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检索条件"任意字段=2019 International Conference on Applied Machine Learning and Data Science, ICAMLDS 2019"

共 11127 条记录，以下是31-40 订阅

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SwinMixer Detector: A Two-Level Traffic Flow Detection System Based on Swin Transformer and MLP-Mixer 24th

SwinMixer Detector: A Two-Level Traffic Flow Detection Syste...

引用

24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Lu, Lu Fang, Menghao Li, Xia Li, Minghui Lei, Tianwei Kong, Zixiao School of Cyber Science and Engineering University of International Relations Beijing100091 China Institute of Disaster and Emergency Medicine Tianjin University Tianjin300072 China Marine Engineering of College Dalian Maritime University Dalian116026 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819615414

This paper explores the problem of boundary data classification ambiguity that arises when machine learning techniques are applied in the field of intrusion detection. The features and attributes of the boundary data place them in the boundaries or boundaries between different categories in the classification decision, and these data points may be close to the decision boundaries of different categories in the feature space, resulting in relative uncertainty or some ambiguity in their classification. To address this problem, this study innovatively proposes a secondary detection system called the SwinMixer Detector, which utilises the Swin Transformer and MLP-Mixer architecture. The system consists of a primary triage detection platform utilizing Swin Transformer classifier and a secondary detection platform utilizing MLP-Mixer, which can effectively solve the problem of fuzzy classification of boundary data. By validating on the SR-BH2020 dataset and the CIC-DDOS2019 dataset, the method in this paper achieves a precision of 96.40% and 95.81%, respectively, which provides a new research idea and solution for the field of intrusion detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mixers (machinery)

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Evaluation of Efficient and Performance-Retaining cVEP-BCI Class Augmentation 13

Evaluation of Efficient and Performance-Retaining cVEP-BCI C...

引用

13th international Winter conference on Brain-Computer Interface, BCI 2025

作者： Fodor, Milán András Cantürk, Atilla Le, Thi-Phuong-Ngan Heisenberg, Gernot Volosyak, Ivan Faculty of Technology and Bionics Rhine-Waal University of Applied Sciences Kleve47533 Germany Institute of Information Science Technical University of Applied Sciences Cologne Cologne50678 Germany

ISBN: (纸本)9798331521929

Brain–computer interfaces (BCIs) enable direct communication between the brain and external devices. BCI spellers are one of their most significant applications, allowing users to operate a keyboard with their brain. BCIs based on code-modulated visual evoked potentials (cVEPs) offer the potential for class augmentation due to the cyclical nature of their stimuli. This approach allows for the use of less training data while creating additional classes in the spelling interface, without requiring additional data or causing significant performance loss. To definitively address concerns regarding the potential limitations of this technique, we derived four extra classes from a single class in our BCI speller and evaluated their performance. The results demonstrated no performance loss and no evidence of anomalously poor participant performance. This research advocates for class augmentation as a standard practice to reduce calibration time and improve the scalability, accessibility, and overall effectiveness of cVEP-based BCI spellers. © 2025 IEEE.

关键词： Electroencephalography

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EEG-based Multimodal Representation learning for Emotion Recognition 13

EEG-based Multimodal Representation Learning for Emotion Rec...

引用

13th international Winter conference on Brain-Computer Interface, BCI 2025

作者： Yin, Kang Shin, Hye-Bin Li, Dan Lee, Seong-Whan Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

Multimodal learning has been a popular area of research, yet integrating electroencephalogram (EEG) data poses unique challenges due to its inherent variability and limited availability. In this paper, we introduce a novel multimodal framework that accommodates not only conventional modalities such as video, images, and audio, but also incorporates EEG data. Our framework is designed to flexibly handle varying input sizes, while dynamically adjusting attention to account for feature importance across modalities. We evaluate our approach on a recently introduced emotion recognition dataset that combines data from three modalities, making it an ideal testbed for multimodal learning. The experimental results provide a benchmark for the dataset and demonstrate the effectiveness of the proposed framework. This work highlights the potential of integrating EEG into multimodal systems, paving the way for more robust and comprehensive applications in emotion recognition and beyond. © 2025 IEEE.

关键词： Emotion Recognition

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Application of Deep learning for Wildfire Risk Management: Preliminary Results 4th

Application of Deep Learning for Wildfire Risk Management:...

