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An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping

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Geocarto International 2023年第1期38卷

作者： Hai, Tao Theruvil Sayed, Biju Majdi, Ali Zhou, Jincheng Sagban, Rafid Band, Shahab S. Mosavi, Amir School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Computer Science Dhofar University Salalah Oman Department of Building and Construction Technologies Engineering Al-Mustaqbal University College Hilla Iraq Department of Computer Technology Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan John von Neumann Faculty of Informatics Obuda University Budapest Hungary German Research Center for Artificial Intelligence Oldenburg Germany Institute of the Information Society University of Public Service Budapest Hungary

A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： artificial intelligence cat swarm optimization geospatial machine learning natural hazard Wildfire susceptibility

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PAT: Preference-Aware Transfer Learning for Recommendation with Heterogeneous Feedback

PAT: Preference-Aware Transfer Learning for Recommendation w...

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International Joint Conference on Neural Networks (IJCNN)

作者： Feng Liang Wei Dai Yunfeng Huang Weike Pan Zhong Ming National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In this paper, we study an important recommendation problem with heterogeneous feedback of users' grade scores such as 5-star grade scores and like/dislike binary ratings assigned to items. As a response, we address the problem from a transfer learning perspective, i.e., taking the grade scores as the target data and the binary ratings as the auxiliary data, in order to share knowledge between two different types of data more sufficiently. Technically, besides the observed explicit feedback of grade scores and binary ratings, we propose to exploit the implicit preference context beneath the feedback, which is incorporated into the prediction process of users' grade scores to items. Finally, we develop a novel and generic transfer learning solution, i.e., preference-aware transfer (PAT), which embodies several recent algorithms as parts of our solution and special cases. To verify our novel solution, we then conduct extensive empirical studies on two large and public datasets and find that our PAT performs significantly better than the state-of-the-art methods.

关键词： Collaboration Matrix decomposition Predictive models data models Probabilistic logic Training Context modeling

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Adaptive Transfer Learning for Heterogeneous One-Class Collaborative Filtering

Adaptive Transfer Learning for Heterogeneous One-Class Colla...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiancong Chen Weike Pan Zhong Ming National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In this paper, we study a recent and important recommendation problem called heterogeneous one-class collaborative filtering (HOCCF), where we have two different types of one-class feedback, i.e., a set of browses and a set of purchases, for preference learning. Previous methods exploit the browses and purchases by extending some existing methods on modeling homogeneous one-class feedback in an integrative or sequential manner. However, an integrative method may be of high complexity in model training due to the expanded prediction rule, and a sequential method is of inconvenience in deployment because more than one parametric models have to be maintained. In this paper, we convert the HOCCF problem to an adaptive transfer learning task, where we first model browses via a factorization model from a perspective of the role of browser, and then adaptively refine the learned model parameters via purchases from a perspective of the dependent role of purchaser. Based on this conversion, we design a novel solution called role-based adaptive factorization (ROAF), and then derive two specific variants with pairwise preference learning and pointwise preference learning. Finally, we conduct extensive empirical studies on two large datasets, and find that our ROAF is a very promising solution in terms of recommendation accuracy, besides its convenience of one single parametric model in deployment.

关键词： Browsers Adaptation models Parametric statistics Training Bayes methods Task analysis

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International Conference on big data and Smart Computing (bigCOMP)

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

In this paper, we focus on a recently studied problem in recommendation called heterogeneous one-class collaborative filtering (HOCCF), which consists of different types of feedback, e.g., target feedback and auxiliary feedback. The main challenge of HOCCF is how to make good use of the large-scale and ambiguous auxiliary feedback to assist the task of learning users'true preferences. Inspired by a recent method that jointly learns the similarity among items associated with the target feedback and auxiliary feedback in a transfer learning manner, we further extend this method by learning additional similarity among users for better modeling users' preferences. Specifically, we jointly learn the item-based similarity and user-based similarity by leveraging the target feedback and auxiliary feedback, and then aggregate the dual similarity into one single prediction rule for estimating users' preferences, which is thus termed as dual similarity learning model (DSLM). Extensive empirical studies on three public datasets show that our DSLM with dual similarity is more effective in terms of recommendation accuracy, demonstrating that our method strikes a good balance between the item-based similarity and user-based similarity.

关键词： Collaboration IP networks Aggregates big data Predictive models Indexes Uncertainty

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Sequence-Aware Factored Mixed Similarity Model for Next-Item Recommendation

Sequence-Aware Factored Mixed Similarity Model for Next-Item...

