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Performance Evaluation of machine learning algorithms in Change Detection and Change Prediction of a Watershed's Land Use and Land Cover

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH 2023年第2期17卷 1-14页

作者： Mousavinezhad, Mirhossein Feizi, Atabak Aalipour, Mehdi Univ Mohaghegh Ardabili Fac Engn Dept Civil Engn Ardebil Iran Univ Tehran Fac Nat Resources Dept Environm Sci Tehran Iran

In recent years, owing to frequent natural and human-caused changes in land use and land cover (LULC), the need to use accurate methods in investigating LULC changes in the study area has gained double importance. In this article, the accuracy and performance of two methods of random forest (RF) and maximum likelihood (ML) were investigated in the classification of the images, determination of factors affecting changes, detection of change, and prediction of LULC. For this purpose, the Gorganrood Watershed was considered the study area. The accuracy assessment results of classified images demonstrated the RF method's superiority over ML. RF's kappa coefficient and overall accuracy achieved percentages of (75.31, 79.63%), (78.31, 82.14%), (88.92, 90.9%), and (75.74, 80%), while the ML rates were (54.7, 62.5%), (76.27, 80.35%), (78.79, 82.46%) and (64.69, 70.9%) for the years 1991, 2001, 2011, and 2021, respectively. According to both outputs of the transition potential algorithm, elevation and aspect had the most and least influence on LULC, respectively. Additionally, change detection of classified images using RF and ML revealed that urban fabric, grassland, and permanently irrigated land experienced an increase with percentages of (7.58, 2.52%), (67.75, 60.6%), (15.1, 7.29%). On the contrary, from 1991 to 2021, forest, non-irrigated arable land, and water bodies had reduction rates of (13.42, 12.02%), (75.79, 94.17%), and (56.94, 44.87%), respectively. The results of change prediction using RF also indicated that grassland, forest, non-irrigated arable land and water bodies would decrease by 15.03, 39.65, and 7%, 0.96% during 20 years from 2031 to 2051, whereas urban fabric and permanently irrigated land would increase 17.95, and 44.69%. Similarly, the predicted images using ML depicted that forest and non-irrigated arable land and urban fabric decreased by 14.2, 10.3, and 6.72%, while grassland, permanently irrigated land and water bodies increased by 27.88, 1.54

关键词： Change detection Change prediction Land use land cover CA-Markov machine learning algorithms Remote sensing

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Artificial intelligence-driven education evaluation and scoring: Comparative exploration of machine learning algorithms

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JOURNAL OF INTELLIGENT SYSTEMS 2024年第1期33卷

作者： Ma, Xiangfen Puyang Vocat & Tech Coll Fac Math & Informat Engn Puyang 457000 Peoples R China

With the widespread popularity of intelligent education, artificial intelligence plays an important role in the field of education. Currently, there are issues such as low accuracy and low adaptability. By comparing algorithms such as logistic regression, decision tree, random forest (RF), support vector machine, and long short-term memory (LSTM) recurrent neural network (RNN), this article adopted a multi-classification fusion strategy and fully considered the adaptability of the algorithm to evaluate and grade students in two scenarios with different grades and teaching quality. By encoding and normalizing student grades, six evaluation parameters were selected for the evaluation criteria of teaching quality through principal component analysis feature selection. Multi-classifier models were used to fuse the five models in pairs, improving the accuracy of the experimental evaluation. Finally, the experimental data of the six fused multi-classification models in the scenarios of student performance estimation and teaching quality estimation were compared, and the experimental effects of education evaluation and grading under different models were analyzed. The experimental results showed that the LSTM RNN-RF model had the strongest adaptability in the scenario of student performance estimation, with an estimation accuracy of 98.5%, which was 12.9% higher than a single RF model. This experiment was closely related to educational scenarios and fully considered the adaptability of different machine learning algorithms to different scenarios, improving the prediction and classification accuracy of the model.

