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An Enhanced Multi-Scale Collaborative Learning Network for Medical Image Segmentation in Internet of Medical Things

IEEE Internet of Things Journal 2025年

作者： Yang, Aimin Li, Jiahao Bai, Yunjie Li, Jie Yang, Xingwang North China University of Science and Technology College of Science Hebei Tangshan063210 China North China University of Science and Technology Hebei Key Laboratory of Data Science and Application Hebei Tangshan063210 China North China University of Science and Technology Hebei Engineering Research Center of Iron Ore Optimization and Iron Pre-Process Intelligence Tangshan063210 China

Multi-organ segmentation in the abdomen is a key area in medical image segmentation and is essential for accurate diagnosis and treatment of diseases. The diversity of abdominal organs, differences in size, and ambiguous boundaries pose significant challenges to segmentation tasks, making it difficult for existing models to meet clinical demands in complex scenarios. To address this, this paper proposes a Multi-Scale Collaborative Learning Network (MSCL-Net) in conjunction with a medical Internet of Things (IoMT) platform, aiming to enhance the accuracy of abdominal organ segmentation. The introduction of IoMT technology dramatically expands the channels for obtaining medical image data. By collecting massive amounts of data in real-time from various scenarios, such as outpatient clinics and hospitals, doctors can quickly use the segmentation model to obtain preliminary segmentation results of CT images, which specialists can rapidly correct. This workflow improves clinical work efficiency and provides a large volume of highquality data for training the model, further enhancing its accuracy. Regarding model design, the Feature Space Module is used for global feature extraction, the Feature Extraction Module captures local semantic information at different scales, and the Feature Aggregation Module performs deep integration of local and global information. A considerable number of experimental results based on the Synapse dataset demonstrate that MSCL-Net outperforms existing advanced algorithms and achieves 81.71% in the Dice Similarity Coefficient (DSC) and 17.81 in the Hausdorff Distance at 95% (HD95) metrics, demonstrating its outstanding ability in medical image segmentation. © 2014 IEEE.

关键词： Diagnosis

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Three-way selection random forest algorithm based on decision boundary entropy

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Applied Intelligence 2022年第12期52卷 13384-13397页

作者： Zhang, Chunying Ren, Jing Liu, Fengchun Li, Xiaoqi Liu, Shouyue College of Science North China University of Science and Technology Tangshan China Key Laboratory of Data Science and Application of Hebei Province Tangshan China College of Qian’an North China University of Science and Technology Tangshan China

Aiming at the problem of high probability of negative impact about redundant attributes in random forest algorithms, a Three-way Selection Random Forest algorithm based on decision boundary entropy (TSRF) is proposed without losing randomness and reducing the influence of redundant attributes on decision-making results. According to the characteristics of the attribute, the concept of decision boundary entropy is defined. Then a measuring method of attribute importance based on decision boundary entropy is proposed and set as an evaluation standard. Three-way decision is constructed and the attribute is divided into three candidate domains, namely positive domain, negative domain and boundary domain. In order to ensure the randomness of attributes, three-way attribute random selection rules based on attribute randomness are established and a certain number of attributes are randomly selected from the three candidate domains. Combine the samples selected by the bootstrap sampling method with attribute sets selected by three-way decision to produce training sample sets so that we can train the decision trees and generate forest. Six datasets are selected for the experiment. Two parameters of attribute randomness and three-way decision thresholds are analyzed to verify the theoretical conclusions respectively. The results show that the TSRF algorithm can meet the different requirements of different data sets by adjusting the parameters. The classification effect on the binary data is basically the same as the comparison algorithm, but TSRF has a significant improvement effect on the multi-class data compared with other algorithms. The proposed TSRF algorithm widens the idea for the measurement method of significance of attribute, innovates the random forest three-way selection integration method, and provides a better model framework for solving multi-classification problems.

