11,762 research outputs found
Using Machine Learning for Handover Optimization in Vehicular Fog Computing
Smart mobility management would be an important prerequisite for future fog
computing systems. In this research, we propose a learning-based handover
optimization for the Internet of Vehicles that would assist the smooth
transition of device connections and offloaded tasks between fog nodes. To
accomplish this, we make use of machine learning algorithms to learn from
vehicle interactions with fog nodes. Our approach uses a three-layer
feed-forward neural network to predict the correct fog node at a given location
and time with 99.2 % accuracy on a test set. We also implement a dual stacked
recurrent neural network (RNN) with long short-term memory (LSTM) cells capable
of learning the latency, or cost, associated with these service requests. We
create a simulation in JAMScript using a dataset of real-world vehicle
movements to create a dataset to train these networks. We further propose the
use of this predictive system in a smarter request routing mechanism to
minimize the service interruption during handovers between fog nodes and to
anticipate areas of low coverage through a series of experiments and test the
models' performance on a test set
A deep learning integrated Lee-Carter model
In the field of mortality, the LeeâCarter based approach can be considered the milestone
to forecast mortality rates among stochastic models. We could define a âLeeâCarter model familyâ
that embraces all developments of this model, including its first formulation (1992) that remains the
benchmark for comparing the performance of future models. In the LeeâCarter model, the kt parameter,
describing the mortality trend over time, plays an important role about the future mortality behavior.
The traditional ARIMA process usually used to model kt shows evident limitations to describe the future
mortality shape. Concerning forecasting phase, academics should approach a more plausible way in
order to think a nonlinear shape of the projected mortality rates. Therefore, we propose an alternative
approach the ARIMA processes based on a deep learning technique. More precisely, in order to catch
the pattern of kt series over time more accurately, we apply a Recurrent Neural Network with a Long
Short-Term Memory architecture and integrate the LeeâCarter model to improve its predictive capacity.
The proposed approach provides significant performance in terms of predictive accuracy and also allow
for avoiding the time-chunksâ a priori selection. Indeed, it is a common practice among academics to
delete the time in which the noise is overflowing or the data quality is insufficient. The strength of
the Long Short-Term Memory network lies in its ability to treat this noise and adequately reproduce it
into the forecasted trend, due to its own architecture enabling to take into account significant long-term
patterns
The Role of Emerging Predictive IT Tools in Effective Migration Governance
Predicting mass migration is one of the main challenges for policymakers and NGOs working with migrants worldwide. Recently there has been a considerable increase in the use of computational techniques to predict migration flows, and advances have allowed for application of improved algorithms in the field. However, given the rapid pace of technological development facilitating these new predictive tools and methods for migration, it is important to address the extent to which such instruments and techniques engage with and impact migration governance. This study provides an in-depth examination of selected existing predictive tools in the migration field and their impact on the governance of migratory flows. It focuses on a comparative qualitative examination of these toolsâ scope, as well as how these characteristics link to their respective underlying migration theory, research question, or objective. It overviews how several organisations have developed tools to predict short- or longer-term migration patterns, or to assess and estimate migration uncertainties. At the same time, it demonstrates how and why these instruments continue to face limitations that in turn affect migration management, especially as it relates to increasing EU institutional and stakeholder efforts to forecast or predict mixed migration. The main predictive migration tools in use today cover different scopes and uses, and as such are equally valid in shaping the requirements for a future, fully comprehensive predictive migration tool. This article provides clarity on the requirements and features for such a tool and draws conclusions as to the risks and opportunities any such tool could present for the future of EU migration governance
The Role of emerging predictive IT tools in effective Mmigration governance
