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Evolutionary optimization of Yagi–Uda antenna design using grey wolf optimizer

Neural Computing and Applications 2025年第6期37卷 4155-4183页

作者： Braik, Malik Sheta, Alaa Aljahdali, Sultan El-Hefnawi, Fatma Al-Hiary, Heba Elashmawi, Walaa H. Computer Science Department Al-Balqa Applied University Salt Jordan Department of Computer Science Southern Connecticut State University New HavenCT United States Computer Science Department Taif University Taif Saudi Arabia Microwave Department Electronics Research Institute Giza Egypt Department of Computer Information System Al-Balqa Applied University Salt Jordan Department of Computer Science Faculty of Computers & Informatics Suez Canal University Ismailia Egypt Department of Computer Science Faculty of Computer Science Misr International University Cairo Egypt

Meta-heuristic optimization algorithms have become widely used due to their outstanding features, such as gradient-free mechanisms, high flexibility, and great potential for avoiding local optimal solutions. This research explored the grey wolf optimizer (GWO) to find the ideal configuration for a six-element Yagi–Uda antenna. The GWO algorithm adjusted the lengths of the antenna wires and the spacings between them. The goal was to maximize the antenna’s ability to transmit signals (throughput gain). Optimal antenna selection relies on various parameters, including gain, bandwidth, impedance matching, frequency, side-lobe levels, etc. The optimization of a six-element Yagi–Uda antenna presents a challenging engineering design problem due to its multimodal and nonlinear nature. Achieving optimal performance hinges on the intricate interplay between the lengths of the constituent elements and the spacing configurations. To this end, a multiobjective function was adopted to design this antenna. The performance of several meta-heuristic algorithms, including genetic algorithms, biogeography-based optimization, simulated annealing, and grey wolf optimizer, was compared. The GWO-based approach has performed better than its competitors. This optimized antenna design based on GWO reported a gain of 14.21 decibel. Therefore, the GWO-based method optimizes antennas that can be further investigated for other antenna design problems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Simulated annealing

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Heart Disease Identification Using Ensemble Learning and Deep Learning 3rd

Heart Disease Identification Using Ensemble Learning and Dee...

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3rd International Conference on Intelligent systems and Sustainable Computing, ICISSC 2023

作者： Siruvoru, Vahini Nampally, Vijay Kumar Thati, Deepak Devanaboyina, Sai Abhinav Sanagala, Sai Dheeraj Reddy Department of Computer Science and Business System B V Raju Institute of Technology Telangana Medak Narsapur India

ISBN: (纸本)9789819783540

Technology in the field of health care has been constantly enhanced incorporating machine learning and various other technologies for clinical decision support systems or computer-aided healthcare systems which assist in examining the complications. Heart Disease is an issue of concern for any age group and gender, which depends on innumerable factors like cholesterol, blood pressure, chest pain and more. Furthermore, initial methods of getting a check-up from the doctor to determine whether the patient is suffering or will be suffering from the disease is time consuming as well as laborious. Considering this, by using Machine Learning and Deep Learning, it is possible to overcome the complications and produce an effective and accurate means of detecting heart disease. This is achieved by deploying ANN, performing feature reduction by PCA as well and creating an ensemble model of Random Forest, Decision Tree, and KNN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Drone-Assisted Load Distribution Framework for Traffic Optimization in IoT Networks 17

Drone-Assisted Load Distribution Framework for Traffic Optim...

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17th International Conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Soni, Priyanka Mense, Omkar Manohar Kanti Addya, Sourav National Institute of Technology Cloud and Smart System Services Lab Department of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798331531195

Device connectivity has been redefined by the rapid development of the Internet of Things (IoT) technology, enabling diverse applications in areas such as smart cities, smart homes, and healthcare. These applications produce massive amounts of data, making it a significant challenge to offload tasks to cloud servers. However, the physical distance between devices often leads to latency issues for IoT systems, impacting time-sensitive applications. Fog computing tackles this issue by processing data closer to IoT devices. However, a high user density can strain the capacity of a macro base station, potentially leading to congestion. Drone Base Stations (DBSs) provide a flexible solution by acting as mobile relays to reduce latency and traffic burden. To address this issue, this work proposes a drone-assisted load distribution strategy in IoT networks. In our proposed approach, we deploy DBS efficiently using a greedy optimization technique. However, after the deployment of DBS, an optimal base station assignment framework dynamically connects user equipment to the most suitable base station, helping to minimize latency and enhance network performance. This dual-phase approach provides a scalable and practical solution for realtime IoT applications. Through extensive simulations, ensures that our proposed approach achieves a more balanced assignment compared to baseline algorithms. © 2025 IEEE.

