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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Machine learning methods for condition assessment of transportation infrastructure 11

Machine learning methods for condition assessment of transpo...

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11th international conference on Structural Health Monitoring of intelligent Infrastructure, SHMII 2022

作者： Almheiri, Zainab Meguid, Mohamed Department of Civil Engineering McGill University MontréalQC Canada Peter Guo-Hua Fu School of Architecture Faculty of Engineering McGill University MontréalQC Canada

the process of damage identification methods for civil structures and infrastructures is defined as structural health monitoring (SHM). SHM examines the health condition of structures/infrastructures using advanced sensor technologies to collect periodically sampled data. these data can then be fed into intelligent algorithms to identify changes to the structural material and geometric properties of structures. this paper reviews the state-of-the-art research on machine learning methods to assess transportation infrastructure performance, focusing on journal articles. Machine learning can leverage valuable information from real data, thus avoiding environmental and economic catastrophes. therefore, it is necessary to define efficient and accurate machine learning techniques for researchers and decision-makers to assess the performance of critical infrastructures. this review proves limited studies relied on machine learning methods for condition assessment of the selected transportation infrastructure. In addition, this review shows that research is still ongoing to provide a robust approach in this field. Challenges and future directions for decision-makers are also discussed in this paper. © 2022 international conference on Structural Health Monitoring of intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.

关键词： condition assessment energy pipelines Machine learning roadways transportation infrastructure water

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Future Perspectives on automated Machine learning in Biomedical Signal Processing 4th

Future Perspectives on Automated Machine Learning in Biomedi...

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4th international conference on intelligent Technologies and Applications (INTAP)

作者： Lopez-Ramos, Luis Miguel Univ Agder SFI Offshore Mech Ctr Grimstad Norway Univ Agder Dept ICT Grimstad Norway

ISBN: (纸本)9783031105258;9783031105241

Recent developemnts in Machine learning (ML) and Signal Processing (SP) are of paramount importance for the advancement of artificial intelligence in the health sector. the implications of these enabling technologies encompass several broad fields from mathematics to product development in connection with the practitioners and patients. this paper aims at guiding researchers and entrepreneurs in the journey from basic research to application of data-driven technology for the health sector. Two key topics related to automatization of data processing, namely sparsity-based processing, and automated ML, are introduced and discussed in relation to biomedical SP. the discussed topics are exemplified in the context of cardiac signal processing.

关键词： Signal processing Biomedical engineering Interpretability Machine learning automated machine learning Autonomous information management

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Hybrid Deep learning Model for Arch of Aorta Classification Based on Slime Mould Algorithm 11

Hybrid Deep Learning Model for Arch of Aorta Classification ...

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11th international conference on Bioinformatics and Computational Biology (ICBCB)

作者： Cheng, Shi-Hang Cao, Jun-Zhe Dalian Univ Technol Sch Control Sci & Engn Minist Educ Key Lab Intelligent Control & Optimizat Ind Equip Dalian Peoples R China

ISBN: (纸本)9798350397871

As an important anatomical structure, the mediastinum can develop a variety of lesions. Endoscopic ultrasonography-guided fine needle aspiration is a novel auxiliary diagnostic method for these diseases at present. Since the images presented by endoscopic ultrasonography are not direct views, even experienced doctors may have certain difficulties in identifying the exact location of lesions in clinical diagnosis. therefore, deep learning can be used to classify endoscopic images to assist doctors in the positioning of endoscopic ultrasound (EUS). In this paper, a hybrid model combining the pre-trained deep learning networkResNet and the metaheuristic algorithm called Slime Mould Algorithm (SMA) is proposed to identify the structure of the aortic arch by distinguishing the aortic arch from non-aortic arch to assist doctors in positioning rapidly. the hybrid model utilizes ResNet50 for a raw feature extraction, adopts SMA for a further feature selection based on the extracted features, and finally uses the Support Vector Machine classifier to classify the filtered features. the hybrid model takes advantage of the excellent feature extraction ability of the deep network and the good search characteristics in the solution space of the SMA. this model achieves an average accuracy of 92.21% for classification on practical data of 14 patients' EUS images. Experimental results show that the proposed model outperforms other deep networks and metaheuristic algorithms. It shows that this model has achieved good results in the aortic arch classification problem.

