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DB-FSCIL: Few-Shot Class-Incremental learning Using Dual Bridges 21st

DB-FSCIL: Few-Shot Class-Incremental Learning Using Dual Bri...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Bao, Kexin Lin, Fanzhao Liu, Ruyue Ge, Shiming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China NARI Technology Company Limited Nanjing China

ISBN: (纸本)9789819601219

Few-shot class-incremental learning (FSCIL) performs classification continuously with limited new samples. Some mainstream methods constrain the model in incremental sessions or allocate spaces for incoming classes in the base session. However, these methods inevitably suffer from an overlap in the latent space of new and old classes, obstructing the discriminative ability of the model. To address that, we propose a novel two-stage FSCIL method using dual bridges containing sample bridge and memory bridge. In the base session, we train a model with the sample bridge, which is built by marriage of real samples, to preview knowledge. In incremental sessions, we align limited samples and the classifier with the help of memory bridge, which stores fixed feature centers. The dual bridges effectively link previous and subsequent sessions, minimizing the overlap. Extensive experiments on three publicly available datasets validate our method’s state-of-the-art performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Zero-shot learning

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CNN-KNN Model for Assessing Hair Health in Telogen Effluvium 3

CNN-KNN Model for Assessing Hair Health in Telogen Effluvium

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Gotte Ranjith Shubham Banerjee, Deepak School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

The abstract provides a concise overview of a study that focuses on predicting hair breakage levels using a CNN-KNN hybrid model. The study evaluates the model's performance across five Breakage Degrees (BDs) by utilizing precision, recall, and F1-score metrics. Among these degrees, BD2 stands out with precision, recall, and F1-score values of 94.20%, indicating the model's ability to accurately classify hair damage levels between 21% and 40%. The support values, which indicate the number and proportion of instances in each BD, demonstrate a well-balanced distribution across all categories, ensuring the model's robust training. Researchers used a dataset of 10,050 images to reach an impressive 94.02% accuracy in predicting hair breakage levels caused by Telogen Effluvium in the research. Moreover, the model achieves an impressive overall accuracy of 98%, showcasing its effectiveness in distinguishing between various degrees of hair breakage severity. This high accuracy level highlights the model's potential application in clinical settings for dermatological diagnostics and personalized treatment planning in hair care management. The abstract underscores the CNN-KNN hybrid approach as a promising methodology for automated hair health assessment, providing healthcare professionals with a dependable tool to improve diagnostic accuracy and treatment efficacy. By incorporating machine learning techniques, specifically convolutional and k-nearest neighbor algorithms, the study contributes to the advancement of computational methods in dermatology and cosmetology. The results suggest practical implications for enhancing patient care through early detection and intervention of hair-related conditions, ultimately supporting better health outcomes and quality of life for individuals affected by hair breakage concerns. © 2025 IEEE.

关键词： Nearest neighbor search

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A Variational Approach to Personalized Federated learning and Its Improvement 20th

A Variational Approach to Personalized Federated Learning a...

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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： Wu, Huiwen Zhang, Shuo Research Center for Data Hub and Security Zhejiang Laboratory Hangzhou310000 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and System Sciences Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789819600250

Federated learning (FL) is a new approach that allows clients from different locations to work together on a global model without sharing raw data. However, training one standard model is not optimal for local optimization due to heterogeneity. Thus, personalized federated learning (PFL) paradigms aim to combine local and global information while preserving diversity among local clients. However, these methods require more theoretical motivation. This paper formalizes the unfired analysis form by defining a linear mapping on the server’s aggregation step for a deeper understanding of the implementation design and to improve existing methods further. Moreover, we show that the existing FL and PFL methods are specific cases in our framework with selected mapping matrices, including FedAvg, FedMTL, FedProx, MAML, and pure local training. Based on that, we propose a novel data-driven graph Laplacian-induced PFL scheme named GLIP-FL to address statistical heterogeneity issues and improve computation efficiency. We implement evaluation experiments of the proposed method on four popular data sets with five FL baselines in three data partitions, both homogeneous and heterogeneous, and the comprehensive experiments show the great potential of our method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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MSAN: Multi-Scale Adaptive Network Guided by Human Attention for Accident Prediction 21st

MSAN: Multi-Scale Adaptive Network Guided by Human Attentio...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Qiao, Yu Yang, Xiaohui Lu, Anran School of Information Science and Engineering University of Jinan Jinan250022 China Jinan Inspur Data Technology Co. Ltd. Jinan250101 China

ISBN: (纸本)9789819601240

It is a major challenge to understand the spatio-temporal interactions of driving scenarios in accident prediction tasks of intelligent vehicle systems. Given that the gaze information of experienced drivers during the driving process involves complex spatio-temporal interactions, this information can serve as human attention to provide guidance for the training of accident prediction models. However, most existing studies use the human attention information as one of the multitask labels and do not leverage multi-scale spatial channel information of human attention for supervision. To address this gap, we propose a Multi-Scale Adaptive Network (MSAN) guided by human attention for traffic accident prediction. This network efficiently learns multi-scale spatial channel information from human attention composed of driver’s eye movement information. Additionally, we introduce a novel strategy for the recursive transmission of time information to mitigate the impact of varying previous results on current model inference. After experiment, our model has been proven to excel over state-of-the-art approaches on two benchmark datasets, which serves as a practical solution to bolster the safety of intelligent vehicles. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： intelligent systems

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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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Enhance statistical Features with Changepoint Detection for Driver Behaviour Analysis 21st

