Proposed Machine Learning Model Research Articles

Detecting contract cheating through linguistic fingerprint

Contract cheating, the act of students enlisting others to complete academic assignments on their behalf, poses a significant challenge in academic settings, undermining the integrity of education and assessment. It involves submitting work that is falsely represented as the student’s own, thus violating academic standards and ethics. The advent of artificial intelligence-based language models, such as ChatGPT, has raised concerns about the potential impact of contract cheating. As these language models can generate human-like text with ease, there are concerns about their role in facilitating and increasing contract cheating incidents. Innovative approaches are thus needed to detect contract cheating and address its implications for academic integrity. This study introduces a machine learning (ML) model focused on identifying deviations from a learner’s unique writing style (or their linguistic fingerprint) to detect contract cheating, complementing traditional plagiarism detection methods. The study involved 150 learners majoring in engineering and business who were studying English as a foreign language at a college in Saudi Arabia. The participants were asked to produce descriptive essays in English within a consistent genre over one semester. The proposed approach involved data preprocessing, followed by transformation using Term Frequency-Inverse Document Frequency (TF-IDF). To address data imbalance, random oversampling was applied, and logistic regression (LR) was trained with optimal hyperparameters obtained through grid search. Performance evaluation was conducted using various metrics. The results showed that the ML model was effective in identifying non-consistent essays with improved accuracy after implementing random oversampling. The LR model achieved an accuracy of 98.03%, precision of 98.52%, recall of 98.03%, and F1-score of 98.24%. The proposed ML model shows promise as an indicator of contract cheating incidents, providing an additional tool for educators and institutions to uphold academic integrity. However, it is essential to interpret the model results cautiously, as they do not constitute unequivocal evidence of cheating but rather serve as grounds for further investigation. We also emphasize the ethical implications of such approaches and suggest avenues for future research to explore the model’s applicability among first-language writers and to conduct longitudinal studies on second-language learners’ language development over longer periods.

Early prediction of Sepsis: A comparative assessment on patients’ covariates

The early prediction of Sepsis is a highly important task regarding its impact on the mortality ratio for ICU patients. The earlier the Sepsis occurrence is foreseen, the sooner the patient can have the chance to be intervened and the patient can be prevented from getting major organ damage. However, it is a challenging task for healthcare professionals even though they have expert knowledge and standardized prediction scoring systems. Therefore the machine learning models that evaluate the patients’ regularly measured covariates as features, and make predictions by utilizing them have been acknowledged and have gathered attention to automatize and improve the early prediction process. However, the nature of the problem of early prediction of Sepsis requires patients to make an estimation based on past measurements, and reliable estimation cannot be made with the use of instant measurement results alone. To this end, additional features obtained by recent measurements have been proposed to be involved in the machine learning model’s estimation process by basing a specific dataset with a specific definition of the illness. This study broadly evaluates the previously proposed features to measure the effectiveness of their single and joint use. Unlike the previous research, this evaluation was done by multiple datasets, different types of additional features, and various machine learning methods for validation purposes. Experimental results have proved that the proposed machine learning model has a significant effect on early prediction, and the best improvement is obtained with the XGBoost classifier. However, it should be noted that quantity, especially the quality of the features used to feed the model has a critical effect on the model’s performance. Consequently, using additional features enhances the model’s performance dramatically.

A Machine Learning Model to Predict the Seismic Lifecycle Behavior of a Cross-Sea Cable-Stayed Bridge

Cross-sea cable-stayed bridges encounter challenges associated with cable corrosion and cable-force relaxation during their service life, which significantly affects their structural performance and seismic response. This study focuses on a cross-sea cable-stayed bridge located in Hainan Province. Utilizing an LSTM deep learning model, this study aims to fill in the gaps in short-term cable-monitoring data from the past year using the available cable-force-monitoring data from the same period. The authors of this study interpolated the cable-force data in the absence of sensors and employed a SARIMA machine learning time-series-prediction model to predict the future trends of all cable forces. A finite-element model was constructed, and a dynamic time-history analysis of the seismic response of the cross-sea cable-stayed bridge was conducted, considering the influence of cable-force relaxation and cable corrosion in the future. The findings indicate that the LSTM-SARIMA model predicted an average decrease of 11.81% in the cable force of the cable-stayed bridge after 20 years. During the lifecycle of the cables, cable corrosion exerts a significant impact on the variation in cable stress within the bridge structure during earthquakes, while cable-force relaxation has a more pronounced effect on the vertical displacement of the main beam of the bridge structure during seismic events. Compared to when using the traditional model that only considers cable corrosion, the maximum negative vertical displacement of the main beam increases by 29.7% when using the proposed model if the earthquake intensity is 0.35 g after 20 years, which indicates that the proposed machine learning model can exactly determine the seismic behavior of the lifecycle cross-sea cable-stayed bridge, considering the impacts of both cable-force relaxation and cable corrosion.