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Fs-Woa-Stacking: A Novel Ensemble Model for Early Diagnosis of Breast Cancer

SSRN

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SSRN 2024年

作者： Xiao, Tianyun Kong, Shanshan Zhang, Zichen Liu, Fengchun Yang, Aimin Hua, Dianbo Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Hebei Tangshan063210 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Hebei Tangshan China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Hebei Tangshan China Beijing Sitairui Cancer Data Analysis Joint Laboratory Beijing101149 China

In this paper, a new integrated scheme is proposed to accurately predict breast cancer, help doctors make early diagnosis and treatment plans, and improve the prognosis of patients. We selects five mainstream machine learning models: support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost). The Wisconsin Breast Cancer database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) are used as datasets to investigate the predictive performance of these single and ensemble models. Then, we use multiple linear regression method for feature selection (FS), the experimental results show that the change of feature subset will significantly affect the performance of the model. After that, we apply whale optimization algorithm (WOA) to optimize the hyperparameters of the model to improve their prediction performance. In addition, we ensemble these models by stacking, investigate the performance changes of the ensemble model when different models are used as meta learners. Finally, the FS-WOA-Stacking model achieves 99.56% accuracy on WBCD and 99.65% accuracy on WDBC. Compared with the existing breast cancer prediction models, the performance of the proposed model is at an excellent level. © 2024, The Authors. All rights reserved.

关键词： Adaptive boosting

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Msnoa-Catboost: A Novel Pan-Cancer Diagnosis Model with Feature Subset Selection and Hyperparameter Optimization

SSRN

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SSRN 2024年

作者： Xiao, Tianyun Zhang, Zichen Kong, Shanshan Liu, Fengchun Yang, Aimin Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Hebei Tangshan063210 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Hebei Tangshan China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Hebei Tangshan China

Cancer, as a global epidemic disease, remains a significant threat to human life and health. Prevention and early diagnosis are widely recognized as effective ways to tackle cancer. With the development of big data technology, many data-driven cancer diagnosis models have been proposed. However, the accuracy and generalization ability of existing models often fail to meet practical application needs. In order to address these challenges, this paper proposes a novel and improved protocol for high-precision pan-cancer diagnosis based on five publicly available cancer datasets from the UCI database and Kaggle, covering different cancer types such as breast, lung, and glioma. To achieve feature subset selection and hyperparameter optimization, we first introduce the nutcracker optimization algorithm (NOA), and improve it by using a multi-strategy method (MSNOA) to enhance optimization performance and efficiency. In the comparative experiment phase, we compare several popular machine learning models and identify MSNOA-CatBoost as the proposed model. The experimental results show that MSNOA-CatBoost improves the accuracy of the five datasets by an average of 9.12% compared to the baseline optimal model, and outperforms existing models in terms of performance. In summary, this study presents a promising approach to achieving high-precision pan-cancer diagnosis by leveraging the power of big data and advanced machine learning techniques. The proposed MSNOA-CatBoost model shows remarkable performance improvement over existing models, and has the potential to be applied in clinical practice for cancer diagnosis and treatment planning. © 2024, The Authors. All rights reserved.

关键词： Optimization

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Meta-Transfer Learning Based Cross-Domain Gesture Recognition Using WiFi Channel State Information

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IEEE Transactions on Consumer Electronics 2025年

作者： Dai, Penglin Zhou, Junfei Ma, Jialong Zhang, Hao Wu, Xiao Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China Ministry of Education Engineering Research Center of Sustainable Urban Intelligent Transportation China Tangshan Institute of Southwest Jiaotong University Tangshan063000 China Chongqing University of Posts and Telecommunications College of Computer Science and Technology Key Laboratory of Data Engineering and Visual Computing Chongqing400065 China

