Article
Computer Science, Information Systems
Cun Ji, Mingsen Du, Yanxuan Wei, Yupeng Hu, Shijun Liu, Li Pan, Xiangwei Zheng
Summary: Time series classification is widely used in various domains, including EEG/ECG classification, device anomaly detection, and speaker authentication. Despite the existence of many methods, selecting intuitive temporal features for accurate classification remains a challenge. Therefore, this paper proposes a new method called TSC-RTF, which utilizes random temporal features, and shows that it can compete with state-of-the-art methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Mathematics, Applied
Laifan Pei, Zhaohui Li, Jie Liu
Summary: In this study, a texture classification algorithm named TCIVG was designed based on a newly proposed image visibility graph network constructing method. Experimental results on the Brodatz texture image database showed high classification accuracy for both artificial and natural texture images. The results outperformed some existing literature studies based on the same image database.
Article
Chemistry, Analytical
Tianjiao Kong, Jie Shao, Jiuyuan Hu, Xin Yang, Shiyiling Yang, Reza Malekian
Summary: In this study, complex network features were extracted from EEG signals for emotion recognition through the construction of two types of complex networks and fusion of feature matrices. The proposed method achieved high emotion recognition accuracies in valence and arousal dimensions, and further improved classification accuracies when combined with time-domain features.
Article
Engineering, Electrical & Electronic
Sayanjit Singha Roy, Soumya Chatterjee
Summary: This article proposes a novel method for identification of partial discharge signals using horizontal visibility graph spectral analysis, achieving high recognition accuracy even in presence of noise. The method involves extracting spectral graph features, employing machine learning classifiers, and adjusting penetrable distance to improve detection accuracy.
IEEE SENSORS JOURNAL
(2021)
Article
Computer Science, Interdisciplinary Applications
Bingtao Zhang, Dan Wei, Guanghui Yan, Tao Lei, Haishu Cai, Zhifei Yang
Summary: In this study, a depression recognition framework based on the feature-level fusion of spatial-temporal electroencephalography (EEG) was proposed. By analyzing EEG data and performing feature fusion, depression could be effectively recognized. The experimental results showed that the method achieved a high accuracy rate.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Cun Ji, Mingsen Du, Yupeng Hu, Shijun Liu, Li Pan, Xiangwei Zheng
Summary: With the increasing application of Internet of Things technology, time series classification has become a research hotspot in the field of data mining. This paper proposes a new method for time series classification based on temporal features (TSC-TF), which generates temporal feature candidates through time series segmentation and selects important features with the help of a random forest. The experimental results on various datasets demonstrate the superiority of the proposed method.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Musrrat Ali, Sanoj Kumar, Rahul Pal, Manoj K. Singh, Deepika Saini
Summary: Texture analysis is an important task in image processing and computer vision. This paper proposes a method for texture classification using graphs, specifically the natural and horizontal visibility graphs. The suggested method outperforms traditional techniques and even approaches the performance of convolutional neural networks. The results show the potential of graph methods for texture classification.
Review
Engineering, Mechanical
Tao Wen, Huiling Chen, Kang Hao Cheong
Summary: The analysis of time series and images is significant in various fields. Visibility graph algorithms are used to map time series and images into different types of complex networks in order to explore their topological structure and information. By using local random walk algorithms and information fusion methods, time series can be forecasted, and images can be classified using machine learning models. The visibility graph algorithm outperforms existing algorithms in time series prediction and image classification, making complex networks an important tool for understanding the characteristics of time series and images.
NONLINEAR DYNAMICS
(2022)
Article
Physics, Multidisciplinary
Le Cheng, Peican Zhu, Wu Sun, Zhen Han, Keke Tang, Xiaodong Cui
Summary: The analysis and discrimination of time series data have practical significance. Transforming time series data into networks using visibility graph (VG) methods is an effective approach for classifying the data through GNNs. However, there are two main obstacles: efficiency and complexity in weighted graph construction, and difficulty in assigning node importance. To overcome these challenges, an improved weighted visibility graph algorithm (WLVG) is proposed, which intelligently assigns weights based on Euclidean distance and removes unimportant edges. The graph isomorphism network (GIN) is used to aggregate information among neighbors. Experimental results show WLVG outperforms baseline methods on practical datasets, demonstrating its effectiveness.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Yusheng Huang, Xiaoyan Mao, Yong Deng
Summary: The degree sequence of the NVG transformation provides useful motif information for practical usage, as shown in a study on stock trend prediction. The proposed natural visibility encoding and moving window strategy have been proven effective and robust in classifying time series. Further investigation into the degree sequence of the NVG transformation is encouraged based on the success of the proposed framework.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Mingsen Du, Yanxuan Wei, Xiangwei Zheng, Cun Ji
Summary: Multivariate time series classification is widely used in various real-life applications and has attracted significant research interest. However, existing methods only focus on local or global features and overlook the spatial dependency features among multiple variables. In this study, we propose a multi-feature based network (MF-Net) that captures both local and global features through the attention-based mechanism and integrates them to capture spatial dependency features. Experimental results on UEA datasets demonstrate that our method performs competitively with state-of-the-art methods.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Zeynab Mohammadpoory, Mahda Nasrolahzadeh, Sekineh Asadi Amiri
Summary: Recently, the concept of transforming time series into graphs has been widely used in various studies. This particular study focuses on the visibility graph (VG) algorithms for epileptic seizure detection. Single-channel EEG signals are transformed into five different VG graphs, and 13 features are extracted. The results show that the proposed VG algorithms are efficient for classification, with high accuracy for two or three classes, making it effective for seizure detection.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Shaocong Wu, Mengxia Liang, Xiaolong Wang, Qingcai Chen
Summary: Time series classification is important in time series analysis research and has gained attention from researchers. Representation learning and feature space expansion play vital roles in improving the performance of classifiers. Inspired by complex network analysis, non-Euclidean structural representation is introduced to highlight and characterize important features. To explore this approach further, an ensemble learning framework based on visibility graph representation (VGbel) is proposed, which incorporates directed series-to-graph transformation, multiscale feature extraction, and stacking-based ensemble modeling. Extensive evaluation on UCR time series archive demonstrates the effectiveness and competitiveness of the proposed method.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Timo De Waele, Adnan Shahid, Daniel Peralta, Anniek Eerdekens, Margot Deruyck, Frank A. M. Tuyttens, Eli De Poorter
Summary: To track the activities and performance of horses, inertial measurement units (IMUs) combined with machine learning algorithms are commonly used. A data-efficient algorithm is proposed that only requires 3 minutes of labeled calibration data. This algorithm achieved a 95% accuracy on datasets captured with leg-mounted IMUs and neck-mounted IMU. However, when the algorithm was calibrated on multiple horses and evaluated on unfamiliar horses, there was a 15% drop in classification accuracy.
