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Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke:development and validation of a hybrid machine learning model

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Stroke & Vascular Neurology 2024年第6期9卷 631-639页

作者： Ximing Nie Jinxu Yang Xinxin Li Tianming Zhan Dongdong Liu Hongyi Yan Yufei Wei Xiran Liu Jiaping Chen Guoyang Gong Zhenzhou Wu Zhonghua Yang Miao Wen Weibin Gu Yuesong Pan Yong Jiang Xia Meng Tao Liu Jian Cheng Zixiao Li Zhongrong Miao Liping Liu Department of Neurology Capital Medical UniversityBeijingChina China National Clinical Research Center for Neurological Diseases Beijing Tiantan HospitalBeijingChina School of Computer and Communication Engineering University of Science and TechnologyBeijingChina Department of Radiology Beijing Tiantan HospitalBeijingChina Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationSchool of Biological Science and Medical Engineering International Research Institute for Multidisciplinary ScienceBeihang UniversityBeijingChina Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Computer Science and EngineeringBeihang UniversityBeijingChina Department of Interventional Neuroradiology Beijing Tiantan HospitalCapital Medical UniversityBeijingChina

Background Identification of futile recanalisation following endovascular therapy(EVT)in patients with acute ischaemic stroke is both crucial and ***,we present a novel risk stratification system based on hybrid machine learning method for predicting futile *** Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management *** models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following *** optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation *** Using a hybrid feature selection approach,we trained and tested multiple classifiers on two independent patient cohorts(n=1122)to develop a hybrid machine learning-based prediction *** model demonstrated superior discriminative ability compared with other models and scoring systems(area under the curve=0.80,95%CI 0.73 to 0.87)and was transformed into a web application(RESCUE-FR Index)that provides a risk stratification system for individual prediction(accessible online atfr-index.biomind.cn/RESCUE-FR/).Conclusions The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.

关键词： database prediction optimal

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Bidirectional Attention Mechanism-Based Deep Learning Model for Text Classification Under Natural Language Processing

Bidirectional Attention Mechanism-Based Deep Learning Model ...

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Intelligent Computing and Networking - Proceedings of IC-ICN 2023

作者： Pande, Sagar Dhanraj Kumaresan, T. Lanke, Govinda Rajulu Degadwala, Sheshang Dhiman, Gaurav Soni, Mukesh School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Sathyamangalam India Data Science and Engineering Birla Institute of Technology and Science Rajasthan Pilani India Department of Computer Engineering Sigma Institute of Engineering Gujarat Vadodara India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of CSE University Centre for Research and Development Chandigarh University Punjab Mohali140413 India

ISBN: (纸本)9789819931767

Existing text classification models based on graph convolutional networks usually update node representations simply by fusing neighborhood information of different orders through adjacency matrices, resulting in an insufficient representation of node semantic information. In addition, models based on conventional attention mechanisms are only Word vectors which are forward-weighted representations, ignoring the impact of negative words on the final classification. This paper proposes a model based on a bidirectional attention mechanism and a gated graph convolutional network to solve the above problems. The model first uses the gated graph convolutional network, selectively fuses the multi-order neighbourhood information of the nodes in the graph, retains the information of the previous order, and enriches the feature representation of the nodes. Secondly, the influence of different words on the classification results is learned through a two-way attention mechanism. While giving positive weights to terms that play a positive role in classification, negative consequences are given to words that have adverse effects of weakening their influence in vector representation, thereby improving the model's ability to discriminate nodes with different properties in documents. Finally, maximum pooling and average pooling are used to fuse the vector representation of the word, and the document representation is obtained for the final classification. Experiments are carried out on four benchmark data sets, and the results show that the method is significantly better than the baseline model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Comparative Analysis of Optimizers and Transfer Learning Methods for Headgear Detection Using Convolutional Neural Network

Comparative Analysis of Optimizers and Transfer Learning Met...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Sri Sai Teja, M.S. Ullas, P. Vikas, K.R. Kumarswamy, S. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Department of Computer Science and Engineering Karnataka Bangalore India Global Academy of Technology Department of Artificial Intelligence and Data Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350300826

