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High Accuracy COVID-19 prediction Using Optimized Union Ensemble Feature Selection Approach

IEEE ACCESS 2024年 12卷 122942-122958页

作者： Jafar, Abbas Lee, Myungho Myongji Univ Dept Comp Engn Yongin 17058 South Korea

Recently, the world has been dealing with a severe outbreak of COVID-19. The rapid transmission of the virus causes mild to severe cases of cough, fever, body aches, organ failures, and death. An increasing number of patients, fewer diagnostic options, and extended waiting periods for test results all put pressure on healthcare systems, increasing the virus's spread. A concise and accurate automatic diagnosis is crucial to identify infected patients in the early stage. This paper proposes a machine learning-based predictive framework to identify COVID-19 cases from clinical data using an optimized union ensemble feature selection (OUEFS) approach. The OUEFS is based on the union ensemble of the feature subsets obtained through a rigorous feature selection (FS) process. It also involves a performance optimization of the ML classifiers. Initially the OUEFS identified key features from the publicly accessible COVID-19 dataset using FS methods such as Mutual Information Feature Selection (MIFS), Recursive Feature Elimination (RFE), and the RidgeCV. The most important features were selected using Top-k thresholding technique. Then selected subsets of features were integrated using a union ensemble approach where an optimal combination of features with enhanced predictive power is derived. This composite feature set was subsequently utilized for model training and evaluation. The classification was conducted using machine learning algorithms such as linear SVM, gradient boosting (GB), logistic regression (LR), and Adaboost to compare their effectiveness on individual and combined feature subsets. We also conducted a Genetic Algorithm (GA) based hyperparameter optimization (HPO) which further refined our training process and enhanced the accuracy of our proposed approach. Experimental results show that the union ensemble of MIFS and RidgeCV FS techniques and the Adaboost classifier and GA HPO achieved 96.30% accuracy. Our optimized union ensemble approach demonstrated supe

关键词： COVID-19 Feature extraction Radio frequency Support vector machines Accuracy prediction algorithms Machine learning Ensemble learning Hyperparameter optimization feature selection COVID-19 classification ensemble learning hyper-parameter optimization

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Tri-Branch Convolutional Neural Networks for Top-k Focused Academic Performance prediction

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第1期35卷 439-450页

作者： Cui, Chaoran Zong, Jian Ma, Yuling Wang, Xinhua Guo, Lei Chen, Meng Yin, Yilong Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan 250014 Peoples R China Shandong Univ Sch Software Jinan 250101 Peoples R China Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250101 Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China

Academic performance prediction aims to leverage student-related information to predict their future academic outcomes, which is beneficial to numerous educational applications, such as personalized teaching and academic early warning. In this article, we reveal the students' behavior trajectories by mining campus smartcard records, and capture the characteristics inherent in trajectories for academic performance prediction. Particularly, we carefully design a tri-branch convolutional neural network (CNN) architecture, which is equipped with rowwise, columnwise, and depthwise convolutions and attention operations, to effectively capture the persistence, regularity, and temporal distribution of student behavior in an end-to-end manner, respectively. However, different from existing works mainly targeting at improving the prediction performance for the whole students, we propose to cast academic performance prediction as a top-k ranking problem, and introduce a top-k focused loss to ensure the accuracy of identifying academically at-risk students. Extensive experiments were carried out on a large-scale real-world dataset, and we show that our approach substantially outperforms recently proposed methods for academic performance prediction. For the sake of reproducibility, our codes have been released at https://***/ZongJ1111/Academic-Performance-prediction.

关键词： Behavioral sciences Trajectory prediction algorithms Electronic learning Convolutional neural networks Tensors Smart phones Academic performance prediction at-risk student identification campus behavior trajectory convolutional neural networks

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Pedestrian Trajectory prediction and Pose Estimation Considering Behavioral Characteristic Relationships and Noise Inhibition

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IEEE ACCESS 2024年 12卷 46907-46918页

作者： Zhou, Jincao Fu, Weiping Ning, Benyu He, Siyuan Xian Univ Technol Coll Mech & Precis Instrument Engn Xian 710048 Peoples R China Xian Int Univ Coll Engn Xian 710077 Peoples R China

