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machine learning algorithms for the Estimation of Water Quality Parameters in Lake Llanquihue in Southern Chile

WATER 2023年第11期15卷 1994页

作者： Rodriguez-Lopez, Lien Bustos Usta, David Bravo Alvarez, Lisandra Duran-Llacer, Iongel Lami, Andrea Martinez-Retureta, Rebeca Urrutia, Roberto Univ San Sebastian Fac Ingn Arquitectura & Diseno Lientur 1457 Concepcion 4030000 Chile Univ Concepcion Fac Oceanog Concepcion 4030000 Chile Univ Concepcion Dept Elect Engn Edmundo Larenas 219 Concepcion 4030000 Chile Univ Mayor Fac Ciencias Ingn & Tecnol Hemera Ctr Observac Tierra Camino La Piramide 5750 Santiago 8580745 Chile Inst Water Res IRSA Sez Verbania I-1000015 Verbania CP Italy Univ Concepcion Fac Ciencias Ambientales Concepcion 4030000 Chile

The world's water ecosystems have been affected by various human activities. Artificial intelligence techniques, especially machine learning, have become an important tool for predicting the water quality of inland aquatic ecosystems. As an excellent biological indicator, chlorophyll-a was studied to determine the state of water quality in Lake Llanquihue, located in southern Chile. A 31-year time series (1989 to 2020) of data collected in situ was used to determine the evolution of limnological parameters at eight spaced stations covering all of the main points of the lake, and the year, month, day, and hour time intervals were selected. Using machine learning techniques, out of eight estimation algorithms that were applied with real data to estimate chlorophyll-a, three models showed better performance (XGBoost, LightGBM, and AdaBoost). The results for the best models show excellent performance, with a coefficient of determination between 0.81 and 0.99, a root-mean-square error of between 0.03 ug/L and 0.46 ug/L, and a mean bias error of between 0.01 and 0.27 ug/L. These models are scalable and applicable to other lake systems of interest that present similar conditions and can support decision making related to water resources.

关键词： machine learning algorithms chlorophyll-a lake

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Medical Internet of things using machine learning algorithms for lung cancer detection

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Journal of Management Analytics 2020年第4期7卷 591-623页

作者： Kanchan Pradhan Priyanka Chawla Lovely Professional University CSEPhagwaraIndia

This paper empirically evaluates the several machine learning algorithms adaptable for lung cancer detection linked with IoT *** this work,a review of nearly 65 papers for predicting different diseases,using machine learning algorithms,has been *** analysis mainly focuses on various machine learning algorithms used for detecting several diseases in order to search for a gap toward the future improvement for detecting lung cancer in medical *** technique was analyzed on each step,and the overall drawbacks are pointed *** addition,it also analyzes the type of data used for predicting the concerned disease,whether it is the benchmark or manually collected ***,research directions have been identified and depicted based on the various existing *** will be helpful for the upcoming researchers to detect the cancerous patients accurately in early stages without any flaws.

关键词： disease prediction lung cancer machine learning algorithms internet of things

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Detection of Malfunctioning Photovoltaic Modules Based on machine learning algorithms

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IEEE ACCESS 2021年 9卷 37210-37219页

作者： Hwang, Humble Po-Ching Ku, Cooper Cheng-Yuan Chan, James Chi-Chang Natl Yang Ming Chiao Tung Univ Inst Informat Management Hsinchu 30010 Taiwan Ind Technol Res Inst ITRI Hsinchu 31040 Taiwan

In recent years, with the rise of environmental awareness worldwide, the number of solar power plants has significantly increased. However, the maintenance of solar power plants is not an easy job, especially the detection of malfunctioning photovoltaic (PV) cells in large-scale or remote power plants. Therefore, finding these cells and replacing them in time before severe events occur is increasingly important. In this paper, we propose a hybrid scheme with three embedded learning methods to enhance the detection of malfunctioning PV modules with validated efficiencies. For the first method, we combine the improved gamma correction function (preprocess) with a convolutional neural network (CNN). Infrared (IR) thermographic images of solar modules are used to train the abovementioned improved algorithm. For the second method, we train a CNN model using the IR temperatures of PV modules with the preprocessing of a threshold function. A compression procedure is then designed to cut the time-consuming preprocesses. The third method is to replace the CNN with the eXtreme Gradient Boosting (XGBoost) algorithm and the selected temperature statistics. The experimental results show that all three methods can be implemented with high detection accuracy and low time consumption, and furthermore, the hybrid scheme provides an even better accuracy.

