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Maragno D, Buti G, Birbil Şİ, Liao Z, Bortfeld T, den Hertog D, Ajdari A. Embedding machine learning based toxicity models within radiotherapy treatment plan optimization. Phys Med Biol 2024; 69:075003. [PMID: 38412530 DOI: 10.1088/1361-6560/ad2d7e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 02/27/2024] [Indexed: 02/29/2024]
Abstract
Objective.This study addresses radiation-induced toxicity (RIT) challenges in radiotherapy (RT) by developing a personalized treatment planning framework. It leverages patient-specific data and dosimetric information to create an optimization model that limits adverse side effects using constraints learned from historical data.Approach.The study uses the optimization with constraint learning (OCL) framework, incorporating patient-specific factors into the optimization process. It consists of three steps: optimizing the baseline treatment plan using population-wide dosimetric constraints; training a machine learning (ML) model to estimate the patient's RIT for the baseline plan; and adapting the treatment plan to minimize RIT using ML-learned patient-specific constraints. Various predictive models, including classification trees, ensembles of trees, and neural networks, are applied to predict the probability of grade 2+ radiation pneumonitis (RP2+) for non-small cell lung (NSCLC) cancer patients three months post-RT. The methodology is assessed with four high RP2+ risk NSCLC patients, with the goal of optimizing the dose distribution to constrain the RP2+ outcome below a pre-specified threshold. Conventional and OCL-enhanced plans are compared based on dosimetric parameters and predicted RP2+ risk. Sensitivity analysis on risk thresholds and data uncertainty is performed using a toy NSCLC case.Main results.Experiments show the methodology's capacity to directly incorporate all predictive models into RT treatment planning. In the four patients studied, mean lung dose and V20 were reduced by an average of 1.78 Gy and 3.66%, resulting in an average RP2+ risk reduction from 95% to 42%. Notably, this reduction maintains tumor coverage, although in two cases, sparing the lung slightly increased spinal cord max-dose (0.23 and 0.79 Gy).Significance.By integrating patient-specific information into learned constraints, the study significantly reduces adverse side effects like RP2+ without compromising target coverage. This unified framework bridges the gap between predicting toxicities and optimizing treatment plans in personalized RT decision-making.
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Affiliation(s)
- Donato Maragno
- Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands
| | - Gregory Buti
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Division of Radiation BioPhysics, Boston, MA, United States of America
| | - Ş İlker Birbil
- Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands
| | - Zhongxing Liao
- University of Texas' MD Anderson Cancer Center, Department of Radiation Oncology, Division of Radiation Oncology, Houston, TX, United States of America
| | - Thomas Bortfeld
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Division of Radiation BioPhysics, Boston, MA, United States of America
| | - Dick den Hertog
- Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands
| | - Ali Ajdari
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Division of Radiation BioPhysics, Boston, MA, United States of America
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Luo Q, Zhu H, Zhu J, Li Y, Yu Y, Lei L, Lin F, Zhou M, Cui L, Zhu T, Li X, Zuo H, Yang X. Artificial intelligence-enabled 8-lead ECG detection of atrial septal defect among adults: a novel diagnostic tool. Front Cardiovasc Med 2023; 10:1279324. [PMID: 38028503 PMCID: PMC10679442 DOI: 10.3389/fcvm.2023.1279324] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
Background Patients with atrial septal defect (ASD) exhibit distinctive electrocardiogram (ECG) patterns. However, ASD cannot be diagnosed solely based on these differences. Artificial intelligence (AI) has been widely used for specifically diagnosing cardiovascular diseases other than arrhythmia. Our study aimed to develop an artificial intelligence-enabled 8-lead ECG to detect ASD among adults. Method In this study, our AI model was trained and validated using 526 ECGs from patients with ASD and 2,124 ECGs from a control group with a normal cardiac structure in our hospital. External testing was conducted at Wuhan Central Hospital, involving 50 ECGs from the ASD group and 46 ECGs from the normal group. The model was based on a convolutional neural network (CNN) with a residual network to classify 8-lead ECG data into either the ASD or normal group. We employed a 10-fold cross-validation approach. Results Statistically significant differences (p < 0.05) were observed in the cited ECG features between the ASD and normal groups. Our AI model performed well in identifying ECGs in both the ASD group [accuracy of 0.97, precision of 0.90, recall of 0.97, specificity of 0.97, F1 score of 0.93, and area under the curve (AUC) of 0.99] and the normal group within the training and validation datasets from our hospital. Furthermore, these corresponding indices performed impressively in the external test data set with the accuracy of 0.82, precision of 0.90, recall of 0.74, specificity of 0.91, F1 score of 0.81 and the AUC of 0.87. And the series of experiments of subgroups to discuss specific clinic situations associated to this issue was remarkable as well. Conclusion An ECG-based detection of ASD using an artificial intelligence algorithm can be achieved with high diagnostic performance, and it shows great clinical promise. Our research on AI-enabled 8-lead ECG detection of ASD in adults is expected to provide robust references for early detection of ASD, healthy pregnancies, and related decision-making. A lower number of leads is also more favorable for the application of portable devices, which it is expected that this technology will bring significant economic and societal benefits.
