AdaBoost Research Articles

Conditional Generative Adversarial Networks with Optimized Machine Learning for Fault Detection of Triplex Pump in Industrial Digital Twin

In recent years, digital twin (DT) technology has garnered significant interest from both academia and industry. However, the development of effective fault detection and diagnosis models remains challenging due to the lack of comprehensive datasets. To address this issue, we propose the use of Generative Adversarial Networks (GANs) to generate synthetic data that replicate real-world data, capturing essential features indicative of health-related information without directly referencing actual industrial DT systems. This paper introduces an intelligent fault detection and diagnosis framework for industrial triplex pumps, enhancing fault recognition capabilities and offering a robust solution for real-time industrial applications within the DT paradigm. The proposed framework leverages Conditional GANs (CGANs) alongside the Harris Hawk Optimization (HHO) as a metaheuristic method to optimize feature selection from input data to enhance the performance of machine learning (ML) models such as Bagged Ensemble (BE), AdaBoost (AD), Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Decision Tree (DT), and Naive Bayes (NB). The efficacy of the approach is evaluated using key performance metrics such as accuracy, precision, recall, and F-measure on a triplex pump dataset. Experimental results indicate that hybrid-optimized ML algorithms (denoted by “ML-HHO”) generally outperform or match their classical counterparts across these metrics. BE-HHO achieves the highest accuracy at 95.24%, while other optimized models also demonstrate marginal improvements, highlighting the framework’s effectiveness for real-time fault detection in DT systems, where SVM-HHO attains 94.86% accuracy, marginally higher than SVM’s 94.48%. KNN-HHO outperforms KNNs with 94.73% accuracy compared to 93.14%. Both DT-HHO and DT achieve 94.73% accuracy, with DT-HHO exhibiting slightly better precision and recall. NB-HHO and NB show near-equivalent performance, with NB-HHO at 94.73% accuracy versus NB’s 94.6%. Overall, the optimized algorithms demonstrate consistent, albeit marginal, improvements over their classical versions.

A bidirectional gated recurrent unit based novel stacking ensemble regressor for foretelling the global horizontal irradiance

The rapid expansion of solar power generation has led to new challenges in solar intermittency, requiring precise forecasts of Global Horizontal Irradiance (GHI). Accurate GHI predictions are crucial for integrating sustainable energy sources into traditional electrical grid management. The article proposes an innovative solution, the novel Enhanced Stack Ensemble with a Bi-directional Gated Recurrent Unit (ESE-Bi-GRU), which uses machine learning (ML) boosting regressors such as Ada Boost, Cat Boost, Extreme Gradient Boost, and Gradient Boost, and Light Gradient Boost Machine acts as a base learner and the deep learning (DL) algorithms such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for both directions are taken as a meta-learner. The predictive performance of the proposed ESE-Bi-GRU model is evaluated against individual models, showing significant reductions in mean absolute error (MAE) by 86.03 % and root mean squared error (RMSE) by 66.43 %. The model's ability to minimize prediction errors, such as MAE and RMSE holds promise for more effective planning and utilization of sporadic solar resources. By improving GHI forecast accuracy, the ESE-Bi-GRU model contributes to optimizing the integration of sustainable energy sources within the broader energy grid, fostering a more sustainable and environmentally conscious approach to energy management.

Comprehensive Analysis of Bile Medicines Based on UHPLC-QTOF-MSE and Machine Learning.

Based on UHPLC-QTOF-MSE analysis and quantized processing, combined with machine learning algorithms, data modeling was carried out to realize digital identification of bear bile powder (BBP), chicken bile powder (CIBP), duck bile powder (DBP), cow bile powder (CBP), sheep bile powder (SBP), pig bile powder (PBP), snake bile powder (SNBP), rabbit bile powder (RBP), and goose bile powder (GBP). First, 173 batches of bile samples were analyzed by UHPLC-QTOF-MSE to obtain the retention time-exact mass (RTEM) data pair to identify bile acid-like chemical components. Then, the data were modeled by combining support vector machine (SVM), random forest (RF), artificial neural network (ANN), gradient boosting (GB), AdaBoost (AB), and Naive Bayes (NB), and the models were evaluated by the parameters of accuracy (Acc), precision (P), and area under the curve (AUC). Finally, the bile medicines were digitally identified based on the optimal model. The results showed that the RF model constructed based on the identified 12 bile acid-like chemical constituents and random forest algorithm is optimal with ACC, P, and AUC > 0.950. In addition, the accuracy of external identification verification of 42 batches of bile medicines detected at different times is 100.0%. So based on UHPLC-QTOF-MSE analysis and combined with the RF algorithm, it can efficiently and accurately realize the digital identification of bile medicines, which can provide reference and assistance for the quality control of bile medicines. In addition, hyodeoxycholic acid, glycohyodeoxycholic acid, and taurochenodeoxycholic acid, and so forth are the most important bile acid constituents for the identification of nine bile medicines.

