Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-series data.The second method classifies the ECG by patient experience.The third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer information.Because ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and diagnosis.Classifications using all three approaches have not been examined till now.Several researchers found that Machine Learning(ML)techniques can improve ECG classification.This study will compare popular machine learning techniques to evaluate ECG features.Four algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization results.SVM plus prior knowledge has the highest accuracy(99%)of the four ML methods.QRS characteristics failed to identify signals without chaos theory.With 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

High-throughput calculations combining machine learning to investigate the corrosion properties of binary Mg alloys

Magnesium(Mg)alloys have shown great prospects as both structural and biomedical materials,while poor corrosion resistance limits their further application.In this work,to avoid the time-consuming and laborious experiment trial,a high-throughput computational strategy based on first-principles calculations is designed for screening corrosion-resistant binary Mg alloy with intermetallics,from both the thermodynamic and kinetic perspectives.The stable binary Mg intermetallics with low equilibrium potential difference with respect to the Mg matrix are firstly identified.Then,the hydrogen adsorption energies on the surfaces of these Mg intermetallics are calculated,and the corrosion exchange current density is further calculated by a hydrogen evolution reaction(HER)kinetic model.Several intermetallics,e.g.Y_(3)Mg,Y_(2)Mg and La_(5)Mg,are identified to be promising intermetallics which might effectively hinder the cathodic HER.Furthermore,machine learning(ML)models are developed to predict Mg intermetallics with proper hydrogen adsorption energy employing work function(W_(f))and weighted first ionization energy(WFIE).The generalization of the ML models is tested on five new binary Mg intermetallics with the average root mean square error(RMSE)of 0.11 eV.This study not only predicts some promising binary Mg intermetallics which may suppress the galvanic corrosion,but also provides a high-throughput screening strategy and ML models for the design of corrosion-resistant alloy,which can be extended to ternary Mg alloys or other alloy systems.

Machine learning with active pharmaceutical ingredient/polymer interaction mechanism:Prediction for complex phase behaviors of pharmaceuticals and formulations

The high throughput prediction of the thermodynamic phase behavior of active pharmaceutical ingredients(APIs)with pharmaceutically relevant excipients remains a major scientific challenge in the screening of pharmaceutical formulations.In this work,a developed machine-learning model efficiently predicts the solubility of APIs in polymers by learning the phase equilibrium principle and using a few molecular descriptors.Under the few-shot learning framework,thermodynamic theory(perturbed-chain statistical associating fluid theory)was used for data augmentation,and computational chemistry was applied for molecular descriptors'screening.The results showed that the developed machine-learning model can predict the API-polymer phase diagram accurately,broaden the solubility data of APIs in polymers,and reproduce the relationship between API solubility and the interaction mechanisms between API and polymer successfully,which provided efficient guidance for the development of pharmaceutical formulations.

Fundamental error in tree-based machine learning model selection for reservoir characterisation

Over the past two decades,machine learning techniques have been extensively used in predicting reservoir properties.While this approach has significantly contributed to the industry,selecting an appropriate model is still challenging for most researchers.Relying solely on statistical metrics to select the best model for a particular problem may not always be the most effective approach.This study encourages researchers to incorporate data visualization in their analysis and model selection process.To evaluate the suitability of different models in predicting horizontal permeability in the Volve field,wireline logs were used to train Extra-Trees,Ridge,Bagging,and XGBoost models.The Random Forest feature selection technique was applied to select the relevant logs as inputs for the models.Based on statistical metrics,the Extra-Trees model achieved the highest test accuracy of 0.996,RMSE of 19.54 mD,and MAE of 3.18 mD,with XGBoost coming in second.However,when the results were visualised,it was discovered that the XGBoost model was more suitable for the problem being tackled.The XGBoost model was a better predictor within the sandstone interval,while the Extra-Trees model was more appropriate in non-sandstone intervals.Since this study aims to predict permeability in the reservoir interval,the XGBoost model is the most suitable.These contrasting results demonstrate the importance of incorporating data visualisation techniques as an evaluation metric.Given the heterogeneity of the subsurface,relying solely on statistical metrics may not be sufficient to determine which model is best suited for a particular problem.

Hybrid data-driven framework for shale gas production performance analysis via game theory, machine learning, and optimization approaches

A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.

Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County,Three Gorges Reservoir, China

Landslide susceptibility mapping is vital for landslide risk management and urban planning.In this study,we used three statistical models[frequency ratio,certainty factor and index of entropy(IOE)]and a machine learning model[random forest(RF)]for landslide susceptibility mapping in Wanzhou County,China.First,a landslide inventory map was prepared using earlier geotechnical investigation reports,aerial images,and field surveys.Then,the redundant factors were excluded from the initial fourteen landslide causal factors via factor correlation analysis.To determine the most effective causal factors,landslide susceptibility evaluations were performed based on four cases with different combinations of factors("cases").In the analysis,465(70%)landslide locations were randomly selected for model training,and 200(30%)landslide locations were selected for verification.The results showed that case 3 produced the best performance for the statistical models and that case 2 produced the best performance for the RF model.Finally,the receiver operating characteristic(ROC)curve was used to verify the accuracy of each model's results for its respective optimal case.The ROC curve analysis showed that the machine learning model performed better than the other three models,and among the three statistical models,the IOE model with weight coefficients was superior.

Classifying Machine Learning Features Extracted from Vibration Signal with Logistic Model Tree to Monitor Automobile Tyre Pressure

Tyre pressure monitoring system(TPMS)is compulsory in most countries like the United States and European Union.The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data.A difference in wheel speed would trigger an alarm based on the algorithm implemented.In this paper,machine learning approach is proposed as a new method to monitor tyre pressure by extracting the vertical vibrations from a wheel hub of a moving vehicle using an accelerometer.The obtained signals will be used to compute through statistical features and histogram features for the feature extraction process.The LMT(Logistic Model Tree)was used as the classifier and attained a classification accuracy of 92.5%with 10-fold cross validation for statistical features and 90.5% with 10-fold cross validation for histogram features.The proposed model can be used for monitoring the automobile tyre pressure successfully.

Simulation and forecasting of fishery weather based on statistical machine learning

As the new generation of artificial intelligence(AI)continues to evolve,weather big data and statistical machine learning(SML)technologies complement each other and are deeply integrated to significantly improve the processing and forecasting accuracy of fishery weather.Accurate fishery weather services play a crucial role in fishery production,serving as a great safeguard for economic benefits and personal safety,enabling fishermen to carry out fishery production better,and contributing to the sustainable development of the fishery industry.The objective of this paper is to offer an understanding of the present state of research and development in SML technology for simulating and forecasting fishery weather.Specifically,we analyze the current state of research and technical features of SML in weather and summarize the applications of SML in simulation and forecasting of fishery weather,which mainly include three aspects:fishery weather scenario generation,fishery weather forecasting,and fishery extreme weather warning.We also illustrate the main technical means and principles of SML technology.Finally,we summarize the most advanced SML fields and provide an outlook on their application value in the field of fishery weather.

Computational discovery of energy materials in the era of big data and machine learning:A critical review

The discovery of novel materials with desired properties is essential to the advancements of energy-related technologies.Despite the rapid development of computational infrastructures and theoretical approaches,progress so far has been limited by the empirical and serial nature of experimental work.Fortunately,the situation is changing thanks to the maturation of theoretical tools such as density functional theory,high-throughput screening,crystal structure prediction,and emerging approaches based on machine learning.Together these recent innovations in computational chemistry,data informatics,and machine learning have acted as catalysts for revolutionizing material design and hopefully will lead to faster kinetics in the development of energy-related industries.In this report,recent advances in material discovery methods are reviewed for energy devices.Three paradigms based on empiricism-driven experiments,database-driven high-throughput screening,and data informatics-driven machine learning are discussed critically.Key methodological advancements involved are reviewed including high-throughput screening,crystal structure prediction,and generative models for target material design.Their applications in energy-related devices such as batteries,catalysts,and photovoltaics are selectively showcased.

