AutoRhythmAI: A Hybrid Machine and Deep Learning Approach for Automated Diagnosis of Arrhythmias

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.

Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

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.

Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province,China

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.

Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

File labeling techniques have a long history in analyzing the anthological trends in computational linguistics.The situation becomes worse in the case of files downloaded into systems from the Internet.Currently,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user files.Currently,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic modeling.However,one major drawback of current topic modeling approaches is better results.They rely on specific language types and domain similarity of the data.In this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the files.The results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

AID4I:An Intrusion Detection Framework for Industrial Internet of Things Using Automated Machine Learning

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.

AutoML: A systematic review on automated machine learning with neural architecture search

AutoML(Automated Machine Learning)is an emerging field that aims to automate the process of building machine learning models.AutoML emerged to increase productivity and efficiency by automating as much as possible the inefficient work that occurs while repeating this process whenever machine learning is applied.In particular,research has been conducted for a long time on technologies that can effectively develop high-quality models by minimizing the intervention of model developers in the process from data preprocessing to algorithm selection and tuning.In this semantic review research,we summarize the data processing requirements for AutoML approaches and provide a detailed explanation.We place greater emphasis on neural architecture search(NAS)as it currently represents a highly popular sub-topic within the field of AutoML.NAS methods use machine learning algorithms to search through a large space of possible architectures and find the one that performs best on a given task.We provide a summary of the performance achieved by representative NAS algorithms on the CIFAR-10,CIFAR-100,ImageNet and wellknown benchmark datasets.Additionally,we delve into several noteworthy research directions in NAS methods including one/two-stage NAS,one-shot NAS and joint hyperparameter with architecture optimization.We discussed how the search space size and complexity in NAS can vary depending on the specific problem being addressed.To conclude,we examine several open problems(SOTA problems)within current AutoML methods that assure further investigation in future research.

Automated deep learning system for power line inspection image analysis and processing: architecture and design issues

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 .

Automated Machine Learning for Epileptic Seizure Detection Based on EEG Signals

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.

Groundwater contaminant source identification considering unknown boundary condition based on an automated machine learning surrogate

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.

Auto machine learning-based modelling and prediction of excavationinduced tunnel displacement

The influence of a deep excavation on existing shield tunnels nearby is a vital issue in tunnelling engineering.Whereas,there lacks robust methods to predict excavation-induced tunnel displacements.In this study,an auto machine learning(AutoML)-based approach is proposed to precisely solve the issue.Seven input parameters are considered in the database covering two physical aspects,namely soil property,and spatial characteristics of the deep excavation.The 10-fold cross-validation method is employed to overcome the scarcity of data,and promote model’s robustness.Six genetic algorithm(GA)-ML models are established as well for comparison.The results indicated that the proposed AutoML model is a comprehensive model that integrates efficiency and robustness.Importance analysis reveals that the ratio of the average shear strength to the vertical effective stress E_(ur)/σ′_(v),the excavation depth H,and the excavation width B are the most influential variables for the displacements.Finally,the AutoML model is further validated by practical engineering.The prediction results are in a good agreement with monitoring data,signifying that our model can be applied in real projects.

Early Detection of Diabetic Retinopathy Using Machine Intelligence throughDeep Transfer and Representational Learning

Diabetic retinopathy (DR) is a retinal disease that causes irreversible blindness.DR occurs due to the high blood sugar level of the patient, and it is clumsy tobe detected at an early stage as no early symptoms appear at the initial level. To preventblindness, early detection and regular treatment are needed. Automated detectionbased on machine intelligence may assist the ophthalmologist in examining thepatients’ condition more accurately and efficiently. The purpose of this study is toproduce an automated screening system for recognition and grading of diabetic retinopathyusing machine learning through deep transfer and representational learning.The artificial intelligence technique used is transfer learning on the deep neural network,Inception-v4. Two configuration variants of transfer learning are applied onInception-v4: Fine-tune mode and fixed feature extractor mode. Both configurationmodes have achieved decent accuracy values, but the fine-tuning method outperformsthe fixed feature extractor configuration mode. Fine-tune configuration modehas gained 96.6% accuracy in early detection of DR and 97.7% accuracy in gradingthe disease and has outperformed the state of the art methods in the relevant literature.

