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Evaluating machine learning models and feature selection for reference evapotranspiration estimation in semi-arid regions: a case study in Doukkala, Morocco

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Theoretical and Applied Climatology 2025年第6期156卷 1-24页

作者： Belarbi, Zaid EL Younoussi, Yacine Information System and Software Engineering Laboratory Abdelmalek Essaadi University Tetouan Morocco

Accurate estimation of reference evapotranspiration (ETo) is crucial for effective water resource management and irrigation planning, particularly in regions with significant agricultural activity, such as Doukkala, Morocco. This study explores the potential of machine learning (ML) models to estimate daily ETo using 18 years of climatic data collected from two meteorological stations in the region. The dataset includes key meteorological variables—temperature, humidity, solar radiation, wind speed, and rainfall—with ETo calculated using the FAO-56 Penman-Monteith (PM) equation as the reference. A range of ML models, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), XGBoost, Random Forest (RF), k-Nearest Neighbors (KNN), Decision Trees (DT), and Multiple Linear Regression (MLR), were trained and compared. Additionally, deep learning architectures such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) were evaluated for their ability to capture temporal dependencies within the data. Among the tested models, ANN demonstrated the highest performance, achieving an R² of 0.9868 and an MSE of 0.0440, followed closely by SVR and XGBoost, both of which also exhibited high predictive accuracy. In contrast, simpler models like MLR and DT showed comparatively lower performance, underscoring the advantages of more advanced ML techniques for ETo estimation. While LSTM displayed moderate success, CNN underperformed relative to other models. The inclusion of data from two stations enabled the evaluation of model generalizability across distinct locations within the region. However, this study has some limitations. While cross-site validation improves generalizability, the findings are based on two stations within the same semi-arid region, which may not fully represent ETo dynamics in more diverse climatic conditions. Additionally, while ML models demonstrated strong predictive capabilities, further improvements could be achieved b

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BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation

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Frontiers of Computer Science 2023年第5期17卷 103-118页

作者： Jinwei LUO Mingkai HE Weike PAN Zhong MING College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen 518060China

Session-based recommendation(SBR)and multibehavior recommendation(MBR)are both important problems and have attracted the attention of many researchers and *** from SBR that solely uses one single type of behavior sequences and MBR that neglects sequential dynamics,heterogeneous SBR(HSBR)that exploits different types of behavioral information(e.g.,examinations like clicks or browses,purchases,adds-to-carts and adds-to-favorites)in sequences is more consistent with real-world recommendation scenarios,but it is rarely *** efforts towards HSBR focus on distinguishing different types of behaviors or exploiting homogeneous behavior transitions in a sequence with the same type of ***,all the existing solutions for HSBR do not exploit the rich heterogeneous behavior transitions in an explicit way and thus may fail to capture the semantic relations between different types of ***,all the existing solutions for HSBR do not model the rich heterogeneous behavior transitions in the form of graphs and thus may fail to capture the semantic relations between different types of *** limitation hinders the development of HSBR and results in unsatisfactory *** a response,we propose a novel behavior-aware graph neural network(BGNN)for *** BGNN adopts a dual-channel learning strategy for differentiated modeling of two different types of behavior sequences in a ***,our BGNN integrates the information of both homogeneous behavior transitions and heterogeneous behavior transitions in a unified *** then conduct extensive empirical studies on three real-world datasets,and find that our BGNN outperforms the best baseline by 21.87%,18.49%,and 37.16%on average correspondingly.A series of further experiments and visualization studies demonstrate the rationality and effectiveness of our *** exploratory study on extending our BGNN to handle more than two types of behaviors show that our BGNN can e

关键词： session-based recommendation graph neural network heterogeneous behaviors

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An Uncertainty Quantization-Based Method for Anti-UAV Detection in Infrared Images

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Computers, Materials & Continua 2025年第4期83卷 1439-1458页

作者： Can Wu Wenyi Tang Yunbo Rao Yinjie Chen Hui Ding Shuzhen Zhu Yuanyuan Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China State Key Laboratory of Air Traffic Management System Nanjing210000China National Airspace Management Center Beijing100094China

Infrared unmanned aerial vehicle(UAV)target detection presents significant challenges due to the inter-play between small targets and complex *** methods,while effective in controlled environments,often fail in scenarios involving long-range targets,high noise levels,or intricate backgrounds,highlighting the need for more robust *** address these challenges,we propose a novel three-stage UAV segmentation framework that leverages uncertainty quantification to enhance target *** framework incorporates a Bayesian convolutional neural network capable of generating both segmentation maps and probabilistic uncertainty *** utilizing uncer-tainty predictions,our method refines segmentation outcomes,achieving superior detection ***,this marks the first application of uncertainty modeling within the context of infrared UAV target *** evaluations on three publicly available infrared UAV datasets demonstrate the effectiveness of the proposed *** results reveal significant improvements in both detection precision and robustness when compared to state-of-the-art deep learning *** approach also extends the capabilities of encoder-decoder convolutional neural networks by introducing uncertainty modeling,enabling the network to better handle the challenges posed by small targets and complex environmental *** bridging the gap between theoretical uncertainty modeling and practical detection tasks,our work offers a new perspective on enhancing model interpretability and *** codes of this work are available openly at https://***/general-learner/UQ_Anti_UAV(acceessed on 11 November 2024).

