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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是121-130 订阅

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11th international Workshop on Ophthalmic Medical Image Analysis, OMIA-XI 2024 was held in conjunction with the 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

11th International Workshop on Ophthalmic Medical Image Anal...

引用

11th international Workshop on Ophthalmic Medical Image Analysis, OMIA-XI 2024 was held in conjunction with the 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031731181

The proceedings contain 16 papers. The special focus in this conference is on Ophthalmic Medical Image Analysis. The topics include: VNR-AV: Structural Post-processing for Retinal Arteries and Veins Segmentation;wavelet Deep learning Network for Objective Retinal Functional Estimation from Multimodal Retinal Imaging;inter-frame Sclera Vessel Rotation Tracking for Toric Intraocular Lens Implantation Navigation;data Heterogeneity-Aware Personalized Federated learning for Diagnosis;MM-UNet: A Mixed MLP Architecture for Improved Ophthalmic Image Segmentation;Coral-CVDs: A Consistent Ordinal Regression Model for Cardiovascular Diseases Grading;affordable Deep learning for Diagnosing Inherited and Common Retinal Diseases via Color Fundus Photography;Comparative Analysis of data Augmentation for Retinal OCT Biomarker Segmentation;advanced Diabetic Retinopathy Classification: Integrating Pathological Indicators Segmentation and Morphological Feature Analysis;Masked Image Modelling for Retinal OCT Understanding;A Dual-Stream Network for Langerhans’ Cells Segmentation in CCM Images;formula-Driven data Augmentation and Partial Retinal Layer Copying for Retinal Layer Segmentation;Enhancing Community Vision Screening: AI-Driven Retinal Photography for Early Disease Detection and Patient Trust;enhancing Large Foundation Models to Identify Fundus Diseases Based on Contrastive Enhanced Low-Rank Adaptation Prompt.

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Self-supervised Pre-training Tasks for an fMRI Time-Series Transformer in Autism Detection 7th

Self-supervised Pre-training Tasks for an fMRI Time-Series...

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7th international Workshop on machine learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Zhou, Yinchi Duan, Peiyu Du, Yuexi Dvornek, Nicha C. Department of Biomedical Engineering New HavenCT United States Department of Radiology and Biomedical Imaging Yale University New HavenCT United States

ISBN: (纸本)9783031787607

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that encompasses a wide variety of symptoms and degrees of impairment, which makes the diagnosis and treatment challenging. Functional magnetic resonance imaging (fMRI) has been extensively used to study brain activity in ASD, and machine learning methods have been applied to analyze resting state fMRI (rs-fMRI) data. However, fewer studies have explored the recent transformer-based models on rs-fMRI data. Given the superiority of transformer models in capturing long-range dependencies in sequence data, we have developed a transformer-based self-supervised framework that directly analyzes time-series fMRI data without computing functional connectivity. To address over-fitting in small datasets and enhance the model performance, we propose self-supervised pre-training tasks to reconstruct the randomly masked fMRI time-series data, investigating the effects of various masking strategies. We then fine-tune the model for the ASD classification task and evaluate it using two public datasets and five-fold cross-validation with different amounts of training data. The experiments show that randomly masking entire ROIs gives better model performance than randomly masking time points in the pre-training step, resulting in an average improvement of 10.8% for AUC and 9.3% for subject accuracy compared with the transformer model trained from scratch across different levels of training data availability. Our code is available on GitHub. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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ITiCSE WGR 2024 - Publication of the 2024 Working Group Reports on Innovation and Technology in Computer science Education

ITiCSE WGR 2024 - Publication of the 2024 Working Group Repo...

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29th Working Group Reports on Innovation and Technology in Computer science Education, ITiCSE WGR 2024

ISBN: (纸本)9798400712081

The proceedings contain 10 papers. The topics discussed include: computing education in African countries: a literature review and contextualized learning materials;AI integration in the IT professional workplace: a scoping review and interview study with implications for education and professional competencies;instructors’ perspectives on capstone courses in computing fields: a mixed-methods study;data systems education: curriculum recommendations, course syllabi, and industry needs;an international examination of non-technical skills and professional dispositions in computing – identifying the present day academia-industry gap;diversity, equity, and inclusion in computing science: culture is the key, curriculum contributes;what we talk about when we talk about K-12 computing education;and a pedagogical framework for developing abstraction skills.

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8th international conference on Computer science and Application Engineering, CSAE 2024

8th International Conference on Computer Science and Applica...