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Affiliation(s)
- Qiushi Luo
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongling Zhu
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiabing Zhu
- Wuhan Zoncare Bio-Medical Electronics Co., Ltd, Wuhan, China
| | - Yi Li
- Wuhan Zoncare Bio-Medical Electronics Co., Ltd, Wuhan, China
| | - Yang Yu
- Division of Cardiology, the Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lei Lei
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fan Lin
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Minghe Zhou
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Longyan Cui
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tao Zhu
- Wuhan Zoncare Bio-Medical Electronics Co., Ltd, Wuhan, China
| | - Xuefei Li
- Wuhan National High Magnetic Field Center, Huazhong University of Science and Technology, Wuhan, China
| | - Huakun Zuo
- Wuhan National High Magnetic Field Center, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoyun Yang
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Koksal ES, Aydin E. Physics Informed Piecewise Linear Neural Networks for Process Optimization. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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4
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Baader FJ, Althaus P, Bardow A, Dahmen M. Demand response for flat nonlinear MIMO processes using dynamic ramping constraints. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
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Thorbjarnarson T, Yorke-Smith N. Optimal training of integer-valued neural networks with mixed integer programming. PLoS One 2023; 18:e0261029. [PMID: 36724189 PMCID: PMC9891529 DOI: 10.1371/journal.pone.0261029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 11/22/2021] [Indexed: 02/02/2023] Open
Abstract
Recent work has shown potential in using Mixed Integer Programming (MIP) solvers to optimize certain aspects of neural networks (NNs). However the intriguing approach of training NNs with MIP solvers is under-explored. State-of-the-art-methods to train NNs are typically gradient-based and require significant data, computation on GPUs, and extensive hyper-parameter tuning. In contrast, training with MIP solvers does not require GPUs or heavy hyper-parameter tuning, but currently cannot handle anything but small amounts of data. This article builds on recent advances that train binarized NNs using MIP solvers. We go beyond current work by formulating new MIP models which improve training efficiency and which can train the important class of integer-valued neural networks (INNs). We provide two novel methods to further the potential significance of using MIP to train NNs. The first method optimizes the number of neurons in the NN while training. This reduces the need for deciding on network architecture before training. The second method addresses the amount of training data which MIP can feasibly handle: we provide a batch training method that dramatically increases the amount of data that MIP solvers can use to train. We thus provide a promising step towards using much more data than before when training NNs using MIP models. Experimental results on two real-world data-limited datasets demonstrate that our approach strongly outperforms the previous state of the art in training NN with MIP, in terms of accuracy, training time and amount of data. Our methodology is proficient at training NNs when minimal training data is available, and at training with minimal memory requirements-which is potentially valuable for deploying to low-memory devices.