Advancing infrastructure resilience: machine learning-based prediction of bridges’ rating factors under autonomous truck platoons

AbstractThe operational characteristics of freight shipment will significantly change after the implementation of Autonomous and Connected Trucks (ACT). This change will have a significant impact on freight mobility, transportation safety, and the sustainability of infrastructure. Truck platooning is an emerging truck configuration that is expected to become operational in the future due to the rapid advancements in connected vehicle technology and autonomous driving assistance. The platooning configuration enables trucks to be connected with themselves and the surrounding infrastructure. This arrangement has shown to be a promising solution to improve the vehicles’ fuel efficiency, reduce carbon dioxide emission, reduce traffic congestion, and improve transportation service. However, platooning may accelerate the damage accumulation of pavement and bridge structures due to the formation of multiple load axles within each platoon since those structures were not designed for such loads. According to AASHTO, bridges are designed based on a notional live load model comprised of one or two trucks per lane in conjunction with or separate from an applied uniform load (AASHTO, LRFD 2022). This damage, if accumulated, its repair would require billions of dollars from the government and would impede the movement of both people and goods. The potential damage to infrastructure may arise due to various factors such as the number of trucks in a platoon, gap spacing between trucks, and the type of trucks. This research work includes a thorough parametric study with 295,200 computer simulations using SAP 2000. The goal was to evaluate the effect of different truck platooning configurations on the load rating of existing bridges. The obtained results served as the dataset for training various machine learning models, including Random Tree, Random Forest, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), K-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost). Results showed that Random Forest model performed the best, with the lowest prediction errors. The proposed machine learning model has shown its effectiveness in identifying optimal platooning configurations for bridge structures within the scope of the study. Graphical Abstract

Improving the quality of hires via the use of machine learning and an expansion of the person–environment fit theory

PurposeOur proposed machine learning model contributes to improving the quality of Hire by providing a more nuanced and comprehensive analysis of candidate attributes. Instead of focusing solely on obvious factors, such as qualifications and experience, our model also considers various dimensions of fit, including person-job fit and person-organization fit. By integrating these dimensions of fit into the model, we can better predict a candidate’s potential contribution to the organization, hence enhancing the Quality of Hire.Design/methodology/approachWithin the scope of the investigation, the competencies of the personnel working in the IT department of one in the largest state banks of the country were used. The entire data collection includes information on 1,850 individual employees as well as 13 different characteristics. For analysis, Python’s “keras” and “seaborn” modules were used. The Gower coefficient was used to determine the distance between different records.FindingsThe K-NN method resulted in the formation of five clusters, represented as a scatter plot. The axis illustrates the cohesion that exists between things (employees) that are similar to one another and the separateness that exists between things that have their own individual identities. This shows that the clustering process is effective in improving both the degree of similarity within each cluster and the degree of dissimilarity between clusters.Research limitations/implicationsEmployee competencies were evaluated within the scope of the investigation. Additionally, other criteria requested from the employee were not included in the application.Originality/valueThis study will be beneficial for academics, professionals, and researchers in their attempts to overcome the ongoing obstacles and challenges related to the securing the proper talent for an organization. In addition to creating a mechanism to use big data in the form of structured and unstructured data from multiple sources and deriving insights using ML algorithms, it contributes to the debates on the quality of hire in an entire organization. This is done in addition to developing a mechanism for using big data in the form of structured and unstructured data from multiple sources.