Feature-Based Vibration Monitoring of a Hydraulic Brake System Using Machine Learning

Hydraulic brakes in automobiles are an important control component used not only for the safety of the passenger but also for others moving on the road.Therefore,monitoring the condition of the brake components is inevitable.The brake elements can be monitored by studying the vibration characteristics obtained from the brake system using a proper signal processing technique through machine learning approaches.The vibration signals were captured using an accelerometer sensor under a various fault condition.The acquired vibration signals were processed for extracting meaningful information as features.The condition of the brake system can be predicted using a feature based machine learning approach through the extracted features.This study focuses on a mechatronics system for data acquisitions and a signal processing technique for extracting features such as statistical,histogram and wavelets.Comparative results have been carried out using an experimental study for finding the effectiveness of the suggested signal processing techniques for monitoring the condition of the brake system.

Application of Machine Learning for Tool Condition Monitoring in Turning

The machining process is primarily used to remove material using cutting tools.Any variation in tool state affects the quality of a finished job and causes disturbances.So,a tool monitoring scheme(TMS)for categorization and supervision of failures has become the utmost priority.To respond,traditional TMS followed by the machine learning(ML)analysis is advocated in this paper.Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation.In the current study,investigation on the single point cutting tool is carried out while turning a stainless steel(SS)workpeice on the manual lathe trainer.The vibrations developed during this activity are examined for failure-free and various failure states of a tool.The statistical modeling is then incorporated to trace vital signs from vibration signals.The multiple-binary-rule-based model for categorization is designed using the decision tree.Lastly,various tree-based algorithms are used for the categorization of tool conditions.The Random Forest offered the highest classification accuracy,i.e.,92.6%.

An Incentive Mechanism for Federated Learning:A Continuous Zero-Determinant Strategy Approach

As a representative emerging machine learning technique, federated learning(FL) has gained considerable popularity for its special feature of “making data available but not visible”. However, potential problems remain, including privacy breaches, imbalances in payment, and inequitable distribution.These shortcomings let devices reluctantly contribute relevant data to, or even refuse to participate in FL. Therefore, in the application of FL, an important but also challenging issue is to motivate as many participants as possible to provide high-quality data to FL. In this paper, we propose an incentive mechanism for FL based on the continuous zero-determinant(CZD) strategies from the perspective of game theory. We first model the interaction between the server and the devices during the FL process as a continuous iterative game. We then apply the CZD strategies for two players and then multiple players to optimize the social welfare of FL, for which we prove that the server can keep social welfare at a high and stable level. Subsequently, we design an incentive mechanism based on the CZD strategies to attract devices to contribute all of their high-accuracy data to FL.Finally, we perform simulations to demonstrate that our proposed CZD-based incentive mechanism can indeed generate high and stable social welfare in FL.

Machine learning accelerated DFT research on platinum-modified amorphous alloy surface catalysts

Pt-modified amorphous alloy(Pt@PdNiCuP)catalyst exhibits excellent electro-catalytic activity and high experimental durability for hydrogen evolution reaction(HER).However,the physical origin of the catalytically active remains unclear.In this paper,we constructed a distance contribution descriptor(DCD)for the feature engineering of machine learning(ML)potential,and calculated the Gibbs free energies(ΔGH)of 46,000*H binding sites on the Pt@Pd Ni Cu P surface by ML-accelerated density functional theory(DFT).The relationship betweenΔGHand DCD revealed that in the H-Pt distance region of 2.0-2.5 A where the parabolic tail and disordered scatters coexist,the H-metal bonding configuration is mainly the bridge-or hollow-bonding type.The contribution analysis of DCD indicates that the joint effect of Pt,Pd and Ni atoms determines the catalytical behavior of amorphous alloy,which agrees well with experimental results.By counting atomic percentages in different energy intervals,we obtained the atomic ratio for the best catalytic performance(Pt:Pd:Ni:Cu:P=0.33:0.17:0.155:0.16:0.185).Projected density of states(PDOS)show that H 1s orbital,Pt 5d orbital,and Pd 4d orbital form a bonding state at-2 e V.These results provide new ideas for designing more active amorphous alloy catalysts.

Machine learning transforms the inference of the nuclear equation of state

Our knowledge of the properties of dense nuclear matter is usually obtained indirectly via nuclear experiments,astrophysical observations,and nuclear theory calculations.Advancing our understanding of the nuclear equation of state(EOS,which is one of the most important properties and of central interest in nuclear physics)has relied on various data produced from experiments and calculations.We review how machine learning is revolutionizing the way we extract EOS from these data,and summarize the challenges and opportunities that come with the use of machine learning.

