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EffiDenseGenOp: Ensemble Transfer Learning With Hyperparameter Tuning Using genetic algorithm optimization for PCOS Detection From Ultrasound Sonography Images

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IEEE ACCESS 2025年 13卷 54285-54312页

作者： Ghosh, Ananya Srinivasan, Kathiravan Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 India

In this research, we present the revolutionary 'EffiDenseGenOp' framework for Polycystic Ovary Syndrome (PCOS) detection, leveraging the amalgamation of Ensembled Transfer Learning Models. By harnessing the synergies of Ensembled EfficientNetB7 and DenseNet201, our approach transcends conventional models, offering a robust solution for PCOS detection. Notably, we introduce a genetic algorithm-based hyperparameter tuning mechanism, optimizing the model configuration to ensure superior generalization and performance. Our contributions encompass a meticulous comparative analysis, pitting machine learning models, deep learning models, transfer learning models, and our proposed Ensembled Transfer Learning Models against each other. The ensemble technique strategically captures complementary patterns and features from each base model, significantly amplifying the overall predictive power. Moreover, we conduct a comprehensive exploration of hyperparameters, employing extensive tuning to enhance model performance and generalization. The efficiency of genetic algorithm is underscored through a rigorous comparative analysis. Additionally, a novel Fuzzy Inference System is introduced for image quality enhancement, designed after meticulous examinations of image behavior under varying noise levels and membership functions. Our model undergoes rigorous training through diverse image variations, employing data augmentation techniques and noise addition. Performance evaluation reveals superior accuracy (99.58%), precision (98.87%), recall (99.20%), F1-score (99.01%), and AUC-ROC score (98.97%), substantiated by detailed analyses including confusion matrices and AUC-ROC curves. Compared to existing models, our proposed model outperforms several state-of-the-art techniques, such as VGGNet16, PCOSNet, and ResNet50, with an accuracy of 99.95%, highlighting a significant improvement in PCOS detection performance. Robustness is ensured through exhaustive K-fold cross-validation, while v

关键词： Polycystic ovary syndrome transfer learning genetic algorithm optimization hyperparameter tuning Polycystic ovary syndrome transfer learning genetic algorithm optimization hyperparameter tuning

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Thermal design and genetic algorithm optimization of geothermal and solar-assisted multi-energy and hydrogen production using artificial neural networks

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ENERGY 2025年 324卷

作者： Yilmaz, Ceyhun Arslan, Muhammed Ozdemir, Safiye Nur Tokgoz, Nehir Sakarya Univ Dept Mech Engn Sakarya Turkiye Afyon Kocatepe Univ Motor Vehicles & Transportat Technol Afyonkarahisar Turkiye

A comprehensive thermodynamic and economic analysis of an innovative multiple energy and hydrogen production system that integrates solar and geothermal energy sources is presented in the study. The system is operated and optimized using a genetic algorithm (GA) based on an artificial neural network (ANN), which adapts in real-time and improves performance. The proposed system employs solar and geothermal energy to power a variety of processes, such as the generation of electricity, the production of hydrogen through electrolysis, and the heating of space. By optimizing the exergetic unit costs of hydrogen, heating, and electricity, we assess the economic feasibility of the system. The economic competitiveness of renewable energy sources is illustrated by the exergetic unit cost of 0.011 $/kWh (3.05 $/GJ) generated by the geothermal and solar-assisted power facility. The electrolysis unit generates hydrogen at a discharge rate of 0.0154 kg/s, with an exergetic unit cost of 1.491 $/kg H2 (12.42 $/GJ). This method is cost-effective for the production of pure fuel. In addition, the conversion of hydrogen to electricity by a fuel cell results in an optimized unit cost of electricity of 0.0778 $/kWh (21.61 $/GJ). The system also offers space heating at a unit heating cost of 0.005 $/kWh (1.38 $/GJ). The overall system's thermodynamic performance assessment indicates that the energy and exergy efficiencies are optimized at 34.5 % and 46 %, respectively. These results emphasize the integrated system's potential for sustainable and effective energy production, providing a prospective solution for various energy requirements while guaranteeing economic feasibility.