关键词： Drones

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Heuristic Search with Cut Point Based Strategy for Critical Node Problem

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Journal of computer science & Technology 2024年第6期39卷 1328-1340页

作者： Zhi-Han Chen Shao-Wei Cai Jian Gao Shi-Ke Ge Chan-Juan Liu Jin-Kun Lin School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 101408China Key Laboratory of System Software(Chinese Academy of Sciences)and State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China College of Information Science and Technology Northeast Normal UniversityChangchun 130024China School of Computer Science and Technology Dalian University of TechnologyDalian 116024China SeedMath Technology Limited Beijing 100086China

The critical node problem(CNP)aims to deal with critical node identification in a graph,which has extensive applications in many *** CNP is a challenging task due to its computational complexity,and it attracts much attention from both academia and *** this paper,we propose a population-based heuristic search algorithm called CPHS(Cut Point Based Heuristic Search)to solve CNP,which integrates two main *** first one is a cut point based greedy strategy in the local search,and the second one involves the functions used to update the solution pool of the ***,a mutation strategy is applied to solutions with probability based on the overall average similarity to maintain the diversity of the solution *** are performed on a synthetic benchmark,a real-world benchmark,and a large-scale network benchmark to evaluate our *** with state-of-the-art algorithms,our algorithm has better performance in terms of both solution quality and run time on all the three benchmarks.

关键词： local search cut point heuristic search critical node problem

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Personalized Collaborative Search Engine for Decision Making

Personalized Collaborative Search Engine for Decision Making

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Murshleen, Mohd. Prabhu, A. Jain, Garima Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Computer Science & Business System Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350366846

Web search engines are used for retrieving information but still there exists ambiguity in their retrieval process. It is so because traditional search engines laid emphasis on matching of keywords rather than maintaining semantic relations among terms lack of ability to produce precise results, presence of ambiguous keywords and verifying needs of users are few hindrances that are acting as scenario for web search engines. The paper focuses on improving search quality of users by collecting user experiences through creating hierarchical user profile it leads to collaborative search engine that helps in reaching at particular decision then the collected user's data are uploaded to recommendation server which are than displayed a search engines like Google/Yahoo. © 2024 IEEE.

关键词： Search engines

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Dynamic iterative imputation algorithm based on rough fuzzy C-Means clustering 3

Dynamic iterative imputation algorithm based on rough fuzzy ...

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2023 3rd International Conference on Computational Modeling, Simulation and Data Analysis, CMSDA 2023

作者： Gong, Zheng Yan, Chun Liu, Wei Mathematics and System Science Institute Shandong University of Science and Technology Qingdao Shandong266590 China Computer Science Institute Shandong University of Science and Technology Qingdao Shandong266590 China

Missing data is a very common phenomenon in daily life. To mitigate its adverse impact on data analysis, this paper proposes a dynamic iterative imputation algorithm based on the rough fuzzy C-means algorithm. The algorithm incorporates dynamic approximations to partition and iterate the cluster assignment of missing sample points, utilizing information from complete sample points within the same cluster for iterative imputation. The imputation results are evaluated by using MAE and RMSE metrics, and the optimal number of iterations and imputation values are determined by minimizing these metrics. To verify the effectiveness of the proposed algorithm, 6 UCI public datasets are selected for imputation tests, and the change curves of metric value with iterations are generated under different missing proportions in each dataset. The optimal imputation result is obtained based on the lowest point of the curve. Subsequently, the performance of the proposed algorithm is compared with three cluster-based and five machine learning-based imputation algorithms. The results demonstrate the significant superiority of the proposed algorithm over the other methods, achieving a more approximate imputation of missing values in the dataset. © Published under licence by IOP Publishing Ltd.

关键词： Statistical tests

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Social Distance and Face Mask Detection using vision-based sensors and IP cameras 3

Social Distance and Face Mask Detection using vision-based s...