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Affiliation(s)
- Tómas Thorbjarnarson
- Algorithmics Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Neil Yorke-Smith
- Algorithmics Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
- * E-mail:
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Iftakher A, Monjur MS, Hasan MMF. An Overview of Computer‐aided Molecular and Process Design. CHEM-ING-TECH 2023. [DOI: 10.1002/cite.202200172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Ashfaq Iftakher
- Texas A&M University Artie McFerrin Department of Chemical Engineering 100 Spence St. TX 77843-3122 College Station USA
| | - Mohammed Sadaf Monjur
- Texas A&M University Artie McFerrin Department of Chemical Engineering 100 Spence St. TX 77843-3122 College Station USA
| | - M. M. Faruque Hasan
- Texas A&M University Artie McFerrin Department of Chemical Engineering 100 Spence St. TX 77843-3122 College Station USA
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Luo Y, Ierapetritou M. Multifeedstock and Multiproduct Process Design Using Neural Network Surrogate Flexibility Constraints. Ind Eng Chem Res 2023. [DOI: 10.1021/acs.iecr.2c02968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Yuqing Luo
- University of Delaware, Department of Chemical and Biomolecular Engineering, 150 Academy Street, Newark, Delaware19716, United States
| | - Marianthi Ierapetritou
- University of Delaware, Department of Chemical and Biomolecular Engineering, 150 Academy Street, Newark, Delaware19716, United States
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8
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A Parametric Approach to Identify Synergistic Domains of Process Intensification for Reactive Separation. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.118337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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9
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Ma K, Sahinidis NV, Bindlish R, Bury SJ, Haghpanah R, Rajagopalan S. Data-driven strategies for extractive distillation unit optimization. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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10
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Sildir H, Sarrafi S, Aydin E. Optimal artificial neural network architecture design for modeling an industrial ethylene oxide plant. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107850] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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11
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12
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Misra S, Sundar K, Sharma R, Brink K. Deployable, Data-Driven Unmanned Vehicle Navigation System in GPS-Denied, Feature-Deficient Environments. J INTELL ROBOT SYST 2022. [DOI: 10.1007/s10846-022-01647-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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13
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Langiu M, Dahmen M, Mitsos A. Simultaneous optimization of design and operation of an air-cooled geothermal ORC under consideration of multiple operating points. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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14
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Multilevel surrogate modeling of an amine scrubbing process for
CO
2
capture. AIChE J 2022. [DOI: 10.1002/aic.17705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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A Neural Network Based Superstructure Optimization Approach to Reverse Osmosis Desalination Plants. MEMBRANES 2022; 12:membranes12020199. [PMID: 35207120 PMCID: PMC8879670 DOI: 10.3390/membranes12020199] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/05/2022] [Accepted: 02/07/2022] [Indexed: 12/10/2022]
Abstract
An ever-growing population together with globally depleting water resources pose immense stresses for water supply systems. Desalination technologies can reduce these stresses by generating fresh water from saline water sources. Reverse osmosis (RO), as the industry leading desalination technology, typically involves a complex network of membrane modules that separate unwanted particles from water. The optimal design and operation of these complex RO systems can be computationally expensive. In this work, we present a modeling and optimization strategy for addressing the optimal operation of an industrial-scale RO plant. We employ a feed-forward artificial neural network (ANN) surrogate modeling representation with rectified linear units as activation functions to capture the membrane behavior accurately. Several ANN set-ups and surrogate models are presented and evaluated, based on collected data from the H2Oaks RO desalination plant in South-Central Texas. The developed ANN is then transformed into a mixed-integer linear programming formulation for the purpose of minimizing energy consumption while maximizing water utilization. Trade-offs between the two competing objectives are visualized in a Pareto front, where indirect savings can be uncovered by comparing energy consumption for an array of water recoveries and feed flows.
