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Time-aware bike flow prediction framework with dynamic edge fusion and memory integration

Knowledge-Based Systems 2025年 322卷

作者： M.Rajeh, Taha Li, Tianrui Luo, Zhpeng Javed, Muhammad Hafeez Alhaek, Fares School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu 611756 China Stirling College Chengdu University Chengdu 610106 China Computing Science Faculty of Natural Sciences University of Stirling Stirling FK9 4LA United Kingdom

Bike-Sharing Systems (BSSs) have become a popular solution to urban sustainability concerns, providing affordable and environmentally friendly transportation. However, modeling and predicting shared-bike flow patterns are essential for developing intelligent transportation systems and facilitating city management. Despite the existence of several inflow and outflow prediction models, certain unresolved issues remain, including the inability to differentiate and capture heterogeneous dynamic relationships within daily flows, and the suboptimal utilization of historical flow data for forecasting. To address these limitations, we propose MVRC (Multi-View Relational Framework based on Clustering), a novel deep-learning framework for precise inflow and outflow prediction. The MVRC framework models three types of heterogeneous dynamic flow relationships during different periods of the day. Initially, we introduce a novel clustering method to group nearby stations exhibiting similar patterns, followed by creating a dynamic memory module to store and update edge attributes based on the time of day. Furthermore, the dynamic edge fusion mechanism effectively integrates heterogeneous, time-evolving flow features, capturing their varying impacts. To further enhance the model's capabilities, an offline component that leverages historical data is incorporated, improving performance during testing. Extensive experiments conducted on real datasets from New York City and Washington, D.C., demonstrate the superior prediction efficiency and framework effectiveness of the MVRC framework compared to existing state-of-the-art techniques. These results highlight the practicality and efficacy of MVRC in terms of prediction accuracy and stability. © 2025

关键词： Attention Bike flow Clustering data fusion Flow prediction Graph convolutional networks Urban computing

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CNN-Based Automated Classification of SPECT Bone Scan Images

CNN-Based Automated Classification of SPECT Bone Scan Images

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2022 International Conference on Neural Networks, Information, and Communication Engineering, NNICE 2022

作者： Man, Zhengxing Lin, Qiang Cao, Yongchun School of Mathematics and Computer Science Northwest Minzu University Gansu Lanzhou730124 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Gansu Lanzhou730030 China

ISBN: (纸本)9781510655171

Functional medicine imaging has been successfully applied to capture functional changes in pathological tissues of the body in recent years. SPECT nuclear medicine functional imaging has the potential to acquire information about areas of concern (e.g., lesions and organs) in a non-invasive manner, enabling semi-automated or automated decision-making for the purposes of disease diagnosis, treatment, evaluation, and prediction. To reliably identify that whether or not at least one hotspot or lesion presents in a whole-body SPECT image, in this work, we develop a group of CNN-based classifiers. Specifically, we first propose a preprocessing method that transforms each original SPECT file into the required form by deep learning model, including normalization, 3-channel construction, rotation and scaling, size standardization, and size adapting. Second, six different classifiers are constructed by fine-tuning parameters of the standard VGG-16 model. Last, a group of real-world SPECT whole-body bone scan files were utilized to evaluate the developed classifiers. Experiment results shows that our classifiers are workable for the 2-class classification of SPECT images, achieving a best value of 0.7641, 0.6678, 1.000, and 0.6574 for defined evaluation metrics Acc, Pre, Rec, and AUC, respectively. © 2022 SPIE.

关键词： Image classification

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Multi-disease classification of whole-body scintigraphy images based on deep learning 7

Multi-disease classification of whole-body scintigraphy imag...

