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A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2018年 144卷 325-340页

作者： Teluguntla, Pardhasaradhi Thenkabail, Prasad S. Oliphant, Adam Xiong, Jun Gumma, Murali Krishna Congalton, Russell G. Yadav, Kamini Huete, Alfredo US Geol Survey 2255 N Gemini Dr Flagstaff AZ 86001 USA BAERI NASA Res Pk Moffett Field CA 94035 USA Int Crops Res Inst Semi Arid Trop Hyderabad Telangana India Univ New Hampshire Dept Nat Resources & Environm 56 Coll Rd Durham NH 03824 USA Univ Technol Sydney POB 123 Broadway NSW Australia

Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013-2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4-6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32-48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was

关键词： Cropland mapping Landsat machine learning algorithm Random forest Google Earth Engine Australia China

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A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section

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BMC ANESTHESIOLOGY 2021年第1期21卷 116-116页

作者： Wei, Chang-na Wang, Li-ying Chang, Xiang-yang Zhou, Qing-he Jiaxing Univ Affiliated Women & Children Hosp Dept Anesthesia Jiaxing Zhejiang Peoples R China Jiaxing Univ Affiliated Hosp Dept Anesthesia Jiaxing Zhejiang Peoples R China

Background The intrathecal hyperbaric bupivacaine dosage for cesarean section is difficult to predetermine. This study aimed to develop a decision-support model using a machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose based on physical variables during cesarean section. Methods Term parturients presenting for elective cesarean section under spinal anaesthesia were enrolled. Spinal anesthesia was performed at the L3/4 interspace with 0.5% hyperbaric bupivacaine at dosages determined by the anesthesiologist. A spinal spread level between T4-T6 was considered the appropriate block level. We used a machine-learning algorithm to identify relevant parameters. The dataset was split into derivation (80%) and validation (20%) cohorts. A decision-support model was developed for obtaining the regression equation between optimized intrathecal 0.5% hyperbaric bupivacaine volume and physical variables. Results A total of 684 parturients were included, of whom 516 (75.44%) and 168 (24.56%) had block levels between T4 and T6, and less than T6 or higher than T4, respectively. The appropriate block level rate was 75.44%, with the mean bupivacaine volume [1.965, 95%CI (1.945,1.984)]ml. In lasso regression, based on the principle of predicting a reasonable dose of intrathecal bupivacaine with fewer physical variables, the model is "Y=0.5922+ 0.055117* X-1-0.017599*X-2" (Y: bupivacaine volume;X-1: vertebral column length;X-2: abdominal girth), with lambda 0.055, MSE 0.0087, and R-2 0.807. Conclusions After applying a machine-learning algorithm, we developed a decision model with R-2 0.8070 and MSE due to error 0.0087 using abdominal girth and vertebral column length for predicting the optimized intrathecal 0.5% hyperbaric bupivacaine dosage during term cesarean sections.

关键词： machine learning algorithm Bupivacaine dosage Cesarean section Spinal anesthesia Physical variables

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A Comparative Study between machine learning algorithm and Artificial Intelligence Neural Network in Detecting Minor Bearing Fault of Induction Motors

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ENERGIES 2019年第11期12卷 2105页

作者： Pandarakone, Shrinathan Esakimuthu Mizuno, Yukio Nakamura, Hisahide Nagoya Inst Technol Dept Elect & Mech Engn Nagoya Aichi 4668555 Japan TOENEC Corp Res & Dev Div Nagoya Aichi 4570819 Japan

Most of the mechanical systems in industries are made to run through induction motors (IM). To maintain the performance of the IM, earlier detection of minor fault and continuous monitoring (CM) are required. Among IM faults, bearing faults are considered as indispensable because of its high probability incidence nature. CM mainly depends upon signal processing and fault detection techniques. In recent decades, various methods have been involved in detecting the bearing fault using machine learning (ML) algorithms. Additionally, the role of artificial intelligence (AI), a growing technology, has also been used in fault diagnosis of IM. Taking the necessity of minor fault detection and the detailed study about the role of ML and AI to detect the bearing fault, the present study is performed. A comprehensive study is conducted by considering various diagnosis methods from ML and AI for detecting a minor bearing fault (hole and scratch). This study helps in understanding the difference between the diagnosis approach and their effectiveness in detecting an IM bearing fault. It is accomplished through FFT (fast Fourier transform) analysis of the load current and the extracted features are used to train the algorithm. The application is extended by comparing the result of ML and AI, and then explaining the specific purpose of use.

