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A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae ( Auxenochlorella pyrenoidosa)

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ENVIRONMENT INTERNATIONAL 2024年 194卷 109162页

作者： Qin, Li-Tang Tian, Xue-Fang Zhang, Jun-Yao Liang, Yan-Peng Zeng, Hong-Hu Mo, Ling-Yun Guilin Univ Technol Coll Environm Sci & Engn Guilin 541004 Peoples R China Guilin Univ Technol Guangxi Key Lab Theory & Technol Environm Pollut C Guilin 541006 Peoples R China Guilin Univ Technol Collaborat Innovat Ctr Water Pollut Control & Wate Guilin 541006 Peoples R China

Quantitative structure-activity relationships (QSARs) have been used to predict mixture toxicity. However, current research faces gaps in achieving accurate predictions of the mixture toxicity of azole fungicides. To address this gap, the application of machine learning (ML) algorithms has emerged as an effective strategy. In this study, we applied 12 algorithms, namely, k-nearest neighbor (KNN), kernel k-nearest neighbors (KKNN), support vector machine (SVM), random forest (RF), stochastic gradient boosting (GBM), cubist, bagged multivariate adaptive regression splines (Bagged MARS), eXtreme gradient boosting (XGBoost), boosted generalized linear model (GLMBoost), boosted generalized additive model (GAMBoost), bayesian regularized neural networks (BRNN), and recursive partitioning and regression trees (CART) to build ML models for 225 mixture toxicity of azole fungicides towards Auxenochlorella pyrenoidosa. A total of 36 single ML models and 12 consensus models were developed. The results indicated that models employing concentration addition (CA), independent action (IA), and molecular descriptors (MD) as variables demonstrated superior predictive abilities. The consensus model combining SVM and RF algorithms (labeled as CM0) demonstrated the highest level of accuracy in fitting the data, with a coefficient of determination of 0.980. Additionally, it showed strong predictive abilities when tested with external data, achieving an external R2 value of 0.945 and a Concordance Correlation Coefficient of 0.967. This study provides a positive contribution to the ecological risk assessment of a mixture of azole fungicides.

关键词： Azole Fungicides QSARs machine learning algorithms Consensus Model

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Effluent parameters prediction of a biological nutrient removal (BNR) process using different machine learning methods: A case study

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024年 351卷 119899页

作者： Manav-Demir, Neslihan Gelgor, Huseyin Baran Oz, Ersoy Ilhan, Fatih Ulucan-Altuntas, Kubra Tiwary, Abhishek Debik, Eyup Yildiz Tech Univ Environm Engn Dept TR-34220 Istanbul Turkiye Yildiz Tech Univ Stat Dept TR-34220 Istanbul Turkiye Istanbul Tech Univ Environm Engn Dept TR-34469 Istanbul Turkiye De Montfort Univ Sch Engn & Sustainable Dev Gateway Leicester LE1 9BH England

This paper proposes a novel targeted blend of machine learning (ML) based approaches for controlling waste-water treatment plant (WWTP) operation by predicting distributions of key effluent parameters of a biological nutrient removal (BNR) process. Two years of data were collected from Plajyolu wastewater treatment plant in Kocaeli, Turkiye and the effluent parameters were predicted using six machine learning algorithms to compare their performances. Based on mean absolute percentage error (MAPE) metric only, support vector regression machine (SVRM) with linear kernel method showed a good agreement for COD and BOD5, with the MAPE values of about 9% and 0.9%, respectively. Random Forest (RF) and EXtreme Gradient Boosting (XGBoost) regression were found to be the best algorithms for TN and TP effluent parameters, with the MAPE values of about 34% and 27%, respectively. Further, when the results were evaluated together according to all the performance metrics, RF, SVRM (with both linear kernel and RBF kernel), and Hybrid Regression algorithms generally made more successful predictions than Light GBM and XGBoost algorithms for all the parameters. Through this case study we demonstrated selective application of ML algorithms can be used to predict different effluent parameters more effectively. Wider implementation of this approach can potentially reduce the resource demands for active monitoring the environmental performance of WWTPs.