关键词： machine learning algorithms Deep learning Classification algorithms Feature extraction Training Temperature distribution Data models Solar power generation fault detection infrared imaging image processing machine learning

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Performance evaluation of machine learning algorithms in predicting dew point pressure of gas condensate reservoirs

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SN APPLIED SCIENCES 2020年第12期2卷 2124页

作者： Ikpeka, Princewill Ugwu, Johnson Russell, Paul Pillai, Gobind Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough Cleveland England

Accurate knowledge of the dew point pressure for a gas condensate reservoir is necessary for optimizing mitigation operations during field development plan. This study explores the use of machine learning models in predicting the dew point pressure of gas condensate reservoirs. 535 experimental dew point pressure data-points with maximum temperature and pressure of 304 degrees F and 10,500 psi were used for this analysis. First, a standard multiple linear regression (MLR) was used as a benchmark for comparing the performance of the machine learning models. Multilayer perceptron Neural Networks (MLP) [optimized for the number of neurons and hidden layers], Support Vector machine (SVM) [using radial basis function kernel] and Decision Tree [Gradient boost Method (GBM) and XG Boost (XGB)] algorithms were used to predict the dew point pressure. The performance of these algorithms was then compared with results obtained from published machine learning models. The input parameters for the model include;gas composition, specific gravity, the molecular weight of the heavier component and compressibility factor. The performances of these algorithms were analysed using root mean square error (RMSE), absolute average relative deviation percentage (AARD %) and coefficient of determination (R-2).

关键词： Dew point pressure prediction machine learning algorithms Gas condensate reservoir

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Assessing PM2.5 concentrations in Tehran, Iran, from space using MAIAC, deep blue, and dark target AOD and machine learning algorithms

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ATMOSPHERIC POLLUTION RESEARCH 2019年第3期10卷 889-903页

作者： Nabavi, Seyed Omid Haimberger, Leopold Abbasi, Esmail Univ Vienna Dept Meteorol & Geophys Vienna Austria Persian Gulf Univ Dept Environm Studies Bushehr Iran

This study aims to explore the spatial estimation of fine particulate matter (PM2.5) using 10-km merged dark target and deep blue (DB_DT) Aerosol Optical Depth (AOD) and 1-km Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD over Tehran. The ability of four machine learning algorithms (MLAs) to predict PM2.5 concentrations is also investigated. Results show that the association of satellite AOD with surface PM significantly increases after considering the contribution of relative humidity in PM mass concentration and normalization of AOD to Planetary boundary layer height (PBLH). The examination of derived aerosol layer height (ALH) from 159 Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) profiles shows that PBLH could successfully represent the top of aerosol-laden layers. Surprisingly, the highest correlation was found between normalized 10-km DB_DT AOD and corrected PM2.5 measurements. Consequently, random forest (RF) fed by this AOD product has yielded the best performance (R-2 = 0.68, RMSE = 17.52 and MRE = 27.46%). Importance analysis of variables reveals that DB_DT and meteorological fields are of highest and least importance among selected variables, respectively. The RF performance is less satisfactory during summer which is assumed to be caused by the omission of unknown features representing the formation of secondary aerosols. The inferior accuracy of estimation in the north and east of Tehran is also linked to lacking features which could feed the transportation of PM(2.5 )from west to the east of the study area into MLAs.

关键词： MAIAC AOD DB_DT AOD Vertical distribution of aerosols Fine particulate matters machine learning algorithms

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Prediction of risk factors for first trimester pregnancy loss in frozen-thawed good-quality embryo transfer cycles using machine learning algorithms

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JOURNAL OF ASSISTED REPRODUCTION AND GENETICS 2023年第2期40卷 279-288页

作者： Ozer, Gonul Akca, Aysu Yuksel, Beril Duzguner, Ipek Pehlivanli, Ayca Cakmak Kahraman, Semra Mem Sisli Hosp IVF & Reprod Genet Ctr Piyalepasa Bulvari TR-35385 Istanbul Turkey Mimar Sinan Fine Arts Univ Fac Sci Bomonti Campus TR-34380 Istanbul Turkey Mimar Sinan Fine Arts Univ Letters Stat Dept Bomonti Campus TR-34380 Istanbul Turkey