关键词： Endoscopic Ultrasound (EUS) aortic arch Resnet50 Slime Mould Algorithm (SMA)

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TOWARDS STABLE TEST-TIME ADAPTATION IN DYNAMIC WILD WORLD 11

TOWARDS STABLE TEST-TIME ADAPTATION IN DYNAMIC WILD WORLD

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11th international conference on learning Representations, ICLR 2023

作者： Niu, Shuaicheng Wu, Jiaxiang Zhang, Yifan Wen, Zhiquan Chen, Yaofo Zhao, Peilin Tan, Mingkui South China University of Technology China Tencent AI Lab China National University of Singapore Singapore Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Pazhou Laboratory China

Test-time adaptation (TTA) has shown to be effective at tackling distribution shifts between training and testing data by adapting a given model on test samples. However, the online model updating of TTA may be unstable and this is often a key obstacle preventing existing TTA methods from being deployed in the real world. Specifically, TTA may fail to improve or even harm the model performance when test data have: 1) mixed distribution shifts, 2) small batch sizes, and 3) online imbalanced label distribution shifts, which are quite common in practice. In this paper, we investigate the unstable reasons and find that the batch norm layer is a crucial factor hindering TTA stability. Conversely, TTA can perform more stably with batch-agnostic norm layers, i.e., group or layer norm. However, we observe that TTA with group and layer norms does not always succeed and still suffers many failure cases. By digging into the failure cases, we find that certain noisy test samples with large gradients may disturb the model adaption and result in collapsed trivial solutions, i.e., assigning the same class label for all samples. To address the above collapse issue, we propose a sharpness-aware and reliable entropy minimization method, called SAR, for further stabilizing TTA from two aspects: 1) remove partial noisy samples with large gradients, 2) encourage model weights to go to a flat minimum so that the model is robust to the remaining noisy samples. Promising results demonstrate that SAR performs more stably over prior methods and is computationally efficient under the above wild test scenarios. the source code is available at https://***/mr-eggplant/SAR. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Entropy

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On Development of Computer Aided System for Detecting Brain Neurological Disorders 11

On Development of Computer Aided System for Detecting Brain ...

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11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： El-Askary, Nada S. Mabrouk Morsey, Mohamed Mahmoud, Abeer M. El-Arif, Taha Ibrahim Ain Shams University Faculty of Computer and Information Science Computer Science Department Cairo Egypt

ISBN: (纸本)9798350322101

Brain Neurological disorder is a serious disease that affects human's brain and may cause him to suffer. Computer Aided Systems have emerged in our daily life to provide support for the doctors and have become promising tools that assist healthcare professionals in predicting and diagnosing pathologies and recommend treatments for individual patients. this survey paper aims to provide a comprehensive overview of the current state of computer-aided systems for detecting brain neurological disorders including Alzheimer's disease and Autism spectrum disorders. the paper focuses on gathering the most popular datasets with different types such as EEG and fMRI that were utilized in the development and evaluation of these systems. We explored diverse range of features employed in these datasets which are vitally important for the precise identification and classification of brain neurological disorders. Additionally, recent studies in this field are collected and analyzed, with a particular emphasis on the methodologies utilized in the design and implementation of their models. these encompass traditional machine learning algorithms such as SVM, RF, DT and K-NN, as well as deep learning approaches, including CNN, DNN and Autoencoders. the findings are presented in a table format, showcasing the accuracy results of the different methodologies, sorted in descending order. Furthermore, the paper concludes by presenting insightful charts for statistical analysis that can guide researchers in understanding the current state of the art in this field to assist them in the development of more accurate, efficient, and accessible tools, utilizing the data accumulated throughout the survey paper, to enable early detection, diagnosis and ultimately improving patient outcomes and quality of life. © 2023 IEEE.

关键词： Deep learning

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Cybersecurity Audits for Emerging and Existing Cutting Edge Technologies 11

Cybersecurity Audits for Emerging and Existing Cutting Edge ...

