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共 3173 条记录，以下是941-950 订阅

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

Longitudinal Segmentation of MS Lesions via Temporal Diffe...

引用

Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, Stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031845246

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/Longitudinal-Difference-Weighting. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Transferability feature recognition of university biomedical patent technology based on machine learning

Transferability feature recognition of university biomedical...

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international conference on Computer Engineering and Applications (ICCEA)

作者： Yirui Tang Xingdong Shao Department of Economics and Management Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

In the era of digitalization and intelligence, identifying the transferability characteristics of biomedical patent technologies in higher education institutions is of great significance. this study takes seven intellectual property demonstration pilot universities in Shanghai as the research subjects, and constructs a biomedical patent technology transferability evaluation index system from three aspects: technical dimension, market dimension, and legal dimension. Four algorithms based on machine learning, including Support Vector machine (SVM), Decision Tree (DT), Random Forest (RF), and Adaptive Boosting (AdaBoost), are respectively used for training and prediction. through comparison and selection, it is finally confirmed that the Random Forest model has the best prediction effect for the constructed index system. It is considered to apply the algorithm model of this research to the massive data of biomedical industry-related patent technologies, to promote the transformation of achievements and intelligent development of the biomedical industry.

关键词： Support vector machines Training Radio frequency Patents Adaptation models machine learning algorithms Biological system modeling

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Rolling Bearing Fault Diagnosis Algorithm Based on RCMSDE Fusion Transfer learning

Rolling Bearing Fault Diagnosis Algorithm Based on RCMSDE Fu...

引用

machine learning and Computer Application (ICMLCA), international conference on

作者： Xiangyu Yu Yulong Ying Kun Li School of Energy and Mechanical Engineering Shanghai University of Electric Power Shanghai China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

To address the issue of poor accuracy in cross- condition diagnosis with traditional data-driven fault diagnosis methods, a bearing fault diagnosis method based on refined composite multiscale symbolic dynamic entropy integrated with transfer learning and least squares support vector machine is proposed. the refined composite multiscale symbolic dynamic entropy is used to extract fault features, combined with balanced distribution adaptation for feature mapping, and Bayesian optimization is applied for parameter tuning. the mapped feature vectors, with optimized parameters, are then input into an LSSVM optimized by the improved grey wolf algorithm to obtain the final fault identification accuracy. the proposed method was applied for cross-condition fault diagnosis experiments on the Case Western Reserve University dataset and the Paderborn University dataset, construct the test dataset using data with multiple fault types and varying severity levels. Experimental results show that the migration experiments between different working conditions on the two datasets achieved an average accuracy of 97% and 87%, respectively, and the highest accuracy rate can reach 100 % , It shows that the proposed method can accurately identify the fault type, and has a higher average accuracy than the comparison algorithm.

关键词： Fault diagnosis Vibrations Accuracy Heuristic algorithms Transfer learning Rolling bearings Feature extraction Entropy Classification algorithms data mining

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data augmentation for fairness-aware machine learning: Preventing algorithmic bias in law enforcement systems 22

Data augmentation for fairness-aware machine learning: Preve...

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5th ACM conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Pastaltzidis, Ioannis Dimitriou, Nikolaos Quezada-Tavarez, Katherine Aidinlis, Stergios Marquenie, thomas Gurzawska, Agata Tzovaras, Dimitrios Information Technologies Institute Centre for Research and Technology Hellas Greece Centre for It and Ip Law Ku Leuven Belgium Trilateral Research United Kingdom

ISBN: (纸本)9781450393522

Researchers and practitioners in the fairness community have highlighted the ethical and legal challenges of using biased datasets in data-driven systems, with algorithmic bias being a major concern. Despite the rapidly growing body of literature on fairness in algorithmic decision-making, there remains a paucity of fairness scholarship on machine learning algorithms for the real-time detection of crime. this contribution presents an approach for fairness-aware machine learning to mitigate the algorithmic bias / discrimination issues posed by the reliance on biased data when building law enforcement technology. Our analysis is based on RWF-2000, which has served as the basis for violent activity recognition tasks in data-driven law enforcement projects. We reveal issues of overrepresentation of minority subjects in violence situations that limit the external validity of the dataset for real-time crime detection systems and propose data augmentation techniques to rebalance the dataset. the experiments on real world data show the potential to create more balanced datasets by synthetically generated samples, thus mitigating bias and discrimination concerns in law enforcement applications. © 2022 ACM.