Identifying severe community-acquired pneumonia using radiomics and clinical data: a machine learning approach

Evaluating Community-Acquired Pneumonia (CAP) is crucial for determining appropriate treatment methods. In this study, we established a machine learning model using radiomics and clinical features to rapidly and accurately identify Severe Community-Acquired Pneumonia (SCAP). A total of 174 CAP patients were included in the study, with 64 cases classified as SCAP. Radiomic features were extracted from chest CT scans using radiomics techniques, and screened to remove irrelevant features. Additionally, clinical indicators of patients were similarly screened and constituted the clinical feature set. Subsequently, eight common machine learning models were employed to complete the SCAP identification task. Specifically, interpretability analysis was conducted on the models. In the end, we screened out 15 radiomic features (such as LeastAxisLength, Maximum2DDiameterColumn and ZonePercentage) and two clinical features: Lymphocyte (p = 0.041) and Albumin (p = 0.044). Using radiomic features as inputs in model predictions yielded the highest AUC of 0.85 on the test set. When using the clinical feature set as model inputs, the AUC was 0.82. Combining the two sets of features as model inputs, Ada Boost achieved the best performance with an AUC of 0.89. Our study demonstrates that combining radiomics and clinical data using machine learning methods can more accurately identify SCAP patients.

Enhancing PVT property predictions for black oil reservoirs through the application of supervised machine learning techniques

The study presents an innovative approach utilizing machine learning (ML) techniques to forecast essential characteristics of black oil reservoirs, namely the oil formation volume factor (Bo) and the solution gas-oil ratio (Rs). A suite of four established ML algorithms-Random Forest, Linear Regression, Ada Boost, and Gradient Boost were extensively conditioned using a dataset that includes 297 individual data points and evaluation is done using test data and a blind test of models. The study showcases the practical efficacy of ML in accurately predicting Bo and Rs, even with a relatively modest dataset. The potential of ML models to save time and costs by reducing the dependence on laborious laboratory measurements is underscored, presenting a transformative shift in reservoir engineering practices. Furthermore, the current research extends its impact by comparing ML predictions with various empirical correlations and actual values, demonstrating that ML models, particularly Gradient Boost, consistently outperform traditional correlations and it can be used to predict values in the undersaturated region too. It is observed from the blind test results that for the prediction of Bo Gradient boost and random forest models (R2 0.97, RMSE 0.01) is a preferable choice and for the prediction of Rs the Gradient boost model (R2 0.97, RMSE 22) can be preferred. The adaptability of ML models to integrate new data over time is a significant feature, ensuring continuous improvement in accuracy. This study adds to the expanding reservoir engineering knowledge base, highlighting the practical use of machine learning for accurately predicting crucial reservoir properties. Its significance goes beyond theoretical progress, providing tangible tools for reservoir engineers and geologists to improve decision-making in the management of oil and gas reservoirs.

Prediction of metabolic syndrome using machine learning approaches based on genetic and nutritional factors: a 14-year prospective-based cohort study

