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Comparative Assessment of Reference Evapotranspiration Estimation Using Conventional Method and machine learning algorithms in Four Climatic Regions

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PURE AND APPLIED GEOPHYSICS 2020年第9期177卷 4479-4508页

作者： Raza, Ali Shoaib, Muhammad Faiz, Muhammad Abrar Baig, Faisal Khan, Mudasser Muneer Ullah, Muhammad Kaleem Zubair, Muhammad Bahauddin Zakariya Univ Dept Agr Engn Multan Pakistan Northeast Agr Univ Coll Water Conservancy & Civil Engn Harbin Peoples R China Bahauddin Zakariya Univ Dept Civil Engn Multan Pakistan Univ Lahore Dept Civil Engn Lahore Pakistan NFC Inst Engn & Technol Dept Civil Engn Multan Pakistan

Reference evapotranspiration (ETo) is considered as an essential component in hydrological and agro meteorological processes. Its accurate estimation becomes an imperative in the planning and management of irrigation practices. ETo estimation also plays a vital role in improving the irrigation efficiency, water reuse and irrigation scheduling. The conventional physical model of Penmen Montieth (PM56) developed by Food and agriculture organization (FAO) has been recommended worldwide for ETo estimation. This model was firstly used in this study to determine ETo by using required meteorological data and obtained results used as referenced values. Afterward, five data machine learning algorithms/data driven models, support vector machine (SVM), multilayer perceptron (MLP), group method of data handling (GMDH), general regression neural network (GRNN) and cascade correlation neural network (CCNN), were applied to estimate ETo values. The climatic data of maximum and minimum temperatures, wind speed, average relative humidity and sunshine hours of six stations from Pakistan was used to train and test data driven model. Data driven models were also applied on other climatic stations without training data which lie in China, New Zealand and USA to further validate and investigate their performance. Comparison results indicated that model efficiency (ME) and correlation coefficient (r) of SVM were obtained (ranges: ME = 95-99%;r = 0.96-1) maximum for all the selected stations. Alternatively, model errors (RMSE = 0.016, MSE = 0.0001 & MAE = 0.08) for SVM were found minimum in comparison to GMDH, MLP, CCNN and GRNN. In addition, all data driven models show enough divergence from hyper arid to high humid climate except SVM which shows almost identical results for all the climatic zones in comparison to standard FAO-PM56 method. Finally, it can be concluded that SVM could be considered as a reliable alternative method for ETo estimation among data driven models.

关键词： Reference evapotranspiration machine learning algorithms cross validation climatic regions

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The predictive effect of different machine learning algorithms for pressure injuries in hospitalized patients: A network meta-analyses

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HELIYON 2022年第11期8卷 e11361页

作者： Qu, Chaoran Luo, Weixiang Zeng, Zhixiong Lin, Xiaoxu Gong, Xuemei Wang, Xiujuan Zhang, Yu Li, Yun Jinan Univ Shenzhen Peoples Hosp Dept Operating Room Clin Med Coll 2 Shenzhen 518020 Guangdong Peoples R China Southern Univ Sci & Technol Affiliated Hosp 1 Shenzhen 518020 Guangdong Peoples R China Jinan Univ Shenzhen Peoples Hosp Dept Nursing Dept Clin Med Coll 2 Shenzhen 518020 Guangdong Peoples R China Shenzhen Univ Shenzhen Peoples Hosp 2 Affiliated Hosp 1 Dept Operating Room Shenzhen 518040 Guangdong Peoples R China

Background: Pressure injury has always been a focus and difficulty of nursing. With the development of nursing informatization, a large amount of structured and unstructured data has been generated, and it is difficult for traditional methods to utilize these data. With the intersection of artificial intelligence and nursing, it has become a new trend to apply machine learning algorithms to build pressure injury prediction models to manage pressure injuries. However, there is no evidence on the effectiveness of the method and which of a large number of al-gorithms for machine learning is more applicable to pressure injuries. Objective: This review aims to systematically synthesize existing evidence to determine the effectiveness of applying machine learning algorithms for pressure injury management, to further evaluate and compare pressure injury prediction models constructed by numerous machine learning algorithms, and to derive evidence for the best algorithms for predicting and managing pressure injuries. Design: Systematic review and network meta-analysis. Methods: A systematic electronic search was conducted in the EBSCO, Embase, PubMed, and Web of Science databases. We included all retrospective diagnostic accuracy trials and prospective diagnostic accuracy trials constructing a predictive model by machine learning for pressure injuries up to December 2021. Two review authors independently selected relevant studies and extracted data using the Cochrane handbook for systematic reviews of diagnostic test accuracy. The network meta-analysis was conducted using statistical software R and STATA. The certainty of the evidence was rated using the QUADAS-2 tool. Result: Twenty-five clinical diagnostic trials with a total of 237397 participants were identified in this review. The results of our study revealed that pressure injury machine learning models can effectively predict these injuries. Combining the algorithms separately yields the main results: decision trees (

