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StreamDFP: A General Stream Mining Framework for Adaptive Disk Failure prediction

IEEE TRANSACTIONS ON COMPUTERS 2023年第2期72卷 520-534页

作者： Han, Shujie Lee, Patrick P. C. Shen, Zhirong He, Cheng Liu, Yi Huang, Tao Peking Univ Beijing 100871 Peoples R China Chinese Univ Hong Kong Shatin Hong Kong Peoples R China Xiamen Univ Xiamen 361005 Peoples R China Alibaba Grp Hangzhou 310052 Peoples R China

We explore machine learning for accurately predicting imminent disk failures and hence providing proactive fault tolerance for modern large-scale storage systems. Current disk failure prediction approaches are mostly offline and assume that the disk logs required for training learning models are available a priori. However, disk logs are often continuously generated as an evolving data stream, in which the statistical patterns vary over time (also known as concept drift). Such a challenge motivates the need of online techniques that perform training and prediction on the incoming stream of disk logs in real time, while being adaptive to concept drift. We first measure and demonstrate the existence of concept drift on various disk models in production. Motivated by our study, we design StreamDFP, a general stream mining framework for disk failure prediction with concept-drift adaptation based on three key techniques, namely online labeling, concept-drift-aware training, and general prediction, with a primary objective of supporting various machine learning algorithms. We extend StreamDFP to support online transfer learning for minority disk models with concept-drift adaptation. Our evaluation shows that StreamDFP improves the prediction accuracy significantly compared to without concept-drift adaptation under various settings, and achieves reasonably high stream processing performance.

关键词： Random forests Predictive models Training prediction algorithms Adaptation models Machine learning algorithms Production Disk failure prediction stream mining concept drift and online transfer learning

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Ensemble Remaining Useful Life prediction for Lithium-Ion Batteries With the Fusion of Historical and Real-Time Degradation Data

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第5期72卷 5934-5947页

作者： Lin, Yan-Hui Tian, Ling-Ling Ding, Ze-Qi Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China Data Ctr China Life Insurance Co Ltd Beijing 100020 Peoples R China

Remaining useful life (RUL) prediction is a critical task in prognostics and health management. The performances of traditional RUL prediction approaches for lithium-ion batteries are usually affected by the uncertainties involved in the data analysis and model selection. This paper proposes an ensemble prognostic approach under the particle filter (PF) framework to improve the prediction accuracy in consideration of the uncertainties. In PF algorithm, an optimal weights initialization method is proposed with the comprehensive consideration of model bias and variance, and a novel weighting scheme is proposed to optimize the ensemble model performance by assigning time-varying and degradation-dependent weights with the fusion of historical and real-time degradation data. Besides, a data noise quantification method is proposed and applied in the PF algorithm to solve the hyperparameter setting problem. The effectiveness of the proposed approach is illustrated through the real datasets obtained from two types of lithium-ion batteries.

关键词： Degradation Data models Predictive models Batteries Real-time systems prediction algorithms Uncertainty Lithium-ion battery remaining useful life prediction ensemble model weighting scheme particle filter

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prediction of Dilatory Behavior in eLearning: A Comparison of Multiple Machine Learning Models

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IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 2023年第5期16卷 648-663页

作者： Imhof, Christof Comsa, Ioan-Sorin Hlosta, Martin Parsaeifard, Behnam Moser, Ivan Bergamin, Per Swiss Distance Univ Appl Sci Inst Res Open Distance & eLearning CH-3900 Brig Switzerland North West Univ ZA-2531 Potchefstroom South Africa

Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include a higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems (LMS) and learning analytics (LA), indicators of such behavior can be detected, enabling predictions of future procrastination and other dilatory behavior. However, research focusing on such predictions is scarce. Moreover, studies involving different types of predictors and comparisons between the predictive performance of various methods are virtually nonexistent. In this article, we aim to fill these research gaps by analyzing the performance of multiple machine learning algorithms when predicting the delayed or timely submission of online assignments in a higher education setting with two categories of predictors: 1) subjective, questionnaire-based variables and 2) objective, log-data-based indicators extracted from a learning management system. The results show that models with objective predictors consistently outperform models with subjective predictors, and a combination of both variable types performs slightly better with an accuracy of 70%. For each of these three options, a different approach prevailed (gradient boosting machines for the subjective, Bayesian multilevel models for the objective, and Random Forest for the combined predictors). We conclude that careful attention should be paid to the selection of predictors and algorithms before implementing such models in learning management systems.

