scholarly journals Evaluation of Altitude Sensors for a Crop Spraying Drone

Drones ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 25 ◽  
Author(s):  
Matheus Hentschke ◽  
Edison Pignaton de Freitas ◽  
Carlos Hennig ◽  
Igor Girardi da Veiga
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Precision Agriculture ◽  
Data Extraction ◽  
Measurement Reliability ◽  
Extraction Techniques ◽  
Intrinsic Factors ◽  
Operation Environment ◽  
Aperture Control ◽  
Harsh Conditions

This work aims to study and compare different range finders applied to altitude sensing on a rotating wings UAV. The specific application is the altitude maintenance for the fluid deployment valve aperture control in an unmanned pulverization aircraft used in precision agriculture. The influence of a variety of parameters are analyzed, including the tolerance for crop inconsistencies, density variations and intrinsic factors to the process, such as the pulverization fluid interference in the sensor’s readings, as well as their vulnerability to harsh conditions of the operation environment. Filtering and data extraction techniques were applied and analyzed in order to enhance the measurement reliability. As a result, a wide study was performed, enabling better decision making about choosing the most appropriate sensor for each situation under analysis. The performed data analysis was able to provide a reliable baseline to compare the sensors. With a baseline set, it was possible to counterweight the sensors errors and other factors such as the MSE for each environment to provide a summarized score of the sensors. The sensors which provided the best performance in the used metrics and tested environment were Lightware SF11-C and LeddarTech M16.

Medical Diagnosis from a Clinician’s Point of View

1978 ◽  
Vol 17 (01) ◽  
pp. 28-35
Author(s):  
F. T. De Dombal
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Computer Science ◽  
Medical Diagnosis ◽  
Information Science ◽  
Point Of View ◽  
Joint Research ◽  
The World

This paper discusses medical diagnosis from the clinicians point of view. The aim of the paper is to identify areas where computer science and information science may be of help to the practising clinician. Collection of data, analysis, and decision-making are discussed in turn. Finally, some specific recommendations are made for further joint research on the basis of experience around the world to date.

Data Extraction Techniques for Spreadsheet Records

2007 ◽  
Vol 2 (1) ◽  
pp. 119-129 ◽  
Author(s):  
Mark G. Simkin
Keyword(s):  
Data Extraction ◽  
Stratified Sampling ◽  
Specific Information ◽  
Extraction Techniques ◽  
Filtering Techniques

Abstract Many accounting applications use spreadsheets as repositories of accounting records, and a common requirement is the need to extract specific information from them. This paper describes a number of techniques that accountants can use to perform such tasks directly using common spreadsheet tools. These techniques include (1) simple and advanced filtering techniques, (2) database functions, (3) methods for both simple and stratified sampling, and, (4) tools for finding duplicate or unmatched records.

scholarly journals Personalized treatment options for chronic diseases using precision cohort analytics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kenney Ng ◽  
Uri Kartoun ◽  
Harry Stavropoulos ◽  
John A. Zambrano ◽  
Paul C. Tang
Keyword(s):  
Decision Making ◽  
Chronic Diseases ◽  
Primary Care Physicians ◽  
Treatment Guidelines ◽  
Point Of Care ◽  
Data Extraction ◽  
Treatment Options ◽  
Personalized Treatment ◽  
Medical Decision ◽  
Similarity Model

AbstractTo support point-of-care decision making by presenting outcomes of past treatment choices for cohorts of similar patients based on observational data from electronic health records (EHRs), a machine-learning precision cohort treatment option (PCTO) workflow consisting of (1) data extraction, (2) similarity model training, (3) precision cohort identification, and (4) treatment options analysis was developed. The similarity model is used to dynamically create a cohort of similar patients, to inform clinical decisions about an individual patient. The workflow was implemented using EHR data from a large health care provider for three different highly prevalent chronic diseases: hypertension (HTN), type 2 diabetes mellitus (T2DM), and hyperlipidemia (HL). A retrospective analysis demonstrated that treatment options with better outcomes were available for a majority of cases (75%, 74%, 85% for HTN, T2DM, HL, respectively). The models for HTN and T2DM were deployed in a pilot study with primary care physicians using it during clinic visits. A novel data-analytic workflow was developed to create patient-similarity models that dynamically generate personalized treatment insights at the point-of-care. By leveraging both knowledge-driven treatment guidelines and data-driven EHR data, physicians can incorporate real-world evidence in their medical decision-making process when considering treatment options for individual patients.

