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检索条件"任意字段=2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023"

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Diagnosis Then Aggregation: An Adaptive Ensemble Strategy for Keyphrase Extraction 3rd

Diagnosis Then Aggregation: An Adaptive Ensemble Strategy fo...

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3rd CAAI international conference on Artificial Intelligence (CICAI)

作者： Jin, Xin Liu, Qi Yue, Linan Liu, Ye Zhao, Lili Gao, Weibo Gong, Zheng Zhang, Kai Bi, Haoyang Univ Sci & Technol China Anhui Prov Key Lab Big Data Anal & Applicat Hefei Peoples R China State Key Lab Cognit Intelligence Hefei Peoples R China

ISBN: (纸本)9789819988495;9789819988501

Keyphrase extraction (KE) is a fundamental task in the information extraction, which has recently gained increasing attention. However, when facing text with complex structure or high noise, current individual keyphrase extraction methods fail to handle capturing multiple features and limit the performance of the keyphrase extraction. To solve that, ensemble learning methods are employed to achieve better performance. Unfortunately, traditional ensemble strategies rely only on the extraction performance (e.g., Accuracy) of each algorithm on the whole dataset for keyphrase extraction, and the aggregated weights are commonly fixed, lacking fine-grained considerations and adaptiveness to the data. To this end, in this paper, we propose an Adaptive Ensemble strategy for Keyphrase Extraction (AEKE) that can aggregate individual KE models adaptively. Specifically, we first obtain the multi-dimensional abilities of individual KE models by employing cognitive diagnosis methods. Then, based on the diagnostic abilities, we introduce an adaptive ensemble strategy to yield an accurate and reliable weight distribution for model aggregation when facing new data, and further apply it to improve keyphrase extraction in the model aggregation. Extensive experimental results on real-world datasets clearly validate the effectiveness of AEKE. Code is released at https://***/kingiv4/AEKE.

关键词： Keyphrase Extraction Ensemble Learning cognitive Diagnosis

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10th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2022

10th International Conference on Frontiers of Intelligent Co...

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10th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2022

ISBN: (纸本)9789811975233

The proceedings contain 55 papers. The special focus in this conference is on Frontiers of Intelligent computing: Theory and Applications. The topics include: Physical Layer Security in MIMO Relay-Based cognitive Radio Network;A Novel Approach for Bug Triaging Using TOPSIS;energy-Efficient Resource Allocation in cognitive Radio Networks;Medical Internet of Things and data Analytics for Post-COVID Care: An Analysis;Mammography Image Classification and Detection by Bi-LSTM with Residual Network Using XG-Boost Approach;ECG Biometric Recognition by Convolutional Neural Networks with Transfer Learning Using Random Forest Approach;design and Analysis of a Metamaterial-Based Butterworth Microstrip Filter;internet of Things-Enabled Irrigation System in Precision Agriculture;A Hybrid Feature Selection Framework for Breast Cancer Prediction Using Mutual Information and AdaBoost-RFE;Weighted Low-Rank and Sparse Matrix Decomposition Models for Separating Background and Foreground in Dynamic MRI;2D-CTM and DNA-Based computing for Medical Image Encryption;a Review of Financial Fraud Detection in E-Commerce Using Machine Learning;ReEDNet-An Encoder–Decoder Framework for Single Image Dehazing;detection of Flood Events from Satellite Images Using Deep Learning;Development and Implementation of an Efficient Deep Residual Network for ECG Classification;study of Class Incremental Learning Strategies for Intrusion Detection System;classification of High-Priority Tweets for Effective Rescue Operations During Natural Disaster Combining Twitter’s Textual and Non-textual Features;An Energy Efficient Offloading Technique for UAV-Assisted MEC Using Nature Inspired Algorithm;Trajectory Planning and data Collection of UAVs Over Disaster a Affected Areas;hand Gesture Recognition on Skeletal data Using Multi-head Neural Network;Remote Sensing Image Fusion Based on PCA and Wavelets;machine Learning Approach to Analyze Breast Cancer.

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ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environmental Microorganism Image Object Detection

ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environm...

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2023 IEEE international conference on Big data, Bigdata 2023

作者： Yang, Hechen Yang, Jinzhu Jiang, Tao Zhao, Xin Zhang, Jinghua Zhao, Peng Chen, Ao Nie, Qianqing Grzegorzek, Marcin Li, Chen Northeastern University Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Shenyang China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang China Chengdu University of Traditional Chinese Medicine School of Intelligent Medicine Chengdu China Chengdu University of Information Technology International Joint Institute of Robotics and Intelligent Systems Chengdu China Institute of Medical Informatics University of Luebeck Luebeck Germany

ISBN: (纸本)9798350324457

The detection of environmental microorganisms is always a difficult task, e specially when the multi-scale environment is complex. For tiny objects in microscopic images, current detection methods face the challenge of accurate identification and localization. In contrast, we propose a convolutional neural network (ECA-RetinaNet) for microscopic object detection of which underlying dataset is a high-quality EMDS-7 dataset. The accuracy of ECA-RetinaNet is high, with a high mean Average Precision (mAP) value of 81.42% in the Environmental Microorganisms (EMS) detection task. Its accuracy has been higher than that of the two-stage object detection network. © 2023 IEEE.

