Deep convolutional neural networks as a decision support tool in medical problems – malignant melanoma case study

2017 ◽  
pp. 848-856 ◽  
Author(s):  
Arkadiusz Kwasigroch ◽  
Agnieszka Mikołajczyk ◽  
Michał Grochowski
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Malignant Melanoma ◽  
Convolutional Neural Networks ◽  
Decision Support Tool ◽  
Deep Convolutional Neural Networks ◽  
Medical Problems ◽  
Support Tool ◽  
Melanoma Case

A Data-Driven Decision Support Tool at the service of Energy suppliers and Utilities for Tackling Energy Poverty: A case study in Greece

2021 ◽  
Author(s):  
Apostolos Arsenopoulos ◽  
Elissaios Sarmas ◽  
Andriana Stavrakaki ◽  
Ioanna Giannouli ◽  
John Psarras
Keyword(s):  
Decision Support ◽  
Decision Support Tool ◽  
Data Driven ◽  
Energy Poverty ◽  
Support Tool

SOSeas Web App: An assessment web-based decision support tool to predict dynamic risk of drowning on beaches using deep neural networks

2021 ◽  
pp. 1-20
Author(s):  
Javier García-Alba ◽  
Javier F. Bárcena ◽  
Luis Pedraz ◽  
Felipe Fernández ◽  
Andrés García ◽  
...  
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Deep Neural Networks ◽  
Decision Support Tool ◽  
Web Based ◽  
Support Tool ◽  
Dynamic Risk ◽  
Web App

Artificial Neural Network Models as a Decision Support Tool for Selection in Sugarcane: A Case Study Using Seedling Populations

Crop Science ◽  
2011 ◽  
Vol 51 (1) ◽  
pp. 21-31 ◽  
Author(s):  
Marvellous M. Zhou ◽  
Collins A. Kimbeng ◽  
Thomas L. Tew ◽  
Kenneth A. Gravois ◽  
Michael J. Pontif
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Decision Support ◽  
Network Models ◽  
Decision Support Tool ◽  
Neural Network Models ◽  
Support Tool ◽  
Artificial Neural ◽  
Artificial Neural Network Models

A Web-based GIS Decision Support Tool for Determining Corn Aflatoxin Risk: A Case Study Data from Southern Georgia, USA

2017 ◽  
Vol 8 (2) ◽  
pp. 718-723 ◽  
Author(s):  
F. Navarro ◽  
B. Ingram ◽  
R. Kerry ◽  
B. V. Ortiz ◽  
B. T. Scully
Keyword(s):  
Decision Support ◽  
Decision Support Tool ◽  
Study Data ◽  
Corn Production ◽  
Web Based ◽  
Support Tool ◽  
Risk Areas ◽  
Southern Georgia ◽  
Case Study Data

Aflatoxin is a fungal toxin contaminating corn and causing liver cancer in humans and animals. Contamination is driven by high temperatures and drought. Aflatoxin assessment is expensive so extension services need to identify high risk areas so irrigation, planting strategies and corn varieties can be adapted. This research presents a web-based decision support tool for risk illustrated with a case study from southern Georgia. The tool employs the approach, developed by Kerry et al. (2017b) where exceedance of key thresholds in temperatures, rainfall, soil type and corn production are used to determine risk. The tool also includes NDVI to indicate drought stress and could be further expanded to include new risk factors and adapted to other crops.

On the Design of a Sustainable Production Line: The MetaCAM Tool

2015 ◽  
Author(s):  
Apostolos Fysikopoulos ◽  
Theocharis Alexopoulos ◽  
George Pastras ◽  
Panos Stavropoulos ◽  
Georgios Chryssolouris
Keyword(s):  
Energy Efficiency ◽  
Decision Support ◽  
Value Chain ◽  
Production Line ◽  
Optimization Procedure ◽  
Decision Support Tool ◽  
Life Cycles ◽  
Support Tool ◽  
Industrial Sectors

Nowadays, manufacturing enterprises face enormous environmental challenges, due to complex and diverse economic trends, including shorter product life cycles, rapid advances in science and technology, increased diversity in customer demands and globalization of production activities. Consequently, the cost is highly affected by environmentally related factors. Energy efficiency is one of the main factors, which together with waste management, affect manufacturing decisions. The complexity and diversity of the factors that determine energy efficiency require intelligent systems for their optimization at each “manufacturing level”. Manufacturing decisions should be taken as fast as possible and with the highest possible accuracy. Artificial intelligence/machine learning tools have made significant progress during the last decade and are suitable for such applications. The main objective of the current study is that an architecture for the development of a networked, online, decision support tool, be provided towards achieving sustainable value chain management. The main idea behind the proposed design is that stakeholders be assisted in taking decisions towards improving the energy and eco-efficiency of the entire value chain or parts of it. This is suggested within the context of a multi-objective optimization procedure, taking into account other important decision making attributes, such as flexibility, quality and time for the final reduction in the overall cost. This architecture incorporates real time information modules that interact with online monitoring systems, using any available information within the value chain and the existing IT tools. A partial realization of the proposed idea is implemented in the form of a user friendly software tool (the MetaCAM tool). This based, decision support tool aiming to optimize a current production line or to propose alternatives for the manufacturing of a product. The tool performs optimization based on a set of predefined criteria, namely energy, waste, cost and time. For each of these criteria, the end-user selects the desired weight factor in order to drive the optimization procedure accordingly. The tool presents the characteristics of the setup of the proposed optimized line and maintains all used data and calculations in order to be reused when necessary. For the tool’s validation, three real case studies from different industrial sectors have been used. The first case study comes from the domestic appliances sector (refrigerator door panel), the second one from the automotive sector (a two seat bench for light commercial vehicles) and finally, the third case study derives from the aeronautics sector and deals with the production of the loading ramp hinge of a military aircraft.

Data Mining in Decision Support for Bioenergy Production

2010 ◽  
pp. 1091-1108
Author(s):  
Nasser Ayoub ◽  
Yuji Naka
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Tool ◽  
Geographical Information ◽  
Support Tool ◽  
Bioenergy Production ◽  
Decision Making Problem ◽  
Biomass Resources

This chapter presents Data Mining, DM, as a planning and decision support tool for biomass resources management to produce bioenergy. Furthermore, the decision making problem for bioenergy production is defined. A Decision Support System, DSS that utilizes a DM technique, e.g. clustering, integrated with other group of techniques and tools, such as Genetic Algorithms, GA, Life Cycle Assessment, Geographical Information System, GIS, etc, is presented. A case study that shows how to tackle the decision making problem is also shown.

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