关键词： Artificial neural networks genetic algorithm optimization Geothermal energy Hydrogen production Solar energy Thermoeconomic analysis

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Theme park greening VR design based on entertainment robots and genetic algorithm optimization: Digital entertainment design experience

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ENTERTAINMENT COMPUTING 2025年 52卷

作者： Huang, Yue Ke, Xinxia Jiang, Yulin Qingdao Univ Acad Fine Arts Qingdao 200071 Shandong Peoples R China Chizhou Univ Coll Arts & Educ Chizhou 247000 Anhui Peoples R China

Theme parks should provide attractive green design and digital interactive experiences to attract tourists and improve their satisfaction. Therefore, this study aims to achieve a digital interactive experience in theme park greening design through the use of entertainment robots and genetic algorithm optimization. The study analyzed the characteristics of experiential theme parks and introduced landscape 3D modeling techniques for creating interactive 3D models of green spaces in theme parks. By using technologies such as sensors and cameras, entertainment robots can perceive the presence and behavior of tourists. Entertainment robots can interact with tourists in real-time, customize them according to their interests and needs, and provide unique and enjoyable experiences for tourists. By collecting and analyzing feedback and behavioral data from tourists, the advantages and improvement points of digital interactive experiences can be identified. Through comparative experimental analysis with traditional experience methods, it was found that digital interactive experience can significantly improve tourist participation and satisfaction. Through user data analysis of interactive experience, this design can effectively improve the greening design of theme parks, enhance tourist participation and entertainment experience.

关键词： Entertainment robots genetic algorithm optimization Theme park greening design Digital interactive experience

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genetic algorithm optimization Design of Gradient Conformal Chiral Metamaterials and 3D Printing Verifiction for Morphing Wings

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Chinese Journal of Mechanical Engineering 2024年第6期37卷 346-364页

作者： Qian Zheng Weijun Zhu Quan Zhi Henglun Sun Dongsheng Li Xilun Ding School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China

This paper proposes a gradient conformal design technique to modify the multi-directional stiffness characteristics of 3D printed chiral metamaterials,using various airfoil *** method ensures the integrity of chiral cell nodal circles while improving load transmission efficiency and enhancing manufacturing precision for 3D printing applications.A parametric design framework,integrating finite element analysis and optimization modules,is developed to enhance the wing’s multidirectional *** optimization process demonstrates that the distribution of chiral structural ligaments and nodal circles significantly affects wing *** stiffness gradient optimization results reveal a variation of over 78%in tail stiffness performance between the best and worst parameter *** outcomes suggest that this strategy can develop metamaterials with enhanced deformability,offering a promising approach for designing morphing wings.

关键词： Morphing wings Chiral metamaterials Gradient conformal design genetic algorithm optimization 3D printing

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genetic algorithm optimization of a molecular mechanics force field for technetium

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INORGANICA CHIMICA ACTA 2000年第0期300卷 113-124页

作者： Cundari, TR Fu, WT Univ Memphis Dept Chem Computat Res Mat Inst CROMIUM Memphis TN 38152 USA Univ Illinois Ctr Pharmaceut Biotechnol Chicago IL 60607 USA

A new force field was developed using genetic algorithms (GAs) to optimize molecular mechanics (MM) parameters. The GA-MM approach was applied to the study of technetium (Tc) complexes with coordination numbers of 5 or 6 and formal oxidation states of + 3 to + 6 on the metal. Both soft and hard donor ligands, coordinated through dative, single, and multiple bonds to Tc, were studied. The new GA-MM force field was tested for the prediction of metric parameters (bond lengths, bond angles and dihedral angles) and overall geometry in reference to X-ray crystallographic data. Despite the chemical diversity found in technetium coordination chemistry, good modeling was achieved in a fraction of the time of higher-level, quantum-based methods, with considerably less computational resources. The GA-MM approach is general enough to be applicable to other d-block metals. (C) 2000 Elsevier Science S.A. All rights reserved.

关键词： genetic algorithm optimization molecular mechanics force field technetium complexes

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genetic algorithm optimization of a CPW fed UWB circular ring monopole with modified ground plane

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2016年第6期58卷 1319-1323页

作者： Rodrigues, E. J. B. D'Assuncao, A. G. Lins, H. W. C. Peixeiro, C. Univ Fed Rio Grande do Norte Dept Commun Engn Caixa Postal 1655 BR-59072970 Natal RN Brazil Anatel Natl Agcy Telecommun BR-58043010 Joao Pessoa PB Brazil Univ Lisbon Inst Telecomunicacoes Inst Super Tecn Av Rovisco Pais 1 P-1049001 Lisbon Portugal

In this paper a coplanar waveguide (CPW) fed ultra-wideband (UWB) circular ring monopole antenna with a modified ground plane is proposed. The dimensions of the antenna are obtained using a genetic algorithm (GA) optimization procedure. To proof the concept and validate the design antenna prototypes have been fabricated and tested. A good agreement is obtained between numerical simulation and experimental results. The input reflection coefficient is below -10 dB in the frequency range 2.52 GHz to 12 GHz, which corresponds to an impedance bandwidth of about 4.8:1, and the gain is about 3 dBi. The proposed antenna element seems to be suitable for UWB and cognitive radio spectrum sensing applications. (c) 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:1319-1323, 2016

关键词： genetic algorithm optimization UWB monopole CPW feeding circular ring monopole modified ground plane cognitive radio

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genetic algorithm optimization for Water Curtain Cooling Fuzzy Control System

Genetic Algorithm Optimization for Water Curtain Cooling Fuz...

