The Warning Decision Support System–Integrated Information

2007 ◽  
Vol 22 (3) ◽  
pp. 596-612 ◽  
Author(s):  
Valliappa Lakshmanan ◽  
Travis Smith ◽  
Gregory Stumpf ◽  
Kurt Hondl
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Severe Weather ◽  
Weather Data ◽  
Spatial Coverage ◽  
Analysis System ◽  
Automated Algorithms ◽  
The Individual ◽  
Integrated Information

Abstract The Warning Decision Support System–Integrated Information (WDSS-II) is the second generation of a system of tools for the analysis, diagnosis, and visualization of remotely sensed weather data. WDSS-II provides a number of automated algorithms that operate on data from multiple radars to provide information with a greater temporal resolution and better spatial coverage than their currently operational counterparts. The individual automated algorithms that have been developed using the WDSS-II infrastructure together yield a forecasting and analysis system providing real-time products useful in severe weather nowcasting. The purposes of the individual algorithms and their relationships to each other are described, as is the method of dissemination of the created products.

scholarly journals Weighted Deep Neural Network Based Clinical Decision Support System for the Determination of Fetal Health

2019 ◽  
Vol 8 (4) ◽  
pp. 8564-8569
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Deep Neural Network ◽  
Predictive Analytics ◽  
Clinical Decision ◽  
Svm Classifier ◽  
Healthcare Data ◽  
The Individual

Healthcare industry is undergoing changes at a tremendous rate due to healthcare innovations. Predictive analytics is increasingly being used to diagnose the patient’s ailments and provide actionable insights into already existing healthcare data. The paper looks at a decision support system for determining the health status of the foetus from cardiotographic data using deep learning neural networks. The foetal health records are classified as normal, suspect and pathological. As the multiclass cardiotographic datset of the foetus shows a high degree of imbalance a weighted deep neural network is applied. To overcome the accuracy paradox due to the multiclass imbalance, relevant metrics such as the sensitivity, specificity, F1 Score and Gmean are used to measure the performance of the classifier rather than accuracy. The metrics are applied to the individual classes to ensure that the positive cases are identified correctly. The weighted DNN based classifier is able to classify the positive instances with Gmean score of 91% which is better than than the SVM classifier.

scholarly journals A Decision Support System For Integration Of Vendor Selection Task

2011 ◽  
Vol 10 (1) ◽  
pp. 132 ◽  
Author(s):  
Raydel Tullous ◽  
Richard Lee Utecht
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Analytic Hierarchy Process ◽  
Support System ◽  
Selection Task ◽  
Vendor Selection ◽  
Analytic Hierarchy ◽  
Purchasing Decision ◽  
The Individual ◽  
Hierarchy Process

<span>The purpose of this paper is to illustrate the usefulness of a particular decision support system, the Analytic Hierarchy Process (AHP), for developing and implementing integrated procurement systems. The purchasing decision support system (PDSS) proposed in this paper provides each participant involved in the purchasing process with a procedure to communicate their preferences and the reasons for those preferences. All of the participants preferences may be aggregated to determine an overall preference, or in some cases, the results may simply be used to gain a better understanding of the values the individual place on various attributes.</span>

scholarly journals Decision Support System For Employee Bonus Determination With Web-Based Simple Additive Weighting (SAW) Method In PT. Mayatama Solusindo

2020 ◽  
Vol 2 (1) ◽  
pp. 7-13
Author(s):  
Yuda Irawan
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Program Implementation ◽  
System Development ◽  
Employee Performance ◽  
Weighting Method ◽  
Simple Additive Weighting ◽  
Analysis System

Decision Support System is a computerized system designed to increase effectiveness in decision making to solve semi-structured and unstructured problems so that the decision-making process can be of higher quality. One method of solving MADM problems is by using the Simple Additive Weighting method. The SAW method is to find the weighted sum of the performance ratings for each alternative of all attributes. This study aims to design and create a system to determine which employees are entitled to receive bonuses, for that we need a decision support for giving employee bonuses decisions. In this study using the Simple Additive Weighting method. The system development model used is a waterfall. Waterfall has several stages, namely needs analysis, system design, writing program code, program testing, program implementation and maintenance. The results showed the benefits of the SAW method as a decision support system for determining employee bonuses based on the employee performance of PT. Mayatama Solusindo can assist administrators in determining employee bonuses quickly and effectively. So the bonus that employees get using the SAW method is the basic salary times the percentage of the ranking value.