关键词： artificial intelligence machine learning algorithms educational assessment and scoring multi-classifier models adaptability and accuracy

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Exploring machine learning algorithms to predict not using modern family planning methods among reproductive age women in East Africa

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BMC HEALTH SERVICES RESEARCH 2024年第1期24卷 1-12页

作者： Yehuala, Tirualem Zeleke Univ Gondar Inst Publ Hlth Coll Med & Hlth Sci Dept Hlth Informat Gondar Ethiopia

BackgroundThe use of the modern family planning method provides chances for women to reach optimal child spacing, increase quality of life, increase economic status, achieve the desired family size, and prevent unsafe abortions and maternal and perinatal deaths. However, use modern family planning is low among reproductive age women in East Africa. Therefore, this study aimed at predicting not using modern family planning and identifying its associated factors among reproductive age women in east Africa using machine learning *** data set was obtained from a demographic health survey in East Africa. This study used data on weighted sample of 92,564 women who were in their reproductive age. Python software was utilized for data processing, and machine learning models such as Random Forest (RF), Decision Tree (DT), XGB (Extreme Gradient Boosting), SVM (Support Victor machine), and K-Nearest Neighbor Decision Tree (DT) were implemented. In this work, we evaluated the predictive models' performance using performance assessment criteria such as accuracy, precision, recall, and the AUC *** this study, 92,564 reproductive women's was used in the final analysis. Among the proposed machine learning models, XGB performed best overall in the proposed classifier, with an accuracy of 98.7%, precision of 99.8%, recall of 98%, 99.9% AUC score, had high specificity 98%, sensitivity 99.8%, and low error rate discovery was 0.013%. The most significant determinants of not using modern family planning in East Africa: lack of education, age 25-29, living in a rural area, being single, not knowing how to use a contraceptive, and smoking *** Gradient Boosting proved effective in predicting not using modern family planning, offering valuable insights for interventions and policy decisions in East Africa. machine learning could help achieve early prediction and intervention on reproductive women's not using modern family planning. This leads

关键词： Family planning Prediction machine learning algorithms East Africa

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Big data and machine learning algorithms for health-care delivery (vol 20, pg e262, 2019)

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LANCET ONCOLOGY 2019年第6期20卷 E293-E293页

作者： Ngiam, K. Y. Khor, I. W. [a]Department of Surgery National University of Singapore Singapore [b]Department of Medicine Yong Loo Lin School of Medicine National University of Singapore Singapore [c]Division of General Surgery (Thyroid and Endocrine Surgery) University Surgical Cluster National University Hospital Singapore [d]National University Health System Corporate Office Singapore

Summary Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. However, to effectively use machine learning tools in health care, several limitations must be addressed and key issues considered, such as its clinical implementation and ethics in health-care delivery. Advantages of machine learning include flexibility and scalability compared with traditional biostatistical methods, which makes it deployable for many tasks, such as risk stratification, diagnosis and classification, and survival predictions. Another advantage of machine learning algorithms is the ability to analyse diverse data types (eg, demographic data, laboratory findings, imaging data, and doctors’ free-text notes) and incorporate them into predictions for disease risk, diagnosis, prognosis, and appropriate treatments. Despite these advantages, the application of machine learning in health-care delivery also presents unique challenges that require data pre-processing, model training, and refinement of the system with respect to the actual clinical problem. Also crucial are ethical considerations, which include medico-legal implications, doctors’ understanding of machine learning tools, and data privacy and security. In this Review, we discuss some of the benefits and challenges of big data and machine learning in health care.

