To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and ex...To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and expert experience,which limits their adaptability under variable operating conditions and strong noise environments,severely affecting the generalization capability of diagnostic models.To address this issue,this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning(AutoML).The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary fault representations.On this basis,automated machine learning techniques are introduced to enable end-to-end diagnostic workflow construction and optimal model configuration acquisition.Finally,diagnostic decisions are achieved by automatically integrating the predictions of multiple high-performance base models.Experimental results on a centrifugal pump vibration and acoustic dataset demonstrate that the proposed framework achieves high diagnostic accuracy under noise-free conditions and maintains strong robustness under noisy interference,validating its efficiency,scalability,and practical value for rotating machinery fault diagnosis.展开更多
The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods...The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource demands.Automated Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification tasks.This paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these limitations.Our method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature groups.The Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization problem.Empirical validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.展开更多
Landslide hazard mapping is essential for regional landslide hazard management.The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County,China based on an automated machin...Landslide hazard mapping is essential for regional landslide hazard management.The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County,China based on an automated machine learning framework(AutoGluon).A total of 2241 landslides were identified from satellite images before and after the rainfall event,and 10 impact factors including elevation,slope,aspect,normalized difference vegetation index(NDVI),topographic wetness index(TWI),lithology,land cover,distance to roads,distance to rivers,and rainfall were selected as indicators.The WeightedEnsemble model,which is an ensemble of 13 basic machine learning models weighted together,was used to output the landslide hazard assessment results.The results indicate that landslides mainly occurred in the central part of the study area,especially in Hetian and Shanghu.Totally 102.44 s were spent to train all the models,and the ensemble model WeightedEnsemble has an Area Under the Curve(AUC)value of92.36%in the test set.In addition,14.95%of the study area was determined to be at very high hazard,with a landslide density of 12.02 per square kilometer.This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County.展开更多
By identifying and responding to any malicious behavior that could endanger the system,the Intrusion Detection System(IDS)is crucial for preserving the security of the Industrial Internet of Things(IIoT)network.The be...By identifying and responding to any malicious behavior that could endanger the system,the Intrusion Detection System(IDS)is crucial for preserving the security of the Industrial Internet of Things(IIoT)network.The benefit of anomaly-based IDS is that they are able to recognize zeroday attacks due to the fact that they do not rely on a signature database to identify abnormal activity.In order to improve control over datasets and the process,this study proposes using an automated machine learning(AutoML)technique to automate the machine learning processes for IDS.Our groundbreaking architecture,known as AID4I,makes use of automatic machine learning methods for intrusion detection.Through automation of preprocessing,feature selection,model selection,and hyperparameter tuning,the objective is to identify an appropriate machine learning model for intrusion detection.Experimental studies demonstrate that the AID4I framework successfully proposes a suitablemodel.The integrity,security,and confidentiality of data transmitted across the IIoT network can be ensured by automating machine learning processes in the IDS to enhance its capacity to identify and stop threatening activities.With a comprehensive solution that takes advantage of the latest advances in automated machine learning methods to improve network security,AID4I is a powerful and effective instrument for intrusion detection.In preprocessing module,three distinct imputation methods are utilized to handle missing data,ensuring the robustness of the intrusion detection system in the presence of incomplete information.Feature selection module adopts a hybrid approach that combines Shapley values and genetic algorithm.The Parameter Optimization module encompasses a diverse set of 14 classification methods,allowing for thorough exploration and optimization of the parameters associated with each algorithm.By carefully tuning these parameters,the framework enhances its adaptability and accuracy in identifying potential intrusions.Experimental results demonstrate that the AID4I framework can achieve high levels of accuracy in detecting network intrusions up to 14.39%on public datasets,outperforming traditional intrusion detection methods while concurrently reducing the elapsed time for training and testing.展开更多
Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically ...Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual involvement.This manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series models.The complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple junctures.To surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series prediction.However,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series tasks.Therefore,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time series.ARIMA is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing efforts.Using multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term.Furthermore,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural Networks.Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.展开更多
Epilepsy is a common neurological disease and severely affects the daily life of patients.The automatic detection and diagnosis system of epilepsy based on electroencephalogram(EEG)is of great significance to help pat...Epilepsy is a common neurological disease and severely affects the daily life of patients.The automatic detection and diagnosis system of epilepsy based on electroencephalogram(EEG)is of great significance to help patients with epilepsy return to normal life.With the development of deep learning technology and the increase in the amount of EEG data,the performance of deep learning based automatic detection algorithm for epilepsy EEG has gradually surpassed the traditional hand-crafted approaches.However,the neural architecture design for epilepsy EEG analysis is time-consuming and laborious,and the designed structure is difficult to adapt to the changing EEG collection environment,which limits the application of the epilepsy EEG automatic detection system.In this paper,we explore the possibility of Automated Machine Learning(AutoML)playing a role in the task of epilepsy EEG detection.We apply the neural architecture search(NAS)algorithm in the AutoKeras platform to design the model for epilepsy EEG analysis and utilize feature interpretability methods to ensure the reliability of the searched model.The experimental results show that the model obtained through NAS outperforms the baseline model in performance.The searched model improves classification accuracy,F1-score and Cohen’s kappa coefficient by 7.68%,7.82%and 9.60%respectively than the baseline model.Furthermore,NASbased model is capable of extracting EEG features related to seizures for classification.展开更多