IntroductionMetabolic syndrome is a chronic disease associated with multiple comorbidities. Over the last few years, machine learning techniques have been used to predict metabolic syndrome. However, studies incorporating demographic, clinical, laboratory, dietary, and genetic factors to predict the incidence of metabolic syndrome in Koreans are limited. In the present study, we propose a genome-wide polygenic risk score for the prediction of metabolic syndrome, along with other factors, to improve the prediction accuracy of metabolic syndrome.MethodsWe developed 7 machine learning-based models and used Cox multivariable regression, deep neural network (DNN), support vector machine (SVM), stochastic gradient descent (SGD), random forest (RAF), Naïve Bayes (NBA) classifier, and AdaBoost (ADB) to predict the incidence of metabolic syndrome at year 14 using the dataset from the Korean Genome and Epidemiology Study (KoGES) Ansan and Ansung.ResultsOf the 5440 patients, 2,120 were considered to have new-onset metabolic syndrome. The AUC values of model, which included sex, age, alcohol intake, energy intake, marital status, education status, income status, smoking status, dried laver intake, and genome-wide polygenic risk score (gPRS) Z-score based on 344,447 SNPs (p-value < 1.0), were the highest for RAF (0.994 [95% CI 0.985, 1.000]) and ADB (0.994 [95% CI 0.986, 1.000]).ConclusionsIncorporating both gPRS and demographic, clinical, laboratory, and seaweed data led to enhanced metabolic syndrome risk prediction by capturing the distinct etiologies of metabolic syndrome development. The RAF- and ADB-based models predicted metabolic syndrome more accurately than the NBA-based model for the Korean population.

Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations

AbstractAgriculture constitutes the foundational pillar of the global economy, engaging a substantial segment of the workforce and making a considerable contribution to the Gross Domestic Product (GDP). However, agricultural productivity faces numerous challenges, including varying climatic conditions, soil types, and limited access to modern farming practices. Developing intelligent agricultural systems becomes imperative to address these challenges and enhance agricultural productivity. Therefore, this paper aims to present a Machine Learning (ML) based crop recommendation system tailored for the farming landscape. The proposed system utilizes historical data on climatic conditions, soil properties, crop yields, and farmer preferences to provide personalized crop recommendations. The goal of this study is to appraise the efficacy of nine distinct ML models—Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Bagging (BG), AdaBoost (AB), Gradient Boosting (GB), and Extra Trees (ET) to generate practical recommendations for crop selection. Numerous preprocessing methods are employed to cleanse and normalize the data, thereby ensuring its appropriateness for model training. The ML models are trained using historical data sets, including temperature, rainfall, humidity, soil pH, and nutrient levels, where crop yields are correlated with environmental and agronomic factors. The models undergo fine-tuning through methods such as cross-validation to enhance their performance and ensure robustness. Among those models, Radom Forest has achieved the highest accuracy (99.31%). The proposed Machine Learning-based crop recommendation system offers a promising approach to addressing the challenges faced by the farmers. By leveraging advanced data analytics and artificial intelligence techniques, the system empowers farmers with timely and personalized recommendations, ultimately leading to improved agricultural productivity, food security, and economic prosperity.

Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation.

To determine if an explainable artificial intelligence (XAI) model enhances the accuracy and transparency of predicting embryo ploidy status based on embryonic characteristics and clinical data. This retrospective study utilized a dataset of 1908 blastocyst embryos. The dataset includes ploidy status, morphokinetic features, morphology grades, and 11 clinical variables. Six machine learning (ML) models including Random Forest (RF), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Support Vector Machine (SVM), AdaBoost (ADA), and Light Gradient-Boosting Machine (LGBM) were trained to predict ploidy status probabilities across three distinct datasets: high-grade embryos (HGE, n = 1107), low-grade embryos (LGE, n = 364), and all-grade embryos (AGE, n = 1471). The model's performance was interpreted using XAI, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) techniques. The mean maternal age was 38.5 ± 3.85years. The Random Forest (RF) model exhibited superior performance compared to the other five ML models, achieving an accuracy of 0.749 and an AUC of 0.808 for AGE. In the external test set, the RF model achieved an accuracy of 0.714 and an AUC of 0.750 (95% CI, 0.702-0.796). SHAP's feature impact analysis highlighted that maternal age, paternal age, time to blastocyst (tB), and day 5 morphology grade significantly impacted the predictive model. In addition, LIME offered specific case-ploidy prediction probabilities, revealing the model's assigned values for each variable within a finite range. The model highlights the potential of using XAI algorithms to enhance ploidy prediction, optimize embryo selection as patient-centric consultation, and provides reliability and transparent insights into the decision-making process.

