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Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation

IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Lu, Mengkang Wu, Yicheng Xia, Yong Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Xi’an710072 China The University of Adelaide Australian Institute for Machine Learning AdelaideSA5000 Australia Monash University Faculty of Information Technology Department of Data Science and AI Australia

The scarcity of annotations has become a significant obstacle in training powerful deep-learning models for medical image segmentation, limiting their clinical application. To overcome this, semi-supervised learning that leverages abundant unlabeled data is highly desirable to enhance model training. However, most existing works still focus on specific medical tasks and underestimate the potential of learning across diverse tasks and datasets. In this paper, we propose a Versatile Semi-supervised framework (VerSemi) to present a new perspective that integrates various SSL tasks into a unified model with an extensive label space, exploiting more unlabeled data for semi-supervised medical image segmentation. Specifically, we introduce a dynamic task-prompted design to segment various targets from different datasets. Next, this unified model is used to identify the foreground regions from all labeled data, capturing cross-dataset semantics. Particularly, we create a synthetic task with a CutMix strategy to augment foreground targets within the expanded label space. To effectively utilize unlabeled data, we introduce a consistency constraint that aligns aggregated predictions from various tasks with those from the synthetic task, further guiding the model to accurately segment foreground regions during training. We evaluated our VerSemi framework against seven established SSL methods on four public benchmarking datasets. Our results suggest that VerSemi consistently outperforms all competing methods, beating the second-best method with a 2.69% average Dice gain on four datasets and setting a new state of the art for semi-supervised medical image segmentation. Code is available at https://***/maxwell0027/VerSemi. © 1982-2012 IEEE.

关键词： Self-supervised learning

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Clustering Analysis of Water Usage Structures Based on Knowledge Graph

Clustering Analysis of Water Usage Structures Based on Knowl...

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Computer, Big data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Jianzhuo Yan Han Jiao Hang Zhang Yongchuan Yu Jianhui Chen Shiwei Zhou Faculty of Information Technology Beijing University of Technology Beijing China Institute of Big Data Analysis and Applications Beijing Institute of Water Science and Technology Beijing China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

A key issue in the management of water resources in China is the adoption of a water supply total quantity control framework at the regional level based on socioeconomic development. This necessitates more effective management of water resource demand to achieve a balance between supply and demand. This paper aims to conduct an in-depth analysis of the industrial water usage structure, identify the primary influencing factors, and thereby provide insights for the design of demand management policies. Using a typical application scenario of "water extraction and supply" in a subsidiary of a water supply company in Beijing, a water knowledge graph incorporating multiple data sources is constructed. The study proposes a knowledge graph embedding model based on graph convolutional neural networks. Over the 36 months from 2019 to 2021, the water units in the accommodation, office, urban resident, healthcare, retail, education, catering, and residential services industries are clustered, and the water usage is analyzed. The results of this study are expected to provide references for optimizing urban water resource utilization.

关键词： Industries Water Supply and demand Soft sensors Knowledge graphs Water conservation Convolutional neural networks

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Feature Aggregation Network for Memory-Based Industrial Anomaly Detection 9

Feature Aggregation Network for Memory-Based Industrial Anom...

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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Li, Min Han, Delong Shao, Rui He, Jinghui City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

In the process of large-scale industrial production, complex defects of different shapes and sizes can occur in industrial products and their components, and these defects seriously affect the performance of industrial products and their components, and being able to quickly and accurately identify defective industrial products and their components is an important task. Memory-based methods are usually used for anomaly detection, and these methods often rely on pre-trained models for feature extraction, but have high requirements on feature quality. In addition, patch-based methods mainly focus on local differences in feature embedding when detecting anomalies without explicitly exploiting broader contextual information, thus greatly affecting the robustness and accuracy of anomaly detection. Aiming at the above problems and requirements, this paper proposes a Feature Aggregation Network for Memory-Based Industrial Anomaly Detection (FANet), which is based on the pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-level and multi-scale features, and the FANet provides advanced performance in industrial image anomaly detection and localisation by pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-layered and multi-scale features, and introduction of environmental information in the anomaly score computation to perform industrial anomaly detection tasks, and using core set subsampling to reduce storage requirements. On the MVTec AD, FANet has an image-level AUROC score of 99.1% and a pixel-level AUROC score of 98.2%. Similarly high detection results were achieved on the ViSA dataset, further demonstrating the robustness of FANet. © 2024 IEEE.