Machine Learning Guided Strategies to Develop High Efficiency Indoor Perovskite Solar Cells

AbstractIndoor Perovskite Solar Cells (IPSCs) have recently gathered massive research attention, driven by their promising role in powering the continuously expanding Internet of Things (IoT) devices and simultaneous advancements in the Perovskite solar field. To further accelerate the development of IPSCs, a machine learning (ML) approach to assist the advancement of IPSCs is proposed in the current study. Here, a ML model to predict the most important performance parameters such as short circuit current (JSC), open circuit voltage (VOC), fill factor (FF), and power conversion efficiency (PCE) of IPSCs under various light sources and intensities is presented. This developed model can effectively predict the performances of Perovskite Solar Cells (PSCs) operated under indoor illumination close to the true/experimental values. The factors affecting the IPSC performance by Correlation matrix and SHAPley analysis are also analyzed. These findings demonstrate that the proposed ML model provides accurate predictions of VOC, JSC, FF, and PCE of IPSCs, ultimately contributing to the optimization of solar cell performance under indoor environments and the advancement of renewable energy technology.

Proposed Machine learning model for predicting Egyptian Parliament Election Results

Political life and election have become one of the most important comments on social media sites. Governments have shown a keen interest in predicting the results of elections, whether presidential or parliamentary. The purpose of this study is to predict the results of the Egyptian Parliament elections using sentiment analysis, specifically Support Vector Machines (SVM), Naive Bayes, Decision Trees, and Random Forests in the context of machine learning. In this study, a sentiment analysis approach is employed to analyze public sentiment towards political parties and candidates leading up to Parliament elections. The sentiment analysis techniques are utilized to classify sentiment from textual data collected from Tweeter; Data were obtained in November 2020 before and during election days. The study utilizes a machine learning framework to train and test the models using a labeled dataset of sentiment-labeled political texts. The findings of this study reveal that sentiment analysis using machine learning can effectively predict the results of Parliament elections. The accuracy and performance of each technique are evaluated and compared to determine the most accurate and reliable predictor of election outcomes. This study contributes to the existing literature by applying sentiment analysis techniques to predict Parliament election results. The use of machine learning algorithms in combination with sentiment analysis, offers a novel approach to election forecasting. The findings of this study can be valuable for political analysts, election strategists, and policymakers seeking to understand public sentiment and predict election outcomes accurately.

New formulation for predicting total dissolved gas supersaturation in dam reservoir: application of hybrid artificial intelligence models based on multiple signal decomposition

Total dissolved gas (TDG) concentration plays an important role in the control of the aquatic life. Elevated TDG can cause gas-bubble trauma in fish (GBT). Therefore, controlling TDG fluctuation has become of great importance for different disciplines of surface water environmental engineering.. Nowadays, direct estimation of TDG is expensive and time-consuming. Hence, this work proposes a new modelling framework for predicting TDG based on the integration of machine learning (ML) models and multiresolution signal decomposition. The proposed ML models were trained and validated using hourly data obtained from four stations at the United States Geological Survey. The dataset are composed from: (i) water temperature (Tw), (ii) barometric pressure (BP), and (iii) discharge (Q), which were used as the input variables for TDG prediction. The modelling strategy is conducted based on two different steps. First, six singles ML model namely: (i) multilayer perceptron neural network, (ii) Gaussian process regression, (iii) random forest regression, (iv) random vector functional link, (v) adaptive boosting, and (vi) Bootstrap aggregating (Bagging), were developed for predicting TDG using Tw, BP, and Q, and their performances were compared. Second, a new framework was introduced based on the combination of empirical mode decomposition (EMD), the variational mode decomposition (VMD), and the empirical wavelet transform (EWT) preprocessing signal decomposition algorithms with ML models for building new hybrid ML models. Hence, the Tw, BP, and Q signals were decomposed to extract the intrinsic mode functions (IMFs) by using the EMD and VMD methods and the multiresolution analysis (MRA) components by using the EWT method. Then after, the IMFs and MRA components were selected and regraded as new input variables for the ML models and used as an integral part thereof. The single and hybrid prediction models were compared using several statistical metrics namely, root mean square error, mean absolute error, coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). The single and hybrid models were trained several times with high number of repetitions, depending on the kind of modeling process. The obtained results using single models gave good agreement between the predicted TDG and the situ measured dataset. Overall, the Bagging model performed better than the other five models with R2 and NSE values of 0.906 and 0.902, respectively. However, the extracted IMFs and MRA components using the EMD, VMD and the EWT have contributed to an improvement of the hybrid models’ performances, for which the R2 and NSE were significantly increased reaching the values of 0.996 and 0.995. Experimental results showed the superiority of hybrid models and more importantly the importance of signal decomposition in improving the predictive accuracy of TDG.Graphical abstract