Analyzing electroencephalogram signals with machine learning to comprehend online learning media

&lt;p&gt;In E-learning, evaluating students' comprehension of lecture video content is significant. The surge in online platform usage due to the pandemic has been remarkable, but the pressing issue is that learning outcomes still need to match the growth. Addressing this, a scientific system that gauges the comprehensibility of lecture videos becomes crucial for the effective design of future courses. This research paper is based on a cognitive approach utilizing EEG signals to determine student's level of comprehension. The study involves the design, evaluation, and comparison of multiple machines learning models, aiming to contribute to developing an efficient learning system. Fifteen distinct machine learning (ML) classifiers were implemented, among them AdaBoost (ADA), gradient boosting (GBC), extreme gradient boosting (XGboost), extra trees (ET), random forest (RF), light gradient boosting machine (light gum), and decision tree (DT) algorithms standouts. The DT exhibited exceptional performance across metrics such as area under the curve (AUC), accuracy, recall, F1 score, Kappa, precision, and matthews correlation coefficient (MCC). It achieved nearly 1.0 in these metrics while taking a short training time of only 1.7 seconds. This reveals its potential as an efficient classifier for electroencephalography (EEG) datasets and highlights its viability for practical implementation.&lt;/p&gt;

Quantitative assessment of brain structural abnormalities in children with autism spectrum disorder based on artificial intelligence automatic brain segmentation technology and machine learning methods

Rationale and objectivesTo explore the characteristics of brain structure in Chinese children with autism spectrum disorder (ASD) using artificial intelligence automatic brain segmentation technique, and to diagnose children with ASD using machine learning (ML) methods in combination with structural magnetic resonance imaging (sMRI) features. MethodsA total of 60 ASD children and 48 age- and sex-matched typically developing (TD) children were prospectively enrolled from January 2023 to April 2024. All subjects were scanned using 3D-T1 sequences. Automated brain segmentation techniques were utilized to obtain the standardized volume of each brain structure (the ratio of the absolute volume of brain structure to the whole brain volume). The standardized volumes of each brain structure in the two groups were statistically compared, and the volume data of brain areas with significant differences were combined with ML methods to diagnose and predict ASD patients. ResultsCompared with the TD group, the volumes of the right lateral orbitofrontal cortex, right medial orbitofrontal cortex, right pars opercularis, right pars triangularis, left hippocampus, bilateral parahippocampal gyrus, left fusiform gyrus, right superior temporal gyrus, bilateral insula, bilateral inferior parietal cortex, right precuneus cortex, bilateral putamen, left pallidum, and right thalamus were significantly increased in the ASD group (P< 0.05). Among six ML algorithms, support vector machine (SVM) and adaboost (AB) had better performance in differentiating subjects with ASD from those TD children, with their average area under curve (AUC) reaching 0.91 and 0.92, respectively. ConclusionAutomatic brain segmentation technology based on artificial intelligence can rapidly and directly measure and display the volume of brain structures in children with autism spectrum disorder and typically developing children. Children with ASD show abnormalities in multiple brain structures, and when paired with sMRI features, ML algorithms perform well in the diagnosis of ASD.

An ensemble machine learning-based approach to predict cervical cancer using hybrid feature selection

Cervical cancer has recently emerged as the leading cause of premature death among women. Around 85% of cervical cancer cases occur in underdeveloped countries. There are several risk factors associated with cervical cancer. This study describes a novel predictive model that uses early screening and risk trends from individual health records to forecast cervical cancer patients' prognoses. This study uses machine learning classification techniques to investigate the risk factors for cervical cancer. Additionally, use the voting method to evaluate all models and select the most appropriate model. The dataset used in this study contains missing values and shows a significant imbalance. Thus, the Random Oversampling technique was used as a sampling method. We used Principal Component Analysis (PCA) and XGBoost feature selection techniques to determine the most important features. To predict the accuracy, we used several machine learning classifiers, including Support Vector Machines (SVM), Random Forest (RF), k-nearest Neighbors (KNN), Decision Trees (DT), Naive Bayes (NB), Logistic Regression (LR), AdaBoost (AdB), Gradient Boosting (GB), Multilayer Perceptron (MLP), and Nearest Centroid Classifier (NCC). To demonstrate the efficacy of the suggested model, a comparison of its accuracy, sensitivity, and specificity was performed. We used the Random Oversampling approach along with the Ensemble ML method, hard voting on RF and MLP, and achieved 99.19% accuracy. It is demonstrated that the ensemble ML classifier (hard voting) performs better at handling classification problems when features are decreased and the high-class imbalance problem is handled.

