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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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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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Predicting Stroke Through Retinal Graphs and Multimodal Self-supervised learning

Predicting Stroke Through Retinal Graphs and Multimodal Sel...

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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： Huang, Yuqing Wittmann, Bastian Demler, Olga Menze, Bjoern Davoudi, Neda Department of Computer Science ETH Zürich Zürich Switzerland ETH AI Center ETH Zürich Zürich Switzerland Department of Quantitative Biomedicine University of Zurich Zürich Switzerland Harvard Medical School BostonMA United States

ISBN: (纸本)9783031845246

Early identification of stroke is crucial for intervention, requiring reliable models. We proposed an efficient retinal image representation together with clinical information to capture a comprehensive overview of cardiovascular health, leveraging large multimodal datasets for new medical insights. Our approach is one of the first contrastive frameworks that integrates graph and tabular data, using vessel graphs derived from retinal images for efficient representation. This method, combined with multimodal contrastive learning, significantly enhances stroke prediction accuracy by integrating data from multiple sources and using contrastive learning for transfer learning. The self-supervised learning techniques employed allow the model to learn effectively from unlabeled data, reducing the dependency on large annotated datasets. Our framework showed an AUROC improvement of 3.78% from supervised to self-supervised approaches. Additionally, the graph-level representation approach achieved superior performance to image encoders while significantly reducing pre-training and fine-tuning runtimes. These findings indicate that retinal images are a cost-effective method for improving cardiovascular disease predictions and pave the way for future research into retinal and cerebral vessel connections and the use of graph-based retinal vessel representations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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The Research on V2V Cooperative Caching Method Based on Vehicle Attributes 24

The Research on V2V Cooperative Caching Method Based on Vehi...

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2024 international conference on Artificial Intelligence of Things and computing, AITC 2024

作者： Wang, Zirui Wang, Hui Wan, Jianxiong Liu, Chuyi Sun, Pengfei Li, Leixiao School of Data Science and Application Inner Mongolia University of Technology Inner Mongolia Autonomous Region Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Inner Mongolia Autonomous Region Hohhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Republic of China Inner Mongolia Autonomous Region Hohhot China

ISBN: (纸本)9798400709869

To address the increasingly severe issue of insufficient vehicular caching in the Internet of Vehicles (IoV), Vehicular edge computing uses base stations (BS) and vehicles with computing and storage capabilities as edge servers to cache content. However, most existing studies focus on reducing task costs, assuming vehicles can simultaneously act as requesters and providers, without considering whether a vehicle is suitable to serve as a provider, willing to share resources or ensure secure data transmission, thus lowering matching efficiency. Deep reinforcement learning and blockchain technology were integrated into the Internet of Vehicles to address distributed vehicle-to-vehicle collaborative caching. An optimal collaborative caching strategy was designed, considering dynamic vehicle attributes, with the system's objective being to maximize vehicle utility to encourage resource sharing. Simulation results confirm that the proposed blockchain-based vehicular edge computing collaborative caching scheme with deep reinforcement learning significantly outperforms other strategies. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Blockchain

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Diagnosis of Autism Spectrum Disorders Based on fMRI 24

Diagnosis of Autism Spectrum Disorders Based on fMRI

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13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Zhao, Xiaoxue Wu, Peng School of Information Science and Engineering University of Jinan Jinan China

ISBN: (纸本)9798400717482

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in social communication and repetitive behaviors, with its exact etiology remaining unclear. Early and accurate diagnosis is critical for effective interventions. This paper introduces a diagnostic method based on functional magnetic resonance imaging (fMRI) data, aiming to extract more effective features and understand the relationships between them. This study utilizes a multi-site fMRI dataset provided by the Autism Brain Imaging data Exchange (ABIDE) to objectively identify functional connectivity patterns in ASD participants. A deep learning model combining autoencoders and Transformers is proposed to learn complex patterns within the brain's functional connectivity matrix and capture interactions between different brain regions. The study evaluated 883 subjects from the ABIDE cohort and, by comparing its performance with that of the ASD-DiagNet[12] model on the same dataset, validated the efficacy of the proposed method. Experimental results demonstrate that the method achieved high classification accuracy in identifying ASD, laying a foundation for future research and suggesting potential applications in clinical diagnosis of ASD. © 2024 Copyright held by the owner/author(s).