Predicting and understanding the ductility of BCC high entropy alloys via knowledge-integrated machine learning

Despite the fact that body-centered cubic high entropy alloys (BCC HEAs) exhibit exceptional strengthening mechanisms over a wide temperature range, their application is often hindered by poor room temperature ductility. Consequently, the accurate prediction of BCC HEAs′ ductility becomes critical in alloy design but is challenged by the intricate nature of these alloy systems. In this study, a comprehensive machine learning (ML) strategy with integrated material knowledge is proposed to address this problem. By referring to plasticity theories, thermodynamics as well as atomic properties, we develop a set of cost-effective material descriptors with various mathematical forms such as averaging, mismatching, maximum-minimum ranging and averaging as binary pairs for HEAs. These descriptors are proved to effectively incorporate material knowledge into ML modelling. They also offer potential alternatives to those obtained from costly first principles calculations. Classification and regression tasks are conducted to predict the ductility of BCC HEAs, and Gradient boosting algorithm exhibiting the best performance for both tasks is eventually selected. The established optimized models achieve high predictive accuracies exceeding 0.85. Through feature engineering, five key features related to valence electron concentration (VEC) and atomic size are screened out. These features lay the foundation for explicit formulae developed via Symbolic Classification, enabling the straightforward numerical prediction of BCC HEAs′ ductility. These key features are further analyzed by an interpretable ML method and compared against experimental and computational plasticity factors, elucidating their influences on various plasticity mechanisms. An in-depth exploration of why the proposed ML models outperform conventional plasticity criteria for HEAs is also presented. This study highlights an effective knowledge-integrated ML approach in predicting material properties resulting from complex mechanisms, with a particular focus on HEAs that often exhibit sparse data.

Using a Machine Learning Approach to Predict the Need for Elective Revision and Unplanned Surgery after Implant-based Breast Reconstruction.

Two-stage implant-based reconstruction after mastectomy may require secondary revision procedures to treat complications, correct defects, and improve aesthetic outcomes. Patients should be counseled on the possibility of additional procedures during the initial visit, but the likelihood of requiring another procedure is dependent on many patient- and surgeon-specific factors. This study aims to identify patient-specific factors and surgical techniques associated with higher rates of secondary procedures and offer a machine learning model to compute individualized assessments for preoperative counseling. A training set of 209 patients (406 breasts) who underwent two-stage alloplastic reconstruction was created, with 45.57% of breasts (185 of 406) requiring revisional or unplanned surgery. On multivariate analysis, hypertension, no tobacco use, and textured expander use corresponded to lower odds of additional surgery. In contrast, higher initial tissue expander volume, vertical radial incision, and larger nipple-inframammary fold distance conferred higher odds of additional surgery. The neural network model trained on clinically significant variables achieved the highest collective performance metrics, with ROC AUC of 0.74, sensitivity of 84.2, specificity of 63.6, and accuracy of 62.1. The proposed machine learning model trained on a single surgeon's data offers a precise and reliable tool to assess an individual patient's risk of secondary procedures. Machine learning models enable physicians to tailor surgical planning and empower patients to make informed decisions aligned with their lifestyle and preferences. The utilization of this technology is especially applicable to plastic surgery, where outcomes are subject to a variety of patient-specific factors and surgeon practices, including threshold to perform secondary procedures.