In the domain of pedestrian trajectory prediction(PTP) from a roadbed perspective, the visibility of pedestrian feature points is inevitably compromised by external noise interference, impacting both pedestrian pose estimation(PPE) and PTP. This paper presents an innovative model for PTP. The model not only tackles noise interference but also takes into account the inherent correlation between pedestrian pose features and trajectory coordinates. To tackle the challenge of noise interference during pedestrian crossing, we reframe it as an anomalous feature detection problem using the Graph Deviation Network (GDN). Subsequently, we enhance the Long Short-Term Memory (LSTM) module by incorporating a time-domain anomaly suppression module, resulting in the development of an Anomaly Inhibition-LSTM (AI-LSTM) with robust noise suppression capabilities. Finally, by integrating the predicted values of behavioral pose and trajectory position, considering the behavioral characteristic relationship resolved by the GDN algorithm, we achieve accurate prediction and pose estimation of pedestrian crossing trajectories amidst noise interference. Experimental results demonstrate superior performance of our algorithm in the PPE task when compared to GDN and LSTM algorithms. In the PTP task, our algorithm exhibits performance comparable to the Transformer-based method, with the added advantage of improved interpretability.

关键词： Pedestrians Trajectory Interference Pose estimation Personal protective equipment Long short term memory prediction algorithms Predictive models Autonomous vehicles Trajectory prediction pose estimation autonomous vehicles

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Refined Chroma From Luma prediction in AV1 Based on Color Component Grouping

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第6期20卷 8940-8948页

作者： Huo, Junyan Zhang, Zhenyao Song, Jiarun Ma, Yanzhuo Yang, Fuzheng Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

Chroma from luma (CfL) in AOMedia Video 1 (AV1) utilizes the correlation between color components to derive the predicted chroma from the reconstructed luma. The mean of the predicted chroma, i.e., dc, is set to the chroma average of the neighboring regions. Since the neighboring regions have limited reference samples, the distribution of the coding block is hard to be predicted from the neighboring regions, resulting in an error in the predicted dc. A new feature of CfL is that the luma of the coding block has been reconstructed. Using the luma information, the ground-truth distribution of the coding block can be built. Based on this observation, a refined dc prediction is proposed based on color component grouping (GDC). We design an adaptive grouping scheme and use the number of luma and the chroma average in each group to derive a refined dc. The offline experiment verifies that the proposed method provides a dc with high prediction accuracy. Compared with the libaom of AV1, the proposed GDC achieves BD-rate reductions of 0.15%, 0.90%, and 0.98% for the Y, Cb, and Cr components. With the increased group numbers, additional coding gains can be provided. The proposed GDC can be implemented with a small complexity, which is hardware-friendly.

关键词： Encoding Color Histograms Video coding prediction algorithms Image color analysis Vectors AOMedia Video 1 (AV1) chroma from luma (CfL) cross component prediction (CCP) grouping video coding

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Real-Time TCP Packet Loss prediction Using Machine Learning

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IEEE ACCESS 2024年 12卷 159622-159634页

作者： Welzl, Michael Islam, Safiqul von Stephanides, Maximilian Univ Oslo Dept Informat Oslo Norway Oslo Metropolitan Univ Dept Comp Sci Oslo Norway CESifo Oslo Norway

Congestion and resulting packet loss in TCP connections can lead to performance degradation and reduce the Quality of Experience (QoE) for end users. Many common TCP congestion control algorithms therefore adjust their sending rate proactively, using heuristics based on measured delay and/or throughput. We have investigated whether it would be possible to replace such heuristics with Machine Learning (ML), such that Internet hosts could meaningfully react when a packet loss is predicted to happen. For example, such a reaction could involve the use of Forward Erasure Correction (FEC) to protect against the loss, or to dynamically tune parameters of TCP. We present two ML models that were trained for Reno and Cubic flows, respectively, and show that they can be beneficially applied in real time. We do this by informing a TCP sender to reduce its rate via a local, artificially generated Explicit Congestion Notification (ECN) signal at the sender before packet loss would have happened.

