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.

The Impact of High Speed Machining on Computing and Automation

Machine tool technologies, especially Computer Numerical Control （CNC） High Speed Machining （HSM） have emerged as effective mechanisms for Rapid Tooling and Manufacturing applications. These new technologies are attractive for competitive manufacturing because of their technical advantages, i.e. a significant reduction in lead-time, high product accuracy, and good surface finish. However, HSM not only stimulates advancements in cutting tools and materials, it also demands increasingly sophisticated CAD^CAM software, and powerful CNC controllers that require more support technologies. This paper explores the computational requirement and impact of HSM on CNC controller, wear detection, look ahead programming, simulation, and tool management.

High Quality Fixture Quenching Fixture Types, Levels of Automation and Machine Types

The paper shows, how the quality of workpieces to be heat treated can be improved by using the technology of fixture-quenching. Different fixture systems, like fixed mandrel, expanding mandrel and lamellar mandrel are described. In the next section there is a comparison between manual operation of hardening machines vs. automated lines. Since there are applications for each, manual and fully automated hardening systems, HEESS has not only focused to develop automated lines, but also refined manual operated hardening machines (SP-Series). These take advantage of the latest technology, like for example quick tool change and PLC-control with workpiece parameter database. An overview over different machine types is given.

基于Automation Studio的造纸机液压系统设计与仿真

液压系统给造纸机压辊提供稳定和可靠的压力来完成纸页的压榨脱水和压光。本文使用Automation Studio设计造纸机的液压系统,并对造纸机各压榨上辊的落下预压、加压和抬起复位等工作过程进行模拟仿真,仿真结果表明,设计的液压系统能以很好的压力稳定性和可靠性完成压榨工作。将设计的液压系统应用于造纸机,液压系统的运行效果基本与仿真结果相同。表明这种设计和仿真方法能准确地模拟实际液压系统的工作状态,它建模过程简单,运行参数调节方便,为液压系统设计及运行优化提供了一种好工具。

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。

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.

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.

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.

Increasing Crop Quality and Yield with a Machine Learning-Based Crop Monitoring System

Farming is cultivating the soil,producing crops,and keeping livestock.The agricultural sector plays a crucial role in a country’s economic growth.This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield.In the first stage,machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops.The recommended crops are based on various factors such as weather conditions,soil analysis,and the amount of fertilizers and pesticides required.In the second stage,a transfer learningbased model for plant seedlings,pests,and plant leaf disease datasets is used to detect weeds,pesticides,and diseases in the crop.The proposed model achieved an average accuracy of 95%,97%,and 98% in plant seedlings,pests,and plant leaf disease detection,respectively.The system can help farmers pinpoint the precise measures required at the right time to increase yields.

Real-Time Crop Prediction Based on Soil Fertility and Weather Forecast Using IoT and a Machine Learning Algorithm

The aim of this article is to assist farmers in making better crop selection decisions based on soil fertility and weather forecast through the use of IoT and AI (smart farming). To accomplish this, a prototype was developed capable of predicting the best suitable crop for a specific plot of land based on soil fertility and making recommendations based on weather forecast. Random Forest machine learning algorithm was used and trained with Jupyter in the Anaconda framework to achieve an accuracy of about 99%. Based on this process, IoT with the Message Queuing Telemetry Transport (MQTT) protocol, a machine learning algorithm, based on Random Forest, and weather forecast API for crop prediction and recommendations were used. The prototype accepts nitrogen, phosphorus, potassium, humidity, temperature and pH as input parameters from the IoT sensors, as well as the weather API for data forecasting. The approach was tested in a suburban area of Yaounde (Cameroon). Taking into account future meteorological parameters (rainfall, wind and temperature) in this project produced better recommendations and therefore better crop selection. All necessary results can be accessed from anywhere and at any time using the IoT system via a web browser.

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 .

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。

数控技术在农业机械自动化生产中的应用

数控技术广泛应用于机械加工和自动化领域,通过计算机控制来实现工作机器的精确运动和操作。随着人口增长和资源有限性的挑战,提高农业生产效率和质量变得尤为重要,数控技术在农业机械自动化生产中发挥着关键作用。该文以农业机械化作业和精准种植施肥为切入点,系统阐述了数控技术的应用原理,如计算机编程、坐标控制和运动控制等,数控技术通过精确的控制农机的运动和操作,提高了生产效率和一致性。

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。

智能农机多机协同收获作业控制方法与试验

为提高多台无人化智能收获机和运粮车协同作业效率,该研究以2台不同型号水稻收获机和1台运粮车为研究对象,开展了智能农机多机协同收获作业控制方法研究。根据协同作业控制决策约束条件,建立协同收获作业中有限个状态过程的改进型连续时间马尔科夫链模型。以减少非作业时间为优化目标,通过模型预测未来一段时间内每台收获机的卸粮时间,动态更新每台收获机的卸粮顺序和时间。仿真试验结果表明:该研究控制方法相对于仓满后再召唤运粮车的卸粮方式有效减少了作业时间,协同收获任务的农机平均作业时间减少了13.58%。田间试验结果表明:智能农机多机协同作业控制方法实现了2台水稻收获机和1台运粮车协同自主作业,在场景1中,相对于仓满召唤卸粮模式,收获机1和收获机2非作业时间分别减少了71.25%和42%,收获效率提高了6.65%和5.22%;在场景2中,相对于仓满召唤卸粮模式,收获机1和收获机2非作业时间分别减少了77.64%和37.09%,收获效率提高了12.07%和5.78%。该文提出的控制方法可以实现收获-卸粮转运自主作业,减少了收获机的非作业时间,提高了作业效率,可为无人农场智能收获协同作业提供支撑。

三边封基质自动包装机的设计与试验

无土栽培模式在我国的应用逐渐广泛,但基质栽培袋的机械化生产发展缓慢,造价昂贵的流水线式生产模式并不适合我国个体用户及中小企业的生产情况。为此,针对市场上缺乏小型、实用、低成本、自动化的基质袋生产设备的现状,设计了三边封基质自动包装机。机具在基质包装领域创新使用先装填再制袋的方法,采用PLC为核心的控制系统,由搅拌装置、定料装置、压缩装置、热封装置、换膜装置、机架、传动系统和控制系统组成。在此,介绍了整机的工作原理及各个装置的设计情况,并对整机的生产功能进行了试验验证。结果表明:三边封基质自动包装机能够自动化完成基质袋的生产,生产速率为36袋/h,包装质量满足设计要求,为无土栽培产业的机械化发展提供了技术支持。