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For patients with stroke at home, strategies have been formulated for emotional nursing, sports rehabilitation nursing, and interventions for poor lifestyle habits such as smoking, drinking, and picky eating. Data were obtained through tracking investigation, effect evaluation indexes were developed according to Hamilton depression scale (HAMD), activities of daily living (ADL) and other rating scale; C4.5 decision tree algorithm was used to analyze the effect of nursing intervention strategy, then we derived the corresponding knowledge rules. We come to conclusions: ① Effective emotional care and bad living habits interventions are contributed to reduce the risk of stroke. ② Smoking, drinking, picky eating, exercising and other factors are associated, so we should combine and intervene them as to better perfect the risk of stroke to provide decision-making reference for home nursing and rehabilitation intervention of stroke patients.

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Yuanyuan Chen ◽  
Xiufeng He ◽  
Jia Xu ◽  
Lin Guo ◽  
Yanyan Lu ◽  

PurposeAs one of the world's most productive ecosystems, ecological land plays an important role in regional and global environments. Utilizing advanced optical and synthetic aperture radar (SAR) data for land cover/land use research becomes increasingly popular. This research aims to investigate the complementarity of fully polarimetric SAR and optical imaging for ecological land classification in the eastern coastal area of China.Design/methodology/approachFour polarimetric decomposition methods, namely, H/Alpha, Yamaguchi3, VanZyl3 and Krogager, were applied to Advanced Land Observing Satellite (ALOS) SAR image for scattering parameter extraction. These parameters were merged with ALOS optical parameters for subsequent classification using the object-based quick, unbiased, efficient statistical tree decision tree method.FindingsThe experimental results indicate that an improved classification performance was obtained in the decision level when merging the two data sources. In fact, unlike classification using only optical images, the proposed approach allowed to distinguish ecological land with similar spectrum but different scattering. Moreover, unlike classification using only polarimetric information, the integration of polarimetric and optical data allows to accurately distinguish reed from artemisia and sand from salt field and therefore achieve a detailed classification of the coastal area characteristics.Originality/valueThis research proposed an integrated classification method for coastal ecological land with polarimetric SAR and optical data. The object-based and decision-level fusion enables effective ecological land classification in coastal area was verified.

2021 ◽  
Vol 7 (6) ◽  
Subhasmita Barad ◽  
ParathaSarathi Mishra ◽  
Pramod Chandra Sahu ◽  
Tanmay Sarkar ◽  
Mohamad Faiz Mohd Amin ◽  

2021 ◽  
Ayria Behdinian ◽  
Kamran Rezaie ◽  
Ali Bozorgi-Amiri

Abstract BackgroundEmployee health is an essential issue for Human Resource Management (HRM). The employees' health level is undeniably correlated to the job position in which they work since it may harm their well-being, and they may not be capable of performing their duties properly. Prompt diagnosis and resolution of employees' physical complications are highly critical.MethodsMachine learning (ML) is the state-of-the-art method potentially utilized to make early predictions to safeguard employees' healthiness. The technical laborers within the food manufacturing company are included in this Research. The functional classification models, namely, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree, are exploited to predict the employees' wellness for their vocation. K-fold Cross-Validation (KCV) and Confusion Matrix were applied in this study, the former for estimating the model's functionality and the latter for forecasting accuracy.ResultsAfter implementing four models on the 231 employees, the accuracy was extracted out, SVM with 78%, KNN with 78%, Decision Tree with 80%, and the highest for LR algorithm with 84%.ConclusionsIn this Research, the LR algorithm was opted to paving the way for Human Resources Managers in order to utilize a functional system to predict the Suitability of factory workers concerning their healthiness. The Hearing condition was picked out as a leading factor in selecting employees for their job position. Consequently, it is significant to planning a hearing conservation program for employees, especially those exposed to excessive noise.Trial Registration: Retrospectively registered.

2021 ◽  
Vol 8 ◽  
Robyn M. Samuel ◽  
Raissa Meyer ◽  
Pier Luigi Buttigieg ◽  
Neil Davies ◽  
Nicholas W. Jeffery ◽  

Biomolecular ocean observing and research is a rapidly evolving field that uses omics approaches to describe biodiversity at its foundational level, giving insight into the structure and function of marine ecosystems over time and space. It is an especially effective approach for investigating the marine microbiome. To mature marine microbiome research and operations within a global ocean biomolecular observing network (OBON) for the UN Decade of Ocean Science for Sustainable Development and beyond, research groups will need a system to effectively share, discover, and compare “omic” practices and protocols. While numerous informatic tools and standards exist, there is currently no global, publicly-supported platform specifically designed for sharing marine omics [or any omics] protocols across the entire value-chain from initiating a study to the publication and use of its results. Toward that goal, we propose the development of the Minimum Information for an Omic Protocol (MIOP), a community-developed guide of curated, standardized metadata tags and categories that will orient protocols in the value-chain for the facilitated, structured, and user-driven discovery of suitable protocol suites on the Ocean Best Practices System. Users can annotate their protocols with these tags, or use them as search criteria to find appropriate protocols. Implementing such a curated repository is an essential step toward establishing best practices. Sharing protocols and encouraging comparisons through this repository will be the first steps toward designing a decision tree to guide users to community endorsed best practices.

