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Spatio-temporal classification and prediction of land use and land cover change for the Vembanad Lake system, Kerala: a machine learning approach

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第57期29卷 86220-86236页

作者： Sundar, Parthasarathy Kulithalai Shiyam Deka, Paresh Chandra Natl Inst Technol Karnataka Dept Water Resources & Ocean Engn Mangalore India

Land use and land cover (LULC) change has become a critical issue for decision planners and conservationists due to inappropriate growth and its effect on natural ecosystems. As a result, the goal of this study is to identify the LULC for the Vembanad Lake system (VLS), Kerala, in the short term, i.e., within a decade, utilizing three standard machine learning approaches, random forest (RF), classification and regression trees (CART), and support vector machines (SVM), on the Google Earth Engine (GEE) platform. When comparing the three techniques, SVM performed poor at an average accuracy of around 82.5%, CART being the next at accuracy of 87.5%, and the RF model being good at the average of 89.5%. The RF outperformed the SVM and CART in almost identical spectral classes such as barren land and built-up areas. As a result, RF-classified LULC is considered to predict the spatio-temporal distribution of LULC transition analysis for 2035 and 2050. The study was conducted in Idrisi TerrSet software using the cellular automata (CA)-Markov chain analysis. The model's efficiency is evaluated by comparing the projected 2019 image to the actual 2019 classified image. The efficiency was good with more than 94.5% accuracy for the classes except for barren land, which might have resulted from the recent natural calamities and the accelerated anthropogenic activity in the area.

关键词： Google Earth Engine Random forest Support vector machines classification classification and regression trees Land use and land cover CA-Markov chain analysis LULC prediction

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Evaluation of ML-based Breast cancer diagnosis using AWS SageMaker 3

Evaluation of ML-based Breast cancer diagnosis using AWS Sag...

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3rd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025

作者： Vardhan, T.A.S. Kolli, Charishma Kumar, Bittu Pamarty, Khalvida Badve, Sanika Rahul Shrivastava, Amit Koneru Lakshmaiah Education Foundation Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798331521691

This study explores the application of various machine learning methods for diagnosing breast cancer through binary classification. Algorithms such as Support Vector Machines (SVM), Naive Bayes, k-Nearest Neighbors (KNN), and classification and regression trees (CART) were employed to classify tumors as benign or malignant based on data from the Breast Cancer Wisconsin Dataset. The objective is to identify the most effective model for accurate breast cancer detection by evaluating these algorithms using metrics such as accuracy and precision. Results indicate that CART, KNN, and Naive Bayes achieved approximately 92% accuracy, whereas SVM outperformed with an accuracy exceeding 99.1%. This study demonstrates the potential of SVM and other machine learning models to enhance early breast cancer detection, which could significantly improve patient survival rates. The findings highlight the critical role of machine learning in advancing medical diagnostics and improving healthcare outcomes. © 2025 IEEE.

关键词： AWS Sage-Maker Breast cancer diagnosis classification and regression trees k-Nearest Neighbors Support Vector Machines

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Pseudo-optimal five-level DCC modulation based on machine learning

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 158卷

作者： Montero-Robina, Pablo Gordillo, Francisco Gomez-Estern, Fabio Cuesta, Federico Autel Iberia SL Barcelona Spain Univ Seville Escuela Tecn Super Ingn Seville Spain Univ Loyola Andalucia Escuela Tecn Super Ingn Seville Spain

This paper presents a method for the control design of five-level DCC converters based on mixed-integer optimization and machine learning. The resulting controller is computationally simple and can be easily implemented on low-resource control hardware using simple nested "if-else"statements. The optimization problem is recalled from previous work by modifying the cost function to further enhance the dynamic performance. Additionally, and in contrast to previous works, the online implementation accomplished in this paper allows the system to cover a wider range of operating points. For this, the optimization problem is solved offline for several operating conditions, and the results are gathered into a dataset to train classification and regression trees (CARTs), which are later used online. Due to the generalization capability of the CARTs, a more flexible and less resource-intensive implementation is achieved which is capable of operating at points outside the ones considered in the training dataset. The resulting control strategy is compared in simulation and experiments with several alternative approaches found in the literature. This approach can be extended to other power converter topologies, allowing the implementation of optimized modulations.

关键词： classification and regression trees Diode-clamped converter Mixed-integer linear optimization Multilevel converter

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Spatio-temporal classification of land use and land cover and its changes in Kerala using remote sensing and machine learning approach

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2024年第5期196卷 459-459页

作者： Vijay, Anjali Varija, K. Natl Inst Technol Karnataka Dept Water Resources & Ocean Engn Mangalore 575025 India

Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform. RF and CART are versatile machine learning algorithms frequently employed for classification and regression, offering effective tools for predictive modelling across diverse domains due to their flexibility and data-handling capabilities. Normalised Difference Vegetation Index (NDVI), Normalised Differences Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), and Bare soil index (BSI) are integral indices utilised to enhance the precision of land use and land cover classification in satellite imagery, playing a crucial role by providing valuable insights into specific landscape attributes that may be challenging to identify using individual spectral bands alone. The results showed that the performance of RF is better than that of CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values point out the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren land. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted the area of the barren class, as well as the water class, decreased steadily from

关键词： Land use and land cover Google Earth Engine classification and regression trees Random forest NDVI NDBI MNDWI BSI Kerala

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Synthetic Individual Income Tax Data: Methodology, Utility, and Privacy Implications

Synthetic Individual Income Tax Data: Methodology, Utility, ...