The uniaxial compressive strength(UCS)of rocks is a crucial indicator for evaluating the bearing capacity of geological structures in rock engineering,and it holds significant implications for disaster management.Howe...The uniaxial compressive strength(UCS)of rocks is a crucial indicator for evaluating the bearing capacity of geological structures in rock engineering,and it holds significant implications for disaster management.However,direct measurement poses a significant challenge.Therefore,simpler alternatives such as Schmidt hammer rebound number(SRn),P-wave velocity(Vp),and point load index(Is)are frequently used to estimate UCS indirectly.In this study,we compiled a comprehensive dataset of 1168 samples that included SRn,Vp,Is,and UCS values.The dataset was refined using an isolation forest algorithm,which identified and removed 280 outliers,leaving a dataset of 888 samples for analysis.We developed and assessed an automated machine learning(AutoML)model for predicting UCS,introducing a novel approach to tackle this prediction challenge.Additionally,we compared models enhanced by Bayesian optimization,including multi-layer perceptron(MLP),support vector machine(SVM),Gaussian process regression(GPR),and K-nearest neighbor(KNN).Among these,the AutoML model demonstrated superior performance in UCS prediction,offering a rapid and efficient method for estimating UCS in engineering applications and enabling intelligent classification of rock masses.The study also evaluated the sensitivity and contribution of SRn,Vp,and Is in UCS estimation by various techniques,including permutation feature importance(PFI),SHapley Additive exPlanations(SHAP),and local interpretable model-agnostic explanations(LIME).The results underscore that the AutoML approach not only streamlines UCS modeling but also provides a robust and comprehensive solution,significantly enhancing the accuracy and efficiency of the prediction process.展开更多
Groundwater contamination source identification(GCSI)is a prerequisite for contamination risk evaluation and efficient groundwater contamination remediation programs.The boundary condition generally is set as known va...Groundwater contamination source identification(GCSI)is a prerequisite for contamination risk evaluation and efficient groundwater contamination remediation programs.The boundary condition generally is set as known variables in previous GCSI studies.However,in many practical cases,the boundary condition is complicated and cannot be estimated accurately in advance.Setting the boundary condition as known variables may seriously deviate from the actual situation and lead to distorted identification results.And the results of GCSI are affected by multiple factors,including contaminant source information,model parameters,boundary condition,etc.Therefore,if the boundary condition is not estimated accurately,other factors will also be estimated inaccurately.This study focuses on the unknown boundary condition and proposed to identify three types of unknown variables(contaminant source information,model parameters and boundary condition)innovatively.When simulation-optimization(S-O)method is applied to GCSI,the huge computational load is usually reduced by building surrogate models.However,when building surrogate models,the researchers need to select the models and optimize the hyperparameters to make the model powerful,which can be a lengthy process.The automated machine learning(AutoML)method was used to build surrogate model,which automates the model selection and hyperparameter optimization in machine learning engineering,largely reducing human operations and saving time.The accuracy of AutoML surrogate model is compared with the surrogate model used in eXtreme Gradient Boosting method(XGBoost),random forest method(RF),extra trees regressor method(ETR)and elasticnet method(EN)respectively,which are automatically selected in AutoML engineering.The results show that the surrogate model constructed by AutoML method has the best accuracy compared with the other four methods.This study provides reliable and strong support for GCSI.展开更多
The plethora of complex Artificial Intelligence(AI)algorithms and available High-Performance Computing(HPC)power stimulates the expeditious development of AI components with heterogeneous designs.Consequently,the need...The plethora of complex Artificial Intelligence(AI)algorithms and available High-Performance Computing(HPC)power stimulates the expeditious development of AI components with heterogeneous designs.Consequently,the need for cross-stack performance benchmarking of AI-HPC systems has rapidly emerged.In particular,the de facto HPC benchmark,LINPACK,cannot reflect the AI computing power and input/output performance without a representative workload.Current popular AI benchmarks,such as MLPerf,have a fixed problem size and therefore limited scalability.To address these issues,we propose an end-to-end benchmark suite utilizing automated machine learning,which not only represents real AI scenarios,but also is auto-adaptively scalable to various scales of machines.We implement the algorithms in a highly parallel and flexible way to ensure the efficiency and optimization potential on diverse systems with customizable configurations.We utilize Operations Per Second(OPS),which is measured in an analytical and systematic approach,as a major metric to quantify the AI performance.We perform evaluations on various systems to ensure the benchmark’s stability and scalability,from 4 nodes with 32 NVIDIA Tesla T4(56.1 Tera-OPS measured)up to 512 nodes with 4096 Huawei Ascend 910(194.53 Peta-OPS measured),and the results show near-linear weak scalability.With a flexible workload and single metric,AIPerf can easily scale on and rank AI-HPC,providing a powerful benchmark suite for the coming supercomputing era.展开更多
In healthcare,the persistent challenge of arrhythmias,a leading cause of global mortality,has sparked extensive research into the automation of detection using machine learning(ML)algorithms.However,traditional ML and...In healthcare,the persistent challenge of arrhythmias,a leading cause of global mortality,has sparked extensive research into the automation of detection using machine learning(ML)algorithms.However,traditional ML and AutoML approaches have revealed their limitations,notably regarding feature generalization and automation efficiency.This glaring research gap has motivated the development of AutoRhythmAI,an innovative solution that integrates both machine and deep learning to revolutionize the diagnosis of arrhythmias.Our approach encompasses two distinct pipelines tailored for binary-class and multi-class arrhythmia detection,effectively bridging the gap between data preprocessing and model selection.To validate our system,we have rigorously tested AutoRhythmAI using a multimodal dataset,surpassing the accuracy achieved using a single dataset and underscoring the robustness of our methodology.In the first pipeline,we employ signal filtering and ML algorithms for preprocessing,followed by data balancing and split for training.The second pipeline is dedicated to feature extraction and classification,utilizing deep learning models.Notably,we introduce the‘RRI-convoluted trans-former model’as a novel addition for binary-class arrhythmias.An ensemble-based approach then amalgamates all models,considering their respective weights,resulting in an optimal model pipeline.In our study,the VGGRes Model achieved impressive results in multi-class arrhythmia detection,with an accuracy of 97.39%and firm performance in precision(82.13%),recall(31.91%),and F1-score(82.61%).In the binary-class task,the proposed model achieved an outstanding accuracy of 96.60%.These results highlight the effectiveness of our approach in improving arrhythmia detection,with notably high accuracy and well-balanced performance metrics.展开更多
