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Boosted machine learning model for predicting intradialytic hypotension using serum biomarkers of nutrition

COMPUTERS IN BIOLOGY AND MEDICINE 2022年 147卷 105752-105752页

作者： Yang, Xiao Zhao, Dong Yu, Fanhua Heidari, Ali Asghar Bano, Yasmeen Ibrohimov, Alisherjon Liu, Yi Cai, Zhennao Chen, Huiling Chen, Xumin Changchun Univ Sci & Technol Sch Comp Sci & Technol Changchun 130022 Jilin Peoples R China Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Jilin Peoples R China Beihua Univ Coll Comp Sci & Technol Jilin 132013 Jilin Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Wenzhou Med Univ Dept Urol Affiliated Hosp 1 Wenzhou 325000 Zhejiang Peoples R China Wenzhou Med Univ Dept Nephrol Affiliated Hosp 1 Wenzhou 325000 Zhejiang Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China

Intradialytic hypotension (IDH) is a serious complication of hemodialysis (HD), with an incidence of more than 20%. IDH induces ischemic organ damage and even reduces the ultrafiltration and duration of HD sessions. Frequent attacks of IDH are a risk factor for death in HD patients. Malnutrition is common in HD patients and is also associated with mortality. Although the link between IDH episodes and malnutrition has been observed in practice, it has not been supported by the data. To study the relationship, we propose a promising hybrid model called BSCWJAYA_KELM, which is a wrapper feature selection method based on a variant of the JAYA optimization algorithm (SCWJAYA) and Kernel extreme learning machine (KELM). In this paper, we verify the optimization capability of the SCWJAYA algorithm in the model by comparing experiments with some state-of-the-art methods for IEEE CEC2014, IEEE CEC2017, and IEEE CEC2019 benchmark functions. The prediction accuracy of BSCWJAYA_KELM is validated by the public datasets and the HD dataset. In the experiments on the HD dataset, 1940 HD sessions of 178 HD patients are analyzed by the developed BSCWJAYA_KELM model. The key indicators selected from vast amounts of data are serum uric acid, dialysis vintage, age, diastolic pressure, and albumin. The BSCWJAYA_KELM method is a stable and excellent prediction model that can achieve a more accurate prediction of IDH.

关键词： Intradialytic hypotension Hemodialysis optimization algorithm Feature selection Kernel extreme learning machine KELM ELM Machine learning IEEE CEC

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Image and video dehazing based on transmission estimation and refinement using Jaya algorithm

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OPTIK 2022年 265卷

作者： Ashwini, K. Nenavath, Hathiram Jatoth, Ravi Kumar Natl Inst Technol Dept Elect & Commun Engn Warangal 506004 Andhra Pradesh India Vardhaman Coll Engn Autonomous Dept Elect & Commun Engn Hyderabad 501218 India

Due to its in-built ill-posed nature, image and video dehazing is an immensely thought-provoking problem, and it is an essential pre-processing step for various applications such as ADAS (automatic driver-assistance systems), security and surveillance, remote sensing, and long-range imaging. We proposed a novel single image and video dehazing method, called JA-dehaze, by using Jaya algorithm (JA) to address this problem. The technique consists of three factors: Discrete Haar Wavelet Transform (DHWT) for sub-bands, local atmospheric light estimation and transmission map estimation and refinement using Jaya algorithm. We formulated haze removal as an optimization problem, and the fitness function for the single image is taken from the information loss term, distance and the haze density term. The JA-dehaze method improves the gradient and preserves the information optimally by minimizing the fitness function. Furthermore, we extended the single image dehazing process to real-time video dehazing. Experimental results demonstrate that the proposed JA-dehaze method can successfully improve the quality of a foggy image without sacrificing color fidelity and can retain image details sufficiently.

关键词： Video dehazing Jaya algorithm JA-dehaze Transmission estimation optimization algorithm

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Analyzing the Societal Cost of Electric Roads Compared to Batteries and Oil for All Forms of Road Transport

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ENERGIES 2022年第5期15卷

作者： Coban, Hasan Huseyin Rehman, Aysha Mohamed, Abdullah Ardahan Univ Dept Elect Engn TR-75002 Ardahan Turkey Univ Gujrat Dept Math Gujrat 50700 Pakistan Future Univ Egypt Univ Res Ctr New Cairo 11745 Egypt

This article is designed to demonstrate that electric roads are an affordable way to electrify all forms of road transport-not only cars, but also buses and trucks. Electric roads represent a way to power electric vehicles without relying solely on batteries. The idea is that when an electric vehicle reaches an electric road, it stops using power from the battery and instead uses power directly from the road itself. The primary challenge for electric vehicles is still the perception of a compromised quality of life in owning an electric vehicle due to a limited range compared with petrol and diesel cars, today. This paper introduces a new technology, currently experiencing rapid development, that can not only overcome range anxiety but make electric vehicles better, in terms of range, than petrol and diesel cars today. Furthermore, not only can this research help to arrange this, but it can also help, for the first time, to cost-effectively electrify heavy-duty transport, such as trucks and buses, which would be a huge breakthrough in terms of sustainability, as it is very important to start supplying electricity to heavy-duty vehicles. The case study provides a very hypothetical example of a trip with and without an electric road, covering a total of 26,011 km of highways and main roads. The results indicate that building electric roads is cheaper than many other alternatives. If a large battery is replaced with a smaller battery for each new vehicle sold, after 3 years, enough savings will be made to electrify all highways and main roads in Turkey. This paper can help transport operators and policymakers develop strategies to accelerate the adoption of electric vehicles by appropriately implementing electric road infrastructure.

