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Adaptive knowledge transfer based on machine learning method for evolutionary multitasking optimization

INFORMATION SCIENCES 2025年 702卷

作者： Shen, Jiangtao Dong, Huachao Tian, Ye Wang, Xinjing Chen, Weixi Zhu, Haijia Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei 230601 Peoples R China Univ Victoria Dept Mech Engn Victoria BC Canada

In recent years, evolutionary multitasking has exhibited significant potential in solving multiple optimization tasks synergistically by the evolution of a single population. The paradigm enables different tasks to share underlying similarities by transferring information to each other, which has been shown to accelerate the convergence of similar tasks. In the absence of prior knowledge of the relationships between optimization tasks, it is not trivial to control the degree of intertask knowledge transfer, thus negative knowledge transfer between tasks frequently occurs to impede convergence behavior. In this paper, we propose a multifactorial evolutionary algorithm (MFEA) based on the machine learning method, termed MFEA-ML, to learn to adaptively transfer online at the individual level to alleviate negative transfer and boost positive transfer. Different from most of the existing algorithms that measure intertask similarities for adaptive knowledge transfer, the proposed method collects training data by tracing the survival status of the individuals generated by intertask transfer and accordingly constructs a machine learning model to guide the transfer of genetic materials from the perspective of individual pairs. The efficacy of MFEA-ML is demonstrated on a series of benchmark problems as well as a practical engineering design scenario involving simultaneous consideration of two mission requirements. In the future, modifying the proposed method to handle expensive multitask optimization problems is a promising direction.

关键词： Evolutionary multitasking Multifactorial optimization Online data-driven learning Negative transfer machine learning method

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Dynamic Recrystallization Grain Identification for a Duplex-Phase Titanium Alloy Based on a machine learning method

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METALS AND MATERIALS INTERNATIONAL 2025年 1-15页

作者： Zhang, Shuai Zhang, Haoyu Sun, Jie Yan, Liyuan Wang, Chuan Zhou, Ge Chen, Lijia Shenyang Univ Technol Sch Mat Sci & Engn Shenyang 110870 Liaoning Peoples R China Shenyang Univ Technol Shenyang Key Lab Adv Struct Mat & Applicat Shenyang 110870 Liaoning Peoples R China Northeastern Univ State Key Lab Rolling & Automat Shenyang 110819 Liaoning Peoples R China Liaoning Univ Coll Light Ind Shenyang 110036 Liaoning Peoples R China

Single-pass isothermal compression experiments for a duplex-phase titanium alloy (Ti-10V-5Al-2.5Fe-0.1B alloy) at 800-920 degrees C and strain rate of 0.0005-0.05 s-1 were carried out. A dynamic recrystallization (DRX) grain identification method based on the optimized random forest model by sparrow search algorithm (SSA-RF) was used to identify DRX grains in the microstructure and realize the prediction of the degree of DRX in the microstructure under the new parameters. To verify the predictive ability of the SSA-RF model, the Extreme Gradient Boosting (XGBoost) model is introduced to identify DRX grains, and the prediction results are compared with those of the SSA-RF model. After statistically calculating the identified DRX grains, the research results show that the average relative absolute errors between the true and predicted values for DRX fraction and average DRX grain size are 9.43% and 14.04%, respectively, which are lower than those of the XGBoost model. The SSA-RF model has a higher precision in identifying DRX grains and predicting the degree of DRX at the new process parameters. In order to realize the prediction of DRX degree under new process parameters without experimental data, new data was constructed as the predicting data for the SSA-RF model based on the average grain size and the average length-diameter ratio data predicted by the SSA-RF model. The predicted results show that the errors between the predicted and true values for average DRX grain size and DRX fraction are 4.21 mu m and 15.53%, respectively. The predicted results give a good indication of the true degree of DRX.

关键词： Duplex-phase titanium alloy DRX grain identification machine learning method

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System for Predicting Neurological Outcomes Following Cardiac Arrest Based on Clinical Predictors Using a machine learning method: The Neurological Outcomes After Cardiac Arrest method

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NEUROCRITICAL CARE 2025年 1-10页

作者： Kim, Tae Jung Suh, Jungyo Park, Soo-Hyun Kim, Youngjoon Ko, Sang-Bae Seoul Natl Univ Coll Med Dept Neurol Seoul South Korea Seoul Natl Univ Hosp Dept Crit Care Med Seoul South Korea Ulsan Univ Asan Med Ctr Dept Urol Coll Med Seoul South Korea Soonchunhyang Univ Hosp Seoul Dept Neurol Seoul South Korea

