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An Early Warning Method Based on Blending of Deep Generative Model and Oversampling Model for Online learning

IEEE ACCESS 2025年 13卷 72248-72268页

作者： Zhang, Mingyan Wang, Yiqing Hung, Jui-Long Wang, Jie Duan, Chao Zhejiang Normal Univ Coll Educ Jinhua 321004 Zhejiang Peoples R China Zhejiang Normal Univ Zhejiang Key Lab Intelligent Educ Technol & Applic Jinhua 321004 Zhejiang Peoples R China Boise State Univ Dept Educ Technol Boise ID 83725 USA

Early warning for learning performance requires to identify the maximum number of at-risk students as early as possible within a semester. However, educational data often suffer from the issue of data imbalance, making it challenging to simultaneously achieve both high precision (accurate identification) and high recall (comprehensive coverage) in at-risk student detection. Deep generative models and oversampling models are effective methods to solve data imbalance issues, which can improve classification performance. This paper proposes a method that combines the advantages of deep generative models and oversampling models to build a blending model for dealing with imbalanced educational data, which can effectively improve the precision, recall, F1-score and AUC for online learning early warning. First, we compare baseline models to select the best classifier, then choose the highest-precision deep generative model and the highest-recall oversampling model to construct blending models, which are shown to improve early warning prediction metrics. Finally, interpretable models are used to analyze differences in at-risk student prediction between the blending model, deep generative model, and oversampling model. The proposed models are validated on both extremely imbalanced datasets and new semester datasets. Results show that: (1) Compared to the baseline model, both the base learners built by the deep generative model and the oversampling model can improve the evaluation metrics of the model, the deep generative base learners achieve higher precision than the oversampling model, while the oversampling base learners achieve higher recall than the deep generative base learners. (2) The blending model composed of deep generative base learner and oversampling base learner can further improve the F1-score and AUC based on their individual strengths, the proposed blending model can also conduct effective early warning three units earlier than baseline models. (3) Compared

关键词： Predictive models Data models Education Analytical models Accuracy Measurement machine learning algorithms Autoencoders Support vector machines Solid modeling Online learning early warning deep generative model oversampling model blending model interpretable AI at-risk student identification

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Data-Driven Insights into Climate Change Effects on Groundwater Levels Using machine learning

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WATER RESOURCES MANAGEMENT 2025年第7期39卷 1-16页

作者： Lu, Xinyong Wang, Zimo Zhao, Menghao Peng, Songzhe Geng, Song Ghorbani, Hamzeh Zhongkai Univ Agr & Engn Coll Econ & Trade Guangzhou 510550 Guangdong Peoples R China Nankai Univ Sch Philosophy Tianjin 300350 Peoples R China Qingdao Univ Appl & Tech Educ Coll Qingdao 266061 Shandong Peoples R China Nanjing Univ Posts & Telecommun Sch Populat Studies Nanjing 210042 Jiangsu Peoples R China Shanghai Normal Univ Tianhua Coll Shanghai 201815 Peoples R China Islamic Azad Univ Ahvaz Branch Young Researchers & Elite Club Ahvaz Iran

Climate change disrupts groundwater levels (GWL) by modifying precipitation patterns, reducing recharge rates, and limiting water availability. Rising temperatures and evolving weather patterns further degrade surface and groundwater quality. These changes exacerbate competition for water resources, heightening allocation challenges and ecological disruptions. Groundwater fluctuations adversely affect ecosystems, causing habitat disturbances and biodiversity loss. This study explores the impacts of climate change on GWL using machine learning techniques to analyze 9,430 time series data points (1993-2021) from Northern China. Four distinct classes of top-performing machine learning models were evaluated. The CNN model demonstrated superior performance, achieving an R-2 value of 0.9924 and an RMSE of 0.1832, highlighting its efficacy in processing complex patterns. Pearson correlation analysis revealed that Average Annual Precipitation (AAP), Average Soil Moisture (ASM), and Evapotranspiration (EV) positively influence GWL, while Severe Wet Potential (SWP), Severe Drought Potential (SDP), and Temperature (T) exhibit negative correlations. Feature ranking identified AAP as the most critical factor for groundwater recharge, followed by ASM and EV, which also play significant roles in groundwater dynamics. These findings provide a robust understanding of the key drivers influencing groundwater recharge and storage, offering valuable insights to inform sustainable water resource management in the context of climate change.

关键词： Climate Change Groundwater Level Water risk Water Resources Management machine learning algorithms

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Methodologies and Application of machine learning algorithms to Classify the Performance of High Performance Cluster Components 16

Methodologies and Application of Machine Learning Algorithms...

