Machine learning approach for the ground level aerosol concentration analysis

Laboratory-based hyperspectral imaging (HSI) is an optical non-destructive technology used to extract mineralogical information from bedrock drill cores. In the present study, drill core scanning in the long-wave infrared (LWIR; 8000–12,000 nm) wavelength region was used to map the dominant minerals in HSI pixels. Machine learning classification algorithms, including random forest (RF) and support vector machine, have previously been applied to the mineral characterization of drill core hyperspectral data. The objectives of this study are to expand semi-automated mineral mapping by investigating the mapping accuracy, generalization potential, and classification ability of cutting-edge methods, such as various ensemble machine learning algorithms and deep learning semantic segmentation. In the present study, the mapping of quartz, talc, chlorite, and mixtures thereof in HSI data was performed using the ENVINet5 algorithm, which is based on the U-net deep learning network and four decision tree ensemble algorithms, including RF, gradient-boosting decision tree (GBDT), light gradient-boosting machine (LightGBM), AdaBoost, and bagging. Prior to training the classification models, endmember selection was employed using the Sequential Maximum Angle Convex Cone endmember extraction method to prepare the samples used in the model training and evaluation of the classification results. The results show that the GBDT and LightGBM classifiers outperformed the other classification models with overall accuracies of 89.43% and 89.22%, respectively. The results of the other classifiers showed overall accuracies of 87.32%, 87.33%, 82.74%, and 78.32% for RF, bagging, ENVINet5, and AdaBoost, respectively. Therefore, the findings of this study confirm that the ensemble machine learning algorithms are efficient tools to analyze drill core HSI data and map dominant minerals. Moreover, the implementation of deep learning methods for mineral mapping from HSI drill core data should be further explored and adjusted.

Accurate oil palm yield prediction is necessary to sustain oil palm production for food security and economic return. However, there are limited studies on comprehensive mapping and accurate oil palm yield prediction using advanced machine learning algorithms. Using multi-temporal remote sensing data, this paper proposed a new approach to predict oil palm yield based on the normalized difference vegetation index (NDVI) and ensemble machine learning algorithm. ReliefF algorithm with linear projection was employed to select the best combination of spectral indices in oil palm discrimination. Oil palm land cover was classified using random forest (RF) and modified AdaBoost algorithms. A time-series approach known as walk-forward validation was firstly introduced to train the model using the 2016-2019 data and the one-step prediction was performed for 2020 using RF and AdaBoost. Result of the study revealed that the RF model (RMSE = 0.384; MSE = 0.148; MAE = 0.147) outperformed the AdaBoost model (RMSE = 0.410; MSE = 0.168; MAE = 0.176). Our research has demonstrated the value of detailed mapping and subsequent yield prediction by developing a novel approach utilising time-series satellite imagery, ensemble machine learning, and NDVI, which will assist decision-makers in managing their practices related to oil palm.

Warpage prediction of Injection-molded PVC part using ensemble machine learning algorithm

This study looked at novel data sources for cardiovascular risk prediction including detailed lifestyle questionnaire and continuous blood pressure monitoring, using ensemble machine learning algorithms (MLAs). The reference conventional risk score compared against was the Framingham Risk Score (FRS). The outcome variables were low or high risk based on calcium score 0 or calcium score 100 and above. Ensemble MLAs were built based on naive bayes, random forest and support vector classifier for low risk and generalized linear regression, support vector regressor and stochastic gradient descent regressor for high risk categories. MLAs were trained on 600 Southeast Asians aged 21 to 69 years free of cardiovascular disease. All MLAs outperformed the FRS for low and high-risk categories. MLA based on lifestyle questionnaire only achieved AUC of 0.715 (95% CI 0.681, 0.750) and 0.710 (95% CI 0.653, 0.766) for low and high risk respectively. Combining all groups of risk factors (lifestyle survey questionnaires, clinical blood tests, 24-h ambulatory blood pressure and heart rate monitoring) along with feature selection, prediction of low and high CVD risk groups were further enhanced to 0.791 (95% CI 0.759, 0.822) and 0.790 (95% CI 0.745, 0.836). Besides conventional predictors, self-reported physical activity, average daily heart rate, awake blood pressure variability and percentage time in diastolic hypertension were important contributors to CVD risk classification.

