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Enhancing PCOS diagnosis accuracy with modified Giza Pyramids Optimizer, SLOGPO, and PCOSNet: advanced classification and optimization techniques

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Cluster Computing 2025年第5期28卷 1-27页

作者： Aldosary, Abdallah El-Shafai, Walid Mohamed, Ehab Mahmoud Emara, Heba M. Department of Computer Engineering College of Engineering in Wadi Addwasir Prince Sattam bin Abdulaziz University Wadi Addwasir11991 Saudi Arabia Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering College of Engineering in Wadi Addawasir Prince Sattam Bin Abdulaziz University Wadi Addawasir11991 Saudi Arabia Department of Electronics and Electrical Communications Engineering High Institute of Electronic Engineering Ministry of Higher Education Bilbis-Sharqiya44621 Egypt

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting women during their reproductive years, characterized by a range of hormonal imbalances and reproductive dysfunctions. Accurate diagnosis of PCOS is crucial, as it serves as the foundation for timely interventions and personalized management strategies. In this context, this paper presents a comprehensive evaluation of two advanced classification models: the Stacked Learning Optimized by Giza Pyramids Optimizer (SLOGPO) and PCOSNet, a model leveraging 1-Dimensional Convolutional Neural Networks (CNN) integrated with a BORUTA-based feature selection framework. The study employs three distinct datasets, each containing a variety of physical and clinical parameters. The SLOGPO model, which combines stacked learning, the Improved Giza Pyramids Optimizer (IGPO), and BORUTA for feature selection, demonstrated strong generalization capabilities, achieving accuracies of 96% on Dataset I, 96% on Dataset II, and 95% on Dataset III. In contrast, the PCOSNet model, based on a 1D CNN architecture with BORUTA, achieved accuracies of 94% on Dataset I, 93% on Dataset II, and 84% on Dataset III. These results underscore the superior generalization ability of the SLOGPO model across varied datasets, while the performance of PCOSNet indicates areas for further improvement, particularly in handling diverse datasets. This study provides valuable insights into the impact of feature selection techniques and dataset variability on the performance of PCOS detection models. The findings emphasize the importance of evaluating models across multiple datasets to assess their generalizability and robustness, laying the groundwork for future research in PCOS detection and related medical applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Wideband Circularly-Polarized Hexagonal SIW Cavity-Backed Slot Antenna With Enhanced Bandwidth and Compact Design

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IEEE Access 2025年 13卷 80407-80415页

作者： Sarani, Vahid Mousa Ali, Esraa Shariatifar, Milad Virdee, Bal S. Mariyanayagam, Dion Rashid, Nasr Alibakhshikenari, Mohammad Dalarsson, Mariana Ferdowsi University of Mashhad Electrical Engineering Department Mashhad*** Iran Al-Ahliyya Amman University Communications and Computer Engineering Department Amman19111 Jordan University of Rome "Tor Vergata " Electronics Engineering Department Rome00133 Italy London Metropolitan University Center for Communications Technology LondonN7 8DB United Kingdom Jouf University College of Engineering Electrical Engineering Department Al Jowf Sakaka72388 Saudi Arabia Al-Azhar University Faculty of Engineering Department of Electrical Engineering Nasr City Cairo11884 Egypt Dogus University Department of Electrical and Electronics Engineering Istanbul Umraniye34775 Turkey KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper presents a novel wideband circularly polarized (CP) cavity-backed slot antenna based on Substrate Integrated Waveguide (SIW) technology, designed for compact and high-efficiency performance. The proposed antenna utilizes a hexagonal SIW cavity to simultaneously excite two closely spaced resonant modes (TM110 and TM210), resulting in enhanced bandwidth for linear polarization (LP). To achieve circular polarization, a passive, single-layer linear-to-circular polarization converter is integrated above the cavity, offering a structurally simple and PCB-compatible solution. Unlike conventional CP designs that rely on complex feeding networks or multilayered structures, this configuration maintains a planar profile and efficient performance. A fabricated prototype demonstrates strong agreement between simulation and measurement, achieving a peak gain of 9.2 dBic and a 14% axial ratio (AR) bandwidth. These results highlight the antenna’s suitability for modern wireless systems requiring wideband CP functionality, including satellite communications, 5G modules, and compact embedded devices. © 2013 IEEE.