Gesture recognition plays a crucial role in a wide range of consumer electronics applications, including human-computer interaction and virtual reality, by enabling the identification and interpretation of human gestures. In recent times, WiFi-based gesture recognition has garnered significant attention due to its privacy protection and unobtrusive nature. However, this approach heavily depends on neural network-based models and is notably influenced by environmental conditions, such as specific location and orientation. To address these environmental impacts, we propose a network framework that leverages transfer learning and meta-learning. The primary focus is on utilizing transfer learning to train a convolutional neural network (ResNet) for feature extraction, enabling the extraction of domain-independent features specifically related to gestures, rather than being influenced by environmental factors. Additionally, we employ the meta-learning algorithm MAML to train the fully connected network for gesture classification. Following training, a small set of samples can be utilized for rapid adaptation to diverse domains, maintaining high accuracy across different domains to achieve cross-domain gesture recognition. Our performance evaluation is based on the Widar 3.0 dataset, encompassing both in-domain and cross-domain scenarios. The simulation results demonstrate that our proposed algorithm surpasses Widar3.0 and WiGRUNT by approximately 7.9% and 1.12% in gesture recognition accuracy across all scenarios. © 2025 IEEE.

关键词： Wi-Fi

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Effect of basicity on metallurgical properties of magnesium fluxed pellets

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Journal of Iron and Steel Research International 2020年第3期27卷 239-247页

作者： Jie Li Hai-fei An Wei-xing Liu Ai-min Yang Man-sheng Chu College of Metallurgy and Energy North China University of Science and TechnologyTangshan 063000.HebeiChina Hebei Key Laboratory of Data Science and Applications North China University of Science and TechnologyTangshan 063000HebeiChina School of Metallurgy Northeastern UniversityShenyang 110819LiaoningChina

Magnesium fluxed pellets are the focus of blast furnace burden research for reducing environmental *** pelletizing,roasting and metallurgical properties of a Chinese fine magnetite ore with the addition of magnesium flux were experimentally tested,and the effects of basicity on the consolidation behavior,compressive strength,and reducibility of magnesium fluxed pellets were systematically ***,the mechanisms were analyzed by means of thermodynamics calculation and scanning electron microscopy-energy-dispersive spectrometry analysis *** results show that the consolidation behavior of magnesium fluxed pellets during roasting process was obviously promoted with increasing the basicity of the magnesium fluxed *** compressive strength increased firstly and then decreased,reaching the maximum value of 2352 N/pellet with the basicity of *** reduction degree increased gradually with enhancing the basicity owing to the fact that the decomposition of the added CaCO^could increase the porosity of pellets,thereby increasing the CO diffusion in pellet during ***,the reduction swelling index was improved with increasing the basicity because the generated calcium feirite could effectively suppress the growth of iron whiskers.

关键词： Magnesium fluxed pellet Basicity Compressive strength Reduction degree Reduction swelling

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The Development of Green Olympic Games: A Comparison of CO2Emission Reduction Strategies between PyeongChang 2018 and Beijing 2022 9th

The Development of Green Olympic Games: A Comparison of CO2E...

引用

9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Ding, Xuesong Jiang, Yilin Yang, Banban Su, Jingchun Chen, Liming Li, Na School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Chinese Academy of Fiscal Sciences Ministry of Finance Beijing100142 China Division of Sports Science and Physical Education Tsinghua University Beijing100084 China Division of Sports Science and Physical Education Tangshan Polytechnic College Tangshan063299 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy Sciences Beijing100190 China

The Olympic Games are striking a balance between economic and ecological benefits. Taking the 5 ice event venues of the 2018 PyeongChang Winter Olympic Games as the research object, the CO2 emissions under the 2018 PyeongChang Low-carbon scenario and 2022 Beijing Low-carbon scenario are compared. The empirical results show that, in terms of venue operation, the Beijing strategies can reduce CO2 emissions by 715.7 t compared with the PyeongChang strategies. In terms of the refrigerant escape, Beijing's adoption of CO2 refrigerant can reduce the emission of CO2 by 1202.2 t compared with the R404 refrigerant used in PyeongChang. This paper confirms the progress of the Green Winter Olympic Games, and may provide implications for the future host countries as well as the cities which in the green transformation. © 2022 The Author(s).