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7th International Conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Liu, Liangxia Cao, Yongchun Lin, Qiang Man, Zhengxing Wang, Weiqiong Key Laboratory of China's Ethnic Languages and Information Technology Ministry of Education Northwest Minzu University China School of Mathematics and Computer Science Northwest Minzu University China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University China

ISBN: (纸本)9781665478571

Bone scan images has important clinical application value for the early diagnosis and treatment of skeletal lesions in the whole-body. In order to realize classification of multiple diseases (i.e., normal, bone metastases, arthritis, and thyroid cancer) in whole-body bone scintigraphy images, In this paper, the multi-disease classification method based on convolutional neural network was studied on bone scan images, and a self-defined classification model was proposed, which was named SPNT9. The model consists of nine convolutional layers, five pooling layers, two fully connected layers and one softmax layer. Finally, the model was validated with real SPECT images collected. By comparing with VGGNet, ResNet and DenseNet models, The SPNT9 model obtained the best classification performance in the test samples, with Accuracy, Precision, Recall and F-1 of 0.8814, 0.7806, 0.7900 and 0.7899, respectively. The results show that the proposed model can be used to accurately classify multiple diseases in SPECT images. © 2022 IEEE.

关键词： Convolutional neural networks

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Classification of Thoracic Bone Scintigraphic Images Using ResNet with Attention Modules 2

Classification of Thoracic Bone Scintigraphic Images Using R...

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2nd International Conference on Signal Image Processing and Communication, ICSIPC 2022

作者： Cao, Xu Cao, Yongchun Lin, Qiang Man, Zhengxing Wang, Yubo Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University China School of Mathematics and Computer Science Northwest Minzu University China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University China

ISBN: (纸本)9781510657694

Whole-body bone scan is an effective tool for diagnosing bone metastasis from malignant tumors, including lung cancer. However, due to the low spatial resolution of Scintigraphic images, manual analysis of the images is very difficult. In order to accurately diagnose bone metastases, a self-defined convolutional neural network is studied and constructed in this paper for detecting lung cancer-Caused skeletal metastasis in bone scan images Firstly, since thoracic regions such as ribs and spine are the most frequent areas of bone metastases, we preprocessed the collected SPECT whole-body bone imaging data to extract the thoracic regions. Then based on the feature that ResNet can alleviate the vanishing gradient problem caused by increasing depth in deep neural networks and the feature that attention can focus on important information to improve network expression. A deep SPECT image classification network Att-ResNet24 with residual module and hybrid attention mechanism is developed for classification of these images, including bone metastases and non-bone metastases. Experiments on a set of real SPETC whole-body bone images show that self-defined classification networks can be effectively used for the detection of bone metastases, obtaining the best scores 0.737, 0.744, 0.736, and 0.735 for accuracy, precision, recall, and F-1 score, respectively. © 2022 SPIE.

关键词： Image classification

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FedLGMatch: Federated semi-supervised learning via joint local and global pseudo labeling

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Knowledge-Based Systems 2025年 320卷

作者： Zhao, Qing Chu, Jielei Li, Zhaoyu Huang, Wei Luo, Zhipeng Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China China Railway Engineering Group Limited Beijing100039 China China Railway Eryuan Engineering Group Co. Ltd. Sichuan Chengdu610031 China College of Computer and Data Science Fuzhou University China

The bulk of existing Federated Learning (FL) algorithms pay attention to supervised setting and assume that clients have fully labeled data. However, it may be impractical for all clients to obtain plenty of labels due to high annotation costs. Hence, the Federated Semi-Supervised Learning (FSSL) as a promising paradigm has better prospect in many realistic applications (e.g. medical scenario). Under Labels-at-Server scenario, most pseudo labeling based FSSL approaches use only the global model to generate pseudo-labels for unlabeled data, while the local models are ignored. When the local data distribution is much more different from the central server (e.g., Non-IID setting), the generated pseudo-labels may contain much noise, thus, resulting in more serious confirmation bias. To tackle the crucial issue, a novel Federated Semi-Supervised Learning via Joint Local and Global Pseudo Labeling (FedLGMatch) framework is proposed in this paper. The prominent advantage of the proposed FedLGMatch is that it allows local models trained in the last communication round to assist global model in generating pseudo-labels, which successfully emphasizes more clean pseudo-label learning at each client. Experimental results also show that FedLGMatch achieves significant performance improvements than other state-of-the-art models on the standard benchmark datasets. © 2025 Elsevier B.V.