关键词： Circular polarization

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Safety Critical Control of a Quadrupedal Robot Using MPC-CBF

Safety Critical Control of a Quadrupedal Robot Using MPC-CBF

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Mcconnellogue, Peter James Van, Mien Mcmullan, Rhyss Mcelroy, Ronan Naeem, Wasif School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast United Kingdom

ISBN: (纸本)9798331533892

Quadrupedal robots have been deployed in numerous challenging environments, such as nuclear facility inspections and industrial surveys. These unstructured and dynamic environments expose the robots to significant safety risks. Therefore, this paper proposes a novel design for a safety-critical control system for quadrupedal robots, integrating Model Predictive Control (MPC) with Control Barrier Functions (CBF) to enhance overall system safety. A comprehensive simulation environment has been established, and results demonstrate that the proposed MPCCBF controller outperforms the conventional MPC controller. Specifically, while the MPC controller alone provides adequate tracking of a desired trajectory in a trot gait, integrating CBF enhances obstacle avoidance and improves stability. This combination allows for navigation through more challenging environments while maintaining accurate trajectory tracking. © 2025 IEEE.

关键词： Predictive control systems

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Reducing order modelling of Zeta Converter through Hurwitz polynomial and Padé Approximation

Reducing order modelling of Zeta Converter through Hurwitz p...

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2025 Australian and New Zealand Control Conference, ANZCC 2025

作者： Kumari, Khushboo Kumar, Deepak Sreeram, Victor The EED MNNIT Allahabad India The School of Electrical Electronics and Computer Engineering The University of Western Australia Australia

ISBN: (纸本)9798331532192

This paper introduces a new interval model reduction approach for simplifying the dynamic modeling of a Zeta converter using Hurwitz polynomial and Padé approximation. A Zeta converter, characterized as a fourth-order DC-DC buck-boost converter with a non-inverting output, typically exhibits complex dynamic behavior due to variations in its components. To address this, the converter is modeled as an interval system, accounting for ±2% and ±10% variations in system parameters. The interval model is then reduced to a lower-order system by applying the proposed technique. The effectiveness of the reduced-order models is evaluated through time response analysis. The results confirm that the proposed method reduces the model's complexity while preserving essential characteristics, demonstrating the robustness and practicality of the suggested technique. © 2025 IEEE.

关键词： Buck converter

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Enhanced Position-Aided Beam Prediction Using Real-World Data and Enhanced-Convolutional Neural Networks

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IEEE Access 2025年 13卷 74913-74925页

作者： Abd El Moaty Mohamed Gouda, Ahmed Hamad, Ehab K. I. Hussein, Aziza I. Mabrook, M. Mourad Donkol, A.A. Nahda University Faculty of Engineering Electrical Engineering Department Beni Suef62764 Egypt Aswan University Faculty of Engineering Department of Electrical Engineering Aswan81542 Egypt Effat University Department of Electrical and Computer Engineering Jeddah22332 Saudi Arabia Beni-Suef University Faculty of Navigation Science and Space Technology Space Communication Department Beni Suef62511 Egypt South Valley University Faculty of Engineering Electrical Engineering Communication and Electronics Department Qena83523 Egypt

Millimeter-wave (mmWave) communication systems utilize narrow beamforming to ensure adequate signal power. However, beam alignment requires significant training overhead, especially in high mobility scenarios. Previous research has utilized synthetic data for position-aided beam prediction, which does not fully capture real-world complexities. In this work, an Enhanced Convolutional Neural Network model (E-CNN) is proposed for optimal prediction of beam indices with the aid of real-world GPS position data. The proposed E-CNN model has been investigated across nine different scenarios from the DeepSense 6G dataset and compared against the conventional algorithms. For 64-beams Scenario 1, the E-CNN model showed an increase in average top-1 accuracy from 55.57% to 63.92%, and in case of 32-beams, the accuracy increased from 71.34 % to 82.06%. For 16-beams, the accuracy increased from 86.17% to 94.64 %, while for 8-beams, the accuracy increased from 90.24% to 97.11%. In addition, besides showing significant power loss reduction in various scenarios, the proposed E-CNN model has demonstrated robustness regarding real-word conditions and adaptability for various beam setups. The model realized as high as a 50% power loss reduction in arguably the most challenging graphs, which is an exercise in reliability. This research fills the existing gap between the simulated aid beam alignment and real-world position beam aided alignment, which can be useful in improving beamforming in the upcoming wireless networks. © 2013 IEEE.