2021 ◽  
Vol 8 (1) ◽  
Heru Nugroho ◽  
Nugraha Priya Utama ◽  
Kridanto Surendro

AbstractA missing value is one of the factors that often cause incomplete data in almost all studies, even those that are well-designed and controlled. It can also decrease a study’s statistical power or result in inaccurate estimations and conclusions. Hence, data normalization and missing value handling are considered the major problems in the data pre-processing stage, while classification algorithms are adopted to handle numerical features. In cases where the observed data contained outliers, the missing value estimated results are sometimes unreliable or even differ greatly from the true values. Therefore, this study aims to propose the combination of normalization and outlier removals before imputing missing values on the class center-based firefly algorithm method (ON  +  C3FA). Moreover, some standard imputation techniques like mean, a random value, regression, as well as multiple imputation, KNN imputation, and decision tree (DT)-based missing value imputation were utilized as a comparison of the proposed method. Experimental results on the sonar dataset showed normalization and outlier removals effect in the methods. According to the proposed method (ON  +  C3FA), AUC, accuracy, F1-Score, Precision, Recall, and AUC-PR had 0.972, 0.906, 0.906, 0.908, 0.906, 0.61 respectively. The result showed combining normalization and outlier removals in C3-FA (ON  +  C3FA) was an efficient technique for obtaining actual data in handling missing values, and it also outperformed the previous studies methods with r and RMSE values of 0.935 and 0.02. Meanwhile, the Dks value obtained from this technique was 0.04, which indicated that it could maintain the values or distribution accuracy.

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6715
Yuequn Zhang ◽  
Lei Luo ◽  
Xu Ji ◽  
Yiyang Dai

Fault detection and diagnosis (FDD) has received considerable attention with the advent of big data. Many data-driven FDD procedures have been proposed, but most of them may not be accurate when data missing occurs. Therefore, this paper proposes an improved random forest (RF) based on decision paths, named DPRF, utilizing correction coefficients to compensate for the influence of incomplete data. In this DPRF model, intact training samples are firstly used to grow all the decision trees in the RF. Then, for each test sample that possibly contains missing values, the decision paths and the corresponding nodes importance scores are obtained, so that for each tree in the RF, the reliability score for the sample can be inferred. Thus, the prediction results of each decision tree for the sample will be assigned to certain reliability scores. The final prediction result is obtained according to the majority voting law, combining both the predicting results and the corresponding reliability scores. To prove the feasibility and effectiveness of the proposed method, the Tennessee Eastman (TE) process is tested. Compared with other FDD methods, the proposed DPRF model shows better performance on incomplete data.

Luis Vives ◽  
Gurpreet Singh Tuteja ◽  
A. Sai Manideep ◽  
Sonika Jindal ◽  
Navjot Sidhu ◽  

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2790
Abdul Hannan ◽  
Jagadeesh Anmala

The classification of stream waters using parameters such as fecal coliforms into the classes of body contact and recreation, fishing and boating, domestic utilization, and danger itself is a significant practical problem of water quality prediction worldwide. Various statistical and causal approaches are used routinely to solve the problem from a causal modeling perspective. However, a transparent process in the form of Decision Trees is used to shed more light on the structure of input variables such as climate and land use in predicting the stream water quality in the current paper. The Decision Tree algorithms such as classification and regression tree (CART), iterative dichotomiser (ID3), random forest (RF), and ensemble methods such as bagging and boosting are applied to predict and classify the unknown stream water quality behavior from the input variables. The variants of bagging and boosting have also been looked at for more effective modeling results. Although the Random Forest, Gradient Boosting, and Extremely Randomized Tree models have been found to yield consistent classification results, DTs with Adaptive Boosting and Bagging gave the best testing accuracies out of all the attempted modeling approaches for the classification of Fecal Coliforms in the Upper Green River watershed, Kentucky, USA. Separately, a discussion of the Decision Support System (DSS) that uses Decision Tree Classifier (DTC) is provided.

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6682
Md Abdullah Al Zubaer ◽  
Keshav Thapa ◽  
Sung-Hyun Yang

R peak detection is crucial in electrocardiogram (ECG) signal analysis to detect and diagnose cardiovascular diseases (CVDs). Herein, the dynamic mode selected energy (DMSE) and adaptive window sizing (AWS) algorithm are proposed for detecting R peaks with better efficiency. The DMSE algorithm adaptively separates the QRS components and all non-objective components from the ECG signal. Based on local peaks in QRS components, the AWS algorithm adaptively determines the Region of Interest (ROI). The Feature Extraction process computes the statistical properties of energy, frequency, and noise from each ROI. The Sequential Forward Selection (SFS) procedure is used to find the best subsets of features. Based on these characteristics, an ensemble of decision tree algorithms detects the R peaks. Finally, the R peak position on the initial ECG signal is adjusted using the R location correction (RLC) algorithm. The proposed method has an experimental accuracy of 99.94%, a sensitivity of 99.98%, positive predictability of 99.96%, and a detection error rate of 0.06%. Given the high efficiency in detection and fast processing speed, the proposed approach is ideal for intelligent medical and wearable devices in the diagnosis of CVDs.

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