Android smartphones have become an integral part of our daily lives,becoming targets for ransomware attacks.Such attacks encrypt user information and ask for payment to recover it.Conventional detection mechanisms,suc...Android smartphones have become an integral part of our daily lives,becoming targets for ransomware attacks.Such attacks encrypt user information and ask for payment to recover it.Conventional detection mechanisms,such as signature-based and heuristic techniques,often fail to detect new and polymorphic ransomware samples.To address this challenge,we employed various ensemble classifiers,such as Random Forest,Gradient Boosting,Bagging,and AutoML models.We aimed to showcase how AutoML can automate processes such as model selection,feature engineering,and hyperparameter optimization,to minimize manual effort while ensuring or enhancing performance compared to traditional approaches.We used this framework to test it with a publicly available dataset from the Kaggle repository,which contains features for Android ransomware network traffic.The dataset comprises 392,024 flow records,divided into eleven groups.There are ten classes for various ransomware types,including SVpeng,PornDroid,Koler,WannaLocker,and Lockerpin.There is also a class for regular traffic.We applied a three-step procedure to select themost relevant features:filter,wrapper,and embeddedmethods.The Bagging classifier was highly accurate,correctly getting 99.84%of the time.The FLAML AutoML framework was evenmore accurate,correctly getting 99.85%of the time.This is indicative of howwellAutoML performs in improving things with minimal human assistance.Our findings indicate that AutoML is an efficient,scalable,and flexible method to discover Android ransomware,and it will facilitate the development of next-generation intrusion detection systems.展开更多
The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its...The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible .展开更多
The present study extracts human-understandable insights from machine learning(ML)-based mesoscale closure in fluid-particle flows via several novel data-driven analysis approaches,i.e.,maximal information coefficient...The present study extracts human-understandable insights from machine learning(ML)-based mesoscale closure in fluid-particle flows via several novel data-driven analysis approaches,i.e.,maximal information coefficient(MIC),interpretable ML,and automated ML.It is previously shown that the solidvolume fraction has the greatest effect on the drag force.The present study aims to quantitativelyinvestigate the influence of flow properties on mesoscale drag correction(H_(d)).The MIC results showstrong correlations between the features(i.e.,slip velocity(u^(*)_(sy))and particle volume fraction(εs))and thelabel H_(d).The interpretable ML analysis confirms this conclusion,and quantifies the contribution of u^(*)_(sy),εs and gas pressure gradient to the model as 71.9%,27.2%and 0.9%,respectively.Automated ML without theneed to select the model structure and hyperparameters is used for modeling,improving the predictionaccuracy over our previous model(Zhu et al.,2020;Ouyang,Zhu,Su,&Luo,2021).展开更多
Improving early diagnosis of autism spectrum disorder(ASD)in children increasingly relies on predictive models that are reliable and accessible to non-experts.This study aims to develop such models using Python-based ...Improving early diagnosis of autism spectrum disorder(ASD)in children increasingly relies on predictive models that are reliable and accessible to non-experts.This study aims to develop such models using Python-based tools to improve ASD diagnosis in clinical settings.We performed exploratory data analysis to ensure data quality and identify key patterns in pediatric ASD data.We selected the categorical boosting(CatBoost)algorithm to effectively handle the large number of categorical variables.We used the PyCaret automated machine learning(AutoML)tool to make the models user-friendly for clinicians without extensive machine learning expertise.In addition,we applied Shapley additive explanations(SHAP),an explainable artificial intelligence(XAI)technique,to improve the interpretability of the models.Models developed using CatBoost and other AI algorithms showed high accuracy in diagnosing ASD in children.SHAP provided clear insights into the influence of each variable on diagnostic outcomes,making model decisions transparent and understandable to healthcare professionals.By integrating robust machine learning methods with user-friendly tools such as PyCaret and leveraging XAI techniques such as SHAP,this study contributes to the development of reliable,interpretable,and accessible diagnostic tools for ASD.These advances hold great promise for supporting informed decision-making in clinical settings,ultimately improving early identification and intervention strategies for ASD in the pediatric population.However,the study is limited by the dataset’s demographic imbalance and the lack of external clinical validation,which should be addressed in future research.展开更多
Calorific value is one of the most important properties of coal.Machine learning(ML)can be used in the prediction of calorific value to reduce experimental costs.China is one of the world’s largest coal production co...Calorific value is one of the most important properties of coal.Machine learning(ML)can be used in the prediction of calorific value to reduce experimental costs.China is one of the world’s largest coal production countries and coal occupies an important position in its national energy structure.However,ML models with a large database for the overall regions of China are still missing.Based on the extensive coal gasification practices in East China University of Science and Technology,we have built ML models with a large database for overall regions of China.An AutoML model was proposed and achieved a minimum MSE of 1.021.SHAP method was used to increase the model interpretability,and model validity was proved with literature data and additional in-house experiments.The model adaptability was discussed based on the databases of China and USA,showing that geography-specific ML models are essential.This study integrated a large coal database and AutoML method for accurate calorific value prediction and could offer key tools for Chinese coal industry.展开更多
Neural architecture search(NAS)optimizes neural network architectures to align with specific data and objectives,thereby enabling the design of high-performance models without specialized expertise.However,a significa...Neural architecture search(NAS)optimizes neural network architectures to align with specific data and objectives,thereby enabling the design of high-performance models without specialized expertise.However,a significant limitation of NAS is that it requires extensive computational resources and time.Consequently,performing a comprehensive architectural search for each new dataset is inefficient.Given the continuous expansion of available datasets,there is an urgent need to predict the optimal architecture for the previously unknown datasets.This study proposes a novel framework that generates architectures tailored to unknown datasets by mapping architectures that have demonstrated effectiveness on the existing dataset into a latent feature space.As NAS is inherently represented as graph structures,we employed an encoder-decoder transformation model based on variational graph auto-encoders to perform this latent feature mapping.The encoder-decoder transformation model demonstrates strong capability in extracting features from graph structures,making it particularly well-suited for mapping NAS architectures.By training variational graph auto-encoders on existing high-quality architectures,the proposed method constructs a latent space and facilitates the design of optimal architectures for diverse datasets.Furthermore,to effectively define similarity amongarchitectures,wepropose constructing the latent spaceby incorporatingbothdataset andtaskfeatures.Experimental results indicate that our approach significantly enhances search efficiency and outperforms conventional methods in terms of model performance.展开更多
