scholarly journals Using machine learning to predict the impact of agricultural factors on communities of soil microarthropods

2005 ◽  
Vol 2 (1) ◽  
Author(s):  
Damjan Demšar ◽  
Sašo Džeroski ◽  
Paul Henning Krogh ◽  
Thomas Larsen
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Soil Fauna ◽  
Linear Equations ◽  
Sustainable Use ◽  
Individual Species ◽  
Soil Microarthropods ◽  
The Impact

With the newly arisen ecological awareness in the agriculture the sustainable use and development of the land is getting more important. With the sustainable use of soil in mind, we are developing a decision support system that helps making decisions on managing agricultural systems and is able to handle both conventional and genetically modified crops as a part of the ECOGEN project. The decision support system considers economical and agricultural factors and actions including crop selection, crop sequence, pest and weed control actions etc. For such decision support system to work, it needs modules that predict results of different agricultural actions. One of the most important factors for sustainable use and fertility of soil is soil flora and fauna. Any change of that community can influence the short or long term soil fertility and soil usability. With soil fauna being one of the most important factors we first need to model it. However, since the function of the individual species is not known, the only action we have is to try and model the community of soil fauna. We start by modelling the community soil microarthropods. For that goal we used machine learning methods - regression trees, model trees and linear equations. We identified previous crops and time since different kinds of tillage as the most important factors for the community of soil microarthropods.

scholarly journals Decision Support System for Stock Portfolio Selection Using Artificial Intelligence and Machine Learning

2021 ◽  
Vol 26 (1) ◽  
pp. 87-93
Author(s):  
Sandeep Patalay ◽  
Madhusudhan Rao Bandlamudi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Stock Market ◽  
Decision Support System ◽  
Portfolio Selection ◽  
Support System ◽  
Financial Analysis ◽  
Traditional Approach ◽  
Stock Portfolio

Investing in stock market requires in-depth knowledge of finance and stock market dynamics. Stock Portfolio Selection and management involve complex financial analysis and decision making policies. An Individual investor seeking to invest in stock portfolio is need of a support system which can guide him to create a portfolio of stocks based on sound financial analysis. In this paper the authors designed a Financial Decision Support System (DSS) for creating and managing a portfolio of stock which is based on Artificial Intelligence (AI) and Machine learning (ML) and combining the traditional approach of mathematical models. We believe this a unique approach to perform stock portfolio, the results of this study are quite encouraging as the stock portfolios created by the DSS are based on strong financial health indices which in turn are giving Return on Investment (ROI) in the range of more than 11% in the short term and more than 61% in the long term, therefore beating the market index by a factor of 15%. This system has the potential to help millions of Individual Investors who can make their financial decisions on stocks and may eventually contribute to a more efficient financial system.

scholarly journals Methodological Approaches to Regional Socio-Economic System Management

2020 ◽  
Vol 23 (6) ◽  
pp. 148-160
Author(s):  
E. A. Averchenkova
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Economic System ◽  
Support System ◽  
Feedback System ◽  
Management Theory ◽  
Practical Implementation ◽  
System Management ◽  
Management Decisions ◽  
The Impact

Purpose of research. This paper is a description of the methodology for regional socio-economic system management based on the principles and concepts of management theory. Methods. A methodology for regional socio-economic system managing has been developed, taking into account the impact of National projects and the influence of the external environment. The methodology consists of six stages and fourteen techniques that allow describing the regional socio-economic system management in terms and tools of the management theory: the region itself is considered as an object of management experiencing a controlling action formed under some affecting influence. The methodology also assumes the formalization of a negative feedback system and a control system in the developed model of regional socio-economic system management. Results. The methodology of managing the regional socio-economic system can be used in the management process. Those who make management decisions at the regional level usually rely on their own professional skills, past experience, and intuition. However, the heuristic approach to regional management can be extended by the capabilities of the developed methodology, the practical implementation of which can be presented as a decision support system. This will allow regional governments to improve the effectiveness of management decisions based on monitoring the state of socio-economic systems. Conclusion. The methodology for managing the regional socio-economic system provides a complete management cycle: from the formalization of basic concepts to the description of the control and feedback system. The information implementation of the methodology is presented in the form of an automated product – a decision support system - that can be used in the formation of an automated workplace for civil servants. 