关键词： machine learning algorithms big data

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Screening and Analysis of Core Genes for Osteoporosis Based on Bioinformatics Analysis and machine learning algorithms

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INDIAN JOURNAL OF ORTHOPAEDICS 2024年第7期58卷 944-954页

作者： Lu, Yongxia Wang, Wei Yang, Baiyuan Cao, Gui Du, Yue Liu, Jingyu Chengdu Seventh Peoples Hosp Dept Endocrinol & Metab Chengdu Peoples R China Chengdu Seventh Peoples Hosp Dept Neurol Chengdu Peoples R China

ObjectiveThis study aimed to identify osteoporosis-related core genes using bioinformatics analysis and machine learning *** expression profiles of osteoporosis patients were obtained from the Gene Expression Profiles (GEO) database, with GEO35958 and GEO84500 used as training sets, and GEO35957 and GSE56116 as validation sets. Differential gene expression analysis was performed using the R software "limma" package. A weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and modular genes of osteoporosis. Kyoto Gene and Genome Encyclopedia (KEGG), Gene Ontology (GO), and gene set enrichment analysis (GSEA) were performed on the differentially expressed genes. LASSO, SVM-RFE, and RF machine learning algorithms were used to screen for core genes, which were subsequently validated in the validation set. Predicted microRNAs (miRNAs) from the core genes were also analyzed, and differential miRNAs were validated using quantitative real-time PCR (qPCR) *** total of 1280 differentially expressed genes were identified. A disease key module and 215 module key genes were identified by WGCNA. Three core genes (ADAMTS5, COL10A1, KIAA0040) were screened by machine learning algorithms, and COL10A1 had high diagnostic value for osteoporosis. Four core miRNAs (has-miR-148a-3p, has-miR-195-3p, has-miR-148b-3p, has-miR-4531) were found by intersecting predicted miRNAs with differential miRNAs from the dataset (GSE64433, GSE74209). The qPCR experiments validated that the expression of has-miR-195-3p, has-miR-148b-3p, and has-miR-4531 was significantly increased in osteoporosis *** study demonstrated the utility of bioinformatics analysis and machine learning algorithms in identifying core genes associated with osteoporosis.

关键词： Bioinformatics machine learning algorithms Osteoporosis Core genes

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Predicting whole-life carbon emissions for buildings using different machine learning algorithms: A case study on typical residential properties in Cornwall, UK

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APPLIED ENERGY 2024年 357卷

作者： Zheng, Lin Mueller, Markus Luo, Chunbo Yan, Xiaoyu Univ Exeter Fac Environm Engn Dept Renewable Energy Grp Penryn TR10 9FE England Univ Exeter Environm & Sustainabil Inst Fac Environm Sci & Econ Penryn Campus Penryn TR10 9FE England Univ Exeter Fac Environm Sci & Econ Dept Earth & Environm Sci Penryn Campus Penryn TR10 9FE England Univ Exeter Inst Data Sci & Artificial Intelligence Exeter EX4 4RN England Univ Exeter Fac Environm Sci & Econ Dept Comp Sci Exeter EX4 4RN England Univ Exeter Penryn Campus Penryn TR10 9FE England

Whole-life carbon emissions (WLCE) studies are critical in assessing the environmental impact of buildings and promoting sustainable design practices. However, existing methods for estimating WLCE are time-consuming and data-intensive, limiting their usefulness in the early building design stages. In response to this, this research introduces a novel approach by harnessing various machine learning algorithms to predict WLCE and WLCE intensity (normalised by floor area) for buildings. To evaluate the suitability of machine learning algorithms, we conducted an experiment involving ten algorithms to build the prediction models. These models were trained using data from 150 typical residential properties in Cornwall, UK, along with 28 features obtained from a comprehensive survey, including floor area, heating type, and occupant characteristics. The ten algorithms include Multiple Linear Regression, and non-linear algorithms such as Decision Tree, Random Forest. Performance evaluation metrics, such as coefficient of determination (R2), mean absolute error (MAE), means squared error (MSE), root-mean-square error (RMSE), and elapsed time, were employed. Our research contributes to the field by showcasing the effectiveness of machine learning models in predicting building WLCE. We reveal that all the tested machine learning algorithms have the capability to predict WLCE and WLCE intensity, non-linear models outperform linear ones, and the Random Forest (RF) model demonstrates superior performance in terms of accuracy, stability, and efficiency. This research encourages the integration of life cycle studies into the early design stage, even within tight building design schedules, offering practical guidance to architects and designers. Furthermore, these results also benefit a wide range of stakeholders, not only the architects but also the engineers, policymakers, and life cycle assessment (LCA) researchers, contributing to the advancement of data-driven sustainability app

