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Land Subsidence Susceptibility Mapping in South Korea Using machine learning algorithms

SENSORS 2018年第8期18卷 2464-2464页

作者： Dieu Tien Bui Shahabi, Himan Shirzadi, Ataollah Chapi, Kamran Pradhan, Biswajeet Chen, Wei Khosravi, Khabat Panahi, Mahdi Bin Ahmad, Baharin Saro, Lee Ton Duc Thang Univ Geog Informat Sci Res Grp Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Environm & Labour Safety Ho Chi Minh City Vietnam Univ Kurdistan Fac Nat Resources Dept Geomorphol Sanandaj *** Iran Univ Kurdistan Fac Nat Resources Dept Rangeland & Watershed Management Sanandaj *** Iran Univ Technol Sydney Fac Engn & IT Ctr Adv Modelling & Geospatial Informat Syst CAMG Sydney NSW 2007 Australia Sejong Univ Dept Energy & Mineral Resources Engn 209 Neungdong Ro Seoul 05006 South Korea Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Shaanxi Peoples R China Sari Agr & Nat Resources Univ SANRU Fac Nat Resources Dept Watershed Sci Engn POB 48181-68984 Sari Mazandaran Iran Islamic Azad Univ North Tehran Branch Dept Geophys Young Researchers & Elites Club POB 19585-466 Tehran Iran UTM Fac Geoinformat & Real Estate Dept Geoinformat Skudai 81310 Malaysia Korea Inst Geosci & Mineral Resources KIGAM Geol Res Div 124 Gwahak Ro Daejeon 34132 South Korea Korea Univ Sci & Technol Dept Geophys Explorat 217 Gajeong Ro Daejeon 34113 South Korea

In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results.

关键词： land subsidence machine learning algorithms GIS South Korea

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Discrimination of maize transgenic and non-transgenic varieties by laser induced spectroscopy (LIBS) and machine learning algorithms

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MICROCHEMICAL JOURNAL 2024年 203卷

作者： Ribeiro, Matheus Cicero Cabral, Jader Nicolodelli, Gustavo Senesi, Giorgio S. Caires, Anderson R. L. Goncalves, Daniel A. Menegatti, Carlos Milori, Debora Cena, Cicero Marangoni, Bruno UFMS Univ Fed Mato Grosso Programa Posgrad Ciencia Mat Campo Grande MS Brazil Univ Fed Uberlandia Inst Fis POB 593 BR-38400902 Uberlandia MG Brazil UFSC Univ Fed Santa Catarina Florianopolis SC Brazil CNR Ist Sci & Tecnol Plasmi ISTP Sede Bari Via Amendola 122-D I-70126 Bari Italy Univ Fed Grande Dourados UFGD Fac Exact Sci & Technol Dourados MS Brazil Univ Sao Paulo Escola Engn Lorena POB 116 BR-12602810 Lorena SP Brazil EMBRAPA Instrumentacao POB 741 BR-13560970 Sao Carlos SP Brazil Fed Univ Mato Grosso UFMS Bairro Univ Inst Phys INFI Ave Costa & Silva S-N BR-79090900 Campo Grande MS Brazil

Background: In the last decades, the production and consumption of genetically modified agricultural products have increased markedly due to the worldwide population growth and rising demand for food and feed. Consequently, genetically modified crops have been extensively produced and consumed, which required identifying and discriminating transgenic and non-transgenic products. Results: Laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was applied to identify and discriminate two varieties of conventional (not-genetically modified, NGM) maize from four varieties of transgenic maize (genetically modified, GM). The LIBS spectra acquired under reduced pressure (100 Torr) conditions over two ranges, i.e., 175-330 nm and 275-770 nm, were subjected to Standard Normal Variation (SNV) and multivariate methods such as Principal Component Analysis (PCA) to reduce data matrices dimensionality and spectral noise. The supervised machine learning algorithms k-nearest neighbor (k-NN) and support vector machine (SVM) have been applied to discriminate among NGM and GM maize reserving 25 % of data for external validation. The training data were employed for hyperparameter optimization of classifiers using the Leave-One-Out Cross-Validation (LOOCV) method. Considering all six maize varieties simultaneously, the highest training accuracy achieved was 90.56 %, with an external validation accuracy of 88.33 %. In an alternative approach based on pairwise combinations of one GM variety against one NGM variety, the best outcome achieved was 100 % LOOCV and external validation accuracy. Conclusions: These results showed that LIBS supported by appropriate chemometric methods represents an alternative screening technique for identifying and discriminating transgenic from non-transgenic maize.