关键词： Diseases

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Classification of Sarcoma Based on Genomic data Using machine learning Models 4

Classification of Sarcoma Based on Genomic Data Using Machin...

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4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Gala, Pratham Pandloskar, Yash Godbole, Shubham Hakim, Fayed Kanani, Pratik Kurup, Lakshmi Dwarkadas J. Sanghvi College of Engineering Mumbai India Dept. of Artificial Intelligence and Data Science Dwarkadas J. Sanghvi College of Engineering Mumbai India

The proposed work provides a new machine-learnt classification approach for the various types of soft tissue sarcoma based on genomics data which addresses a considerable gap in sarcoma diagnostics. The previous studies have investigated various aspects of sarcoma but this study is unique in that it targets the predicting sarcoma variant types using genetic information, which has not been done before. Random Forest was used as the meta-estimator and a stacking ensemble model comprising of Random Forest, Extreme Gradient Boosting and LightGBM were used for this study. The model which was trained and validated on a complete dataset of 206 adult soft tissue sarcoma samples containing genomic alterations, transcriptomic, epigenomic and proteomic data achieved an accuracy of 89.44% at a precision level as high as 91%. Stratified k-fold cross validation is employed to ensure that class imbalance is not a hindrance to performance. This innovative approach outmatches single classifiers and traditional single model methods at great length hence making it possible and effective to use machine learning on genomic data for predicting sarcoma variants. Thus, the findings from this research could change cancer diagnosis forever;they promise more accurate classification as well as personalized treatment modalities while also providing a framework for analogous applications in other rare complex cancers. © 2025 The Authors.

关键词： Adversarial machine learning

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19th international conference on Hybrid Artificial Intelligence Systems, HAIS 2024

19th International Conference on Hybrid Artificial Intellige...

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19th international conference on Hybrid Artificial Intelligence Systems, HAIS 2024

ISBN: (纸本)9783031741852

The proceedings contain 52 papers. The special focus in this conference is on Hybrid Artificial Intelligence Systems. The topics include: The Third Codon Nucleotide’s Role in Genetic Recombination Within SARS-CoV-2 Spike Protein: A Pilot Study;machine learning for the Identification of Biomarker and Risk Factors associated with Depression in Adult Population: Preliminary Results on a Small Cohort;Unveiling HIV-1 U Sequences: Shedding Light Through Transfer learning on Genomic Spectrograms;model to Early Detection of Autism Spectrum Disorder Through Opinion Mining Approach;A Short Analysis of Hybrid Approaches in COVID‑19 for Detection and Diagnosing;A Graph Neural Network with Multi-head Attention for Universal Brain Disease Diagnosis from fMRI Images;a Computer Vision Approach to Detect Facial Characteristics Related to Encephalopathy in Term Infants;SPADE Norms: a Distributed General Framework for Normative Multi-agent Systems;Finding Optimal Classroom Arrangements to Minimize Cheating in Exams Using a Hybrid AI System;resilience to the Flowing Unknown: An Open Set Recognition Framework for data Streams;a Comparison Procedure for the Evaluation of Metaheuristics;Nyström and RFF Ensembles for Large-Scale Kernel Predictions;application of Transfer learning to Online Models in Malware Detection;a New Training Algorithm for Support Vector machines;soft Adaptive Segments for Bio-Inspired Temporal Memory;assessing Generative Artificial Intelligence in Fundamental Physics with Gaussian Processes;implementation of Classical Decision Trees in a Quantum computing Paradigm;machine learning Methods as Robust Quantum Noise Estimators;The Impact of data Annotations on the Performance of Object Detection Models in Icon Detection for GUI Images;bayesian Model Selection Pruning in Predictive Maintenance;neonates Crying Detection Through Feature Extraction and machine learning Methods;differentiable Prototypes with Distributed Memory Network for Continual learning;efficient Continu

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19th international conference on Hybrid Artificial Intelligence Systems, HAIS 2024

19th International Conference on Hybrid Artificial Intellige...