In untapped nations, cars are quite uncommon because motorcycles have always dominated the transportation industry. Over the past few years, there has been an increase in motorbike accidents. Motorcyclists who do not use fluorescent helmets because they don't think they offer enough protection are frequently engaged in traffic accidents. Today, it is against the law to operate a motorbike without a helmet, which increases accidents and fatalities in India. Every motorcyclist must wear a helmet when operating a motorcycle by law for the protection of the public. However, the majority of people continue to disprove this by driving without wearing helmets. In order to solve this problem, we provide a method in this work that uses real-time surveillance footage to automatically identify motorcycle riders without helmets and identify license plates. When the traffic police observe motorbike riders riding without helmets in a group at intersections, they also use CCTV footage to seize control of the drivers of those cars and fine the helmet-less riders. But only human initiative and dedication can make it happen. Because of its great accuracy, Convolutional neural networks are utilized for picture categorization and recognition. The Convolutional neural network uses a hierarchical model that builds a network in the shape of a funnel and eventually produces a fully connected layer where all the neurons are connected to one another and the output is processed. The 764 photos in the helmet detection dataset that is accessible on Kaggle have bounding box annotations in the PASCAL VOC format. © 2023 IEEE.

关键词： Convolutional neural networks

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Inductive Relation Prediction by Disentangled Subgraph Structure

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Tsinghua science and Technology 2024年第5期29卷 1566-1579页

作者： Guiduo Duan Rui Guo Wenlong Luo Guangchun Luo Tianxi Huang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731Chinaand also with Trusted Cloud Computing and Big Data Key Laboratory of Sichuan ProvinceChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Hangzhou NetEase Cloud Music Technology Co. Ltd.Hangzhou 310052China Department of Fundamental Courses Chengdu Textile CollegeChengdu 611700China

Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict ***,subgraphs may contain disconnected regions,which usually represent different semantic *** not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic *** indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are *** disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph ***,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the ***,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@*** prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.

关键词： disentangled subgraph structure knowledge graph completion Gated Recurrent Unit(GRU) feature updating

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Research on a Technology of Mail Anti-virus Gateway for Industrial Internet

Research on a Technology of Mail Anti-virus Gateway for Indu...

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2023 IEEE International Conference on Image Processing and computer Applications, ICIPCA 2023

作者： Wu, Yuhong Lv, Yunshan Zhang, Ling School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication Key Laboratory of Public Big Data Security Technology Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798350314670

With the rapid rise of the Industrial Internet, emails that convey a large amount of information require a strong network security environment. Aiming at the increasingly complex mail virus prevention and control work under the background of the industrial Internet, this paper designs and implements a mail anti-virus gateway system. First, design the overall process and structure of the mail virus processing system;then, design an extended mail receiving and sending protocol on the basis of the public framework, thereby ensuring the convenience of the system for use in different roles and environments. In the detection and filtering part of mail virus, an anti-virus engine containing multiple sub-function modules is designed to detect and filter viruses in mail text and attachments. Through the function test of 1000 kinds of virus mails, it is found that the mail anti-virus gateway system designed in this article can detect and filter most types of mail viruses. Compared with the virus detection on the client and server, the anti-virus gateway system has better performance And efficiency. © 2023 IEEE.

关键词： Network security

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

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Theory of Consistency Diffusion Models: Distribution Estimation Meets Fast Sampling 41

Theory of Consistency Diffusion Models: Distribution Estimat...

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41st International Conference on Machine Learning, ICML 2024

作者： Dou, Zehao Chen, Minshuo Wang, Mengdi Yang, Zhuoran Department of Statistics and Data Science Yale University New Haven United States Electrical and Computer Engineering Princeton University Princeton United States

Diffusion models have revolutionized various application domains, including computer vision and audio generation. Despite the state-of-the-art performance, diffusion models are known for their slow sample generation due to the extensive number of steps involved. In response, consistency models have been developed to merge multiple steps in the sampling process, thereby significantly boosting the speed of sample generation without compromising quality. This paper contributes towards the first statistical theory for consistency models, formulating their training as a distribution discrepancy minimization problem. Our analysis yields statistical estimation rates based on the Wasserstein distance for consistency models, matching those of vanilla diffusion models. Additionally, our results encompass the training of consistency models through both distillation and isolation methods, demystifying their underlying advantage. Copyright 2024 by the author(s)

