Zhu²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071644 - 12 January 2026

Abstract In order to address the challenges posed by complex background interference, high miss-detection rates of micro-scale defects, and limited model deployment efficiency in photovoltaic (PV) module defect detection, this paper proposes an efficient detection framework based on an improved YOLOv11 architecture. First, a Re-parameterized Convolution (RepConv) module is integrated into the backbone to enhance the model’s sensitivity to fine-grained defects—such as micro-cracks and hot spots—while maintaining high inference efficiency. Second, a Multi-Scale Feature Fusion Convolutional Block Attention Mechanism (MSFF-CBAM) is designed to guide the network toward critical defect regions by jointly modeling channel-wise and spatial… More >

Xiaorui Zhang^1,2,*, Chunlin Yuan³, Wei Sun⁴, Ting Wang⁵

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.071043 - 09 December 2025

Abstract Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression, but existing models have significant shortcomings. On the one hand, Transformer multi-head self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity. On the other hand, fixed-size convolution kernels are often used, which have weak perception ability for emotional regions of different scales. Therefore, this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling. In terms of space, multi-branch large-kernel stripe convolution is used to More >