Acute kidney injury (AKI) is a common postoperative complication among patients in the neurological intensive care unit (NICU), often resulting in poor prognosis and high mortality. In this retrospective cohort study, we established a model for predicting AKI following brain surgery based on an ensemble machine learning algorithm using data from 582 postoperative patients admitted to the NICU at the Dongyang People's Hospital from March 1, 2017, to January 31, 2020. Demographic, clinical, and intraoperative data were collected. Four machine learning algorithms (C5.0, support vector machine, Bayes, and XGBoost) were used to develop the ensemble algorithm. The AKI incidence in critically ill patients after brain surgery was 20.8%. Intraoperative blood pressure; postoperative oxygenation index; oxygen saturation; and creatinine, albumin, urea, and calcium levels were associated with the postoperative AKI occurrence. The area under the curve value for the ensembled model was 0.85. The accuracy, precision, specificity, recall, and balanced accuracy values were 0.81, 0.86, 0.44, 0.91, and 0.68, respectively, indicating good predictive ability. Ultimately, the models using perioperative variables exhibited good discriminatory ability for early prediction of postoperative AKI risk in patients admitted to the NICU. Thus, the ensemble machine learning algorithm may be a valuable tool for forecasting AKI.

Disastrous natural hazards, such as landslides, floods, and forest fires cause a serious threat to natural resources, assets and human lives. Consequently, landslide risk assessment has become requisite for managing the resources in future. This study was designed to develop four ensemble metaheuristic machine learning algorithms, such as grey wolf optimized based artificial neural network (GW-ANN), grey wolf optimized based random forest (GW-RF), particle swarm optimization optimized based ANN (PSO-ANN), and PSO optimized based RF for modeling rainfall-induced landslide susceptibility (LS) in Aqabat Al-Sulbat, Asir region, Saudi Arabia, which observes landslide frequently. To obtain very high precision and robust prediction from machine learning algorithms, the grey wolf and PSO optimization algorithms were integrated to develop new ensemble machine learning techniques. Subsequently, LS maps produced by training dataset were validated using the receiver operating characteristics (ROC) curve based on the testing dataset. Based on the area under curve (AUC) value of ROC curve, the best method for LS modeling was selected. We developed ROC curve-based sensitivity analysis to investigate the influence of the parameters for LS modeling. The Gumble extreme value distribution was employed to estimate the rainfall at 2, 5, 10, 20, 50, and 100 year return periods. Then, the landslide hazard maps were prepared at different return periods by integrating the best LS model and estimated rainfall at different return periods. The theory of danger pixels was employed to prepare a final risk assessment of the resources, which have been exposed to the landslide. The results showed that 27–42 and 6–15 km2 were predicted as the very high and high LS zones using four ensemble metaheuristic machine learning algorithms. Based on the area under curve (AUC) of ROC, GR-ANN (AUC-0.905) appeared as the best model for LS modeling. The areas under high and very high landslide hazard were gradually increased over the progression of time (26 km2 at the 2 year return period and 40 km2 at the 100 year return period for the high landslide hazard zone, and 6 km2 at the 2 year return period and 20 km2 at the 100 year return period for the very high landslide hazard zone). Similarly, the areas of danger pixel also increased gradually from the 2 to 100 year return periods (37 km2 to 62 km2). Various natural resources, such as scrubland, built up, and sparse vegetation, were identified under risk zone due to landslide hazards. In addition, these resources would be exposed extensively to landslides over the advancement of return periods. Therefore, the outcome of the present study will help planners and scientists to propose high precision management plans for protecting natural resources, which have been exposed to landslides.

P125. Development of a novel ensemble machine learning algorithm for prediction of complications and readmission after anterior cervical spinal fusion