ACCOUNTING MODEL OF DECISION SUPPORT SYSTEM

2020 ◽  
Vol 10 (4) ◽  
pp. 612-619
Author(s):  
P.E. Shumilin ◽  
◽  
V.A. Eremenko ◽  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Financial Stability ◽  
Primary Data ◽  
Management Decision ◽  
Corporate Management ◽  
Managerial Decisions ◽  
Accounting Model ◽  
The Impact

The digital development of the economy opens up new horizons for accounting. On the one hand, dissolution of accounting in corporate management systems takes place, on the other hand, the accounting functions for managing economic information remain relevant. This article uses the accounting modeling method. We offer a five-blocks accounting model of the decision support system. The model is formed by such blocks as the interface for collecting primary data on company transactions in the context of the formation of financial, managerial, strategic accounting accounts, ETL (extract, transform, loading) of processes for combining credentials from various sources within the framework of a structured work plan of accounts; predicted accounting iterations, having a synergistic, reorganization, reorganization, immunization, hedging and other areas; express audit of the management decision, which consists in assessing the impact of the management decision on the effectiveness of the company, which includes such elements as tax and legal expertise; SWOT analysis; reporting visualization tools that allow you to generate different types of reporting: financial, managerial, statistical, not just in tabular form, but using digital visualization methods; accounting and analytical indicators of managerial decisions, which can be described as a system of indicators reflecting the financial and economic situation of the enterprise under the influence of managerial decisions; the state of its financial stability, profitability, solvency, liquidity; the size of the property of the founders. The introduction and use of this model will allow generating relevant accounting information based on the needs of management, supporting the adoption of management decisions at a scientifically sound level that meets the criteria of business efficiency and protect the interests of owners.

Decision support system for NPK fertilization: a solution method for minimizing the impact on human health, climate change, ecosystem quality and resources

2020 ◽  
Vol 55 (11) ◽  
pp. 1267-1282
Author(s):  
Ramiro Meza-Palacios ◽  
Alberto A. Aguilar-Lasserre ◽  
Luis F. Morales-Mendoza ◽  
José O. Rico-Contreras ◽  
Luis H. Sánchez-Medel ◽  
...  
Keyword(s):  
Climate Change ◽  
Decision Support ◽  
Decision Support System ◽  
Human Health ◽  
Support System ◽  
Solution Method ◽  
Ecosystem Quality ◽  
The Impact ◽  
Npk Fertilization

Gold coast seaway smartrelease decision support system: optimising recycled water release in a sub tropical estuarine environment

2009 ◽  
Vol 60 (8) ◽  
pp. 2077-2084 ◽  
Author(s):  
G. Stuart ◽  
A. Hollingsworth ◽  
F. Thomsen ◽  
S. Szylkarski ◽  
S. Khan ◽  
...  
Keyword(s):  
Water Quality ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Numerical Models ◽  
Water Levels ◽  
Gold Coast ◽  
Recycled Water ◽  
Water Release ◽  
The Impact

Gold Coast Water is responsible for the management of the water, recycled water and wastewater assets of the City of the Gold Coast on Australia's east coast. Excess treated recycled water is released at the Gold Coast Seaway, a man-made channel connecting the Broadwater Estuary with the Pacific Ocean, on an outgoing tide in order for the recycled water to be dispersed before the tide changes and re-enters the Broadwater estuary. Rapid population growth has placed increasing demands on the city's recycled water release system and an investigation of the capacity of the Broadwater to assimilate a greater volume of recycled water over a longer release period was undertaken in 2007. As an outcome, Gold Coast Water was granted an extension of the existing release licence from 10.5 hours per day to 13.3 hours per day from the Coombabah wastewater treatment plant (WWTP). The Seaway SmartRelease Project has been designed to optimise the release of the recycled water from the Coombabah WWTP in order to minimise the impact to the receiving estuarine water quality and maximise the cost efficiency of pumping. In order achieve this; an optimisation study that involves intensive hydrodynamic and water quality monitoring, numerical modelling and a web-based decision support system is underway. An intensive monitoring campaign provided information on water levels, currents, winds, waves, nutrients and bacterial levels within the Broadwater. This data was then used to calibrate and verify numerical models using the MIKE by DHI suite of software. The Decision Support System will then collect continually measured data such as water levels, interact with the WWTP SCADA system, run the numerical models and provide the optimal time window to release the required amount of recycled water from the WWTP within the licence specifications.

Variables Influencing Machine Learning-Based Cardiac Decision Support System: A Systematic Literature Review

2019 ◽  
Vol 892 ◽  
pp. 274-283
Author(s):  
Mohammed Ashikur Rahman ◽  
Afidalina Tumian
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision ◽  
Machine Learning Techniques ◽  
Cardiac Care ◽  
Learning Techniques

Now a day, clinical decision support systems (CDSS) are widely used in the cardiac care due to the complexity of the cardiac disease. The objective of this systematic literature review (SLR) is to identify the most common variables and machine learning techniques used to build machine learning-based clinical decision support system for cardiac care. This SLR adopts the Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) format. Out of 530 papers, only 21 papers met the inclusion criteria. Amongst the 22 most common variables are age, gender, heart rate, respiration rate, systolic blood pressure and medical information variables. In addition, our results have shown that Simplified Acute Physiology Score (SAPS), Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) are some of the most common assessment scales used in CDSS for cardiac care. Logistic regression and support vector machine are the most common machine learning techniques applied in CDSS to predict mortality and other cardiac diseases like sepsis, cardiac arrest, heart failure and septic shock. These variables and assessment tools can be used to build a machine learning-based CDSS.

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