Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (150)
  • Open Access

    REVIEW

    A Survey of Knowledge Graph Construction Using Machine Learning

    Zhigang Zhao1, Xiong Luo1,2,3,*, Maojian Chen1,2,3, Ling Ma1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 225-257, 2024, DOI:10.32604/cmes.2023.031513 - 30 December 2023

    Abstract Knowledge graph (KG) serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework. This framework facilitates a transformation in information retrieval, transitioning it from mere string matching to far more sophisticated entity matching. In this transformative process, the advancement of artificial intelligence and intelligent information services is invigorated. Meanwhile, the role of machine learning method in the construction of KG is important, and these techniques have already achieved initial success. This article embarks on a comprehensive journey through the last strides in the field of KG via machine More >

  • Open Access

    ARTICLE

    CFSA-Net: Efficient Large-Scale Point Cloud Semantic Segmentation Based on Cross-Fusion Self-Attention

    Jun Shu1,2, Shuai Wang1,2, Shiqi Yu1,2, Jie Zhang3,*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2677-2697, 2023, DOI:10.32604/cmc.2023.045818 - 26 December 2023

    Abstract Traditional models for semantic segmentation in point clouds primarily focus on smaller scales. However, in real-world applications, point clouds often exhibit larger scales, leading to heavy computational and memory requirements. The key to handling large-scale point clouds lies in leveraging random sampling, which offers higher computational efficiency and lower memory consumption compared to other sampling methods. Nevertheless, the use of random sampling can potentially result in the loss of crucial points during the encoding stage. To address these issues, this paper proposes cross-fusion self-attention network (CFSA-Net), a lightweight and efficient network architecture specifically designed for… More >

  • Open Access

    REVIEW

    Visual SLAM Based on Object Detection Network: A Review

    Jiansheng Peng1,2,*, Dunhua Chen1, Qing Yang1, Chengjun Yang2, Yong Xu2, Yong Qin2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3209-3236, 2023, DOI:10.32604/cmc.2023.041898 - 26 December 2023

    Abstract Visual simultaneous localization and mapping (SLAM) is crucial in robotics and autonomous driving. However, traditional visual SLAM faces challenges in dynamic environments. To address this issue, researchers have proposed semantic SLAM, which combines object detection, semantic segmentation, instance segmentation, and visual SLAM. Despite the growing body of literature on semantic SLAM, there is currently a lack of comprehensive research on the integration of object detection and visual SLAM. Therefore, this study aims to gather information from multiple databases and review relevant literature using specific keywords. It focuses on visual SLAM based on object detection, covering… More >

  • Open Access

    ARTICLE

    A Lightweight Road Scene Semantic Segmentation Algorithm

    Jiansheng Peng1,2,*, Qing Yang1, Yaru Hou1

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1929-1948, 2023, DOI:10.32604/cmc.2023.043524 - 29 November 2023

    Abstract In recent years, with the continuous deepening of smart city construction, there have been significant changes and improvements in the field of intelligent transportation. The semantic segmentation of road scenes has important practical significance in the fields of automatic driving, transportation planning, and intelligent transportation systems. However, the current mainstream lightweight semantic segmentation models in road scene segmentation face problems such as poor segmentation performance of small targets and insufficient refinement of segmentation edges. Therefore, this article proposes a lightweight semantic segmentation model based on the LiteSeg model improvement to address these issues. The model… More >

  • Open Access

    ARTICLE

    Solving Arithmetic Word Problems of Entailing Deep Implicit Relations by Qualia Syntax-Semantic Model

    Hao Meng, Xinguo Yu*, Bin He, Litian Huang, Liang Xue, Zongyou Qiu

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 541-555, 2023, DOI:10.32604/cmc.2023.041508 - 31 October 2023

    Abstract Solving arithmetic word problems that entail deep implicit relations is still a challenging problem. However, significant progress has been made in solving Arithmetic Word Problems (AWP) over the past six decades. This paper proposes to discover deep implicit relations by qualia inference to solve Arithmetic Word Problems entailing Deep Implicit Relations (DIR-AWP), such as entailing commonsense or subject-domain knowledge involved in the problem-solving process. This paper proposes to take three steps to solve DIR-AWPs, in which the first three steps are used to conduct the qualia inference process. The first step uses the prepared set… More >

  • Open Access

    ARTICLE

    Solving Algebraic Problems with Geometry Diagrams Using Syntax-Semantics Diagram Understanding