Operational Margin From Weather and Motion Database for Heavy Transport Vessels

2011 ◽  
Author(s):  
Jan B. de Jonge ◽  
Onno A. J. Peters
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Ship Motion ◽  
Environmental Data ◽  
Weather Data ◽  
Background Information ◽  
Motion Data ◽  
Design Values ◽  
Wave Heights

While shipping large and heavy cargo like jack-up rigs or semi-submersibles, the Motion Monitoring and Captain Decision Support system is a valuable tool to ensure a safe and economical voyage. Using the dynamic characteristics of the vessel, in combination with 5-day weather forecasts and design limits like maximum accelerations at the cargo location, roll motion and/or leg bending moments, more and better information is available to the Master to choose safe route, heading and speed. This way the best knowledge of what to expect is contributing to the safety of cargo, vessel and crew. The Octopus onboard system gathers a large amount of information about ship position, speed, heading, nowcast weather data and corresponding ship motion data. Reference is made to the paper of Peters [2] for background information of the Octopus Motion Monitoring and Decision Support system and an overview of methods used by the motion measurement system. In May 2008 the first Dockwise vessel started to gather weather and ship motion data. It is estimated that each vessel gathers around 50.000 nautical miles of data in a year, which is all collected in a database. The paper presents how this information is used for general research to environmental data, ship motion data and comparison to design values. Scatter diagrams from nowcast weather data can be produced. After collecting a certain amount of measurements, so called Dockwise scatter diagrams could be used as input for future voyage calculations. With this engineering approach Masters decisions for weather routing and bad weather avoidance is taken into account. This could lead for example to reduced design wave for a passage around the Cape of Good Hope. Now casted weather data and ship motions data is compared to design values from the cargo securing manual. Statistics like maximum difference, average difference give extensive data and insight in the operational margin of Dockwise transports. The calculation of the operational margin is independent of the standard safety margin valid for each transport. The conclusion is that the recorded nowcast significant wave height for the analyzed voyages never exceeded 5.0 [m]. With larger design wave heights the minimum operational margin increases to more than 40%, while the lowest operational margin occurs at design wave heights around 4.5 [m]. The database built by gathering all relevant information from the system and from crew observations, increases insight in the operational margins, which contributes to increased knowledge and safety.

scholarly journals Planning and Managing the Integrated Water System: A Spatial Decision Support System to Analyze the Infrastructure Performances

2020 ◽  
Vol 12 (16) ◽  
pp. 6432
Author(s):  
Michele Grimaldi ◽  
Monica Sebillo ◽  
Giuliana Vitiello ◽  
Vincenzo Pellecchia
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Water System ◽  
Decision Makers ◽  
Spatial Decision Support System ◽  
Human Beings ◽  
Spatial Decision Support ◽  
System A ◽  
The Individual

The demand for water is constantly increasing, while there are factors related to climate change and pollution that make it less and less available. Addressing this problem means being able to face it with a global approach, which takes into account that human beings need water to survive, as well as all the systems on which they rely, namely sanitation, health, education, business, and industry. While human behavior is influenced by the growing awareness on this topic promoted by organizations specifically targeting this mission, the need to protect water resources in operational terms has led mainly to the need for smart urban infrastructure planning, consistent with the objective of promoting sustainable development. To this aim, the authorities in charge of monitoring the implementation of the investment plans by operators need to perform accurate evaluations of the technical quality of the services provided. The present paper introduces a framework to design a Multi-criteria Spatial Decision Support System, conceived to help decision-makers define and analyze the investment priorities of the individual service operators. By building a knowledge model of the network under investigation, decision-makers are aware of physical components of the whole system and are provided with an intervention priority index related to the network objects that could be affected by the planning action to be implemented.

scholarly journals DECISION SUPPORT SYSTEM FOR IRRIGATED CROPS GROWERS

2018 ◽  
pp. 38-45
Author(s):  
Alexander Sadovski ◽  
Christov Ilia
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Crop Yields ◽  
Irrigation Management ◽  
Field Trials ◽  
Scientific Management ◽  
Agricultural Field ◽  
Irrigated Crops ◽  
The Individual

Abstract: Against the background of climate change, which reduces water availability in many areas of the world, every year the global Agriculture, the world's largest user of the planet's water resources, spends a huge amount of water without achieving ptimal crop yields. Finding a universally applicable way to ensure the efficient use of irrigation water in agriculture is a real business need and its successful transformation into a fully functional automated decision support system is a technology that can lead to creation of a product, which will be a novelty for irrigation management. The article describes a comprehensive technology that allows scientific management of the state of irrigated crops for virtually any agricultural field that has been tested in long-term field trials and brought to the TRL6 software prototype. The structure of the Decision Support System is presented and links between the individual partial mathematical models and their technological relationship with the databases used are shown.

