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Application of Machine learning for Aboveground Biomass Modeling in Tropical and Temperate Forests from Airborne Hyperspectral Imagery

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FOREsTs 2025年第3期16卷 477-477页

作者： Darko, Patrick Osei Metari, samy Arroyo-Mora, J. Pablo Fagan, Matthew E. Kalacska, Margaret McGill Univ Dept Geog Appl Remote Sensing Lab Montreal PQ H3A 0B9 Canada Natl Res Council Canada Automot & Surface Transportat Ottawa ON K1A 0R6 Canada Natl Res Council Canada Flight Res Lab Ottawa ON K1A 0R6 Canada Univ Maryland Baltimore Cty Dept Geog & Environm Syst Baltimore MD 21250 USA

Accurate operational methods used to measure, verify, and report changes in biomass at large spatial scales are required to support conservation initiatives. In this study, we demonstrate that machine learning can be used to model aboveground biomass (AGB) in both tropical and temperate forest ecosystems when provided with a sufficiently large training dataset. Using wavelet-transformed airborne hyperspectral imagery, we trained a shallow neural network (sNN) to model AGB. An existing global AGB map developed as part of the European space Agency's DUE GlobBiomass project served as the training data for all study sites. At the temperate site, we also trained the model on airborne-LiDAR-derived AGB. In comparison, for all study sites, we also trained a separate deep convolutional neural network (3D-CNN) with the hyperspectral imagery. Our results show that extracting both spatial and spectral features with the 3D-CNN produced the lowest RMsE across all study sites. For example, at the tropical forest site the Tortuguero conservation area, with the 3D-CNN, an RMsE of 21.12 Mg/ha (R-2 of 0.94) was reached in comparison to the sNN model, which had an RMsE of 43.47 Mg/ha (R-2 0.72), accounting for a similar to 50% reduction in prediction uncertainty. The 3D-CNN models developed for the other tropical and temperate sites produced similar results, with a range in RMsE of 13.5 Mg/ha-31.18 Mg/ha. In the future, as sufficiently large field-based datasets become available (e.g., the national forest inventory), a 3D-CNN approach could help to reduce the uncertainty between hyperspectral reflectance and forest biomass estimates across tropical and temperate bioclimatic domains.

关键词： convolutional neural network REDD wavelet scattering continuous wavelet transform deep learning spectra-spatial feature extraction

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Real-time multi-depth holographic display using complex-valued neural network

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Optics Express 2025年第4期33卷 7380-7395页

作者： Zhang, Yuanzhe Cheng, Dewen Wang, Yesheng Wang, Yongdong shan, Yuefan Yang, Tong Wang, Yongtian Beijing Engineering Research Center of Mixed Reality and Advanced Display School of Optics and Photonics Beijing Institute of Technology Beijing100081 China Beijing NED+AR Ltd. Beijing100081 China

computer-generated holography (CGH) has made significant advancements and is considered a leading approach for near-eye 3D displays. Recent learning-based CGH methods address the time-quality trade-off of traditional approaches but often face challenges related to efficiency and computational demands, especially with real-valued networks in multi-depth settings. To overcome these issues, this study proposes a residual block-based complex-valued convolutional neural network (ResC-CNN) structure, integrated into a symmetric dual-network framework driven by a diffraction model, for real-time generation of multi-depth holographic displays. This approach enhances the network’s ability to handle complex domain calculations in CGH, making the learning process more efficient. A layered depth image (LDI) dataset is also incorporated to improve scene information prediction accuracy. Numerical and optical experiment results indicate that our proposed framework significantly increases the real-time generation frame rate of holograms and enhances the fidelity of displayed details, offering a practical solution for high-quality, real-time multi-depth holographic displays in applications such as augmented reality. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Augmented reality

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Machine learning for Wireless Network Optimization: Algorithms and Applications for Adaptive Communication 1

Machine Learning for Wireless Network Optimization: Algorith...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： srinivas, Madhira surya, s. Thakar, Prachi Deepak Jain, Amit Kumar Department of Computer Science & Engineering Hyderabad501301 India Chennai600116 India KJ college of Engineering & Management Research Department of Computer Engineering Pune India Poornima University Department of Electrical & Electronics Engineering Rajasthan Jaipur303905 India

ISBN: (纸本)9798331518578

This project looks into the possibility of applying machine learning to optimize wireless networks for adaptive communication. Using 5G resource data, it applies preprocessing, exploratory analysis, and visualization to identify key trends: signal strength, latency, and bandwidth utilization. Machine learning algorithms predict resource allocation based on latency-sensitive and bandwidth-demanding applications. The approach emphasizes energy efficiency, scalability, and service quality, and dynamically adjusts communication strategies to meet diverse network demands. The findings indicate the machine learning importance in enhancing elasticity and robustness in a wireless network and its general function. © 2025 IEEE.

