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Assessing industrial wastewater effluent toxicity using boosting algorithms in machine learning: A case study on ecotoxicity prediction and control strategy development

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ENVIRONMENTAL POLLUTION 2024年 341卷 123017页

作者： Nguyen, Duc-Viet Park, Jihae Lee, Hojun Han, Taejun Wu, Di Univ Ghent Ctr Environm & Energy Res Global Campus Incheon 21985 South Korea Univ Ghent Ctr Adv Proc Technol Urban Resource Recovery CAPTU Dept Green Chem & Technol B-9000 Ghent Belgium Univ Ghent Dept Anim Sci & Aquat Ecol B-9000 Ghent Belgium Univ Ghent Bio Environm Sci & Technol BEST Lab Global Campus119-5 Songdomunhwa Ro Incheon 21985 South Korea

Trace heavy metals have a tendency to persist in the effluent of industrial wastewater treatment facilities, leading to toxic effects on downstream water bodies. Traditional assessment methods relied on animal testing, but ethical concerns have rendered them unacceptable. An alternative solution is to evaluate wastewater toxicity using trophic-level aquatic organisms as bioassays. However, these bioassay methods involve costly and timeconsuming chemical and biological analytical experiments. In this study, an artificial intelligence-powered water quality assessment (AiWA) approach is proposed for predicting industrial effluent ecotoxicity to further enhance the quick and cost-effective ecotoxicity assessment process. Initially, 99 samples were collected from industrial wastewater treatment plants representing 21 different industries in the Republic of Korea. Fourteen parameters were measured, encompassing both physicochemical and ecotoxicological aspects. boosting algorithms, especially extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost), were employed for model development. XGBoost outperformed AdaBoost in terms of model performance. Feature selection analysis revealed that conductivity, copper, lead, selenium, pH, and zinc concentrations were the most suitable inputs for training the boosting model. The innovated XGBoost-based AiWA model demonstrated significantly higher performance (i.e., up to 80%) compared to conventional models with an R2 value of exceeding 0.94 and root mean square error of 3.5 toxicity unit for predicting the integrated toxicity unit (ITU). Additionally, pH and conductivity emerged as crucial indicators for reflecting ecotoxicity levels. Specially, this case study indicated that non-toxic/directly dischargeable levels (TU <= 1) were achieved when the pH ranged from 6.8 to 8.4 and the conductivity remained below 1651 mu S/cm. These findings are expected to facilitate rapid and cost-effective detection of heavy metal ecotoxici

关键词： Artificial intelligence boosting algorithms Heavy metals Ecotoxicity prediction and classification Industrial wastewater management

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boosting algorithms Empowering Heart Disease Prediction for Enhanced Medical Accuracy 3

Boosting Algorithms Empowering Heart Disease Prediction for ...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Sharma, Urvashi Saxena, Kanak Samrat Ashok Technological Institute Computer Science & Engineering Department Vidisha India Samrat Ashok Technological Institute Head Computer Science & Engineering Department Vidisha India

ISBN: (纸本)9798331523893

Heart disease remains a leading cause of mortality worldwide, necessitating accurate and reliable predictive models to aid early diagnosis and treatment. Traditional machine learning methods like LR and DT Classifiers offer reasonable recall rates but suffer from lower precision and accuracy. To address this challenge, we employed advanced boosting algorithms, specifically GradientboostingClassifier and XGBClassifier, to enhance predictive accuracy in heart disease detection. Our study shows that these algorithms achieve accuracy rates above 93%, with precision exceeding 52%, and a significantly low False Positive Rate below 0.5%. However, the trade-off involves lower recall rates, suggesting some true cases remain undetected. boosting algorithms thus provide a robust approach for heart disease prediction, though attention to false negatives is essential for comprehensive application. © 2025 IEEE.