关键词： Refrigerants

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Fractional Modeling and Dynamic Analysis of Two Kinds of Viscoelastic Thin Plates with Gaussian White Noise

SSRN

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SSRN 2023年

作者： Deng, Jinna Liu, Qiumei Yang, Aimin Chen, Yiming College of Science North China University of Science and Technology Tangshan063210 China Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Tangshan063210 China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Tangshan063210 China School of Science Yanshan University Qinhuangdao066004 China LE STUDIUM Loire Valley Institute for Advanced Studies Orléans & Tours Orléans45000 France

According to fractional order model, the governing equation of viscoelastic plate is established. Using the properties of the shifted Bernstein polynomials, the fractional order equation of viscoelastic plate is resolved, the conversion of fractional differential equations to algebraic equations, and operator matrices represented by Kronecker products are solved in the domain. The convergence study validates the algorithm's efficacy and precision in solving differential equations. The displacement of viscoelastic metal thin plate under different external loads (uniform distribution and linear distribution), the effect of white Gaussian noise on the thin metal plate displacement, the effect of different boundary conditions on thin metal plate displacement under harmonic load are studied, the displacements of metal and polymer plates under different loads are contrasted and stress and strain relationship of two kinds of plates are discussed. Based on the outcomes, it can be concluded that the shift Bernstein polynomial approach is useful for numerically simulating viscoelastic plates. © 2023, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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Further results on permutation pentanomials over Fq3 in characteristic two

arXiv

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arXiv 2025年

作者： Zhang, Tongliang Zheng, Lijing Wang, Hengtai Peng, Jie Li, Yanjun College of Science North China University of Science and Technology Hebei Tangshan063000 China Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063000 China School of Mathematics and Physics University of South China Hunan Hengyang421001 China Mathematics and Science College Shanghai Normal University Shanghai200234 China Institute of Statistics and Applied Mathematics Anhui University of Finance and Economics Anhui Bengbu233030 China

Let q = 2m. In a recent paper [31], Zhang and Zheng investigated several classes of permutation pentanomials of the form ϵ0xd0+L(ϵ1xd1+ϵ2xd2) over Fq3 (d0 = 1, 2, 4) from some certain linearized polynomial L(x) by using multivariate method and some techniques to determine the number of the solutions of some equations. They proposed an open problem that there are still some permutation pentanomials of that form have not been proven. In this paper, inspired by the idea of [12], we further characterize the permutation property of the pentanomials with the above form over Fq3 (d0 = 1, 2, 4). The techniques in this paper would be useful to investigate more new classes of permutation pentanomials. Copyright © 2025, The Authors. All rights reserved.

关键词： Polynomials

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RNaAE: A Novel Approach for Identifying Unseen Railway Anomalies

RNaAE: A Novel Approach for Identifying Unseen Railway Anoma...

引用

International Conference on Signal Processing Proceedings (ICSP)

作者： Jiayu Zhang Qingji Guan Junbo Liu Yaping Huang Jianyong Guo Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Infrastructure Inspector Center China Academy of Railway Science Corporation Limited Beijing China Tangshan Research Institute of Beijing Jiaotong University Tangshan China Urban Rail Transit National Engineering Research Center for Digital Construction and Evaluation Tianjin China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Detecting abnormal objects in railway track inspection images using vision-based technology is a crucial task for ensuring the safety of railway transportation. Traditional supervised object detection methods fail to achieve satisfactory results due to the diverse categories of abnormal objects and the lack of abnormal samples. Even though the widely used Autoencoder can leverage reconstruction errors to detect anomalies without using abnormal data, they tend to generate a relatively high number of false positives. In this paper, we address the task in an unsupervised manner and propose a novel Random Network-Assisted Autoencoder, called RNaAE, for identifying unseen abnormal objects. Specifically, we first design a learnable network to fit a randomly initialized stochastic network with fixed weights, where the difference between two predictions can then be used to estimate whether the candidate object is anomalous. After combined with a traditional Autoencoder, a Gaussian mixture model is then used to classify the candidate box into normal and abnormal by anomaly scores. Extensive experiments conducted on our collected railway anomaly dataset demonstrate that the proposed RNaAE exceeds previous stateof-the-art methods, achieving 98.23% and 92.02% in terms of AUROC and F1-score.

关键词： Autoencoders Transportation Stochastic processes Object detection Signal processing Inspection Rail transportation Safety Object recognition Anomaly detection

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