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International Conference on big data and Smart Computing (bigCOMP)

作者： Liulan Zhong Jing Lin Weike Pan Zhong Ming National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Sequence-aware next-item recommendation has recently been studied because of the noteworthy usefulness of the sequential information integrated into recommendation algorithms. Following the development thread of sequential recommendation methods, especially the factored Markov chains segment, we propose a novel sequence-aware factored mixed similarity model (S-FMSM) based on a well-designed fusing similarity model with factored high-order Markov chains (i.e., Fossil). Unlike the baseline methods that model the general representation and the sequential representation in two divided factorization components, we use a factored mixed similarity model that unites the general similarity and the sequential relationship between two successive items for their sequential representation learning. Experiments on six datasets show that our newly introduced general similarity can notably improve the results of the recommended ranking lists. And a study on tuning the prior tradeoff parameter indicates the importance of the general similarity on different datasets.

关键词： Markov processes Linear programming Probability big data Collaboration Feeds Conferences

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Super-Resolution Domain Adaptation Networks for Semantic Segmentation via Pixel and Output Level Aligning

arXiv

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arXiv 2020年

作者： Wu, Junfeng Tang, Zhenjie Xu, Congan Liu, Enhai Gao, Long Yan, Wenjun The Naval Aeronautical University Shandong264000 China The School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Hebei Province Key Laboratory of Big Data Calculation Tianjin300401 China

Recently, Unsupervised Domain Adaptation (UDA) has attracted increasing attention to address the domain shift problem in the semantic segmentation task. Although previous UDA methods have achieved promising performance, they still suffer from the distribution gaps between source and target domains, especially the resolution discrepany in the remote sensing images. To address this problem, this paper designs a novel end-to-end semantic segmentation network, namely Super-Resolution Domain Adaptation Network (SRDA-Net). SRDA-Net can simultaneously achieve the super-resolution task and the domain adaptation task, thus satisfying the requirement of semantic segmentation for remote sensing images which usually involve various resolution images. The proposed SRDA-Net includes three parts: a Super-Resolution and Segmentation (SRS) model which focuses on recovering high-resolution image and predicting segmentation map, a Pixel-level Domain Classifier (PDC) for determining which domain the pixel belongs to, and an Output-space Domain Classifier (ODC) for distinguishing which domain the pixel contribution is from. By jointly optimizing SRS with two classifiers, the proposed method can not only eliminates the resolution difference between source and target domains, but also improve the performance of the semantic segmentation task. Experimental results on two remote sensing datasets with different resolutions demonstrate that SRDA-Net performs favorably against some state-of-the-art methods in terms of accuracy and visual quality. Code and models are available at https://***/tangzhenjie/SRDA-Net. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Image super-resolution with an enhanced group convolutional neural network

arXiv

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arXiv 2022年

作者： Tian, Chunwei Yuan, Yixuan Zhang, Shichao Lin, Chia-Wen Zuo, Wangmeng Zhang, David School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China Department of Electrical Engineering City University of Hong Kong Hong Kong School of Computer Science and Engineering Central South University Hunan Changsha410083 China Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR. Code is found at https://***/hellloxiaotian/ESRGCNN. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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Executive summary for the China Kidney Disease Network (CK-NET) 2017–2018 Annual data Report

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Kidney International 2025年第6期107卷 980-984页

作者： Yang, Chao Gao, Bixia Wang, Fang Chu, Hong Wang, Jinwei Sun, Xiaoyu Zhang, Ping Xu, Hui Mao, Huijuan Xing, Changying Chen, Menghua Zuo, Li Wang, Yue Yu, Feng Zhang, Hong Wang, Haibo Chen, Jianghua Zhang, Luxia Zhao, Ming-Hui Renal Division Department of Medicine Peking University First Hospital Beijing China Peking University Institute of Nephrology Beijing China Center for Digital Health and Artificial Intelligence Peking University First Hospital Beijing China Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases Chinese Academy of Medical Sciences Beijing China Advanced Institute of Information Technology Peking University Zhejiang Hangzhou China State Key Laboratory of Vascular Homeostasis and Remodeling Peking University Beijing China National Institute of Health Data Science at Peking University Beijing China Kidney Disease Center The First Affiliated Hospital College of Medicine Zhejiang University Zhejiang Hangzhou China Department of Nephrology Xiangya Hospital Central South University Hunan Changsha China Department of Nephrology The First Affiliated Hospital of Nanjing Medical University Nanjing Medical University Jiangsu Nanjing China Department of Nephrology General Hospital of Ningxia Medical University Ningxia Yinchuang China Department of Nephrology Peking University People's Hospital Beijing China Department of Nephrology Peking University Third Hospital Beijing China Department of Nephrology Peking University International Hospital Beijing China Research Centre of Big Data and Artificial Intelligence for Medicine First Affiliated Hospital of Sun Yat-Sen University Guangdong Guangzhou China Peking-Tsinghua Center for Life Sciences Beijing China