关键词： Dilatory behavior learning analytics (LA) machine learning (ML) predictive performance procrastination Delays Predictive models Behavioral sciences prediction algorithms Task analysis Data models Classification algorithms

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Comparative Analysis Study for Air Quality prediction in Smart Cities Using Regression Techniques

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IEEE ACCESS 2023年 11卷 115140-115149页

作者： Al-Eidi, Shorouq Amsaad, Fathi Darwish, Omar Tashtoush, Yahya Alqahtani, Ali Niveshitha, Niveshitha Tafila Tech Univ Comp Sci Dept Tafila 66110 Jordan Wright State Univ Dept Comp Sci & Engn Dayton OH 45435 USA Eastern Michigan Univ Informat Secur & Appl Comp Dept Ypsilanti MI 48197 USA Jordan Univ Sci & Technol Dept Comp Sci Irbid 22110 Jordan Najran Univ Dept Networks & Commun Engn Najran 61441 Saudi Arabia

In smart cities, air pollution has detrimental impacts on human physical health and the quality of living environment. Therefore, correctly predicting air quality plays an important effective action plan to mitigate air pollution and create healthier and more sustainable environments. Monitoring and predicting air pollution is crucial to empower individuals to make informed decisions that protect their health. This research presents a comprehensive comparative analysis focused on air quality prediction using three distinct regression techniques- Random Forest regression, Linear regression, and Decision Tree regression. The main goal of this study is to discern the most effective model by considering a range of evaluation criteria, including Mean Absolute Error and R-2 measures. Moreover, it considers the crucial aspects of minimizing prediction errors and enhancing computational efficiency by evaluating the regression models within two frameworks. The findings of this study underscore the superiority of the Decision Tree regression approach over the other models, demonstrating its exceptional accuracy with a high R-2 score and a minimal error rate. Moreover, integrating cloud computing technology has resulted in substantial improvements in the execution time of these approaches. This technology enhancement significantly affects the overall efficiency of the air quality prediction process. By leveraging distributed computing resources, real-time air quality forecasting becomes feasible, enabling timely decision-making and proactive measures to address air pollution episodes effectively.

关键词： Air pollution Atmospheric modeling Predictive models prediction algorithms Computational modeling Regression tree analysis Random forests Machine learning Air quality Internet of Things Smart cities machine learning IoT smart city air quality index

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Magi-Net: Meta Negative Network for Early Activity prediction

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 3254-3265页

作者： Wang, Wenqian Chang, Faliang Zhang, Junhao Yan, Rui Liu, Chunsheng Wang, Bin Shou, Mike Zheng Natl Univ Singapore Dept Elect & Comp Engn Show Lab Singapore 117583 Singapore Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China

Early activity prediction/recognition aims to recognize action categories before they are fully conveyed. Compared to full-length action sequences, partial video sequences only provide insufficient discrimination information, which makes predicting the class labels for some similar activities challenging, especially when only very few frames can be observed. To address this challenge, in this paper, we propose a novel meta negative network, namely, Magi-Net, that utilizes a contrastive learning scheme to alleviate the insufficiency of discriminative information. In our Magi-Net model, the positive samples are generated by augmenting an input anchor conditioned on all observation ratios, while the negative samples are selected from a trainable negative look-up memory (LUM) table, which stores the training samples and the corresponding misleading categories. Furthermore, a meta negative sample optimization strategy (MetaSOS) is proposed to boost the training of Magi-Net by encouraging the model to learn from the most informative negative samples via a meta learning scheme. Extensive experiments are conducted on several public skeleton-based activity datasets, and the results show the efficacy of the proposed Magi-Net model.

关键词： Predictive models Video sequences Task analysis Optimization prediction algorithms Adaptation models Training Early activity prediction skeleton-based action recognition meta learning

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Advancing Learned Video Compression With In-Loop Frame prediction

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第5期33卷 2410-2423页

作者： Yang, Ren Timofte, Radu Gool, Luc Van Swiss Fed Inst Technol Comp Vis Lab D ITET CH-8092 Zurich Switzerland Julius Maximilian Univ Wurzburg Comp Vis Lab D-97070 Wurzburg Germany Katholieke Univ Leuven Dept Elect Engn ESAT B-3000 Leuven Belgium