Machine Learning for Breast Cancer Detection with Dual-Port Textile UWB MIMO Bra-Tenna System

A wearable textile bra-tenna system based on dual-polarization sensors for breast cancer (BC) detection is presented in this paper. The core concept behind our work is to investigate which type of polarization is most effective for BC detection, using the combination of orthogonal polarization signals with machine learning (ML) techniques to enhance detection accuracy. The bra-tenna sensors have a bandwidth ranging from 2–12 GHz. To complement the proposed system, detection based on machine learning algorithms (MLAs) is developed and tested to enhance its functionality. Using scattered signals at different polarizations, the bra-tenna system uses MLAs to predict BC in its early stages. Classification techniques are highly effective for data classification, especially in the biomedical field. Two scenarios are considered: Scenario 1, where the system detects a tumor or non-tumor, and Scenario 2, where the system detects three classes of one, two, and non-tumors. This confirms that MLAs can detect tumors as small as 10 mm. ML techniques, including eight algorithms such as the Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Methods (GBMs), Decision Tree (DT) classifier, Ada Boost (AD), CatBoost, Extreme Gradient Boosting (XG Boost), and Logistic Regression (LR), are applied to this balanced dataset. For optimal analysis of the BC, a performance evaluation is performed. Notably, SVM achieves outstanding performance in both scenarios, with metrics such as its F1 score, recall, accuracy, receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), and precision all exceeding 90%, helping doctors to effectively investigate BC. Furthermore, the Horizontal-Horizontal (HH) sensor configuration achieved the highest accuracy of 98% and 99% for SVMs in the two scenarios, respectively.

Exploring the Impact of Artificial Intelligence and Machine Learning on Predicting Outcomes in Regional Development Conventions: A Case Study of the Tanger-Tetouan-Al Hoceïma Region, Morocco

Objectives: For over six years, Morocco has been engaged in an initiative aimed at promoting inclusive and sustainable territorial development, characterized by the implementation of an advanced regionalization experiment. However, the main challenge facing government leaders today is managing a growing number of regional development conventions. Methods: To achieve these objectives, we developed a predictive model using artificial intelligence and machine learning aimed at anticipating the outcomes of regional development conventions. Through this method, we were able to identify the risks associated with their potential failure and forecast their impact. Simultaneously, various data extraction methods and classification algorithms were implemented. We collected and analyzed information from 310 past and current regional development conventions, spanning from 2016 to 2020, considering various factors influencing their success or failure. We conducted extensive experiments to assess the effectiveness of various predictive models. Results: The results we obtained indicate that the classifiers Ada Boost, Random Forest, and Bernoulli NB are the three most effective algorithms for predicting the outcomes of regional development conventions. Conclusion: This study contributes to shaping a broader discourse on the practical application of artificial intelligence in public policies and regional development, highlighting its potential to enhance resource allocation and alleviate the burden of repetitive administrative tasks for organizations with limited resources.