关键词： Object segmentation uncertainty quantification bayesian convolutional neural network

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A Hybrid Method to Measure Distribution Consistency of Mixed-Attribute Datasets

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 182-196页

作者： He, Yulin Ye, Xuan Huang, Defa Fournier-Viger, Philippe Huang, Joshua Zhexue College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Random sample partition (RSP) is a newly developed data management and processing model for Big Data processing and analysis. To apply the RSP model for Big Data computation tasks, it is very important to measure the distribution consistency of different datasets. Existing measurement methods for continuous-attribute and discrete-attribute datasets cannot directly deal with mixed-attribute datasets. In this article, we design a hybrid method to measure the distribution consistency among different mixed-attribute datasets by using a multilayer extreme learning machine (MLELM) and the generalized maximum mean discrepancy (GMMD) criterion, abbreviated as MLELM-GMMD. MLELM is first used to transform original mixed-attribute datasets into corresponding deep encoding datasets. Then, the GMMD criterion is applied to check the distribution consistency of the deep encoding datasets. Four experiments have been done to validate the feasibility and effectiveness of MLELM-GMMD, i.e., the impact of MLELM on the amount of information during mixed-attribute data transformation, the impact of MLELM on distributions of mixed-attribute data, the distribution consistencies of RSP and non-RSP data blocks, and the comparison with other measurement methods. Experimental results show that the proposed MLELM-GMMD method can measure the distribution consistency of mixed-attribute datasets more accurately than one-hot encoding-based methods. © 2022 IEEE.

关键词： Data mining

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Tree Regularization for Visual Explanations of Cancer Cell Classification

Tree Regularization for Visual Explanations of Cancer Cell C...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Raza, Muhammad Umair Chen, Jie Wang, Li Nawaz, Adil Leung, Victor C.M. Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China

ISBN: (纸本)9798350386226

The challenge of interpretability remains a significant barrier to adopting deep neural networks in healthcare domains. Although tree regularization aims to align a deep neural network's decisions with a single axis-aligned decision tree, however, relying on one tree for all inputs often leads to sub-optimal performance and interoperability. To address this limitation, we propose an enhanced tree regularization method that integrates a post-hoc visual explainable model such as Grad-CAM. This approach guides the deep model to be well-approximated by decision trees tailored to the salient regions identified by Grad-CAM in the input space. We rigorously validate the effectiveness of this framework on two cancer cell datasets: CNMC, which focuses on acute lymphoblastic leukemia cells, and ISBI2016, which comprises benign and malignant skin lesions. The results demonstrate that the proposed method delivers simpler and more interpretable explanations without compromising accuracy, thereby advancing the interpretability of deep learning models in critical healthcare applications. © 2024 IEEE.

关键词： Cancer cells

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Bug localization based on syntactical and semantic information of source code

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Journal of systems engineering and Electronics 2023年第1期34卷 236-246页

作者： YAN Xuefeng CHENG Shasha GUO Liqin College of Computer Science Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 211106China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System EngineeringBeijing 100854China

The existing software bug localization models treat the source file as natural language, which leads to the loss of syntactical and structure information of the source file. A bug localization model based on syntactical and semantic information of source code is proposed. Firstly, abstract syntax tree(AST) is divided based on node category to obtain statement sequence. The statement tree is encoded into vectors to capture lexical and syntactical knowledge at the statement ***, the source code is transformed into vector representation by the sequence naturalness of the statement. Therefore,the problem of gradient vanishing and explosion caused by a large AST size is obviated when using AST to the represent source code. Finally, the correlation between bug reports and source files are comprehensively analyzed from three aspects of syntax, semantics and text to locate the buggy code. Experiments show that compared with other standard models, the proposed model improves the performance of bug localization, and it has good advantages in mean reciprocal rank(MRR), mean average precision(MAP) and Top N Rank.

