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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Surendra Maharjan Wenzhao Li Shahryar Fazli Hani Sewilam Hesham El-Askary Computational and Data Sciences Graduate Program Schmid College of Science and Technology Chapman University Orange CA USA Earth Systems Science and Data Solutions Lab Chapman University Orange CA USA The Center for Applied Research on the Environment and Sustainability The American University in Cairo Cairo Egypt Department of Engineering Hydrology RWTH Aachen University Aachen Germany Department of Environmental Sciences Faculty of Science Alexandria University Alexandria Egypt

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Vapor Pressure Deficit (VPD) is crucial in meteorology and agriculture for understanding plant-environment interactions. Its application as an indicator in agricultural practices notably advances Sustainable Development Goals such as Zero Hunger (SDG 2) and Climate Action (SDG 13). This research focuses on the impact of climate change on agricultural productivity and food security in the Nile River Basin (NRB), emphasizing the role of VPD, temperature, and precipitation. Utilizing Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets from NEX-GDDP-CMIP6, the study analyzes key climatic variables that influence agricultural conditions. The study applies the Mann-Kendall test to evaluate VPD trends from 2000 to 2060 under two Shared Socioeconomic Pathways (SSPs), SSP2-4.5 and SSP5-8.5. The study's findings on the implications of rising VPD levels in the Nile River Basin (NRB), particularly under the SSP 5-8.5 scenario, highlight a critical challenge for the region's agricultural productivity and food security. The increased VPD, indicative of drier conditions, leads to a moisture deficit for crops, potentially reducing agricultural yields. This scenario poses a significant threat to food security, as lower crop yields can result in food shortages and higher food prices, adversely affecting vulnerable populations. The study underscores the necessity of integrating VPD insights into agricultural and water resource management strategies to uphold food security against climatic variations in support of the SDGs.

关键词： Productivity Climate change Precipitation Food security Agriculture Rivers Sustainable development

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Pyramid Scene Parsing Net Model for Automated Paddy Field Map using SPOT 6 Satellite Images 5

Pyramid Scene Parsing Net Model for Automated Paddy Field Ma...

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5th IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology, AGERS 2022

作者： Heryadi, Yaya Irwansyah, Edy Miranda, Eka Soeparno, Haryono Herlawati Hashimoto, Kiyota Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Prince of Songkla University Faculty of Technology and Environment Phuket83120 Thailand

ISBN: (纸本)9798350398212

Food sustainability is still one of the main priorities for many countries as it contributes to the economy and stability of the nation. For government in many countries whose peoples consumes rice as its staple food, food self-sufficiency initiatives highly depend on accurate prediction of paddy field map. Mapping paddy field task is a challenging problem which cannot be handled manually especially when the paddy fields are spread out in very wide geographical areas such as those in Indonesia. Fortunately, wide availability of satellite imagery and the advent of deep learning technology in the past ten years have made it possible to improve efficiency of most parts of those manual works involving image semantic segmentation tasks. However, satellite image-based semantic segmentation is a challenging task. High object complexity, cloud partial occlusion, larger image size than a computer memory can stored can hinder accuracy of the image segmentation results. This paper presents a method for paddy field map generating using semantic image segmentation approach in which Pyramid Scene Parsing Net model is used for segmenting satellite imagery. The generated paddy map can be used as a basis for decision-making, especially in the agricultural sector. Analysis of local land use/land cover dynamics. The results of his experiments using SPOT 6 satellite imagery from the Pahung region of Central Kalimantan achieved average training accuracy, best training accuracy and test accuracy of 0.85, 0.86 and 0.89 respectively. These results indicated that the semantic segmentation model is suitable for addressing the same task in different crops. © 2022 IEEE.

关键词： Image enhancement

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TreeXGNN: can gradient-boosted decision trees help boost heterogeneous graph neural networks?