关键词： Transgenic maize Non-transgenic maize LIBS Chemometric analysis machine learning algorithms

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User-centric Activity Recognition and Prediction Model using machine learning algorithms

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第12期12卷 501-510页

作者： Roy, Namrata Ahmed, Rafiul Huq, Mohammad Rezwanul Shahriar, Mohammad Munem East West Univ Dept Comp Sci & Engn Dhaka Bangladesh

Human Activity Recognition has been a dynamic research area in recent years. Various methods of collecting data and analyzing them to detect activity have been well investigated. Some machine learning algorithms have shown excellent performance in activity recognition, based on which many applications and systems are being developed. Unlike this, the prediction of the next activity is an emerging field of study. This work proposes a conceptual model that uses machine learning algorithms to detect activity from sensor data and predict the next activity from the previously seen activity sequence. We created our activity recognition dataset and used six machine learning algorithms to evaluate the recognition task. We have proposed a method for the next activity prediction from the sequence of activities by converting a sequence prediction problem into a supervised learning problem using the windowing technique. Three classification algorithms were used to evaluate the next activity prediction task. Gradient Boosting performs best for activity recognition, yielding 87.8% accuracy for the next activity prediction over a 16-day timeframe. We have also measured the performance of an LSTM sequence prediction model for predicting the next activity, where the optimum accuracy is 70.90%.

关键词： machine learning algorithms activity recognition gradient boosting next activity prediction LSTM sequence prediction model

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Dental age assessment based on CBCT images using machine learning algorithms

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FORENSIC SCIENCE INTERNATIONAL 2022年 334卷 111245-111245页

作者： Saric, Rijad Kevric, Jasmin Hadziabdic, Naida Osmanovic, Ahmed Kadic, Mirsad Saracevic, Muzafer Jokic, Dejan Rajs, Vladimir La Trobe Univ Sch Engn & Math Sci Dept Engn Melbourne Vic 3086 Australia Int Burch Univ Fac Engn & Nat Sci Dept Elect & Elect Engn Sarajevo 71000 Bosnia & Herceg Univ Sarajevo Fac Dent Med Clin Dept Oral Surg Bolnicka 4a Sarajevo 71000 Bosnia & Herceg Olawell Inc Dept Biol Manchester MA 01944 USA Publ Hlth Inst Kakanj Kakanj 72240 Bosnia & Herceg Univ Novi Pazar Dept Comp Sci Novi Pazar 36300 Serbia Univ Novi Sad Fac Tech Sci Dept Power Energy Elect & Telecommun Novi Sad 21000 Serbia

Age estimation has become inordinately significant for human beings for many reasons, such as detecting legal and criminal responsibility and other social events like a marriage license, birth certificate, etc. This paper aims to decide on the most desirable machine learning algorithm (from conventional machine learning algorithms to deep learning) for dental age estimation based on buccal bone level. The database consisted of 150 CBCT images (73 males and 77 females) from an existing base of the Faculty of Dental Medicine with Clinics, University of Sarajevo, aged 20-69. Results were obtained using the Waikato Environment for Knowledge Analysis (Weka), machine learning software in Java. Left and Right Buccal Alveolar Bone Levels are increasing with age, so they showed to be the most important attributes, especially the latter. Random Forest classifier provided the greatest result with the correlation coefficient of 0.803 and the mean absolute error of 6.022. We have also shown that considering sinus-related features can be a significant addition to the databases. Our paper is probably one of the first studies where regression algorithms based on the Support Vector machines and Random Forest were utilized. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Dental age estimation machine learning algorithms Feature Selection

