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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61-70 订阅

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Monitoring the Concentration of Air Pollutants and Its Health Hazards Using machine learning Models 5th

Monitoring the Concentration of Air Pollutants and Its Healt...

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5th international conference on computing science, Communication and Security

作者： Jain, Aditi Shenoy, Aditya Adiga, Ananya Anekal, Anirudha Prajwal, Saritha PES Univ Bengaluru 560085 Karnataka India

ISBN: (纸本)9783031751691;9783031751707

With the world moving rapidly towards industrialization driven by economic growth and technological advancements, there is an alarming surge of air pollution leading to significant health concerns. In response, this work introduces a research-driven approach for a continuous air quality monitoring system, designed to continuously track air quality in real-time in the proximity and proactively predict potential health hazards for the user. Central to the system's efficacy is a state-of-the-art hybrid machine learning model, seamlessly amalgamating the strengths of Adaptive Long Short-Term Memory (LSTM) and Auto-Regressive Integrated Moving Average (ARIMA) models, renowned for their ability in handling intricate time series data. This model is securely deployed on a cloud platform, ensuring not only accessibility but also scalability to meet current and future technological standards. The system primarily concentrates on the monitoring of air pollutants, such as PM2.5, PM10, and CO, ensuring that users have access to immediate and up-to-date insights into the air quality in their surroundings. Beyond this, the system goes a step further by employing this data to assess users' potential risk of developing lung cancer. Through the use of Internet of Things (IoT) sensors, the system can issue timely and potentially life-saving insights, providing users with valuable information for decision-making improving their well-being. In a world, where the link between air quality and health is increasingly evident, our research-based initiative serves as a beacon for a healthier future, while also fostering environmental consciousness and public well-being.

关键词： Lung Cancer IOT Adaptive LSTM ARIMA AQI Air Pollutants

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Federated learning as a Service for Hierarchical Edge Networks with Heterogeneous Models 22nd

Federated Learning as a Service for Hierarchical Edge Networ...

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22nd international conference on Service Oriented computing

作者： Gao, Wentao Tavallaie, Omid Chen, Shuaijun Zomaya, Albert Univ Sydney Sch Comp Sci Sydney NSW Australia Univ Oxford Dept Engn Sci Oxford England

ISBN: (纸本)9789819608041;9789819608058

Federated learning (FL) is a distributed machine learning (ML) framework that is capable of training a new global model by aggregating clients' locally trained models without sharing users' original data. Federated learning as a service (FLaaS) offers a privacy-preserving approach for training machine learning models on devices with various computational resources. Most proposed FL-based methods train the same model in all client devices regardless of their computational resources. However, in practical Internet of Things (IoT) scenarios, IoT devices with limited computational resources may not be capable of training models that client devices with greater hardware performance hosted. Most of the existing FL frameworks that aim to solve the problem of aggregating heterogeneous models are designed for Independent and Identical Distributed (IID) data, which may make it hard to reach the target algorithm performance when encountering non-IID scenarios. To address these problems in hierarchical networks, in this paper, we propose a heterogeneous aggregation framework for hierarchical edge systems called HAF-Edge. In our proposed framework, we introduce a communication-efficient model aggregation method designed for FL systems with two-level model aggregations running at the edge and cloud levels. This approach enhances the convergence rate of the global model by leveraging selective knowledge transfer during the aggregation of heterogeneous models. To the best of our knowledge, this work is pioneering in addressing the problem of aggregating heterogeneous models within hierarchical FL systems spanning IoT, edge, and cloud environments. We conducted extensive experiments to validate the performance of our proposed method. The evaluation results demonstrate that HAF-Edge significantly outperforms state-of-the-art methods.

关键词： Federated learning as a Service (FLaaS) Model Heterogeneity Edge computing IoT Networks

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Detection of Medicinal Plants Using machine learning 12th

Detection of Medicinal Plants Using Machine Learning

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12th international conference on Recent Trends in computing, ICRTC 2024

作者： Ravikumar, S. Eugene Berna, I. Babu, R. Arockia Raj, Y. Vijay, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

An exciting new direction in plant categorization and identification is the use of machine learning techniques for medicinal plant detection. Using a variety of photo datasets containing medicinal plants, this technique trains models using ANNs. The ML model learns the complex traits that distinguish various plant species by using data from pre-trained models or by developing bespoke architectures. Thanks to the trained model’s precision in visually recognizing therapeutic plants, everyone from researchers to ecologists can reap the benefits. Methods for medicinal plant identification that are both accurate and objective are crucial to biodiversity research and attempts to safeguard resources. Considerations of ethics, model calibration, and data curation should be incorporated into the construction of such systems. Gather any and all images of plants used for medical purposes. Various viewpoints, lighting scenarios, and plant types will enhance the set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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Information Content of machine learning Under Robo-Advisory Inference 14th

Information Content of Machine Learning Under Robo-Advisory ...