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International Conference on Fuzzy System and Data Mining (FSDM)

作者： Shi, Xiao-Ying Xu, Zhi-Bang Gong, Jing-Hong Yang, Hui East China Jiaotong Univ Acad Elect & Elect Engn Nanchang 330013 Peoples R China Nanchang Univ Nanchang 330013 Peoples R China

ISBN: (纸本)9781614996194;9781614996187

Intelligent optimization control combines intelligent optimization and intelligent control in order to optimize the local or global performance of control systems;it provides an effective way to solve control problems in complex systems. In this paper, a fuzzy logic controller is employed based on the genetic algorithm for control rules in a water curtain cooling system. Simulation results and industrial experiments indicate that fuzzy logic control based on the genetic algorithm optimization for the water curtain cooling process of accelerating steel is effective using this control model.

关键词： genetic algorithm optimization fuzzy reference rules fuzzy logic controller water curtain cooling system

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Adaptive robust PID control subject to supervisory decoupled sliding mode control based upon genetic algorithm optimization

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2015年第4期37卷 505-514页

作者： Mahmoodabadi, M. J. Taherkhorsandi, M. Talebipour, M. Castillo-Villar, K. K. Sirjan Univ Technol Dept Mech Engn Sirjan Kerman Iran Univ Texas San Antonio Dept Mech Engn San Antonio TX 78249 USA

While proportional-integral-derivative (PID) control is the most applicable controller in industry, it does not provide important qualities, such as stability in case of uncertainties, being robust to disturbances, and optimal control inputs. To address the issue, this paper presents adaptive robust PID control subject to supervisory decoupled sliding mode control for an inverted pendulum system optimized by using a genetic algorithm optimization. Decoupled sliding mode control is a variable structure control method having appropriate features, such as good tracking performance and robustness with regard to disturbances. To this end, decoupled sliding mode control as a supervisory controller is utilized in accordance with PID control to deliver necessary control inputs and enhance the performance of the controller. Effectual methods such as the transfer function which resulted in having minimum chattering in the controller is employed in this paper. An adaptation mechanism is used to update the proportional, derivative, and integral gains of PID control. Then, the parameters of the controller are ascertained by using a genetic algorithm. The results and analysis prove the proper performance of the controller via providing an optimal smooth control input, proper tracking performance, and the elimination of the chattering problem.

关键词： Adaptive robust control decoupled sliding mode control genetic algorithm optimization PID control the inverted pendulum and cart system

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Hybrid quantum search with genetic algorithm optimization

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2210-e2210页

作者： Ardelean, Sebastian Mihai Udrescu, Mihai Univ Politehn Timisoara Dept Comp & Informat Technol Timisoara Timis Romania

Quantum genetic algorithms (QGA) integrate genetic programming and quantum computing to address search and optimization problems. The standard strategy of the hybrid QGA approach is to add quantum resources to classical genetic algorithms (GA), thus improving their efficacy ( i.e. , quantum optimization of a classical algorithm). However, the extent of such improvements is still unclear. Conversely, Reduced Quantum genetic algorithm (RQGA) is a fully quantum algorithm that reduces the GA search for the best fitness in a population of potential solutions to running Grover's algorithm. Unfortunately, RQGA finds the best fitness value and its corresponding chromosome ( i.e. , the solution or one of the solutions of the problem) in exponential runtime, O(2n/2), n/2 ), where n is the number of qubits in the individuals' quantum register. This article introduces a novel QGA optimization strategy, namely a classical optimization of a fully quantum algorithm, to address the RQGA complexity problem. Accordingly, we control the complexity of the RQGA algorithm by selecting a limited number of qubits in the individuals' register and fixing the remaining ones as classical values of '0' and '1' with a genetic algorithm. We also improve the performance of RQGA by discarding unfit solutions and bounding the search only in the area of valid individuals. As a result, our Hybrid Quantum algorithm with genetic optimization (HQAGO) solves search problems in O(2 (n-k)/2 ) oracle queries, where k is the number of fixed classical bits in the individuals' register.

关键词： Quantum computing Quantum genetic algorithms genetic algorithm optimization Hybrid quantum genetic algorithm

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Standard and micro genetic algorithm optimization of profiled corrugated horn antennas

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2002年第6期35卷 449-453页

作者： Sinton, S Robinson, J Rahmat-Samil, Y Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

The genetic algorithm (GA) optimization technique is applied to the optimization of a profiled corrugated horn, designed as a feed for a space-borne remote sensing reflector-antenna system. In addition to using the standard GA (SGA) optimization technique, a profiled corrugated horn is also designed and analyzed using micro GA (muGA). A comparative study has been performed among the various designs. (C) 2002 Wiley Periodicals, Inc.

关键词： genetic algorithm optimization micro GA profiled corrugated horn antennas offset reflector antennas

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