Enhance Statistical Features with Changepoint Detection for ...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Maktoubian, Jamal Tran, Son N. Shillabeer, Anna Amin, Muhammad Bilal Sambrooks, Lawrence School of Information and Communication Technology University of Tasmania Hobart Australia School of Information Technology Deakin University VIC Australia School of Computing Mathematics and Engineering Charles Sturt University NSW Australia

ISBN: (纸本)9789819601240

Driver behaviour modelling is a critical field that addresses complex and dynamic driving behaviours on roads with the goal of enhancing road safety, reducing air pollution, and improving vehicle performance. Recent advancements in sensor technology and machine learning (ML) techniques have facilitated the capture and analysis of driver behaviour patterns. Nonetheless, the efficacy of ML models heavily relies on the quality of the data used. Therefore, developing feature extraction techniques that provide high-quality inputs is crucial. In this paper, we conceptualised, implemented, and evaluated a novel feature model called Changepoint-based statistical Feature (C-bSF). Initially, we extracted various statistical functions from raw sensor data, which were then aggregated using lagging windows. Following this, a changepoint detection method was used to derive the C-bSF feature. We compared the performance metrics of this new approach with other feature extraction methods, demonstrating the superiority of C-bSF in driver behaviour classification tasks across three datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Supporting Elderly Care Through an AI-Driven and FHIR-Based Remote Monitoring System 21st

Supporting Elderly Care Through an AI-Driven and FHIR-Based ...

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21st international Symposium on Distributed Computing and Artificial Intelligence, DCAI 2024

作者： Martins, Rafael Pereira, Hugo Corrente, Gustavo Xavier, William Conceição, Luís Freitas, Alberto Marreiros, Goreti GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering – Polytechnic of Porto Porto Portugal LASI – Intelligent Systems Associate Laboratory Guimarães Portugal Wiseware Solutions Gafanha da Encarnação Portugal MEDCIDS – Department of Community Medicine Information and Health Decision Sciences Faculty of Medicine University of Porto Porto Portugal CINTESIS@RISE – Center for Health Technology and Services Faculty of Medicine University of Porto Porto Portugal

ISBN: (纸本)9783031809453

An increase in the elderly population has led to a growing demand for constant health care. Remote monitoring of seniors aims to provide more effective care and promote patient independence, whether they live in residential homes, or their own homes supported by a domiciliary care service. It encompasses several issues present in their lives that may benefit from being assisted and monitored remotely. There are many successful studies and products focused on one of these issues, but there are no systems, to the best of our knowledge, that serve as a broad remote monitoring solution for multiple of these topics simultaneously. This paper proposes an architecture for an intelligent health monitoring ecosystem to interact with these various aspects of the elderly’s daily life, including monitoring of vital signs, movement and medication intake, detection of falls, sleep quality analysis, access control, and patient–caregiver communication. This solution will make use of wearables and medication dispensers, and it will securely store all patient-related information into an HL7 FHIR standardized database, from where machine learning models can import data for predicting important information and generating alerts when necessary. Next steps involve the development of this proposed system and testing it with elderly volunteers living in a residential home or subscribed to its domiciliary care services. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Remote patient monitoring

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Personalized Federated learning with Feature Alignment via Knowledge Distillation 21st

Personalized Federated Learning with Feature Alignment via...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Qi, Guangfei Qu, Zhihao Lyu, Shen-Huan Jia, Ninghui Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9789819601189

Personalized Federated learning (PFL) has gained significant attention for its ability to handle heterogeneous data effectively. Parameter decoupling is a typical approach to PFL. It decouples the model into a feature extractor and a classifier head, where the feature extractor is trained collaboratively to learn a common representation and the classifier head is personalized for local data. Since local training only learns personalized feature information and ignores global information, the generalization ability of the feature extractor is limited. To improve the performance of the local model, a feasible approach is to make the local feature extractor more generalized. However, prior work requires the transmission of additional feature data beyond the transmission of model parameters, which leads to privacy leakage and higher communication overhead. To address these shortcomings, we propose a PFL algorithm with feature alignment via knowledge distillation, named PFAKD. PFAKD enhances the training of local feature extractors by explicitly aligning each sample’s local features with global features, providing more detailed guidance. Meanwhile, it avoids additional communication overhead and the risk of privacy leakage. We conduct extensive experiments in heterogeneous data scenarios. PFAKD outperforms other state-of-the-art methods by up to 4.35% in terms of model accuracy. Our code is available at https://***/fei0829/PFAKD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Role of Machine learning in Climate Change Prediction and Mitigation

Role of Machine Learning in Climate Change Prediction and Mi...

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Machine learning and Autonomous Systems (ICMLAS), international conference on

作者： Nayan Jikar Yash Tale Abhay Tale Aditya Barhate Prateek Verma Aman Jikar Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Climate change is one of the biggest challenges of the 21st century, with profound environmental, economic, and social impacts. Machine learning (ML) has emerged as a transformative tool to address climate challenges through data-driven solutions, offering predictive capabilities for extreme weather events and enhancing the efficiency of clean-energy systems. This technology benefits the optimization of renewable energy generation, intelligent grid management, and resource utilization towards reducing greenhouse gas emissions. Moreover, ML promotes climate justice through targeted region-specific intervention and inclusive solution-making for the most vulnerable. Several technical and ethical challenges associated with data quality, scalability, and model generalization must still be overcome for ML to realize its potential. This paper reviews the role of ML in climate change prediction and mitigation discusses ongoing real-world applications and outlines the future research agenda necessary to address existing challenges. Integrating ML with emerging technologies like the Internet of Things (IoT) and explainable AI, alongside global collaboration and a focus on climate justice, will be essential in advancing effective climate action.

关键词： Climate change Renewable energy sources Ethics Prevention and mitigation Scalability data integrity Collaboration data models Internet of Things Meteorology

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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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