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Reducing the vicissitudes of heterologous prochiral substrate catalysis by alcohol dehydrogenases through machine learning algorithms

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BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS 2024年 691卷 149298页

作者： Ghatak, Arindam Shanbhag, Anirudh P. Datta, Santanu Univ Calcutta Dept Biophys Mol Biol & Bioinformat Kolkata India UAS GKVK Biomoneta Res Pvt Ltd Natl Ctr Biol Sci NCBS C CAMP Bangalore 560065 India UAS GKVK Bugworks Res India Pvt Ltd Natl Ctr Biol Sci NCBS C CAMP Bangalore 560065 India

Alcohol dehydrogenases (ADHs) are popular catalysts for synthesizing chiral synthons a vital step for active pharmaceutical intermediate (API) production. They are grouped into three superfamilies namely, medium-chain (MDRs), short-chain dehydrogenase/reductases (SDRs), and iron-containing alcohol dehydrogenases. The former two are used extensively for producing various chiral synthons. Many studies screen multiple enzymes or engineer a specific enzyme for catalyzing a substrate of interest. These processes are resource-intensive and intricate. The current study attempts to decipher the ability to match different ADHs with their ideal substrates using machine learning algorithms. We explore the catalysis of 284 antibacterial ketone intermediates, against MDRs and SDRs to demonstrate a unique pattern of activity. To facilitate machine learning we curated a dataset comprising 33 features, encompassing 4 descriptors for each compound. Subsequently, an ensemble of machine learning techniques viz. Partial Least Squares (PLS) regression, k-Nearest Neighbors (kNN) regression, and Support Vector machine (SVM) regression, was harnessed. Moreover, the assimilation of Principal Component Analysis (PCA) augmented precision and accuracy, thereby refining and demarcating diverse compound classes. As such, this classification is useful for discerning substrates amenable to diverse alcohol dehydrogenases, thereby mitigating the reliance on high-throughput screening or engineering in identifying the optimal enzyme for specific substrate.

关键词： Chiral intermediates Alcohol dehydrogenases Biocatalysis machine learning algorithms Catalytic turnover

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Marketing Strategy of Private Enterprises Based on Bayesian Dynamic Panel Model of machine learning algorithms

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND WEB ENGINEERING 2024年第1期19卷 1页

作者： Sun, Siyu Long, Juan Shanghai Univ Finance & Econ Zhejiang Coll Shanghai Peoples R China Chongqing City Vocat Coll Chongqing Peoples R China

machine learning algorithms have attracted widespread attention in both industry and academia. This article mainly studies the marketing strategy decision-making of private listed enterprises based on Bayesian panel data model. By constructing a Bayesian static panel data model and a Bayesian dynamic panel data model, an empirical analysis was conducted on the debt financing decisions of private enterprises from two aspects: external financial environment and internal governance. The experimental results show that the MC error and standard deviation of parameter estimation for Bayesian static panel data model and Bayesian dynamic panel data model are both very small. This method contains more information, increases observation data and degrees of freedom. This article provides important theoretical guidance for the coordinated development of private listed enterprises and state-owned enterprises. It is conducive to promoting the coordinated development of the entire national economy.

关键词： Bayesian Template Dynamic Panel Model machine learning algorithms Marketing Strategy Private Enterprise

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Establishing a differential diagnosis model between primary membranous nephropathy and non-primary membranous nephropathy by machine learning algorithms

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RENAL FAILURE 2024年第2期46卷 2380752页

作者： Cao, Shangmei Yang, Shaozhe Chen, Bolin Chen, Xixia Fu, Xiuhong Tang, Shuifu Luohe Med Coll Affiliated Hosp 1 Henan Key Lab Fertil Protect & Aristogenesis Dept Sci & Technol Innovat CtrLuohe Cent Hosp Renmin St 56 Luohe 462000 Peoples R China Guangzhou Univ Chinese Med Affiliated Hosp 1 Div Nephrol 16 Ji Chang Rd Guangzhou 510405 Guangdong Peoples R China