关键词： boosting algorithms Gradient boosting Classifier Heart Disease Prediction Machine Learning Medical Accuracy XGBClassifier

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Data-Driven Insights: boosting algorithms to Uncover Electricity Theft Patterns in AMI

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Khan, Inam Ullah Ali, Arshid Taylor, C. James Ma, Xiandong Southern Methodist Univ Bobby B Lyle Sch Engn Dallas TX 75205 USA South Dakota State Univ Dept Elect Engn & Comp Sci Brookings SD 57007 USA Univ Lancaster Sch Engn Lancaster LA1 4YW England

This study introduces a sophisticated supervised machine learning method for electric theft detection utilizing a customized histogram gradient boosting (HGB) algorithm. Comprehensive preprocessing, including imputation, normalization, outlier management, and resampling, ensures that the time-series data are accurately prepared for analysis. The synthetic minority oversampling technique-edited nearest neighbor (SMOTE-ENN) algorithm corrects class imbalances, preparing the data for the feature optimization stage, in which key features are selected and extracted. The HGB algorithm, enhanced through Bayesian optimization, is central to the training process, resulting in a model that precisely classifies electricity consumption patterns as genuine or fraudulent. The robustness of the model is evaluated against other recognized boosting methods, such as adaptive boosting (ADB), gradient boosting decision tree (GBDT), and LightGBM, alongside various ensemble and traditional machine learning models. Utilizing key performance metrics such as accuracy, F1-score, and area under the curve (AUC) for validation, the proposed model yields very promising results, with 93% accuracy, 95% F1-score, and 98% AUC, outperforming the comparison group under similar dataset and hyperparameter conditions. This underscores the model's potential as a highly accurate tool for combating electricity theft within an advanced metering infrastructure (AMI).

关键词： Feature extraction boosting Electricity Meters Computational modeling Classification algorithms Smart meters Smart grids Costs Accuracy Advanced metering infrastructure (AMI) boosting algorithms class balancing electricity theft detection (ETD) feature engineering smart grid

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Application of deep learning in civil engineering: boosting algorithms for predicting strength of concrete

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第5期45卷 9109-9122页

作者： Xie, Canrong Wang, Jianjun Wu, Zhiwen Nie, Shaojun Hu, Yichan Huang, Sheng Guangxi Rd & Bridge Engn Grp Co Ltd Nanning Peoples R China Guangxi Univ Coll Civil Engn & Architecture Key Lab Disaster Prevent & Struct Safety Minist Nanning Peoples R China

Machine learning (ML) has been applied in civil engineering to predict the compressive strength of concrete with high accuracy. In this paper, five boosting ensemble algorithms, i.e., XGBoost, AdaBoost, GBDT, LightGBM, and CatBoost, were used to predict the compressive strength of high-performance concrete (HPC). The models were evaluated using performance indicators such as R-2, root mean square error (RMSE), and mean absolute error (MAE). The results showed that the CatBoost model had the highest accuracy with a R-2 (0.970) and a RMSE (2.916). The prediction accuracy of the model was increased through hyperparameter optimization, which got a higher with a R-2 (0.975) and a RMSE (2.863). Meanwhile, the SHapley Additive exPlanations (SHAP) method was used to explain the output results of the optimal model (CatBoost), which generated explainable insights that further revealed the complex relationship between the prediction model parameters. The results showed that AGE, W/B, and W/C had the most impact on high-performance concrete compressive strength (HPCCS) prediction, which was similar to the results of sensitivity analysis. This study provided a theoretical basis and technical guidance for developing the mix design of a new high-performance concrete (HPC) system. In the future, the interpretable results of the model output should be iteratively checked and validated in the actual laboratory in order to provide guidance for engineering practice.

关键词： High-Performance Concrete (HPC) compressive strength machine learning boosting algorithms game theory

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Comparative analysis of boosting algorithms for predicting personal default

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COGENT ECONOMICS & FINANCE 2025年第1期13卷

作者： Nguyen, Nhat Ngo, Duy Ho Chi Minh Univ Banking Ho Chi Minh Vietnam

Accurately predicting personal default risk is crucial for financial institutions to manage credit risk effectively. This study conducts a comparative analysis of the performance of boosting algorithms, including AdaBoost, XGBoost, LightGBM, and CatBoost, in predicting personal defaults. The dataset used in the study comprises 7,542 individual customers collected from Vietnamese commercial banks and financial institutions between 2014 and 2022, with 12 features related to the financial and demographic characteristics of the borrowers. All customer-related information is fully anonymized and encrypted during the data collection process to ensure compliance with research ethics. The predictive models are evaluated based on six criteria: Accuracy, Precision, Sensitivity, Specificity, F1 score, and AUC. The results indicate that the LightGBM model has the best performance, demonstrating the ability to efficiently handle large and complex datasets. Additionally, the study identifies the five most significant factors influencing personal default risk: Monthly Liability, Credit Balance, Credit History Length, Max Credit Limit, and Yearly Income. However, the study's limitations in the size and scope of the dataset may reduce the generalizability of the results when applied to other regions. These findings provide valuable insights that help financial institutions enhance their strategies for managing credit risk effectively.

