scholarly journals Methods for small area population forecasts: state-of-the-art and research needs

2021 ◽  
Author(s):  
Thomas Wilson ◽  
Irina Grossman ◽  
Monica Alexander ◽  
Philip Rees ◽  
Jeromey Temple
Keyword(s):  
Machine Learning ◽  
Small Area ◽  
Model Averaging ◽  
Population Forecasts ◽  
Current State ◽  
Forecasting Methods ◽  
Population Forecasting ◽  
Socio Economic Variables ◽  
User Friendly ◽  
Quantifying Uncertainty

Small area population forecasts are widely used by government and business for a variety of planning, research and policy purposes, and often influence major investment decisions. Yet the toolbox of small area population forecasting methods and techniques is modest relative to that for national and large subnational regional forecasting. In this paper we assess the current state of small area population forecasting, and suggest areas for further research. The paper provides a review of the literature on small area population forecasting methods published over the period 2001-2020. The key themes covered by the review are: extrapolative and comparative methods, simplified cohort-component methods, model averaging and combining, incorporating socio-economic variables and spatial relationships, ‘downscaling’ and disaggregation approaches, linking population with housing, estimating and projecting small area component input data, microsimulation, machine learning, and forecast uncertainty. Several avenues for further research are then suggested, including more work on model averaging and combining, developing new forecasting methods for situations which current models cannot handle, quantifying uncertainty, exploring methodologies such as machine learning and spatial statistics, creating user-friendly tools for practitioners, and understanding more about how forecasts are used.

Quantifying Uncertainty to Improve Decision Making in Machine Learning.

2018 ◽  
Author(s):  
David John Stracuzzi ◽  
Michael Christopher Darling ◽  
Matthew Gregor Peterson ◽  
Maximillian Gene Chen
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Quantifying Uncertainty

scholarly journals A Systematic Survey of ML Datasets for Prime CV Research Areas—Media and Metadata

Data ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 12
Author(s):  
Helder F. Castro ◽  
Jaime S. Cardoso ◽  
Maria T. Andrade
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Qualitative Analysis ◽  
Continuous Growth ◽  
Scientific Databases ◽  
Extensive Survey ◽  
Research Areas ◽  
Current State ◽  
Global View ◽  
Cooperative Production

The ever-growing capabilities of computers have enabled pursuing Computer Vision through Machine Learning (i.e., MLCV). ML tools require large amounts of information to learn from (ML datasets). These are costly to produce but have received reduced attention regarding standardization. This prevents the cooperative production and exploitation of these resources, impedes countless synergies, and hinders ML research. No global view exists of the MLCV dataset tissue. Acquiring it is fundamental to enable standardization. We provide an extensive survey of the evolution and current state of MLCV datasets (1994 to 2019) for a set of specific CV areas as well as a quantitative and qualitative analysis of the results. Data were gathered from online scientific databases (e.g., Google Scholar, CiteSeerX). We reveal the heterogeneous plethora that comprises the MLCV dataset tissue; their continuous growth in volume and complexity; the specificities of the evolution of their media and metadata components regarding a range of aspects; and that MLCV progress requires the construction of a global standardized (structuring, manipulating, and sharing) MLCV “library”. Accordingly, we formulate a novel interpretation of this dataset collective as a global tissue of synthetic cognitive visual memories and define the immediately necessary steps to advance its standardization and integration.

scholarly journals Sampo-UI: A full stack JavaScript framework for developing semantic portal user interfaces

Semantic Web ◽  
2021 ◽  
pp. 1-16
Author(s):  
Esko Ikkala ◽  
Eero Hyvönen ◽  
Heikki Rantala ◽  
Mikko Koho
Keyword(s):  
User Interfaces ◽  
Linked Data ◽  
State Of The Art ◽  
Software Framework ◽  
End User ◽  
Faceted Search ◽  
Software Developer ◽  
Current State ◽  
Knowledge Graphs ◽  
User Friendly

This paper presents a new software framework, Sampo-UI, for developing user interfaces for semantic portals. The goal is to provide the end-user with multiple application perspectives to Linked Data knowledge graphs, and a two-step usage cycle based on faceted search combined with ready-to-use tooling for data analysis. For the software developer, the Sampo-UI framework makes it possible to create highly customizable, user-friendly, and responsive user interfaces using current state-of-the-art JavaScript libraries and data from SPARQL endpoints, while saving substantial coding effort. Sampo-UI is published on GitHub under the open MIT License and has been utilized in several internal and external projects. The framework has been used thus far in creating six published and five forth-coming portals, mostly related to the Cultural Heritage domain, that have had tens of thousands of end-users on the Web.

scholarly journals Review for the Solar Radiation Forecasting Methods Based on Machine Learning Approaches

2021 ◽  
Vol 1964 (4) ◽  
pp. 042065
Author(s):  
U Hemavathi ◽  
Ann C V Medona ◽  
V Dhilip Kumar ◽  
R Raja Sekar
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Learning Approaches ◽  
Forecasting Methods

scholarly journals A New Model Averaging Approach in Predicting Credit Risk Default