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14th international conference on Frontier computing, FC 2024

作者： Yeh, Chih-Chuan Department of Finance National Taichung University of Science and Technology No.129 Sec. 3 Sanmin Rd. North Dist Taichung City40401 Taiwan

ISBN: (纸本)9789819623945

This article examines the transformative impact of Robo-Advisors on wealth management through the integration of big data and artificial intelligence (AI) in algorithmic trading strategies. We explore how AI technologies, including machine learning and natural language processing, can analyze large datasets, identify market trends, and optimize investment portfolios. This study compares the performance of various forecasting algorithms within the robo-advisory framework, taking the efficient frontier into consideration as a key metric. The research highlights the potential of AI-driven innovations to improve investment decision-making and shape the future of financial services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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SegHeD: Segmentation of Heterogeneous data for Multiple Sclerosis Lesions with Anatomical Constraints

SegHeD: Segmentation of Heterogeneous Data for Multiple Sc...

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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： Basaran, Berke Doga Zhang, Xinru Matthews, Paul M. Bai, Wenjia Department of Computing Imperial College London London United Kingdom Data Science Institute Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom School of Integrated Circuits and Electronics Beijing Institute of Technology CN Beijing China UK Dementia Research Institute Imperial College London London United Kingdom Rosalind Franklin Institute Didcot United Kingdom

ISBN: (纸本)9783031845246

Assessment of lesions and their longitudinal progression from brain magnetic resonance (MR) images plays a crucial role in diagnosing and monitoring multiple sclerosis (MS). machine learning models have demonstrated a great potential for automated MS lesion segmentation. Training such models typically requires large-scale high-quality datasets that are consistently annotated. However, MS imaging datasets are often small, segregated across multiple sites, with different formats (cross-sectional or longitudinal), and diverse annotation styles. This poses a significant challenge to train a unified MS lesion segmentation model. To tackle this challenge, we present SegHeD, a novel multi-dataset multi-task segmentation model that can incorporate heterogeneous data as input and perform all-lesion, new-lesion, as well as vanishing-lesion segmentation. Furthermore, we account for domain knowledge about MS lesions, incorporating longitudinal, spatial, and volumetric constraints into the segmentation model. SegHeD is assessed on five MS datasets and achieves a high performance in all, new, and vanishing-lesion segmentation, outperforming several state-of-the-art methods in this field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Research on machine learning Prediction Algorithm in Stability of Mountain Rock Slope 14th

Research on Machine Learning Prediction Algorithm in Stabili...

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14th international conference on Frontier computing, FC 2024

作者： Xiang, Juan Zhang, Xuguang Li, Yongde School of Architecture and Civil Engineering Chongqing Metropolitan College of Science and Technology Chongqing China Architectural Design Institute China Construction Fifth Engineering Division Corp Ltd. Changsha China

ISBN: (纸本)9789819627974

In order to predict the slope stability, a method based on machine learning is put forward. By analyzing the slope instability characteristics, six typical slope parameters (pore water pressure, slope height) are selected and analyzed, and the corresponding evaluation data set is established. The slope stability model is established by using machine learning methods such as gradient hoist (GBM) and support vector machine (SVM), and verified by 50% cross-validation. By analyzing the characteristics of slope instability and combining with domestic slope cases, according to the sensitivity of characteristic parameters, the slope protection measures for different sensitive factors are put forward. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Slope stability

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FedGS: Federated Gradient Scaling for Heterogeneous Medical Image Segmentation 9th

FedGS: Federated Gradient Scaling for Heterogeneous Medical ...

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27th international conference on Medical Image computing and Computer Assisted Intervention (MICCAI)

作者： Schutte, Philip Corbetta, Valentina Beets-Tan, Regina Silva, Wilson Univ Amsterdam Amsterdam Netherlands Netherlands Canc Inst Dept Radiol Amsterdam Netherlands Univ Utrecht Dept Informat & Comp Sci Dept Biol AI Technol Life Utrecht Netherlands Maastricht Univ GROW Sch Oncol & Dev Biol Med Ctr Maastricht Netherlands

ISBN: (纸本)9783031776090;9783031776106

Federated learning (FL) in Deep learning (DL)-automated medical image segmentation helps preserving privacy by enabling collaborative model training without sharing patient data. However, FL faces challenges with data heterogeneity among institutions, leading to suboptimal global models. Integrating Disentangled Representation learning (DRL) in FL can enhance robustness by separating data into distinct representations. Existing DRL methods assume heterogeneity lies solely in style features, overlooking content-based variability like lesion size and shape. We propose FedGS, a novel FL aggregation method, to improve segmentation performance on small, under-represented targets while maintaining overall efficacy. FedGS demonstrates superior performance over FedAvg, particularly for small lesions, across PolypGen and LiTS datasets. The code and pre-trained checkpoints are available at the following link: https://***/Trustworthy-AI-UU-NKI/Federated-learning-Disentanglement.