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International Conference on Privacy in Statistical Databases (PSD)

作者： Bowen, Claire McKay Bryant, Victoria Burman, Leonard Czajka, John Khitatrakun, Surachai MacDonald, Graham McClelland, Robert Mucciolo, Livia Pickens, Madeline Ueyama, Kyle Williams, Aaron R. Wissoker, Doug Zwiefel, Noah Urban Inst Washington DC 20024 USA Internal Revenue Serv Washington DC 20002 USA Coiled New York NY 10018 USA UCL London England

ISBN: (纸本)9783031139451;9783031139444

The United States Internal Revenue Service Statistics of Income (SOI) Division possesses invaluable administrative tax data from individual income tax returns that could vastly expand our understanding of how tax policies affect behavior and how those policies could be made more effective. However, only a small number of government analysts and researchers can access the raw data. The public use file (PUF) that SOI has produced for more than 60 years has become increasingly difficult to protect using traditional statistical disclosure control methods. The vast amount of personal information available in public and private databases combined with enormous computational power create unprecedented disclosure risks. SOI and researchers at the Urban Institute are developing synthetic data that represent the statistical properties of the administrative data without revealing any individual taxpayer information. This paper presents quality estimates of the first fully synthetic PUF and shows how it performs in tax model microsimulations as compared with the PUF and the confidential administrative data.

关键词： Disclosure control Synthetic data Utility classification and regression trees

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Do young people really engage in sustainable behaviors in their lifestyles?

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SOCIAL INDICATORS RESEARCH 2022年第3期163卷 1467-1485页

作者： Piscitelli, Alfonso D'Uggento, Angela Maria Univ Naples Federico II Dept Agr Sci Naples Italy Univ Bari Aldo Moro Dept Econ & Finance Bari Italy

In recent years, environmental problems have become a serious issue worldwide due to the increasing damage caused by climate change. People's environmental awareness has grown, and public opinion is now demanding effective action from governments. Young people around the world are playing an important role in this, with the Fridays For Future movement, calling on policymakers to make environmental protection one of their political priorities. Through a survey of 1,975 high school students, this paper aims to contribute to the study of young people's sustainable behaviors and their awareness to take effective action against environmental degradation, to explore their concerns and opinions about environmental issues, and to find out what ecological practices they are willing to adopt in their daily lives. Data analysis is conducted using tree-based methods to examine the sustainable behaviors and define the key practices that constitute them. The results of classification tree show that sustainable behaviors impact lifestyles, whether through less demanding actions such as turning off the faucet or appliances, or willingness to work as a volunteer, among others. The Random Forest provides us with a ranked list of sustainable behaviors that young people engage in to reduce and stop environmental degradation. The results of this study may be of interest to policy makers who need to plan educational pathways for students from elementary school to university, as environmental culture must be a cornerstone of our society.

关键词： Environmental problems Sustainable behaviors classification and regression trees Random forest

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Intrinsic versus Extrinsic Drivers: An Investigation into Young People's Motives to Engage in Under-Declared Employment and Undeclared Work

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EASTERN EUROPEAN ECONOMICS 2022年第6期60卷 559-577页

作者： Ianole-Calin, Rodica Horodnic, Ioana Alexandra Plopeanu, Aurelian-Petrus Univ Bucharest Fac Business & Adm Dept Appl Econ & Quantitat Anal Bucharest Romania Alexandru Ioan Cuza Univ Fac Econ & Business Adm Dept Management Mkt & Business Adm Iasi Romania Alexandru Ioan Cuza Univ Inst Interdisciplinary Res Dept Social Sci & Humanities Iasi Romania

The paper aims to deepen understanding of the reasons for participation in undeclared work and under-declared employment. Alongside the main theories used so far in the field, self-determination theory is introduced by investigating the role of internalized and extrinsic motivation. Logistic regression analysis and classification and regression trees are used on a sample of 1,059 Romanian youths. The results emphasize the central role of tax morale, in generating intolerance toward informal practices and internalized motivations for compliance. In addition, peer influence shapes the decision to adopt informal practices whereas deterrent measures have no significant impact.