Current successes in artificial intelligence domain have revitalized interest in spacecraft pursuit-evasion game,which is an interception problem with a non-cooperative maneuvering target.The paper presents an automat...Current successes in artificial intelligence domain have revitalized interest in spacecraft pursuit-evasion game,which is an interception problem with a non-cooperative maneuvering target.The paper presents an automated machine learning(AutoML)based method to generate optimal trajectories in long-distance scenarios.Compared with conventional deep neural network(DNN)methods,the proposed method dramatically reduces the reliance on manual intervention and machine learning expertise.Firstly,based on differential game theory and costate normalization technique,the trajectory optimization problem is formulated under the assumption of continuous thrust.Secondly,the AutoML technique based on sequential model-based optimization(SMBO)framework is introduced to automate DNN design in deep learning process.If recommended DNN architecture exists,the tree-structured Parzen estimator(TPE)is used,otherwise the efficient neural architecture search(NAS)with network morphism is used.Thus,a novel trajectory optimization method with high computational efficiency is achieved.Finally,numerical results demonstrate the feasibility and efficiency of the proposed method.展开更多
Rock samples'TOC content is the best indicator of the organic matter in source rocks.The origin rock samples’analysis is used to calculate it manually by specialists.This method requires time and resources becaus...Rock samples'TOC content is the best indicator of the organic matter in source rocks.The origin rock samples’analysis is used to calculate it manually by specialists.This method requires time and resources because it relies on samples from many well intervals in source rocks.Therefore,research has been done to aid this effort.Machine learning algorithms can estimate total organic carbon instead of well logs and stratigraphic studies.In light of these efforts,the current work present a study on automating the total organic carbon estimation using machine learning approaches improved by an evolutionary methodology to give the model flexibility and precision.Genetic algorithms,differential evolution,particle swarm optimization,grey wolf optimization,artificial bee colony,and evolution strategies were used to improve machine learning models to predict TOC.The six metaheuristics were integrated into four machine learning methods:extreme learning machine,elastic net linear model,linear support vector regression,and multivariate adaptive regression splines.Core samples from the YuDong-Nan shale gas field,located in the Sichuan basin,were used to evaluate the hybrid strategy.The findings show that combining machine learning models with an evolutionary algorithms in a hybrid fashion produce flexible models that accurately predict TOC.The results show that,independent of the metaheuristic used to guide the model selection,optimized extreme learning machines attained the best performance scores according to six metrics.Such hybrid models can be used in exploratory geological research,particularly for unconventional oil and gas resources.展开更多
Quantitative investment(abbreviated as“quant”in this paper)is an interdisciplinary field combining financial engineering,computer science,mathematics,statistics,etc.Quant has become one of the mainstream investment ...Quantitative investment(abbreviated as“quant”in this paper)is an interdisciplinary field combining financial engineering,computer science,mathematics,statistics,etc.Quant has become one of the mainstream investment methodologies over the past decades,and has experienced three generations:quant 1.0,trading by mathematical modeling to discover mis-priced assets in markets;quant 2.0,shifting the quant research pipeline from small“strategy workshops”to large“alpha factories”;quant 3.0,applying deep learning techniques to discover complex nonlinear pricing rules.Despite its advantage in prediction,deep learning relies on extremely large data volume and labor-intensive tuning of“black-box”neural network models.To address these limitations,in this paper,we introduce quant 4.0 and provide an engineering perspective for next-generation quant.Quant 4.0 has three key differentiating components.First,automated artificial intelligence(AI)changes the quant pipeline from traditional hand-crafted modeling to state-of-the-art automated modeling and employs the philosophy of“algorithm produces algorithm,model builds model,and eventually AI creates AI.”Second,explainable AI develops new techniques to better understand and interpret investment decisions made by machine learning black boxes,and explains complicated and hidden risk exposures.Third,knowledge-driven AI supplements data-driven AI such as deep learning and incorporates prior knowledge into modeling to improve investment decisions,in particular for quantitative value investing.Putting all these together,we discuss how to build a system that practices the quant 4.0 concept.We also discuss the application of large language models in quantitative finance.Finally,we propose 10 challenging research problems for quant technology,and discuss potential solutions,research directions,and future trends.展开更多
Conventional automated machine learning(AutoML)technologies fall short in preprocessing low-quality raw data and adapting to varying indoor and outdoor environments,leading to accuracy reduction in forecasting short-t...Conventional automated machine learning(AutoML)technologies fall short in preprocessing low-quality raw data and adapting to varying indoor and outdoor environments,leading to accuracy reduction in forecasting short-term building energy loads.Moreover,their predictions are not transparent because of their black box nature.Hence,the building field currently lacks an AutoML framework capable of data quality enhancement,environment self-adaptation,and model interpretation.To address this research gap,an improved AutoML-based end-to-end data-driven modeling framework is proposed.Bayesian optimization is applied by this framework to find an optimal data preprocessing process for quality improvement of raw data.It bridges the gap where conventional AutoML technologies cannot automatically handle missing data and outliers.A sliding window-based model retraining strategy is utilized to achieve environment self-adaptation,contributing to the accuracy enhancement of AutoML technologies.Moreover,a local interpretable model-agnostic explanations-based approach is developed to interpret predictions made by the improved framework.It overcomes the poor interpretability of conventional AutoML technologies.The performance of the improved framework in forecasting one-hour ahead cooling loads is evaluated using two-year operational data from a real building.It is discovered that the accuracy of the improved framework increases by 4.24%–8.79%compared with four conventional frameworks for buildings with not only high-quality but also low-quality operational data.Furthermore,it is demonstrated that the developed model interpretation approach can effectively explain the predictions of the improved framework.The improved framework offers a novel perspective on creating accurate and reliable AutoML frameworks tailored to building energy load prediction tasks and other similar tasks.展开更多
基金supported in part by the National Natural Science Foundation of China under Grants 52475102 and 52205101in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515240021+1 种基金in part by the Young Talent Support Project of Guangzhou Association for Science and Technology(QT-2024-28)in part by the Youth Development Initiative of Guangdong Association for Science and Technology(SKXRC2025254).