关键词： Municipal wastewater treatment Biological nutrient removal (BNR) process machine learning machine learning algorithms

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Fine-Scale Mangrove Species Classification Based on UAV Multispectral and Hyperspectral Remote Sensing Using machine learning

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REMOTE SENSING 2024年第16期16卷 3093页

作者： Yang, Yuanzheng Meng, Zhouju Zu, Jiaxing Cai, Wenhua Wang, Jiali Su, Hongxin Yang, Jian Nanning Normal Univ Key Lab Environm Change & Resources Use Beibu Gulf Nanning 530001 Peoples R China Guangxi Beihai Wetland Ecosyst Natl Observat & Res Beihai 536001 Peoples R China Univ Kentucky Dept Forestry & Nat Resources TP Cooper Bldg Lexington KY 40546 USA

Mangrove ecosystems play an irreplaceable role in coastal environments by providing essential ecosystem services. Diverse mangrove species have different functions due to their morphological and physiological characteristics. A precise spatial distribution map of mangrove species is therefore crucial for biodiversity maintenance and environmental conservation of coastal ecosystems. Traditional satellite data are limited in fine-scale mangrove species classification due to low spatial resolution and less spectral information. This study employed unmanned aerial vehicle (UAV) technology to acquire high-resolution multispectral and hyperspectral mangrove forest imagery in Guangxi, China. We leveraged advanced algorithms, including RFE-RF for feature selection and machine learning models (Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Light Gradient Boosting machine (LightGBM)), to achieve mangrove species mapping with high classification accuracy. The study assessed the classification performance of these four machine learning models for two types of image data (UAV multispectral and hyperspectral imagery), respectively. The results demonstrated that hyperspectral imagery had superiority over multispectral data by offering enhanced noise reduction and classification performance. Hyperspectral imagery produced mangrove species classification with overall accuracy (OA) higher than 91% across the four machine learning models. LightGBM achieved the highest OA of 97.15% and kappa coefficient (Kappa) of 0.97 based on hyperspectral imagery. Dimensionality reduction and feature extraction techniques were effectively applied to the UAV data, with vegetation indices proving to be particularly valuable for species classification. The present research underscored the effectiveness of UAV hyperspectral images using machine learning models for fine-scale mangrove species classification. This approach has the potential to significantly impr

关键词： mangrove hyperspectral machine learning algorithms unmanned aerial vehicle species classification

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Energy-Efficient Satellite Range Scheduling Using a Reinforcement learning-Based Memetic Algorithm

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第4期60卷 4073-4087页

作者： Song, Yanjie Suganthan, Ponnuthurai Nagaratnam Pedrycz, Witold Yan, Ran Fan, Dongming Zhang, Yue Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Qatar Univ Coll Engn KINDI Ctr Comp Res Doha 2713 Qatar Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada Polish Acad Sci Syst Res Inst PL-00901 Warsaw Poland Istinye Univ Res Ctr Performance & Prod Anal TR-34010 Istanbul Turkiye Nanyang Technol Univ Sch Civil & Environm Engn Nanyang 639798 Singapore Beihang Univ Sch Transportat Sci Engn Beijing 10091 Peoples R China Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China Natl Univ Singapore Dept Ind Syst Engn & Management Singapore 119077 Singapore

The rapid expansion of the satellite industry has presented numerous opportunities across various sectors and significantly transformed people's daily lives. However, the high energy consumption resulting from frequent task execution poses challenges for satellite management. Energy consumption has become an important factor to be considered in the design of future satellite management systems. The energy-efficient satellite range scheduling problem (EESRSP) aims to optimize task sequencing profits within the satellite management system while simultaneously conserving energy. To address this problem, a mixed-integer scheduling model is constructed, taking into account the energy consumption of ground stations during telemetry, tracking, and command (TT&C) operations. Then, we propose a reinforcement learning-based memetic algorithm (RL-MA) that incorporates a heuristic initialization method (HIM). The HIM enables the algorithm to rapidly generate high-quality initial solutions by leveraging task features associated with EESRSRP. RL-MA employs both population search and local search (LS) techniques to explore the satellite TT&C task plan. RL-MA incorporates two genetic operators, crossover and mutation, into the population-based search. In the LS stage, multiple random and heuristic LS operators are incorporated through an ensemble LS strategy. To improve search performance, Q-learning, a classical class of reinforcement learning (RL) methods tailored to problem characteristics, is utilized for selecting effective operators. RL dynamically adjusts LS operators based on strategy performance. Experimental results demonstrate that the proposed RL-MA can effectively generate sound solutions for EESRSP with varying task scales. Furthermore, the improvement strategies employed in the algorithm are validated to enhance the scheduling performance of RL-MA. This study reveals that integrating RL with an ensemble of LS operators can significantly enhance the algorithm's exploi