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11th international conference on intelligent Systems and Embedded Design, ISED 2023

作者： Saravanan, Sivasankaran Menon, Anup Saravanan, Kavya Hariharan, Shanmugasundaram Nelson, Leema Gopalakrishnan, Jegadeesh Sri Sairam Engineering College Department of Information Technology Chennai India Vardhaman College of Engineering Department of Cse Hyderabad India Chitkara University Institute of Engineering and Technology Punjab India Sri Sairam Engineering College Department of It Chennai India

ISBN: (纸本)9798350318159

the evolution of cybersecurity insurance is spurred by financial institutions aiming to manage costs from cyber incidents, particularly in the early stages like cloud-based services. Analyzing big data, secure assessments of cyber events are done, linking repository data for various risk scenarios. Challenges arise in securing cloud services offered by giants like Google, Amazon, and Microsoft, as control over security is ceded. Research develops models countering cloud attacks to boost security. the role of cybersecurity in cloud service and application development is stressed. A comprehensive approach addresses Distributed Denial of Service (DDoS) attacks, evaluating algorithms using metrics. Blockchain's integration with cybersecurity is explored via text mining, revealing themes, vulnerabilities, and risks. Businesses moving to the cloud raise security concerns. data-driven methods identify cloud vulnerabilities and propose solutions. Blockchain's potential and challenges in cybersecurity are highlighted. AI-driven defenses for Big data are crucial. AI, Big data, and blockchain's synergy is examined for security implications. Blockchain bolsters Cyber-Physical Systems security and healthcare record integrity. AI eases cybersecurity with machine learning. Collaborative mechanisms for sharing threat information are discussed. the study explores AI-driven cybersecurity frameworks for autonomous threat detection, analyzing their defensive and offensive roles amidst evolving challenges. the complex interplay between AI, cyber warfare, and modern cybersecurity is emphasized. © 2023 IEEE.

关键词： Cybersecurity

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Handling Imbalanced datasets in Researcher Scientific Classification Using Oversampling and Undersampling Approaches 11

Handling Imbalanced Datasets in Researcher Scientific Classi...

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11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Zulius, Antoni Defit, Sarjon Stiawan, Deris Abdiansah, Abdiansah Samsuryadi, Samsuryadi Rahmi, Nadya Alinda Universitas Bina Insan Lubuklinggau Universitas Sriwijaya Faculty of Engineering Sciences Faculty of Engineering Palembang Indonesia Universitas Putra Indonesia YPTK Department of Information Technology Faculty of Computer Science Padang Indonesia Universitas Sriwijaya Department of Computer Engineering Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Department of Informatic Engineering Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9798350355314

the challenge of class imbalance in datasets is a prevalent issue in machine learning, often resulting in biased and inaccurate predictions. this study investigates the effectiveness of different data balancing techniques in enhancing classifier accuracy when applied to an imbalanced Indonesian National Scientific Manuscript dataset. Specifically, we examine how various classifiers respond to three widely used data balancing methods: Synthetic Minority Over-sampling Technique (SMOTE), Random Under-sampling, and Random Over-sampling. We assess the performance of six classifiers-Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Naïve Bayes (NB), and Gradient Boosting (GB)-when these techniques are applied. the analysis reveals diverse outcomes, with some classifiers showing substantial accuracy gains, while others experience moderate or even adverse effects. the findings underscore the importance of selecting an appropriate balancing method based on the dataset's characteristics and the specific classification algorithm employed in the knowledge classification process. © 2024 IEEE.

关键词： Adversarial machine learning

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A Fault Detection Mechanism for database Management Systems on Mobile Edge Computing 11

A Fault Detection Mechanism for Database Management Systems ...

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11th IEEE international conference on Mobile Cloud Computing, Services, and engineering, MobileCloud 2023

作者： Voutsas, Fotios Violos, John Leivadeas, Aris Netdata Inc. San Francisco United States École de Technologie Supérieure Department of Software and IT Engineering Montreal Canada

ISBN: (纸本)9798350322798

the ever-increasing demand for reliable data storage solutions at mobile edge computing makes it a necessity to develop timely fault detection mechanisms. In this paper, we address the research challenge of detecting failed data queries in an application and infrastructure agnostic way. In this context, the mobile edge infrastructure includes a database Management System (DBMS) that runs on server nodes and client nodes that retrieve, store, update and delete data in the DBMS. Specifically, we propose a rule-based algorithm with thresholds that takes as input utilization metrics and detects the faults. It does so without using sensitive DBMS credentials. To verify the applicability and the generality of the proposed algorithm, we made an experimental evaluation with six different query generation functions and testbed configurations. the comparison with other popular machine learning methods used for anomaly detection in monitoring systems, showed that the proposed algorithm significantly surpasses the other methods in terms of Precision and F1-score. During the operation of the mobile edge computing we gathered the utilization metrics, the queries success and fail status and created six datasets that we make them publicly available. © 2023 IEEE.