    Litian Huang, Xinguo Yu, Lei Niu*, Zihan Feng

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 517-539, 2023, DOI:10.32604/cmc.2023.041206 - 31 October 2023

    Abstract Solving Algebraic Problems with Geometry Diagrams (APGDs) poses a significant challenge in artificial intelligence due to the complex and diverse geometric relations among geometric objects. Problems typically involve both textual descriptions and geometry diagrams, requiring a joint understanding of these modalities. Although considerable progress has been made in solving math word problems, research on solving APGDs still cannot discover implicit geometry knowledge for solving APGDs, which limits their ability to effectively solve problems. In this study, a systematic and modular three-phase scheme is proposed to design an algorithm for solving APGDs that involve textual and… More >

  • Open Access

    ARTICLE

    Ontology-Based Crime News Semantic Retrieval System

    Fiaz Majeed1, Afzaal Ahmad1, Muhammad Awais Hassan2, Muhammad Shafiq3,*, Jin-Ghoo Choi3, Habib Hamam4,5,6,7

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 601-614, 2023, DOI:10.32604/cmc.2023.036074 - 31 October 2023

    Abstract Every day, the media reports tons of crimes that are considered by a large number of users and accumulate on a regular basis. Crime news exists on the Internet in unstructured formats such as books, websites, documents, and journals. From such homogeneous data, it is very challenging to extract relevant information which is a time-consuming and critical task for the public and law enforcement agencies. Keyword-based Information Retrieval (IR) systems rely on statistics to retrieve results, making it difficult to obtain relevant results. They are unable to understand the user's query and thus face word… More >

  • Open Access

    REVIEW

    The Electrophysiology of Semantic Processing in Individuals with Autism Spectrum Disorder: A Meta-Analysis

    Danfeng Yuan1, Xiangyun Yang1, Lijuan Yang1, Zhanjiang Li1,2,*

    International Journal of Mental Health Promotion, Vol.25, No.10, pp. 1067-1079, 2023, DOI:10.32604/ijmhp.2023.041430 - 03 November 2023

    Abstract Language difficulties vary widely among people with autism spectrum disorder (ASD). However, the semantic processing of autistic person and its underlying electrophysiological mechanism are still unclear. This meta-analysis aimed to explore the disturbance of semantic processing in patients with ASD. PubMed, Web of Science, and Embase were searched for event-related potential (ERP) studies on semantic processing in autistic people published in English before September 01, 2022. Pooled estimates were calculated by fixed-effects or random-effects models according to the heterogeneity using Comprehensive Meta-Analysis 2.0. The potential moderators were explored by meta-regression and subgroup analysis. This meta-analysis… More >

  • Open Access

    ARTICLE

    Intelligent Traffic Surveillance through Multi-Label Semantic Segmentation and Filter-Based Tracking

    Asifa Mehmood Qureshi1, Nouf Abdullah Almujally2, Saud S. Alotaibi3, Mohammed Hamad Alatiyyah4, Jeongmin Park5,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3707-3725, 2023, DOI:10.32604/cmc.2023.040738 - 08 October 2023

    Abstract Road congestion, air pollution, and accident rates have all increased as a result of rising traffic density and worldwide population growth. Over the past ten years, the total number of automobiles has increased significantly over the world. In this paper, a novel method for intelligent traffic surveillance is presented. The proposed model is based on multilabel semantic segmentation using a random forest classifier which classifies the images into five classes. To improve the results, mean-shift clustering was applied to the segmented images. Afterward, the pixels given the label for the vehicle were extracted and blob… More >

  • Open Access

    ARTICLE

    Multi-Objective Image Optimization of Product Appearance Based on Improved NSGA-Ⅱ

    Yinxue Ao1, Jian Lv1,*, Qingsheng Xie1, Zhengming Zhang2

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3049-3074, 2023, DOI:10.32604/cmc.2023.040088 - 08 October 2023

    Abstract A second-generation fast Non-dominated Sorting Genetic Algorithm product shape multi-objective imagery optimization model based on degradation (DNSGA-II) strategy is proposed to make the product appearance optimization scheme meet the complex emotional needs of users for the product. First, the semantic differential method and K-Means cluster analysis are applied to extract the multi-objective imagery of users; then, the product multidimensional scale analysis is applied to classify the research objects, and again the reference samples are screened by the semantic differential method, and the samples are parametrized in two dimensions by using elliptic Fourier analysis; finally, the… More >

Displaying 31-40 on page 4 of 150. Per Page