关键词： Satellites Task analysis Search problems machine learning algorithms Memetics Genetic algorithms Energy consumption Energy-efficient ensemble of local search (LS) methods heuristic memetic algorithm (MA) reinforcement learning (RL) satellite range scheduling

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How do non-deposit sites influence the performance of machine learning-based gold prospectivity mapping? A study case in the Pitangui Greenstone Belt, Brazil

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JOURNAL OF GEOCHEMICAL EXPLORATION 2024年 264卷

作者： Ribeiro, Brener Otavio Luiz Barbuena, Danilo de Melo, Gustavo Henrique Coelho Motta, Joao Gabriel Marques, Eduardo Duarte Marinho, Marcelo de Souza Univ Fed Vales Jequitinhonha & Mucuri Lab Estudos Tecton LESTE Campus JKMGT 367Km 5835000 Alto da Jacuba BR-39100000 Diamantina MG Brazil Univ Fed Ouro Preto Campus Morro Cruzeiro BR-35400000 Ouro Preto MG Brazil Eurasian Resources Grp Dubai U Arab Emirates Belo Horizonte Reg Off Geol Survey Brazil SGB CPRM BR-30140002 Belo Horizonte Brazil

One of the greatest challenges in mineral prospectivity mapping (MPM) research nowadays is to find a solid methodology that ensures the reliability of the prospectivity model during the learning and prediction procedures. Multiple uncertainties such as the location of non-deposit sites or the type of machine learning algorithm (MLA) can bias the MPM. To investigate these effects, we used multiple training datasets with different nondeposits locations, randomly created, and MLAs such as Artificial Neural Network (ANN), Random Forests (RF) and Support Vector machine (SVM), to model orogenic-Au prospectivity in the Pitangui Greenstone Belt (PGB, Brazil). Regarding the implications in the methodology for MPM, there are great differences between the models' performances in mapping prospective zones when there is a slightly change in the location of negative samples. These changes can be observed by using the Shapley additive explanation metrics (SHAP values), which can help mitigate such effects by choosing an optimal model among all randomly created datasets. The SHAP values of non-deposit sites also showed that ANN and SVM present overfitting problems despite the use of balanced data. RF on the other hand outperformed in all ten datasets and showed great recognition and adjustment to the negative samples. The results presented in this research are also promising to the prospective studies in the PGB, as it shows a map capable to correctly predict 97 % of the known deposits and occurrences in 3 % of the total area and points the new frontiers for gold exploration in the PGB.

关键词： Mineral prospectivity mapping Pitangui Greenstone Belt machine learning algorithms Gold mineralization

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PIML-SM: Physics-Informed machine learning to Estimate Surface Soil Moisture From Multisensor Satellite Images by Leveraging Swarm Intelligence

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Singh, Abhilash Gaurav, Kumar IISER FGRS Lab Bhopal 462066 India Univ Leeds Sch Math Leeds LS2 9JT England

We introduce a physics-informed machine learning (PIML) algorithm based on a feed-forward neural network (FFNN) to estimate surface soil moisture from limited in situ measurements and Sentinel-1/2 satellite images on the alluvial fan of the Kosi River by leveraging radar physics. We set up a learning bias PIML by modifying the loss function of the FFNN by using the improved integral equation model (I2EM). A particle swarm optimization (PSO) algorithm is used to optimize the tuning parameters of the PIML. The effectiveness of the proposed model is compared with ten benchmark algorithms. The performance of PIML model is superior among the benchmark algorithms, achieving a correlation coefficient (R) of 0.94, a root mean square error (RMSE = 0.019 m3/m3), and bias $ = -0.03$ m3/m3. We conclude that the PIML model can accurately estimate soil moisture solely from satellite images, achieving higher spatial and temporal resolutions, even with limited in situ observations. The findings of this study can be applied in agriculture, hydrology, flood management, and drought monitoring, particularly in data-scarce regions.