关键词： Convolutional neural networks

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LiDAR-Only Navigation of UGVs in Dynamic Environments via Graph Attention Networks and Deep Reinforcement Learning

LiDAR-Only Navigation of UGVs in Dynamic Environments via Gr...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Maguire, Luke Van, Mien Olayemi, Kabirat Mcloone, Sean School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast United Kingdom

ISBN: (纸本)9798331533892

A novel deep reinforcement learning (DRL) framework is proposed for autonomous navigation of unmanned ground vehicles (UGVs) in dynamic environments using LiDAR data. Utilizing a Graph Attention Network (GAT), the framework processes high-dimensional LiDAR data into a meaningful state representation, enabling informed navigation decisions and adaptability to changing obstacles. The approach is validated through simulations of a Husky A200 UGV, demonstrating significant performance improvements over baseline TD3 algorithms. Key results include an improved success rate in trained environments and enhanced generalization in unseen scenarios, with reduced collisions and shorter navigation times. The findings highlight the potential of GAT-enhanced DRL for efficient and cost-effective autonomous navigation in real-world applications. © 2025 IEEE.

关键词： Deep reinforcement learning

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The design and analysis of an advanced hybrid renewable energy microgrid for Cyangugu Remote rural community in Rwanda using Homer Pro

The design and analysis of an advanced hybrid renewable ener...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Asiegbu, Adimchinobi Almaktoof, Ali Adonis, Marco Electrical Electronics and Computer Engineering Department Cape Peninsula University of Technology Cape Town South Africa

ISBN: (纸本)9781032803722

In Rwanda, and in Cyangugu Remote community near the Kivu Lake, there is high solar sunshine energy, significant wind energy, abundant methane gas, appreciable geothermal energy, peat(biomass), hydro renewable energy sources, and grid power access. To ensure feasibility, accessibility, and sustainability, a balance need to be maintained to optimize this energy mix options, in terms of cost (NPV, COE etc.), maintenance, sensitivity, optimization, and other system parameters. The best combination of these Hybrid Renewable Energy Sources (HRES) considering the community’s energy needs, is modelled using the Hybrid Optimization of Multiple Electric Renewables – Professional (HOMER Pro), innovatively, to design and find out the proposed optimum system requirements with environmental advantages or benefits, best payback duration, lower or zero net GHGs and radiation emissions, performance optimization with lest value of Net Present Cost (NPC) and Cost of Energy (COE), sensitivity analysis involving components supply changes like wind speed change based atmospheric conditions, variable sunshine frequencies based on time of the day, seasons, fuel price changes etc. These seven energy supply components is used to provide access to modern, clean and reliable energy to Cyangugu. © 2025 The Author(s).

关键词： Clean energy

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Optimized FLOPs-Aware Knowledge Distillation for TinyML Applications in Agriculture

Optimized FLOPs-Aware Knowledge Distillation for TinyML Appl...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Alba, Arjay Villaverde, Jocelyn Lacara, Amor Domingo, Jovita Aguirre, Dennis College of Engineering Bulacan State University School of Graduate Studies School of Electrical Electronics and Computer Engineering Mapua University Manila Philippines School of Graduate Studies School of Electrical Electronics and Computer Engineering Mapua University Manila Philippines College of Engineering Bulacan State University Malolos Philippines

