Combining satellite data and Machine Learning techniques for irrigation Decision Support Systems

2019 ◽  
Author(s):  
Loris Francesco Termite ◽  
Alberto Garinei ◽  
Andrea Marini ◽  
Marcello Marconi ◽  
Lorenzo Biondi
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support Systems ◽  
Satellite Data ◽  
Support Systems ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Judges Are from Mars, Pro Se Litigants Are from Venus: Predicting Decisions from Lay Text

2020 ◽  
Author(s):  
Karl Branting ◽  
Carlos Balhana ◽  
Craig Pfeifer ◽  
John Aberdeen ◽  
Bradford Brown
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Support Systems ◽  
Learning Algorithms ◽  
Machine Learning Techniques ◽  
Access To Justice ◽  
Learning Models ◽  
Learning Techniques ◽  
Citizen Complaints ◽  
Machine Learning Models

Access to justice could be significantly expanded if decision support systems were able to accurately interpret statements of fact by pro se (self-represented) litigants. Prior research, which has demonstrated that case decisions can often be predicted by machine-learning models trained on judges’ statements of facts, suggests the hypothesis that these same learning algorithms could be effectively applied to pro se litigants’ fact statements. However, there has been a dearth of corpora on which to test this hypothesis. This paper describes an experiment testing the ability to predict the outcome of pro se litigants’ complaints on a corpus of 5,842 cases initiated by citizen complaints. The results of this experiment were strikingly negative, suggesting that fact statements by unguided pro se litigants are far less amenable to simple machine-learning techniques than judges’ texts and appearing to disconfirm the hypothesis above.

Review of classification studies for machine learning in the development of intelligent management decision support systems

2020 ◽  
Vol 89 ◽  
pp. 20-29
Author(s):  
Sh. K. Kadiev ◽  
◽  
R. Sh. Khabibulin ◽  
P. P. Godlevskiy ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Mathematical Models ◽  
Decision Support Systems ◽  
Support Systems ◽  
Management Decision ◽  
Classification Methods ◽  
Advantages And Disadvantages ◽  
Intelligent Management ◽  
Management Decision Support

Introduction. An overview of research in the field of classification as a method of machine learning is given. Articles containing mathematical models and algorithms for classification were selected. The use of classification in intelligent management decision support systems in various subject areas is also relevant. Goal and objectives. The purpose of the study is to analyze papers on the classification as a machine learning method. To achieve the objective, it is necessary to solve the following tasks: 1) to identify the most used classification methods in machine learning; 2) to highlight the advantages and disadvantages of each of the selected methods; 3) to analyze the possibility of using classification methods in intelligent systems to support management decisions to solve issues of forecasting, prevention and elimination of emergencies. Methods. To obtain the results, general scientific and special methods of scientific knowledge were used - analysis, synthesis, generalization, as well as the classification method. Results and discussion thereof. According to the results of the analysis, studies with a mathematical formulation and the availability of software developments were identified. The issues of classification in the implementation of machine learning in the development of intelligent decision support systems are considered. Conclusion. The analysis revealed that enough algorithms were used to perform the classification while sorting the acquired knowledge within the subject area. The implementation of an accurate classification is one of the fundamental problems in the development of management decision support systems, including for fire and emergency prevention and response. Timely and effective decision by officials of operational shifts for the disaster management is also relevant. Key words: decision support, analysis, classification, machine learning, algorithm, mathematical models.

Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery

2022 ◽  
Vol 305 ◽  
pp. 117834
Author(s):  
Alfredo Nespoli ◽  
Alessandro Niccolai ◽  
Emanuele Ogliari ◽  
Giovanni Perego ◽  
Elena Collino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Machine Learning Techniques ◽  
Solar Irradiation ◽  
Cloud Type ◽  
Learning Techniques ◽  
Type Classification

Benefits and Risks of Machine Learning Decision Support Systems

JAMA ◽  
2017 ◽  
Vol 318 (23) ◽  
pp. 2353 ◽  
Author(s):  
Eta S. Berner ◽  
Bunyamin Ozaydin
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems

Towards the collaborative development of machine learning techniques in planning support systems – a Sydney example

2020 ◽  
pp. 239980832093997 ◽  
Author(s):  
Oliver Lock ◽  
Michael Bain ◽  
Christopher Pettit
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support System ◽  
Support Systems ◽  
Added Value ◽  
Machine Learning Techniques ◽  
Planning Support Systems ◽  
Planning Support ◽  
Learning Techniques ◽  
Planning Support System

The rise of the term ‘big data’ has contributed to recent advances in computational analysis techniques, such as machine learning and more broadly, artificial intelligence, which can extract patterns from large, multi-dimensional datasets. In the field of urban planning, it is pertinent to understand both how such techniques can advance our understanding of cities, and how they can be embedded within transparent and effective digital planning tools, known as planning support systems. This research specifically focuses on two related contributions. First, it investigates the role of planning support systems in supporting a participatory data analytics approach through an iterative process of developing and evaluating a planning support system environment. Second, it investigates how specifically machine learning planning support systems can be co-designed by built environment practitioners and stakeholders in this environment to solve a real planning issue in Sydney, Australia. This paper presents the results of applied research undertaken through the design and implementation of four workshops, involving 57 participants who were involved in a co-design process. The research follows a mixed-methods approach, studying a wide array of measures related to participatory analytics, task load, perceived added value, recordings and observations. The results highlight recommendations regarding the design and evaluation of planning support system environments for co-design and their coupling with machine learning techniques. It was found that consistency and transparency are highly valued and central to the design of a planning support system in this context. General attitudes towards machine learning and artificial intelligence as techniques for planners and developers were positive, as they were seen as both potentially transformative but also as simply another technique to assist with workflows. Some conceptual challenges were encountered driven by practitioners' simultaneous need for concrete scenarios for accurate predictions, paired with a desire for predictions to drive the development of these scenarios. Insights from this work can inform future planning support system evaluation and co-design studies, in particular those aiming to support democracy enhancement, greater inclusion and more efficient resource allocation through a participatory analytics approach.

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