关键词： induction motor minor bearing fault frequency spectrum analysis machine learning algorithm artificial intelligence

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Enhanced neonatal surgical site infection prediction model utilizing statistically and clinically significant variables in combination with a machine learning algorithm

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AMERICAN JOURNAL OF SURGERY 2018年第4期216卷 764-777页

作者： Bartz-Kurycki, Marisa A. Green, Charles Anderson, Kathryn T. Alder, Adam C. Bucher, Brian T. Cina, Robert A. Jamshidi, Ramin Russell, Robert T. Williams, Regan E. Tsao, KuoJen Univ Texas Hlth Sci Ctr Houston McGovern Med Sch 6431 Fannin St Houston TX 77030 USA Childrens Med Ctr 1935 Med Dist Dr Dallas TX 75235 USA Univ Utah Sch Med 30 N 1900 E Salt Lake City UT 84132 USA Med Univ South Carolina 180 Calhoun St Charleston SC 29401 USA Phoenix Childrens Hosp 1919 E Thomas Rd Phoenix AZ 85016 USA Univ Alabama Birmingham Childrens Hosp Alabama 1600 7th Ave S Birmingham AL 35233 USA Univ Tennessee Hlth Sci Ctr 910 Madison Ave Memphis TN 38163 USA

Background: machine-learning can elucidate complex relationships/provide insight to important variables for large datasets. This study aimed to develop an accurate model to predict neonatal surgical site infections (SSI) using different statistical methods. Methods: The 2012-2015 National Surgical Quality Improvement Program-Pediatric for neonates was utilized for development and validations models. The primary outcome was any SSI. Models included different algorithms: full multiple logistic regression (LR), a priori clinical LR, random forest classification (RFC), and a hybrid model (combination of clinical knowledge and significant variables from RF) to maximize predictive power. Results: 16,842 patients (median age 18 days, IQR 3-58) were included. 542 SSIs (4%) were identified. Agreement was observed for multiple covariates among significant variables between models. Area under the curve for each model was similar (full model 0.65, clinical model 0.67, RF 0.68, hybrid LR 0.67);however, the hybrid model utilized the fewest variables (18). Conclusions: The hybrid model had similar predictability as other models with fewer and more clinically relevant variables. machine-learning algorithms can identify important novel characteristics, which enhance clinical prediction models. (c) 2018 Elsevier Inc. All rights reserved.

关键词： Prediction model Surgical wound infection Infants machine learning algorithm Risk factors

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Big data processing with Apache Spark in university institutions: spark streaming and machine learning algorithm

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INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG learning 2019年第1-2期29卷 5-20页

作者： Boachie, Emmanuel Li, Chunlin Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430063 Hubei Peoples R China Kumasi Tech Univ Dept Comp Sci Box 854 Kumasi Ghana

Data processing is an effective tool for educational sector, which can improve admission selection procedures and decisions. Most research papers focus on computational and theoretical aspect of education though little effort have been put on technological aspect of applying data mining techniques on students admission process. We therefore design a simple spark streaming framework together with machine learning algorithm to guide admission processing. We implement the spark streaming model and the proposed machine learning algorithm in a selected university using its admissions' data. The focus is on the number of students that can be admitted and those that should be rejected to reduce time and cost. The case study we evaluated show the practical usefulness of Spark streaming and machine learning algorithm for data processing in a real-time to reduce time and cost. The experiment results also confirm meaningful graphical interpretation of data using spark streaming and machine learning algorithm for students' selection for admissions.

关键词： spark streaming big data processing machine learning algorithm

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Prediction of pathologic stage in non-small cell lung cancer using machine learning algorithm based on CT image feature analysis

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BMC CANCER 2019年第1期19卷 464-464页

作者： Yu, Lingming Tao, Guangyu Zhu, Lei Wang, Gang Li, Ziming Yi, Jianding Chen, Qunhui Shanghai Jiao Tong Univ Affiliated Chest Hosp Shanghai Chest Hosp Dept Radiol 241 Huaihai West Rd Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Affiliated Chest Hosp Shanghai Chest Hosp Dept Pathol Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Affiliated Chest Hosp Shanghai Chest Hosp Ctr Stat Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Affiliated Chest Hosp Shanghai Chest Hosp Ctr Lung Tumor Clin Med Shanghai 200030 Peoples R China

PurposeTo explore imaging biomarkers that can be used for diagnosis and prediction of pathologic stage in non-small cell lung cancer (NSCLC) using multiple machine learning algorithms based on CT image feature *** with stage IA to IV NSCLC were included, and the whole dataset was divided into training and testing sets and an external validation set. To tackle imbalanced datasets in NSCLC, we generated a new dataset and achieved equilibrium of class distribution by using SMOTE algorithm. The datasets were randomly split up into a training/testing set. We calculated the importance value of CT image features by means of mean decrease gini impurity generated by random forest algorithm and selected optimal features according to feature importance (mean decrease gini impurity >0.005). The performance of prediction model in training and testing sets were evaluated from the perspectives of classification accuracy, average precision (AP) score and precision-recall curve. The predictive accuracy of the model was externally validated using lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples from TCGA *** prediction model that incorporated nine image features exhibited a high classification accuracy, precision and recall scores in the training and testing sets. In the external validation, the predictive accuracy of the model in LUAD outperformed that in *** pathologic stage of patients with NSCLC can be accurately predicted based on CT image features, especially for LUAD. Our findings extend the application of machine learning algorithms in CT image feature prediction for pathologic staging and identify potential imaging biomarkers that can be used for diagnosis of pathologic stage in NSCLC patients.