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8th international conference on Computer science and Application Engineering, CSAE 2024

ISBN: (纸本)9798400718090

The proceedings contain 26 papers. The topics discussed include: building extraction from high-resolution remote sensing image based on improved ResUNet network;a visual based algorithm for measuring the speed of scrap metal vibration feeding;semantic segmentation for virtual-real fusion data processing in nonferrous metal process industry;an evaluation method based on fuzzy model for the autonomous and controllable of the spacecraft component production;discrete-time physics-informed neural networks for two-phase flow interface capturing;comprehensive review: advancing cognitive computing through theory of mind integration and deep learning in artificial intelligence;and kinematics behaviors evaluation of a naval ship survival training simulation system in case of longitudinal shaking.

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Scalable machine learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios 24

Scalable Machine Learning Architectures for IPA-Driven Maint...

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7th international conference on Information science and Systems, ICISS 2024

作者： Huang, Zhimeng Liu, Xiaodong Romdhani, Imed Shih, Chi-Sheng School of Computing Engineering and Built Environment Edinburgh Napier University Edinburgh United Kingdom College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan

ISBN: (纸本)9798400717567

This research presents a groundbreaking approach to Building Maintenance Management (BMM) by introducing an Intelligent Process Automation (IPA)-Driven Building Maintenance Management (IBMM) model. This innovative model harnesses the synergies between Artificial Intelligence (AI), machine learning (ML), and Internet of Things (IoT) technologies to transition from reactive to proactive and predictive building maintenance strategies. The study highlights the critical gap in current BMM practices—the absence of intelligent systems for anticipating and addressing maintenance issues before they escalate. Through an extensive literature review, the transformative potential of AI and IoT for enhancing building maintenance management within smart cities is explored, establishing a foundation for the IBMM model’s application. The core of this research lies in its novel application of scalable machine learning architectures to automate and optimize maintenance task allocation in large-scale building portfolios. The practicality of the IBMM model is demonstrated via a proof of concept (POC) in an industrial setting, evidencing its capacity to improve efficiency, reduce costs, and bolster sustainability in building maintenance operations. The model epitomizes a paradigm shift in BMM by integrating IPA, which combines AI and ML, facilitating automated, intelligent decision-making and task allocation. Among its advancements, the IBMM model introduces enhanced predictive maintenance through real-time data analysis, adaptive learning and optimization, automated decision-making, and human-machine collaboration, contributing to energy efficiency and alignment with smart city objectives. The paper delineates the methodology, design, and implementation of a machine learning model for engineer task assignments, culminating in a case study that validates the model’s efficacy. This research not only signifies a significant advancement in BMM by leveraging IPA technologies for autonomous p

关键词： Predictive maintenance

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3rd international conference on Smart Technologies and Innovation Management, MTYMEX 2024

3rd International Conference on Smart Technologies and Innov...

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3rd international conference on Smart Technologies and Innovation Management, MTYMEX 2024

ISBN: (纸本)9783031649561

The proceedings contain 15 papers. The special focus in this conference is on Smart Technologies and Innovation Management. The topics include: Prospective Directions in the Computer Systems Industry Foundation Classes (IFC) Shaping data Exchange for Cities Sustainability and Resilience;a Proposal of Model Combining Semantic Analysis and Knowledge Graph for Question Answering System in Vietnamese Law of science and Technology;trends and Innovative Tactics in Startup Innovation: A Survey;interactive Web Mapping for Geolocation of Socio-community Infrastructure in a Developing Country;development of Digital Capabilities for E-commerce Agribusiness: An Exploratory Research;a Social-Software-Based Telemedicine Information System for Facilitating Healthcare Services;lab-to-Market Product Innovation: Implications of Using a Translational Research & Development Model and Methodology;a Multilayered Process Framework for Predicting Students’ Academic Performance in Open and Distance learning;a New Notification System for Tracking Ethereum Transactions with Social Media Software;A Secure Consortium-Blockchain-Enabled Communication Scheme for DNA-Based Smart Health Systems;review on machine learning for Zero-Day Exploit Detection and Response;A Review of AI in Spear Phishing Defense: Detecting and Thwarting Advanced Email Threats;energy-Efficient Dynamic Adaptive Encryption for Low-Resource Internet of Things.

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Cost-Aware Dynamic Cloud Workflow Scheduling Using Self-attention and Evolutionary Reinforcement learning 22nd

Cost-Aware Dynamic Cloud Workflow Scheduling Using Self-atte...