关键词： Fault detection

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11th international Workshop on Biomedical Image Registration, WBIR 2024, held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

11th International Workshop on Biomedical Image Registration...

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11th international Workshop on Biomedical Image Registration, WBIR 2024, held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031734793

the proceedings contain 28 papers. the special focus in this conference is on Biomedical Image Registration. the topics include: Large Deformation Registration with A Confidence-Guided Network;unleashing Registration: Diffusion Models for Synthetic Paired 3D Training data;feedback Attention for Unsupervised Cardiac Motion Estimation in 3D Echocardiography;learning Deformable Intra-Patient Liver Registration with Graph Cross-Attention;mamba? Catch the Hype Or Rethink What Really Helps for Image Registration;assessing the Robustness of Image Registration Models Under Domain Shifts with Learnable Input Images;challenging the Robustness of Image Registration Similarity Metrics with Adversarial Attacks;Comparative Study on Co-registration Techniques for Diffusion-Weighted Breast MRI and Improved ADC Mapping;a learning-Free Approach to Mitigate Abnormal Deformations in Medical Image Registration;Deformable MRI Sequence Registration for AI-Based Prostate Cancer Diagnosis;SINA: Sharp Implicit Neural Atlases by Joint Optimisation of Representation and Deformation;A Novel Fusion of CT/MRI and US Images Based on Depth Camera and Electromagnetic Tracking;deep learning Multi-channel Structural and Diffusion Tensor Neonatal Image Registration;registration by Regression (RbR): A Framework for Interpretable and Flexible Atlas Registration;Diffusion Model-Based Hierarchical Registration of Whole-Body PET/CT Images;unsupervised Similarity learning for Image Registration with Energy-Based Models;Segmentation by Registration-Enabled SAM Prompt engineering Using Five Reference Images;electron Microscopy Image Registration with Twin Axial Transformer and Progressive Training;general Vision Encoder Features as Guidance in Medical Image Registration;rigid Single-Slice-in-Volume Registration via Rotation-Equivariant 2D/3D Feature Matching;a Self-supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer;CAR: Contrast-Agnostic Deformable Medical

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Machine learning Model for Anxiety Disorder Diagnosis Based on Sensory Time-Series data 11th

Machine Learning Model for Anxiety Disorder Diagnosis Based ...

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11th international Work-conference on Bioinformatics and Biomedical engineering (IWBBIO)

作者： Fakhar, Usman Alsmadi, Malek Alkhateeb, Abedalrhman Lakehead Univ Comp Sci Dept 955 Oliver Rd Thunder Bay ON P7B 5E1 Canada Confederat Coll Sch Engn Technol Trades & Aviat 1450 Nakina Dr Thunder Bay ON P7B 0E5 Canada

ISBN: (纸本)9783031646287;9783031646294

Anxiety disorders are common and serious mental disorders that affect many people and disrupt their lives. this study explores the potential of machine learning techniques in aiding the diagnosis of anxiety disorders through the analysis of sensory data. By leveraging features extracted from time-series bio-behavioral data, including motor activity and heart rate, predictive models are trained to differentiate between individuals with and without anxiety. Feature selection methods are employed to identify discriminative features for classification to enhance the predictive accuracy of the models. three classification algorithms, Multilayer Perceptron (MLP), Support Vector Machine (SVM), and XGBoost are evaluated on a dataset of 68 samples from individuals with and without anxiety. the results show that the MLP classifier outperforms SVM and XGBoost with AUCROC = 0.94, precision = 0.87, recall = 1.0, and accuracy = 0.86 in classifying anxiety disorders from non-anxiety samples. this proposed machine learning model may serve as a potential diagnostics model for anxiety and may help understand the disorder's relevant bio-behavioral and motor features.

关键词： Anxiety Machine learning Time-Series Feature Selection MLP Classification

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