Geostatistical and Data Segmentation Approaches for Determining Yield Monitoring System Delay Time

e17529 Background: One of the most important reasons of BC mortality is delay in treatment. Due to lack of BC awareness and organized screening programs, total delay time (from first symptoms of BC to initiation of treatment; TDT) is longer in low-middle income countries. The primary goal of this survey was to identify factors affecting TDT in patients with BC. Methods: As a part of previously presented multinational survey, a total of 1.031 BC patients from 12 cities of 5 districts in Turkey were surveyed using a uniform questionnaire. TDT was determined using 8 individual scales, including one pertaining to patient delay and 7 related to subsequent steps in a typical diagnostic process. Regression models were constructed using 17 variables concerning diverse contextual and personal patient characteristics. Time between first symptom and first medical visit (Patient Delay Time; PDT) and time between first medical visit and start of therapy (System Delay Time; SDT) were modeled separately with multilevel regression. Results: Mean PDT, SDT and TDT were 4.8, 10.5 and 13.8 weeks respectively, with 42% of the patients with a delay of >12 weeks. Multilevel regression equation indicated that disregard of discovered symptoms (p<0.001) and having at least secondary level of education (p=0.021) were significantly correlated with longer PDT. Patients with stronger self-examination habits (p=0.009), reporting more support from friends and family (p<0.001) and living in metropolitan areas (>500k) (p=0.006) had shorter PDT. Predictors of longer SDT included disregard (p<0.001) and having a PDT>4 weeks (p<0.001). Shorter SDT was correlated to being older than 60 years of age (p=0.027). Results revealed that diagnosis during periodic check-up or opportunistic mammography vs. symptomatic BC (p<0.001) and having first medical examination by a surgeon resulted in shorter SDT. Conclusions: TDT in Turkey is unacceptably long and system delay accounted for a substantial part of the total delay experienced by breast cancer patients. This points to a need for shortening clinical pathways if possible. A long patient delay calls for research into patient awareness of BC.

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Multi sensor data and temporal image fusion cross validation technique for Agri yield monitoring system

10.21203/rs.3.rs-943821/v1 ◽

2021 ◽

Author(s):

Swati V. Shinde ◽

Rajveer Shastri ◽

Atul Kumar Dwivedi ◽

Anandakumar Haldorai ◽

Varsha Sahni ◽

...

Keyword(s):

Remote Sensing ◽

Monitoring System ◽

Cross Validation ◽

Sensor Data ◽

Temporal Data ◽

Multiple Sensors ◽

Multi Sensor Data Fusion ◽

Validation Technique ◽

The Cross ◽

Yield Monitoring

Abstract In recent years, the diverse application in various disciplines and the versatility has gained a huge interest for the researchers to research on the multi-sensor data fusion technology. The remote sensing process involves the measurement and recording of the data from a scene. Thus, the remote sensing systems are known to be a powerful tool as they help in the earth's atmosphere and surface monitor at different scales. The remote sensing of the data faces a serious challenge as the data captured by the multiple sensors are heterogeneous. This affects the efficient processing and the effectiveness of the data that is being sensed. Thus, the increase in the diversity in data increases the ancillary datasets. These multimodal datasets are used jointly to improve the processing performance as per the application requirement. Initially, the fusion of the temporal data with the backscattered/temporal data is possible from the data retrieved from remote sensing. Many researchers made several types of research on fusing the multi-temporal and multimodal data and gave different ideas for a different type of researchers. This paper presents the cross-validation technique for monitoring the yield. This monitoring system is developed by fusing the multi-sensor data and the temporal images. This fusion is performed, and the performance of the yield monitoring system is analyzed from the results obtained. By using the cross-validation technique, the efficiency of the system is found to be improved.

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