Predicting mass migration is one of the main challenges for policymakers and NGOs working with migrants worldwide. Recently there has been a considerable increase in the use of computational techniques to predict migration flows, and advances have allowed for application of improved algorithms in the field. However, given the rapid pace of technological development facilitating these new predictive tools and methods for migration, it is important to address the extent to which such instruments and techniques engage with and impact migration governance. This study provides an in-depth examination of selected existing predictive tools in the migration field and their impact on the governance of migratory flows. It focuses on a comparative qualitative examination of these tools' scope, as well as how these characteristics link to their respective underlying migration theory, research question, or objective. It overviews how several organisations have developed tools to predict short- or longer-term migration patterns, or to assess and estimate migration uncertain- ties. At the same time, it demonstrates how and why these instruments continue to face limitations that in turn affect migration management, especially as it relates to increasing EU institutional and stakeholder efforts to forecast or predict mixed migration. The main predictive migration tools in use today cover different scopes and uses, and as such are equally valid in shaping the requirements for a future, fully comprehensive predictive migration tool. This article provides clarity on the requirements and features for such a tool and draws conclusions as to the risks and opportunities any such tool could present for the future of EU migration governance
Smart Grid Technologies in Europe: An Overview
The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity networkâthe smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio
Predicting irregular migration: high hopes, meagre results
German and European migration policy operates in permanent crisis mode. Sudden increases in irregular immigration create a sense of loss of control, which is instrumentalised by populist forces. This has generated great interest in quantitative migration predictions. High expectations are placed in the AI-based tools currently under develÂopÂment for forecasting irregular migration. The potential applications of these tools are manifold. They range from managing and strengthening the EUâs reception capacity and border protections to configuring humanitarian aid provision and longer-term planning of development programmes. There is a significant gap between the expectations placed in the new instruments and their practical utility. Technical limits exist, medium-term forecasts are methodologically implausible, and channels for feeding the results into political decision-making processes are lacking. The great demand for predictions is driven by the political functions of migration prediction, which include its uses in political communication, funding acquisition and legitimisation of political decisions. Investment in the quality of the underlying data will be more productive than developing a succession of new prediction tools. Funding for appliÂcations in emergency relief and development cooperation should be prioritised. Crisis early warning and risk analysis should also be strengthened and their networking improved. (author's abstract
Ambulance Emergency Response Optimization in Developing Countries
The lack of emergency medical transportation is viewed as the main barrier to
the access of emergency medical care in low and middle-income countries
(LMICs). In this paper, we present a robust optimization approach to optimize
both the location and routing of emergency response vehicles, accounting for
uncertainty in travel times and spatial demand characteristic of LMICs. We
traveled to Dhaka, Bangladesh, the sixth largest and third most densely
populated city in the world, to conduct field research resulting in the
collection of two unique datasets that inform our approach. This data is
leveraged to develop machine learning methodologies to estimate demand for
emergency medical services in a LMIC setting and to predict the travel time
between any two locations in the road network for different times of day and
days of the week. We combine our robust optimization and machine learning
frameworks with real data to provide an in-depth investigation into three
policy-related questions. First, we demonstrate that outpost locations
optimized for weekday rush hour lead to good performance for all times of day
and days of the week. Second, we find that significant improvements in
emergency response times can be achieved by re-locating a small number of
outposts and that the performance of the current system could be replicated
using only 30% of the resources. Lastly, we show that a fleet of small
motorcycle-based ambulances has the potential to significantly outperform
traditional ambulance vans. In particular, they are able to capture three times
more demand while reducing the median response time by 42% due to increased
routing flexibility offered by nimble vehicles on a larger road network. Our
results provide practical insights for emergency response optimization that can
be leveraged by hospital-based and private ambulance providers in Dhaka and
other urban centers in LMICs
Load-shifting inside production lines
This thesis will have the goal to investigate possibilities for optimization of schedules of
factory s production lines, to minimize the cost of electrical energy used. During this research,
different operational constrains will have to be identified and taken into account, during
optimization. At the end, based on discovered limitations, and other necessary assumptions,
models for factory process will be devised in order to be optimized based on suitable
algorithms
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