关键词： machine learning

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Human Behavior Analysis: Applications and machine learning Algorithms 7th

Human Behavior Analysis: Applications and Machine Learning A...

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7th international conference on Computing in Engineering and Technology, ICCET 2022

作者： Patil, Jayshree Adamuthe, Amol Patil, Sudarshan Rajarambapu Institute of Technology MS Rajaramnagar India

ISBN: (纸本)9789811927188

Human behavior analysis is essential in many fields such as robotics, affective computing, biometrics, and pattern recognition. this paper aims to identify the different applications of human behavior analysis in different fields, input capturing mechanism and features used for human behavior analysis, and review of machine learning algorithms used. the domains like education, transportation, healthcare, and online social networks, survey alliance has a variety of applications of human behavior analysis with different objectives. the physiological features like eye gaze, head pose, facial expression, body pose, and human activity data are used to analyze human behavior. these features are captured with the help of computer vision systems’ input devices like web cameras, depth cameras, eye tracker devices, cameras from smartphones. A wide range of machine learning algorithms such as SVM, decision tree, Naive Bayes, CNN, RNN is investigated for human behavior analysis. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Eye tracking

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Network Security Intrusion Detection System Based on Deep learning

Network Security Intrusion Detection System Based on Deep Le...

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5th international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2024

作者： Fang, Jingwen Leng, Fuchen School of E-commerce Wuhan Technology and Business University Wuhan430065 China

In order to build a new, efficient, and accurate network security intrusion detection system, this paper is based on machine algorithms of deep learning. Firstly, a corresponding network data processing layer is set up, which involves data collection and data preprocessing, and can effectively process and convert various raw network data. then, a deep learning detection layer is set up, which can timely and accurately identify various network intrusion detection threats. Finally, a decision and feedback layer is set up, which can timely warn of identified network intrusion detection threats and take effective defense measures to ensure network security. After experimental verification, the results show that deep learning based network security intrusion detection systems have excellent stability and accuracy in both intrusion behavior classification and recognition when facing network intrusions. In terms of detection on different datasets, network security intrusion detection systems based on deep learning are significantly superior to other systems. this result confirms that the system's generalization ability is outstanding when testing different datasets. © 2025 the Authors. Published by Elsevier B.V.

关键词： Network intrusion

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data mining Technology Integrates Analysis in Petrochemical Testing data

Data Mining Technology Integrates Analysis in Petrochemical ...

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5th international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2024

作者： Wang, Li Dongying Vocational Institute Shandong Dongying257091 China

With the continuous improvement of information technology in the petroleum industry, the basic data of petroleum is growing exponentially. An urgent issue that needs to be addressed is how to optimize management and efficiently analyze it, which has become a challenge in the petrochemical industry. this article aimed to integrate testing data from different sources, optimize the testing process, improve testing efficiency and quality, reduce production costs, and achieve sustainable development of enterprises. this article applied data mining techniques to extract the patterns and characteristics behind massive detection data, providing scientific basis for monitoring and managing production processes. the results of this article indicated that integrating and analyzing detection data can optimize production processes, improve production efficiency, improve product quality, reduce costs, and thus enhance the competitiveness of enterprises. © 2025 the Authors. Published by Elsevier B.V.