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19th international conference on Hybrid Artificial Intelligence Systems, HAIS 2024

ISBN: (纸本)9783031741821

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Decision Systems in Cybersecurity: A machine learning Perspective 12th

Decision Systems in Cybersecurity: A Machine Learning Perspe...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

作者： Dhablia, Anishkumar Ganesh, R. Senthil Rizvi, Nuzhat Arora, Himanshu Borade, Monali Ravindra Bhujbal, Pallavi Altimetrik India Pvt. Ltd Maharashtra Pune India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Nagpur Campus Pune India Computer Science and Engineering Arya College of Engineering Jaipur India Artificial Intelligence and Data Science VIIT Pune India Department of Information Technology School of Computing MIT ADT University Loni Kalbhor Pune India

ISBN: (纸本)9789819601462

The evolving digital environment requires sophisticated decision systems to protect important information assets from the growing complexity and variety of cyber threats. This review paper examines the impact of machine learning (ML) on cybersecurity decision systems, with a specific focus on analyzing ML and deep learning (DL) methods. The study employs the UNSW-NB15 dataset, which is a widely used benchmark dataset in the field of network security. The paper starts with an overview of the present cybersecurity environment, emphasizing the difficulties presented by advanced and changing cyber threats. It highlights the importance of adaptive and intelligent decision systems that can efficiently identify and reduce cyberattacks as they occur. A significant part of the paper focuses on a thorough analysis of different machine learning and deep learning techniques used in cybersecurity. The review discusses conventional machine learning algorithms like decision trees, support vector machines, and ensemble methods, along with sophisticated deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Each approach is thoroughly assessed for its strengths and limitations based on factors like accuracy, interpretability, and scalability. The performance of various ML and DL models is evaluated using the UNSW-NB15 dataset as a benchmark. The dataset covers various cyberattack scenarios, enabling a thorough assessment of algorithms’ ability to detect anomalies and classify malicious activities. The paper explores the incorporation of machine learning (ML) and deep learning (DL) into decision support systems, highlighting the significance of explainability and interpretability in the realm of cybersecurity. The conversation covers the difficulties of implementing machine learning (ML) and deep learning (DL) models in practical situations, such as concerns about data privacy, adversarial attacks, and model resilience. This rev

关键词： Adversarial machine learning

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Language Models Meet Anomaly Detection for Better Interpretability and Generalizability

Language Models Meet Anomaly Detection for Better Interpret...

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作者： Li, Jun Kim, Su Hwan Müller, Philip Felsner, Lina Rueckert, Daniel Wiestler, Benedikt Schnabel, Julia A. Bercea, Cosmin I. Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Helmholtz AI and Helmholtz Munich Munich Germany Klinikum Rechts der Isar Munich Germany Imperial College London London United Kingdom Kings’s College London London United Kingdom

ISBN: (纸本)9783031845246

This research explores the integration of language models and unsupervised anomaly detection in medical imaging, addressing two key questions: (1) Can language models enhance the interpretability of anomaly detection maps? and (2) Can anomaly maps improve the generalizability of language models in open-set anomaly detection tasks? To investigate these questions, we introduce a new dataset for multi-image visual question-answering on brain magnetic resonance images encompassing multiple conditions. We propose KQ-Former (Knowledge Querying Transformer), which is designed to optimally align visual and textual information in limited-sample contexts. Our model achieves a 60.81% accuracy on closed questions, covering disease classification and severity across 15 different classes. For open questions, KQ-Former demonstrates a 70% improvement over the baseline with a BLEU-4 score of 0.41, and achieves the highest entailment ratios (up to 71.9%) and lowest contradiction ratios (down to 10.0%) among various natural language inference models. Furthermore, integrating anomaly maps results in an 18% accuracy increase in detecting open-set anomalies, thereby enhancing the language model’s generalizability to previously unseen medical conditions. The code and dataset are available at: https://***/compai-lab/miccai-2024-junli?tab=readme-ov-file. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Visual languages

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

Longitudinal Segmentation of MS Lesions via Temporal Diffe...

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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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