A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms

Today, crop suggestions and necessary guidance have become a regular need for a farmer. Farmers generally depend on their local agriculture officers regarding this, and it may be difficult to obtain the right guidance at the right time. Nowadays, crop datasets are available on different websites in the agriculture sector, and they play a crucial role in suggesting suitable crops. So, a decision support system that analyzes the crop dataset using machine learning techniques can assist farmers in making better choices regarding crop selections. The main objective of this research is to provide quick guidance to farmers with more accurate and effective crop recommendations by utilizing machine learning methods, global positioning system coordinates, and crop cloud data. Here, the recommendation can be more personalized, which enables the farmers to predict crops in their specific geographical context, taking into account factors like climate, soil composition, water availability, and local conditions. In this regard, an existing historical crop dataset that contains the state, district, year, area-wise production rate, crop name, and season was collected for 246,091 sample records from the Dataworld website, which holds data on 37 different crops from different areas of India. Also, for better analysis, a dataset was collected from the agriculture offices of the Rayagada, Koraput, and Gajapati districts in Odisha state, India. Both of these datasets were combined and stored using a Firebase cloud service. Thirteen different machine learning algorithms have been applied to the dataset to identify dependencies within the data. To facilitate this process, an Android application was developed using Android Studio (Electric Eel | 2023.1.1) Emulator (Version 32.1.14), Software Development Kit (SDK, Android SDK 33), and Tools. A model has been proposed that implements the SMOTE (Synthetic Minority Oversampling Technique) to balance the dataset, and then it allows for the implementation of 13 different classifiers, such as logistic regression, decision tree (DT), K-Nearest Neighbor (KNN), SVC (Support Vector Classifier), random forest (RF), Gradient Boost (GB), Bagged Tree, extreme gradient boosting (XGB classifier), Ada Boost Classifier, Cat Boost, HGB (Histogram-based Gradient Boosting), SGDC (Stochastic Gradient Descent), and MNB (Multinomial Naive Bayes) on the cloud dataset. It is observed that the performance of the SGDC method is 1.00 in accuracy, precision, recall, F1-score, and ROC AUC (Receiver Operating Characteristics–Area Under the Curve) and is 0.91 in sensitivity and 0.54 in specificity after applying the SMOTE. Overall, SGDC has a better performance compared to all other classifiers implemented in the predictions.

Predicting viscosity for steelmaking slag: A stacking regression approach

The viscosity of steel slag plays a pivotal role in steelmaking, impacting various aspects of the steelmaking operation and product quality. This paper introduces a novel approach for predicting the viscosity of turndown slag from a basic oxygen furnace using a stacking regression model. The stacking model in this approach incorporates four base models: multiple linear regression (LR), random forest (RF), k-nearest neighbour (KNN) and AdaBoost (ADB), while random forest is selected as a meta-model. The selection of meta-models is done through a systematic framework provided for identifying suitable meta-models in a stacking model. Similarly, the framework also demonstrated the workflow for constructing and evaluating a series of stacking models with different configurations. On the evaluation of 20 stacking models (including two and three-layers), the optimal configuration is found to be LR + KNN with R2 value of 0.993 and the shortest training time. Additionally, a correlation analysis of slag viscosity with composition and temperature is done, which will help the operators of steel melting shops in optimize the steelmaking process based on the required slag viscosity. Ultimately, this study emphasizes the efficacy of optimal-configured stacking regression models in industrial applications, achieving both maximum accuracy and minimal computational time.

APPLYING MACHINE LEARNING TO AUDIT DATA: ENHANCING FRAUD DETECTION, RISK ASSESSMENT AND AUDIT EFFICIENCY

Machine learning (ML) is used globally as a tool for predictive analysis. Within auditing, the use of audit data helps to uncover fraud indicators, identify risk areas and implement predictive models for continuous audit monitoring. Researchers are using various machine learning methods to analyze large and complex audit data to facilitate prediction. In this study, an online UCI dataset of 776 lines and 27 features is used. Out of these 27 features, 13 are eliminated due to their low impact on the target dataset or due to the ‘important feature selection’ algorithm. In this analysis, I used supervised learning methods, namely K-Nearest Neighbors, Logistic Regression, Random Forest, Support Vector Machine, Decision Tree, Linear Discriminant Analysis, Gaussian Naive Bayes, Extra Tree Algorithm, Gradient Boosting Algorithm, Ada Boosting Algorithm and XGBoost Algorithms. The experimental results highlight the power of eight neighbor KNN and evaluate its effectiveness, sensitivity, precision, accuracy and F1 score in comparison with other methods such as Naive Bayes, SVM (Linear Kernel), Decision Tree Classifier and Random Forest Classifier.