Predictive and experimental analysis of forces in die-less forming using artificial intelligence techniques

The use of a novel technology for producing the components of lightweight materials and to reduce the requirements of power utilized during manufacturing processes can be a great aspect to decrease pollution and save resources. Single point incremental forming (SPIF) is the viable and novel approach for manufacturing the parts of high strength and lightweight materials without involving dedicated tools and dies economically. This die-less forming technique outperforms the conventional forming techniques by saving the energy and materials. In this work, the estimation and investigation of forming forces have been accomplished to ensure the secure uses for the SPIF machines for performing this process for the designed conditions on AA2024 sheets which is a lightweight aluminum alloy being widely used in aerospace and automotive sectors. To predict the peak deforming load, machine learning (ML) techniques are employed in the current work along with the artificial neural network (ANN) by taking experimental results as the input dataset. The proposed ML model revealed better accuracy (99%) than previous work performed using similar approaches. The proposed ANN model produced lower mean absolute percentage error 4.35 as compared to other models. Authors also calculated the computing time taken during estimation of forming force. Combination of the Flatend-R1 tool and the 1.6 mm blank thickness increased the deforming loads drastically and can become the limiting factor for forming machine which should be avoided whereas the combination of hemispherical tool and lower blank thickness (0.5 mm) reduced the deforming loads that are needed to manufacture the conical frustum. It was also noticed that as the tool shape was changed from hemispherical-end to Flatend-R1, the axial peak forces were increased by 13.16%, 16.59%, 20.43%, and 22.78% for the levels 1, 2, 3, and 4 of the blank thickness, respectively.

Grid-tuned ensemble models for 2D spectrogram-based autism classification

BackgroundAutism spectrum disorder (ASD) is a prevalent collection of developmental disorders that affects around one in every 36 children worldwide. Since autistic children exhibit aberrant electroencephalogram (EEG) signals in comparison to typically developing (TD) children, many authors have recently proposed several machine learning (ML) and deep learning (DL) models to classify ASD patients using EEG signals. However, these models lack extensive and large-scale feature extraction and apply multiple benchmark datasets. Furthermore, most of the proposed ML models need feature engineering to extract key features for classification. MethodThis study proposed a DL-based weighted ensemble model to avoid the need for feature engineering. The proposed model is based on finding the best suitable combination of weights using a grid search strategy, treating the highest weighted model as the most accurate, and giving more weightage. The strength of the suggested technique involves extracting features from EEG recordings by transforming raw EEG into multi-channel two-dimensional time–frequency maps (2D spectrograms) using the short-time Fourier transform (STFT). Furthermore, it applies two widely-researched public EEG datasets [i.e., the King Abdulaziz University (KAU) and the University of Sheffield (TUOS) databases] for performance comparison and better generalizability. Additionally, the performance of the proposed model has been compared with other well-known DL models, and our proposed straightforward, easily implemented models surpass these models in terms of accuracy and training time. ResultsThe suggested model yields an accuracy of 94.71% for ASD classification using the KAU dataset and 96.22% accuracy for utilizing the TUOS dataset. ConclusionsEnsembled models can efficiently enhance accuracy, stability, and reproducibility. The demonstrated finding solidifies the applicability of the weighted ensemble model to classify ASD patients utilizing EEG data. The suggested model might be used as a supplemental tool to assist neurologists in diagnosing ASD.

Sound Speed Inversion Based on Multi-Source Ocean Remote Sensing Observations and Machine Learning

Ocean sound speed is important for underwater acoustic applications, such as communications, navigation and localization, where the assumption of uniformly distributed sound speed profiles (SSPs) is generally used and greatly degrades the performance of underwater acoustic systems. The acquisition of SSPs is necessary for the corrections of the sound ray propagation paths. However, the inversion of SSPs is challenging due to the intricate relations of interrelated physical ocean elements and suffers from the high costs of calculations and hardware deployments. This paper proposes a novel sound speed inversion method based on multi-source ocean remote sensing observations and machine learning, which adapts to large-scale sea regions. Firstly, the datasets of SSPs are generated utilizing the Argo thermohaline profiles and the empirical formulas of the sound speed. Then, the SSPs are analyzed utilizing the empirical orthogonal functions (EOFs) to reduce the dimensions of the feature space as well as the computational load. Considering the nonlinear regression relations of SSPs and the observed datasets, a general framework for sound speed inversion is formulated, which combines the designed machine learning models with the reduced-dimensional feature representations, multi-source ocean remote sensing observations and water temperature data. After being well trained, the proposed machine learning models realize the accurate inversion of the targeted ocean region by inputting the real-time ocean environmental data. The experiments verify the advantages of the proposed method in terms of the accuracy and effectiveness compared with conventional methods.

