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DLMNN Based Heart Disease Prediction with PD-SS optimization algorithm

Intelligent Automation & Soft Computing 2023年第2期35卷 1353-1368页

作者： S.Raghavendra Vasudev Parvati R.Manjula Ashok Kumar Nanda Ruby Singh D.Lakshmi S.Velmurugan Department of Information and Communication Technology Manipal Institute of TechnologyManipal Academy of Higher EducationManipal576104India Department of Information Science and Engineering SDM College of Engineering and TechnologyDharwad580002India Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering B.V Raju Institute of TechnologyTelangana502313India Faculty of Engineering and Technology SRM Institute of Science and TechnologyNCR CampusUttar Prasesh201204India School of Computing Science and Engineering VIT Bhopal UniversityMadhya Pradesh466114India Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India

In contemporary medicine,cardiovascular disease is a major public health *** diseases are one of the leading causes of death *** are classiﬁed as vascular,ischemic,or *** information contained in patients’Electronic Health Records(EHR)enables clin-icians to identify and monitor heart *** failure rates have risen drama-tically in recent years as a result of changes in modern *** diseases are becoming more prevalent in today’s medical *** year,a substantial number of people die as a result of cardiac *** primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of *** improve the efﬁciency of the classiﬁcation algo-rithms,we construct a data pre-processing stage using feature ***-ments using unidirectional and bidirectional neural network models found that a Deep Learning Modiﬁed Neural Network(DLMNN)model combined with the Pet Dog-Smell Sensing(PD-SS)algorithm predicted the highest classiﬁcation performance on the UCI Machine Learning Heart Disease *** DLMNN-based PDSS achieved an accuracy of 94.21%,an F-score of 92.38%,a recall of 94.62%,and a precision of 93.86%.These results are competitive and promising for a heart disease *** demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease *** proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data.

关键词： Machine learning random forest coronary heart disease cardiovascular optimization algorithm

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Real-time combined heat and power operational strategy using a hierarchical optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY 2011年第A4期225卷 403-412页

作者： Yun, K. Cho, H. Luck, R. Mago, P. J. Mississippi State Univ Dept Mech Engn Mississippi State MS 39762 USA Pacific NW Natl Lab Richland WA 99352 USA

Existing attempts to optimize the operation of combined heat and power (CHP) systems for building applications have two major limitations: the electrical and thermal loads are obtained from historical weather profiles;and the CHP system models ignore transient responses by using constant equipment efficiencies. This article considers the transient response of a building combined with a hierarchical CHP optimal control algorithm to obtain a real-time integrated system that uses the most recent weather and electric load information. This is accomplished by running concurrent simulations of two transient building models. The first transient building model uses current as well as forecast input information to obtain short-term predictions of the thermal and electric building loads. The predictions are then used by an optimization algorithm (i.e. a hierarchical controller that decides the amount of fuel and of electrical energy to be allocated at the current time step). In a simulation, the actual physical building is not available and, hence, to simulate a real-time environment, a second, building model with similar but not identical input loads are used to represent the actual building. A state-variable feedback loop is completed at the beginning of each time step by copying (i.e. measuring, the state variable from the actual building and restarting the predictive model using these 'measured' values as initial conditions). The simulation environment presented in this article features non-linear effects such as the dependence of the heat exchanger effectiveness on their operating conditions. The results indicate that the CHP engine operation dictated by the proposed hierarchical controller with uncertain weather conditions has the potential to yield significant savings when compared with conventional systems using current values of electricity and fuel prices.

关键词： combined heat and power real-time optimization optimization algorithm

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An optimization algorithm based on stochastic sensitivity analysis for noisy objective landscapes

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2003年第2期79卷 245-252页

作者： Okano, H Koda, A Univ Tsukuba Inst Policy & Planning Sci Tsukuba Ibaraki 3058573 Japan IBM Res Corp Tokyo Res Lab Yamato Kanagawa 2428502 Japan

A function minimization algorithm that updates solutions based on approximated derivative information is proposed. The algorithm generates sample points with Gaussian white noise, and approximates derivatives based on stochastic sensitivity analysis. Unlike standard trust region methods which calculate gradients with n or more sample points, where n is the number of variables, the proposed algorithm allows the number of sample points M to be less than n. Furthermore, it ignores small amounts of noise within a trust region. This paper addresses the following two questions: how does the derivative approximation become worse when the number of sample points is small? Can the algorithm find good solutions with inexact derivative information when the objective landscape is noisy? Through intensive numerical experiments using quadratic functions, the algorithm is shown to be able to approximate derivatives when M is about n/10 or more. The experiments using a formulation of the traveling salesman problem show that the algorithm can find reasonably good solutions for noisy objective landscapes with inexact derivatives information. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： optimization algorithm stochastic sensitivity gradient method traveling salesman problem