TreeXGNN: can gradient-boosted decision trees help boost het...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ming-Yi Hong Shih-Yen Chang Hao-Wei Hsu Yi-Hsiang Huang Chih-Yu Wang Che Lin Graduate Program of Data Science National Taiwan University and Academia Sinica Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan Department of Electrical Engineering National Taiwan University Taiwan

Graph neural networks are a promising deep learning method that can apply graph structures to various tasks. In real-world scenarios, we often have heterogeneous graphs, wherein different node and edge types capture complex interactions between nodes. High-dimensional node features provide rich information about the target nodes. Conventional heterogeneous graph neural networks (HGNN) focus more on graph structures than node features and may have difficulties extracting knowledge from complex node features. In this study, we propose a novel framework, the tree-boosted heterogeneous graph neural network abbreviated as TreeXGNN, which could efficiently and automatically extract target node features via gradient-boosted decision trees (GBDT). It integrates community structure information with proper fusion modules and a shared feature space design on HGNN. We achieved state-of-the-art performance on the three well-known heterogeneous graph benchmark datasets, IMDB, DBLP, and ACM, and significantly improved performance compared to previous studies. Our work paves the foundation for integrating tree-based models to boost HGNNs for general community analysis.

关键词： Training Knowledge engineering Deep learning Signal processing Feature extraction Graph neural networks Decision trees

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Graph Laplacian Learning with Exponential Family Noise

arXiv

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arXiv 2023年

作者： Shi, Changhao Mishne, Gal The Electrical and Computer Engineering Department UC San Diego CA92093 United States The Halıcıoglu Data Science Institute The Neurosciences Graduate Program UC San Diego CA92093 United States

Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matrix to reveal the spectral decomposition of signals in the graph frequency domain. However, a common challenge in applying GSP methods is that in many scenarios the underlying graph of a system is unknown. A solution in such cases is to construct the unobserved graph from available data, which is commonly referred to as graph or network inference. Although different graph inference methods exist, these are restricted to learning from either smooth graph signals or simple additive Gaussian noise. Other types of noisy data, such as discrete counts or binary digits, are rather common in real-world applications, yet are underexplored in graph inference. In this paper, we propose a versatile graph inference framework for learning from graph signals corrupted by exponential family noise. Our framework generalizes previous methods from continuous smooth graph signals to various data types. We propose an alternating algorithm that jointly estimates the graph Laplacian and the unobserved smooth representation from the noisy signals. We also extend our approach to a variational form to account for the inherent stochasticity of the latent smooth representation. Finally, since real-world graph signals are frequently non-independent and temporally correlated, we further adapt our original setting to a time-vertex formulation. We demonstrate on synthetic and real-world data that our new algorithms outperform competing Laplacian estimation methods that suffer from noise model mismatch. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal processing

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Tmf-Gnn: Temporal Matrix Factorization-Based Graph Neural Network for Multivariate Time Series Forecasting with Missing Values

SSRN

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SSRN 2023年

作者： Kim, Suhyeon Lee, Taek-Ho Lee, Junghye Graduate School of Data Science Kyungpook National University Daegu41566 Korea Republic of Technology Management Economics and Policy Program & Institute of Engineering Research Seoul National University Seoul08826 Korea Republic of Technology Management Economics and Policy Program & Graduate School of Engineering Practice Seoul National University Seoul08826 Korea Republic of

Missing data in multivariate time series (MTS) is a very common issue, often caused by unreliable sensors and data storage or transmission problems. Particularly, such missing data can cause some errors and biases in the MTS forecasting tasks of real-world applications, implying that proper handling of the missing data is essential. Therefore, in this study, we propose a new method for MTS forecasting with missing values, called a temporal matrix factorization-based graph neural network (TMF-GNN), to improve predictive performance outcomes. TMF-GNN basically uses the concept of TMF, which reconstructs partially observed MTS data. We newly present a data-adaptive regularization method for TMF based on graph-based and sequential deep learning algorithms to capture both the variable- and sample-wise information of MTS data affected by the concept known as missingness. We demonstrate the feasibility of the proposed method by conducting various experiments on three MTS datasets and show how it outperforms baseline methods. We believe that our study will have an impact on several MTS-related tasks and that it can be a useful alternative for handling missing values in MTS data. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Learner Action Patterns in the Problem-Solving Process Related to program Code Composition Based on Tracking System Activities 7

Learner Action Patterns in the Problem-Solving Process Relat...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Syahidi, Aulia Akhrian Supianto, Ahmad Afif Hirashima, Tsukasa Watanobe, Yutaka Politeknik Negeri Banjarmasin Smart City Information System Applied Undergraduate Program Banjarmasin City Indonesia Research Center for Data and Information Sciences Bandung City Indonesia Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Learning Engineering Laboratory Higashihiroshima Japan University of Aizu Division of Information Systems Department of Computer Science and Engineering Fukushima AizuWakamatsu Japan

ISBN: (纸本)9798350345711

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the 'if-then' rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process. © 2022 IEEE.

