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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31-40 订阅

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Interpretable Grade Prediction Based on ECOC and Shapley Theory 24

Interpretable Grade Prediction Based on ECOC and Shapley The...

引用

8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Yang, Dapeng Cao, Longhan Pan, Lifang Xie, Shutong School of Computer Engineering Jimei University Fujian Xiamen China School of Science Jimei University Fujian Xiamen China

ISBN: (纸本)9798400710094

In recent years, blended learning models combining online and offline instructional methods have been widely implemented in higher education institutions. Online learning platforms accumulate a large number of fine-grained records of students’ learning behavior, offering an excellent opportunity to analyze students’ learning behavior. Firstly, a year’s worth of student behavior log data was collected from the "Java Language Programming" blended learning course at a college. Secondly, we propose an interpretable grade prediction method based on the error correcting output code (ECOC) with Shapley theory. This approach employs Recursive Feature Elimination (RFE) to select features from the original features, and creates an ECOC classification matrix to predict student performance. Finally, the Shapley values for each feature were used to conduct an explainable analysis of the prediction results. Experimental results show that the proposed method outperforms other classical classification algorithms. This study provides a visual and educational analysis of the major factors affecting academic performance, thereby offering educational administrators more intuitive decision support. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Feature Selection Based on Membrane Clustering 7th

Feature Selection Based on Membrane Clustering

引用

7th international conference on Optimization and learning

作者： Sheuly, Sharmin Sultana Deivard, Johannes Seceleanu, Tiberiu Xiong, Ning Malardalen Univ Vasteras Sweden Dev Solut AB Eskilstuna Sweden

ISBN: (纸本)9783031779404;9783031779411

Given increased complex data with high dimensions, feature selection aims to select a subset of features to increase the efficiency of machine learning. This paper proposes a new feature selection method based on membrane computing. The proposed method has two main advantages. First, it provides a new solution to search for feature combinations while requiring no model construction (which is time-consuming) to evaluate a feature subset. Second, feature selection is embedded in a membrane clustering algorithm, which is designed to enable searching for the best feature subset and finding active cluster centres at the same time. The designed clustering algorithm mimics the behavior of multiple cells and it has stronger global search ability than existing evolutionary algorithms. The efficacy of the proposed method has been shown by the evaluation of a set of benchmark data sets.

关键词： feature selection membrane computing clustering

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Sentiment Analysis on 2024 Regional Elections using Hybrid CNN-SVM with Semantic Features and Word2Vec

Sentiment Analysis on 2024 Regional Elections using Hybrid C...

引用

2025 international conference on Advancement in data science, E-learning and Information System, ICADEIS 2025

作者： Andinny, Syahbilla Putri Setiawan, Erwin Budi School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798331513320

Regional Head Election is a political agenda held every five years. Everyone has their point of view on assessing the candidates. These political conversations often appear on platform X, a social media to exchange information, responses, comments, and even emotions. These public expressions can be classified into positive, negative, and neutral sentiments. This political phenomenon is the object of research to understand a hybrid deep learning model that combines Convolutional Neural Network (CNN) and Support Vector machine (SVM), enhanced with TF-IDF extraction features, IndoBERT semantics to understand the deeper context of Indonesian text and Word2Vec that represents words in the form of vectors with similar meanings against testing on three corpus X, IndoNews, and X+IndoNews. This research analyzes the perspectives of Indonesian social media users surrounding the 2024 Regional Elections using 60,000 Indonesian tweets collected with keywords related to the 2024 Regional Elections from the X platform and a similarity corpus of 124,925 from the IndoNews collection. This study uses CNN to extract spatial patterns from textual data, while SVM serves as a classification model for sentiment prediction. The test results show the highest accuracy of 81.67%, with an improvement of +1.83% compared to the CNN baseline and +3.51% compared to the SVM baseline. The completion of sentiment analysis using the hybrid model and features applied in this research successfully detects public opinion for candidates, campaign teams, media, or survey institutions to understand the dynamics of voters surrounding the 2024 Regional Elections. © 2025 IEEE.

关键词： Convolutional neural networks

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Joint UAV Trajectory Optimization and Task Offloading in Integrated Air-Ground Networks 7th

Joint UAV Trajectory Optimization and Task Offloading in Int...