关键词： Federated learning

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A Novel Ensemble learning Approach for Diabetes Prediction in Imbalanced datasets 13

A Novel Ensemble Learning Approach for Diabetes Prediction i...

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13th international conference on Research in computing at Feminine, RIF 2024

作者： Boughareb, Djalila Bouteldja, Said Seridi, Hamid Department of Computer Science LabStic Laboratory University 8 May 1945 Guelma Algeria Department of Computer Science University of 8 May 1945 Guelma Algeria

The incapacity of the body to effectively make or use insulin results in diabetes, a chronic illness. Over time, this illness may cause harm to the kidneys, blood vessels, heart, eyes, nerves, and kidneys. Timely treatment is essential to stop the progression of diabetes and requires early detection. We provide a hybrid machine learning strategy in this work that predicts diabetes by combining two strong algorithms. XGBoost (eXtreme Gradient Boosting)-based voting classifier and bagging classifier are the two main components of our system. We evaluated our model using three distinct datasets: the Pima Indian diabetes dataset (PIDD), its extended version, and the Frankfurt Hospital Germany Diabetes dataset (FHGDD). In comparison to individual algorithms (XGBoost, Bagging with Decision Tree) and other ensemble methods (Voting Classifier, HM-Bag Moov Voting Classifier, XGBoost+ data feature stitching, and Soft), our experimental results show that our proposed approach achieved higher accuracy of 92.7%, precision of 97.1%, recall of 81.7%, and an F1 score of 88.7%%. Therefore, our results imply that the hybrid machine learning approach that has been suggested can be a dependable tool for the early diagnosis of diabetes, resulting in better patient outcomes and more prompt and efficient therapies. © 2024 Copyright for this paper by its authors.

关键词： Bagging Decision Tree Diabetes Prediction Ensemble learning FHGDD machine learning Pima XGBoost

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Ensemble learning with Feature Fusion for Well-Overflow Detection 5th

Ensemble Learning with Feature Fusion for Well-Overflow Dete...

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5th international conference on Neural computing for Advanced Applications (NCAA)

作者： Cui, Ziliang Liu, Li Xiong, Yinzhou Liu, Yinguo Su, Yu Man, Zhimin Wang, Ye Chongqing Univ Posts & Telecommun Chongqing Peoples R China Chongqing Energy Big Data Ctr Co Ltd Chongqing Peoples R China Shandong Zibo Polytech Vocat Sch Chongqing Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Traditional methods for kick prediction rely on experience and basic physical models, but they struggle to maintain high accuracy under complex well conditions and varying geological environments. Moreover, conventional approaches often fail to fully leverage big data resources. Therefore, this study aims to improve the accuracy of kick prediction through machine learning models, especially by adopting an innovative feature fusion strategy to optimize the prediction model. We evaluated the comprehensive performance of six different machine learning models: GBM, LightGBM, CatBoost, XGBoost, Random Forest, and AdaBoost. Based on these evaluations, we proposed a feature score weighted fusion method. This method weights the importance scores of features based on the accuracy of each model, selecting the features that have the most significant impact on the prediction results. Experimental results show that the model utilizing the feature fusion strategy performs better on the test set than any single base model. Specifically, the GBM model, optimized through feature fusion, achieved better values in accuracy, F1 score, and F2 score, proving the effectiveness of this feature fusion strategy in enhancing the accuracy of kick predictions during well control operations. Furthermore, this study compared the performance of different models, providing valuable insights for kick prediction in well control operations. This research not only demonstrates the potential of feature fusion strategies in improving the accuracy of kick prediction but also offers new insights for the future application of machine learning technologies in the field of oilfield data analysis.

关键词： machine learning feature fusion kill well overflow

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data Orchestration Platform for AI Workflows Execution Across computing Continuum 15

Data Orchestration Platform for AI Workflows Execution Acros...

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15th international conference on Cloud computing and Services science, CLOSER 2025

作者： Arcas, Gabriel Ioan Cioara, Tudor Engineering and Data Solutions Department Bosch Engineering Center Cluj-Napoca Romania Computer Science Department Technical University of Cluj Napoca Romania

ISBN: (纸本)9789897587474

Cloud AI technologies have emerged to exploit the vast amount of data produced by digitized activities. However, despite these advancements, they still face challenges in several areas, including data processing, achieving fast response times, and reducing latency. This paper proposes a data orchestration platform for AI workflows, considering the computing continuum setup. The edge layer of the platform focuses on immediate data collection, the fog layer provides intermediate processing, and the cloud layer manages long-term storage and complex data analysis. The orchestration platform incorporates the Lambda Architecture principles for flexibility in managing batch processing and real-time data streams, enabling effective management of large data volumes for AI workflows. The platform was used to manage an AI workflow dealing with the prediction of household energy consumption, showcasing how each layer supports different stages of the machine learning pipeline. The results are promising the models are being trained, validated, and deployed effectively, with reduced latency and use of computational resources. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： data streams

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