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： De Rango, Alessio D’Ambrosio, Donato Mendicino, Giuseppe Department of Environmental Engineering University of Calabria Ponte Pietro Bucci Rende87036 Italy Department of Mathematics and Computer Science University of Calabria Ponte Pietro Bucci Rende87036 Italy

ISBN: (纸本)9783031812439

Due to ongoing climate change and the related increased risk of drought, wildfires in the Mediterranean are expected to become more frequent and intense. This note introduces the use of Deep learning to produce susceptibility maps for wildfire risk assessment to tackle this scenario. Indeed, thanks to the improved computational capabilities of modern GPUs, recent literature has pointed out that it is possible to train Deep learning models on big datasets by outperforming standard approaches in several application domains, including the one object of this study. An Artificial Neural Network (ANN) model has been developed and applied to the Calabria region (southern Italy) study area. The network has been trained and validated over ten years of data by considering both meteorological and geomorphological data, and human factors. Preliminary results are promising, evidencing a higher network’s accuracy to the operational state-of-the-art Fire Weather Index (FWI) method, currently applied for forecasting and mitigation purposes in the study area. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Themes, Knowledge Evolution, and Emerging Trends Related to the Application of Artificial Intelligence in Personalized learning: A Scientometric Analysis in CiteSpace 14

Themes, Knowledge Evolution, and Emerging Trends Related to ...

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14th international conference on Educational and Information Technology, ICEIT 2025

作者： Qin, Qinggui Wang, Jiaming Zhang, Shuhan Macao Polytechnic University Faculty of Applied Sciences China GXNU School of Design Guangxi Normal University Guilin China

ISBN: (纸本)9798331540883

Artificial intelligence (AI) plays a pivotal role in the development and growth of personalized learning. Therefore, it is necessary to conduct scientific and in-depth research on the application of AI in this field, systematically analyzing its development trends and research hotspots to provide references for researchers. This study employs a visual bibliometric analysis as the primary research approach, selecting 922 articles from the Web of science spanning from 2004 to 2024. A comprehensive analysis was conducted using CiteSpace, primarily covering major highly co-cited literature and keywords. The notable findings of this study are as follows: Firstly, the study reveals three major themes related to the application methodology of AI in personalized learning, namely, machine learning, large language models, and personalized recommendation systems. Secondly, the visual timeline reveals the application of Personalized learning in AI across three phases: Part A (2016-2022), Part B (2019-2024), and Part C (2011-2024). Thirdly, the burst analysis illustrates the hotspots, especially 'intelligent tutoring system'. This study deepens our understanding of the fundamentals and cutting-edge research in the application of AI to personalized learning, thus facilitating the identification of future research directions, as well as teaching practice patterns and trends. © 2025 IEEE.

关键词： Teaching

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Comparative Study Using Ensemble Methods and Sampling Techniques for Imbalanced Diabetes data 4th

Comparative Study Using Ensemble Methods and Sampling Techni...

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4th international conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Khemisa, Hana Dendani, Nadjette Khedimi, Yasmine Amara, Nour Djihane Azizi, Nabiha Badji Mokhtar Univ Dept Comp Sci Annaba Algeria

ISBN: (纸本)9783031821523;9783031821530

In medical decision making, the efficacy of classification, a vital component of decision support systems, can be significantly compromised by imbalanced class distributions within training data. This discrepancy is especially critical due to the heightened consequences of misclassification, particularly when a minority class is mistakenly classified as a majority class. Diabetes, posing a substantial public health challenge, underscores the urgency of addressing classification issues. The World Health Organization reports that 30% to 40% of diabetes cases remain undiagnosed, contributing to a 3% increase in diabetes-related mortality rates from 2000 to 2019. In 2019 alone, approximately 2 million deaths were attributed to diabetes and related kidney diseases. Consequently, effective intervention strategies are imperative. Managing imbalanced datasets in medical contexts presents significant obstacles to accurate classification, with profound implications for patient outcomes. To address this challenge, this research proposes leveraging machine learning and artificial intelligence techniques. The research comprises two main components: applying boosting and unbalanced bagging to a dataset containing diabetes-related information. Initial findings indicate that boosting yields promising results in classification accuracy. Building upon this success, we compare boosting with three additional technique Synthetic Minority Over-sampling Technique (SMOTE), Edited Nearest Neighbors (ENN), and Tomek links each sequentially applied to the dataset. Through rigorous experimentation and analysis, we aim to identify the most effective strategy for handling imbalanced datasets in diabetes classification. By enhancing our understanding of classification methods and their impact on imbalanced datasets, this research aims to contribute to improved decision-making processes in healthcare settings, ultimately leading to better outcomes for patients affected by diabetes.

关键词： machine learning Classification Imbalanced data Diabetes Sampling Ensemble methods

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dataset Refinement for Improving the Generalization Ability of the EEG Decoding Model 13

Dataset Refinement for Improving the Generalization Ability ...