关键词： Whole life carbon emissions Life cycle thinking machine learning algorithms Sustainable building Carbon reduction Data -driven approaches

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Remote Diagnosis, Maintenance and Prognosis for Advanced Driver Assistance Systems Using machine learning algorithms

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SAE INTERNATIONAL JOURNAL OF PASSENGER CARS-ELECTRONIC AND ELECTRICAL SYSTEMS 2016年第1期9卷 114-122页

作者： Taie, Mostafa Anwar Moawad, Eman Magdy Diab, Mohammed ElHelw, Mohamed Nile Univ iSAQB Giza Egypt Nile Univ Giza Egypt

New challenges and complexities are continuously increasing in advanced driver assistance systems (ADAS) development (e.g. active safety, driver assistant and autonomous vehicle systems). Therefore, the health management of ADAS' components needs special improvements. Since software contribution in ADAS' development is increasing significantly, remote diagnosis and maintenance for ADAS become more important. Furthermore, it is highly recommended to predict the remaining useful life (RUL) for the prognosis of ADAS' safety critical components;e.g. (Ultrasonic, Cameras, Radar, LIDAR). This paper presents a remote diagnosis, maintenance and prognosis (RDMP) framework for ADAS, which can be used during development phase and mainly after production. An overview of RDMP framework's elements is explained to demonstrate how/when this framework is connected to database servers and remote analysis servers. Moreover, Sensors fusion is used in RDMP to detect some sensor failures and even to predict their RUL. Additionally, some well-known machine learning algorithms (MLA) are used to predict RUL of ADAS' components, and different types of input attributes to these MLA are proposed for some basic ADAS' components. MLA use training data set, which shall be constructed ideally from actual records reported remotely by RDMP (Prognosis Analysis and Self-learning System). However, initial dataset before production of the vehicle can be created from ADAS laboratory tests (e.g. Assessments on test tracks), ADAS simulation and theoretical analytical methods. Also, experiments of using the proposed RDMP in some ADAS' components (Sensor fusion and Braking system as ADAS actuator) are presented. Summary, conclusion with proven results and future work are explained.

关键词： Prognosis Driver Assistance Systems Remote diagnostics machine learning algorithms Sensor fusion advanced driver assistance systems (ADAS) Braking systems multispectral linear arrays

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A data-informed analytical approach to human-scale greenway planning: Integrating multi-sourced urban data with machine learning algorithms

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URBAN FORESTRY & URBAN GREENING 2020年 56卷 126871-126871页

作者： Tang, Ziyi Ye, Yu Jiang, Zhidian Fu, Chaowei Huang, Rong Yao, Dong Tongji Univ Coll Architecture & Urban Planning Shanghai Peoples R China Tongji Univ Joint Int Res Lab Ecourban Design Shanghai Peoples R China Shanghai Tongji Urban Planning & Design Inst Shanghai Peoples R China

Urban greenways have been recognized as an important strategy to improve human-scale quality in high-density built environments. Nevertheless, current greenway suitability analysis mainly focuses on geographical and natural issues, failing to account for human-scale urban design factors. Accordingly, this study proposes a data-informed approach to planning urban greenway networks using a combination of classical urban design theories, multi-sourced urban data, and machine learning algorithms. Maoming City in China was used as a case study. Per classical urban design theories, specifically, Cervero and Ewing's 5D variables, density, diversity, design, dimensions of destination accessibility, and distance-to-transit, were selected as key factors. A series of new urban data, including points of interest (PoIs), location-based service (LBS) positioning data, and street view images, were applied in conjunction with machine learning algorithms and geographical information system (GIS) tools to measure these key factors at a human-scale resolution and generate an optimized greenway suitability analysis. This analytical approach is an attempt to take human-scale concerns into account on a city-wide scale regarding greenway network generation. It also pushes the methodological boundaries of greenway planning by combining classical urban design thinking with new urban data and new techniques.