关键词： Soil moisture machine learning Moisture Predictive models Data models Surface topography Radio frequency machine learning algorithms Surface soil Sentinel-1 Improved integral equation model (I2EM) neural networks physics-informed machine learning (PIML) Sentinel-1/2 soil moisture swarm intelligence

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Anomaly Detection Algorithm of Industrial Internet of Things Data Platform Based on Deep learning

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2024年第3期8卷 1037-1048页

作者： Li, Xing Xie, Chao Zhao, Zhijia Wang, Chunbao Yu, Huajun Dongguan Univ Technol Sch Elect Engn & Intelligentizat Dongguan 523808 Guangdong Peoples R China Northeastern Univ China State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China Wuhan Donghu Univ Sch Management Wuhan 430212 Hubei Peoples R China Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Guangdong Peoples R China MK Smart Robot Co Ltd R&D Dept Zhuhai 519000 Guangdong Peoples R China Shanghai Elect GeniKIT Med Sci & Technol Co Prod Dev Dept Shanghai 200070 Peoples R China

The development of the Internet of Things (IoT) causes most industrial applications to utilize IoT devices to improve their productivity. Applications such as smart cities, energy management, smart homes, smart cars, and supply chain management widely utilize the IoT to manage the industries' efficiency. Industrial IoT devices are frequently affected by cybercriminals and damage information and productivity. Criminal activities can be overcome by applying various machine-learning techniques. Existing methods can process intermediate attacks;however, traditional machine learning techniques have difficulties predicting adversarial and catastrophic attacks. In addition, most of the AI-based industrial applications have heterogeneous and mixed data, requiring robust intruder detection systems. The research issues are addressed by introducing the Meta-Heuristic Optimized Deep Random Neural Networks (MH-DRNN). The system uses the optimization process in feature selection and classification, reducing the heterogeneous data analysis issues. The optimization method selects the features from the feature set according to the sunflower movement, which minimizes the difficulties in computation. In addition, three MLP and three recurrent layers are incorporated into this system to maximize the prediction rate up to 99.2% accuracy.

关键词： Industrial Internet of Things Anomaly detection Deep learning Feature extraction Real-time systems machine learning algorithms Green products Industrial Internet of Things (IIoT) machine learning intermediate attacks meta-heuristic optimized deep random neural networks sunflower movement

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Enhancing Rice Production Prediction in Indonesia Using Advanced machine learning Models

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IEEE ACCESS 2024年 12卷 151161-151177页

作者： Erlin Yunianta, Arda Wulandhari, Lili Ayu Desnelita, Yenny Nasution, Nurliana Junadhi Inst Bisnis & Teknol Pelita Indonesia Dept Informat Engn Pekanbaru 28127 Indonesia King Abdulaziz Univ Fac Comp & Informat Technol Rabigh Dept Informat Syst Jeddah 21589 Saudi Arabia Bina Nusantara Univ Sch Comp Sci Dept Comp Sci Jakarta 11480 Indonesia Inst Bisnis & Teknol Pelita Indonesia Fac Comp Sci Dept Informat Syst Pekanbaru 28127 Indonesia Univ Lancang Kuning Fac Comp Sci Dept Informat Engn Pekanbaru 28265 Indonesia Univ Sains & Teknol Indonesia Dept Informat Engn Pekanbaru 28294 Indonesia