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22nd international conference on Service Oriented computing

作者： Shen, Ya Chen, Gang Ma, Hui Zhang, Mengjie Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Wellington New Zealand Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand

ISBN: (纸本)9789819608072;9789819608089

As a key cloud management problem, Cost-aware Dynamic Multi-Workflow Scheduling (CDMWS) aims to assign virtual machine (VM) instances to execute tasks in workflows so as to minimize the total costs, including both the penalties for violating Service Level Agreement (SLA) and the VM rental fees. Powered by deep neural networks, Reinforcement learning (RL) methods can construct effective scheduling policies for solving CDMWS problems. Traditional policy networks in RL often use basic feedforward architectures to separately determine the suitability of assigning any VM instances, without considering all VMs simultaneously to learn their global information. This paper proposes a novel self-attention policy network for cloud workflow scheduling (SPN-CWS) that captures global information from all VMs. We also develop an Evolution Strategy-based RL (ERL) system to train SPN-CWS reliably and effectively. The trained SPN-CWS can effectively process all candidate VM instances simultaneously to identify the most suitable VM instance to execute every workflow task. Comprehensive experiments show that our method can noticeably outperform several state-of-the-art algorithms on multiple benchmark CDMWS problems.

关键词： Cloud computing Cloud Workflow Management Dynamic Multi-Workflow Scheduling Self-Attention Reinforcement learning

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18th international conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

18th International Conference on Wireless Artificial Intelli...

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18th international conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

ISBN: (纸本)9783031714696

The proceedings contain 108 papers. The special focus in this conference is on Wireless Artificial Intelligent computing Systems and Applications. The topics include: Profinder: Towards Professionals Recognition on Mobile Devices for Users with Cognitive Decline;truthful Double Auction-Based Resource Allocation Mechanisms for Latency-Sensitive Applications in Edge Clouds;V2ICooper: Toward Vehicle-to-Infrastructure Cooperative Perception with Spatiotemporal Asynchronous Fusion;recommendation-Aware Collaborative Edge Caching Strategy in the Internet of Vehicles;towards Robust Internet of Vehicles Security: An Edge Node-Based machine learning Framework for Attack Classification;device-Edge-Cloud Collaborative Video Stream Processing and Scheduling Strategy Based on Deep Reinforcement learning;consistent Low-Latency Scheduling for Microsecond-Scale Tasks in data Centers;multi-scale data Reconstruction Based Policy Optimization Algorithm for Skill learning;Joint Optimization Design of Intelligence Reflecting Surface Assisted MU-MISO System Based on Deep Reinforcement learning;Secure Motion Verification for High Altitude Platforms with a Hybrid AOA-TDOA-FDOA Scheme;variational Autoencoder Based Automatic Clustering for Multivariate Time Series Anomaly Detection;deep Reinforcement learning Based Economic Dispatch with Cost Constraint in Cyber Physical Energy System;inceptionNeXt Network with Relative Position Information for Microexpression Recognition;adMarks: Image Steganography Based on Adversarial Perturbation;a Review on Binary Code Analysis datasets;low-Cost Robot Path Planning Mechanism for Escaping from Dead Ends;towards Communication-Efficient Collaborative Perception: Harnessing Channel-Spatial Attention and Knowledge Distillation;improving Anomaly Scene Recognition with Large Vision-Language Models;ReSU-Net: State Space Model for 3D Abdominal Multi-organ Segmentation;autocue : Targeted Textual Adversarial Attacks with Adversarial Prompts;universal Sign Language

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Multimodality for Diagnosis of Asian Choroidal Vasculopathy: Results from a Novel dataset and Deep-learning Experiments

Multimodality for Diagnosis of Asian Choroidal Vasculopath...

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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： Cho, Daehyun Kim, Young Ho Ahn, Somin Oh, Jaeryung Wallraven, Christian Department of Artificial Intelligence Korea University Seoul Korea Republic of Department of Ophthalmology Korea University College of Medicine Seoul Korea Republic of Department of Ophthalmology Dongguk University Ilsan Hospital Goyang Korea Republic of Department of Artificial Intelligence and Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9783031845246

Deep learning algorithms show tremendous potential for clinical decision-making - for example, in providing automated diagnoses of imaging data. However, typical clinical datasets often are limited in size, modalities, and contain heterogeneous, incomplete data, which presents challenges for deep learning frameworks that necessitate larger, uniform datasets, complicating their deployment especially with new types of disease models. In this work, we present a case study for deep learning in such a challenging setting in the context of diagnosing Asian choroidal Vasculopathy (ACV), which is a retinopathy profile currently under discussion in ophthalmology to be differentiated from age-related macular degeneration (AMD). We first introduce a novel, human-annotated multimodal dataset for ACV versus AMD diagnosis incorporating four different imaging modalities. We next explore the usefulness of "foundation models" for this data, compared to traditional dataset-specific training. Most importantly, we investigate which of the four modalities is most discriminative and whether bi-modal classification is able to enhance performance across multiple fusion approaches. We also discuss first results of salient features using explainability techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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18th international conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

18th International Conference on Wireless Artificial Intelli...

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18th international conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

ISBN: (纸本)9783031714634

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