Length of hospital stay prediction with an integrated approach of statistical-based fuzzy cognitive maps and artificial neural networks

2021 ◽  
Vol 59 (3) ◽  
pp. 483-496 ◽  
Author(s):  
Elif Dogu ◽  
Y. Esra Albayrak ◽  
Esin Tuncay
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hospital Stay ◽  
Length Of Hospital Stay ◽  
Cognitive Maps ◽  
Integrated Approach ◽  
Fuzzy Cognitive Maps ◽  
Artificial Neural

Applications in Machine Learning

2010 ◽  
pp. 149-167 ◽  
Author(s):  
E. Parsopoulos Konstantinos ◽  
N. Vrahatis Michael
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Literature Review ◽  
General Framework ◽  
Computational Models ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Optimization Process ◽  
Learning Problems

This chapter presents the fundamental concepts regarding the application of PSO on machine learning problems. The main objective in such problems is the training of computational models for performing classification and simulation tasks. It is not our intention to provide a literature review of the numerous relative applications. Instead, we aim at providing guidelines for the application and adaptation of PSO on this problem type. To achieve this, we focus on two representative cases, namely the training of artificial neural networks, and learning in fuzzy cognitive maps. In each case, the problem is first defined in a general framework, and then an illustrative example is provided to familiarize readers with the main procedures and possible obstacles that may arise during the optimization process.

scholarly journals Integrated approach to the planning of energy consumption by non-traction railway consumers

2018 ◽  
Vol 239 ◽  
pp. 01056 ◽  
Author(s):  
Alexander Galkin ◽  
Alexey Kovalev ◽  
Timur Shayuhov
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Correlation Analysis ◽  
Power Consumption ◽  
Systematic Approach ◽  
Integrated Approach ◽  
Expert Assessments ◽  
The Subject ◽  
Artificial Neural ◽  
High Degree

Non-traction railway load consumes a significant amount of electricity. Russian Railways supplies electricity not only to its structural units but also to other consumers. Private houses, individual entrepreneurs and small production facilities located near the railway are powered by the RZD. It is very important for the company to plan consumption for non- traction needs. The subject of the study in the paper is a systematic approach to the planning of power consumption using the mathematical apparatus of artificial neural networks, correlation analysis and the method of expert assessments. The method of expert assessments allows identifying the most significant factors that affect the consumption of electricity. It is necessary to attract experienced professionals in the field of electricity, working at a particular enterprise. They are able to determine with a high degree of accuracy those factors that have a significant impact on the consumption of the organization. Correlation analysis allows you to mathematically check the degree of influence of a single factor on the resulting value. The apparatus of artificial neural networks allows building a forecast of power consumption, taking into account the influence of external factors. The authors propose to use a systematic approach to the planning of power consumption. It is necessary to combine three tools: the method of expert assessments, correlation analysis and artificial neural networks. The combination of these tools will improve the accuracy of power consumption planning and, as a result, will lead to increased economic efficiency due to the rational consumption of electricity.

scholarly journals Lithium Potential Mapping Using Artificial Neural Networks: A Case Study from Central Portugal

Minerals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1046
Author(s):  
Martin Köhler ◽  
Delira Hanelli ◽  
Stefan Schaefer ◽  
Andreas Barth ◽  
Andreas Knobloch ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Global Economy ◽  
Integrated Approach ◽  
Industrial Applications ◽  
Geochemical Data ◽  
Spectral Signature ◽  
Ann Model ◽  
Geological Processes ◽  
Artificial Neural

The growing importance and demand of lithium (Li) for industrial applications, in particular rechargeable Li-ion batteries, have led to a significant increase in exploration efforts for Li-bearing minerals. To ensure and expand a stable Li supply to the global economy, extensive research and exploration are necessary. Artificial neural networks (ANNs) provide powerful tools for exploration target identification. They can be cost-effectively applied in various geological settings. This article presents an integrated approach of Li exploration targeting using ANNs for data interpretation. Based on medium resolution geological maps (1:50,000) and stream sediment geochemical data (1 sample per 0.25 km2), the Li potential was calculated for an area of approximately 1200 km2 in the surroundings of Bajoca Mine (Northeast Portugal). Extensive knowledge about geological processes leading to Li mineralisation (such as weathering conditions and diverse Li minerals) proved to be a determining factor in the exploration model. Furthermore, Sentinel-2 satellite imagery was used in a separate ANN model to identify potential Li mine sites exposed on the ground surface by analysing the spectral signature of surface reflectance in well-known Li locations. Finally, the results were combined to design a final map of predicted Li mineralisation occurrences in the study area. The proposed approach reveals how remote sensing data in combination with geological and geochemical data can be used for delineating and ranking exploration targets of almost any deposit type.

scholarly journals Evaluation of the Length of Hospital Stay Through Artificial Neural Networks Based Systems

2020 ◽  
pp. 391-403
Author(s):  
Vasco Abelha ◽  
Fernando Marins ◽  
Henrique Vicente
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Length Of Hospital Stay ◽  
Time Factors ◽  
Knowledge Representation And Reasoning ◽  
Daily Lives ◽  
Specific Patient ◽  
Economic Politics ◽  
Degree Of Confidence ◽  
Artificial Neural

The mentality of savings and eliminating any kind of outgoing costs is undermining our society and our way of living. Cutting funds from Education to Health is at best delaying the inevitable “Crash” that is foreshadowed. Regarding Health, a major concern, can be described as jeopardize the health of Patients – Reduce of the Length of Hospital. As we all know, Human Health is very sensitive and prune to drastic changes in short spaces of time. Factors like age, sex, their ambient context – house conditions, daily lives – should all be important when deciding how long a specific patient should remain safe in a hospital. In no way, ought this be decided by the economic politics. Logic Programming was used for knowledge representation and reasoning, letting the modeling of the universe of discourse in terms of defective data, information and knowledge. Artificial Neural Networks and Genetic Algorithms were used in order to evaluate and predict how long should a patient remain in the hospital in order to minimize the collateral damage of our government approaches, not forgetting the use of Degree of Confidence to demonstrate how feasible the assessment is.

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