基于Azure AutoML的泥沙预报模型构建与应用

泥沙预报是开展水库泥沙实时调度的前提,而水沙作用机理和演进规律的复杂性又导致开展高效、精准的泥沙预报较为困难。基于微软在2018年发布的Azure AutoML自动化机器学习技术,进行了泥沙预报模型构建与应用的探索。选取三峡水库泥沙重要控制站——寸滩、清溪场、万县、黄陵庙站构建了含沙量预报模型,并从模型构建与评估、预报精度、输入因子重要性等角度开展了分析。研究结果表明:Azure AutoML技术可便捷地进行自动化机器学习模型的构建,基于该技术建立的预见期为1~3 d的模型针对沙峰消退阶段和含沙量较小阶段预报效果较好;预见期为1~2 d的模型可以对沙峰开展较为准确的预报;寸滩、清溪场站含沙量主要受到上游来沙的影响,而万县、黄陵庙站的含沙量自相关性较强。

基于纹理特征的AutoML在NBI-ME判断食管癌分期中的应用

目的探讨基于纹理特征的自动化机器学习(Automated Machine Learning,AutoML)在窄带成像技术结合放大内镜(Narrow-Band Imaging-Magnification Endoscopy,NBI-ME)图片中区分早期和进展期食管鳞癌的应用。方法收集苏州大学附属第一医院内镜中心食管鳞癌NBI-ME图片1507张,随机分为训练集(1264张)和验证集(243张)。使用MATLAB软件,提取整张内镜图片,共计32个纹理特征变量。将上述变量载入H2O平台进行AutoML二分类建模。另收集苏州大学附属第二医院内镜图片(278张)作为外部测试集。同时邀请1名低年资和1名高年资内镜医生对外部测试集进行判读。采用受试者工作特征(Receiver Operating Characteristic,ROC)曲线下面积(Area Under Curve,AUC)和准确度(Accuracy,ACC)等评估鉴别效能。结果基于RF算法的AutoML模型在外部测试集中表现最优,其AUC为0.975,ACC为0.939,显著优于其他模型,包括传统的GLM(AUC:0.776、ACC:0.687)和XGBoost模型(AUC:0.968、ACC:0.863);同时也优于低年资内镜医生(AUC:0.868、ACC:0.871)和高年资内镜医生(AUC:0.919、ACC:0.921)。结论基于内镜图片纹理特征的AutoML模型在食管早癌和进展期癌区别中展现出优秀的鉴别能力。

Fully Automated Density-Based Clustering Method

Cluster analysis is a crucial technique in unsupervised machine learning,pattern recognition,and data analysis.However,current clustering algorithms suffer from the need for manual determination of parameter values,low accuracy,and inconsistent performance concerning data size and structure.To address these challenges,a novel clustering algorithm called the fully automated density-based clustering method(FADBC)is proposed.The FADBC method consists of two stages:parameter selection and cluster extraction.In the first stage,a proposed method extracts optimal parameters for the dataset,including the epsilon size and a minimum number of points thresholds.These parameters are then used in a density-based technique to scan each point in the dataset and evaluate neighborhood densities to find clusters.The proposed method was evaluated on different benchmark datasets andmetrics,and the experimental results demonstrate its competitive performance without requiring manual inputs.The results show that the FADBC method outperforms well-known clustering methods such as the agglomerative hierarchical method,k-means,spectral clustering,DBSCAN,FCDCSD,Gaussian mixtures,and density-based spatial clustering methods.It can handle any kind of data set well and perform excellently.

An AutoML based trajectory optimization method for long-distance spacecraft pursuit-evasion game

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.