关键词： Personal default prediction boosting algorithms feature importance analysis machine learning financial institutions

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Sentiment Analysis for Hindi Cinema Using boosting algorithms 8th

Sentiment Analysis for Hindi Cinema Using Boosting Algorithm...

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Mann, Parul Jha, Anmol Rani, Ritu Sharma, Arun Dev, Amita Indira Gandhi Delhi Tech Univ Women New Delhi 110006 India

ISBN: (纸本)9789819713288;9789819713295

In today's rapidly evolving world, with ubiquitous access to technology, there are massive amounts of data being generated. This data contains key insights that shape better decision-making. Hence, tools that help us extract such insights from this data are of the utmost importance. Sentiment analysis is one such tool. It helps us determine the emotions behind a piece of text. Although there are many resources for sentiment analysis in English, resources for Hindi are limited. We aim to remedy this issue with our work where we scrape, annotate, and pre-process our own Hindi review corpus from the field of cinema. We propose a novel methodology to perform Hindi sentiment analysis using various boosting algorithms and create a foundation to aid better model and framework selection for vernacular natural language processing tasks.

关键词： Hindi sentiment analysis Indic languages Natural language processing boosting algorithms Machine learning

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Analysis of uniform resource locator using boosting algorithms for forensic purpose

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COMPUTER COMMUNICATIONS 2022年 190卷 69-77页

作者： Apoorva, K. A. Sangeetha, S. Natl Inst Technol Dept Comp Applicat Tiruchirapalli Tamil Nadu India

Innovations are taking up new roles in all fields. It still has a crucial role in Internet technology, as the ease with which the Internet is available everywhere and accessible from any device has resulted in a slew of cyber-attacks., A prevalent scenario during and before a pandemic is phishing, which is accomplished by smartly altering the URL as a legitimate one and then redirecting the user to other sites and extracting personal information. The benchmark URL datasets used for the study considering an equal balance between phishing/ malicious URLs and benign/ legitimate URLs. URLs are parsed in this procedure to extract valuable elements that aid in the identification of URL phishing. Our research emphasized using different machine learning boosting algorithms such as Extreme Gradient boosting, Light Gradient boosting, Adaptive boosting, and Gradient boosting and have achieved an accuracy of more than 98% for most of the algorithms considered.

关键词： Phishing Machine learning boosting algorithms URL features Cyber-attacks

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Productivity modelling of an inclined stepped solar still for seawater desalination using boosting algorithms based on experimental data

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DESALINATION AND WATER TREATMENT 2022年 276卷 28-39页

作者： Wazirali, Raniyah Abujazar, Mohammed Shadi S. Abujayyab, Sohaib K. M. Ahmad, Rami Fatihah, Suja Kabeel, A. E. Karaagac, Sakine Ugurlu Abu Amr, Salem S. Alazaiza, Motasem Y. D. Bashir, Mohammed J. K. Sokar, Ibrahim Y. Saudi Elect Univ Coll Comp & Informat Riyadh 11673 Saudi Arabia Al Aqsa Univ Al Aqsa Community Intermediate Coll PB 4051 Gaza Palestine Karabuk Univ Fac Engn Dept Environm Engn TR-78050 Karabuk Turkey Int Coll Engn & Management Fire Safety Engn Muscat 112 Oman Amer Univ Emirates Coll Comp Informat Technol Dubai 503000 U Arab Emirates Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Civil Engn Bangi 43600 Selangor Malaysia Tanta Univ Fac Engn Mech Power Engn Dept Tanta Egypt Int Coll Engn & Management 111 St Muscat Oman A Sharqiyah Univ Coll Engn Dept Civil & Environm Engn Ibra 400 Oman Univ Tunku Abdul Rahman Fac Engn & Green Technol Dept Environm Engn Kampar 31900 Perak Malaysia Gaza Univ Fac Comp Sci & Informat Technol Gaza Palestine