关键词： Packet loss Real-time systems Delays Measurement Internet prediction algorithms Training data Throughput Predictive models Monitoring Telecommunication congestion control Machine learning TCP congestion control machine learning ECN

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Interactive prediction and Decision-Making for Autonomous Vehicles: Online Active Learning With Traffic Entropy Minimization

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 17718-17732页

作者： Zhang, Yiran Lou, Shanhe Hang, Peng Huang, Wenhui Yang, Lie Lv, Chen Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore Nanyang Technol Univ Dept Elect & Comp Engn Singapore 639798 Singapore Tongji Univ Dept Traff Engn Shanghai 200070 Peoples R China

Interacting with the surrounding road users is crucial for autonomous vehicles (AV). However, the inherent multimodality and uncertainties associated with traffic participants (TP) pose challenges in AVs' prediction and decision-making (PnD). A primary challenge is adapting predictors trained on static offline datasets to the dynamic, diverse data streams encountered in reality. Secondly, utilizing one single forecast trajectory with the highest probability for decision-making contains potential risks as it neglects that even a small probability represents a subset of TP behaviors. Based on the existing prediction backbone, we propose an online learning approach incorporating pseudo-labels inferred from partial feedback as compensation for conventional methodologies, considering both the commonsense and personalization facets of driving. Drawing inspiration from the second law of thermodynamics, we propose to minimize microscopic traffic entropy as an additional objective in decision-making. This objective aims to reduce the chaos of traffic scenes, thus achieving more predictable future interactions and, conversely, making future decisions easier. Through real-time human-in-the-loop experiments, we quantifiably and comparably reveal that adopting one single trajectory without online learning in PnD is risky. However, this reliability is verified to be significantly improved by our proposed techniques, and the efficacy is further analyzed in a subsequent qualitative study. A static experiment transferring the prediction algorithm trained exclusively on Argoverse 2 to datasets including NGSIM, HighD, RounD, and NuScenes is also conducted, demonstrating that the proposed correction can effectively mitigate the gap between the datasets and real-world scenarios.

关键词： Decision making Trajectory Entropy Uncertainty Vehicle dynamics prediction algorithms Predictive models Trajectory prediction decision-making autonomous driving online learning microscopic traffic entropy

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Group Penalized Multinomial Logit Models and Stock Return Direction prediction

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第6期70卷 4297-4318页

作者： Hu, Xuemei Yang, Junwen Chongqing Technol & Business Univ Chongqing Key Lab Stat Intelligent Comp & Monitori Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Sch Math & Stat Chongqing 400067 Peoples R China

Multinomial logit model(MLM) has been proposed as the most frequently regression model for multi-category response. To deal with correlated data, in this paper we propose G-LASSO/G-SCAD/G-MCP penalized MLM to exert class discovery and class prediction for high-dimensional multi-category classification problems. Firstly, we develop a group coordinate descent(GCD) algorithm to simultaneously complete group selection and group estimation, and prove the convergence to the proposed GCD algorithm under mild conditions. Secondly, we apply the training set and group estimations to obtain class probability estimators, choose the Bayes classifier to identify class index information, and introduce the testing set and a few measures to assess multi-category prediction performance. Simulations show that the proposed methods outperform LASSO/SCAD/MCP penalized MLM, 3 deep learning methods and 3 machine learning methods in terms of Kappa, PDI(polytomous discrimination index), Optimal or Average Accuracy. Finally, we combine group penalized MLM with 58 technical indicators to predict up trends, sideways trends and down trends for stock returns, and show that the proposed methods outperform the other 9 methods in terms of Accuracy, PDI, Kappa and HUM. Therefore, the proposed method can not only accommodate the correlation information, but also improve multi-category prediction performance by shrinking group coefficients.

关键词： prediction algorithms Logistic regression Vectors Convergence Market research Optimization Machine learning algorithms Deep learning models gCD algorithm group estimations machine learning models multi-category prediction performance

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Wireless Edge Caching and Content Popularity prediction Using Machine Learning

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IEEE CONSUMER ELECTRONICS MAGAZINE 2024年第4期13卷 32-41页

作者： Krishnendu, S. Bharath, B. N. Bhatia, Vimal Nebhen, Jamel Dobrovolny, Michal Ratnarajah, Tharmalingam Indian Inst Technol Indore Dept Elect Engn Indore India Indian Inst Technol Dharwad Dept Elect Engn Dharwad India Indian Inst Technol Indore Indore India Univ Hradec Kralove Hradec Kralove Czech Republic Prince Sattam Bin Abdulaziz Univ Alkharj Saudi Arabia Univ Hradec Kralove Appl Informat Hradec Kralove Czech Republic Univ Edinburgh Inst Digital Commun Edinburgh Midlothian Scotland