关键词： electric road system battery-electric car electric vehicle sustainable transport profitability charging infrastructure optimization algorithm

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Optimized Bezier-curve-based command generation and robust inverse optimal control for attitude tracking of spacecraft

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AEROSPACE SCIENCE AND TECHNOLOGY 2022年 121卷

作者： Wang, Peng Zhang, Xiaobing Nanjing Univ Sci & Technol Sch Energy & Power Engn Nanjing Peoples R China

In this paper we address the optimized attitude command generation and robust inverse optimal control design for the spacecraft attitude tracking with large angle slews. Firstly, with the reference attitude command described in a Bezier curve through the spherical linear interpolation, an optimized reference command is generated to meet the principles of small energy consumption and short turning time. The applied optimization combines the particle swarm optimization algorithm and genetic algorithm based on information entropy to guarantee the diversity of the population and improve the global search capabilities. Next, a robust inverse optimal control is developed for the attitude tracking of the obtained optimized reference attitude command, consisting of an inverse optimal Lyapunov approach for performance guarantee and an arctangent-based integral sliding mode control for the disturbance attenuation. A stronger generality of the control Lyapunov function is obtained for the inverse optimality. Furthermore, the developed control efforts are of smaller amplitudes due to introducing the auxiliary angular velocity for the smooth transition to the reference attitude command. Finally, simulation results are included to show the performance of the proposed scheme. (c) 2021 Elsevier Masson SAS. All rights reserved.

关键词： Attitude control Inverse optimal control optimization algorithm Bezier curve Arctangent-based sliding mode control

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A Framework of gradient-based shape optimization using feature-based CAD parameterization

A Framework of gradient-based shape optimization using featu...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Sun, Liang Yao, Weigang Robinson, Trevor T. Marques, Simao Armstrong, Cecil G. Queens Univ Belfast Sch Mech & Aerosp Engn Belfast BT9 5AH Antrim North Ireland Univ Surrey Dept Mech Sci Guildford GU2 7XH Surrey England

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

This paper presents a framework of performing aerodynamic shape optimization using the feature-based CAD system, which is widely used in aerospace industry. An interface is set up to communicate between the system installs the CAD package and the system installs the CFD solver. Mesh is adapted to fit the new CAD model after each parametric update by performing surface mesh deformation and volume mesh deformation in sequence. The proposed framework can be easily integrated into the industrial product design and analysis process.

关键词： Shape optimization Aerodynamic Shape optimization CAD System Aerospace Industry Lift Coefficient Mesh Generation Open Source Software Pressure Coefficient Linear Elasticity optimization algorithm

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Review of optimization-Based Feature Selection algorithms on Healthcare Dataset 2nd

Review of Optimization-Based Feature Selection Algorithms on...

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2nd International Conference on Computing, Communications and Data Engineering (CCODE)

作者： Manonmani, M. Balakrishnan, Sarojini Avinashilingam Inst Home Sci & Higher Educ Women Dept Comp Sci Coimbatore Tamil Nadu India

ISBN: (纸本)9789811501357;9789811501340

Feature selection is an important function in classification and prediction technique, especially in medical data mining. It is embedded with the task of selecting a subset of relevant features that can be used in constructing a model. optimization algorithms play a significant role in medical data mining especially in diagnosing chronic disease because it offers good efficiency in less computational cost and time. Also, the classification algorithms yield better results when the feature selection algorithm is based on an objective function. This paper aims to provide a review of optimized feature selection algorithms and an overview of the proposed improved teaching learning based optimization algorithm (ITLBO) for classification and prediction of chronic kidney disease (CKD). The proposed algorithm aims to reduce the number of features required for diagnosing the CKD with an objective function that optimizes the features and selects them based on the updated weight of the fitness function.

关键词： optimization algorithm Objective function Chronic kidney disease (CKD) Improved teacher-learner-based optimization algorithm (ITLBO) Fitness function

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Simultaneous layout and topology optimization of curved stiffened panels

Simultaneous layout and topology optimization of curved stif...