Chronic kidney disease (CKD) has become a public health issue. Rapid urbanization in China has been intimately linked to shifts in the spectrum of CKD, particularly as regards CKD related to diabetes mellitus. By amalgamating diverse data streams and harnessing cutting-edge technologies, the China Kidney Disease Network aspires to evolve into a comprehensive surveillance framework for kidney diseases in China. The China Kidney Disease Network 2017–2018 Annual data Report is the fourth nationwide report produced by the China Kidney Disease Network team. Through the analysis of data from 3 large national databases and 4 provincial dialysis quality control centers, this report seeks to provide a robust foundation for understanding the current state of kidney disease in China. Specifically, in addition to providing epidemiological and economic data on CKD and dialysis from 2017 to 2018, hot topics such as environmental pollution and CKD, as well as digital intelligence in nephrology, have been addressed. This report could serve as a valuable resource for researchers, policymakers, and health care professionals alike, inspiring new ideas, fostering collaborations, and ultimately leading to improved outcomes for those affected by kidney diseases. © 2025 International Society of Nephrology

关键词： big data chronic kidney disease dialysis epidemiology surveillance

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A Pitch-aware Speaker Extraction Serial Network

A Pitch-aware Speaker Extraction Serial Network

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Yu Jiang Meng Ge Longbiao Wang Jianwu Dang Kiyoshi Honda Sulin Zhang Bo Yu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Japan Advanced Institute of Science and Technology Ishikawa Japan Automotive Data of China Co. Ltd

ISBN: (数字)9789881476883

ISBN: (纸本)9781728181301

Despite deep learning has an excellent performance in monaural speaker extraction, it's still a challenge to extract speakers when facing the same gender, i.e., male-male and female-female. On the other hand, it has been proved that pitch tracking is effective for same-gender speech separation. In this study, we proposed a pitch-aware speaker extraction serial network (PSESNet) to improve extraction performance. We designed a serial system and compared it with multi-task learning, we tried to use the target speaker's pitch information to optimize the loss function rather than as input to the extraction network. The extraction part uses SpeakerBeam-FE (SBF) with magnitude and temporal spectrum approximation loss (MTSAL) and speaker embedding concatenation. After extracting the spectrogram of the target speaker, we connected the spectrogram to predict the pitch information to do further optimization. Experimental results show that serial system performs better than multi-task learning and proposed method improves performance in both same and opposite gender conditions. On average, PSESNet achieves 4.7% and 3.8% relative improvements on WSJ0 dataset over the SBF-MTSAL-Concat baseline on signal-to-distortion ratio (SDR) under both closed and open condition.

关键词： data mining Spectrogram Estimation Training Target tracking Optimization Acceleration

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LG-Umer: UNet-like Network Integrate Local-Global Feature with Novel Attention for Road Extraction from Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Niu, Penghui Cai, Taotao Zhang, Yajuan Zhang, Ping Xu, Wenjia Gu, Junhua Han, Jungong Hebei University of Technology School of Artificial Intelligence Tianjin300401 China University of Southern Queensland Toowoomba487- 535 Australia Hebei Prospecting Institute of Hydrogeology and Engineering Geological Shijiazhuang050021 China Hebei University of Technology Hebei Province Key Laboratory of Big Data Calculation Tianjin300401 China University of Sheffield Department of Computer Science S10 2TN United Kingdom

Road extraction from remote sensing images (RSIs) is a key research area in smart city development. While deep learning techniques have demonstrated remarkable effectiveness in this domain, existing approaches exhibit limitations: convolutional neural network (CNN)-based methods struggle to capture global contextual information for long-range road networks, vision transformer (ViT)-based methods fail to adequately extract multi-scale local features, and hybrid CNN-ViT architectures overlook the synergistic guidance between local and global features. To address these challenges, we propose LG-Umer, a UNet-like network that integrates Local-Global features with a novel attention mechanism, combining the complementary strengths of CNNs and ViTs within an encoder-decoder framework. Specifically, the encoder employs a Multi-scale Strip Deformational (MSD) module, which utilizes deformable convolutions to adaptively extract topological structures and variable-shaped local road features. In the decoder, a Multi-stage Gate Unit (MGU) module is introduced, incorporating a novel attention mechanism to model long-range dependencies by leveraging local features as attention operators for global feature refinement. Extensive experiments on three public benchmarks demonstrate the superiority of LG-Umer. It achieves IoU scores of 70.4%, 71.2% and 68.7% on the Massachusetts Road, DeepGlobe Road, and CHN6-CUG datasets, respectively, surpassing recent state-ofthe-art (SOTA) methods by 1.2%, 0.9%, and 1.1%. These results validate the effectiveness of our approach in balancing local detail preservation and global contextual modeling for road extraction tasks. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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