引用

13th international Winter conference on Brain-Computer Interface, BCI 2025

作者： Kim, Sung-Jin Lee, Dae-Hyeok Han, Hyeon-Taek Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

Electroencephalography (EEG) is a generally used neuroimaging approach in brain-computer interfaces due to its non-invasive characteristics and convenience, making it an effective tool for understanding human intentions. Therefore, recent research has focused on decoding human intentions from EEG signals utilizing deep learning methods. However, since EEG signals are highly susceptible to noise during acquisition, there is a high possibility of the existence of noisy data in the dataset. Although pioneer studies have generally assumed that the dataset is well-curated, this assumption is not always met in the EEG dataset. In this paper, we addressed this issue by designing a dataset refinement algorithm that can eliminate noisy data based on metrics evaluating data influence during the training process. We applied the proposed algorithm to two motor imagery EEG public datasets and three different models to perform dataset refinement. The results indicated that retraining the model with the refined dataset consistently led to better generalization performance compared to using the original dataset. Hence, we demonstrated that removing noisy data from the training dataset alone can effectively improve the generalization performance of deep learning models in the EEG domain. © 2025 IEEE.

关键词： Deep learning

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A Semi-automatic Pipeline for the Decay Mapping and the State of Conservation Assessment of Architectural Heritage Through Point Clouds

A Semi-automatic Pipeline for the Decay Mapping and the Stat...

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4th international conference on Advanced Research in Technologies Information Innovation and Sustainability-ARTIIS-Annual

作者： Lasorella, Margherita Cantatore, Elena Rondinelli, Maria Felicia Letizia Fatiguso, Fabio Politecn Bari Via Edoardo Orabona 4 I-70125 Bari Italy Univ Basilicata Via Ateneo Lucano 10 I-85100 Potenza Italy

ISBN: (纸本)9783031834318;9783031834325

The integration of automation and artificial intelligence has revolutionized the documentation, conservation, and restoration of architectural cultural heritage. This research presents a pipeline leveraging machine learning (ML) algorithms for the automatic decay mapping of digitalized architectures, combined with informative system to support experts in the assessment of a synthetic index of the conservation state. Moreover, the setup of a web-based system as a platform for the expert acknowledgment and management of decays and properties helps them in a fast and coherent final assessment. The proposed pipeline combines such technologies to the architectural recovery theories and ensures a standardized procedure compliant with international and national regulations and standards such as ICOMOS-ISCS Glossary and UNI 11182:2006, UNI 8290-1:1981 and UNI CEN/TS 17385:2019. Method and tools are applied to the Labriola Palace in Tursi, Italy, demonstrating its efficacy in assessing the conservation state of architectural heritage, as well as the higher collaborative levels to reach also among technicians with different levels of computer science skill.

关键词： Digital model Historic Building Informative System Condition Assessment machine learning Technical users

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Nearest Neighbors Counterfactuals 10th

Nearest Neighbors Counterfactuals

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10th international conference on machine learning, Optimization, and data science, LOD 2024

作者： Magagnini, Marica Carrizosa, Emilio De Leone, Renato School of Science and Technology Università di Camerino Camerino62032 Italy Instituto de Matemáticas de la Universidad de Sevilla Seville41012 Spain

ISBN: (纸本)9783031824807

machine learning algorithms usually make decisions without providing any explanations on how they were reached. As a result, such models are not understood and trusted by the users. Counterfactuals are user-friendly explanations that provide valuable information to determine what should be changed in order to modify the outcome of a black box decision-making model without revealing the underlying algorithmic details. We present a novel technique to obtain counterfactual explanations by solving an optimization problem when a k-Nearest Neighborhood classifier is employed in a binary classification. Results from artificial and real datasets demonstrate the validity of the proposal. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Virtual learning machine for Tiny Devices 20th

Virtual Learning Machine for Tiny Devices

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20th Principle and Practice of data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Kitagawa, Nozomi Yamauchi, Koichiro Graduate school of Engineering Department of Computer Science Chubu University Kasugai Japan

ISBN: (纸本)9789819600250

Recent developments in artificial intelligence (AI) have increased the demand for high-performance computational devices. However, edge devices are highly restricted in terms of computational power and memory capacity. To address this, a learning engine called swap kernel regression (SKR) [Masaharu et al. 2019] was developed that enables both inference and learning using an edge device. The key feature of SKR is ability to extend beyond the real physical capacity by collaborating with a second storage device. SKR thereby enables the virtual construction of a large-scale learning machine on a tiny device. We developed an improved version of SKR that maximizes performance by solving the thrashing problem and further reduces the computational cost. Using this improved version, we aim to achieve few-shot class-incremental learning (FSCIL), which continuously adapts to new tasks with limited samples, envisioned for on-site implementation. The proposed method uses a kernel machine that operates in a restricted environment in collaboration with a secondary storage system. The kernel parameters, which are not essential for calculating the output values for upcoming inputs, are stored in secondary storage to create a space in the main memory. The essential kernel parameters stored in secondary storage are loaded into main memory when required. Using this strategy, the system can realize recognition or regression tasks without compromising its generalization capability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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