关键词： machine learning algorithms Network meta -analysis Wound management Information management Information technology

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Detection of pepper fusarium disease using machine learning algorithms based on spectral reflectance

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2020年 28卷

作者： Karadag, Kerim Tenekeci, Mehmet Emin Tasaltin, Ramazan Bilgili, Aysin Harran Univ Osmanbey Campus Elect Elect Engn Dept Sanliurfa Turkey Harran Univ Osmanbey Campus Comp Engn Dept Sanliurfa Turkey GAPTAEM Sanliurfa Turkey

The development of computerized automated diagnostic systems ensures more effective health screening in plants. In this way, the damage caused by diseases can be reduced by early detection. Light reflections from plant leaves are known to carry information about plant health. In the study, healthy and fusarium diseased peppers (capsicum annuum) was detected from the reflections obtained from the pepper leaves with the aid of spectroradiometer. Reflections were taken from four groups of pepper leaves (healthy, fusarium-diseased, mycorrhizal fungus, fusarium-diseased and mycorrhizal fungus) grown in a closed environment at wavelengths between 350 nm and 2500 nm. Pepper disease detection takes place in two stages. In the first step, the feature vector is obtained. In the second step, the feature vectors of the input data are classified. The feature vector consist of the coefficients of wavelet decomposition and the statistical values of these coefficients. Artificial Neural Networks (ANN), Naive Bayes (NB) and K-nearest Neighbor (KNN) were used for classification. In detection the health case of pepper, the average success rates of different classification algorithms for the first two groups (diseased and healthy peppers) were calculated as 100% for KNN, 97.5% for ANN and 90% for NB. Likewise, these rates for the classification of all groups were calculated as 100% for KNN, 88.125% for ANN and 82% for NB. Overall, the results have shown that leaf reflections can be successfully used in disease detection. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Pepper disease detection Spectral reflectance machine learning algorithms Wavelet Classification

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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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Undecorated GFET for determinations of heavy metal ions aided by machine learning algorithms

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TALANTA OPEN 2023年 7卷

作者： Hao, Tiantian Zhang, Rong Ren, Shuhui Jia, Yunfang Nankai Univ Coll Elect Informat & Opt Engn Tianjin Peoples R China Tianjin Univ Technol Sch Integrated Circuit Sci & Engn Tianjin Peoples R China

The Graphene field effect transistor (GFET) has become one of the most popular biochemical-sensing platforms. The sensitivity of traditional GFET sensor usually depends on functionalization of aptamers. Here, we reported a machine learning algorithms assisted heavy metal ions detecting method by using pristine GFET. In this work, GFETs are prepared by screen printing technology, which has the advantages of total solution processes and low costs. The graphene ink (G-ink) used in screen printing procedures of GFET is synthesized by combining liquid exfoliation with solvent exchange method. The FTIR and XPS show there are oxygen-containing groups on the graphene channel, which have different affinities for heavy metal ions and can be used to determine ions. Seven algorithms are used to establish and train prediction models by using the data of transfer curves as inputs and ions' types and concentrations as labels, the results indicate that the accuracy of AdaBoost (0.91) is the highest, which demonstrate this machine learning assisted GFET sensing platform can be used to determine metal ions' types and concentrations at the same time. Moreover, the complicated processes for bio-functionalizing GFET and data analysis are omitted, which is benefit to simplify the fabricating procedures, we believe it will be an attractive strategy to promote the development of GFET.