关键词： Anomaly detection

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Exam Cheating Classification Using a Deep Learning Approach

Exam Cheating Classification Using a Deep Learning Approach

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Bishaw, Molla Ejigu Salau, Ayodeji Olalekan Vats, Satvik Kaushik, Keshav Alemu, Wubie Belay Tin, Ting Tin Debre Tabor University Department of Information Technology Debre Tabor Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Graphic Era Hill University Department of Cse Uttarakhand Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun248007 India Amity School of Engineering and Technology Amity University Mohali Punjab India Debark University Department of Information Technology Debark Ethiopia Inti International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia

ISBN: (纸本)9798350379716

Exam cheating has become a significant concern in educational institutions due to its potential negative impact on academic integrity. Researchers have attempted to overcome the issue. However, the previous research did not use multiple cheating activities in their datasets and the attributes of the studies used were not strong enough to represent exam cheating, particularly about paper positions. The study utilizes the dataset containing cheating and non-cheating images to develop a model using deep learning approaches. The dataset consists of images collected from network colleges, confirming the presence of cheating and non-cheating instances. Preprocessing techniques, including image resizing, noise removal, histogram equalization, augmentation and feature extraction are applied to the dataset, and both original and augmented versions of the datasets are used in experimental studies. Different input image sizes are employed for the deep CNN, AlexNet, GoogLeNet, and ResNet architectures and evaluated for their performance in classification in a paper based exam cheating. As the experimental results demonstrate from the models used in this study the customized CNN model achieves the highest accuracy of 93% and exhibits excellent performance in terms of loss function and accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Online and Offline Hybrid Teaching Mode Based on Scenario Cases: A Case Study of MySQL databases 4

Online and Offline Hybrid Teaching Mode Based on Scenario Ca...

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4th International Conference on Computer science and Educational Informatization, CSEI 2022

作者： You, Jinguo Wang, Ziwen Zhao, Xiaoxia Kou, Weili Zhang, Yunwei Zhang, Ting Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming China Computer Center Kunming University of Science and Technology Yunnan Kunming China College of Big Data and Intelligent Engineering Southwest Forestry University Yunnan Kunming China School of Information Engineering Jiaozuo University Henan Jiaozuo China

ISBN: (数字)9781839537882

ISBN: (纸本)9781839537882

Teaching mode design is a process to achieve the learning goal. Under the background of information technology, online and offline hybrid teaching mode has become the new form of education, and the closely related teaching mode design plays an essential role in the success of teaching implementation. Scenario case teaching can stimulate students' interest in learning and improve their ability to analyze and solve problems. Taking the MySQL database course as an example, this paper introduces the design and implementation process of online and offline hybrid teaching modes based on scenario cases. The practical results demonstrate that the hybrid teaching mode far outperforms the traditional teaching effect in terms of the aspects of students' basic knowledge of databases, practical operation level, and self-learning ability. Furthermore, this teaching mode has gained popularity in the studying of both our university and league universities. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Design

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Quantile-based Clustering for Functional data via Modelling Functional Principal Components Scores

Quantile-based Clustering for Functional Data via Modelling ...