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16th IEEE International Conference on Cluster Computing (CLUSTER)

作者： Romero, Phil Idler, Craig Los Alamos Natl Lab High Performance Comp 1 Los Alamos NM 87545 USA Los Alamos Natl Lab High Performance Comp 5 Los Alamos NM USA

ISBN: (纸本)9781479955480

High Performance Computing Clusters are designed to host highly parallelized applications, often in excess of thousands of nodes allocated to a job. These jobs, especially those that require a high level of synchronous communication, can be greatly affected by a single poor, or even sub-standard performing component. These components, often referred to as a node, are typically comprised of CPUs, accelerator processors, memory, a communication bus, and so on. Consequently it is important to identify and eliminate these sub-standard performing nodes before a job is scheduled onto them. In this paper we will describe the process used to measure and the methodology used to quantify poor performing nodes or classify suspect performing nodes into groups, or clusters, that can be later used to identify future performance issues. This process is more involved than simply running a scientific calculation across all the nodes, finding one that was "slow", and labeling it as a bad node. At Los Alamos, this methodology has been used successfully to find problem nodes and has helped characterize the components of other clusters to aid in the proactive elimination of potential problems.

关键词： learning (artificial intelligence) parallel processing performance evaluation CPU accelerator processors high performance computing clusters parallelized applications poor performing nodes scientific calculation suspect performing nodes synchronous communication Bandwidth Clustering algorithms Graphics processing units Principal component analysis Standards Testing synchronous communication Clustering algorithms central processing units PARALLEL PROCESSING (COMPUTERS) Ganglia Bandwidth Performance Evaluation machine learning algorithms principal components analysis Graphics Processing Unit jobs machine APPLICATIONS

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Optimized Identification of Sentence-Level Multiclass Events on Urdu-Language-Text Using machine learning Techniques

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IEEE ACCESS 2025年 13卷 1-25页

作者： Ali, Somia Jamil, Uzma Younas, Muhammad Zafar, Bushra Hanif, Muhammad Kashif Univ Faisalabad Dept Comp Sci Govt Coll Faisalabad 38040 Pakistan

In today's digital world, social media platforms generate a plethora of unstructured information. However, for low-resource languages like Urdu, there is a scarcity of well-structured data for specific tasks such as event classification. Urdu, a language prominent in South Asia, has boasted a complex morphological structure with unique features but has lacked standard linguistic resources like datasets. Long-text classification has demanded more effort than short-text classification due to its expansive vocabulary, information redundancy, and noise. Text processing has been the latest trend in research, with many machine learning and deep learning techniques widely used for it. Multiclass classification has been utilized to classify different languages for various purposes. In this research, a multiclass classification for the Urdu language was performed using a text dataset taken from five different social media platforms including Geo News, Samaa News, Dawn News, Express News, and Urdu Blogs totaling 103,771 sentences. We used sentence-level classification to categorize sentences including terrorist attacks, national news, sports, entertainment, politics, safety, earthquakes, fraud and corruption, sexual assault, weather, accidents, forces, inflation, murder and death, education, and international news. Deep learning, transformer-based and machine learning classifiers are used for event classification. The SMFCNN classifier achieved the greatest accuracy of 88.29%. We incorporated transformer-based models, with the proposed XLM-R+ model demonstrating superior performance with an accuracy of 89.8%. Our results were compared to previously reported techniques that used traditional models, highlighting the significant improvements offered by our approaches. The novelty of this research lies in the inclusion of 16 event categories to broaden coverage and the implementation of the SMFCNN and transformer-based algorithms. This study highlights the potential of deep learn

关键词： Text categorization Accuracy Natural language processing Deep learning Writing Transformers Social networking (online) machine learning algorithms Convolutional neural networks Training Sentence level classification deep learning machine learning Urdu language event classification

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learning-Aided Iterated Local Search Algorithm for Integrated Order Batching, Picker Assignment, Batch Sequencing, and Picker Routing Problem

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 7421-7434页

作者： Cao, ZhengCai Lv, XinSai Lin, ChengRan Harbin Inst Technol State Key Lab Robot & Syst Harbin 150080 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

