Hangjun Zhou¹, Tingting Shen^{1, *}, Xinglian Liu¹, Yurong Zhang¹, Peng Guo^{1, 2}, Jianjun Zhang³

Journal on Artificial Intelligence, Vol.2, No.2, pp. 89-101, 2020, DOI:10.32604/jai.2020.09968 - 15 July 2020

Abstract With the advent of the era of big data, knowledge engineering has received extensive attention. How to extract useful knowledge from massive data is the key to big data analysis. Knowledge graph technology is an important part of artificial intelligence, which provides a method to extract structured knowledge from massive texts and images, and has broad application prospects. The knowledge base with semantic processing capability and open interconnection ability can be used to generate application value in intelligent information services such as intelligent search, intelligent question answering and personalized recommendation. Although knowledge graph has been More >

Jinyingming Zhang¹ , Jin Liu^{1, *}, Xinyue Lin¹

Journal on Artificial Intelligence, Vol.2, No.2, pp. 79-88, 2020, DOI:10.32604/jai.2020.010476 - 15 July 2020

Abstract Neural Machine Translation (NMT) based system is an important technology for translation applications. However, there is plenty of rooms for the improvement of NMT. In the process of NMT, traditional word vector cannot distinguish the same words under different parts of speech (POS). Aiming to alleviate this problem, this paper proposed a new word vector training method based on POS feature. It can efficiently improve the quality of translation by adding POS feature to the training process of word vectors. In the experiments, we conducted extensive experiments to evaluate our methods. The experimental result shows More >

Chen Song¹, Xu Cheng^{1, *}, Yongxiang Gu¹, Beijing Chen¹, Zhangjie Fu¹

Journal on Artificial Intelligence, Vol.2, No.2, pp. 59-77, 2020, DOI:10.32604/jai.2020.010193 - 15 July 2020

Abstract Object detection is one of the most fundamental, longstanding and significant problems in the field of computer vision, where detection involves object classification and location. Compared with the traditional object detection algorithms, deep learning makes full use of its powerful feature learning capabilities showing better detection performance. Meanwhile, the emergence of large datasets and tremendous improvement in computer computing power have also contributed to the vigorous development of this field. In the paper, many aspects of generic object detection are introduced and summarized such as traditional object detection algorithms, datasets, evaluation metrics, detection frameworks based More >

Wei Fang^{1, 2, 3, ∗}, Lin Pang¹, Weinan Yi¹

Journal on Artificial Intelligence, Vol.2, No.1, pp. 39-58, 2020, DOI:10.32604/jai.2020.09789 - 15 July 2020

Abstract In recent years, with the rapid development of human society, more and more complex tasks have emerged that require deep learning to automatically extract abstract feature representations from a large amount of data, and use reinforcement learning to learn the best strategy to complete the task. Through the combination of deep learning and reinforcement learning, end-to-end input and output can be achieved, and substantial breakthroughs have been made in many planning and decision-making systems with infinite states, such as games, in particular, AlphaGo, robotics, natural language processing, dialogue systems, machine translation, and computer vision. In More >

Xueming Qiao¹, Yingxue Xia¹, Weiyi Zhu², Dongjie Zhu^{3, *}, Liang Kong¹, Chunxu Lin³, Zhenhao Guo³, Yiheng Sun³

Journal on Artificial Intelligence, Vol.2, No.1, pp. 29-37, 2020, DOI:10.32604/jai.2020.09955 - 15 July 2020

Abstract Natural scene recognition has important significance and value in the fields of image retrieval, autonomous navigation, human-computer interaction and industrial automation. Firstly, the natural scene image non-text content takes up relatively high proportion; secondly, the natural scene images have a cluttered background and complex lighting conditions, angle, font and color. Therefore, how to extract text extreme regions efficiently from complex and varied natural scene images plays an important role in natural scene image text recognition. In this paper, a Text extremum region Extraction algorithm based on Joint-Channels (TEJC) is proposed. On the one hand, it More >

Peng Cen¹, Kexin Zhang¹, Desheng Zheng^{1, *}

Journal on Artificial Intelligence, Vol.2, No.1, pp. 17-27, 2020, DOI:10.32604/jai.2020.010132 - 15 July 2020

Abstract Deep learning has made a great breakthrough in the field of speech and image recognition. Mature deep learning neural network has completely changed the field of nat ural language processing (NLP). Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the Internet and other media, sentiment analysis has become one of the most active research fields in natural language processing. This paper introduces three deep learning networks applied in IMDB movie reviews sent iment analysis. Dataset was divided to 50% positive reviews and 50% negative reviews. Recurrent Neural More >

Kun Fang^{1, 2}, Jianquan Ouyang^{1, *}

Journal on Artificial Intelligence, Vol.2, No.1, pp. 1-15, 2020, DOI:10.32604/jai.2020.09738 - 15 July 2020

Abstract Generating an Adversarial network (GAN) has shown great development prospects in image generation and semi-supervised learning and has evolved into TripleGAN. However, there are still two problems that need to be solved in Triple-GAN: based on the KL divergence distribution structure, gradients are easy to disappear and training instability occurs. Since Triple-GAN tags the samples manually, the manual marking workload is too large. Marked uneven and so on. This article builds on this improved Triple-GAN model (Improved Triple-GAN), which uses Random Forests to classify real samples, automate tagging of leaf nodes, and use Least Squares More >

Qiuyan Wang^{1, *}, Haibing Dong²

Journal on Artificial Intelligence, Vol.1, No.2, pp. 101-110, 2019, DOI:10.32604/jai.2019.08170

Abstract It is the development trend of library information management, which applies the mature and cutting-edge information technology to library information retrieval. In order to realize the rapid retrieval of massive book information, this paper proposes a book retrieval method combining QR code with image retrieval technology. This method analyzes the visual features of book images, design a book image retrieval method based on boundary contour and regional pixel distribution features, and realizes the association retrieval of book information combined with the QR code, so as to improve the efficiency of book retrieval. The experimental results More >

Won-Chol Yang^1.*, Son-Hak Chon¹, Chol-Min Choe¹, Un-Ha Kim¹

Journal on Artificial Intelligence, Vol.1, No.2, pp. 89-99, 2019, DOI:10.32604/jai.2019.07885

Abstract Materials selection is a multiple attribute decision making (MADM) problem. A lot of MADM methods are applicable to materials selection, and it may produce considerable differences between the results of materials selection. But it is unknown which MADM method is better. So it is desirable to decide reasonable final result of materials selection in consideration of the individual results from different MADM methods. In this paper, materials selection method combined with different MADM methods is proposed. The method is based on final ranks of alternative materials, where the final ranks are determined from the ranks More >

Shuai Yuan¹, Guizhi Wang^1,*, Jibo Chen¹, Wei Guo²

Journal on Artificial Intelligence, Vol.1, No.2, pp. 69-88, 2019, DOI:10.32604/jai.2019.06535

Abstract This paper develops a joint model utilizing the principal component analysis (PCA) and the back propagation (BP) neural network model optimized by the Levenberg Marquardt (LM) algorithm, and as an application of the joint model to investigate the damages caused by typhoons for a coastal province, Fujian Province, China in 2005-2015 (latest). First, the PCA is applied to analyze comprehensively the relationship between hazard factors, hazard bearing factors and disaster factors. Then five integrated indices, overall disaster level, typhoon intensity, damaged condition of houses, medical rescue and self-rescue capability, are extracted through the PCA; Finally,… More >