关键词： 5G Optimization Adaptive Communication Bandwidth Data Pre-processing EDA Energy Efficiency Latency Machine learning Network Elasticity Predictive Models Resource Allocation scalability signal strength Wireless Networks

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Privacy and security Challenges in Federated learning for UAV systems: A systematic Review

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IEEE Access 2025年 13卷 86599-86615页

作者： Farsi, Ahmed Al Khan, Ajmal Mughal, Muhammad Rizwan Bait-suwailam, Mohammed M. Sultan Qaboos University Communication and Information Research Center Muscat Oman Sultan Qaboos University Department of Electrical and Computer Engineering Muscat Oman

Unmanned Aerial Vehicles have become indispensable assets across various sectors, leveraging their mobility and data collection capabilities. However, privacy and security concerns have fueled interest in Federated learning as a potential solution. Federated learning (FL), being decentralized and collaborative, holds promise in addressing the privacy risks inherent in centralized data processing while enhancing model performance. In this review, we explore the privacy and security implications of FL in UAV ecosystems. We highlight FL’s potential to mitigate privacy risks by aggregating model updates locally, minimizing data transmission needs. In addition, we examine security challenges and evaluate protective mechanisms. Through a systematic review of the literature, we identified gaps and proposed future research directions, with the aim of improving the security and privacy of FL in UAV applications. Our findings highlight advanced strategies, such as secure aggregation, adversarial defenses, and lightweight cryptographic techniques, to mitigate privacy and security threats in Federated learning-based UAV. They also underscore the need for real-world validations and regulatory frameworks to ensure resilient and ethically governed deployments in UAV networks. © 2013 IEEE.

关键词： Blockchain Decentralized learning Federated learning Privacy Preservation FL UAV

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Data imputation in large and small-scale spatiotemporal time series gaps using BackForward Bi-LsTM

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Journal of Big Data 2025年第1期12卷 1-18页

作者： Galdelli, Alessandro Narang, Gagan Tomassini, selene D’Agostino, Lorenzo Tassetti, Anna Nora Mancini, Adriano Department of Information Engineering Università Politecnica delle Marche Ancona Italy National Biodiversity Future Center Organization Palermo Italy Department of Information Engineering and Computer Science University of Trento Trento Italy Institute for Marine Biological Resources and Biotechnology National Research Council Ancona Italy

In recent years, tracking systems have transformed the understanding of spatiotemporal dynamic processes. However, these systems often face challenges due to missing data caused by technical limitations or intentional manipulation, leading to classification bias and hidden suspicious activities. In response, we propose a deep learning algorithm based on sequential Bidirectional Long short-Term Memory (BiLsTM) to impute missing trajectories in spatiotemporal datasets. Our approach employs both backward and forward BiLsTMs resulting in BF-BiLsTM architecture to model temporal dependencies before and after the missing trajectories. We evaluated the algorithm on a realistic marine and aerial dataset, considering missing data trajectories on vessels and flights with missing data rates ranging from 5% to 30%. The model improves performance in terms of MAE (0.011°), MsE (0.056%), MAPE (0.054%) and ADE (0.017°) when compared to state-of-the-art approaches. By effectively addressing challenges in spatiotemporal datasets and improving existing benchmarks, our algorithm provides a robust solution for enhancing trajectory imputation in the context of monitoring systems potentially across diverse application domains. © The Author(s) 2025.