关键词： computer vision

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A benchmark for visual analysis of insider threat detection

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science China(Information sciences) 2022年第9期65卷 294-296页

作者： Ying ZHAO Kui YANG Siming CHEN Zhuo ZHANG Xin HUANG Qiusheng LI Qi MA Xinyue LUAN Xiaoping FAN School of Computer Science and Engineering Central South University School of Data Science Fudan University Qi An Xin Group School of Information Technology and Management Hunan University of Finance and Economics

Dear editor,Malicious insiders are trusted entities that are given the power to violate rules in a given security policy, and insider threats occur when trusted entities abuse such a power. In modern networked work environments, malicious insiders and their malicious activities pose serious threats to organizations and businesses. Trusted employees travel the intranet of an organization with a high degree of freedom.

关键词： visual analytics visualization evaluation benchmark intellligence analysis

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Optimized Transfer Learning Model for Lung Cancer Stage Classification Using Computed Tomography Images 5

Optimized Transfer Learning Model for Lung Cancer Stage Clas...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Deepika, R. Shanmugam, P. Moorthi, K. Kumar, P. K. Manoj Swarna, S.L. Ramnath, K. B v Raju Institute of Technology Department of Artificial Intelligence and Data Science Telangana Narsapur India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Sri Sairam Engineering College Department of Electronics and Communication Engineering Chennai India School of Computational Intelligence Joy University Tirunelveli India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India K.L.N College of Information Technology Department of Computer Science and Engineering Tamilnadu Madurai India

ISBN: (纸本)9798331518097

Lung cancer has become one of the primary causes of death worldwide due to cancer, which indicates the necessity of early diagnosis. In this regard, the present work proposes an optimized model of transfer learning based on characterizing different stages of lung cancer by using computed tomography images acquired from the IQ-OTH/NCCD lung cancer database downloadable from Kaggle. It makes use of a two-step process: first, a U-Net model is used on the patient's CT images to reconstruct anatomical features and reduce noise;second, a ShuffleNet architecture is used to predict the patient's CT stage using the reconstructed image. In addition, to enhance classification, PSO was incorporated into the model so that it can optimize the hyperparameters. The accuracy of the model was at 97.85 percent, and it suggests that the model can become a very useful tool for clinicians to bring about earliest identification and staging for lung cancer. © 2024 IEEE.

关键词： Lung cancer

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A novel Approach for optimizing the crop yield by considering climatic and soil features through fusion of Machine Learning and Deep Learning algorithms 15

A novel Approach for optimizing the crop yield by considerin...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Suganthavalli, K. Meenakshi, C. Parvathy, K. Department of Computer Science Chennai Tamil Nadu Pallavaram India Department of Computer Application Chennai Tamil Nadu Pallavaram India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350370249

The agricultural sector is the scientific and artistic cultivation of plants and animals. Agriculture has an important role in India's economy, which covers a significant 60.45% of the country's land and stands as the world's second-largest agricultural sector, driving economic growth through its diverse produce and associated industries. Soil composition really matters for growing stuff. Elements like Nitrogen, Phosphorous, & Potassium play a big role. The way you rotate crops, soil moisture, and even temperature all affect how well things grow. Rainfall is important too. Research has shown that using machine learning can help figure out the best crops to grow. They utilize methods that include Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN) for this. The study also looks into deep learning methods to make the model even better. In addition to predicting crop yields, it also provides detailed information on the required amounts of soil elements and their associated costs. The new model offers superior precision compared to the current one. The system examines the provided data and assists farmers in forecasting a crop, which subsequently aids in achieving financial gains. The prediction of a suitable yield takes into account the meteorological and soil parameters of the area. This Python-based system utilizes advanced algorithms to optimize decision-making, forecasting the most lucrative outcomes in various situations while streamlining costs and boosting productivity. The study employs Support Vector Machines (SVM) as an example of Machine Learning (ML) method, while Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) are used as Deep Learning (DL) techniques. © 2024 IEEE.

关键词： Agriculture crop Prediction DL LSTM and RNN ML SVM

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