Home / Journals / JAI / Vol.3, No.4, 2021
  • Open Access

    ARTICLE

    Solving the Feature Diversity Problem Based on Multi-Model Scheme

    Guanghao Jin1, Na Zhao1, Chunmei Pei1, Hengguang Li2, Qingzeng Song3, Jing Yu1,*
    Journal on Artificial Intelligence, Vol.3, No.4, pp. 135-143, 2021, DOI:10.32604/jai.2021.027154
    Abstract Generally, the performance of deep learning models is related to the captured features of training samples. When the training samples belong to different domains, the diverse features may increase the difficulty of training high performance models. In this paper, we built a new framework that generates multiple models on the organized samples to increase the accuracy of classification. Firstly, our framework selects some existing models and trains each of them on organized training sets to get multiple trained models. Secondly, we select some of them based on a validation set. Finally, we use some fusion method on the outputs of… More >

  • Open Access

    ARTICLE

    Research of Insect Recognition Based on Improved YOLOv5

    Zhong Yuan1, Wei Fang1,2,*, Yongming Zhao3,*, Victor S. Sheng4
    Journal on Artificial Intelligence, Vol.3, No.4, pp. 145-152, 2021, DOI:10.32604/jai.2021.026902
    Abstract Insects play an important role in the natural ecology, it is of great significance for ecology to research on insects. Nowadays, the invasion of alien species has brought serious troubles and a lot of losses to local life. However, there is still much room for improvement in the accuracy of insect recognition to effectively prevent the invasion of alien species. As the latest target detection algorithm, YOLOv5 has been used in various scene detection tasks, because of its powerful recognition capabilities and extremely high accuracy. As the problem of imbalance of feature maps at different scales will affect the accuracy… More >

  • Open Access

    ARTICLE

    Construction and Application of Knowledge Graph for Quality and Safety Supervision of Transportation Engineering

    Sheng Huang, Chuanle Liu*
    Journal on Artificial Intelligence, Vol.3, No.4, pp. 153-162, 2021, DOI:10.32604/jai.2021.025175
    Abstract Knowledge graph technology play a more and more important role in various fields of industry and academia. This paper firstly introduces the general framework of the knowledge graph construction, which includes three stages: information extraction, knowledge fusion and knowledge processing. In order to improve the efficiency of quality and safety supervision of transportation engineering construction, this paper constructs a knowledge graph by acquiring multi-sources heterogeneous data from supervision of transportation engineering quality and safety. It employs a bottom-up construction strategy and some natural language processing methods to solve the problems of the knowledge extraction for transportation engineering construction. We use… More >

  • Open Access

    ARTICLE

    Answer Classification via Machine Learning in Community Question Answering

    Yue Jiang, Xinyu Zhang, Wohuan Jia, Li Xu*
    Journal on Artificial Intelligence, Vol.3, No.4, pp. 163-169, 2021, DOI:10.32604/jai.2021.027590
    Abstract As a new type of knowledge sharing platform, the community question answer website realizes the acquisition and sharing of knowledge, and is loved and sought after by the majority of users. But for multi-answer questions, answer quality assessment becomes a challenge. The answer selection in CQA (Community Question Answer) was proposed as a challenge task in the SemEval competition, which gave a data set and proposed two subtasks. Task-A is to give a question (including short title and extended description) and its answers, and divide each answer into absolutely relevant (good), potentially relevant (potential) and bad or irrelevant (bad, dialog,… More >

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