关键词： Greenway planning Multi-sourced urban data Data-informed urban design machine learning algorithms

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A Novel Automatic Image Intensity Analysis Using machine learning algorithms and Characterization of Tensile Fracture Surface of AA8011-B4C Nano-composite

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TRANSACTIONS OF THE INDIAN INSTITUTE OF METALS 2022年第11期75卷 2891-2903页

作者： Arun, J. Raj, T. G. Ansalam Roy, K. E. Reby Suresh, S. Prema, C. Emmy Marian Engn Coll Dept Mech Engn Trivandrum Kerala India Valia Koonambaikulathamma Coll Engn & Technol Dept Mech Engn Trivandrum Kerala India TKM Coll Engn & Technol Dept Mech Engn Kollam Kerala India Univ Coll Engn Dept Mech Engn Nagercoil Tamil Nadu India Bethlahem Inst Engn Dept Elect & Commun Engn Karungal 629157 Tamil Nadu India

The growths in e-mobility are the factor that impetus the research in lightweight material with improved mechanical properties, especially strength. The research concentrates on developing a novel metal matrix nano-composite of high-quality AA8011 aluminium alloy reinforced with varying weight percentages of nano-particles of B4C (0, 0.3, 0.6, 0.9, 1.2 and 1.5 wt%). The high-energy electromagnetic frequency stir casting technique was used to fabricate nano-composite, which enhanced the wetting of matrix and reinforcement. The rupture strength of nano-composite primarily hinges on the structure and formation of nano-composite. The morphology and distribution of nano-particles in metal matrix nano-composites (MMNC) were characterized by using Field Emission Scanning Electron Microscope. The yield strength, yield point, tensile strength, elongation, and reduction of area of MMNC under uniaxial tensile stresses were determined by Universal Testing machine. The machine learning algorithms automatically characterized the fractured material texture in conjunction with the described techniques. The unique textural information extracted from each fractured sample distinguished the surfaces as agglomeration, brittle and ductile texture. The fractography analysis of MMNCs revealed the transition of composite from cup and cone to cleavage fracture, which ensured the property change of matrix by the addition of nano-reinforced particles.

关键词： AA8011 Nano B4C Stir casting Tensile strength machine learning algorithms

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Forecasting drinking milk price based on economic, social, and environmental factors using machine learning algorithms

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AGRIBUSINESS 2023年第1期39卷 214-241页

作者： Atalan, Abdulkadir Gaziantep Islam Sci & Technol Univ Dept Ind Engn Gaziantep Turkey

The study aimed to describe and test machine learning (ML)-based algorithms to evaluate the unit price of drinking milk. The algorithms were applied to the data collected over 8 years in 2014 and 2021 related to the price of drinking milk in Turkey. The economic, social, and environmental factors that have an impact on the unit price of drinking milk were evaluated. Five ML algorithms, including random forest, gradient boosting, support vector machine (SVM), neural network, and AdaBoost algorithms, were utilized to predict the drinking milk unit price. ML also applied hyperparameter tuning with nested cross-validation to calculate the prediction accuracy for each algorithm. The results show that the random forest algorithm based on the features of the ML algorithms has the best performance, with the accuracy of 99.30% for training and 98.10% for testing the dataset. The average accuracy of gradient boosting, SVM, neural network, and AdaBoost are obtained as 97.30%, 96.15%, 95.65%, and 96.05%, respectively. Random forest performed best as the target variable with the lowest deviation values of mean squared error (MSE) (0.004), root mean square error (RMSE) (0.060), and mean absolute error (MAE) (0.029) in the training and MSE (0.009), RMSE (0.096), and MA (0.055) in the testing dataset. This study presents an interesting perspective with practical potential to adopt ML methods in the dairy industry. The developed ML algorithms can provide dairy investors and policymakers with important decision-support information. [EconLit Citations: C13, C53, L66, C88].

关键词： factors machine learning algorithms milk price prediction

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