BackgroundA multimodal approach may prove effective for predicting clinical outcomes following cardiac arrest (CA). We aimed to develop a practical predictive model that incorporates clinical factors related to CA and multiple prognostic tests using machine learning *** neurological outcomes after CA (NOCA) method for predicting poor outcomes were developed using data from 390 patients with CA between May 2018 and June 2023. The outcome was poor neurological outcome, defined as a Cerebral Performance Category score of 3-5 at discharge. We analyzed 31 variables describing the circumstances at CA, demographics, comorbidities, and prognostic studies. The prognostic method was developed based on an extreme gradient-boosting algorithm with threefold cross-validation and hyperparameter optimization. The performance of the predictive model was evaluated using the receiver operating characteristic curve analysis and calculating the area under the curve (AUC).ResultsOf the 390 total patients (mean age 64.2 years;71.3% male), 235 (60.3%) experienced poor outcomes at discharge. We selected variables to predict poor neurological outcomes using least absolute shrinkage and selection operator regression. The Glasgow Coma Scale-M (best motor response), electroencephalographic features, the neurological pupil index, time from CA to return of spontaneous circulation, and brain imaging were found to be important key parameters in the NOCA score. The AUC of the NOCA method was 0.965 (95% confidence interval 0.941-0.976).ConclusionsThe NOCA score represents a simple method for predicting neurological outcomes, with good performance in patients with CA, using a machine learning analysis that incorporates widely available variables.

关键词： Cardiac arrest Prognosis Prognostic study machine learning method

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machine learning method for knowledge discovery experimented with otoneurological data

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COMPUTER methodS AND PROGRAMS IN BIOMEDICINE 2008年第2期91卷 154-164页

作者： Varpa, Kirsi Iltanen, Kati Juhola, Martti Univ Tampere Dept Comp Sci FI-33014 Tampere Finland

We have been interested in developing an otoneurological decision support system that supports diagnostics of vertigo diseases. in this study, we concentrate on testing its inference mechanism and knowledge discovery method. Knowledge is presented as patterns of classes. Each pattern includes attributes with weight and fitness values concerning the class. With the knowledge discovery method it is possible to form fitness values from data. Knowledge formation is based on frequency distributions of attributes. Knowledge formed by the knowledge discovery method is tested with two vertigo data sets and compared to experts' knowledge. The experts' and machine learnt knowledge are also combined in various ways in order to examine effects of weights on classification accuracy. The classification accuracy of knowledge discovery method is compared to 1- and 5-nearest neighbour method and Naive-Bayes classifier. The results showed that knowledge bases combining machine learnt knowledge with the experts' knowledge yielded the best classification accuracies. Further, attribute weighting had an important effect on the classification capability of the system. When considering different diseases in the used data sets, the performance of the knowledge discovery method and the inference method is comparable to other methods employed in this study. (C) 2008 Elsevier Ireland Ltd. All rights reserved.

关键词： knowledge discovery machine learning method otoneurology

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A machine learning method for Differentiating and Predicting Human-Infective Coronavirus Based on Physicochemical Features and Composition of the Spike Protein

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Chinese Journal of Electronics 2021年第5期30卷 815-823页

作者： WANG Chao ZOU Quan Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Hainan Key Laboratory for Computational Science and Application Hainan Normal University

Several Coronaviruses(CoVs) are epidemic pathogens that cause severe respiratory syndrome and are associated with significant morbidity and mortality. In this paper, a machine learning method was developed for predicting the risk of human infection posed by CoVs as an early warning system. The proposed Spike-SVM(Support vector machine) model achieved an accuracy of 97.36% for Human-infective CoV(HCoV) and Nonhuman-infective CoV(Non-HCoV) classification. The top informative features that discriminate HCoVs and Non-HCoVs were identified. Spike-SVM is anticipated to be a useful bioinformatics tool for predicting the infection risk posed by CoVs to humans.

关键词： Spike protein NonHCoVs genetics microorganisms Support vector machine mortality Spike-SVM model Human-infective CoV severe respiratory syndrome machine learning method biology computing predicting Human-infective coronavirus diseases proteins HCoV human infection differentiating Nonhuman-infective CoV infection risk CoVs discriminate HCoVs learning (artificial intelligence) bioinformatics support vector machines informative features significant morbidity molecular biophysics early warning system epidemic pathogens Coronaviruses

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Real-time determination of sandy soil stiffness during vibratory compaction incorporating machine learning method for intelligent compaction

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第5期14卷 1609-1625页

作者： Zhengheng Xu Hadi Khabbaz Behzad Fatahi Di Wu School of Civil and Environmental Engineering University of Technology SydneySydneyAustralia