Risks ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 114
Author(s):  
Paritosh Navinchandra Jha ◽  
Marco Cucculelli
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Model Averaging ◽  
Research Direction ◽  
Future Research ◽  
Classification Problems ◽  
Average Technique ◽  
Model Average ◽  
Novel Approach ◽  
Unbalanced Dataset

The paper introduces a novel approach to ensemble modeling as a weighted model average technique. The proposed idea is prudent, simple to understand, and easy to implement compared to the Bayesian and frequentist approach. The paper provides both theoretical and empirical contributions for assessing credit risk (probability of default) effectively in a new way by creating an ensemble model as a weighted linear combination of machine learning models. The idea can be generalized to any classification problems in other domains where ensemble-type modeling is a subject of interest and is not limited to an unbalanced dataset or credit risk assessment. The results suggest a better forecasting performance compared to the single best well-known machine learning of parametric, non-parametric, and other ensemble models. The scope of our approach can be extended to any further improvement in estimating weights differently that may be beneficial to enhance the performance of the model average as a future research direction.

scholarly journals Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 99 ◽  
Author(s):  
Kleopatra Pirpinia ◽  
Peter A. N. Bosman ◽  
Jan-Jakob Sonke ◽  
Marcel van Herk ◽  
Tanja Alderliesten
Keyword(s):  
Machine Learning ◽  
Image Registration ◽  
State Of The Art ◽  
Deformable Image Registration ◽  
Optimization Approach ◽  
High Quality ◽  
Trade Off ◽  
Multi Objective ◽  
Current State ◽  
Image Artefacts

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.

Small-area population forecasting in an urban setting: a spatial regression approach

2011 ◽  
Vol 28 (2-3) ◽  
pp. 185-201 ◽  
Author(s):  
Guangqing Chi ◽  
Xuan Zhou ◽  
Paul R. Voss
Keyword(s):  
Small Area ◽  
Spatial Regression ◽  
Urban Setting ◽  
Regression Approach ◽  
Population Forecasting

ARESIBO HORIZON 2020 EUROPEAN RESEARCH PROJECT – ENRICHED SITUATION AWARENESS FOR BORDER SURVEILLANCE

2021 ◽  
Vol 17 (1) ◽  
pp. 247-255
Author(s):  
Konstantinos CHARISI ◽  
Andreas TSIGOPOULOS ◽  
Spyridon KINTZIOS ◽  
Vassilis PAPATAXIARHIS
Keyword(s):  
Augmented Reality ◽  
Situation Awareness ◽  
User Interfaces ◽  
Border Security ◽  
Multiple Sources ◽  
Horizon 2020 ◽  
Research And Innovation ◽  
Current State ◽  
Available Information ◽  
User Friendly

Abstract. The paper aims to introduce the ARESIBO project to a greater but targeted audience and outline its main scope and achievements. ARESIBO stands for “Augmented Reality Enriched Situation awareness for Border security”. In the recent years, border security has become one of the highest political priorities in EU and needs the support of every Member State. ARESIBO project is developed under HORIZON 2020 EC Research and Innovation program and it is the joint effort of 20 participant entities from 11 countries. Scientific excellence and technological innovation are top priorities as ARESIBO enhances the current state-of-the-art through technological breakthroughs in Mobile Augmented Reality and Wearables, Robust and Secure Telecommunications, Robots swarming technique and Planning of Context-Aware Autonomous Missions, and Artificial Intelligence (AI), in order to implement user-friendly tools for border and coast guards. The system aims to improve the cognitive capabilities and the perception of border guards through intuitive user interfaces that will help them acquire an improved situation awareness by filtering the huge amount of available information from multiple sources. Ultimately, it will help them respond faster and more effectively when a critical situation occurs.

scholarly journals Imitating a Safe Human Driver Behaviour in Roundabouts Through Deep Learning

2020 ◽  
Vol 19 (1) ◽  
pp. 85-88
Author(s):  
A. S. J. Cervera ◽  
F. J. Alonso ◽  
F. S. García ◽  
A. D. Alvarez
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Driver Behaviour ◽  
Safety Issues ◽  
Current State ◽  
Surrounding Environment ◽  
Human Driver ◽  
State Of The Environment

Roundabouts provide safe and fast circulation as well as many environmental advantages, but drivers adopting unsafe behaviours while circulating through them may cause safety issues, provoking accidents. In this paper we propose a way of training an autonomous vehicle in order to behave in a human and safe way when entering a roundabout. By placing a number of cameras in our vehicle and processing their video feeds through a series of algorithms, including Machine Learning, we can build a representation of the state of the surrounding environment. Then, we use another set of Deep Learning algorithms to analyze the data and determine the safest way of circulating through a roundabout given the current state of the environment, including nearby vehicles with their estimated positions, speeds and accelerations. By watching multiple attempts of a human entering a roundabout with both safe and unsafe behaviours, our second set of algorithms can learn to mimic the human’s good attempts and act in the same way as him, which is key to a safe implementation of autonomous vehicles. This work details the series of steps that we took, from building the representation of our environment to acting according to it in order to attain safe entry into single lane roundabouts.

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