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optimization algorithm applied to extended range fuel cell hybrid vehicles. Contribution to road transport decarbonization

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ENERGY 2023年第1期267卷

作者： Perez-Davila, Oriana Fernandez, Roberto Alvarez Univ Nebrija Calle Sta Cruz de Marcenado 27 Madrid 28015 Spain

Progressive decarbonization of road transport is underway to reduce greenhouse gas (GHG) emissions and their harmful effects. Although different options have already been considered, hydrogen-electric hybridization seems to be an interesting option to reduce emissions by offering vehicles with sufficient range and competitive performance compared to fossil fuel vehicles. The development of energy management systems (EMS) that achieve efficient use of energy is crucial to extend the vehicles range. In this paper we propose two energy management systems applied to two hydrogen hybrid vehicles: a Plug-in Hybrid Electric Vehicle (PHEV) and a RangeExtended Fuel Cell Hybrid Vehicle (FC-EREV). The proposed EMSs are based on single and multi-level approaches, that consider the amount of hydrogen in the tank to implement a rule-based strategy (RBS) that distributes the current demanded by the motor between the fuel cell and the battery. The EMS parameters for both approaches were selected using a particle swarm optimization (PSO) algorithm in order to find the optimized set of values for both EMS. The proposals have been tested for different driving cycles, showing improvements up to 9% and 12% in range, for the single and multi-level approaches, respectively, when compared to previous works.

关键词： Hydrogen Fuel cell Power management Hybrid vehicle optimization algorithm Range

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Food System Driven by Obstacle Degree optimization algorithm: System Design and algorithm Analysis 2021

Food System Driven by Obstacle Degree Optimization Algorithm...

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2nd International Conference on Artificial Intelligence and Information Systems (ICAIIS )

作者： Gao, Ruotong Wang, Jiacheng Chen, Zixuan Hebei Univ Coll Math & Informat Baoding 071000 Peoples R China

ISBN: (纸本)9781450390200

Food issues have long been the subject of people's concern, and in recent years, frequent disasters have exacerbated the instability of the food system. The contemporary food system is highly efficient, but it neglects equity and sustainability. In order to solve the problem, this paper establishes an obstacle-driven food system impact index model to evaluate and optimize the contemporary food system, choosing China and the United States as the typical representatives of developing and developed countries and puts forward a series of algorithm optimization strategies for the two countries' food systems. By making time-series predictions on the indicators of equity and sustainability, we propose an obstacle degree optimization algorithm for the food systems of the two countries. We predict that by 2030, the obstacle degree to the stability of the Chinese food system in terms of equity and sustainability will be 20 percent lower than in 2018. Also, we predict that by 2025, the obstacle degree of the U.S. will fall from 0.411 in 2018 to zero.

关键词： Food system Obstacle degree EWM optimization algorithm

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Landing performance optimization algorithm of civil aircrafts

Landing performance optimization Algorithm of civil aircraft...

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2nd International Conference on Civil Engineering and Transportation (ICCET 2012)

作者： Chen, Hongying Luo, Wentian Civil Aviat Flight Univ China Flight Technol Coll Guanghan City Si Chuan Peoples R China

ISBN: (纸本)9783037855645

This paper explains and demonstrates how to calculate and optimize landing performance. According regulation requirement, it needs to consider the different requirements [1], including landing field length, missed approach climb gradient, brake energy and tire speed. Weights limited by the different requirements are calculated, the minimum of these weights limited are determined as the actual allowed maximum landing weight.

关键词： Landing performance optimization algorithm Operating landing distance Landing speed Landing weight

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A Novel Energy-Minimized optimization algorithm for Mobile Image Transmission

A Novel Energy-Minimized Optimization Algorithm for Mobile I...

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11th Pacific Rim Conference on Multimedia

作者： Tang, Zhenhua Qin, Tuanfa Liu, Wenyu Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Peoples R China Huazhong Univ Sci & Technol Elect & Informat Engn Dept Wuhan 430074 Peoples R China

ISBN: (纸本)9783642156953

In this paper, we propose a novel energy-minimized optimization algorithm for image coding and transmission over wireless channel. In order to reduce computational complexity of reaching optimal solutions, the presented algorithm makes full use of the intrinsic relations among the system parameters, optimization objective, and system constraints. We present simulation results to demonstrate that the proposed optimization algorithm is effective and it has much lower computational complexity than the conventional sequential quadratic programming (SQP) method. And the proposed algorithm is suitable for mobile image transmission applications.

