scholarly journals A Statistical Model for Shutdowns due to Air Quality Control for a Copper Production Decision Support System

Organizacija ◽  
2015 ◽  
Vol 48 (3) ◽  
pp. 198-202
Author(s):  
Khalid Aboura
Keyword(s):  
Quality Control ◽  
Decision Support ◽  
Air Quality ◽  
Decision Support System ◽  
Statistical Model ◽  
Support System ◽  
Copper Production ◽  
Production Facility ◽  
Air Quality Control ◽  
High Level

Abstract Background: In the mid-1990s, a decision support system for copper production was developed for one of the largest mining companies in Australia. The research was conducted by scientists from the largest Australian research center and involved the use of simulation to analyze options to increase production of a copper production facility. Objectives: We describe a statistical model for shutdowns due to air quality control and some of the data analysis conducted during the simulation project. We point to the fact that the simulation was a sophisticated exercise that consisted of many modules and the statistical model for shutdowns was essential for valid simulation runs. Method: The statistical model made use of a full year of data on daily downtimes and used a combination of techniques to generate replications of the data. Results: The study was conducted with a high level of cooperation between the scientists and the mining company. This contributed to the development of accurate estimates for input into a support system with an EXCEL based interface. Conclusion: The environmental conditions affected greatly the operations of the production facility. A good statistical model was essential for the successful simulation and the high budget expansion decision that ensued.

scholarly journals MACHoice: a Decision Support System for agricultural machinery management

2019 ◽  
Vol 4 (1) ◽  
pp. 305-321 ◽  
Author(s):  
M. Cunha ◽  
S.G. Gonçalves
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Crop Production ◽  
Work Capacity ◽  
Production Costs ◽  
Modern Agriculture ◽  
Level Of Automation ◽  
High Level ◽  
User Friendly

AbstractMechanisation is a key input in modern agriculture, while it accounts for a large part of crop production costs, it can bring considerable farm benefits if well managed. Models for simulated machinery costs, may not replace actual cost measurements but the information obtained through them can replace a farm’s existing records, becoming more valuable to decision makers. MACHoice, a decision support system (DSS) presented in this paper, is a farm machinery cost estimator and break-even analyzer of alternatives for agricultural operations, developed using user-driven expectations and in close collaboration with agronomists and computer engineers. It integrates an innovative algorithm developed for projections of machinery costs under different rates of annual machine use and work capacity processing, which is crucial to decisions on break-even machinery alternatives. A case study based on the comparison of multiple alternatives for grape harvesting operations is presented to demonstrate the typical results that can be expected from MACHoice, and to identify its capabilities and limitations. This DSS offers an integrated and flexible analysis environment with a user-friendly graphical interface as well as a high level of automation of processing chains. The DSS-output consists of charts and tables, evidencing the differences related to costs and carbon emissions between the options inserted by the user for the different intensity of yearly work proceeded. MACHoice is an interactive web-based tool that can be accessed freely for non-commercial use by every known browser.

Individual Exposure Modelling in an Integrated Decision Support System for Air Quality Management

Epidemiology ◽  
2009 ◽  
Vol 20 ◽  
pp. S21-S22
Author(s):  
Alexandra Kuhn ◽  
Miranda Loh ◽  
Lydia Gerharz ◽  
Sandra Torras Ortiz ◽  
Aileen Yang ◽  
...  
Keyword(s):  
Decision Support ◽  
Air Quality ◽  
Quality Management ◽  
Decision Support System ◽  
Support System ◽  
Air Quality Management ◽  
Individual Exposure

scholarly journals Initiative For Thyroid Cancer Diagnosis: Decision Support System For Anaplast Thyroid Cancer

2017 ◽  
Vol 1 (2) ◽  
pp. 48
Author(s):  
Jamil Ahmed Chandio ◽  
M. Abdul Rehman Soomrani ◽  
Attaullah Sehito ◽  
Shafaq Siddiqui
Keyword(s):  
Thyroid Cancer ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Confusion Matrix ◽  
Measurement Techniques ◽  
Research Area ◽  
Classification Problems ◽  
Biomedical Image ◽  
High Level

Due to the high level exposure of biomedical image analysis, Medical image mining has become one of the well-established research area(s) of machine learning. AI (Artificial Intelligence) techniques have been vastly used to solve the complex classification problems of thyroid cancer. Since the persistence of copycat chromatin properties and unavailability of nuclei measurement techniques, it is really problem for doctors to determine the initial phases of nuclei enlargement and to assess the early changes of chromatin distribution. For example involvement of multiple transparent overlapping of nuclei may become the cause of confusion to infer the growth pattern of nuclei variations. Un-decidable nuclei eccentric properties may become one of the leading causes for misdiagnosis in Anaplast cancers. In-order to mitigate all above stated problems this paper proposes a novel methodology so called “Decision Support System for Anaplast Thyroid Cancer” and it proposes a medical data preparation algorithm AD (Analpast_Cancers) which helps to select the appropriate features of Anaplast cancers such as (1) enlargement of nuclei, (2) persistence of irregularity in nuclei and existence of hyper chromatin. Proposed methodology comprises over four major layers, first layer deals with the noise reduction, detection of nuclei edges and object clusters. Second layer selects the features of object of interest such as nuclei enlargement, irregularity and hyper chromatin. Third layer constructs the decision model to extract the hidden patterns of disease associated variables and final layer evaluates the performance evaluation by using confusion matrix, precision and recall measures. The overall classification accuracy is measured about 97.2% with 10-k fold cross validation.

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