This study delves into the application of machine learning techniques for predicting rice production in Indonesia, a country where rice is not just a staple food but also a key component of the agricultural sector. Utilizing data from 2018 to 2023, sourced from the Central Bureau of Statistics of Indonesia and the Meteorology, Climatology, and Geophysics Agency of Indonesia, this research presents a comprehensive approach to agricultural forecasting. The study begins with an Exploratory Data Analysis (EDA) to understand the variability and distribution of variables such as harvested area, production, rainfall, humidity, and temperature. Significant regional disparities in rice production are identified, highlighting the complexity of agricultural forecasting in Indonesia. Five machine learning models- Random Forest, Gradient Boosting, Decision Tree, Support Vector machine, and Artificial Neural Network-are trained and tested. The Random Forest model stands out for its superior performance, as evidenced by the lowest Mean Squared Error (MSE) of 0.016186 and the highest R-squared (R-2) of 0.850039, compared to the other models, indicating its high predictive accuracy and reliability. Hyperparameter tuning using the GridSearchCV technique was conducted on all five models, resulting in performance improvements across the board. Despite these enhancements, the Random Forest model remained the best, achieving a lower MSE of 0.014162 and a higher R-2 value of 0.911257. This research not only underscores the effectiveness of machine learning in improving rice production predictions in Indonesia but also sets the stage for future research. It highlights the potential of advanced analytical techniques in enhancing agricultural productivity and decision-making, paving the way for further explorations into more sophisticated models and a broader range of data, ultimately contributing to the resilience and sustainability of Indonesia's agricultural sector.

关键词： Production machine learning Data models Predictive models Random forests Accuracy Support vector machines Meteorology machine learning algorithms Analytical models Farming Agriculture Agroclimatic variability crop yield production data analysis Indonesia rice production machine learning

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Construction of Design Works Appreciation System Based on machine learning

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IEEE ACCESS 2024年 12卷 105384-105392页

作者： You, Lina Linyi Univ Acad Fine Arts Linyi 276000 Shandong Peoples R China

With the rapid development of the economy, the aspirations for spiritual fulfillment have transcended material demands, fostering a greater appreciation for artistic lifestyles. Artistic design works, embodying humanity's interactions with nature and itself in daily life, interpret people's comprehension and pursuit of beauty through diverse forms of expression. The precise restoration, preservation of artistic value, and conservation of these works are paramount for historical research, cultural transmission, and the perpetuation of artistic legacies. This paper proposes an image restoration algorithm for artistic design works utilizing machine learning, aiming to establish an appreciation system for such works. By leveraging advanced machine learning techniques, the system facilitates efficient restoration and digital appreciation of artistic design works, enhancing restoration quality and broadening artistic appreciation horizons. Through the implementation of this system, it is envisioned that artistic design works will be better preserved and transmitted, enabling individuals to appreciate the grandeur of art.

关键词： Art Image restoration machine learning Protection machine learning algorithms Painting Training Works of artistic design machine learning image restoration

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Enhancing personalized learning with explainable AI: A chaotic particle swarm optimization based decision support system

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APPLIED SOFT COMPUTING 2024年 156卷

作者： Parkavi, R. Karthikeyan, P. Abdullah, A. Sheik Thiagarajar Coll Engn Dept Informat Technol Madurai Tamilnadu India Vellore Inst Technol Sch Comp Sci & Engn SCOPE Chennai Campus Chennai Tamil Nadu India

In the realm of Educational Technology, personalized learning is pivotal, yet predicting students ' learning abilities based on learning styles and ICT remains challenging. We propose a decision support system using machine learning (ML), swarm intelligence, and explainable artificial (XAI) techniques to assess students ' performance. Our model employs Chaotic Particle Swarm Optimization (C-PSO) with Henon execution, outperforming Genetic Algorithm (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Bee Colony Optimization (BCO), Artificial Fish Swarm Algorithm (AFSA), Mayfly Optimization Algorithm (MFOA), Mother Optimization Algorithm (MOA), Fuzzy Self -Tuning PSO (FST-PSO). Evaluating efficiency, effectiveness, and solution quality reveals C-PSO ' s superiority. The study identifies the significant impact of ICT on self -progress and employs Spearman Rank correlation for statistical validation. Findings suggest C-PSO as an effective tool for optimizing educational data analysis and decision -making. Further exploration in real -world educational settings and comparative analyses with alternative optimization techniques are recommended for future research.

关键词： Educational data mining machine learning algorithms Particle Swarm Optimization Meta -heuristic based algorithms Explainable Artificial Intelligence Techniques

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