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2022 International Conference on Applied Mathematics and Digital Simulation, AMDS 2022

作者： Hael, Mohanned A. Qiang Ma, Hai Al-Kuhali, Hamas A. Rizk, Zeinab School of Statistics Jiangxi University of Finance and Economics Nanchang330013 China Department of Data Science and Information Technology Taiz University Taiz9674 Yemen School of Computer & Artificial Intelligence Wuhan University of Technology Wuhan430070 China Faculty of Commerce Damietta University Damietta El-Gadeeda City34511 Egypt

Clustering tasks of functional data arise naturally in many applications, and efficient classification approaches are needed to find groups. The current paper combines the quantile-based model with the principal component analysis of functional data (FPCA). In our proposed procedures, the projection of functional data is first approximated based on (rotated) FPCA. The quantile-based model is then implemented on the space of rotated scores to identify the potential features of underlying clusters. The proposed method overcomes the limitation of using direct basis function expansion such as Fourier, B-spline, or linear fitting, besides representing a nonparametric clustering alternative based on a quantile approach. The proposed method's performance has been evaluated in a comprehensive simulation study and afterward compared with existing functional and non-functional clustering methods. The simulation study results showed that the proposed method performs well in terms of correct classification rate and computing time average. Finally, a real-world application concerning temporal wind speed data has been analyzed to demonstrate the proposed method's advantages and usefulness. © Published under licence by IOP Publishing Ltd.

关键词： Principal component analysis

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Vibration analysis of elastically supported sandwich beam with damaged core and functionally graded face sheets

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Steel and Composite Structures 2025年第1期54卷 71-83页

作者： Wang, Lulu Mohsin, Hasan F. Bains, Pardeep Singh Sharma, Rohit Bohlooli, Nasrin College of Mechanical and Electrical Engineering Guangdong University of Science and Technology Guangdong Dongguan523083 China Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Baghdad10011 Iraq Computer Technical Engineering Department College of Technical Engineering The Islamic University Najaf54001 Iraq University Karnataka Bengaluru560069 India Department of Mechanical Engineering Vivekananda Global University Rajasthan Jaipur303012 India School of Engineering and Technology Shobhit University Uttar Pradesh Gangoh247341 India Department of Mechanical Engineering Arka Jain University Jharkhand Jamshedpur831001 India Nabi Data Science & Computational Intelligence Research Co. Tehran Iran

This paper presents the influence of carbon nanotubes (CNTs) waviness, aspect ratio and damaged core on the vibrational behavior of functionally graded nanocomposite sandwich beams resting on two-parameter elastic foundations. The distributions of CNTs are considered functionally graded (FG) or uniform along the thickness of upper and bottom layers of the sandwich beam and their mechanical properties are estimated by an extended rule of mixture. In this study, the classical theory concerning the mechanical efficiency of a matrix embedding finite length fibers has been modified by introducing the tube-to-tube random contact, which explicitly accounts for the progressive reduction of the tubes’ effective aspect ratio as the filler content increases. A damage model is introduced to provide an analytical description of an irreversible rheological process that causes the decay of the mechanical properties, in terms of engineering constants. An isotropic damage is considered for the core of the sandwich beams. The equations of motion are derived based on Timoshenko beam theory and employing Hamilton’s principle. The problem is modeled using a semi-analytical approach composed of generalized differential quadrature method (GDQM) and series solution adopted to solve the equations of motion. Detailed parametric studies are carried out to investigate the mechanical behavior of these multi-layered structures depending on the damage features, through-the-thickness distribution and boundary conditions. Also, the effects of carbon nanotubes (CNTs) waviness, CNT aspect ratio, Winkler foundation modulus, shear elastic foundation modulus and geometrical conditions on the vibrational behavior of the sandwich structure are studied. © 2025 Techno-Press, Ltd.

关键词： Aspect ratio

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Evaluation of China's Digital Economy: A Case Study using Entropy and TOPSIS 11

Evaluation of China's Digital Economy: A Case Study using En...

