1,773 research outputs found
A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization
Influence Maximization (IM) is a classical combinatorial optimization
problem, which can be widely used in mobile networks, social computing, and
recommendation systems. It aims at selecting a small number of users such that
maximizing the influence spread across the online social network. Because of
its potential commercial and academic value, there are a lot of researchers
focusing on studying the IM problem from different perspectives. The main
challenge comes from the NP-hardness of the IM problem and \#P-hardness of
estimating the influence spread, thus traditional algorithms for overcoming
them can be categorized into two classes: heuristic algorithms and
approximation algorithms. However, there is no theoretical guarantee for
heuristic algorithms, and the theoretical design is close to the limit.
Therefore, it is almost impossible to further optimize and improve their
performance. With the rapid development of artificial intelligence, the
technology based on Machine Learning (ML) has achieved remarkable achievements
in many fields. In view of this, in recent years, a number of new methods have
emerged to solve combinatorial optimization problems by using ML-based
techniques. These methods have the advantages of fast solving speed and strong
generalization ability to unknown graphs, which provide a brand-new direction
for solving combinatorial optimization problems. Therefore, we abandon the
traditional algorithms based on iterative search and review the recent
development of ML-based methods, especially Deep Reinforcement Learning, to
solve the IM problem and other variants in social networks. We focus on
summarizing the relevant background knowledge, basic principles, common
methods, and applied research. Finally, the challenges that need to be solved
urgently in future IM research are pointed out.Comment: 45 page
Inventory Optimization Model Design with Machine Learning Approach in Feed Mill Company
This article aims to address the impacts that companies can have with the application of machine learning to carry out their demand forecasts, knowing that a more accurate demand forecast improves the performance of companies, making them more competitive. The methodology used was a literature review through descriptive, qualitative and with bibliographical surveys in International Journal from 2010 – 2022 by different authors. Findings show that the references prove that demand forecasting with the use of machine learning brings many benefits to organizations, for example, since the results are more accurate, there is better inventory management, consequently customer satisfaction for having the product at the right time and place. Further, this article concludes and suggests that the use of machine learning is able to identify variables that affect the demands, with this it makes a forecast closer to reality and helps managers to make more accurate decisions, improving strategic planning and supply chain management. of company supplies
Machine Learning
Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience
A Survey on Metric Learning for Feature Vectors and Structured Data
The need for appropriate ways to measure the distance or similarity between
data is ubiquitous in machine learning, pattern recognition and data mining,
but handcrafting such good metrics for specific problems is generally
difficult. This has led to the emergence of metric learning, which aims at
automatically learning a metric from data and has attracted a lot of interest
in machine learning and related fields for the past ten years. This survey
paper proposes a systematic review of the metric learning literature,
highlighting the pros and cons of each approach. We pay particular attention to
Mahalanobis distance metric learning, a well-studied and successful framework,
but additionally present a wide range of methods that have recently emerged as
powerful alternatives, including nonlinear metric learning, similarity learning
and local metric learning. Recent trends and extensions, such as
semi-supervised metric learning, metric learning for histogram data and the
derivation of generalization guarantees, are also covered. Finally, this survey
addresses metric learning for structured data, in particular edit distance
learning, and attempts to give an overview of the remaining challenges in
metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved
presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new
method
Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning
Wastewater treatment plants are designed to eliminate pollutants and
alleviate environmental pollution. However, the construction and operation of
WWTPs consume resources, emit greenhouse gases (GHGs) and produce residual
sludge, thus require further optimization. WWTPs are complex to control and
optimize because of high nonlinearity and variation. This study used a novel
technique, multi-agent deep reinforcement learning, to simultaneously optimize
dissolved oxygen and chemical dosage in a WWTP. The reward function was
specially designed from life cycle perspective to achieve sustainable
optimization. Five scenarios were considered: baseline, three different
effluent quality and cost-oriented scenarios. The result shows that
optimization based on LCA has lower environmental impacts compared to baseline
scenario, as cost, energy consumption and greenhouse gas emissions reduce to
0.890 CNY/m3-ww, 0.530 kWh/m3-ww, 2.491 kg CO2-eq/m3-ww respectively. The
cost-oriented control strategy exhibits comparable overall performance to the
LCA driven strategy since it sacrifices environmental bene ts but has lower
cost as 0.873 CNY/m3-ww. It is worth mentioning that the retrofitting of WWTPs
based on resources should be implemented with the consideration of impact
transfer. Specifically, LCA SW scenario decreases 10 kg PO4-eq in
eutrophication potential compared to the baseline within 10 days, while
significantly increases other indicators. The major contributors of each
indicator are identified for future study and improvement. Last, the author
discussed that novel dynamic control strategies required advanced sensors or a
large amount of data, so the selection of control strategies should also
consider economic and ecological conditions
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