关键词： Attention Convolutional Neural Network Environmental Miroorganisms Feature Fusion Object Detection

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Time Series Forecasting Using Parallel Randomized Fuzzy cognitive Maps and Reservoir computing

Time Series Forecasting Using Parallel Randomized Fuzzy Cogn...

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international conference on Emerging Trends and Applications in Artificial Intelligence, ICETAI 2023

作者： Orang, Omid Bitencourt, Hugo Vinicius e Silva, Petrônio Cândido de Lima Guimarães, Frederico Gadelha Laboratory Universidade Federal de Minas Gerais Belo Horizonte Brazil Federal Institute of Education Science and Technology of Northern Minas Gerais Januária Campus Belo Horizonte Brazil Department of Computer Science Universidade Federal de Minas Gerais Belo Horizonte Brazil

ISBN: (纸本)9783031567278

Fuzzy cognitive Maps (FCMs) have been widely employed as nonlinear forecasting methods that are easily interpretable. They have a remarkable capability to enhance accuracy and are well-equipped to handle uncertainty and emulate the dynamics of complex systems. The main goal of this article is to present a new randomized multiple-input multiple-output (MIMO) FCM-based forecasting technique named M-PRFCM to forecast real-world high-dimensional time series in Internet of Things (IoT) applications. M-PRFCM is a first-order forecasting method integrating the concepts of randomized FCMs, Echo State Networks (ESNs), and Kernel Principle Components Analysis (KPCA). The training process of M-PRFCM is accelerated as a result of utilizing the ESN weight initialization trick, which randomly selects weights. The obtained results show the efficacy and validation of the proposed technique in terms of accuracy when compared with other existing approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Fuzzy cognitive Maps

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Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of cognitive Impairment with Structural MRI 26th

Brain Anatomy-Guided MRI Analysis for Assessing Clinical Pro...

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26th international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI)

作者： Zhang, Lintao Wu, Jinjian Wang, Lihong Wang, Li Steffens, David C. Qiu, Shijun Potter, Guy G. Liu, Mingxia Univ North Carolina Dept Radiol Chapel Hill NC 27515 USA Univ North Carolina Biomed Res Imaging Ctr Chapel Hill NC 27515 USA Guangzhou Univ Chinese Med Sch Clin Med 1 Guangzhou Guangdong Peoples R China Univ Connecticut Dept Psychiat Sch Med Farmington CT 06107 USA Duke Univ Med Ctr Dept Psychiat & Behav Sci Durham NC 27708 USA

ISBN: (纸本)9783031439926;9783031439933

Brain structural MRI has been widely used for assessing future progression of cognitive impairment (CI) based on learning-based methods. Previous studies generally suffer from the limited number of labeled training data, while there exists a huge amount of MRIs in large-scale public databases. Even without task-specific label information, brain anatomical structures provided by these MRIs can be used to boost learning performance intuitively. Unfortunately, existing research seldom takes advantage of such brain anatomy prior. To this end, this paper proposes a brain anatomy-guided representation (BAR) learning framework for assessing the clinical progression of cognitive impairment with T1-weighted MRIs. The BAR consists of a pretext model and a downstream model, with a shared brain anatomy-guided encoder for MRI feature extraction. The pretext model also contains a decoder for brain tissue segmentation, while the downstream model relies on a predictor for classification. We first train the pretext model through a brain tissue segmentation task on 9,544 auxiliary T1-weighted MRIs, yielding a generalizable encoder. The downstream model with the learned encoder is further fine-tuned on target MRIs for prediction tasks. We validate the proposed BAR on two CI-related studies with a total of 391 subjects with T1-weighted MRIs. Experimental results suggest that the BAR outperforms several state-of-the-art (SOTA) methods. The source code and pre-trained models are available at https://***/goodaycoder/BAR.

关键词： Structural MRI Brain anatomy cognitive impairment

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PINPOINT-A multidisciplinary framework for semi-automatic risk assessment in military operations and civilian missions

PINPOINT-A multidisciplinary framework for semi-automatic ri...

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conference on cognitive and Computational Aspects of Situation Management, CogSIMA 2023

作者： Backfried, Gerhard Thomas-Aniola, Dorothea Pilutti, Diego Boyer, Martin Hein, Rüdiger Tabatabaei, Amir Zinkanell, Michael Suker, Michael Agathonos, Philipp HENSOLDT Analytics Vienna Austria Vienna Austria IGASPIN Graz Austria Austrian Institute for European and Security Policy Vienna Austria Austrian Ministry of Defence Vienna Austria Austrian Ministry of European and International Affairs Vienna Austria

We present the PINPOINT project aimed at designing a semi-automated risk assessment framework to support decision-making for military operations and civilian missions. These missions typically take place in critical international areas of remote countries with little infrastructure and require specific technical and operational support. To effectively plan and execute risk mitigation strategies, decision-makers require approaches for systematic, comprehensive risk analysis and proposals for measures. During the planning and execution phases, risk analysis must be updated regularly based on available information derived from Open Source data in combination with reliable and accurate Position-, Navigation-and Timing-data (PNT) monitoring. The proposed framework focuses on innovations in the areas of a systematic risk analysis with derived mitigation actions, Open Source Intelligence (OSINT) related to these areas, Artificial Intelligence (AI) technologies for multiple types of sources (sensors, news, social media) and PNT monitoring based on GNSS-data analysis. © 2024, EasyChair. All rights reserved.