Background: Cardiac arrest (CA) poses a significant risk of death and long-term neurological disability yet there remains a lack of accurate and reliable methods to prognosticate post-CA outcome, indicating a major unmet need. Additionally, no existing CA outcome prediction model utilizes physiological time series (PTS) data, which captures transitions in a patient’s health state at high temporal resolution. Materials and Methods: We screened the Philips eICU database, which includes over 200,000 patients in 200 hospitals, extracting data on CA patients within 24 hours of ICU admission. We first extracted PTS and Electronic Health Record (HER) features and selected our features using nested Random Forest. The outcome labels were neurological state or mortality at ICU discharge. We tested various individual and ensemble machine learning algorithms (GLM, Random Forest, Gradient Boosting, Support Vector Machine, Neural Networks) and optimization techniques (hyperparameter tuning, model stacking, and transfer learning) to maximize the accuracy of outcome prediction. Results: For prediction of neurological outcome, our model combining EHR and PTS features achieved a higher sensitivity (0.78), specificity (0.88), and AUC (0.87±0.01) compared to the APACHE baseline standard model (AUC: 0.74±0.01, sensitivity: 0.77, specificity: 0.63). Also, for prediction of mortality our model (AUC: 0.81±0.01, sensitivity: 0.78, specificity: 0.71) outperformed the APACHE clinical baseline (AUC: 0.75±0.01, sensitivity: 0.86, specificity: 0.56) by 7%. The integration of PTS with EHR data increased mean AUC by 4% for neurological outcome and 2% for mortality. Conclusion: Results demonstrate that for both neurological outcome and mortality prediction, our ensemble model was significantly more effective compared to the baseline APACHE model. Additionally, we find that PTS data adds to the discriminative power of clinical models. With appropriate validation in prospective cohorts, these findings could be used as point-of-care references to aid ICU physicians in clinical decision-making.

Analyzing changes in land use and land cover (LULC) is crucial for ensuring long-term ecological sustainability. This study investigated the spatio-temporal dynamics of LULC, with a particular focus on irrigation expansion and its effects on ecosystem services in the Lake Tana sub-basin. An ensemble machine learning algorithm, random forest, was employed in the LULC classification for 1985, 2003, and 2021. The Normalized Vegetation Index (NDVI) was used to differentiate between rain-fed and irrigated lands, and the impact of LULC dynamics on Ecosystem Service Values (ESVs) was evaluated using modified valuation coefficients tailored to Ethiopian biome conditions. Based on the classified maps, the agricultural land increased significantly from 43.95 to 55.22%, and the irrigated agriculture rose from 1.24 to 8.69% between 1985 and 2021, whereas grassland and shrub land decreased from 14.38 to 5.35% and 18.85 to 15.34%, respectively, within the same periods. The largest gain was observed for irrigated agriculture, while the greatest loss was observed for grasslands over 36years. The total ESVs of the sub-basin were estimated at US$2.98 billion in 1985, US$2.96 billion in 2003, and US$2.995 billion in 2021. Over the 36years, approximately US$0.015 billion (0.52%) in ESVs was gained, highlighting the effects of LULC changes on ecosystem services. To mitigate these impacts, strategic land use planning should incorporate ecosystem service valuation into decision-making, reinforce regulatory frameworks, and advance sustainability initiatives. Future research should focus on enhancing classification accuracy by integrating higher-resolution satellite imagery, incorporating socio-economic variables, and simulating future ESV scenarios under various development pathways.

Since cyberbullying impacts both individual victims and entire society, research on abusive language and its detection has attracted attention in recent years. Because social media sites like Facebook, Instagram, Twitter, and others are so widely accessible, hate speech, bullying, sexism, racism, aggressive material, harassment, poisonous comments, and other types of abuse have all substantially increased. Due to the critical requirement to detect, regulate, and limit the spread of harmful content on social networking sites, we conducted this study to automate the detection of offensive language or cyberbullying. We created a new Arabic balanced data set to be used in the offensive detection process because having a balanced data set for a model would result in improved accuracy models. Recently, the performance of single classifiers has been improved using ensemble machine learning. The purpose of this study is to examine the effectiveness of several single and ensemble machine learning algorithms in identifying Arabic text that contains foul language and cyberbullying. Applying them to three Arabic datasets, we have selected three machine learning classifiers and three ensemble models for this aim. Two of them are offensive datasets that are readily accessible in the public, while the third one was created. The results showed that the single learner machine learning strategy is inferior to the ensemble machine learning methodology. Voting performs is the best performing trained ensemble machine learning classifier, outperforming the best single learner classifier (65.1%, 76.2%, and 98%) for the same datasets with accuracy scores of (71.1%, 76.7%, and 98.5%) for each of the three datasets used. Finally, we improve the voting technique’s performance through hyperparameter tuning on the Arabic cyberbullying data set.

