Exploiting Intra- and Inter-Region Relations for Sales Prediction via Graph Convolutional Network

Liu, Yaochang; Guo, Baoshen; Song, Xining; Wang, Shuai; He, Tian

doi:10.1109/globecom48099.2022.10001442

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…In recent years, the rapid development of the Internet of Things and artificial intelligence has led to numerous studies exploring diverse learning‐based applications in various areas of smart cities through spatial‐temporal data mining, for example, online e‐commerce [6, 7], intelligent transportation [20–25], and online‐to‐offline logistics [9, 26–29]. Pang et al.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, the rapid development of the Internet of Things and artificial intelligence has led to numerous studies exploring diverse learning-based applications in various areas of smart cities through spatial-temporal data mining, for example, online e-commerce [6,7], intelligent transportation [20][21][22][23][24][25], and online-to-offline logistics [9,[26][27][28][29]. Pang et al [30] perform anomaly-informed modelling by formulating anomaly detection as a pairwise relation prediction task leveraging labelled anomaly data.…”

Section: Spatial-temporal Learning-based Applicationsmentioning

confidence: 99%

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Trustworthy semi‐supervised anomaly detection for online‐to‐offline logistics business in merchant identification

Li,

Wang,

et al. 2024

CAAI Trans on Intel Tech

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The rise of online‐to‐offline (O2O) e‐commerce business has brought tremendous opportunities to the logistics industry. In the online‐to‐offline logistics business, it is essential to detect anomaly merchants with fraudulent shipping behaviours, such as sending other merchants' packages for profit with their low discounts. This can help reduce the financial losses of platforms and ensure a healthy environment. Existing anomaly detection studies have mainly focused on online fraud behaviour detection, such as fraudulent purchase and comment behaviours in e‐commerce. However, these methods are not suitable for anomaly merchant detection in logistics due to the more complex online and offline operation of package‐sending behaviours and the interpretable requirements of offline deployment in logistics. MultiDet, a semi‐supervised multi‐view fusion‐based Anomaly Detection framework in online‐to‐offline logistics is proposed, which consists of a basic version SemiDet and an attention‐enhanced multi‐view fusion model. In SemiDet, pair‐wise data augmentation is first conducted to promote model robustness and address the challenge of limited labelled anomaly instances. Then, SemiDet calculates the anomaly scoring of each merchant with an auto‐encoder framework. Considering the multi‐relationships among logistics merchants, a multi‐view attention fusion‐based anomaly detection network is further designed to capture merchants' mutual influences and improve the anomaly merchant detection performance. A post‐hoc perturbation‐based interpretation model is designed to output the importance of different views and ensure the trustworthiness of end‐to‐end anomaly detection. The framework based on an eight‐month real‐world dataset collected from one of the largest logistics platforms in China is evaluated, involving 6128 merchants and 16 million historical order consignor records in Beijing. Experimental results show that the proposed model outperforms other baselines in both AUC‐ROC and AUC‐PR metrics.

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Section: Related Workmentioning

confidence: 99%

Section: Spatial-temporal Learning-based Applicationsmentioning

confidence: 99%

Trustworthy semi‐supervised anomaly detection for online‐to‐offline logistics business in merchant identification

Li,

Wang,

et al. 2024

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

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“…We introduce a dynamic forward process that utilizes static-dynamic fused embeddings for similarity-weighted graph construction as the input for the graph neural network, which enhances the representation of patient data. To enhance the representation capability of the graph neural network and adapt the dynamic forward process of graph construction [23], we employ Graph Transformer Networks (GTN) [24] to uncover the patterns in the data. GTN then classifies the nodes and predicts whether the patient is likely to experience postoperative pain based on their current state.…”

Section: Overall Frameworkmentioning

confidence: 99%

DoseFormer: Dynamic Graph Transformer for Postoperative Pain Prediction

Zhang,

Zhao,

Zhou

et al. 2023

Electronics

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Many patients suffer from postoperative pain after surgery, which causes discomfort and influences recovery after the operation. During surgery, the anesthetists usually rely on their own experience when anesthetizing, which is not stable for avoiding postoperative pain. Hence, it is essential to predict postoperative pain and give proper doses accordingly. Recently, the relevance of various clinical parameters and nociception has been investigated in many works, and several indices have been proposed for measuring the level of nociception. However, expensive advanced equipment is required when applying advanced medical technologies, which is not accessible to most institutions. In our work, we propose a deep learning model based on a dynamic graph transformer framework named DoseFormer to predict postoperative pain in a short period after an operation utilizing dynamic patient data recorded in existing widely utilized equipment (e.g., anesthesia monitor). DoseFormer consists of two modules: (i) We design a temporal model utilizing a long short-term memory (LSTM) model with an attention mechanism to capture dynamic intraoperative data of the patient and output a hybrid semantic embedding representing the patient information. (ii) We design a graph transformer network (GTN) to infer the postoperative pain level utilizing the relations across the patient embeddings. We evaluate the DoseFormer system with the medical records of over 999 patients undergoing cardiothoracic surgery in the Fourth Affiliated Hospital of Zhejiang University School of Medicine. The experimental results show that our model achieves 92.16% accuracy for postoperative pain prediction and has a better comprehensive performance compared with baselines.

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