Integrating deep learning models and multiparametric programming

Katz, Justin; Pappas, Iosif; Avraamidou, Styliani; Pistikopoulos, Efstratios N.

doi:10.1016/j.compchemeng.2020.106801

Cited by 42 publications

(31 citation statements)

References 39 publications

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“…DL models are a class of approximate models proven to have strong predictive capabilities for representing complex phenomena [60]. Approximate models are currently experiencing a radical shift due to the advent of DL.…”

Section: Deep Data-driven Modelsmentioning

confidence: 99%

“…However, our research into the existing literature reveals a scarcity of research utilizing DL in approximate modeling. The introduction of DL models into an optimization formulation provides a means to reduce the problem complexity and maintain model accuracy [60]. Recently it has been shown that DL models in the form of neural networks with rectified linear units can be exactly recast as a mixed-integer linear programming formulation.…”

Section: Deep Data-driven Modelsmentioning

confidence: 99%

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Artificial Intelligence and Its Application in Optimization under Uncertainty

Sadeghi¹,

Amiri²,

Mooseloo³

2022

Artificial Intelligence

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Nowadays, the increase in data acquisition and availability and complexity around optimization make it imperative to jointly use artificial intelligence (AI) and optimization for devising data-driven and intelligent decision support systems (DSS). A DSS can be successful if large amounts of interactive data proceed fast and robustly and extract useful information and knowledge to help decision-making. In this context, the data-driven approach has gained prominence due to its provision of insights for decision-making and easy implementation. The data-driven approach can discover various database patterns without relying on prior knowledge while also handling flexible objectives and multiple scenarios. This chapter reviews recent advances in data-driven optimization, highlighting the promise of data-driven optimization that integrates mathematical programming and machine learning (ML) for decision-making under uncertainty and identifies potential research opportunities. This chapter provides guidelines and implications for researchers, managers, and practitioners in operations research who want to advance their decision-making capabilities under uncertainty concerning data-driven optimization. Then, a comprehensive review and classification of the relevant publications on the data-driven stochastic program, data-driven robust optimization, and data-driven chance-constrained are presented. This chapter also identifies fertile avenues for future research that focus on deep-data-driven optimization, deep data-driven models, as well as online learning-based data-driven optimization. Perspectives on reinforcement learning (RL)-based data-driven optimization and deep RL for solving NP-hard problems are discussed. We investigate the application of data-driven optimization in different case studies to demonstrate improvements in operational performance over conventional optimization methodology. Finally, some managerial implications and some future directions are provided.

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Section: Deep Data-driven Modelsmentioning

confidence: 99%

Section: Deep Data-driven Modelsmentioning

confidence: 99%

Artificial Intelligence and Its Application in Optimization under Uncertainty

Sadeghi¹,

Amiri²,

Mooseloo³

2022

Artificial Intelligence

View full text Add to dashboard Cite

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“…The impact of machine learning models to approximate nonlinear functions for modeling purposes in the context of mpMPC has also been explored. Most notably, Katz et al (2020b) presented a framework for the integration of deep neural networks with ReLU as activation functions and multiparametric programming. Given that a ReLU-based neural network includes max operators which are challenging to be introduced in multiparametric optimization formulation, the authors reformulated the neural network as a mixed-integer linear model that can be readily solved through existing multiparametric programming algorithms.…”

Section: Integration Of Machine Learning and Multiparametric Programmingmentioning

confidence: 99%

Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward

et al. 2021

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The inevitable presence of uncertain parameters in critical applications of process optimization can lead to undesirable or infeasible solutions. For this reason, optimization under parametric uncertainty was, and continues to be a core area of research within Process Systems Engineering. Multiparametric programming is a strategy that offers a holistic perspective for the solution of this class of mathematical programming problems. Specifically, multiparametric programming theory enables the derivation of the optimal solution as a function of the uncertain parameters, explicitly revealing the impact of uncertainty in optimal decision-making. By taking advantage of such a relationship, new breakthroughs in the solution of challenging formulations with uncertainty have been created. Apart from that, researchers have utilized multiparametric programming techniques to solve deterministic classes of problems, by treating specific elements of the optimization program as uncertain parameters. In the past years, there has been a significant number of publications in the literature involving multiparametric programming. The present review article covers recent theoretical, algorithmic, and application developments in multiparametric programming. Additionally, several areas for potential contributions in this field are discussed, highlighting the benefits of multiparametric programming in future research efforts.

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“…In contrast, ML, deep learning (DL), and AI excel at automatic pattern recognition from large amounts of biomedical image data. In particular, machine learning and deep learning algorithms (e.g., support vector machine, neural network, and convolutional neural network) have achieved impressive results in biomedical image classification [14][15][16][17][18][19][20][21][22][23]. Classification helps to organize biomedical image databases into image categories before diagnostics [24][25][26][27][28][29][30].…”

Section: A Survey Of Biomedical Imagementioning

confidence: 99%

“…DL particularly CNN has shown an intrinsic ability to automatically extract the high-level representations from big data [36]. CNN is an artificial neural network with many hidden layers of units between the input and output layers and millions or billions of parameters [21,[68][69][70][71]. General, DL architecture is composed of one or more convolutional layers with many hidden networks, one or more max pooling operations, and a full connection layer.…”

Section: Machine Learning Algorithm (Svm or Cnn)mentioning

confidence: 99%

Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms

Tchapga

Mih

Kouanou

et al. 2021

Journal of Healthcare Engineering

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In modern-day medicine, medical imaging has undergone immense advancements and can capture several biomedical images from patients. In the wake of this, to assist medical specialists, these images can be used and trained in an intelligent system in order to aid the determination of the different diseases that can be identified from analyzing these images. Classification plays an important role in this regard; it enhances the grouping of these images into categories of diseases and optimizes the next step of a computer-aided diagnosis system. The concept of classification in machine learning deals with the problem of identifying to which set of categories a new population belongs. When category membership is known, the classification is done on the basis of a training set of data containing observations. The goal of this paper is to perform a survey of classification algorithms for biomedical images. The paper then describes how these algorithms can be applied to a big data architecture by using the Spark framework. This paper further proposes the classification workflow based on the observed optimal algorithms, Support Vector Machine and Deep Learning as drawn from the literature. The algorithm for the feature extraction step during the classification process is presented and can be customized in all other steps of the proposed classification workflow.

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Integrating deep learning models and multiparametric programming

Cited by 42 publications

References 39 publications

Artificial Intelligence and Its Application in Optimization under Uncertainty

Artificial Intelligence and Its Application in Optimization under Uncertainty

Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward

Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms

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