Machine Learning with Operational Costs

This work proposes a way to align statistical modeling with decision making. We provide a method that propagates the uncertainty in predictive modeling to the uncertainty in operational cost, where operational cost is the amount spent by the practitioner in solving the problem. The method allows us to explore the range of operational costs associated with the set of reasonable statistical models, so as to provide a useful way for practitioners to understand uncertainty. To do this, the operational cost is cast as a regularization term in a learning algorithm’s objective function, allowing either an optimistic or pessimistic view of possible costs, depending on the regularization parameter. From another perspective, if we have prior knowledge about the operational cost, for instance that it should be low, this knowledge can help to restrict the hypothesis space, and can help with generalization. We provide a theoretical generalization bound for this scenario. We also show that learning with operational costs is related to robust optimization.Fulbright Program (Science and Technology Fellowship)Solomon Buchsbaum Research FundNational Science Foundation (U.S.) (Grant IIS-1053407

Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach

During the last years, machine learning-based control and optimization systems are playing an important role in the operation of wastewater treatment plants in terms of reduced operational costs and improved effluent quality. In this paper, a machine learning-based control strategy is proposed for optimizing both the consumption and the number of regulation violations of a biological wastewater treatment plant. The methodology proposed in this study uses neural networks as a soft-sensor for on-line prediction of the effluent quality and as an identification model of the plant dynamics, all under a neuro-genetic optimum model-based control approach. The complete scheme was tested on a simulation model of the activated sludge process of a large-scale municipal wastewater treatment plant running under the GPS-X simulation frame and validated with operational gathered data, showing optimal control performance by minimizing operational costs while satisfying the effluent requirements, thus reducing the investment in expensive sensor devices.info:eu-repo/semantics/publishedVersio

Information and communication technology solutions for outdoor navigation in dementia

INTRODUCTION: Information and communication technology (ICT) is potentially mature enough to empower outdoor and social activities in dementia. However, actual ICT-based devices have limited functionality and impact, mainly limited to safety. What is an ideal operational framework to enhance this field to support outdoor and social activities? METHODS: Review of literature and cross-disciplinary expert discussion. RESULTS: A situation-aware ICT requires a flexible fine-tuning by stakeholders of system usability and complexity of function, and of user safety and autonomy. It should operate by artificial intelligence/machine learning and should reflect harmonized stakeholder values, social context, and user residual cognitive functions. ICT services should be proposed at the prodromal stage of dementia and should be carefully validated within the life space of users in terms of quality of life, social activities, and costs. DISCUSSION: The operational framework has the potential to produce ICT and services with high clinical impact but requires substantial investment

Cost benefits of using machine learning features in NIDS for cyber security in UK small medium enterprises (SME)

Cyber security has made an impact and has challenged Small and Medium Enterprises (SMEs) in their approaches towards how they protect and secure data. With an increase in more wired and wireless connections and devices on SME networks, unpredictable malicious activities and interruptions have risen. Finding the harmony between the advancement of technology and costs has always been a balancing act particularly in convincing the finance directors of these SMEs to invest in capital towards their IT infrastructure. This paper looks at various devices that currently are in the market to detect intrusions and look at how these devices handle prevention strategies for SMEs in their working environment both at home and in the office, in terms of their credibility in handling zero-day attacks against the costs of achieving so. The experiment was set up during the 2020 pandemic referred to as COVID-19 when the world experienced an unprecedented event of large scale. The operational working environment of SMEs reflected the context when the UK went into lockdown. Pre-pandemic would have seen this experiment take full control within an operational office environment; however, COVID-19 times has pushed us into a corner to evaluate every aspect of cybersecurity from the office and keeping the data safe within the home environment. The devices chosen for this experiment were OpenSource such as SNORT and pfSense to detect activities within the home environment, and Cisco, a commercial device, set up within an SME network. All three devices operated in a live environment within the SME network structure with employees being both at home and in the office. All three devices were observed from the rules they displayed, their costs and machine learning techniques integrated within them. The results revealed these aspects to be important in how they identified zero-day attacks. The findings showed that OpenSource devices whilst free to download, required a high level of expertise in personnel to implement and embed machine learning rules into the business solution even for staff working from home. However, when using Cisco, the price reflected the buy-in into this expertise and Cisco’s mainframe network, to give up-to-date information on cyber-attacks. The requirements of the UK General Data Protection Regulations Act (GDPR) were also acknowledged as part of the broader framework of the study. Machine learning techniques such as anomaly-based intrusions did show better detection through a commercially subscription-based model for support from Cisco compared to that of the OpenSource model which required internal expertise in machine learning. A cost model was used to compare the outcome of SMEs’ decision making, in getting the right framework in place in securing their data. In conclusion, finding a balance between IT expertise and costs of products that are able to help SMEs protect and secure their data will benefit the SMEs from using a more intelligent controlled environment with applied machine learning techniques, and not compromising on costs.</p