文摘To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and expert experience,which limits their adaptability under variable operating conditions and strong noise environments,severely affecting the generalization capability of diagnostic models.To address this issue,this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning(AutoML).The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary fault representations.On this basis,automated machine learning techniques are introduced to enable end-to-end diagnostic workflow construction and optimal model configuration acquisition.Finally,diagnostic decisions are achieved by automatically integrating the predictions of multiple high-performance base models.Experimental results on a centrifugal pump vibration and acoustic dataset demonstrate that the proposed framework achieves high diagnostic accuracy under noise-free conditions and maintains strong robustness under noisy interference,validating its efficiency,scalability,and practical value for rotating machinery fault diagnosis.
文摘The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource demands.Automated Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification tasks.This paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these limitations.Our method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature groups.The Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization problem.Empirical validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.
基金supported by the State Administration of Science,Technology and Industry for National Defence,PRC(KJSP2020020303)the National Institute of Natural Hazards,Ministry of Emergency Management of China(ZDJ2021-12)。
文摘Landslide hazard mapping is essential for regional landslide hazard management.The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County,China based on an automated machine learning framework(AutoGluon).A total of 2241 landslides were identified from satellite images before and after the rainfall event,and 10 impact factors including elevation,slope,aspect,normalized difference vegetation index(NDVI),topographic wetness index(TWI),lithology,land cover,distance to roads,distance to rivers,and rainfall were selected as indicators.The WeightedEnsemble model,which is an ensemble of 13 basic machine learning models weighted together,was used to output the landslide hazard assessment results.The results indicate that landslides mainly occurred in the central part of the study area,especially in Hetian and Shanghu.Totally 102.44 s were spent to train all the models,and the ensemble model WeightedEnsemble has an Area Under the Curve(AUC)value of92.36%in the test set.In addition,14.95%of the study area was determined to be at very high hazard,with a landslide density of 12.02 per square kilometer.This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County.
文摘By identifying and responding to any malicious behavior that could endanger the system,the Intrusion Detection System(IDS)is crucial for preserving the security of the Industrial Internet of Things(IIoT)network.The benefit of anomaly-based IDS is that they are able to recognize zeroday attacks due to the fact that they do not rely on a signature database to identify abnormal activity.In order to improve control over datasets and the process,this study proposes using an automated machine learning(AutoML)technique to automate the machine learning processes for IDS.Our groundbreaking architecture,known as AID4I,makes use of automatic machine learning methods for intrusion detection.Through automation of preprocessing,feature selection,model selection,and hyperparameter tuning,the objective is to identify an appropriate machine learning model for intrusion detection.Experimental studies demonstrate that the AID4I framework successfully proposes a suitablemodel.The integrity,security,and confidentiality of data transmitted across the IIoT network can be ensured by automating machine learning processes in the IDS to enhance its capacity to identify and stop threatening activities.With a comprehensive solution that takes advantage of the latest advances in automated machine learning methods to improve network security,AID4I is a powerful and effective instrument for intrusion detection.In preprocessing module,three distinct imputation methods are utilized to handle missing data,ensuring the robustness of the intrusion detection system in the presence of incomplete information.Feature selection module adopts a hybrid approach that combines Shapley values and genetic algorithm.The Parameter Optimization module encompasses a diverse set of 14 classification methods,allowing for thorough exploration and optimization of the parameters associated with each algorithm.By carefully tuning these parameters,the framework enhances its adaptability and accuracy in identifying potential intrusions.Experimental results demonstrate that the AID4I framework can achieve high levels of accuracy in detecting network intrusions up to 14.39%on public datasets,outperforming traditional intrusion detection methods while concurrently reducing the elapsed time for training and testing.
文摘Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual involvement.This manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series models.The complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple junctures.To surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series prediction.However,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series tasks.Therefore,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time series.ARIMA is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing efforts.Using multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term.Furthermore,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural Networks.Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.
基金This work is supported by Fundamental Research Funds for the Central Universities(Grant No.FRF-TP-19-006A3).
文摘Epilepsy is a common neurological disease and severely affects the daily life of patients.The automatic detection and diagnosis system of epilepsy based on electroencephalogram(EEG)is of great significance to help patients with epilepsy return to normal life.With the development of deep learning technology and the increase in the amount of EEG data,the performance of deep learning based automatic detection algorithm for epilepsy EEG has gradually surpassed the traditional hand-crafted approaches.However,the neural architecture design for epilepsy EEG analysis is time-consuming and laborious,and the designed structure is difficult to adapt to the changing EEG collection environment,which limits the application of the epilepsy EEG automatic detection system.In this paper,we explore the possibility of Automated Machine Learning(AutoML)playing a role in the task of epilepsy EEG detection.We apply the neural architecture search(NAS)algorithm in the AutoKeras platform to design the model for epilepsy EEG analysis and utilize feature interpretability methods to ensure the reliability of the searched model.The experimental results show that the model obtained through NAS outperforms the baseline model in performance.The searched model improves classification accuracy,F1-score and Cohen’s kappa coefficient by 7.68%,7.82%and 9.60%respectively than the baseline model.Furthermore,NASbased model is capable of extracting EEG features related to seizures for classification.
基金supported by the National Natural Science Foundation of China(Nos.42472351,42177140,52404127,and 42207235)the Natural Science Foundation of Hubei Province(No.2024AFD359)+1 种基金the Young Elite Scientist Sponsorship Program by China Association for Science and Technology(No.YESS20230742)the China Post-doctoral Science Foundation Program(No.2024T170684).