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Schweidtmann AM, Esche E, Fischer A, Kloft M, Repke J, Sager S, Mitsos A. Machine Learning in Chemical Engineering: A Perspective. CHEM-ING-TECH 2021. [DOI: 10.1002/cite.202100083] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Artur M. Schweidtmann
- Delft University of Technology Department of Chemical Engineering Van der Maasweg 9 2629 HZ Delft The Netherlands
- RWTH Aachen University Aachener Verfahrenstechnik Forckenbeckstr. 51 52074 Aachen Germany
| | - Erik Esche
- Technische Universität Berlin Fachgebiet Dynamik und Betrieb technischer Anlagen Straße des 17. Juni 135 10623 Berlin Germany
| | - Asja Fischer
- Ruhr-Universität Bochum Department of Mathematics Universitätsstraße 150 44801 Bochum Germany
| | - Marius Kloft
- Technische Universität Kaiserslautern Department of Computer Science Erwin-Schrödinger-Straße 52 67663 Kaiserslautern Germany
| | - Jens‐Uwe Repke
- Technische Universität Berlin Fachgebiet Dynamik und Betrieb technischer Anlagen Straße des 17. Juni 135 10623 Berlin Germany
| | - Sebastian Sager
- Otto-von-Guericke-Universität Magdeburg Department of Mathematics Universitätsplatz 2 39106 Magdeburg Germany
| | - Alexander Mitsos
- RWTH Aachen University Aachener Verfahrenstechnik Forckenbeckstr. 51 52074 Aachen Germany
- JARA Center for Simulation and Data Science (CSD) Aachen Germany
- Forschungszentrum Jülich Institute for Energy and Climate Research IEK-10 Energy Systems Engineering Wilhelm-Johnen-Straße 52428 Jülich Germany
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Tsay C. Sobolev trained neural network surrogate models for optimization. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Vurtur Badarinath P, Chierichetti M, Davoudi Kakhki F. A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems. SENSORS 2021; 21:s21051654. [PMID: 33673605 PMCID: PMC7957535 DOI: 10.3390/s21051654] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 02/22/2021] [Accepted: 02/23/2021] [Indexed: 11/17/2022]
Abstract
Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.
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Affiliation(s)
| | - Maria Chierichetti
- Department of Aerospace Engineering, San Jose’ State University, San Jose, CA 95192, USA
- Correspondence:
| | - Fatemeh Davoudi Kakhki
- Machine Learning and Safety Analytics Lab, Department of Technology, San Jose’ State University, San Jose, CA 95192, USA;
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Yang SB, Li Z, Wu W. Data-Driven Process Optimization Considering Surrogate Model Prediction Uncertainty: A Mixture Density Network-Based Approach. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.0c04214] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Shu-Bo Yang
- Department of Chemical and Materials Engineering, University of Alberta, 9211 116 Street, Edmonton, Alberta, Canada T6G1H9
| | - Zukui Li
- Department of Chemical and Materials Engineering, University of Alberta, 9211 116 Street, Edmonton, Alberta, Canada T6G1H9
| | - Wei Wu
- Department of Chemical Engineering, National Cheng Kung University, No. 1, University Road, Tainan 70101, Taiwan
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Pappas I, Kenefake D, Burnak B, Avraamidou S, Ganesh HS, Katz J, Diangelakis NA, Pistikopoulos EN. Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward. FRONTIERS IN CHEMICAL ENGINEERING 2021. [DOI: 10.3389/fceng.2020.620168] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
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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Electric Field Evaluation Using the Finite Element Method and Proxy Models for the Design of Stator Slots in a Permanent Magnet Synchronous Motor. ELECTRONICS 2020. [DOI: 10.3390/electronics9111975] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The efficiency of electric motors is being improved every day and projects with design variations can improve their performance. Among electric motors, the Permanent Magnet Synchronous Machine (PMSM) is being increasingly used, because of its growing use in electric vehicles. Simulating design variations using the Finite Element Method (FEM) can improve PMSM design, and by optimizing the parameters based on the FEM, even better results can be achieved. The design of the PMSM stator slots must be evaluated, as conductors are accommodated and an electrical potential is applied at this location. The FEM parameters are varied, and the results can be used to build an approximate model, known as a proxy model. The proxy model can then be used in a mathematical programming problem to optimize the design of stators that have less electric field in certain regions, thus reducing the chance of developing a failure. The results of the proposed methodology show that its application is promising for machine design and can also be used for the design of other systems.