关键词： Non-small cell lung cancer (NSCLC) Computed tomography (CT) Radiomics machine learning algorithm

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Implementation of machine learning algorithm on factors Effecting Divorce Rate

Implementation of Machine learning Algorithm on factors Effe...

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International Conference on Engineering and Emerging Technologies (ICEET)

作者： Sohail, Sana Aziz, Sadia Tahir, Faizan Haqqui, Sufyan Hussain, Ahsan Usman Inst Technol NEDUET Dept Elect Engn Karachi Pakistan Bahria Univ Dept Comp Sci Islamabad Pakistan Dadabhoy Inst Higher Educ Dept Comp Sci Karachi Pakistan

ISBN: (纸本)9781538632055

With the expanding number of marriages the quantity of detachment and separation are additionally expanding. The major factors that prompt separation can be numerous and there are numerous calculations in machine figuring out how to characterize the variables. The quantity of separation can be avoided and diminished on the off chance that we can have a decent information about the fundamental reasons and practices that can lead a connection to its end. In this paper we have taken a sample data set having some major attributes that causes divorce and comprehended it using 3 different classification algorithm and compared their outcomes. On the premise of their outcomes, we have concluded that which algorithm is more effective for the characterization of the data set in this application

关键词： machine learning algorithm Divorce Rate Artificial Intelligence Social Class

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Breast Cancer Diagnosis Using Adaptive Voting Ensemble machine learning algorithm

Breast Cancer Diagnosis Using Adaptive Voting Ensemble Machi...

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IEEMA IEEE Engineer Infinite Conference

作者： Khuriwal, Naresh Mishra, Nidhi Poornima Univ Jaipur Dept Comp Engn Jaipur Rajasthan India

ISBN: (纸本)9781538611388

According to Breast Cancer Institute (BCI), Breast Cancer is one of the most dangerous type of diseases that is very effective for women in the world. As per clinical expert detecting this cancer in its first stage helps in saving lives. As per *** offers individualized guides for more than 120 types of cancer and related hereditary syndromes. For detecting breast cancer mostly machine learning techniques are used. In this paper we proposed adaptive ensemble voting method for diagnosed breast cancer using Wisconsin Breast Cancer database. The aim of this work is to compare and explain how ANN and logistic algorithm provide better solution when its work with ensemble machine learning algorithms for diagnosing breast cancer even the variables are reduced. In this paper we used the Wisconsin Diagnosis Breast Cancer dataset. When compared to related work from the literature. It is shown that the ANN approach with logistic algorithm is achieved 98.50% accuracy from another machine learning algorithm.

关键词： Breast Cancer Neural Network Logistic machine learning algorithm WDBC Dataset

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Applications Research of machine learning algorithm in Translation System

Applications Research of Machine Learning Algorithm in Trans...

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2018 6th International Conference on machinery,Materials and Computing Technology(ICMMCT 2018)

作者： Lu Yang Da Chen Wenxue Wu School of Foreign Languages and Cultures Xihua University School of Science Xihua University

In recent years, machine translation has made outstanding achievements, but there are also the problems such as fuzzy rules, insufficient data and improper order in the machine translation practice, which makes it still unsatisfactory. machine learning can abstract learning features, and establish complex mapping relationship between input and output signals, and effectively improve the problems existing in the translation system. The concepts and common models of machine learning and deep learning are given in this paper. Based on the neural model, the paper analyses the rules, structure and models of machine translation system. According to the requirement of the translation system, this paper develops a translation system based on machine learning, and gives the design process of the pre-processing module, the coding module, the attention module and the decoding module. Practice has proved that the system has a good performance in translation performance.

关键词： machine learning algorithm Deep learning Translation system

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Chinese News Text Classification Based on machine learning algorithm

Chinese News Text Classification Based on Machine learning a...

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International Conference on Intelligent Human-machine Systems and Cybernetics

作者： Fang Miao Pu Zhang Libiao Jin Hongda Wu School of information engineering Communication University of China Beijing China

Text classification is the key technology for mining and organizing text information, which is the process of determining the text types automatically according to the content. Based on machine learning algorithm, text classification system includes four processes, namely text pretreatment, text representation, classifier training and classification. In this paper, a Chinese news text classification system model is designed. And in the classifier training part, we separately chose and compared K-nearest Neighbor, Naive Bayes, and Support Vector machine as our classification algorithm. Then, we tested and analyzed these classifiers with each other and finally got a conclusion. The experimental conclusion shows that the Chinese news text classification system can get satisfied results based on the machine learning algorithm.

关键词： Chinese text classification Chinese word segment Feature extraction machine learning algorithm

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