文摘The uniaxial compressive strength(UCS)of rocks is a crucial indicator for evaluating the bearing capacity of geological structures in rock engineering,and it holds significant implications for disaster management.However,direct measurement poses a significant challenge.Therefore,simpler alternatives such as Schmidt hammer rebound number(SRn),P-wave velocity(Vp),and point load index(Is)are frequently used to estimate UCS indirectly.In this study,we compiled a comprehensive dataset of 1168 samples that included SRn,Vp,Is,and UCS values.The dataset was refined using an isolation forest algorithm,which identified and removed 280 outliers,leaving a dataset of 888 samples for analysis.We developed and assessed an automated machine learning(AutoML)model for predicting UCS,introducing a novel approach to tackle this prediction challenge.Additionally,we compared models enhanced by Bayesian optimization,including multi-layer perceptron(MLP),support vector machine(SVM),Gaussian process regression(GPR),and K-nearest neighbor(KNN).Among these,the AutoML model demonstrated superior performance in UCS prediction,offering a rapid and efficient method for estimating UCS in engineering applications and enabling intelligent classification of rock masses.The study also evaluated the sensitivity and contribution of SRn,Vp,and Is in UCS estimation by various techniques,including permutation feature importance(PFI),SHapley Additive exPlanations(SHAP),and local interpretable model-agnostic explanations(LIME).The results underscore that the AutoML approach not only streamlines UCS modeling but also provides a robust and comprehensive solution,significantly enhancing the accuracy and efficiency of the prediction process.
基金supported by the National Natural Science Foundation of China (Grant Nos.42272283 and 41972252)the Graduate Innovation Fund of Jilin University (No.2022186).
文摘Groundwater contamination source identification(GCSI)is a prerequisite for contamination risk evaluation and efficient groundwater contamination remediation programs.The boundary condition generally is set as known variables in previous GCSI studies.However,in many practical cases,the boundary condition is complicated and cannot be estimated accurately in advance.Setting the boundary condition as known variables may seriously deviate from the actual situation and lead to distorted identification results.And the results of GCSI are affected by multiple factors,including contaminant source information,model parameters,boundary condition,etc.Therefore,if the boundary condition is not estimated accurately,other factors will also be estimated inaccurately.This study focuses on the unknown boundary condition and proposed to identify three types of unknown variables(contaminant source information,model parameters and boundary condition)innovatively.When simulation-optimization(S-O)method is applied to GCSI,the huge computational load is usually reduced by building surrogate models.However,when building surrogate models,the researchers need to select the models and optimize the hyperparameters to make the model powerful,which can be a lengthy process.The automated machine learning(AutoML)method was used to build surrogate model,which automates the model selection and hyperparameter optimization in machine learning engineering,largely reducing human operations and saving time.The accuracy of AutoML surrogate model is compared with the surrogate model used in eXtreme Gradient Boosting method(XGBoost),random forest method(RF),extra trees regressor method(ETR)and elasticnet method(EN)respectively,which are automatically selected in AutoML engineering.The results show that the surrogate model constructed by AutoML method has the best accuracy compared with the other four methods.This study provides reliable and strong support for GCSI.
文摘The plethora of complex Artificial Intelligence(AI)algorithms and available High-Performance Computing(HPC)power stimulates the expeditious development of AI components with heterogeneous designs.Consequently,the need for cross-stack performance benchmarking of AI-HPC systems has rapidly emerged.In particular,the de facto HPC benchmark,LINPACK,cannot reflect the AI computing power and input/output performance without a representative workload.Current popular AI benchmarks,such as MLPerf,have a fixed problem size and therefore limited scalability.To address these issues,we propose an end-to-end benchmark suite utilizing automated machine learning,which not only represents real AI scenarios,but also is auto-adaptively scalable to various scales of machines.We implement the algorithms in a highly parallel and flexible way to ensure the efficiency and optimization potential on diverse systems with customizable configurations.We utilize Operations Per Second(OPS),which is measured in an analytical and systematic approach,as a major metric to quantify the AI performance.We perform evaluations on various systems to ensure the benchmark’s stability and scalability,from 4 nodes with 32 NVIDIA Tesla T4(56.1 Tera-OPS measured)up to 512 nodes with 4096 Huawei Ascend 910(194.53 Peta-OPS measured),and the results show near-linear weak scalability.With a flexible workload and single metric,AIPerf can easily scale on and rank AI-HPC,providing a powerful benchmark suite for the coming supercomputing era.
文摘In healthcare,the persistent challenge of arrhythmias,a leading cause of global mortality,has sparked extensive research into the automation of detection using machine learning(ML)algorithms.However,traditional ML and AutoML approaches have revealed their limitations,notably regarding feature generalization and automation efficiency.This glaring research gap has motivated the development of AutoRhythmAI,an innovative solution that integrates both machine and deep learning to revolutionize the diagnosis of arrhythmias.Our approach encompasses two distinct pipelines tailored for binary-class and multi-class arrhythmia detection,effectively bridging the gap between data preprocessing and model selection.To validate our system,we have rigorously tested AutoRhythmAI using a multimodal dataset,surpassing the accuracy achieved using a single dataset and underscoring the robustness of our methodology.In the first pipeline,we employ signal filtering and ML algorithms for preprocessing,followed by data balancing and split for training.The second pipeline is dedicated to feature extraction and classification,utilizing deep learning models.Notably,we introduce the‘RRI-convoluted trans-former model’as a novel addition for binary-class arrhythmias.An ensemble-based approach then amalgamates all models,considering their respective weights,resulting in an optimal model pipeline.In our study,the VGGRes Model achieved impressive results in multi-class arrhythmia detection,with an accuracy of 97.39%and firm performance in precision(82.13%),recall(31.91%),and F1-score(82.61%).In the binary-class task,the proposed model achieved an outstanding accuracy of 96.60%.These results highlight the effectiveness of our approach in improving arrhythmia detection,with notably high accuracy and well-balanced performance metrics.
基金supported through theOngoing Research Funding Program(ORF-2025-498),King Saud University,Riyadh,Saudi Arabia.