Purpose Can the risk factors that cause first trimester pregnancy loss in good-quality frozen-thawed embryo transfer (FET) cycles be predicted using machine learning algorithms? Methods This is a retrospective cohort study conducted at Sisli Memorial Hospital, ART and Reproductive Genetics Center, between January 2011 and May 2021. A total of 3805 good-quality FET cycles were included in the study. First trimester pregnancy loss rates were evaluated according to female age, paternal age, body mass index (BMI), diagnosis of infertility, endometrial preparation protocols (natural/artificial), embryo quality (top/good), presence of polycystic ovarian syndrome (PCOS), history of recurrent pregnancy loss (RPL), recurrent implantation failure (RIF), severe male infertility, adenomyosis and endometriosis. Results The first trimester pregnancy loss rate was 18.2% (693/ 3805). The presence of RPL increased first trimester pregnancy loss (OR = 7.729, 95%CI = 5.908-10.142, P = 0.000). BMI, which is > 30, increased first trimester pregnancy loss compared to < 25 (OR = 1.418, 95%CI = 1.025-1.950, P = 0.033). Endometrial preparation with artificial cycle increased first trimester pregnancy loss compared to natural cycle (OR = 2.101, 95%CI = 1.630-2.723, P = 0.000). Female age, which is 35-37, increased first trimester pregnancy loss compared to < 30 (OR = 1.617, 95%CI = 1.120-2.316, P = 0.018), and female age, which is > 37, increased first trimester pregnancy loss compared to < 30 (OR = 2.286, 95%CI = 1.146-4,38, P = 0.016). The presence of PCOS increased first trimester pregnancy loss (OR = 1.693, 95%CI = 1.198-2.390, P = 0.002). The number of previous IVF cycles, which is > 3, increased first trimester pregnancy loss compared to < 3 (OR = 2.182, 95%CI = 1.708-2.790, P = 0.000). Conclusions History of RPL, RIF, advanced female age, presence of PCOS, and high BMI (> 30 kg/m(2)) were the factors that increased first trimester pregnancy loss.

关键词： First trimester pregnancy loss Frozen-thawed embryo transfer (FET) cycles In vitro fertilization (IVF) machine learning algorithms Infertility

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Clustering and Candidate Motif Detection in Exosomal miRNAs by Application of machine learning algorithms

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2019年第2期11卷 206-214页

作者： Gaur, Pallavi Chaturvedi, Anoop Univ Allahabad Fac Sci Nehru Sci Ctr Ctr BioinformatInst Inter Disciplinary Studies Allahabad 211002 Uttar Pradesh India Univ Allahabad Fac Sci Nehru Sci Ctr Dept Stat Allahabad 211002 Uttar Pradesh India

BackgroundThe clustering pattern and motifs give immense information about any biological data. An application of machine learning algorithms for clustering and candidate motif detection in miRNAs derived from exosomes is depicted in this paper. Recent progress in the field of exosome research and more particularly regarding exosomal miRNAs has led much bioinformatic-based research to come into existence. The information on clustering pattern and candidate motifs in miRNAs of exosomal origin would help in analyzing existing, as well as newly discovered miRNAs within exosomes. Along with obtaining clustering pattern and candidate motifs in exosomal miRNAs, this work also elaborates the usefulness of the machine learning algorithms that can be efficiently used and executed on various programming languages/*** were clustered and sequence candidate motifs were detected successfully. The results were compared and validated with some available web tools such as BLASTN' and MEME suite'.ConclusionThe machine learning algorithms for aforementioned objectives were applied successfully. This work elaborated utility of machine learning algorithms and language platforms to achieve the tasks of clustering and candidate motif detection in exosomal miRNAs. With the information on mentioned objectives, deeper insight would be gained for analyses of newly discovered miRNAs in exosomes which are considered to be circulating biomarkers. In addition, the execution of machine learning algorithms on various language platforms gives more flexibility to users to try multiple iterations according to their requirements. This approach can be applied to other biological data-mining tasks as well.

关键词： Motif machine learning algorithms Exosomal miRNAs k-Means clustering

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Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms

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GLOBAL ECOLOGY AND CONSERVATION 2020年 22卷

作者： Ge, Genbatu Shi, Zhongjie Zhu, Yuanjun Yang, Xiaohui Hao, Yuguang Chinese Acad Forestry Inst Desertificat Studies Beijing 100091 Peoples R China Chinese Acad Forestry Expt Ctr Desert Forestry Dengkou 015200 Inner Mongolia Peoples R China State Forestry Adm Dengkou Desert Ecosyst Res Stn Dengkou 015200 Peoples R China

The importance of land use and cover change (LUCC) has gradually attracted more attention due to its influence on the climate and ecosystem. Consequently, the necessity of accurate LUCC mapping has become increasingly apparent. Over the past decades, although a large number of machine learning algorithms have been developed to improve the accuracy and reliability of remote sensing image classification, especially for LUCC classification, there is a lack of studies that assess the performance of machine learning algorithms in arid desert-oasis mosaic landscapes. In this study, the main objective is to provide a reference for the extraction of LUCC information in dryland regions with oasis-desert mosaic landscapes by comparing the performances of the k-nearest neighbor (KNN), random forest (RF), support vector machine (SVM) and artificial neural network (ANN) for the LUCC classification of the Dengkou Oasis, China. Landsat-8 Operational Land Imager (OLI) image data were used with spectral indices and auxiliary variables that were derived from a digital terrain model to classify 7 different land cover categories. The highest overall accuracy was produced by the ANN (97.16%), which was closely followed by the RF (96.92%), SVM (96.20%), and finally KNN (93.98%);statistically similar accuracies were obtained for the ANN, SVM and RF. The RF algorithm performed well across several aspects, such as stability, ease of use and processing time during the parameter tuning. Overall, the random forest algorithm is a good first choice method for land-cover classification in this study area, and the elevation and some spectral indices, such as the NDVI, MSAVI2 and MNDWI, should be used as variables to improve the overall accuracy. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Land use/cover change machine learning algorithms Arid area Desert-oasis mosaic landscape