Marketing customer response scoring model based on machine learning data analysis

2020 ◽  
pp. 1-11
Author(s):  
Tang Yan ◽  
Li Pengfei
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Data Extraction ◽  
Machine Learning Algorithms ◽  
Customer Relationship ◽  
Customer Data ◽  
Modeling And Analysis ◽  
Scoring Model ◽  
Model Based ◽  
Learning Data

In marketing, problems such as the increase in customer data, the increase in the difficulty of data extraction and access, the lack of reliability and accuracy of data analysis, the slow efficiency of data processing, and the inability to effectively transform massive amounts of data into valuable information have become increasingly prominent. In order to study the effect of customer response, based on machine learning algorithms, this paper constructs a marketing customer response scoring model based on machine learning data analysis. In the context of supplier customer relationship management, this article analyzes the supplier’s precision marketing status and existing problems and uses its own development and management characteristics to improve marketing strategies. Moreover, this article uses a combination of database and statistical modeling and analysis to try to establish a customer response scoring model suitable for supplier precision marketing. In addition, this article conducts research and analysis with examples. From the research results, it can be seen that the performance of the model constructed in this article is good.

Decision Making To Assess The Maturity Dimensions of MSME Using A Data Analysis Approach

2021 ◽  
Author(s):  
Lena Magdalena ◽  
R. Rizal Isnanto ◽  
Adi Wibowo ◽  
Budi Warsito
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Analysis Approach

Drilling Data Quality Management: Case Study With a Laboratory Scale Drilling Rig

2018 ◽  
Author(s):  
Suranga C. H. Geekiyanage ◽  
Dan Sui ◽  
Bernt S. Aadnoy
Keyword(s):  
Decision Making ◽  
Quality Improvement ◽  
Control System ◽  
Data Analysis ◽  
Quality Management ◽  
Data Quality ◽  
Incomplete Data ◽  
Laboratory Scale ◽  
Data Quality Management ◽  
Drilling Rig

Drilling industry operations heavily depend on digital information. Data analysis is a process of acquiring, transforming, interpreting, modelling, displaying and storing data with an aim of extracting useful information, so that the decision-making, actions executing, events detecting and incident managing of a system can be handled in an efficient and certain manner. This paper aims to provide an approach to understand, cleanse, improve and interpret the post-well or realtime data to preserve or enhance data features, like accuracy, consistency, reliability and validity. Data quality management is a process with three major phases. Phase I is an evaluation of pre-data quality to identify data issues such as missing or incomplete data, non-standard or invalid data and redundant data etc. Phase II is an implementation of different data quality managing practices such as filtering, data assimilation, and data reconciliation to improve data accuracy and discover useful information. The third and final phase is a post-data quality evaluation, which is conducted to assure data quality and enhance the system performance. In this study, a laboratory-scale drilling rig with a control system capable of drilling is utilized for data acquisition and quality improvement. Safe and efficient performance of such control system heavily relies on quality of the data obtained while drilling and its sufficient availability. Pump pressure, top-drive rotational speed, weight on bit, drill string torque and bit depth are available measurements. The data analysis is challenged by issues such as corruption of data due to noises, time delays, missing or incomplete data and external disturbances. In order to solve such issues, different data quality improvement practices are applied for the testing. These techniques help the intelligent system to achieve better decision-making and quicker fault detection. The study from the laboratory-scale drilling rig clearly demonstrates the need for a proper data quality management process and clear understanding of signal processing methods to carry out an intelligent digitalization in oil and gas industry.

scholarly journals Pengaruh Budaya Dan Religiusitas Terhadap Keputusan Akuntan

2019 ◽  
Vol 1 (2) ◽  
pp. 627-645
Author(s):  
Nisa Umahmudah A ◽  
Sany Dwita ◽  
Nayang Helma Yunita
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Accounting Students ◽  
Experiment Data ◽  
Quasi Experiment

This study aims to test empirically about: 1) The influence of culture on the accountant's decision, and 2) the influence of religiousity effect on the accountant's decision. This type of research belongs to a quasi experiment. Data in this study were collected by using questionnaires on 200 accounting students from 2 universities in Padang City and 1 university in Madura. Data analysis was done by using two-way ANOVA. The results of this study conclude that culture affects an accountant in decision making, while religiousity does not affect the accountant's decision. This study focuses on Javanese culture and Minangkabau culture with a construal of self approach in assessing accountant decisions and using accounting students as a subject to examine cultural and religiousity influences on professional accountant decisions.

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