关键词： Energy consumption image transmission optimization algorithm

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Detection and parameter estimation of R peaks in ECG signal using optimization algorithm 6

Detection and parameter estimation of R peaks in ECG signal ...

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6th International Symposium on Computing and Networking (CANDAR) - Across Practical Development and Theoretical Research

作者： Hsieh, Sheng-Ta Lin, Chun-Ling Oriental Inst Technol Dept Commun Engn New Taipei Taiwan Ming Chi Univ Technol Dept Elect Engn New Taipei Taiwan

ISBN: (纸本)9781538691847

Accurate R-peak detection is very important for arrhythmia diagnosis. Our previous effective R detection algorithm consisted of three strategies: band-pass filter, adaptive definition of interesting block and dynamic threshold. Then, it adopted the optimization algorithm to replace the knowledge-based theory and found out the suitable parameters (El, F2, N, WI, W2, beta and mu) in R detection algorithm quickly and obtained the high performance of detecting R peaks (99.77%). In order to improve the performance of the previous study, this study proposes to add the median filter in the algorithm to correct baseline wander components of electrocardiography (ECG) signals. It is necessary to defined two parameters (Ti and T2) in median filter. Therefore, this study adopts particle swarm optimization (PSO) to find the suitable parameters (Ti, T2, F1, F2, N, W1, W2, beta and mu) in the proposed method. The proposed method is applied to MIT-BIH arrhythmia database. The results show that PSO can find out the suitable parameters in R detection algorithm and have a higher accuracy (99.95%) than one of the previous study.

关键词： R-peak detection arrhythmia diagnosis optimization algorithm knowledge-based theory electrocardiography (ECG) particle swarm optimization (PSO) MIT-BIH arrhythmia database

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An optimization algorithm of PCB Assembly Time for Multi-head Placement Machine 36

An Optimization Algorithm of PCB Assembly Time for Multi-hea...

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第36届中国控制会议

作者： Haiming Liu Peng Yuan Jiaxiang Luo Mei Zhang School of Automation Science and Engineering South China University of Technology

ISBN: (纸本)9781538629185

This paper studies the optimization problem of PCB assembly time for multi-head placement machine. Mathematical model is built and analyzed for the problem, which is of a combinatorial nature and computationally intractable. An optimization algorithm based on heuristic strategy and scatter search method is proposed to minimize the PCB assembly time. By relaxing the restrictions on the problem, the algorithm reduces the assembly time by minimizing cycles of pick-and-place, constructing the simultaneous pickups and optimizing sequence of pick-and-place of components. Numerical experiments were conducted to evaluate the proposed algorithm, along with a comparison with a heuristic algorithm(HA) under strong constraints proposed in existed literature. The results show that the proposed algorithm has better performance in optimization results and can shorten PCB assembly time of multi-head placement machine effectively.

关键词： PCB assembly optimization algorithm heuristic algorithm scatter search

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Hardware Implementation of Approximate Fixed-point Divider for Machine Learning optimization algorithm

Hardware Implementation of Approximate Fixed-point Divider f...

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IEEE Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics (PrimeAsia)

作者： Han, Gandong Zhang, Weiyi Niu, Liting Zhang, Chun Wang, Zhihua Wang, Ziqiang Tsinghua Univ Sch Integrated Circuits Beijing Peoples R China Tsinghua Univ Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9781665450690

Division operation is necessary for many applications, especially optimization algorithms for machine learning. Usually, a certain degree of loss is acceptable in calculating nonsignificant intermediate variables for a considerable speed improvement. This paper proposes a specialized divider to accelerate machine learning optimization algorithm implementation on hardware. Inspired by the fast inverse square root algorithm, we designed a hardware implementation method according to the algorithm, which generates an approximate division result with conversion between floating-point and fixed-point numbers and multiplication. This paper includes three versions of divider: fastDiv accuracy, a conventional design with a 35% less delay and minimal error compared to delay-minimized standard divider from the Synopsys DesignWare library;fastDiv area, an area-oriented design with a 67% less delay and acceptable error compared to the standard divider constrained to the same area size;fastDiv speed, the fastest design with a 54% less delay compared to delay-minimized standard divider. All these three versions can be applied in deploying optimization algorithms in FPGA or ASIC design on demand.

关键词： fixed-point division optimization algorithm hardware acceleration fast square root

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