关键词： Deep neural networks

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Graph Neural Networks: Architectures, Applications, and Future Directions

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IEEE ACCEss 2025年 13卷 62870-62891页

作者： Ponzi, Valerio Napoli, Christian Sapienza Univ Rome Dept Comp Control & Management Engn I-00185 Rome Italy Italian Natl Res Council Inst Syst Anal & Comp Sci I-00185 Rome Italy Silesian Tech Univ Dept Math Applicat & Methods Artificial Intelligen PL-44100 Gliwice Poland

In recent years, deep learning has revolutionized fields such as computer vision, speech recognition, and natural language processing, primarily through techniques applied to data in Euclidean spaces. However, many real world applications involve data from non-Euclidean domains, where graphs naturally represent entities and their complex interdependencies. Traditional machine learning methods have often struggled to process such data in an effective manner. Graph Neural Networks represent a crucial advance in the use of deep learning to interpret and extract knowledge from graph-based data. They have opened up new possibilities for tasks such as node categorization, link inference, and comprehensive graph analysis. This paper provides a detailed analysis of Graph Neural Network (GNN) methodologies, emphasizing their architectural diversity and wide ranging applications. GNN models are systematically categorized into fundamental frameworks such as message passing paradigms, spectral and spatial methods, and advanced extensions such as hypergraph neural networks and multigraph approaches. This paper also explores domains such as social network analysis, molecular biology, traffic forecasting, and recommendation systems. In addition, it emphasizes some critical open challenges, including scalability, dynamic graph modeling, and robustness against noisy or incomplete data. The paper concludes with a proposal for future research directions to improve the scalability, interpretability, and adaptability of GNNs in this fast-evolving field.

关键词： Deep learning Graph neural networks social networking (online) scalability Data models Feature extraction Analytical models Vectors Roads Message passing Graph neural network deep learning message passing spectral methods spatial methods hypergraph neural networks multigraph neural networks scalability

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Federated learning and Parallel Prompt scheduling strategies for Large Language Models 24th

Federated Learning and Parallel Prompt Scheduling Strategie...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Lv, Guangtong Gu, Bruce Jia, Xiaocong Gao, Longxiang Qu, Youyang Cui, Lei Jinan China Shandong Provincial Key Laboratory of Computer Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9789819615278

Large language models have garnered significant attention and are widely utilized across different fields due to their impressive performance. However, centralized training of these models can pose privacy risks like data leakage, hindering their application and advancement. As a decentralized learning paradigm, Federated learning(FL) can effectively address data security in large language models and has specific application scenarios. In addition, the fine-tuning process of large language models also incurs significant communication expenses and leads to the loss of computational resources. The utilization of prompt tuning in federated learning is anticipated to decrease training costs, accelerate the training process, and enhance model performance. In this paper, we propose ‘FedLLM-PPs’ to explore a novel approach for large language models within federated learning. This method address data security concerns associated with large language models while enhancing performance and efficiency. Experiments based on several datasets for various Natural Language Processing(NLP) tasks demonstrate superior performance and efficiency compared to baseline methods. © The Author(s), under exclusive license to springer Nature singapore Pte Ltd. 2025.

关键词： Federated learning

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Improving learning material repositories using student profiles

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soft Computing 2025年第5期29卷 2417-2431页

作者： silva Bravo, Natalie Ferraz Martins, André Ferreira de souza Fonseca Rodrigues, Thales Brito Machado, Marcelo Bernardino, Heder soares Vieira, Alex Borges Barbosa, Hélio José Corrêa de souza, Jairo Francisco LApIC Research Group Federal University of Juiz de Fora Minas Gerais Juiz de Fora36036-900 Brazil Postgraduate Program in Computer Science Federal University of Juiz de Fora Minas Gerais Juiz de Fora36036-900 Brazil Postgraduate Program in Informatics Federal University of the State of Rio de Janeiro Rio de Janeiro22290-255 Brazil Department of Computer Science Federal University of Juiz de Fora Minas Gerais Juiz de Fora36036-900 Brazil

aaaa The adaptive learning community seeks to provide solutions to customize and enhance students’ learning experiences when accessing web-based learning systems. The adaptation usually occurs from the use of learning materials and user information data, which turns the adaptation process highly dependent on the quality of the repositories. Then, the best adaptation a system may offer might still not satisfy the users’ needs. In this work, we propose an approach to assist teachers and stakeholders in understanding repositories’ characteristics and their gaps according to students’ needs. Our approach, first, selects the best sequence of learning materials for each student, which is a well-known problem called Adaptive Curriculum sequencing. Then, based on the selected sequences, we use optimization approaches, such as GRAsP and simulated Annealing, to generate new learning materials possibilities that can improve ACs recommendations. This way, our new approach assists teachers in assembling their learning materials. We have evaluated our approach by comparing it to a traditional approach using a real dataset, and the results are promising. In fact, it is possible to design customized materials using a combination of GRAsP and brute force algorithms on the characteristics of the learning materials. © The Author(s), under exclusive licence to springer-Verlag GmbH Germany, part of springer Nature 2025.