文摘Android smartphones have become an integral part of our daily lives,becoming targets for ransomware attacks.Such attacks encrypt user information and ask for payment to recover it.Conventional detection mechanisms,such as signature-based and heuristic techniques,often fail to detect new and polymorphic ransomware samples.To address this challenge,we employed various ensemble classifiers,such as Random Forest,Gradient Boosting,Bagging,and AutoML models.We aimed to showcase how AutoML can automate processes such as model selection,feature engineering,and hyperparameter optimization,to minimize manual effort while ensuring or enhancing performance compared to traditional approaches.We used this framework to test it with a publicly available dataset from the Kaggle repository,which contains features for Android ransomware network traffic.The dataset comprises 392,024 flow records,divided into eleven groups.There are ten classes for various ransomware types,including SVpeng,PornDroid,Koler,WannaLocker,and Lockerpin.There is also a class for regular traffic.We applied a three-step procedure to select themost relevant features:filter,wrapper,and embeddedmethods.The Bagging classifier was highly accurate,correctly getting 99.84%of the time.The FLAML AutoML framework was evenmore accurate,correctly getting 99.85%of the time.This is indicative of howwellAutoML performs in improving things with minimal human assistance.Our findings indicate that AutoML is an efficient,scalable,and flexible method to discover Android ransomware,and it will facilitate the development of next-generation intrusion detection systems.
基金This work was supported by Science and Technology Project of State Grid Corporation“Research on Key Technologies of Power Artificial Intelligence Open Platform”(5700-202155260A-0-0-00).
文摘The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible .
基金This work was supported by the National Natural ScienceFoundation of China(No.U1862201,91834303 and 22208208)the China Postdoctoral Science Foundation(No.2022M712056)the China National Postdoctoral Program for Innovative Talents(No.BX20220205).
文摘The present study extracts human-understandable insights from machine learning(ML)-based mesoscale closure in fluid-particle flows via several novel data-driven analysis approaches,i.e.,maximal information coefficient(MIC),interpretable ML,and automated ML.It is previously shown that the solidvolume fraction has the greatest effect on the drag force.The present study aims to quantitativelyinvestigate the influence of flow properties on mesoscale drag correction(H_(d)).The MIC results showstrong correlations between the features(i.e.,slip velocity(u^(*)_(sy))and particle volume fraction(εs))and thelabel H_(d).The interpretable ML analysis confirms this conclusion,and quantifies the contribution of u^(*)_(sy),εs and gas pressure gradient to the model as 71.9%,27.2%and 0.9%,respectively.Automated ML without theneed to select the model structure and hyperparameters is used for modeling,improving the predictionaccuracy over our previous model(Zhu et al.,2020;Ouyang,Zhu,Su,&Luo,2021).
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.RS-2023-00218176)the Soonchunhyang University Research Fund.
文摘Improving early diagnosis of autism spectrum disorder(ASD)in children increasingly relies on predictive models that are reliable and accessible to non-experts.This study aims to develop such models using Python-based tools to improve ASD diagnosis in clinical settings.We performed exploratory data analysis to ensure data quality and identify key patterns in pediatric ASD data.We selected the categorical boosting(CatBoost)algorithm to effectively handle the large number of categorical variables.We used the PyCaret automated machine learning(AutoML)tool to make the models user-friendly for clinicians without extensive machine learning expertise.In addition,we applied Shapley additive explanations(SHAP),an explainable artificial intelligence(XAI)technique,to improve the interpretability of the models.Models developed using CatBoost and other AI algorithms showed high accuracy in diagnosing ASD in children.SHAP provided clear insights into the influence of each variable on diagnostic outcomes,making model decisions transparent and understandable to healthcare professionals.By integrating robust machine learning methods with user-friendly tools such as PyCaret and leveraging XAI techniques such as SHAP,this study contributes to the development of reliable,interpretable,and accessible diagnostic tools for ASD.These advances hold great promise for supporting informed decision-making in clinical settings,ultimately improving early identification and intervention strategies for ASD in the pediatric population.However,the study is limited by the dataset’s demographic imbalance and the lack of external clinical validation,which should be addressed in future research.
基金Shanghai Yangfan Program,22YF1410300,Yunfei GaoNational Natural Science Foundation of China,22208104,Yunfei Gao+1 种基金Shanghai Chenguang Program,21CGA35,Yunfei GaoNational Key Research and Development Program of China,2022YFA1504701,Yunfei Gao,2022YFB4101900,Yunfei Gao。
文摘Calorific value is one of the most important properties of coal.Machine learning(ML)can be used in the prediction of calorific value to reduce experimental costs.China is one of the world’s largest coal production countries and coal occupies an important position in its national energy structure.However,ML models with a large database for the overall regions of China are still missing.Based on the extensive coal gasification practices in East China University of Science and Technology,we have built ML models with a large database for overall regions of China.An AutoML model was proposed and achieved a minimum MSE of 1.021.SHAP method was used to increase the model interpretability,and model validity was proved with literature data and additional in-house experiments.The model adaptability was discussed based on the databases of China and USA,showing that geography-specific ML models are essential.This study integrated a large coal database and AutoML method for accurate calorific value prediction and could offer key tools for Chinese coal industry.
基金funded by the New Energy and Industrial Technology Development Organization(NEDO),grant number JPNP18002.
文摘Neural architecture search(NAS)optimizes neural network architectures to align with specific data and objectives,thereby enabling the design of high-performance models without specialized expertise.However,a significant limitation of NAS is that it requires extensive computational resources and time.Consequently,performing a comprehensive architectural search for each new dataset is inefficient.Given the continuous expansion of available datasets,there is an urgent need to predict the optimal architecture for the previously unknown datasets.This study proposes a novel framework that generates architectures tailored to unknown datasets by mapping architectures that have demonstrated effectiveness on the existing dataset into a latent feature space.As NAS is inherently represented as graph structures,we employed an encoder-decoder transformation model based on variational graph auto-encoders to perform this latent feature mapping.The encoder-decoder transformation model demonstrates strong capability in extracting features from graph structures,making it particularly well-suited for mapping NAS architectures.By training variational graph auto-encoders on existing high-quality architectures,the proposed method constructs a latent space and facilitates the design of optimal architectures for diverse datasets.Furthermore,to effectively define similarity amongarchitectures,wepropose constructing the latent spaceby incorporatingbothdataset andtaskfeatures.Experimental results indicate that our approach significantly enhances search efficiency and outperforms conventional methods in terms of model performance.