关键词： Graphene field effect transistor machine learning algorithms Graphene ink Screen printing Heavy metal ions

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Sex and age estimation with machine learning algorithms with parameters obtained from cone beam computed tomography images of maxillary first molar and canine teeth

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EGYPTIAN JOURNAL OF FORENSIC SCIENCES 2023年第1期13卷 27-27页

作者： Senol, Deniz Secgin, Yusuf Duman, Burak Suayip Toy, Seyma Oner, Zulal Duzce Univ Fac Med Dept Anat Duzce Turkiye Karabuk Univ Fac Med Dept Anat Karabuk Turkiye Inonu Univ Fac Dent Dept Oral Diag & Radiol Malatya Turkiye Izmir Bakircay Univ Fac Med Dept Anat Izmir Turkiye

BackgroundThe aim of this study is to obtain a highly accurate and objective sex and age estimation by using the parameters of maxillary molar and canine teeth obtained from cone beam computed tomography images in the input of machine learning algorithms. Cone beam computed tomography images of 240 people aged between 25 and 54 were randomly selected from the archive systems of the hospital and transferred to Horos Medikal. 3D curved multiplanar reconstruction was applied to these images and a 3D image was obtained. The resulting image was brought to the orthogonal plane and the measurements were made by superimposing *** results were grouped in four different age groups (25-30, 31-36, 37-49, 50-54) and recorded. As a result of our study, the highest accuracy rate was found as 0.81 in sex estimation with ADA Boost Classifier algorithm, while in age estimation, the highest accuracy rate was found as 0.84 between 25-30 and 31-36 age groups with random forest algorithm, as 0.74 between 25-30 and 37-49 age groups with random forest and ADA Boost Classifier algorithms and as 0.85 between 25-30 and 50-54 age groups with random forest *** study differs from other studies in two aspects;the first is the selection of a sensitive method such as cone beam computed tomography, and the second is the selection of machine learning algorithms. As a result of our study, the highest accuracy rate was found as 0.81 in sex estimation and as 0.85 in age estimation with parameters of maxillary canine and molar teeth.

关键词： Sex estimation Age estimation Cone beam computed tomography machine learning algorithms Maxillary canine and molar teeth

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Predicting breast cancer-specific survival in metaplastic breast cancer patients using machine learning algorithms

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Journal of Pathology Informatics 2023年 14卷 100329-100329页

作者： Feng, Yufan McGuire, Natasha Walton, Alexandra Fox, Stephen Papa, Antonella Lakhani, Sunil R. McCart Reed, Amy E. UQ Centre for Clinical Research Faculty of Medicine The University of Queensland Brisbane 4029 Australia Pathology Queensland The Royal Brisbane and Women's Hospital Brisbane 4029 Australia Peter MacCallum Cancer Centre and University of Melbourne Melbourne 3000 Australia Monash Biomedicine Discovery Institute Monash University Melbourne 3800 Australia

Metaplastic breast cancer (MpBC) is a rare and aggressive subtype of breast cancer, with data emerging on prognostic factors and survival prediction. This study aimed to develop machine learning models to predict breast cancer-specific survival (BCSS) in MpBC patients, utilizing a dataset of 160 patients with clinical, pathological, and biological variables. An in-depth variable selection process was carried out using gain ratio and correlation-based methods, resulting in 10 variables for model estimation. Five models (decision tree with bagging;logistic regression;multilayer perceptron;naïve Bayes;and, random forest algorithms) were evaluated using 10-fold cross-validation. Despite the constraints posed by the absence of therapeutic information, the random forest model exhibited the highest performance in predicting BCSS, with an ROC area of 0.808. This study emphasizes the potential of machine learning algorithms in predicting prognosis for complex and heterogeneous cancer subtypes using clinical datasets, and their potential to contribute to patient management. Further research that incorporates additional variables, such as treatment response, and more advanced machine learning techniques will likely enhance the predictive power of MpBC prognostic models. © 2023 The Authors

关键词： Breast cancer Breast cancer-specific survival (BCSS) machine learning algorithms Metaplastic breast cancer (MpBC) Predictive models Random forest

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An In₂O₃ Nanotubes based Gas Sensor Array combined with machine learning algorithms for Trimethylamine Detection

An In<sub>2</sub>O<sub>3</sub> Nanotubes based Gas Sensor Ar...

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16th IEEE International Conference on Nano/Micro Engineered and Molecular Systems (IEEE-NEMS)

作者： Ren, Wenjie Zhao, Changhui Liu, Yingming Wang, Fei Southern Univ Sci & Technol Sch Microelect Shenzhen 518055 Peoples R China

ISBN: (纸本)9780738124292

This work focuses on developing an electronic nose system with machine learning algorithm for detection of trimethylamine (TMA). Pure and Ga-doped In2O3 nanotubes are synthesized by a simple electrospinning method, and four kinds of gas sensors (pristine, 1% Ga, 10% Ga, and 20% Ga-doped In2O3) are fabricated to form a sensor array. Results show that the sensor array can classify TMA effectively from interference gases (xylene, ethanol, hydrogen sulfide) by a support vector machine (SVM) algorithm. Several algorithms, including radial basis function neural network (RBFNN), back propagation neural network (BPNN) and principal component analysis combined with linear regression (PCA-LR), are used to predict the concentration level of each gas. For TMA gas, the trained algorithms can predict its concentration with average relative errors of 1.22% for RBFNN, 2.5% for BPNN and 13.34% for PCA-LR. Furthermore, the binary mixtures of TMA and ethanol are measured and used to train the above algorithms, and the lowest average relative error of 1.74% is achieved in the case of RBFNN algorithm.