This work tackles an integrated order batching, picker assignment, batch sequencing, and picker routing problem in warehouse environments. A learning-Aided Iterated Local Search (LILS) is proposed to efficiently find its high-quality solutions. The main optimizer is iterated local search. A novel bidirectional long short-term memory network-embedded autoencoder, built through end-to-end unsupervised learning with an encoder and decoder, guides the search direction. To capture the implicit relationships among strongly-coupled subproblems, long short-term memory layers are incorporated in the encoder. A network-aided mutation operator is introduced to enhance global search in a low-dimensional feature space. In the decoder, low-order and high-fit subsolutions are identified and reconstructed to generate mutated offspring solutions using long short-term memory layers and a masking mechanism. To balance the exploration and exploitation of LILS, an information exchange method is developed. Numerical experiments show that LILS outperforms several existing methods in generating high-quality schedules within a reasonable time. Note to Practitioners-In a warehouse environment, an integrated optimization problem is often addressed by using heuristics due to limited computational resources. However, expedient heuristic rules tend to produce subpar results. While meta-heuristics can generate relatively better schedules, they are time-consuming, particularly for population-based algorithms that must iteratively evaluate fitness functions for numerous candidate solutions. In order to strike a balance between computational demands and solution-qualities, our approach combines machine learning techniques with meta-heuristics. Specifically, we integrate a bidirectional long short-term memory-based autoencoder into iterated local search to enhance the latter's global optimization capability. The integration of machine learning and meta-heuristics enables efficient generation of super

关键词： Routing Sequential analysis Logistics Schedules Search problems Metaheuristics machine learning algorithms Heuristic algorithms Automation Robots Integrated scheduling problem iterated local search autoencoder long short term memory machine learning

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Automatically identifying imperfections and attacks in practical quantum key distribution systems via machine learning

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Science China(Information Sciences) 2024年第10期67卷 359-372页

作者： Jiaxin XU Xiao MA Jingyang LIU Chunhui ZHANG Hongwei LI Xingyu ZHOU Qin WANG Institute of Quantum Information and Technology Nanjing University of Posts and Telecommunications Telecommunication and Networks National Engineering Research CenterNanjing University of Posts and Telecommunications Henan Key Laboratory of Quantum Information and Cryptography Strategic Support Force Information Engineering University

The realistic security of quantum key distribution(QKD) systems is currently a hot research topic in the field of quantum communications. There are always defects in practical devices, and eavesdroppers can make use of the security risk points of various devices to obtain key information. To date, current types of security analysis tend to analyze each security risk point individually, thereby posing great challenges for the overall security evaluation of QKD systems. In this paper, for the first time, we employ machine learning algorithms to identify the defects of different devices and certain attacks in real time, with an accuracy of 98%. It provides a novel solution for the practical security evaluation of QKD systems, thereby addressing the bottleneck problem of multiple risk points being difficult to address simultaneously in QKD systems,thus paving the way for the future large-scale application of quantum communication networks.

关键词： quantum key distribution device imperfections eavasdropping attacks real-time detection machine learning algorithms

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AFed: Algorithmic Fair Federated learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2025年 PP卷 PP页

作者： Chen, Huiqiang Zhu, Tianqing Zhou, Wanlei Zhao, Wei City Univ Macau Fac Data Sci Macau Peoples R China Shenzhen Univ Adv Technol Fac Comp Sci & Control Engn Shenzhen 518055 Peoples R China

Federated learning (FL) has gained significant attention as it facilitates collaborative machine learning among multiple clients without centralizing their data on a server. FL ensures the privacy of participating clients by locally storing their data, which creates new challenges in fairness. Traditional debiasing methods assume centralized access to sensitive information, rendering them impractical for the FL setting. Additionally, FL is more susceptible to fairness issues than centralized machine learning due to the diverse client data sources that may be associated with group information. Therefore, training a fair model in FL without access to client local data is important and challenging. This article presents AFed, a straightforward, yet effective framework for promoting group fairness in FL. The core idea is to circumvent restricted data access by learning the global data distribution. This article proposes two approaches: AFed-G, which uses a conditional generator trained on the server side, and AFed-GAN, which improves upon AFed-G by training a conditional GAN on the client side. We augment the client data with the generated samples to help remove bias. Our theoretical analysis justifies the proposed methods, and empirical results on multiple real-world datasets demonstrate a substantial improvement in AFed over several baselines.