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11th International Conference on information technology and Quantitative Management, ITQM 2024

作者： Liu, Junjie Wang, Boya Xue, Jiayu Qu, Yi Shi, Yong Sino-Danish College University of Chinese Academy of Sciences Beijing100049 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 of Sciences Beijing100190 China Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315100 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States

Recently, digital economy in social science has become increasingly significant. A well-constructed evaluation structure could generally and promptly describe and reveal its overall social digitalization development. Building on existent researches, this paper improves the evaluation system, which comprises four first-level indicators and thirty-one second-level indicators. Notably, this paper innovatively incorporates higher education into digital innovation of the evaluation. The Entropy method is utilized to assign weights to various indicators in an objective way, with employing the TOPSIS approach to prioritize the development of digital economy in places based on the closeness to the ideal solution. By analysing related data related from 31 provinces in China between 2017 to 2021, this paper greatly finds: Firstly, the development of digital economy vary significantly across provinces, with most provinces at a relatively poor level, indicating considerable potential for overall growth;Secondly, Guangdong, Jiangsu, Zhejiang, and Shandong consistently ranked in the top four from 2017 to 2021, reflecting their specific development attributes and their emphasis on digital economy;Thirdly, significant disparities exist both between regions and among provinces. Consequently, this paper suggests establishing a comprehensive national digital economy development plan, formulating region-specific digital economy policies tailored to local conditions, and encouraging leading provinces to actively share experiences with other provinces. © 2024 The Authors.

关键词： Digital economy Evaluation Multi-source information TOPSIS

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Prediction of Hypertension Disease Using Machine Learning Techniques: Case Study from Jordan

Prediction of Hypertension Disease Using Machine Learning Te...

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information technology (ACIT)

作者： Raed Alazaidah Ghassan Samara Anwar Katrawi Wael Hadi Maram Y. Al-Safarini Farah Al-Mamoori Ali Elrashidi Muhyeeddin Alqaraleh Haneen Alzoubi Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of IT Zarqa University Zarqa Jordan Information Security Department Faculty of IT Petra University Amman Jordan Department of Pharmaceutical Faculty of Pharmacy Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Software Engineering Faculty of IT Zarqa University Zarqa Jordan Department of Computer Science Facultty of Information Technology Applied Science Private University Amman Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Hypertension is one of the most common diseases in Jordan. It is one of the main reasons of death among Jordanian adult citizens. Worldwide, nearly 13% of all deaths are due to Hypertension with nearly 8 million deaths yearly. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that Random Forest, Random Committee, and MultilayerPerceptron are the best classification models to handle the task of the early prediction of Hypertension in Jordan.

关键词： Hypertension Measurement Predictive models information technology Random forests Diseases

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Classification Attack on IoT Devices with Machine Learning

Classification Attack on IoT Devices with Machine Learning

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International Conference on Computational Intelligence and Applications (ICCIA)

作者： Nutcha Charoenwong Somkiat Kosolsombat Chiabwoot Ratanavilisagul Data Science and Innovation College of Interdisciplinary Studies Thammasat University Pathum Thani Thailand Department of Computer and Information Science Faculty of Applied Science King Mongkut's University of Technology North Bangkok Thailand

ISBN: (数字)9798350352214

ISBN: (纸本)9798350352221

Internet of Things (IoT) devices are small, low-power devices used for detecting and processing data remotely through the internet. These devices have increasingly been integrated into our daily lives, both in the digital and physical world. However, this interconnectedness poses risks of being targeted by malware due to limited resources and connectivity. Machine learning (ML) techniques can be used to identify and prevent malware attacks on IoT devices. In this research report, we investigate the use of ML techniques for classifying malware in IoT devices. Our research includes the implementation and evaluation of decision tree, k-nearest neighbors (KNN), and XGBoost algorithms. Our evaluation metrics include accuracy, precision, recall, and the average of precision and recall. Our method is tested using a dataset of real-world malware attacks on IoT devices, and the XGBoost algorithm achieves the highest accuracy of 98.44%, demonstrating its effectiveness in detecting and classifying malware in IoT devices.

关键词： Measurement Machine learning algorithms Accuracy Machine learning Nearest neighbor methods Malware Classification algorithms Internet of Things Object recognition Decision trees

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