An emerging real-time ground compaction and quality control, known as intelligent compaction(IC), has been applied for efficiently optimising the full-area compaction. Although IC technology can provide real-time assessment of uniformity of the compacted area, accurate determination of the soil stiffness required for quality control and design remains challenging. In this paper, a novel and advanced numerical model simulating the interaction of vibratory drum and soil beneath is developed. The model is capable of evaluating the nonlinear behaviour of underlying soil subjected to dynamic loading by capturing the variations of damping with the cyclic shear strains and degradation of soil modulus. The interaction of the drum and the soil is simulated via the finite element method to develop a comprehensive dataset capturing the dynamic responses of the drum and the soil. Indeed, more than a thousand three-dimensional(3D) numerical models covering various soil characteristics, roller weights, vibration amplitudes and frequencies were adopted. The developed dataset is then used to train the inverse solver using an innovative machine learning approach, i.e. the extended support vector regression, to simulate the stiffness of the compacted soil by adopting drum acceleration records. Furthermore, the impacts of the amplitude and frequency of the vibration on the level of underlying soil compaction are *** proposed machine learning approach is promising for real-time extraction of actual soil stiffness during compaction. Results of the study can be employed by practising engineers to interpret roller drum acceleration data to estimate the level of compaction and ground stiffness during compaction.

关键词： Intelligent compaction machine learning method Finite element modelling Acceleration response

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Automatic recognition of tweek atmospherics and plasma diagnostics in the lower ionosphere with the machine learning method

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Earth and Planetary Physics 2023年第3期7卷 407-413页

作者： Mao Zhang GaoPeng Lu HaiLiang Huang ZhengWei Cheng YaZhou Chen Steven A.Cummer JiaYi Zheng JiuHou Lei School of Earth and Space Sciences University of Science and Technology of ChinaHefei 230026China Key Laboratory of Atmospheric Optics Anhui Institute of Optics and Fine MechanicsHefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China National Key Laboratory on Electromagnetic Environment Effects Army Engineering UniversityShijiazhuang CampusShijiazhuang 050003China Electrical and Computer Engineering Department Duke UniversityDurhamNC 27708USA

Tweek atmospherics are extremely low frequency and very low frequency pulse signals with frequency dispersion characteristics that originate from lightning discharges and that propagate in the Earth–ionosphere waveguide over long *** this study,we developed an automatic method to recognize tweek atmospherics and diagnose the lower ionosphere based on the machine learning *** differences(automatic−manual)in each ionosphere parameter between the automatic method and the manual method were−0.07±2.73 km,0.03±0.92 cm^(−3),and 91±1,068 km for the ionospheric reflection height(h),equivalent electron densities at reflection heights(Ne),and propagation distance(d),***,the automatic method is capable of recognizing higher harmonic tweek *** evaluation results of the model suggest that the automatic method is a powerful tool for investigating the long-term variations in the lower ionosphere.

关键词： machine learning method tweek atmospherics reflection height D-region ionosphere

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Application of Landsat ETM plus and OLI Data for Foliage Fuel Load Monitoring Using Radiative Transfer Model and machine learning method

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 5100-5110页

作者： Quan, Xingwen Li, Yanxi He, Binbin Cary, Geoffrey J. Lai, Gengke Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 610054 Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou Huzhou 313001 Peoples R China Australian Natl Univ Fenner Sch Environm & Social Canberra ACT 2600 Australia

Foliage fuel load (FFL) is a critical factor affecting crown fire intensity and rate of spread. Satellite observations provide the potential for monitoring FFL dynamics across large areas. Previous studies commonly used empirical methods to estimate FFL, which potentially lacks reproducibility. This study applied Landsat 7 ETM+ and 8 OLI data for FFL retrieval using radiative transfer model (RTM) and machine learning method. To this end, the GeoSail, SAIL, and PROSPECT RTMs were first coupled together to model the near-realistic scenario of a two-layered forest structure. Second, available ecological information was applied to constrain the coupled RTM modeling phases in order to decrease the probability of generating unrealistic simulations. Third, the coupled RTMs were linked to three machine learning models-random forest, support vector machine, and multilayer perceptron-as well as the traditional lookup table. Finally, the performance of each method was validated by FFL measurements from Southwest China and Sweden. The resulting multilayer perceptron (R-2 = 0.77, RMSE = 0.13, and rRMSE = 0.43) outperformed the other three methods. The evaluation of the applicability of the FFL estimates was conducted in a southwest China forest where two occurred in 2014 and 2020. The FFL dynamics from 2013 through 2020 showed that the fire was likely to occur when the FFL accumulated to a critical point (around 27 x 10(6) kg), highlighting the relevance of remote sensing derived FFL estimates for understanding potential fire occurrence.