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Automated Screening of Hip X-rays for Osteoporosis by Singh's Index Using machine learning algorithms

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INDIAN JOURNAL OF ORTHOPAEDICS 2024年第10期58卷 1449-1457页

作者： Kalavakonda, Vijaya Mohamed, Sameer Abhay, Lal Muthu, Sathish SRM Inst Sci & Technol Fac Engn & Technol Sch Comp Dept Comp Technol Chennai India Sri Ramachandra Inst Higher Educ & Res Chennai India Orthopaed Res Grp Coimbatore Tamil Nadu India Karpagam Acad Higher Educ Fac Engn Dept Biotechnol Coimbatore Tamil Nadu India Govt Med Coll Dept Orthopaed Karur Tamil Nadu India

IntroductionOsteoporosis is a significant and growing global public health problem, projected to increase in the next decade. The Singh Index (SI) is a simple, semi-quantitative evaluation tool for diagnosing osteoporosis with plain hip radiographs based on the visibility of the trabecular pattern in the proximal femur. This work aims to develop an automated tool to diagnose osteoporosis using SI of hip radiograph images with the help of machine learning *** used 830 hip X-ray images collected from Indian men and women aged between 20 and 70 which were annotated and labeled for appropriate SI. We employed three state-of-the-art machine learning algorithms-Vision Transformer (ViT), MobileNet-V3, and a Stacked Convolutional Neural Network (CNN)-for image pre-processing, feature extraction, and automation. Each algorithm was evaluated and compared for accuracy, precision, recall, and generalization capabilities to diagnose *** ViT model achieved an overall accuracy of 62.6% with macro-averages of 0.672, 0.597, and 0.622 for precision, recall, and F1 score, respectively. MobileNet-V3 presented a more encouraging accuracy of 69.6% with macro-averages for precision, recall, and F1 score of 0.845, 0.636, and 0.652, respectively. The stacked CNN model demonstrated the strongest performance, achieving an accuracy of 93.6% with well-balanced precision, recall, and F1-score *** superior accuracy, precision-recall balance, and high F1-scores of the stacked CNN model make it the most reliable tool for screening radiographs and diagnosing osteoporosis using the SI.

关键词： Osteoporosis screening Singh's index machine learning algorithms Hip radiographs Convolutional neural networks

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Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem

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ECOLOGY AND EVOLUTION 2019年第5期9卷 2562-2574页

作者： Shiferaw, Hailu Bewket, Woldeamlak Eckert, Sandra Addis Ababa Univ Water & Land Resource Ctr Addis Ababa Ethiopia Addis Ababa Univ Dept Geog & Environm Studies Addis Ababa Ethiopia Univ Bern Ctr Dev & Environm Bern Switzerland

In recent years, an increasing number of distribution maps of invasive alien plant species (IAPS) have been published using different machine learning algorithms (MLAs). However, for designing spatially explicit management strategies, distribution maps should include information on the local cover/abundance of the IAPS. This study compares the performances of five MLAs: gradient boosting machine in two different implementations, random forest, support vector machine and deep learning neural network, one ensemble model and a generalized linear model;thereby identifying the best-performing ones in mapping the fractional cover/abundance and distribution of IPAS, in this case called Prosopis juliflora (SW. DC.). Field level Prosopis cover and spatial datasets of seventeen biophysical and anthropogenic variables were collected, processed, and used to train and validate the algorithms so as to generate fractional cover maps of Prosopis in the dryland ecosystem of the Afar Region, Ethiopia. Out of the seven tested algorithms, random forest performed the best with an accuracy of 92% and sensitivity and specificity >0.89. The next best-performing algorithms were the ensemble model and gradient boosting machine with an accuracy of 89% and 88%, respectively. The other tested algorithms achieved comparably low performances. The strong explanatory variables for Prosopis distributions in all models were NDVI, elevation, distance to villages and distance to rivers;rainfall, temperature, near-infrared and red reflectance, whereas topographic variables, except for elevation, did not contribute much to the current distribution of Prosopis. According to the random forest model, a total of 1.173 million ha (12.33% of the study region) was found to be invaded by Prosopis to varying degrees of cover. Our findings demonstrate that MLAs can be successfully used to develop fractional cover maps of plant species, particularly IAPS so as to design targeted and spatially explicit management st

关键词： Afar Region dryland ecosystems Ethiopia fractional cover mapping invasive alien plant species machine learning algorithms Prosopis juliflora

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