关键词： Federated learning

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Machine-learning-based Performance Prediction for CDN Cache Groups in Meta Computing

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IEEE Internet of Things Journal 2025年第10期12卷 13612-13624页

作者： Qi, senmao Wang, Xulong Zou, Yifei Yuan, Yuan Ling, Yihong Lin, Guangzheng Liu, Ruomei Li, Yijun Yu, Dongxiao Shandong University Institute of Intelligent Computing the School of Computer Science and Technology Qingdao266200 China Baishan Cloud Department of Research and Development Guizhou Guiyang550081 China

Meta computing, as an innovative computing paradigm, aims to transform the Internet into a vast and distributed computing resource pool. This paradigm holds significant promise for the Industrial Internet of Things (IIoT), offering efficient, fault-tolerant, and personalized services while ensuring strong security and privacy. Nowadays, content delivery networks (CDNs) are integral to this vision, providing critical network support by reducing latency, alleviating network congestion, and enhancing service quality. Accurate prediction of CDN cache group performance, which involves heterogeneous edge servers handling diverse workloads, is essential for optimal resource utilization, dynamic load balancing, and efficient traffic management in IIoT. This article addresses the challenge of performance prediction in CDNs using machine learning techniques. By leveraging business request data, load information, and other relevant features, our approach aims to predict key performance indicators, such as CPU utilization, bandwidth usage, and I/O operations. We propose a comprehensive feature engineering method that aggregates input metrics across devices, categorizes business requests using clustering, and incorporates time series modeling to capture traffic patterns. Extensive experiments demonstrate the effectiveness of our approach, highlighting its potential to enhance resource management and service quality in CDNs, thereby supporting the deployment of meta computing in IIoT. © 2014 IEEE.

关键词： servers Predictive models Machine learning Industrial Internet of Things Accuracy Resource management Business Measurement Load modeling Computational modeling

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Density-Clustering Aggregation for Personalized Federated learning With AI-Enabled Aerial and Edge Computing in UAVs

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IEEE Internet of Things Journal 2025年第9期12卷 11220-11232页

作者： Chien, Wei-Che Lin, Chih-Hsun Zhu, Tianli Dai, Cheng Garg, sahil Mukherjee, Amrit National Dong Hwa University Department of Computer Science and Information Engineering Hualien City974301 Taiwan Sichuan University College of Computer Science Sichuan Chengdu610017 China École de Technologie Supérieure Electrical Engineering Department MontrealH3C 1K3 Canada Applied Science and Technology Dubai United Arab Emirates Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Rajpura140401 India University of South Bohemia Faculty of Science Department of Computer Science České Budějovice370 05 Czech Republic

This research introduces the density-clustering-based aggregation for personalized federated learning (DCPFL) algorithm, which utilizes DBsCAN clustering to enhance model accuracy in AI-enabled aerial and edge computing contexts, particularly for uncrewed aerial vehicles (UAVs). The DCPFL framework promotes model sharing among clients, fostering the development of personalized and optimized models. DBsCAN is beneficial in automatically determining cluster numbers using EPs neighborhoods and MinPts, with parameter optimization achieved through cross-experimental analysis. We further refined the model exchange mechanism by integrating a moving average prediction model to optimize the timing of these exchanges. Tests conducted on three public datasets covering two different machine learning (ML) tasks show that DCPFL surpasses existing methods, offering greater accuracy and enhanced adaptability in varied data environments. Implementing this algorithm in UAV networks leverages AI capabilities in aerial and edge computing to efficiently balance personalized modeling requirements with high performance, showcasing its potential to push federated learning (FL) forward in complex and dynamic settings. © 2014 IEEE.

关键词： Federated learning

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