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8th Asia Pacific Web and Web-Age Information Management Joint international conference on Web and Big data

作者： Zhang, Yuanwei Shuai, Jiaqi Wen, Weichang Cui, Haixia South China Normal Univ Sch Elect & Informat Engn Foshan 528200 Peoples R China

ISBN: (纸本)9789819600540;9789819600557

As an essential component of the integrated air-ground-space networks, the unmanned aerial vehicle (UAV) is indispensable for the mobile edge computing (MEC) processing. In this paper, considering the highly dynamic nature of information exchange, we apply the multiagent reinforcement learning (MARL) for the UAV-based communication networks and introduce a low-complexity K-means algorithm for the precluster/partition users to improve the system performance. We devise a general reward and punishment strategy within the multi-agent reinforcement learning process to generate the optimal trajectories and promote the coverage maximization. Furthermore, we utilize NOMA technology to provide stable data rate gains and allocate more power to weaker users under optimizing energy consumption so as to maximize the overall system throughput. Simulation results demonstrate that the proposed joint optimization algorithm achieves excellent convergence performance in UAV based integrated air-ground-space networks.

关键词： UAV K-means algorithm multi-agent reinforcement learning

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Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis

Physics-Informed Latent Diffusion for Multimodal Brain MRI ...

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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： Lüpke, Sven Yeganeh, Yousef Adeli, Ehsan Navab, Nassir Farshad, Azade Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Stanford University Stanford United States

ISBN: (纸本)9783031845246

Recent advances in generative models for medical imaging have shown promise in representing multiple modalities. However, the variability in modality availability across datasets limits the general applicability of the synthetic data they produce. To address this, we present a novel physics-informed generative model capable of synthesizing a variable number of brain MRI modalities, including those not present in the original dataset. Our approach utilizes latent diffusion models and a two-step generative process: first, unobserved physical tissue property maps are synthesized using a latent diffusion model, and then these maps are combined with a physical signal model to generate the final MRI scan. Our experiments demonstrate the efficacy of this approach in generating unseen MR contrasts and preserving physical plausibility. Furthermore, we validate the distributions of generated tissue properties by comparing them to those measured in real brain tissue. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image denoising

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Context-Aware Selection of machine learning as a Service (MLaaS) in IoT Environments

Context-Aware Selection of Machine Learning as a Service ...

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web data Analytics, AIWDA 2024 form the 25th international conference on Web Information Systems Engineering, WISE 2024

作者： Patel, Keya Mistry, Sajib Kanneganti, Deepak Krishna, Aneesh School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia

ISBN: (纸本)9789819614820

The integration of machine learning as a Service (MLaaS) into the Internet of Things (IoT) environments presents considerable opportunities for enhancing decision-making and automation. We propose a novel framework for context-aware selection of MLaaS in IoT settings, aimed at optimising the interaction between IoT users’ activities and machine learning services. Our framework considers various contextual dimensions, such as user preferences, locations, IoT device capabilities, and application requirements, to develop a dynamic selection process. By employing context-aware algorithms, our approach seeks to enhance the efficiency, accuracy, and responsiveness of IoT systems. We propose a context change analysis algorithm based on support vector machines (SVM). We develop a contextual bandits algorithm along with skyline queries to achieve optimal mapping between abstract MLaaS services and concrete MLaaS services for quality of service (QoS) attributes. Experiments conducted with real-world and simulated datasets demonstrate the effectiveness of our proposed methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Back to the Future: Challenges of Sparse and Irregular Medical Image Time Series

Back to the Future: Challenges of Sparse and Irregular M...

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作者： Disch, Nico Albert Peretzke, Robin Roy, Saikat Ulrich, Constantin Zimmerer, David Stiefelhagen, Rainer Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing German Cancer Research Center Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Karlsruhe Institute of Technology Karlsruhe Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Medical Faculty Heidelberg University of Heidelberg Heidelberg Germany

ISBN: (纸本)9783031845246

In longitudinal medical image analysis, most work focuses on regularly sampled images, or on tasks like regression or classification. However, in the clinical context, images are frequently generated irregularly due to factors such as cost constraints. This work tackles the problem of irregularly sampled longitudinal medical imaging by evaluating methods in segmentation and reconstruction tasks. We examine the reconstruction for real-life MRI from patients with Alzheimer’s Disease (AD), where the model predicts future MRI scans from an arbitrary number of input images sampled at irregular intervals. However, experiments show that most models cannot surpass the performance of using an image from the same patient at a different time point as a baseline. Therefore, we conducted experiments on a synthetic dataset to better isolate effects in temporal learning. These experiments offer insights into model behavior and serve as a benchmark for validating models’ longitudinal learning capability, thus providing a straightforward method to assess temporal understanding in a controlled environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Towards Adaptive Asynchronous Federated learning for Human Activity Recognition 24

Towards Adaptive Asynchronous Federated Learning for Human A...