关键词： Training Servers Data models Generators machine learning algorithms Feature extraction Classification algorithms Generative adversarial networks Federated learning Accuracy Fair machine learning federated learning (FL) generative model

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Predicting User Purchases From Clickstream Data: A Comparative Analysis of Clickstream Data Representations and machine learning Models

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IEEE ACCESS 2025年 13卷 43796-43817页

作者： Tokuc, A. Aylin Dag, Tamer Kadir Has Univ Dept Comp Engn TR-34083 Fatih Istanbul Turkiye Valinor AI London SE9 4HA England Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait

Predicting purchase events from e-commerce clickstream data is a critical challenge with significant implications for optimizing marketing strategies and enhancing customer experience. This study addresses this challenge by systematically evaluating and comparing multiple data representations - aggregated session attributes, recent user actions, and hybrid combinations - which bridges gaps in the existing literature and demonstrates the superiority of hybrid approaches. Unlike prior research, which typically focuses on single representations, our approach combines aggregated session-level summaries with granular, sequential user actions to capture both long-term and short-term behavioral patterns. Through comprehensive experimentation, we compared multiple machine learning models, including LightGBM, decision trees, gradient boosting, SVC, and logistic regression, using real-world e-commerce clickstream data. Notably, the hybrid representation with LightGBM achieved superior predictive performance, significantly outperforming alternative methods. Feature importance analysis revealed key factors influencing purchase likelihood, such as time since the last event, session duration, and product interactions. This study provides actionable insights into real-time marketing interventions by demonstrating the practical utility of hybrid data representations and efficient tree-based models. Our findings offer a scalable and interpretable framework for e-commerce platforms to enhance purchase predictions and optimize marketing strategies.

关键词： Predictive models Data models Hidden Markov models Electronic commerce Computational modeling Data visualization Analytical models Real-time systems Random forests machine learning algorithms Clickstream data customer behavior modeling data representations feature importance gradient boosting e-commerce lightgbm machine learning model selection purchase prediction

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machine-learning-based ensemble regression for vehicle-to-vehicle distance estimation using a toe-in style stereo camera

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MEASUREMENT 2025年 240卷

作者： Duran, Ozgur Turan, Bulent Kaya, Mahir Gaziosmanpasa Univ Fac Engn & Architecture Dept Comp Engn TR-60150 Tokat Turkiye

Adjusting the following distance from the front vehicle in highway traffic is important to reduce the risk of collision. Distance estimation is an important research area for advanced driver assistance systems. Therefore, this paper presents a methodology that combines the strengths of several machine learning algorithms using joint decision mechanisms and searches for optimal results for vehicle-to-vehicle distance estimation. The hyperparameter optimization of machine learning models is performed by an iterative algorithm that compares combinations of hyperparameter values. In addition, machine learning algorithms are combined and tested with ensemble learning methods to improve the results obtained. According to the experiments, the ensemble voting regression created by combining extreme gradient boosting, categorical boosting and two multi-layer perceptron models achieves the best result with a mean absolute percentage error value of 1.5444. Considering the comparisons made with other methods, the accuracy of the results obtained with the proposed method is quite high. Code is at: https://***/ozgurduran/V2V-VR.

关键词： Distance Estimation machine learning algorithms Ensemble Voting Regression Ensemble Stacking Regression Hyperparameter Optimization

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Enhancing Chronic Disease Prediction in IoMT-Enabled Healthcare 5.0 Using Deep machine learning: Alzheimer's Disease as a Case Study

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IEEE ACCESS 2025年 13卷 14252-14272页

作者： Javed, Rabia Abbas, Tahir Shahzad, Tariq Kanwal, Khadija Ramay, Sadaqat Ali Khan, Muhammad Adnan Ouahada, Khmaies TIMES Inst Dept Comp Sci Multan 60000 Pakistan Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa Women Univ Inst CS & IT Multan 60000 Pakistan Gachon Univ Fac Artificial Intelligence & Software Dept Software Seongnam Si 13557 South Korea

Chronic disease significantly affects health on a global scale. Deep machine learning algorithms have found widespread application in the diagnosis of chronic diseases. Early diagnosis and treatment reduce the chance of a disease getting worse and, as a result, raise related mortality. The main objective of this work is to present a deep machine learning-based approach that provides better results in terms of accuracy. These findings have significance for tailored healthcare 5.0, enabling healthcare professionals to predict chronic disease more efficiently. A comparative examination of the most recent methods has been provided in our work reveals that it might be more advantageous to use the proposed model in which segmentation of the MRI is performed using U-net architecture and then classification is done using transfer learning for chronic disease prediction. Our proposed model provides 96.06% accuracy, it advances our understanding of deep machine learning's potential for chronic disease prediction and emphasizes the need to tailor model selection to specific disease types using data from IoMT enabled devices. In order to make advanced improvement in the field of healthcare 5.0, future studies should focus on refining these models and investigating how well they work with a wider range of datasets.

关键词： Diseases Medical services machine learning Medical diagnostic imaging Predictive models machine learning algorithms Alzheimer's disease Transfer learning Mortality Magnetic resonance imaging Chronic disease Alzheimer disease deep machine learning IoMT transfer learning image segmentation healthcare 5.0

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