Experimental and machine learning based study of compressive strength of geopolymer concrete

In this study, the aim is to investigate and predict the compressive strength of geopolymer concrete (GPC). The effects of curing method, curing time and concrete age on the compressive strength of GPC were evaluated experimentally. Four curing methods, namely room temperature (25°C), mobile dryer (50°C), heating cabinet type 1 (80°C) and heating cabinet type 2 (100°C) were adopted. Additionally, three curing times, of 8 h, 16 h and 24 h, as well as three concrete ages, of 7 days, 14 days and 28 days, were considered. To predict the compressive strength of GPC, 679 test results were collected to develop various machine learning models. The test results indicated that increasing the curing temperature, curing time and concrete age all led to improvements in the compressive strength of GPC. The mobile dryer showed promise as a curing method for cast-in-place GPC. The proposed machine learning models demonstrated good predictive capacity for the compressive strength of GPC with relatively high accuracy. Through sensitivity analysis, concrete age was identified as the most influential variable affecting the final compressive strength of GPC.

Effect of variable thickness cross rolling on microstructure gradient of 2205 duple stainless steel

Quantitative description of the complex relationship between rolling process and microstructure is crucial for improving the performance of 2205 duplex stainless steel products. In this work, four distinct rolling processes were used for three passes of hot rolling of 2205 duplex stainless steel. The study delved into the macroscopic morphology, microstructural gradients, and texture evolution of 2205 duplex stainless steel sheets in various rolling states. In addition, a data expansion method was used to expand the original experimental data, and the support vector machine model optimized by genetic algorithm was used to accurately describe the complex relationship between different rolling processes and microstructures. The results indicate that the proposed variable thickness cross-rolling method renders a more uniform microstructure in the rolled piece, with significantly refined grains and the formation of a distinct structural gradient. Among them, when using process 3 rolling, the grain size in the edge area of the plate after three passes of rolling is finer and the degree of recrystallization is higher, which is conducive to improving the problem of poor thermal plasticity at the edges and providing a basis for studying the suppression of edge crack damage in 2205 duplex stainless steel. In addition, the proposed machine learning model accurately predicted the grain size of plates in different rolling states, aligning closely with experimental findings.

Spatial downscaling of SMAP radiometer soil moisture using radar data: Application of machine learning to the SMAPEx and SMAPVEX campaigns

This study developed a random forest approach for downscaling the coarse-resolution (36 km) soil moisture measured by The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission to 1 km spatial resolution, utilizing airborne remotely sensed data (radar backscatter and radiometer retrieved soil moisture), vegetation characteristics (normalized difference vegetation index), soil properties, topography, and ground soil moisture measurements from before the launch of SMAP for training a random forest model. The 36 km SMAP soil moisture product was then downscaled by the trained model to 1 km resolution using the information from SMAP. The downscaled soil moisture was evaluated using airborne retrieved soil moisture observations and ground soil moisture measurements. Considering the airborne retrieved soil moisture as a reference, the results demonstrated that the proposed random forest model could downscale the SMAP radiometer product to 1 km resolution with a correlation coefficient of 0.97, unbiased Root Mean Square Error of 0.048 m3 m−3 and bias of 0.016 m3 m−3. Accordingly, the downscaled soil moisture captured the spatial and temporal heterogeneity and demonstrated the potential of the proposed machine learning model for soil moisture downscaling.

Machine Learning-Based Discrimination of Cardiovascular Outcomes in Patients With Hypertrophic Cardiomyopathy

BackgroundCurrent risk stratification strategies for patients with hypertrophic cardiomyopathy (HCM) are limited to traditional methodologies. ObjectivesThe authors aimed to establish machine learning (ML)-based models to discriminate major cardiovascular events in patients with HCM. MethodsWe enrolled consecutive HCM patients from 2 tertiary referral centers and used 25 clinical and echocardiographic features to discriminate major adverse cardiovascular events (MACE), including all-cause death, admission for heart failure (HF-adm), and stroke. The best model was selected for each outcome using the area under the receiver operating characteristic curve (AUROC) with 20-fold cross-validation. After testing in the external validation cohort, the relative importance of features in discriminating each outcome was determined using the SHapley Additive exPlanations (SHAP) method. ResultsIn total, 2,111 patients with HCM (age 61.4 ± 13.6 years; 67.6% men) were analyzed. During the median 4.0 years of follow-up, MACE occurred in 341 patients (16.2%). Among the 4 ML models, the logistic regression model achieved the best AUROC of 0.800 (95% CI: 0.760-0.841) for MACE, 0.789 (95% CI: 0.736-0.841) for all-cause death, 0.798 (95% CI: 0.736-0.860) for HF-adm, and 0.807 (95% CI: 0.754-0.859) for stroke. The discriminant ability of the logistic regression model remained excellent when applied to the external validation cohort for MACE (AUROC = 0.768), all-cause death (AUROC = 0.750), and HF-adm (AUROC = 0.806). The SHAP analysis identified left atrial diameter and hypertension as important variables for all outcomes of interest. ConclusionsThe proposed ML models incorporating various phenotypes from patients with HCM accurately discriminated adverse cardiovascular events and provided variables with high importance for each outcome.