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14th international conference on the Internet of Things, IoT 2024

作者： Gajanin, Rastko Danilenka, Anastasiya Morichetta, Andrea Nastic, Stefan Distributed Systems Group TU Wien Vienna Austria Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland

ISBN: (纸本)9798400712852

In this work, we tackle the problem of performing multi-label classification in the case of extremely heterogeneous data and with decentralized machine learning. Solving this issue is very important in IoT scenarios, where data coming from various sources, collected by heterogeneous devices, serve the learning of a distributed ML model through Federated learning (FL). Specifically, we focus on the combination of FL applied to Human Activity Recognition (HAR), where the task is to detect which kind of movements or actions individuals perform. In this case, transitioning from centralized learning (CL) to federated learning is non-trivial as HAR displays heterogeneity in action and devices, leading to significant skews in label and feature distributions. We address this scenario by presenting concrete solutions and tools for transitioning from centralized to FL for non-IID scenarios, outlining the main design decisions that need to be taken. Leveraging an open-sourced HAR dataset, we experimentally evaluate the effects that data augmentation, scaling, optimizer, learning rate, and batch size choices have on the performance of resulting machine learning models. Some of our main findings include using SGD-m as an optimizer, global feature scaling across clients, and persistent feature skew in the presence of heterogeneous HAR data. Finally, we provide an open-source extension of the Flower framework that enables asynchronous FL. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Identifying and Managing Concept Drift in machine learning Through Page-Hinkley Test: Approaches, Obstacles, and Resolutions 12th

Identifying and Managing Concept Drift in Machine Learning T...

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

作者： Francis, Navya Ali, Anooja School of Computer Science and Engineering REVA University Bangalore India Department of Computer Science Kristu Jayanti College Bangalore India

ISBN: (纸本)9789819789450

Concept drift presents a formidable hurdle in the implementation of machine learning models in practical scenarios, owing to the potential changes in underlying data distributions over time. The timely detection and effective mitigation of concept drift are imperative for upholding the precision and dependability of predictive models. A promising avenue for detecting concept drift is the Page-Hinkley test, renowned for its adaptability to non-stationary data streams and its sensitivity to gradual alterations. This research endeavors to scrutinize the Page-Hinkley test's efficacy in concept drift detection, delving into its amalgamation with strategies for managing drift, and scrutinizing the attendant challenges and prospects. By leveraging theoretical scrutiny, empirical investigations, and pragmatic insights, this study aims to furnish a comprehensive comprehension of concept drift detection and alleviation utilizing the Page-Hinkley test within machine learning applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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machine learning for Waste-to-Energy: Optimization and Predictive Analytics 4th

Machine Learning for Waste-to-Energy: Optimization and Predi...

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4th international conference on Advanced Network Technologies and Intelligent computing-ANTIC-Annual

作者： Rana, Sanskar Thakur, Narender Singh Jyoti, Divya Mahato, Dharamender Prasad Natl Inst Technol Hamirpur Hamirpur 177005 Himachal Prades India

ISBN: (纸本)9783031837951;9783031837968

The burgeoning demand for sustainable energy sources has catalyzed interest in Waste-to-Energy solutions offering both environmental benefits and energy generation potential. This paper explores the integration of machine learning (ML) techniques to optimize and predict key aspects of the Waste-to-Energy process. The study leverages historical data from waste facilities incorporating variables such as waste composition, operational parameters, and environmental conditions. In this paper, machine learning models, including regression, classification, and ensemble methods, are employed to optimize combustion efficiency, predict energy output, and enhance the overall operational performance of Waste-to-Energy conversion process. Furthermore, the predictive analytics are employed to anticipate maintenance needs for mitigating the downtime and optimizing the resource allocation. The findings contribute to the growing field of the sustainable energy by showcasing the efficacy of machine learning in Waste-to-Energy systems providing a scalable and adaptive solution for the challenges inherent in this dynamic and complex process.

关键词： Combustion Efficiency Energy Output Prediction Operational Performance Waste Composition machine learning

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