The ever pervasive growth in information services and technology has resulted in the outbreak of demand for data in the wireless networks. This has made the network operators to ponder over the imminent difficulties, such as computing capabilities and fronthaul-backhaul link capacities. Hence, to bridge the gap between the cloud capacity and requirement of mobile services by the network edges, edge computing, and caching techniques have been gaining more and more attention from researchers across the world. Further, motivated by the successful applications of machine learning (ML) in solving complex and dynamic problems, in this article, it has been used to advance edge caching capabilities. The proposed ML-based algorithms have been evaluated and proved to have better performance compared with the existing conventional algorithms. The mean squared error (mse), for the proposed deep learning (DL) algorithm, is 20-times less than the existing algorithms and while comparing with the simple neural network, the gain in mse for the proposed DL algorithm is observed around 27%. Similarly for the federated learning-based caching algorithm, the average cache hit gain is of the order of $10<<^>>{4}$, hence demonstrating the benefit of the proposed algorithms. In addition, opportunities and challenges for a promising upcoming future of ML in edge computing and content popularity prediction have also been discussed.

关键词： Q-learning Wireless networks Machine learning algorithms Device-to-device communication prediction algorithms Markov processes Consumer electronics

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prediction Window Selection in FCS-MPC for Two-Level VSI Applications

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2024年第3期39卷 3135-3143页

作者： Zafra, Eduardo Vazquez, Sergio Dragicevic, Tomislav Alcaide, Abraham M. Leon, Jose I. Franquelo, Leopoldo G. Univ Seville Elect Dept Seville 41092 Spain Univ Seville Lab Engn Energy & Environm Sustainabil Seville 41092 Spain DTU Dept Wind & Energy Syst DK-2800 Kongens Lyngby Denmark

Semiconductors in power converters are operated in switched-mode. For a given sampling frequency, a digital controller computes the next actuation according to several objectives. Typically one desires to regulate current or voltage values with minimal error and harmonic distortion. Higher switching frequency of the semiconductor is helpful as the granularity of the problem is improved and the harmonic spectrum is shifted toward high-order harmonics that are easier to filter with passive elements. However, the switching losses increase. Finite control set model predictive control (FCS-MPC) is proven to be an excellent control method with a great ability to maximize harmonic quality in terms of the switching frequency, particularly when the controller is configured to consider long prediction horizons. Nonetheless, in order to avoid computational overruns, the prediction horizon and the sampling frequency cannot be indefinitely increased. In this article, the tradeoff between these two parameters is studied, providing guidelines on FCS-MPC tuning for different configurations.

关键词： Control systems Predictive models Optimization Switching frequency prediction algorithms Switches Regulation Digital control model predictive control (MPC) three-phase dc-ac power inverters

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Lightweight Fault prediction Method for Edge Networks

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IEEE INTERNET OF THINGS JOURNAL 2024年第6期11卷 11117-11136页

作者： Yang, Yang Mei, Jingting Zhang, Zhenwei Long, Yuhan Liu, Aolun Gao, Zhipeng Rui, Lanlan Beijing Univ Posts & Telecommun State Key Lab Network & Switching Technol Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Network Management Ctr Beijing Peoples R China

The occurrence of faults increases in edge networks as the service types and component architecture become increasingly complex. Traditional centralized cloud fault prediction technology cannot be directly applied to edge network systems that are typically required to handle real-time data due to their high-computational complexity. Therefore, this article proposes a lightweight fault prediction algorithm for edge networks that realizes fault prediction with cross-layer cooperation. First, for edge devices with limited computation resources, the time feature lightweight extraction method of brain neurology fusion long short term memory (LSTM) based on scene reappearance mechanisms is proposed, which solves the single-step dependency problem of neurons and improves the accuracy. And the LSTM neuron connection method based on pulse dynamics is designed. This method prunes the structure of the LSTM network based on relevant knowledge of biological neurology to realize a lightweight time feature extraction model of fault information. Then, on the edge server side, a spatial feature lightweight extraction method based on a two-way residual structure is proposed. This method uses decomposition convolution to reduce the number of network parameters and realize a lightweight model. Finally, the spatio-temporal correlation features of the extracted fault information are spliced to realize fault prediction. To verify the effectiveness of the proposed model, we compare the improved model with the existing fault prediction model. The experiments show that the algorithm proposed in this article has higher accuracy, lower complexity and lower memory requirements. Therefore, it has high-deployment potential in edge network scenarios.

关键词： Feature extraction Servers Predictive models prediction algorithms Telecommunications Probability Neurons Edge networks fault prediction feature extraction spatio-temporal correlation

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