Solar energy has recently become a viable option for desalinating seawater, primarily in arid regions. However, increasing the productivity of solar still by integrating experimental base and modelling methods is still subject to prediction errors;therefore, the main objective of this research is to postulate and test boosting algorithms for predicting the efficiency and productivity of the system. Five boosting regressors were deployed and evaluated: categorical boosting, adaptive boosting, extreme gradient boosting, gradient boosting machine, and gradient boosting machine (LightGBM). The proposed regressors are implemented based on the system's actual recorded dataset (consisting of 720 observations). The dataset consists of input variables, which are the wind speed (V), cloud cover, humidity, ambient temperature (T), solar radiation (SR), (T-io), (T-w), (T-v), and (T-t). Also, the output variable is represented by the productivity of the system. The dataset was separated into training (70%) and testing (30%) sets. In order to decrease regressors errors, hyperparameter optimization was employed. Gradientboosting approach provided the best prediction, with 95% R-2 accuracy and 39.57 root mean square error (RMSE) error. The LightGBM technique achieved 94% R-2 accuracy and 40.07 RMSE error in the testing dataset. The results reveal that Gradientboosting outperforms the cascaded forward neural network in predicting system productivity (CFNN).

关键词： Solar desalination Meteorological data boosting algorithms Modelling Productivity evaluation

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Effects of Dimension Reduction Methods on boosting algorithms for Better Prediction Accuracies on Classifications of Stress EEGs 6

Effects of Dimension Reduction Methods on Boosting Algorithm...

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6th International Conference on Electronics and Electrical Engineering Technology (EEET)

作者： Sim, Doreen Y. Y. Chong, C. K. Univ Nottingham Fac Sci & Engn Sch Comp Sci Malaysia Campus Semenyih Selangor Malaysia

ISBN: (纸本)9798350395600;9798350395594

This research aims to investigate the effects of various dimension reduction methods, namely Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Linear Discriminant Analysis (LDA) on the prediction accuracies of the stressful state in the EEG signaling when performing different mental tasks. The dataset used for this research is the SAM-40 dataset. It consists of 40 subjects performing three different mental tasks, i.e. Arithmetic Problem Solving Task, Stroop Color Word Test and Mirror Image Recognition Task. Each task was carried out with 3 trials. The results after applying the different dimension reduction methods of PCA, ICA and LDA to different boosting algorithms were analyzed and compared meticulously. These boosting algorithms are mainly the ensemble techniques of AdaBoostM1 and RUSBoost algorithms. Among all the experimented results shown, the LDA induced boosted classification methods showed the best prediction accuracy result, i.e. around 30% of prediction accuracy improvement.

关键词： dimension reduction methods stressful state in EEG signaling boosting algorithms AdaBoostM1 RUSBoost LDA induced boosted classification methods

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Using boosting algorithms to predict bank failure: An untold story

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INTERNATIONAL REVIEW OF ECONOMICS & FINANCE 2021年 76卷 40-54页

作者： Pham, Xuan T. T. Ho, Tin H. Univ Econ & Law Ctr Econ & Financial Res Ho Chi Minh City Vietnam Univ Econ & Law Inst Dev & Res Banking Technol Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam

From a modeling point of view, our work provides a novel approach to better use XGBoost for bank failure prediction, determining the essential technical aspects that can improve the predictive accuracy. Of these technical aspects, the two crucial factors are assigning correct values to target variables and careful predictor selection (through ANOVA, correlation, information value tests, and weight of evidence). We also highlight that bank failure could be predicted four to five quarters earlier when all predictive signals simultaneously appear. Hence, we strongly suggest using quarterly data instead of yearly data. In addition to practical implications, our present work also contributed to the existing literature. We confirm the results of existing studies that emphasized that XGBoost has strong predictive power (Carmona, Climent, and Momparler (2018)). Moreover, we provide evidence that XGBoost outperforms other models in the same boosting family, including gradient boosting and AdaBoost, through an intensive comparison of predictive power. These contributions might facilitate future work on bank failure prediction.

关键词： U S banks Bank failure prediction boosting algorithms XGBoost Variable selection techniques Target variables

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