基于人工智能AutoML技术的短波发射机故障预测分析

人工智能(Artificial Intelligence,AI)技术是由深度学习算法衍生出的技术,该技术主要用于特征提取和结果预测。自动机器学习(Auto Machine Learning,AutoML)作为人工智能技术的代表,现已在神经架构搜索(Neural Architecture Search,NAS)、元学习等领域得到广泛应用。以短波发射机为研究对象,介绍预测设备故障所使用AutoML的框架和应用方向,说明基于该技术设计预测系统的一般步骤,并围绕该技术的具体应用展开讨论,以供参考。

Impact of blurs on machine-learning aided digital pathology image analysis

BACKGROUND Digital pathology image(DPI)analysis has been developed by machine learning(ML)techniques.However,little attention has been paid to the reproducibility of ML-based histological classification in heterochronously obtained DPIs of the same hematoxylin and eosin(HE)slide.AIM To elucidate the frequency and preventable causes of discordant classification results of DPI analysis using ML for the heterochronously obtained DPIs.METHODS We created paired DPIs by scanning 298 HE stained slides containing 584 tissues twice with a virtual slide scanner.The paired DPIs were analyzed by our MLaided classification model.We defined non-flipped and flipped groups as the paired DPIs with concordant and discordant classification results,respectively.We compared differences in color and blur between the non-flipped and flipped groups by L1-norm and a blur index,respectively.RESULTS We observed discordant classification results in 23.1%of the paired DPIs obtained by two independent scans of the same microscope slide.We detected no significant difference in the L1-norm of each color channel between the two groups;however,the flipped group showed a significantly higher blur index than the non-flipped group.CONCLUSION Our results suggest that differences in the blur-not the color-of the paired DPIs may cause discordant classification results.An ML-aided classification model for DPI should be tested for this potential cause of the reduced reproducibility of the model.In a future study,a slide scanner and/or a preprocessing method of minimizing DPI blur should be developed.

建筑基本周期多因素机器学习预测模型

建筑物基本周期是其最重要的动力特性参数,影响因素众多。受限于曲线拟合的传统建模手段,目前的基本周期预测模型表达式中仅能包含高度或层数等单一因素,而忽略其他因素的影响。数据驱动机器学习方法的出现,为建筑周期多因素预测模型的建立提供了新思路。研究从大量文献中收集整理了2561条建筑周期的实测数据,形成了包含建筑高度、层数、材料、功能、地区等多因素的建筑周期实测数据库。建立了具有自学习能力的建筑基本周期多因素机器学习预测模型,避免了一般机器学习模型中繁琐的参数调节过程,提升模型的鲁棒性和适用性。与传统模型结果的对比表明:所提预测模型的适用结构类型范围广、准确性更高,配合云端服务器可形成一种全新的、开放式自学习的建筑周期预测模式。

基于贝叶斯优化LightGBM算法的主动式搜索时间调整方法

针对5G配电终端延迟波动较大导致保护闭锁的问题,提出一种基于贝叶斯优化LightGBM算法的主动式搜索时间调整方法。该方法以无线通信终端延迟波动历史数据以及温度、日期时间等特征变量为输入,对延迟进行预测并动态调整设备参数。首先,对原始特征变量进行特征工程预处理,然后同历史延迟数据一并通过LightGBM算法进行数据的拟合,在训练过程中引入贝叶斯优化算法进行参数寻优,并利用最终加权组合,结合终端实时监测延迟进行预测值的调整,最终实现5G终端延迟的高精度预测。以河北南网某5G配网试点的数据进行训练与验证,结果表明所提方法能有效实现延迟预测,较随机森林回归,XGBoost等算法有更高的预测精度。

基于影像组学和自动机器学习的PA患者肾上腺静脉取血结果的研究

目的建立基于高分辨率增强CT影像和自动机器学习技术的原发性醛固酮增多症(Primary Aldosteronism,PA)亚型术前预测模型。方法回顾性研究经肾上腺静脉取血(Adrenal Venous Sampling,AVS)结果亚型诊断的PA患者312例,其中,207例诊断为单侧优势(AVS-右∶AVS-左=93∶114),105例诊断为双侧优势。纳入患者初诊CT影像,基于薄层静脉期图像提取双侧肾上腺影像组学特征,并定义影像组学商值特征为双侧肾上腺对应影像组学特征的比值,再将特征向量输入自动机器学习进行模型训练。结果根据自动模型筛选,随机森林分类器在预测AVS结果方面取得了较好的整体性能,其中准确度为0.7500,召回率为0.7466,受试者工作特征曲线下面积为0.8792。结论本系统在预测PA患者的AVS结果方面展示出了一定的潜力,因此,机器学习模型可以在常规临床实践中辅助预测PA的亚型诊断。