ContextFour algorithms with relatively balanced complexity and accuracy in deep learning classification algorithm were selected for differential diagnosis of primary membranous nephropathy (PMN).ObjectiveThis study explored the most suitable classification algorithm for PMN identification, and to provide data reference for PMN diagnosis *** total of 500 patients were referred to Luo-he Central Hospital from 2019 to 2021. All patients were diagnosed with primary glomerular disease confirmed by renal biopsy, contained 322 cases of PMN, the 178 cases of non-PMN. Using the decision tree, random forest, support vector machine, and extreme gradient boosting (Xgboost) to establish a differential diagnosis model for PMN and non-PMN. Based on the true positive rate, true negative rate, false-positive rate, false-negative rate, accuracy, feature work area under the curve (AUC) of subjects, the best performance of the model was *** efficiency of the Xgboost model based on the above evaluation indicators was the highest, which the diagnosis of PMN of the sensitivity and specificity, respectively 92% and 96%.ConclusionsThe differential diagnosis model for PMN was established successfully and the efficiency performance of the Xgboost model was the best. It could be used for the clinical diagnosis of PMN. Membranous nephropathy (MN) without obvious causes is called primary MN (PMN), This study utilized deep learning classification algorithms for differential diagnosis of PMN and explored the most suitable classification algorithm for PMN recognition, provided data reference for PMN diagnosis research.

关键词： Primary membranous nephropathy machine learning algorithms differential diagnosis anti-PLA2R IgG Xgboost model

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Metabolism pathway-based subtyping in endometrial cancer: An integrated study by multi-omics analysis and machine learning algorithms

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MOLECULAR THERAPY NUCLEIC ACIDS 2024年第2期35卷 102155页

作者： Liu, Xiaodie Wang, Wenhui Zhang, Xiaolei Liang, Jing Feng, Dingqing Li, Yuebo Xue, Ming Ling, Bin Chinese Acad Med Sci & Peking Union Med Coll China Japan Friendship Hosp Inst Clin Med Sci Dept Obstet & Gynecol Beijing 100029 Peoples R China China Japan Friendship Hosp Dept Obstet & Gynecol Beijing 100029 Peoples R China Shandong Univ Qilu Hosp Dept Obstet & Gynecol 107 Wenhua West Rd Jinan 250012 Shandong Peoples R China

Endometrial cancer (EC), the second most common malignancy in the female reproductive system, has garnered increasing attention for its genomic heterogeneity, but understanding of its metabolic characteristics is still poor. We explored metabolic dysfunctions in EC through a comprehensive multi-omics analysis (RNA-seq datasets from The Cancer Genome Atlas [TCGA], Cancer Cell Line Encyclopedia [CCLE], and GEO datasets;the Clinical Proteomic Tumor Analysis Consortium [CPTAC] proteomics;CCLE metabolomics) to develop useful molecular targets for precision therapy. Unsupervised consensus clustering was performed to categorize EC patients into three metabolism -pathway -based subgroups (MPSs). These MPS subgroups had distinct clinical prognoses, transcriptomic and genomic alterations, immune microenvironment landscape, and unique patterns of chemotherapy sensitivity. Moreover, the MPS2 subgroup had a better response to immunotherapy. Finally, three machine learning algorithms (LASSO, random forest, and stepwise multivariate Cox regression) were used for developing a prognostic metagene signature based on metabolic molecules. Thus, a 13 -hub gene -based classifier was constructed to predict patients' MPS subtypes, offering a more accessible and practical approach. This metabolism -based classification system can enhance prognostic predictions and guide clinical strategies for immunotherapy and metabolism -targeted therapy in EC.