关键词： data mining

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer

Predicting Microsatellite Instability from Histology Images ...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

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Gradient Boosting Model for Water Quality Forecasting

Gradient Boosting Model for Water Quality Forecasting

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International Conference on data Analytics and Management, ICDAM 2023

作者： Panda, Parnani Banerjee, Sushovan Mishra, Sushruta Shashidhar, Chitra de Albuquerque, Victor Hugo C. Reis, Marcello Singh, Pooja Kalinga Institute of Industrial Technology Deemed to Be University Bhubaneswar India Department of Commerce and Management Seshadripuram College Bengaluru560020 India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceará Fortaleza Brazil Federal University of Ceará Ceará Brazil Fort Lauderdale United States Lead of the Biomedical Data Analytics Research Group Bengaluru India Federal Institute of Ceará Ceará Brazil Meteora Consultoria Fortaleza Brazil Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819965465

The aim of our study is to evaluate the quality of water using machine learning methods. Potability is a subjective word that quantifies water quality. In this research, a variety of metrics were used to measure general potability of the water. The prescribed elements are applied like variable matrix to denote water quality. The article used three different classification methods, with success rates ranging from 84.27 to 93.16%, to predict the water quality class including ensemble approach like Random Forest (RFC), SVM for classification (SVC), as well as XGBoost. The Gradient Boosting approach, out of all the ones used, received the greatest accuracy (93.16%), precision (96.3%), recall (92.76%) and F-score (94.54%). Also, it achieved the least computational delay with a mean latency of 1.34 s. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Water quality

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Supervised Score-Based Modeling by Gradient Boosting 39

Supervised Score-Based Modeling by Gradient Boosting

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhao, Changyuan Du, Hongyang Liu, Guangyuan Niyato, Dusit College of Computing and Data Science Nanyang Technological University Singapore CNRS@CREATE 1 Create Way # 08-01 Create Tower 138602 Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong The Energy Research Institute @ NTU Interdisciplinary Graduate Program Singapore

ISBN: (纸本)157735897X

Score-based generative models can effectively learn the distribution of data by estimating the gradient of the distribution. Due to the multi-step denoising characteristic, researchers have recently considered combining score-based generative models with the gradient boosting algorithm, a multi-step supervised learning algorithm, to solve supervised learning tasks. However, existing generative model algorithms are often limited by the stochastic nature of the models and the long inference time, impacting prediction performances. Therefore, we propose a Supervised Score-based Model (SSM), which can be viewed as a gradient boosting algorithm combining score matching. We provide a theoretical analysis of learning and sampling for SSM to balance inference time and prediction accuracy. Via the ablation experiment in selected examples, we demonstrate the outstanding performances of the proposed techniques. Additionally, we compare our model with other probabilistic models, including Natural Gradient Boosting (NGboost), Classification and Regression Diffusion Models (CARD), Diffusion Boosted Trees (DBT), and non-probabilistic gradient boosting models. The experimental results show that our model outperforms existing models in both accuracy and inference time. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Supervised learning

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Delineation of Prostate Cancer Via Enhanced AI-Based Algorithm In Ultrasound Images

Delineation of Prostate Cancer Via Enhanced AI-Based Algorit...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yiwen Ruan Rui Jin Zhaorui Liu Caishan Wang Lei Zhang Tao Peng School of Future Science and Engineering Soochow University Suzhou China Department of Ultrasound Second Affiliated Hospital of Soochow University Suzhou China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Kunshan Jiangsu China Department of Radiation Oncology UT Southwestern Medical Center Dallas Texas USA

Delineation of prostate cancer (PCa) on ultrasound images has become an essential technique for early PCa treatment, which still faces several challenges, such as low image contrast and blurred organ boundaries. Facing the aforementioned issues, a novel coarse-fine segmentation framework method is adopted in this work. First, a deep learning algorithm is used for positioning the region of interest (ROI); then, the principal curve-related technique is adopted to fine-tune initial results. Thirdly, an enhanced quantum evolutionary algorithm is used to hunt for the optimal initialization of the backpropagation neural network. Finally, an interpretable mathematical model is introduced for representing the ROI contour by using the parameters of the neural network. A comprehensive evaluation using the clinical data proves the superiority of our method, with Dice similarity coefficient (DSC) of 83.48+3.44%, Jaccard similarity coefficient (Ω) of 82.25+4.12%, and accuracy (ACC) of 83.4+3.57%, respectively.

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