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AIAA Aviation Forum

作者： Chu, Sheng Townsend, Scott Featherston, Carol Kim, H. Alicia Cardiff Univ Sch Engn Cardiff CF24 3AA Wales Univ Calif San Diego Struct Engn Dept La Jolla CA 92093 USA

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

Simultaneous layout and topology optimization of stiffened panels is investigated in this paper by a new level-set-based method. Specifically, plate elements are used to construct a stiffened panel structure. The level set method is then used to manipulate the stiffener layout, with curved members allowed. A free-form mesh deformation method with a control mesh is utilized to adjust the finite element mesh. The level set method is also used to optimize the internal topologies of the stiffeners. Both mass minimization with buckling constraint and critical buckling load factor maximization with mass constraint are investigated. A semi-analytical sensitivity analysis is performed, and the optimization algorithm is outlined. For the buckling-constrained problem, the p-norm function is used to aggregate many buckling modes and a gradient-based optimizer is used with an adaptive scaling method to enforce an exact control of the buckling limit. Numerical investigations demonstrate and validate the proposed method.

关键词： Topology optimization Sensitivity Analysis optimization algorithm Finite Element Analysis Poisson's Ratio Gradient Free optimization Finite Difference Method Particle Swarm optimization Fast Marching Method Structural Engineering

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Self-Organizing Migrating algorithm with narrowing search space strategy for robot path planning

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APPLIED SOFT COMPUTING 2022年 116卷

作者： Diep, Quoc Bao Truong, Thanh Cong Das, Swagatam Zelinka, Ivan VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava Czech Republic Univ Finance Mkt Ho Chi Minh City Vietnam Indian Stat Inst Elect & Commun Sci Unit Kolkata India Ton Duc Thang Univ Fac Elect & Elect Engn Ho Chi Minh City Vietnam

This article introduces a version of the Self-Organizing Migrating algorithm with a narrowing search space strategy named iSOMA. Compared to the previous two versions, SOMA T3A and Pareto that ranked 3rd and 5th respectively in the IEEE CEC (Congress on Evolutionary Computation) 2019 competition, the iSOMA is equipped with more advanced features with notable improvements including applying jumps in the order, immediate update, narrowing the search space instead of searching on the intersecting edges of hyperplanes, and the partial replacement of individuals in the population when the global best improved no further. Moreover, the proposed algorithm is organized into processes named initialization, self-organizing, migrating, and replacement. We tested the performance of this new version by using three benchmark test suites of IEEE CEC 2013, 2015, and 2017, which, together contain a total of 73 functions. Not only is it superior in performance to other SOMAs, but iSOMA also yields promising results against the representatives of well-known algorithmic families such as Differential Evolution and Particle Swarm optimization. Moreover, we demonstrate the application of iSOMA for path planning of a drone, while avoiding static obstacles and catching the target. (C) 2021 The Author(s). Published by Elsevier B.V.

关键词： Self-Organizing Migrating algorithm optimization algorithm Swarm intelligence Numerical optimization Path planning Drone

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Detecting Lung Cancer Region from CT Image Using Meta-Heuristic Optimized Segmentation Approach

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2022年第16期36卷

作者： Shakeel, Pethuraj Mohamed Aboobaider, Burhanuddin bin Mohd Salahuddin, Lizawati Binti Univ Tekn Malaysia Fac Informat & Commun Technol Melaka 76100 Malaysia

Lung tumor detection using computer-aided modeling improves the accuracy of detection and clinical recommendation precision. An optimal tumor detection requires noise reduced computed tomography (CT) images for pixel classification. In this paper, the butterfly optimization algorithm-based K-means clustering (BOAKMC) method is introduced for reducing CT image segmentation uncertainty. The introduced method detects the overlapping features for optimal edge classification. The best-fit features are first trained and verified for their similarity. The clustering process recurrently groups the feature matched pixels into clusters and updates the centroid based on further classifications. In this classification process, the uncertain pixels are identified and mitigated in the tumor detection analysis. The best-fit features are used to train local search instances in the BOA process, which influences the similar pixel grouping in the uncertainty detection process. The proposed BOAKMC improves accuracy and precision by 10.2% and 13.39% and reduces classification failure and time by 11.29% and 11.52%, respectively.

关键词： Lung tumor detection CT images segmentation K-means clustering optimization algorithm

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Efficient Multi-Information Source Multi-Objective Bayesian optimization

Efficient Multi-Information Source Multi-Objective Bayesian ...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Khatamsaz, Danial Peddareddygari, Lalith Friedman, Samuel Allaire, Douglas Texas A&M Univ J Mike Walker 66 Dept Mech Engn College Stn TX 77843 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Multi-objective optimization is often a difficult task owing to the need to balance competing objectives. A typical approach to handling this is to estimate a Pareto frontier in objective space by identifying non-dominated points. This task is typically computationally demanding owing to the need to incorporate information of high enough fidelity to be trusted in design and decision-making processes. In this work, we present a multi-information source framework for enabling efficient multi-objective optimization. The framework allows for the exploitation of all available information and considers both potential improvement and cost. The framework includes ingredients of model fusion, expected hypervolume improvement, and intermediate Gaussian process surrogates. The approach is demonstrated on two test problems and aerostructural wing design problem.

关键词： Bayesian optimization Cumulative Distribution Function Pareto Frontier Surrogate Model optimization algorithm Aerodynamic Performance Finite Element Analysis Bréguet Range Equation Aircraft Wing Design Open Source Software

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