关键词： bug report abstract syntax tree code representation software bug localization

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Exploring implicit influence for social recommendation based on GNN

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Soft Computing 2024年第19期28卷 11001-11014页

作者： Liu, Zhewei Yang, Peilin Hao, Qingbo Zheng, Wenguang Xiao, Yingyuan Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

In recent years, the method of using graph neural networks (GNN) to learn users’ social influence has been widely applied to social recommendation and has shown effectiveness, but several important challenges have not been well addressed: (i) Most work fails to consider the user interests (historical user-item interactions) when first building user-user social relationships, which can make it difficult to capture accurate user embedding and thus prevent the model from better exploring the users’ social influence;(ii) Most of the current methods do not build social neighbors (with the same item-user interaction) that belong to the item and do not aggregate information from the perspective of social neighbors, which makes it possible for the item to lose a lot of details when expressing the user’s interest factors. Therefore, to address the above challenges, we propose Exploring Implicit Influence for Social Recommendation Based on GNN (EIIGNN). First, we construct the initial user embedding with user-item interaction information and use the implicit modeling module in user modeling to explore the implicit influence of interest factors on users. In addition, EIIGNN models the social graph structure of item (an item-item graph) so that item can aggregate information from the perspective of their social neighbors, which helps the model learn a more accurate representation of the item. Finally, extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of EIIGNN. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Graph neural networks

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FBI-Net: Frequency Band Integration Network for Infrared Small Target Segmentation

FBI-Net: Frequency Band Integration Network for Infrared Sma...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xin, Biqiao Li, Qiang Mao, Qianchen Wang, Jinbao Wang, Bingshu School of Software Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

Small targets in infrared imagery exhibit challenging characteristics due to their minimal semantic information and the extremely imbalanced distribution between the targets and the background. In this paper, we propose a frequency band integration network to extract salient features of infrared small targets in both the spatial and frequency domains. To excavate the high-frequency features of the small targets, we propose a frequency decoupling-fusion module. To decrease the semantic loss that occurs in deep networks, we propose a semantic injection mechanism to assist in retaining critical information from shallow layers. Experimental results show that our proposed method reaches higher prediction accuracy and robustness in the infrared small target segmentation task compared with other state-of-the-art approaches. © 2025 IEEE.

关键词： Feature Fusion Infrared Small Target Segmentation Semantic Injection Wavelet Transformation

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A comparison on the effects of coal fines and sand fouling on the shear behaviors of railway ballast using large scale direct shear tests

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Journal of Traffic and Transportation engineering(English Edition) 2025年第1期12卷 52-67页

作者： Xu Zhang Shunying Ji Yang Feng Yongjun Li Chunfa Zhao School of Civil and Transportation Engineering Guangdong University of TechnologyGuangzhou 510006China State Key Laboratory of Rail Transit Vehicle System Southwest jiaotong UniversityChengdu 610031China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024China

Railway ballast is easily contaminated by different fouling material and the fouled ballast always causes the deterioration of railway *** previous studies focused on the effect of a certain kind of fouling material on ballast performance,but few studies compared the influence of different fouling *** paper presents a series of direct shear tests on the clean ballast and the fouled ballast mixed with coal fines or sand to understand the effects of different fouling material.A unified empirical model is proposed to well capture the shear stress-displacement responses of the clean and fouled *** equations are also obtained to characterize the variation of the nonlinear shear strength envelope of the fouled ballast with the fouling *** on these equations,a simple method is proposed to predict the shear strength of the fouled ballast using the shear test results of clean *** measured results demonstrate significant differences between the effects of coal fines and sand on the shear behaviors of ballast,to which particular attention should be paid in order to take targeted maintenance measures for the fouled *** mechanisms for the different effects of coal fines and sand are also analyzed to understand the roles that various fouling material plays in the fouled ballast.

关键词： Railway ballast Fouled ballast Shear strength Direct shear test Contamination

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Batch-subsample Prototype Network Classification Model for Imbalanced and Overlapping Data

Batch-subsample Prototype Network Classification Model for I...

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2022 Chinese Automation Congress, CAC 2022

作者： Shizhao, Wang Shaoguang, Shu Xueshi, Liu Beijing Institute of Control Engineering System Software Engineering Laboratory Beijing China

ISBN: (纸本)9781665465335

The classification of imbalanced and overlapping data is more difficult. When the number of categories is uneven and category areas are overlap, general neural network classifiers can only extract limited features from the data, which makes the model relies too much on existing samples, resulting in a low classification accuracy. Aiming at the above classification problem, this paper proposes a batch-subsample prototype network model. Based on the prototype network, the model achieves the separation of different categories in classification. In the training process, selecting each batch of training data by subsample method, so that the data for training is balanced each time, which effectively reduce the impact of category imbalance and overlapping on model training. This model is tested in the dataset NSL-KDD with the characteristics of imbalance and overlapping. The experimental results show that the classification accuracy of the batch-subsample prototype network (BSPN) model has been effectively improved, and the classification ability of minority categories also be better. To a certain extent, the model solves the problem that the neural network model has poor classification effect on imbalanced and overlapping data. Above all, the batch-subsample prototype network model can improve the classification accuracy in imbalanced and overlapping data, optimize the recognition rate of minority categories, and increase the classification robustness of the model, which is of great significance for imbalanced and overlapping data classification. © 2022 IEEE.

关键词： Classification (of information)

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