Recent years have witnessed an increasing interest in end-to-end learned video compression. Most previous works explore temporal redundancy by detecting and compressing a motion map to warp the reference frame towards the target frame. Yet, it failed to adequately take advantage of the historical priors in the sequential reference frames. In this paper, we propose an Advanced Learned Video Compression (ALVC) approach with the in-loop frame prediction module, which is able to effectively predict the target frame from the previously compressed frames, without consuming any bit-rate. The predicted frame can serve as a better reference than the previously compressed frame, and therefore it benefits the compression performance. The proposed in-loop prediction module is a part of the end-to-end video compression and is jointly optimized in the whole framework. We propose the recurrent and the bi-directional in-loop prediction modules for compressing P-frames and B-frames, respectively. The experiments show the state-of-the-art performance of our ALVC approach in learned video compression. We also outperform the default hierarchical B mode of x265 in terms of PSNR and beat the slowest mode of the SSIM-tuned x265 on MS-SSIM. The project page: https://***/RenYang-home/ALVC.

关键词： Video compression Image coding Streaming media Ions Standards Interpolation prediction algorithms Deep learning video compression in-loop prediction

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Small-Sample Solar Power Interval prediction Based on Instance-Based Transfer Learning

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2023年第5期59卷 5283-5292页

作者： Long, Huan Geng, Runhao Sheng, Wanxing Hui, Hui Li, Rui Gu, Wei Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China China Elect Power Res Inst Beijing Key Lab Distribut Transform Beijing 100192 Peoples R China

In the context of high photovoltaic (PV) penetration, high-quality solar power interval prediction is important for grid system operation. However, in some cases, sufficient amount of data are not available to train a reliable prediction model, especially for new installed PV stations. To tackle this problem, this article proposes a novel small-sample interval prediction model with improved TrAdaBoost (SIPTAB) method for solar power prediction. Sample weight is designed to select the important samples from other data sources. First, Extreme Learning Machine (ELM) with Direct Quantile Regression (DQR) is employed as the base predictor to construct interval boundaries. Second, an improved TrAdaBoost algorithm is proposed to iteratively construct a boosting ensemble interval predictor to enhance the prediction performance with limited amount of data. Third, a two-stage model training strategy is introduced in the architecture to optimize the boosting ensemble interval predictor and further improve prediction quality. Comprehensive experiments based on realistic solar power data are conducted to confirm the superiority of proposed model.

关键词： Predictive models Training Data models Task analysis Boosting Training data prediction algorithms Small-sample scenario interval prediction solar power prediction transfer learning TrAdaBoost

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Variable-Bit-Rate Video Frame-Size prediction by the Extended Kalman Filter Using Levenberg-Marquardt Algorithm

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IEEE TRANSACTIONS ON BROADCASTING 2023年第1期69卷 75-84页

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun San Jose State Univ Dept Appl Data Sci San Jose CA 95192 USA Louisiana State Univ Sch Elect Engn & Comp Sci Baton Rouge LA 70803 USA Guilin Univ Elect Technol Guangxi Key Lab Wireless Wideband Commun & Signal Guilin 541004 Guangxi Peoples R China Guilin Univ Elect Technol Dept Informat & Telecommun Guilin 541004 Guangxi Peoples R China

It is crucial to dynamically predict the future frame-sizes (bit-rates) for multimedia networking. All of the conventional bit-rate predictors are based on the assumption that instantaneous bit-rates are known precisely all the time (in the absence of uncertainty) which is surely not realistic in practice. In this work, we propose a new expectation-maximization (EM) based extended Kalman filter (EKF) to predict the bit-rates, where the EKF state-transition models will be optimized by the Levenberg-Marquardt algorithm (LMA). The main advantages of our proposed novel EKF-based bit-rate prediction approach are given as follows. First, our proposed EKF-based predictor can optimally estimate the bit-rates in the presence of uncertainty and/or noise. Second, our proposed novel EKF-based bit-rate prediction approach does not require a separate classifier to determine the individual frame-types as the conventional approach so our approach would be more robust than the conventional approach. Numerical evaluation of bit-rate (frame-size) prediction is also conducted over three movies encoded by the MPEG-4 standard. Compared to the existing Kalman-filter based bit-rate prediction methods, our proposed new LMA-EKF predictor can achieve much better performance in terms of the normalized mean square error (NMSE) and the inverse of signal-to-noise-ratio (SNR).