IEEE SENSORS JOURNAL
(2023)
Article
Agronomy
Meng Zhou, Hengbiao Zheng, Can He, Peng Liu, G. Mustafa Awan, Xue Wang, Tao Cheng, Yan Zhu, Weixing Cao, Xia Yao
Summary: This study proposes a classification method based on UAV imagery for crop phenology detection. The results show that the combination of spectral and texture features can improve classification accuracy, providing technical guidance for real-time detection of crop phenology.
FIELD CROPS RESEARCH
(2023)
Article
Computer Science, Hardware & Architecture
Cun Ji, Chao Zhao, Shijun Liu, Chenglei Yang, Li Pan, Lei Wu, Xiangxu Meng
Article
Computer Science, Hardware & Architecture
Cun Ji, Chao Zhao, Li Pan, Shijun Liu, Chenglei Yang, Xiangxu Meng
Article
Computer Science, Software Engineering
Cun Ji, Xiunan Zou, Shijun Liu, Li Pan
SOFTWARE-PRACTICE & EXPERIENCE
(2020)
Article
Computer Science, Artificial Intelligence
Yupeng Hu, Cun Ji, Qingke Zhang, Lin Chen, Peng Zhan, Xueqing Li
Article
Engineering, Biomedical
Xiangwei Zheng, Min Zhang, Tiantian Li, Cun Ji, Bin Hu
Summary: The study proposes a novel method utilizing ERP components and nonlinear feature MMSE to recognize consciousness and unconsciousness emotions, achieving high classification accuracy in experimental results. This suggests that the fusion of ERP components and nonlinear feature MMSE is effective for emotion recognition and provides a new research direction for nonlinear time series studies.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Shan Yang, Xiangwei Zheng, Cun Ji, Xuanchi Chen
Summary: Electronic Health Records (EHRs) are digital records associated with hospitalization, diagnosis, medications, etc., and their secondary use can promote clinical informatics applications. The proposed Multi-Layer Representation Learning method (MLRL) can effectively learn patient representation by exploring valuable information hierarchically.
NEURAL PROCESSING LETTERS
(2021)
Article
Computer Science, Interdisciplinary Applications
Tingting Li, Wenqi Niu, Cun Ji
Summary: Edge computing has led to the study of the edge user allocation (EUA) problem, which aims to allocate edge users to edge servers while meeting specific constraints. This paper proposes the EUA-FOA method, which outperforms existing approaches in effectively solving the EUA problem, as demonstrated through experiments.
JOURNAL OF COMPUTATIONAL SCIENCE
(2021)
Proceedings Paper
Biochemical Research Methods
Xiaofang Sun, Bin Hu, Xiangwei Zheng, Yongqiang Yin, Cun Ji
2020 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE
(2020)
Proceedings Paper
Automation & Control Systems
Shilin Zhang, Bin Hu, Cun Ji, Xiangwei Zheng, Min Zhang
Proceedings Paper
Biochemical Research Methods
Yang Yang, Xiangwei Zheng, Cun Ji
2019 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)
(2019)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Chao Zhao, Shijun Liu, Li Pan, Cun Ji, Chenglei Yang
PROCEEDINGS OF THE 2019 IEEE 23RD INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD)
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Xiukai Zhao, Lei Lyu, Chen Lyu, Cun Ji
2018 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Cun Ji, Xiunan Zou, Yupeng Hu, Shijun Liu, Lei Lyu, Xiangwei Zheng
2018 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS
(2019)
Proceedings Paper
Computer Science, Software Engineering
Wei Luo, Qi Zhang, Cun Ji, Peng Zhan, Jiecai Zheng, Xueqing Li
PROCEEDINGS OF 2018 IEEE 9TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS)
(2018)