Examination of machine learning for assessing physical effects:Learning the relativistic continuum mass table with kernel ridge regression

The kernel ridge regression(KRR)method and its extension with odd-even effects(KRRoe)are used to learn the nuclear mass table obtained by the relativistic continuum Hartree-Bogoliubov theory.With respect to the binding energies of 9035 nuclei,the KRR method achieves a root-mean-square deviation of 0.96 MeV,and the KRRoe method remarkably reduces the deviation to 0.17 MeV.By investigating the shell effects,one-nucleon and twonucleon separation energies,odd-even mass differences,and empirical proton-neutron interactions extracted from the learned binding energies,the ability of the machine learning tool to grasp the known physics is discussed.It is found that the shell effects,evolutions of nucleon separation energies,and empirical proton-neutron interactions are well reproduced by both the KRR and KRRoe methods,although the odd-even mass differences can only be reproduced by the KRRoe method.

A survey of multi-modal learning theory

Deep multi-modal learning,a rapidly growing field with a wide range of practical applications,aims to effectively utilize and integrate information from multiple sources,known as modalities.Despite its impressive empirical performance,the theoretical foundations of deep multi-modal learning have yet to be fully explored.In this paper,we will undertake a comprehensive survey of recent developments in multi-modal learning theories,focusing on the fundamental properties that govern this field.Our goal is to provide a thorough collection of current theoretical tools for analyzing multi-modal learning,to clarify their implications for practitioners,and to suggest future directions for the establishment of a solid theoretical foundation for deep multi-modal learning.

Integrating Machine Learning and Evidential Reasoning for User Profiling and Recommendation

User profiles representing users’preferences and interests play an important role in many applications of personalized recommendation.With the rapid growth of social platforms,there is a critical need for efficient solutions to learn user profiles from the information they shared on social platforms so as to improve the quality of recommendation services.The problem of user profile learning is significantly challenging due to difficulty in handling data from multiple sources,in different formats and often associated with uncertainty.In this paper,we introduce an integrated approach that combines advanced Machine Learning techniques with evidential reasoning based on Dempster-Shafer theory of evidence for user profiling and recommendation.The developed methods for user profile learning and multi-criteria collaborative filtering are demonstrated with experimental results and analysis that show the effectiveness and practicality of the integrated approach.A proposal for extending multi-criteria recommendation systems by incorporating user profiles learned from different sources of data into the recommendation process so as to provide better recommendation capabilities is also highlighted.

Statistical machine learning model for capacitor planning considering uncertainties in photovoltaic power

New energy integration and flexible demand response make smart grid operation scenarios complex and change-able,which bring challenges to network planning.If every possible scenario is considered,the solution to the plan-ning can become extremely time-consuming and difficult.This paper introduces statistical machine learning(SML)techniques to carry out multi-scenario based probabilistic power flow calculations and describes their application to the stochastic planning of distribution networks.The proposed SML includes linear regression,probability distribu-tion,Markov chain,isoprobabilistic transformation,maximum likelihood estimator,stochastic response surface and center point method.Based on the above SML model,capricious weather,photovoltaic power generation,thermal load,power flow and uncertainty programming are simulated.Taking a 33-bus distribution system as an example,this paper compares the stochastic planning model based on SML with the traditional models published in the literature.The results verify that the proposed model greatly improves planning performance while meeting accuracy require-ments.The case study also considers a realistic power distribution system operating under stressed conditions.

A Machine Learning Approach for Collusion Detection in Electricity Markets Based on Nash Equilibrium Theory

We aim to provide a tool for independent system operators to detect the collusion and identify the colluding firms by using day-ahead data. In this paper, an approach based on supervised machine learning is presented for collusion detection in electricity markets. The possible scenarios of the collusion among generation firms are firstly identified. Then,for each scenario and possible load demand, market equilibrium is computed. Market equilibrium points under different collusions and their peripheral points are used to train the collusion detection machine using supervised learning approaches such as classification and regression tree(CART) and support vector machine(SVM) algorithms. By applying the proposed approach to a four-firm and ten-generator test system, the accuracy of the proposed approach is evaluated and the efficiency of SVM and CART algorithms in collusion detection are compared with other supervised learning and statistical techniques.