Predicting Individual Tree Mortality of Larix gmelinii var. Principis-rupprechtii in Temperate Forests Using Machine Learning Methods

Accurate prediction of individual tree mortality is essential for informed decision making in forestry. In this study, we proposed machine learning models to forecast individual tree mortality within the temperate Larix gmelinii var. principis-rupprechtii forests in Northern China. Eight distinct machine learning techniques including random forest, logistic regression, artificial neural network, generalized additive model, support vector machine, gradient boosting machine, k-nearest neighbors, and naive Bayes models were employed, to construct an ensemble learning model based on comprehensive dataset from this specific ecosystem. The random forest model emerged as the most accurate, demonstrating 92.9% accuracy and 92.8% sensitivity, making it the best model among those tested. We identified key variables impacting tree mortality, and the results showed that a basal area larger than the target trees (BAL), a diameter at 130 cm (DBH), a basal area (BA), an elevation, a slope, NH4-N, soil moisture, crown density, and the soil’s available phosphorus are important variables in the Larix Principis-rupprechtii individual mortality model. The variable importance calculation results showed that BAL is the most important variable with an importance value of 1.0 in a random forest individual tree mortality model. By analyzing the complex relationships of individual tree factors, stand factors, environmental, and soil factors, our model aids in decision making for temperate Larix gmelinii var. principis-rupprechtii forest conservation.

Development of Machine Learning Models for Studying the Premixed Turbulent Combustion of Gas-To-Liquids (GTL) Fuel Blends

AbstractStudying the spatial and temporal evolution in turbulent flames represents one of the most challenging problems in the combustion community. Based on previous 3D numerical analyses, this study aims to develop data-driven machine learning (ML) models for predicting the flame radius evolution and turbulent flame speeds for diesel, gas-to-liquids (GTL), and their 50/50 blend (by volumetric composition) under different thermodynamic and turbulence operating conditions. Two ML models were developed in this study. Model 1 predicts the variations of the flame radius with time, equivalence ratio, and turbulence intensity, whereas model 2 predicts the variations of the turbulence flame speed with the operating parameters. The k-fold cross-validation technique is used for model training, and the developed neural network-based model is used to investigate the effects of operating parameters on the premixed turbulent flames. In addition, the possible minimum and maximum values of responses at the corresponding operating parameters are found using a genetic algorithm (GA) approach. Model 1 could capture the computational fluid dynamics (CFD) outputs with high precision at different flame radiuses and time instants with a maximum absolute error percentage of 5.46%. For model 2, the maximum absolute error percentage was 6.58%. Overall, this study demonstrates the applicability and promising performance of the proposed ML models, which will be used in subsequent research to analyze turbulent flames a posteriori.

Machine learning-based prediction model for battery levels in IoT devices using meteorological variables

Efficient energy management is vital for the sustainability of IoT devices employing solar harvesting systems, particularly to circumvent battery depletion during periods of diminished solar incidence. Embracing the structured methodology of CRISP-DM, this study introduces machine learning (ML) models that utilise meteorological data to predict battery charge levels in solar-powered IoT devices. These models enable proactive adjustments to the devices’ data sampling frequencies, ensuring effective energy utilisation. The proposed ML models were evaluated using authentic battery charge data and weather forecast records. The empirical results of this study corroborate the predictive prowess of the models, with an average accuracy reaching as high as 94.09% in specific test cases. This substantiates the potential of the developed methodology to significantly enhance the energy autonomy of IoT devices through predictive analytics.