关键词： Semi-supervised learning

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Star coloring for a special class of graphs G1, d

Star coloring for a special class of graphs G1, d

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2021 International Conference on Big data Analysis and Computer Science, BDACS 2021

作者： Cai, Jin Tian, ShuangLiang An, ZhuoMo School of Mathematics and Computer Science Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Northwest for Minzu University Lanzhou China

ISBN: (纸本)9781665425612

In this paper we study the star coloring for a special class of graphs whose degree is either 1 or d. They are graphs obtained by generalized corona products of path, circle, star, complete graph complete bipartite graph and complement of complete graph respectively. Vertex coloring theory of graphs is an important part of graph coloring theory. Star coloring is a special vertex coloring with limited conditions.. © 2021 IEEE.

关键词： Stars

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Adjacent vertex distinguishing incidence coloring of semi-strong product of paths

Adjacent vertex distinguishing incidence coloring of semi-st...

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2020 International Conference on Computer Science and Management Technology, ICCSMT 2020

作者： Peng, Lizhen Tian, Shuangliang Lu, Qiqi Northwest Minzu University School of Mathematics and Computer Science Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Lanzhou China

ISBN: (纸本)9781728186689

Adjacent vertex distinguishing incidence coloring of graph G is an incidence coloring that satisfies that adjacent vertices have different color sets, and the minimum color number is called the adjacent vertex distinguishing incidence coloring number of graph G, which is denoted as χai (G). In this paper, we study the adjacent vertex distinguishing incidence coloring of semi-strong products and strong products of paths of different orders, and obtain the exact values of the corresponding coloring numbers. © 2020 IEEE.

关键词： Coloring

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The acyclic coloring of the Corona product and Edge corona product of special graphs

The acyclic coloring of the Corona product and Edge corona p...

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2020 International Conference on Computer Science and Management Technology, ICCSMT 2020

作者： Lu, Qiqi Tian, Shuangliang Peng, Lizhen Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Northwest Minzu University School of Mathematics and Computer Science Lanzhou China

ISBN: (纸本)9781728186689

An acyclic coloring of a graph is a proper vertex coloring such that there are no bichromatic cycles. The acyclic chromatic number of G, denoted a(G), is the minimum number of colors required for acyclic coloring of a graph G. We study the acyclic coloring of the corona product, edge corona product of paths or circles and any simple graph, and the corresponding acyclic chromatic number are determined. © 2020 IEEE.

关键词： Coloring

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Proper lucky number of product graph of path

Proper lucky number of product graph of path

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2021 International Conference on Big data Analysis and Computer Science, BDACS 2021

作者： An, Zhuomo Tian, Shuangliang Jin, Cai Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Northwest Minzu University School of Mathematics and Computer Science Lanzhou China

The proper lucky label is based on the lucky label to make the color sum of any two adjacent vertices are different. In this paper we determined the proper lucky number of cartesian product, direct product and semi-pr... 详细信息

ISBN: (纸本)9781665425612

关键词： Set theory

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The acyclic coloring of the Direct product and Semi-strong product of special graphs

The acyclic coloring of the Direct product and Semi-strong p...

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2021 International Conference on Big data Analysis and Computer Science, BDACS 2021

ISBN: (纸本)9781665425612

An acyclic coloring of a graph is a proper vertex coloring without dichromatic cycles. The acyclic chromatic number of G, denoted a(G), is the minimum number of colors required for acyclic coloring of a graph G. We study the acyclic coloring of the direct product, semi-strong product of paths and stars, and the corresponding acyclic chromatic number are determined. © 2021 IEEE.

关键词： Coloring

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