DSS Using Visualization of Multi-Algorithms Voting

2011 ◽  
pp. 296-305 ◽  
Author(s):  
Ran M. Bittmann ◽  
Roy M. Gelbard
Keyword(s):  
Image Processing ◽  
Decision Support ◽  
Decision Support System ◽  
Computer Science ◽  
Support System ◽  
Decision Problem ◽  
Set Up ◽  
The Individual ◽  
Personal Expertise ◽  
Multiple Cluster

The problem of analyzing datasets and classifying them into clusters based on known properties is a well known problem with implementations in fields such as finance (e.g., pricing), computer science (e.g., image processing), marketing (e.g., market segmentation), and medicine (e.g., diagnostics), among others (Cadez, Heckerman, Meek, Smyth, & White, 2003; Clifford & Stevenson, 2005; Erlich, Gelbard, & Spiegler, 2002; Jain & Dubes, 1988; Jain, Murty, & Flynn, 1999; Sharan & Shamir, 2002). Currently, researchers and business analysts alike must try out and test out each diverse algorithm and parameter separately in order to set up and establish their preference concerning the individual decision problem they face. Moreover, there is no supportive model or tool available to help them compare different results-clusters yielded by these algorithm and parameter combinations. Commercial products neither show the resulting clusters of multiple methods, nor provide the researcher with effective tools with which to analyze and compare the outcomes of the different tools. To overcome these challenges, a decision support system (DSS) has been developed. The DSS uses a matrix presentation of multiple cluster divisions based on the application of multiple algorithms. The presentation is independent of the actual algorithms used and it is up to the researcher to choose the most appropriate algorithms based on his or her personal expertise.

scholarly journals Evaluation of decision support system for agrotechnology transfer SUBSTOR potato model (v4.5) under tropical conditions

2016 ◽  
Vol 34 (1) ◽  
pp. 1 ◽  
Author(s):  
Moleen Monita Nand ◽  
Viliamu Iese ◽  
Upendra Singh ◽  
Morgan Wairiu ◽  
Anjeela Jokhan ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Dry Weight ◽  
Growing Season ◽  
Daily Basis ◽  
Weather Data ◽  
Experimental Conditions ◽  
Plant Component ◽  
Tropical Conditions

Decision Support System for Agrotechnology Transfer (DSSAT) SUBSTOR Potato model (v4.5) was calibrated using Desiree variety. DSSAT SUBSTOR Potato model simulates on a daily basis the development and growth of potatoes using inputs such as climate, soil and crop management. The experiment was conducted in Banisogosogo, Fiji Islands, during the potato growing season of 2012. Fresh and dry weights of belowground plant component (tubers) were taken during progressive harvests. The DSSAT SUBSTOR Potato model was calibrated using experimental field data, soil and weather data of the growing season. The manual calibration steps involved recalculation of soil water content and the adjustments of genetic co-efficient to suit the temperature and daylength regime similar to the experimental conditions. Tuber dry weight was used as the main parameter to evaluate the model. The R2 values of the observed and simulated model outputs before calibration for replicate plot 1, replicate plot 2 and replicate plot 3 were 0.52, 0.49 and 0.61 respectively. After calibration, the R2 values for tuber dry yield for replicate plot 1, replicate plot 2 and replicate plot 3 were 0.88, 0.66 and 0.92 respectively indicating a strong positive relationship between the simulated and the observed yield.

scholarly journals Intelligent decision support system approach for predicting the performance of students based on three-level machine learning technique

2021 ◽  
Vol 30 (1) ◽  
pp. 739-749
Author(s):  
Sohaib Latif ◽  
Fang XianWen ◽  
Li-li Wang
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Research Work ◽  
Individual Student ◽  
Intelligent Decision Support System ◽  
Intelligent Decision ◽  
The Individual ◽  
Intelligent Decision Support

Abstract In this research work, a user-friendly decision support framework is developed to analyze the behavior of Pakistani students in academics. The purpose of this article is to analyze the performance of the Pakistani students using an intelligent decision support system (DSS) based on the three-level machine learning (ML) technique. The neural network used a three-level classifier approach for the prediction of Pakistani student achievement. A self-recorded dataset of 1,011 respondents of graduate students of English and Physics courses are used. The ten interviews along with ten questions were conducted to determine the perception of the individual student. The chi-squared ( χ ) \left(\chi ) test was applied to test statistical significancy of the questionnaire. The statistical calculations and computation of data were performed by using the statistical package of IBMM SPSS version 21.0. The seven different algorithms were tested to improve the data classification. The Java-based environment was used for the development of numerous prediction classifiers. C4.5 algorithm shows the finest accuracy, whereas Naïve Bayes (NB) algorithm shows the least. The results depict that the classifier’s efficiency was improved by using a three-level proposed scheme from 83.2% to 88.8%. This prediction has shown remarkable results when compared with the individual level classifier technique of ML. This improvement in the accuracy of DSSs is used to identify more efficiently the gray areas in the education stratum of Pakistan. This will pave a path for making policies in the higher education system of Pakistan. The presented framework can be deployed on different platforms under numerous operating systems.

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