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Sriporn K, Tsai CF, Tsai CE, Wang P. Analyzing Malaria Disease Using Effective Deep Learning Approach. Diagnostics (Basel) 2020; 10:diagnostics10100744. [PMID: 32987888 PMCID: PMC7601431 DOI: 10.3390/diagnostics10100744] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 09/23/2020] [Accepted: 09/23/2020] [Indexed: 11/16/2022] Open
Abstract
Medical tools used to bolster decision-making by medical specialists who offer malaria treatment include image processing equipment and a computer-aided diagnostic system. Malaria images can be employed to identify and detect malaria using these methods, in order to monitor the symptoms of malaria patients, although there may be atypical cases that need more time for an assessment. This research used 7000 images of Xception, Inception-V3, ResNet-50, NasNetMobile, VGG-16 and AlexNet models for verification and analysis. These are prevalent models that classify the image precision and use a rotational method to improve the performance of validation and the training dataset with convolutional neural network models. Xception, using the state of the art activation function (Mish) and optimizer (Nadam), improved the effectiveness, as found by the outcomes of the convolutional neural model evaluation of these models for classifying the malaria disease from thin blood smear images. In terms of the performance, recall, accuracy, precision, and F1 measure, a combined score of 99.28% was achieved. Consequently, 10% of all non-dataset training and testing images were evaluated utilizing this pattern. Notable aspects for the improvement of a computer-aided diagnostic to produce an optimum malaria detection approach have been found, supported by a 98.86% accuracy level.
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Affiliation(s)
- Krit Sriporn
- Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology, Neipu, Pingtung 91201, Taiwan;
- Department of Information Technology, Suratthani Rajabhat University, Suratthani 84100, Thailand
| | - Cheng-Fa Tsai
- Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
- Correspondence: ; Tel.: +886-08-770-3202 (ext. 7906)
| | - Chia-En Tsai
- Department of Biochemistry and Molecular Biology, National Cheng Kung University, Tainan 70101, Taiwan;
| | - Paohsi Wang
- Department of Food and Beverage Management, Cheng Shiu University, Kaohsiung 83347, Taiwan;
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Katz J, Pappas I, Avraamidou S, Pistikopoulos EN. Integrating deep learning models and multiparametric programming. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106801] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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Analyzing Lung Disease Using Highly Effective Deep Learning Techniques. Healthcare (Basel) 2020; 8:healthcare8020107. [PMID: 32340344 PMCID: PMC7348888 DOI: 10.3390/healthcare8020107] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 04/14/2020] [Accepted: 04/20/2020] [Indexed: 01/14/2023] Open
Abstract
Image processing technologies and computer-aided diagnosis are medical technologies used to support decision-making processes of radiologists and medical professionals who provide treatment for lung disease. These methods involve using chest X-ray images to diagnose and detect lung lesions, but sometimes there are abnormal cases that take some time to occur. This experiment used 5810 images for training and validation with the MobileNet, Densenet-121 and Resnet-50 models, which are popular networks used to classify the accuracy of images, and utilized a rotational technique to adjust the lung disease dataset to support learning with these convolutional neural network models. The results of the convolutional neural network model evaluation showed that Densenet-121, with a state-of-the-art Mish activation function and Nadam-optimized performance. All the rates for accuracy, recall, precision and F1 measures totaled 98.88%. We then used this model to test 10% of the total images from the non-dataset training and validation. The accuracy rate was 98.97% for the result which provided significant components for the development of a computer-aided diagnosis system to yield the best performance for the detection of lung lesions.
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A Critical Review of the Modeling and Optimization of Combined Heat and Power Dispatch. Processes (Basel) 2020. [DOI: 10.3390/pr8040441] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Combined heat and power (CHP) systems are attracting increasing attention for their ability to improve the economics and sustainability of the electricity system. Determining how to best operate these systems is difficult because they can consist of many generating units whose operation is governed by complex nonlinear physics. Mathematical programming is a useful tool to support the operation of CHP systems, and has been the subject of substantial research attention since the early 1990s. This paper critically reviews the modeling and optimization work that has been done on the CHP economic dispatch problem, and the CHP economic and emission dispatch problem. A summary of the common models used for these problems is provided, along with comments on future modeling work that would beneficial to the field. The majority of optimization approaches studied for CHP system operation are metaheuristic algorithms. A discussion of the limitations and benefits of metaheuristic algorithms is given. Finally, a case study optimizing five classic CHP system test instances demonstrates the advantages of the using deterministic global search algorithms over metaheuristic search algorithms.
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