Estimation of minimum sample size for identification of the most important features: a case study providing a qualitative B2B sales data set

An important task in machine learning is to reduce data set dimensionality, which in turn contributes to reducing computational load and data collection costs, while improving human understanding and interpretation of models. We introduce an operational guideline for determining the minimum number of instances sufficient to identify correct ranks of features with the highest impact. We conduct tests based on qualitative B2B sales forecasting data. The results show that a relatively small instance subset is sufficient for identifying the most important features when rank is not important

Bin Packing through Machine Learning

In this thesis project we propose a wide range of Machine Learning techniques for dealing with the Bin Packing problem. The business domain is transportation optimization, a popular application field of Operational Research methods. The work is inspired by a real project by the consulting firm Horsa Group. The aim is to inspect the business problem from a mathematical point of view and to focus on different state-of-the-art techniques involving Machine Learning. The objective is to give an overview of the different possible approaches for further developments and compare the pros and cons of possible solutions. We will also compare the performances of those techniques on generated example data and real-world data. The final goal is to reduce the costs of the shipping process by increasing efficiency. The focus will be on how the shipping pallets are composed, packing the items with an efficient and scalable framework. The road map consists in defining in a formal way the Operational Research problem and the business problem, to compare classical approaches with some of the methods that nowadays are more and more popular and involve Machine Learning techniques. Some of those approaches involve Deep Reinforcement Learning and Graph Neural Networks. Finally, we will inspect a wide range of possibilities for making the bin packing process more efficient, simulating different real case scenarios. The aim is to give a clear overview of future developments in Bin Packing Optimization algorithms. Those developments can make the company’s shipping software scalable and well-performing, with more efficient use of resources.In this thesis project we propose a wide range of Machine Learning techniques for dealing with the Bin Packing problem. The business domain is transportation optimization, a popular application field of Operational Research methods. The work is inspired by a real project by the consulting firm Horsa Group. The aim is to inspect the business problem from a mathematical point of view and to focus on different state-of-the-art techniques involving Machine Learning. The objective is to give an overview of the different possible approaches for further developments and compare the pros and cons of possible solutions. We will also compare the performances of those techniques on generated example data and real-world data. The final goal is to reduce the costs of the shipping process by increasing efficiency. The focus will be on how the shipping pallets are composed, packing the items with an efficient and scalable framework. The road map consists in defining in a formal way the Operational Research problem and the business problem, to compare classical approaches with some of the methods that nowadays are more and more popular and involve Machine Learning techniques. Some of those approaches involve Deep Reinforcement Learning and Graph Neural Networks. Finally, we will inspect a wide range of possibilities for making the bin packing process more efficient, simulating different real case scenarios. The aim is to give a clear overview of future developments in Bin Packing Optimization algorithms. Those developments can make the company’s shipping software scalable and well-performing, with more efficient use of resources