ISBN: (纸本)9798331513320

This study introduces a novel FLOPs-Aware Knowledge Distillation (FAKD) framework tailored for TinyML applications in agriculture. By integrating Floating Point Operations (FLOPs) as a regularization term into the distillation loss, the framework ensures computational efficiency while preserving accuracy. The teacher-student architecture employs ResNet-50 and MobileNetV2, respectively, with the latter optimized for edge deployment. The methodology includes advanced techniques such as pruning and INT4 quantization, enabling the model to fit stringent hardware constraints, such as the ESP32's 1MB PSRAM, while maintaining high test accuracy. Experimental results on the PlantVillage dataset reveal a significant accuracy improvement from 92.77% to 96.55% post-FAKD, followed by a tradeoff to 90.15% after optimization for deployment. Furthermore, the study compares the performance of the optimized model across different devices, demonstrating a balance between accuracy, inference time, and power consumption. The results validate the framework's potential for deploying efficient and accurate models on low-power devices in resource-constrained environments, making it suitable for smart agriculture applications. This work underscores the importance of balancing model performance and computational efficiency, paving the way for sustainable TinyML solutions in agriculture. © 2025 IEEE.

关键词： Grain (agricultural product)

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Torque ripple minimization in an 8/6 switched reluctance motor drive: optimization using the AZOA method

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Australian Journal of electrical and electronics engineering 2025年第1期22卷 27-44页

作者： Chaple, Megha D. Deotale, Nitin T. Deotale, Nilesh T. Department of Electrical Engineering Lokmanya Tilak College of Engineering Maharashtra Navi Mumbai India Department of Electronics and Telecommunication Engineering Lokmanya Tilak College of Engineering Maharashtra Navi Mumbai India Department of Computer Engineering St. John College of Engineering and Management Palghar India

In this manuscript, an American Zebra Optimization Algorithm (AZOA) is proposed for minimising torque-ripple in an 8/6 switched reluctance motor (SRM) drive. The major objective of the proposed technique is to improve efficiency, steady state performance, and minimise the torque ripple. The objective is achieved by applying the AZOA to determine the optimal converter topology. The switched reluctance motor is fed by three converters with three legs. It consists of freewheeling diodes, IGBTs, capacitors, and resistors. By then, the proposed technique is executed in the MATLAB software and its performance is evaluated with various existing approaches. The simulation results of the SRM drive with different converter typologies are presented to analyse its performance with torque and speed characteristics. The proposed method demonstrates superior performance across all approaches, including the heap-based optimiser (HBO), salp swarm algorithm (SSA), and cuckoo search algorithm (CSA), yielding better results. From the result, it is concluded that the proposed technique displays a high efficiency of 95% and a low cost of 1.5$ compared with other existing methods. ©, Engineers Australia.

关键词： MATLAB

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UETT4K Anti-UAV: A Large Scale 4K Benchmark Dataset for Vision-based Drone Detection in High-Resolution Imagery

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IEEE Access 2025年 13卷 73553-73568页

作者： Awan, Mughees Sarwar Zaidi, Syed Azhar Ali Mir, Junaid Dr. A. Q. Khan Institute of Computer Sciences and Information Technology Kahuta Pakistan University of Engineering and Technology Department of Electronics Engineering Taxila47080 Pakistan University of Engineering and Technology Department of Electrical Engineering Taxila47080 Pakistan

In recent years, incidents involving unmanned aerial vehicles (UAVs) have increased significantly, raising concerns over security and privacy, especially concerning civilian and military facilities. Vision-based approaches, especially those employing deep convolutional neural networks (DCNNs), show great promise in addressing the need for an accurate and cost-effective UAV detection system. However, DCNNs rely heavily on extensive and well-labeled datasets, which are essential for achieving high accuracy and effectiveness. For drone detection tasks, a dataset of high-resolution images is especially valuable, as it provides more contextual information for DCNNs, enabling more accurate drone detection. This work presents a new drone detection dataset composed of 4K resolution images named University of engineering and Technology Taxila 4K Anti-UAV (UETT4K Anti-UAV). The dataset is created by obtaining real-world videos of different types of drones in diverse environmental and challenging conditions. A custom dataset of 33601 images, manually annotated with hand-labeled bounding boxes, is created from these *** used our dataset to train, validate, and test eight state-of-the-art YOLOv6v3 algorithm models. The test results and qualitative analysis guide selecting the most suitable model for drone detection applications. The proposed extensive 4K dataset offers a valuable resource for effectively training deep learning models to detect drones across diverse conditions accurately. © 2013 IEEE.

关键词： Convolutional neural networks

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