关键词： Risk analysis

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Dimension Reduction in the Sagittal Plane for Diagnosis of Mild cognitive Impairment 14th

Dimension Reduction in the Sagittal Plane for Diagnosis o...

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12th international Advanced computing conference, IACC 2022

作者： Bhasin, Harsh Deshwal, Vishal Jasuja, Arush Manav Rachna University Faridabad India International Centre for Neuromorphic Systems MARCS Institute Western Sydney University SydneyNSW2150 Australia Ripik Technology Pvt. Ltd. Uttar Pradesh Noida India

ISBN: (纸本)9783031356438

Dementia constitutes the major chunk of the diseases in elderly population. The preventive diagnostics of dementia may help the clinicians in handling the symptoms and delaying the progression of the disease. Mild cognitive impairment may be considered precursor to dementia. This work uses the structural-Magnetic Resonance Imaging data obtained from Alzheimer’s Disease Neuroimaging Initiative to classify MCI-Converts from Non-Converts. The information from this 3-D data is difficult to handle using 2-D models, in which the correlation between the slices is lost, and the 3-D models, which are computationally expensive. The proposed model is able to find the correlation between slices of brain volume using s-MRI which grasps the correlation between the slices, is computationally efficient, uses less memory and performs better in comparison with the current state of the art. The work applies Independent Component Analysis and Principal Component Analysis to accomplish the above task. In addition to being more accurate than the state-of-the-art, the proposed model yields an accuracy of 87% with only 714 features. © 2023, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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1st IEEE international conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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Proceedings of the 21st international conference on Business Process Management , BPM 2023

Proceedings of the 21st International Conference on Business...

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Proceedings of the 21st international conference on Business Process Management , BPM 2023

ISBN: (纸本)9783031416194

The proceedings contain 27 papers. The special focus in this conference is on Business Process Management. The topics include: Inferring Missing Entity Identifiers from Context Using Event Knowledge Graphs;process Channels: A New Layer for Process Enactment Based on Blockchain State Channels;action-Evolution Petri Nets: A Framework for Modeling and Solving Dynamic Task Assignment Problems;context-Based Activity Label-Splitting;verifying Resource Compliance Requirements from Natural Language Text over Event Logs;from Text to Performance Measurement: Automatically computing Process Performance Using Textual Descriptions and Event Logs;agent Miner: An Algorithm for Discovering Agent Systems from Event data;interactive Multi-interest Process Pattern Discovery;Increasing RPA Adoption: An Experiment on Countermeasures for Status Quo Bias;can I Trust My Simulation Model? Measuring the Quality of Business Process Simulation Models;stochastic-Aware Comparative Process Mining in Healthcare;on the cognitive Effects of Abstraction and Fragmentation in Modularized Process Models;not Here, But There: Human Resource Allocation Patterns;a Novel Multi-perspective Trace Clustering Technique for IoT-Enhanced Processes: A Case Study in Smart Manufacturing;the Impact of Process complexity on Process Performance: A Study Using Event Log data;Deviation from Standards and Performance in Knowledge-Intensive Processes: Evidence from the Process of Selling Customized IT Solutions;benevolent Business Processes - Design Guidelines Beyond Transactional Value;PEM4PPM: A cognitive Perspective on the Process of Process Mining;event Abstraction for Partial Order Patterns;incremental Discovery of Process Models Using Trace Fragments;approximating Multi-perspective Trace Alignment Using Trace Encodings;POWL: Partially Ordered Workflow Language;polynomial-Time Conformance Checking for Process Trees;investigating the Influence of data-Aware Process States on Activity Probabilities in Simulation Models:

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Emotion Recognition from Eye Movements Using Multi-way Autoregressive Model

Emotion Recognition from Eye Movements Using Multi-way Autor...

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2024 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Tian-Fang Liu, Xuan-Hao Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China

ISBN: (纸本)9798350386226

The application of physiological signals in emotion recognition is a popular research topic in human-computer interactions. Eye movement, as an important physiological signal, plays an essential role in medicine, psychology, cognitive science, and other scientific research fields. Previous studies have successfully identified human emotions by combining various eye-related measurements, selecting features, and utilizing machine learning techniques. However, the exploration of eye movement signals in emotion recognition remains insufficient. In this study, we utilize eye tracking heatmap and eye movement trajectory data for emotion recognition for the first time. Based on the two types of eye movement data, we develop a multi-way autoregressive model capable of processing multi-view eye movement data. Compared to traditional deep learning baseline models, our model better adapts to the structure of eye movement data and significantly improves the classification performance. Furthermore, we integrate heatmap and trajectory data with commonly used eye-related measurement features, which further enhance the performance of emotion recognition beyond previous methods. © 2024 IEEE.

关键词： Emotion Recognition

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