关键词： Fuels Remote sensing Forestry Earth Biological system modeling Vegetation machine learning Fire fire danger foliage fuel load (FFL) forest inversion Landsat machine learning method radiative transfer model (RTM) remote sensing

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Dynamic stability analysis of CFRP sandwich structure reinforced by advanced nanocomposites via both machine learning method and mathematical simulation

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年第30期31卷 12898-12921页

作者： Zhang, Yong Li, Jing Abbas, Mohamed Henan Polytech Inst Nanyang Henan Peoples R China King Khalid Univ Coll Engn Elect Engn Dept Abha Saudi Arabia

This work introduces a quasi-3D refined theory for assessing the transient dynamic reactions of a sandwich annular sector plate subjected to mechanical shock loading. The sandwich structure has two facesheets composed of graphene origami (GOri)-enabled auxetic metal metamaterials (GOEAMs) and a core composed of carbon fiber reinforced polymer. The auxetic quality of the annular plates is mainly determined by the quantity of graphene and the level of folding in the GOri material. The elements are assessed layer by layer across the thickness of the plates. Micromechanical models supported by genetic programming may be used to predict the position-dependent Poisson's ratio and other material parameters. The concept of Hamiltonian is used to deduce the governing equations of the structure. The equations of motion, which vary with time, are solved using the numerical solution process and the Laplace transform. A comprehensive parametric study is carried out to examine the influence of various geometric and physical parameters on the time-dependent behavior of annular sector plates. The current mathematical modeling results are being compared to the findings of previous works, as well as a machine learning technique. By using this machine learning methodology, it is feasible to computationally solve differential equations at a reduced expense, while simultaneously surmounting the challenge of formulation. To use machine learning techniques, a dependable dataset acquired from either experimental or numerical analysis is necessary. The dataset was created using the quantitative findings of the investigation. Furthermore, the machine learning technique demonstrates its capacity to provide very precise outcomes when predicting the transient behavior of the existing structure under novel loading and boundary circumstances.

关键词： transient analysis elasto-dynamic bending metamaterial sandwich structures quasi-3D refined theory machine learning method

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Monitoring the Burden of Seizures and Highly Epileptiform Patterns in Critical Care with a Novel machine learning method

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NEUROCRITICAL CARE 2021年第3期34卷 908-917页

作者： Kamousi, Baharan Karunakaran, Suganya Gururangan, Kapil Markert, Matthew Decker, Barbara Khankhanian, Pouya Mainardi, Laura Quinn, James Woo, Raymond Parvizi, Josef Ceribell Inc 2483 Old Middlefield WaySuite 120 Mountain View CA USA Mt Sinai Hosp Dept Neurol New York NY USA Stanford Univ Dept Neurol & Neurol Sci Med Ctr 300 Pasteur Dr Stanford CA 94305 USA Univ Penn Dept Neurol Perelman Sch Med Philadelphia PA 19104 USA Stanford Univ Dept Emergency Med Med Ctr Stanford CA 94305 USA

Introduction Current electroencephalography (EEG) practice relies on interpretation by expert neurologists, which introduces diagnostic and therapeutic delays that can impact patients' clinical outcomes. As EEG practice expands, these experts are becoming increasingly limited resources. A highly sensitive and specific automated seizure detection system would streamline practice and expedite appropriate management for patients with possible nonconvulsive seizures. We aimed to test the performance of a recently FDA-cleared machine learning method (Clarit gamma, Ceribell Inc.) that measures the burden of seizure activity in real time and generates bedside alerts for possible status epilepticus (SE). methods We retrospectively identified adult patients (n = 353) who underwent evaluation of possible seizures with Rapid Response EEG system (Rapid-EEG, Ceribell Inc.). Automated detection of seizure activity and seizure burden throughout a recording (calculated as the percentage of ten-second epochs with seizure activity in any 5-min EEG segment) was performed with Clarit gamma, and various thresholds of seizure burden were tested (>= 10% indicating >= 30 s of seizure activity in the last 5 min, >= 50% indicating >= 2.5 min of seizure activity, and >= 90% indicating >= 4.5 min of seizure activity and triggering a SE alert). The sensitivity and specificity of Clarit gamma's real-time seizure burden measurements and SE alerts were compared to the majority consensus of at least two expert neurologists. Results Majority consensus of neurologists labeled the 353 EEGs as normal or slow activity (n = 249), highly epileptiform patterns (HEP,n = 87), or seizures [n = 17, nine longer than 5 min (e.g., SE), and eight shorter than 5 min]. The algorithm generated a SE alert (>= 90% seizure burden) with 100% sensitivity and 93% specificity. The sensitivity and specificity of various thresholds for seizure burden during EEG recordings for detecting patients with seizures were 100% and 82%

关键词： machine learning method Neurology Electroencephalography Status epilepticus Seizure burden

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