关键词： Petroleum industry

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Water Quality Prediction for Smart Aquaculture

Water Quality Prediction for Smart Aquaculture

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： R.Praveen Kumar T. Prabhu J. Sowmya A.Nithin Kumar Department of Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

this work presents a unique hybrid approach combining different techniques for precise water quality prediction in smart aquaculture systems. the proposed model integrates the different algorithms with complex pattern recognition, resulting in a robust and efficient predictive system. Incorporating feature engineering and transfer learning enhances the model's adaptability to diverse aquaculture settings, ensuring accurate predictions of critical water parameters. Moreover, our methodology utilizes real-time sensor data, enabling proactive management of water quality conditions. through comprehensive validation, our hybrid model outperforms existing methods, offering a groundbreaking solution for optimizing smart aquaculture operations. this research contributes to the advancement of predictive technologies, providing a novel and effective tool for sustainable aquaculture practices.

关键词： Adaptation models Accuracy Communication systems Transfer learning Water quality Predictive models Prediction algorithms Real-time systems pattern recognition Aquaculture

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A machine learning Approach for Multiclass Sentiment Analysis of Twitter data: A Review 5th

A Machine Learning Approach for Multiclass Sentiment Analysi...

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5th international conference on Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT, ICETCE 2022

作者： Chougule, Bhagyashree B. Patil, Ajit S. Shivaji University Kolhapur416234 India

ISBN: (纸本)9783031070112

Sentiment analysis or opinion mining is a prominent and most demanding research topic in today’s world. the main idea behind this research topic is to recognize the user’s opinions and emotions towards the aspect of service or product via a text basis. Sentiment analysis involves mining text, lexicon construction, extracting features and finally finding polarity of text. Even though numerous amounts of research work were conducted in this field through different methods, opinion mining is still considered a challenging field for research. Most of the prior research concentrated on the binary or ternary classification of sentiments such as positive, negative, neutral. Some studies have done an analysis of Twitter sentiment based on ordinal regression, but by turning the problem of ordinal regression into a problem of binary classification. the aim of this study is to review the multiclass sentiment analysis of Twitter text data using an automated i.e., machine learning approach. this review paper intends to focus on existing work for Twitter sentiment analysis with multiple polarity categorization and explore gaps with future scope in the said research area. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Social networking (online)

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An Optimized Semantic Segmentation Deep learning Model for Automated Coronary Artery Calcification Prediction

An Optimized Semantic Segmentation Deep Learning Model for A...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Nathan Yee Harker School San Jose United States

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Coronary artery disease (CAD) is the leading cause of death in the United States, accounting for 25% of all deaths. Early detection by Coronary Artery Calcium (CAC) scoring of chest computed tomography (CT) scans is crucial but remains a significant challenge due to high costs, time demands, and the need for specialized experts and equipment. While current AI-based methodologies are promising, significant hurdles have impeded their clinical adoption. this study is the first effort to benchmark image classification and image segmentation based deep learning models in direct comparison and demonstrates that image segmentation techniques outperform in automated CAC scoring when benchmarked with image classification. the image classification is performed using ResNet-50, and the image segmentation is performed with an architecture based on Attention U-Net. the study benchmarks the models based on the Stanford AIMI COCA database with gated computed tomography scans of 790 patients and nearly 40,000 total image files. Additionally, this study implements an image optimization strategy that utilizes contrast limited adaptive histogram equalization (CLAHE) and Gaussian blur specific to the CAC score range, thereby predicting a patient's risks for CAD and recommending the appropriate preventative action or treatment. this approach effectively enhances predictive accuracy to above 90% for most CAC score ranges and Dice Coefficient (DCE) to approximately 0.86, a substantial improvement compared to previous efforts using image segmentation deep learning. With increased reliability, the proposed deep learning system demonstrates the viability of AI-driven CAC monitoring for those at risk of CAD to decrease mortality rates.

关键词： Deep learning Solid modeling Computed tomography Semantic segmentation Biological system modeling Benchmark testing Predictive models Arteries Image classification Diseases

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