Using Cross-Validation to Evaluate CERES-Maize Yield Simulations within a Decision Support System for Precision Agriculture

2007 ◽  
Vol 50 (4) ◽  
pp. 1467-1479 ◽  
Author(s):  
K. R. Thorp ◽  
W. D. Batchelor ◽  
J. O. Paz ◽  
A. L. Kaleita ◽  
K. C. DeJonge
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Precision Agriculture ◽  
Cross Validation ◽  
Maize Yield

scholarly journals A Cotton High-Efficiency Water-Fertilizer Control System Using Wireless Sensor Network for Precision Agriculture

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1693
Author(s):  
Chanchan Du ◽  
Lixin Zhang ◽  
Xiao Ma ◽  
Xiaokang Lou ◽  
Yongchao Shan ◽  
...  
Keyword(s):  
Control System ◽  
Decision Support ◽  
Wireless Sensor Network ◽  
Decision Support System ◽  
Sensor Network ◽  
Support System ◽  
Precision Agriculture ◽  
High Efficiency ◽  
Wireless Sensor ◽  
Sensors And Actuators

Scientific researchers have applied newly developed technologies, such as sensors and actuators, to different fields, including environmental monitoring, traffic management, and precision agriculture. Using agricultural technology to assist crop fertilization is an important research innovation that can not only reduce the workload of farmers, but also reduce resource waste and soil pollution. This paper describes the design and development of a water-fertilizer control system based on the soil conductivity threshold. The system uses a low-cost wireless sensor network as a data collection and transmission tool and transmits the data to the decision support system. The decision support system considers the change in soil electrical conductivity (EC) and moisture content to guide the application of water-fertilizer, and then improves the fertilization accuracy of the water-fertilizer control system. In the experiment, the proposed water-fertilizer control system was tested, and it was concluded that, compared with the existing traditional water-fertilizer integration control system, the amount of fertilizer used by the system was reduced by 10.89% on average, and it could save 0.76–0.87 tons of fertilizer throughout the whole growth period of cotton.

scholarly journals A COVID-19 Risk Assessment Decision Support System for General Practitioners: Design and Development Study (Preprint)

2020 ◽  
Author(s):  
Ying Liu ◽  
Zhixiao Wang ◽  
Jingjing Ren ◽  
Yu Tian ◽  
Min Zhou ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Decision Support System ◽  
Risk Stratification ◽  
Support System ◽  
Cross Validation ◽  
Mobile Terminal ◽  
Dynamic Risk ◽  
Risk Stratification Model ◽  
Fold Cross Validation

BACKGROUND The coronavirus disease (COVID-19) has become an urgent and serious global public health crisis. Community engagement is the first line of defense in the fight against infectious diseases, and general practitioners (GPs) play an important role in it. GPs are facing unique challenges from disasters and pandemics in delivering health care. However, there is still no suitable mobile management system that can help GPs collect data, dynamically assess risks, and effectively triage or follow-up with patients with COVID-19. OBJECTIVE The aim of this study is to design, develop, and deploy a mobile-based decision support system for COVID-19 (DDC19) to assist GPs in collecting data, assessing risk, triaging, managing, and following up with patients during the COVID-19 outbreak. METHODS Based on the actual scenarios and the process of patients using health care, we analyzed the key issues that need to be solved and designed the main business flowchart of DDC19. We then constructed a COVID-19 dynamic risk stratification model with high recall and clinical interpretability, which was based on a multiclass logistic regression algorithm. Finally, through a 10-fold cross-validation to quantitatively evaluate the risk stratification ability of the model, a total of 2243 clinical data consisting of 36 dimension clinical features from fever clinics were used for training and evaluation of the model. RESULTS DDC19 is composed of three parts: mobile terminal apps for the patient-end and GP-end, and the database system. All mobile terminal devices were wirelessly connected to the back end data center to implement request sending and data transmission. We used low risk, moderate risk, and high risk as labels, and adopted a 10-fold cross-validation method to evaluate and test the COVID-19 dynamic risk stratification model in different scenarios (different dimensions of personal clinical data accessible at an earlier stage). The data set dimensions were (2243, 15) when only using the data of patients’ demographic information, clinical symptoms, and contact history; (2243, 35) when the results of blood tests were added; and (2243, 36) after obtaining the computed tomography imaging results of the patient. The average value of the three classification results of the macro–area under the curve were all above 0.71 in each scenario. CONCLUSIONS DCC19 is a mobile decision support system designed and developed to assist GPs in providing dynamic risk assessments for patients with suspected COVID-19 during the outbreak, and the model had a good ability to predict risk levels in any scenario it covered.

scholarly journals Morphologically constrained adaptive signal decompositions in studying ventricular arrhythmias

2021 ◽  
Author(s):  
Krishnanand Balasundaram
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Event Detection ◽  
Cross Validation ◽  
Cardiac Tissue ◽  
Clinical Events ◽  
Signal Features ◽  
Physiological Properties ◽  
Time Specific

Ventricular fibrillation (VF) is one of the major causes for sudden cardiac deaths (SCD). The duration from the onset of VF to SCD is a few minutes, making it difficult to study VF. This dissertation proposes methods to extract meaningful information from VF electrograms and formulate associations to underlying structural and physiological properties of the cardiac tissue and clinical events of interest during VF. This was achieved by analyzing clues in the electrograms during VF to infer the underlying anatomical and physiological properties of the cardiac tissue and certain clinical events of interest, which is otherwise not easily available. The proposed methods will be of great assistance for the diagnosis and treatment planning of cardiac arrhythmias. The proposed adaptive time-frequency (TF) signal decomposition was separated into two categories based on two purposes: (1) Time-specific event detection and (2) Time-averaged VA characterization. For the time-specific event detection (in this work rotor detection), electrogram signal features related to the rotor event were identified with an adaptive TF decomposition and amodified criterion function. Using the proposed features and a linear discriminant analysis based classifier with leave-one-out cross validation, overall classification accuracies of 80.77% and 79.41% were achieved in detecting rotor events and separating them from similar but non-rotor events. In the time-averaged ventricular arrhythmia characterization, previously established signal features were used to associate electrogram clues to the structural and physiological characteristics of the cardiac tissue. Using label-consistent K-means singular value decomposition dictionary learning process, dictionaries of TF basis functions were generated to capture specific electric structures and physiological characteristics of the underlying cardiac tissue. The association of these characteristics with the extracted electrogram clues were validated using a cross-validation technique. The cross-validated results ranged from 65.58% to 81.80% for the 7 characteristics used in this study. Further to this, to build a decision-support system with non-linear separable capabilities that could automate and infer the heart events and/or characteristics from the identified electrogram signal structures, neural network models were generated. The cross-validated accuracies ranged from 66.99% to 85.90% for each of the developed models for the decision-support system.

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