关键词： Support vector machines machine learning algorithms Radial basis function networks Nanotubes Interference Prediction algorithms Electronic noses

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Estimation of service length with the machine learning algorithms and neural networks for patients who receiving home health care

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EVALUATION AND PROGRAM PLANNING 2023年第1期100卷

作者： Mentes, Nurettin Cakmak, Mehmet Aziz Kurt, Mehmet Emin Inonu Univ Malatya Vocat Sch Malatya Turkiye Dicle Univ Fac Econ & Adm Sci Dept Hlth Management Diyarbakir Turkiye

The main purpose of the study is to develop an estimation model using machine learning algorithms and to ensure the effective and efficient implementation of home health care service planning in hospitals with these algorithms. The necessary approvals for the study were obtained. The data set was created by obtaining patient data (except for data such as Turkish Republic identification number) from 14 hospitals providing Home Health Care Services in the city of Diyarbakir. The data set was subjected to necessary pre-processing and descriptive statistics were applied. For the estimation model, Decision Tree, Random Forest and Multi-layer Perceptron Neural Network algorithms were used. It was found that the number of days of home health care service, which the patients received, varied depending on their age and gender. It was observed that the patients were generally in the disease groups that required Physiotherapy and Rehabilitation treatments. It was determined that the length of service for patients can be predicted with a high reliability rate (Multi-Layer Model Acc: 90.4%, Decision Tree Model Acc: 86.4%, Random Forest Model Acc: 88.5%) using machine learning algorithms. In the light of the findings and data patterns obtained in the study, it is thought that effective and efficient planning will be made in terms of health management. In addition, it is believed that estimating the average length of service for patients will contribute to strategic planning of human resources for health, and to reducing medical consumables, drugs and hospital expenses.

关键词： machine learning algorithms Home Health Care Services Estimation of Length of Service

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Automated lithological mapping by integrating spectral enhancement techniques and machine learning algorithms using AVIRIS-NG hyperspectral data in Gold-bearing granite-greenstone rocks in Hutti, India

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2020年 86卷

作者： Kumar, Chandan Chatterjee, Snehamoy Oommen, Thomas Guha, Arindam Michigan Technol Univ Geol & Min Engn & Sci 1400 Townsend Dr Houghton MI 49931 USA Indian Space Res Org Natl Remote Sensing Ctr Hyderabad India

In this study, we proposed an automated lithological mapping approach by using spectral enhancement techniques and machine learning algorithms (MLAs) using Airborne Visible Infrared Imaging Spectroradiometer-Next Generation (AVIRIS-NG) hyperspectral data in the greenstone belt of the Hutti area, India. We integrated spectral enhancement techniques such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformation and different MLAs for an accurate mapping of rock types. A conjugate utilization of conventional geological map and spectral enhancement products derived from ASTER data were used for the preparation of a high-resolution reference lithology map. Feature selection and extraction methods were applied on the AVIRIS-NG data to derive different input dataset such as (a) all spectral bands, (b) shortwave infrared bands, (c) Joint Mutual Information Maximization (JMIM) based optimum bands, and (d) optimum bands using PCA, to choose optimum input dataset for automated lithological mapping. The comparative analysis of different MLAs shows that the Support Vector machine (SVM) outperforms other machine learning (ML) models. The SVM achieved an Overall Accuracy (OA) and Kappa Coefficient (k) of 85.48% and 0.83, respectively, using JMIM based optimum bands. The JMIM based optimum bands were more suitable than other input datasets to classify most of the lithological units (i.e. metabasalt, amphibolite, granite, acidic intrusive and migmatite) within the study area. The sensitivity analysis performed in this study illustrates that the SVM is less sensitive to the number of samples and mislabeling in the model training than other MLAs. The obtained high-resolution classified map with accurate litho-contacts of amphibolite, metabasalt, and granite can be coupled with an alteration map of the area for targeting the potential zone of gold mineralization.

关键词： AVIRIS-NG Hyperspectral Lithological mapping machine learning algorithms SVM

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