Quantifying urban air quality through multispectral satellite imagery and Google earth Engine

The escalating concerns surrounding urban air pollution's impact on both the environment and human health have prompted increased attention from researchers, policymakers, and citizens alike. As such, this study addresses growing concerns about urban air pollution's impact on the environment and human health, emphasizing the need for early, high-resolution PM2.5 pollutant measurements. Utilizing Google Earth Engine (GEE) machine learning algorithms, our study evaluates six models over four years in Tehran and Tabriz. Inputs include satellite imagery, meteorological data, and pollutant measurements from air quality stations. Four models—Histogram Gradient Boosting, Random Forest, Extreme Gradient Boosting, and Ada Boosted Decision Trees—outperform Support Vector Machine and Linear Regression. The selected model, a combination of decision tree algorithms and Ada Boost, achieves a notable correlation coefficient of 79.8% and an RMSE of 0.271 g/m3. This superior performance enables the generation of high-resolution (30-m) PM2.5 estimates for the two cities. The study's comprehensive approach, involving various data sources and advanced machine learning techniques, contributes a valuable method for accurate PM2.5 assessment. The findings hold significance for urban air quality management and provide a potential framework for generating detailed PM2.5 datasets based on Landsat images.

Investigation of an amino acid compound as a corrosion inhibitor via ensemble learning

In this study, we evaluate the performance of various machine learning models, including Random Forest (RF), Bagging (BAG), AdaBoost (ADA), Artificial Neural Network (ANN), and Support Vector Machine (SVM), using metrics such as R², Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results indicate that AdaBoost (ADA) achieves the highest performance with an R² of 0.999, RMSE of 2.32, and MAE of 2.24, making it the most accurate model with the smallest prediction errors. Bagging (BAG) also performs exceptionally well, with an R2 of 0.996, RMSE of 3.09, and MAE of 2.92. The Artificial Neural Network (ANN) exhibits a high R2 of 0.999, though RMSE and MAE values are not provided. Random Forest (RF) and Support Vector Machine (SVM) show good performance with R² values of 0.982 and 0.970, respectively, but are outperformed by the ensemble methods. The findings underscore the superiority of ensemble techniques, particularly AdaBoost, in achieving high predictive accuracy and minimal errors in this context.

Predicting sunspot number from topological features in spectral images I: Machine learning approach

This study presents an advanced machine learning approach to predict the number of sunspots using a comprehensive dataset derived from solar images provided by the Solar and Heliospheric Observatory (SOHO). The dataset encompasses various spectral bands, capturing the complex dynamics of solar activity and facilitating interdisciplinary analyses with other solar phenomena. We employed five machine learning models: Random Forest Regressor, Gradient Boosting Regressor, Extra Trees Regressor, Ada Boost Regressor, and Hist Gradient Boosting Regressor, to predict sunspot numbers. These models utilized four key heliospheric variables — Proton Density, Temperature, Bulk Flow Speed and Interplanetary Magnetic Field (IMF) — alongside 14 newly introduced topological variables. These topological features were extracted from solar images using different filters, including HMIIGR, HMIMAG, EIT171, EIT195, EIT284, and EIT304. In total, 60 models were constructed, both incorporating and excluding the topological variables. Our analysis reveals that models incorporating the topological variables achieved significantly higher accuracy, with the r2-score improving from approximately 0.30 to 0.93 on average. The Extra Trees Regressor (ET) emerged as the best-performing model, demonstrating superior predictive capabilities across all datasets. These results underscore the potential of combining machine learning models with additional topological features from spectral analysis, offering deeper insights into the complex dynamics of solar activity and enhancing the precision of sunspot number predictions. This approach provides a novel methodology for improving space weather forecasting and contributes to a more comprehensive understanding of solar-terrestrial interactions.

Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms.

Hospitals that are considered non-profit take into consideration not to make any losses other than seeking profit. A model that ensures that hospital price policies are variable due to hospital revenues depending on patients with appointments is presented in this study. A dynamic pricing approach is presented to prevent patients who have an appointment but do not show up to the hospital from causing financial loss to the hospital. The research leverages three distinct machine learning (ML) algorithms, namely Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB), to analyze the appointment status of 1073 patients across nine different departments in a hospital. A mathematical formula has been developed to apply the penalty fee to evaluate the reappointment situations of the same patients in the first 100 days and the gaps in the appointment system, considering the estimated patient appointment statuses. Average penalty cost rates were calculated based on the ML algorithms used to determine the penalty costs patients will face if they do not show up, such as 22.87% for RF, 19.47% for GB, and 14.28% for AB. As a result, this study provides essential criteria that can help hospital management better understand the potential financial impact of patients missing appointments and can be considered when choosing between these algorithms.