关键词： Undeclared work under-declared employment Romanian youths self-determination theory (SDT) intrinsic motivation extrinsic motivation classification and regression trees

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Quantitative structure-activity relationship analysis of human neutrophil elastase inhibitors using shuffling classification and regression trees and adaptive neuro-fuzzy inference systems

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SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2012年第5-6期23卷 505-520页

作者： Asadollahi-Baboli, M. Babol Univ Technol Dept Sci Babol Sar Iran

The purpose of this study was to develop quantitative structure-activity relationship models for N-benzoylindazole derivatives as inhibitors of human neutrophil elastase. These models were developed with the aid of classification and regression trees (CART) and an adaptive neuro-fuzzy inference system (ANFIS) combined with a shuffling cross-validation technique using interpretable descriptors. More than one hundred meaningful descriptors, representing various structural characteristics for all 51 N-benzoylindazole derivatives in the data set, were calculated and used as the original variables for shuffling CART modelling. Five descriptors of average Wiener index, Kier benzene-likeliness index, subpolarity parameter, average shape profile index of order 2 and folding degree index selected by the shuffling CART technique have been used as inputs of the ANFIS for prediction of inhibition behaviour of N-benzoylindazole derivatives. The results of the developed shuffling CART-ANFIS model compared to other techniques, such as genetic algorithm (GA)-partial least square (PLS)-ANFIS and stepwise multiple linear regression (MLR)-ANFIS, are promising and descriptive. The satisfactory results (r(p)(2) = 0.845, Q(LOO)(2) = 0.861, r(L25%O)(2) = 0.829, RMSELOO = 0.305 and RMSEL25%O = 0.336) demonstrate that shuffling CART-ANFIS models present the relationship between human neutrophil elastase inhibitor activity and molecular descriptors, and they yield predictions in excellent agreement with the experimental values.

关键词： human neutrophil elastase N-benzoylindazole derivatives classification and regression trees adaptive neuro-fuzzy inference systems cross-validation techniques

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Characterization of Drug Resistance in Chronic Myeloid Leukemia Cells Based on Laser Tweezers Raman Spectroscopy

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APPLIED SPECTROSCOPY 2021年第10期75卷 1296-1304页

作者： Zhang, Qian Ye, Minlu Wang, Lingyan Jiang, Dongmei Yao, Shuting Lin, Donghong Chen, Yang Feng, Shangyuan Yang, Ting Hu, Jianda Fujian Med Univ Dept Lab Med Fuzhou Peoples R China Fujian Med Univ Fujian Inst Hematol Dept Hematol Fujian Prov Key Lab HematolUnion Hosp Fuzhou Peoples R China Fujian Med Univ Dept Med Imaging Technol Fuzhou Peoples R China Fujian Normal Univ Key Lab Optoelect Sci & Technol Med Fujian Prov Key Lab Photon Technol Minist Educ Fuzhou Peoples R China

Multidrug resistance is highly associated with poor prognosis of chronic myeloid leukemia. This work aims to explore whether the laser tweezers Raman spectroscopy (LTRS) could be practical in separating adriamycin-resistant chronic myeloid leukemia cells K562/adriamycin from its parental cells K562, and to explore the potential mechanisms. Detection of LTRS initially reflected the spectral differences caused by chemoresistance including bands assigned to carbohydrates, amino acid, protein, lipids, and nucleic acid. In addition, principal components analysis as well as the classification and regression trees algorithms showed that the specificity and sensitivity were above 90%. Moreover, the band data-based classification and regression tree model and receiver operating characteristic curve further determined some important bands and band intensity ratios to be reliable indexes in discriminating K562 chemoresistance status. Finally, we highlighted three metabolism pathways correlated with chemoresistance. This work demonstrates that the label-free LTRS analysis combined with multivariate statistical analyses have great potential to be a novel analytical strategy at the single-cell level for rapid evaluation of the chemoresistance status of K562 cells.

关键词： Chronic myeloid leukemia CML multidrug resistance K562 principal component analysis PCA classification and regression trees CRT laser tweezers Raman spectroscopy LTRS

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Using Diverse Data Sources to Impute Missing Air Quality Data Collected in a Resource-Limited Setting

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ATMOSPHERE 2024年第3期15卷 303页

作者： Kebalepile, Moses Mogakolodi Dzikiti, Loveness Nyaradzo Voyi, Kuku Univ Witwatersrand Sch Clin Med ZA-2050 Johannesburg South Africa Univ Pretoria Sch Hlth Syst & Publ Hlth ZA-0007 Pretoria South Africa Ross Univ Sch Vet Med POB 334 Basseterre St Kitts & Nevi

The sustainable operation of ambient air quality monitoring stations in developing countries is not always possible. Intermittent failures and breakdowns at air quality monitoring stations often affect the continuous measurement of data as required. These failures and breakdowns result in missing data. This study aimed to impute NO2, SO2, O3, and PM 10 to produce complete data sets of daily average exposures from 2010 to 2017. Models were built for (a) an individual pollutant at a monitoring station, (b) a combined model for the same pollutant from different stations, and (c) a data set with all the pollutants from all the monitoring stations. This study sought to evaluate the efficacy of the Multiple Imputation by Chain Equations (MICE) algorithm in successfully imputing air quality data that are missing at random. The application of classification and regression trees (CART) analysis using the MICE package in the R statistical programming language was compared with the predictive mean matching (PMM) method. The CART method performed better, with the pooled R-squared statistics of the imputed data ranging from 0.3 to 0.7, compared to a range of 0.02 to 0.25 for PMM. The MICE algorithm successfully resolved the incompleteness of the data. It was concluded that the CART method produced better reliable data than the PMM method. However, in this study, the pooled R2 values were accurate for NO2, but not so much for other pollutants.

关键词： MICE imputation air quality missing data classification and regression trees

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