Falls represent a major public health risk worldwide for the elderly people. A fall not assisted in time can cause functional impairment in an elderly and a significant decrease in his mobility, independence, and life quality. In this sense, we propose IoTE-Fall system, an intelligent system for detecting falls of elderly people in indoor environments that takes advantages of the Internet of Thing and the ensemble machine learning algorithm. IoTE-Fall system employs a 3D-axis accelerometer embedded into a 6LowPAN wearable device capable of capturing in real time the data of the movements of elderly volunteers. To provide high efficiency in fall detection, in this paper, four machine learning algorithms (classifiers): decision trees, ensemble, logistic regression, and Deepnets are evaluated in terms of AUC ROC, training time and testing time. The acceleration readings are processed and analyzed at the edge of the network using an ensemble-based predictor model that is identified as the most suitable predictor for fall detection. The experiment results from collection data, interoperability services, data processing, data analysis, alert emergency service, and cloud services show that our system achieves accuracy, precision, sensitivity, and specificity above 94%.

Machine Learning is a branch of Artificial Intelligence that predicts several naturally occurring events by training a model with some data and then using unseen data to test it. This paper seeks to analyze the performances of single and ensemble machine learning algorithms on the Cleveland Heart disease data set. Experimental study proves that the accuracy score and area under the ROC curve in the ensemble machine learning model is higher than the single machine learning model in predicting non-CVD and CVD patients.

South Indian millet cultivation remains highly vulnerable to changes in climatic conditions along with soil factors and irregular patterns of rainfall. A machine learning system is developed to make forecasts of millet yield in South Indian districts through the analysis of agro-climatic conditions alongside soil information and crop growing parameters. The information needed preprocessing to encode data and normalize variables before reducing dimensions through Principal Component Analysis. To improve model accuracy the approach employed interaction terms together with polynomial transformations as feature engineering. The experimented model selection process included FCCN-RF alongside ResNet-XGB combined with ResMLP-XGB-LGB and DNN-XGB-LGB-CatBoost. The ensemble model of DNN-XGB-LGB-CatBoost achieved highest performance with 0.9910 R² after conducting assessment with R², MSE, RMSE and MAE. The findings indicated that ResNet-XGB achieved 0.9542 ,ResMLP-XGB-LGB achieved 0.9441 and FCCN-RF achieved 0.9402. Ensemble machine learning algorithms that facilitate sustainable decision-making to address climate-related risks and improve millet production support high accuracy.

Landslide susceptibility assessment along the Karakoram highway, Gilgit Baltistan, Pakistan: A comparative study between ensemble and neighbor-based machine learning algorithms

This paper presents a computational bench-marking assessment of Ensemble Learning algorithms in the prediction of heart disease, combining different Machine Learning algorithms, such as hard voting, soft voting, and stacking, in a single framework. The evaluation was conducted using publicly available cardiovascular dataset obtained from the Kaggle repository (https://www.kaggle.com/datasets/sid321axn/heart-statlog-cleveland-hungary-final) comprising 1,190 instances and 11 clinical features. The process involves data preprocessing, which includes handling missing values, removing outliers, scaling variables and class balancing to ensure uniform input feature selection, based on Random Forest (RF), is used to eliminate unnecessary features. Among the evaluated models, the stacking ensemble classifier achieved the highest overall accuracy of 91.88% on the test dataset. Although additional metrics such as precision, recall and F1-score were computed for comparative analysis, the emphasis of this study remains on methodological benchmarking rather than clinical validation. Various base classifiers, including Decision Tree, Random Forest, AdaBoost, and XGBoost, are applied and tested independently. These models are then combined using ensemble techniques with hard voting, soft voting, and stacking. In stacking, Logistic Regression is used as the meta-model, which is trained on cross-validated predictions of the out-of-fold samples to avoid overfitting. Evaluations are carried out using accuracy as the primary criterion for comparison, so that individual classification systems and their combination strategies can be compared uniformly in the same preprocessing and validation environment. Though performance metrics are provided for comparative indications, the emphasis of the approach lies in the development and evaluation of strategies and not in their clinical assessment. This protocol makes it easy to compare ensemble machine learning algorithms on publicly available cardiovascular datasets and helps to make a systematic comparison of data preprocessing and ensemble configuration approaches.