基金supported by the National Defense Science and Technology Innovation program(18-163-15-LZ-001-004-13).
文摘Current successes in artificial intelligence domain have revitalized interest in spacecraft pursuit-evasion game,which is an interception problem with a non-cooperative maneuvering target.The paper presents an automated machine learning(AutoML)based method to generate optimal trajectories in long-distance scenarios.Compared with conventional deep neural network(DNN)methods,the proposed method dramatically reduces the reliance on manual intervention and machine learning expertise.Firstly,based on differential game theory and costate normalization technique,the trajectory optimization problem is formulated under the assumption of continuous thrust.Secondly,the AutoML technique based on sequential model-based optimization(SMBO)framework is introduced to automate DNN design in deep learning process.If recommended DNN architecture exists,the tree-structured Parzen estimator(TPE)is used,otherwise the efficient neural architecture search(NAS)with network morphism is used.Thus,a novel trajectory optimization method with high computational efficiency is achieved.Finally,numerical results demonstrate the feasibility and efficiency of the proposed method.
基金supported by the Federal University of Juiz de Fora (UFJF).C.S.thanks CAPES (Finance Code 001)L.G.thanks CNPq (401796/2021-3,307688/2022-4,and 409433/2022-5)for their financial support.
文摘Rock samples'TOC content is the best indicator of the organic matter in source rocks.The origin rock samples’analysis is used to calculate it manually by specialists.This method requires time and resources because it relies on samples from many well intervals in source rocks.Therefore,research has been done to aid this effort.Machine learning algorithms can estimate total organic carbon instead of well logs and stratigraphic studies.In light of these efforts,the current work present a study on automating the total organic carbon estimation using machine learning approaches improved by an evolutionary methodology to give the model flexibility and precision.Genetic algorithms,differential evolution,particle swarm optimization,grey wolf optimization,artificial bee colony,and evolution strategies were used to improve machine learning models to predict TOC.The six metaheuristics were integrated into four machine learning methods:extreme learning machine,elastic net linear model,linear support vector regression,and multivariate adaptive regression splines.Core samples from the YuDong-Nan shale gas field,located in the Sichuan basin,were used to evaluate the hybrid strategy.The findings show that combining machine learning models with an evolutionary algorithms in a hybrid fashion produce flexible models that accurately predict TOC.The results show that,independent of the metaheuristic used to guide the model selection,optimized extreme learning machines attained the best performance scores according to six metrics.Such hybrid models can be used in exploratory geological research,particularly for unconventional oil and gas resources.
文摘Quantitative investment(abbreviated as“quant”in this paper)is an interdisciplinary field combining financial engineering,computer science,mathematics,statistics,etc.Quant has become one of the mainstream investment methodologies over the past decades,and has experienced three generations:quant 1.0,trading by mathematical modeling to discover mis-priced assets in markets;quant 2.0,shifting the quant research pipeline from small“strategy workshops”to large“alpha factories”;quant 3.0,applying deep learning techniques to discover complex nonlinear pricing rules.Despite its advantage in prediction,deep learning relies on extremely large data volume and labor-intensive tuning of“black-box”neural network models.To address these limitations,in this paper,we introduce quant 4.0 and provide an engineering perspective for next-generation quant.Quant 4.0 has three key differentiating components.First,automated artificial intelligence(AI)changes the quant pipeline from traditional hand-crafted modeling to state-of-the-art automated modeling and employs the philosophy of“algorithm produces algorithm,model builds model,and eventually AI creates AI.”Second,explainable AI develops new techniques to better understand and interpret investment decisions made by machine learning black boxes,and explains complicated and hidden risk exposures.Third,knowledge-driven AI supplements data-driven AI such as deep learning and incorporates prior knowledge into modeling to improve investment decisions,in particular for quantitative value investing.Putting all these together,we discuss how to build a system that practices the quant 4.0 concept.We also discuss the application of large language models in quantitative finance.Finally,we propose 10 challenging research problems for quant technology,and discuss potential solutions,research directions,and future trends.
基金funded by the National Natural Science Foundation of China(No.52161135202)Hangzhou Key Scientific Research Plan Project(No.2023SZD0028).
文摘Conventional automated machine learning(AutoML)technologies fall short in preprocessing low-quality raw data and adapting to varying indoor and outdoor environments,leading to accuracy reduction in forecasting short-term building energy loads.Moreover,their predictions are not transparent because of their black box nature.Hence,the building field currently lacks an AutoML framework capable of data quality enhancement,environment self-adaptation,and model interpretation.To address this research gap,an improved AutoML-based end-to-end data-driven modeling framework is proposed.Bayesian optimization is applied by this framework to find an optimal data preprocessing process for quality improvement of raw data.It bridges the gap where conventional AutoML technologies cannot automatically handle missing data and outliers.A sliding window-based model retraining strategy is utilized to achieve environment self-adaptation,contributing to the accuracy enhancement of AutoML technologies.Moreover,a local interpretable model-agnostic explanations-based approach is developed to interpret predictions made by the improved framework.It overcomes the poor interpretability of conventional AutoML technologies.The performance of the improved framework in forecasting one-hour ahead cooling loads is evaluated using two-year operational data from a real building.It is discovered that the accuracy of the improved framework increases by 4.24%–8.79%compared with four conventional frameworks for buildings with not only high-quality but also low-quality operational data.Furthermore,it is demonstrated that the developed model interpretation approach can effectively explain the predictions of the improved framework.The improved framework offers a novel perspective on creating accurate and reliable AutoML frameworks tailored to building energy load prediction tasks and other similar tasks.