关键词： MT: Bioinformatics endometrial cancer multi-omics metabolism heterogeneity molecular subgroups machine learning algorithms precision therapy nomogram

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Forecasting frost risk in forest plantations by the combination of spatial data and machine learning algorithms

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AGRICULTURAL AND FOREST METEOROLOGY 2021年 306卷 108450-108450页

作者： Diniz, Ecio Souza Lorenzon, Alexandre Simoes Martins de Castro, Nero Lemos Marcatti, Gustavo Eduardo dos Santos, Osmarino Pires de Deus Junior, Jose Carlos Lopes Cavalcante, Rosane Barbosa Fernandes-Filho, Elpidio Inacio Amaral, Cibele Hummeldo Univ Fed Vicosa Dept Engn Florestal Lab Sensoriamento Remoto & Geoproc Campus UFV S-N BR-36570000 Vicosa MG Brazil Univ Fed Sao Joao Del Rei UFSJ CSL Dept Agr Sci Km 47MG424 Rd BR-35701970 Sete Lagoas MG Brazil CMPC Celulose Rio Grandense Guaiba RS Brazil ITV Inst Tecnol Vale Belem Para Brazil Univ Fed Vicosa Dept Solos Lab Geoproc LabGeo Campus UFV S-N BR-36570000 Vicosa MG Brazil

Brazil is one of the leading timber producers in the world. However, in South Brazil, frost events frequently cause damage and reduce yield in forest plantations, a situation predicted to become more common under global change scenarios. This raises the need for low cost and efficient tools, such as machine learning algorithms to improve forecasting of frost risk. This study used machine learning algorithms to create zoning classifications forecasting frost risk for forest plantations located in the south-central region of Rio Grande do Sul State, Brazil. For this, we gathered and processed data from a local geodatabase (i.e. high-spatial-resolution contour lines, hydrography, and forest stands limits) comprising 30 management units with consistent historical data of frost occurrence. Then, we generated possible local-scale predictors of frost occurrence, which included longitude, latitude, elevation, relative altitude, relief orientation, and Euclidean distance from hydrography. We carried out tests of three machine learning classifiers (Random Forest - RF;Support Vector machine-SVM and Multi-layer Perceptron-MLP) in order to determine which would most accurately predict frost occurrence. We found that RF provided the highest accuracy (> 90%), as well as the smallest percentages of class-specific errors (i.e. commission and omission errors), when compared to SVM and MLP. Latitude was the most important predictor of frost occurrence when using RF. Conversely, MLP performed worst, especially for classifying frost occurrence versus non-occurrence, and therefore had the highest percentage of class-specific errors. Our findings lead us to conclude that RF is the most proficient algorithm for forecasting frost occurrence from local-scale geomorphological data, without the need for high-cost investment in micro-meteorological sensors to monitor climate frost events linking temperature to plant damage. With increasing global climate extreme events, accurate risk zoning is

关键词： machine learning algorithms Climatic forecast Forest production Geoprocessing Frost Events

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Using machine learning for NEETs and sustainability studies: Determining best machine learning algorithms

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SOCIO-ECONOMIC PLANNING SCIENCES 2024年 94卷

作者： Berigel, Muhammet Boztas, Gizem Dilan Rocca, Antonella Neagu, Gabriela Karadeniz Tech Univ TR-61080 Trabzon Turkiye Univ Naples Parthenope I-80142 Naples Italy Res Inst Qual Life Bucharest 050711 Romania

In this study, we apply and compare different algorithms from machine learning to describe and predict NEET rates in 31 European countries in the period from 2005 to 2020. With this aim, we considered eleven indicators describing the socio-economic national context and the level of innovation of the economies. Besides improving knowledge about the use of machine learning algorithms for the description of the NEET phenomenon, we discuss the connections between NEETs and other indicators that connect with other relevant sustainable development goals (SDGs), such as education, the reduction of inequalities, and decent work for everyone. The reduction of NEET rates is the only goal directly addressed to young people, The article underscores the need for evidence-based approaches to measure SDG achievement, especially concerning the heterogeneous NEET population. It emphasizes the importance of machine learning algorithms as a modern methodology for understanding and addressing the NEET phenomenon within the framework of SDGs, considering the complex interrelationships of socio-economic factors contributing to social and economic sustainability.

关键词： Sustainability NEET SDG machine learning algorithms

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