关键词： Kalman filters Streaming media Covariance matrices Transform coding prediction algorithms Heuristic algorithms Signal to noise ratio Extended Kalman filter (EKF) multimedia network MPEG-4 Levenberg-Marquardt algorithm (LMA) expectation-maximization (EM) algorithm variable-bit-rate (VBR) video streaming

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Temporal Link prediction With Motifs for Social Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第3期35卷 3145-3158页

作者： Qiu, Zhenyu Wu, Jia Hu, Wenbin Du, Bo Yuan, Guocai Yu, Philip S. Wuhan Univ Sch Comp Sci Wuhan 430072 Hubei Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Macquarie Univ Dept Comp Sydney NSW 2109 Australia Wuhan Univ Shenzhen Res Inst Sch Comp Sci Wuhan 430072 Hubei Peoples R China Wuhan Maritime Commun Res Inst Dept Informat Network Wuhan 430079 Hubei Peoples R China Univ Illinois Dept Comp Sci Chicago IL 60607 USA

Link prediction has attracted considerable attention. Empiricism and the evolution mechanism based approach are the mainstream methods for link prediction. However, one drawback of such approaches is that they usually ignore the dynamic evolution mechanism of social networks, yet being dynamic is an essential characteristic of a social network that exists in every stage of the network's evolution. In this paper, we address the problem of temporal link prediction and investigate social networks from the time dimension with the purpose of dynamic evolution mechanism capturing. First, we separate a temporal network into a series of snapshots. Then, we propose a triad transition matrix prediction algorithm to learn the change of the distribution of triads among the different snapshots. The learned changes in the distribution of triads can capture the dynamic evolution of the network. With a proposed triad transition influence quantification algorithm, we propose a motifs based link prediction method for temporal link prediction. The proposed method can capture the dynamic evolution of temporal networks and is universal than existing methods. Extensive experiments on disparate real-world networks and model networks with controllable evolution demonstrate the effectiveness of the proposed method.

关键词： Social networking (online) Indexes prediction algorithms Convolutional neural networks Predictive models Force Computational modeling Link prediction social networks dynamic networks motifs

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Imbalanced Classification Methods for Student Grade prediction: A Systematic Literature Review

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IEEE ACCESS 2023年 11卷 1970-1989页

作者： Bujang, Siti Dianah Abdul Selamat, Ali Krejcar, Ondrej Mohamed, Farhan Cheng, Lim Kok Chiu, Po Chan Fujita, Hamido Univ Teknol Malaysia Kuala Lumpur Malaysia Japan Int Inst Technol MJIIT Kuala Lumpur 54100 Malaysia Politekn Sultan Idris Shah Fac Informat & Commun Technol Sungai Ayer Tawar 45100 Selangor Malaysia Univ Teknol Malaysia Fac Engn Malaysia & Media & Games Ctr Excellence MagicX Sch Comp Johor Baharu 81310 Malaysia Univ Hradec Kralove Fac Informat & Management Hradec Kralove 50003 Czech Republic Univ Tenaga Nas Coll Comp & Informat Kajang 43000 Selangor Malaysia Univ Malaysia Sarawak Fac Comp Sci & Informat Technol Kota Samarahan 94300 Sarawak Malaysia Iwate Prefectural Univ Fac Software & Informat Sci Takizawa Iwate 0200693 Japan Univ Granada Andalusian Res Inst Data Sci & Comp Intelligence D Granada 18011 Spain i SOMET Inc Assoc Morioka 0200104 Japan

Student success is essential for improving the higher education system student outcome. One way to measure student success is by predicting students' performance based on their prior academic grades. Concerning the significance of this area, various predictive models are widely developed and applied to help the institution identify students at risk of failure. However, building a high-accuracy predictive model is challenging due to the dataset's imbalanced nature, which caused biased results. Therefore, this study aims to review the existing research article by providing a state-of-the-art approach for handling imbalanced classification in higher education, including the best practices of dataset characteristics, methods, and comparative analysis of the proposed algorithms, focusing on student grade prediction context problems. The study also presents the most common balancing methods published from 2015 to 2021 and highlights their impact on resolving imbalanced classification in three approaches: data-level, algorithm-level, and hybrid-level. The survey results reveal that the data-level approach using SMOTE oversampling is broadly applied in determining imbalanced problems for student grade prediction. However, the application of hybrid and feature selection methods supporting the generalization of the predictive model to boost student grade prediction performance is generally lacking. Other than that, some of the strengths and weaknesses of the proposed methods are discussed and summarized for the direction of future research. The outcomes of this review will guide the professionals, practitioners, and academic researchers in dealing with imbalanced classification, mainly in the higher education field.

关键词： Predictive models Systematics Machine learning prediction algorithms Classification algorithms Data mining Bibliographies Imbalanced classification prediction model machine learning student grade prediction education systematic literature review

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