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3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Vineela, A. Devi, G. Lavanya Deekshit, G. Vinay GMR Institute of Technology Department of Computer Science and Engineering Rajam India Department of Computer Science and System Engineering Vishakhapatnam India

ISBN: (纸本)9781665494007

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

关键词： computer vision

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Lung Cancer Risk Prediction Features Influence Model Based on Machine Learning Techniques 38

Lung Cancer Risk Prediction Features Influence Model Based o...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Mondal, Subhash Maity, Ranjan Rai, Chirag Pramanik, Souptik Nag, Amitava Central Institute of Technology Kokrajhar Computer Science and Engineering Kokrajhar India Meghnad Saha Institute of Technology Computer Science and Bussiness System Kolkata India

ISBN: (纸本)9798350302196

Currently, lung cancer is a very common form of cancer. This is because many people are chain smokers nowadays, and many are affected due to their work hazards. The pollution level in modern cities is also a major cause of this type of cancer. This model is built to predict the chances of the occurrence of lung cancer in an individual with the help of certain conditions. The acquired dataset used in this study contains multiple features, but not all are necessary for predicting the risk of lung cancer. Hence, an embedding feature importance model Light Gradient Boosting Machine (LGBM) is used to find the impact of every feature, and the model had trained using the features with maximum influence. The dataset has been divided into two parts for training and testing the model. The models achieve a k-fold mean accuracy of 97.63% and above with all the features and more than 93% on the reduced features for all the deployed models. The models are developed based on a resource-constrained device perspective over the reduced features low resource dataset and use an algorithm to measure the execution time taken for every model to run and complete its prediction after fitting with the respective classifiers. The model developed on medical data should have maximum accuracy and is necessary for time efficiency that reflects on all the deployed models with stability and efficacy, indicating the robustness and non-overfitted model. © 2023 IEEE.

关键词： Forecasting

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A Noise Shaping SAR ADC with kT/C Noise Cancellation and Mismatch Error Shaping

A Noise Shaping SAR ADC with kT/C Noise Cancellation and Mis...

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2024 IEEE International Conference on Integrated Circuits, Technologies and Applications, ICTA 2024

作者： Yi, Tiange Li, Zhenbing Du, Bing Yang, Shiheng Liu, Jiaxin University of Electronic Science and Technology of China China National Computer System Engineering Research Institute of China China

ISBN: (纸本)9798331530709

The resolution of SAR ADCs is primarily limited by sampling noise, DAC mismatch, and comparator noise. This work presents a noise-shaping SAR ADC design that addresses all these error sources. Sampling noise is mitigated using a kT/C noise cancellation technique combined with resampling. DAC mismatch is managed through data-weighted averaging (DWA) and mismatch error shaping (MES), while comparator noise is reduced using a second-order passive noise-shaping (NS) technique. Designed in a 28nm CMOS process, the ADC achieves 86.8 dB SNDR across a 2.8 MHz bandwidth with a power consumption of 3.8 mW. © 2024 IEEE.

关键词： Comparators (optical)

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A QOE-AWARE STRATEGY FOR ENERGY SAVING IN A FOG-BASED ENVIRONMENTAL MONITORING system

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International Journal of Electrical Engineering 2023年第2期30卷 43-52页

作者： Haung, Yieh-Ran Yin, Chia-Ling Liu, Jenchi Department of Computer Science and Engineering Yuan Ze University Taiwan Electronic and Optoelectronic System Research Laboratories Industrial Technology Research Institute Taiwan

In the Fog-based Environmental Monitoring system (FEMS), the frequency at which Internet of Thing (IoT) devices upload monitoring data and the frequency at which users obtain monitoring data can highly affect energy consumption, data timeliness, and user quality of experience (QoE). How to reduce energy consumption while achieving high data timeliness and user QoE is not only important but also challenging. We propose a QoE-aware strategy to schedule the frequency at which users obtain monitoring data and the frequency at which IoT devices upload monitoring data. The QoE-aware strategy sets the changing tolerance (CT) of monitoring data according to the requirements of different applications in different environments. CT indicates the sensitivity of the application to the change of the environmental data. Only when the changing rate of monitoring data exceeds the CT value set, the IoT device uploads monitoring data to the fog server. Besides, to meet the requirements of reducing data traffic and achieving high data timeliness, users can set different values of QoE Index (QI), i.e., the achievement rate for timely access, according to different environmental requirements. With the QI value, the fog server can calculate the optimal time interval (i.e., access period, AP) for users to access monitoring data. Consequently, by setting the values of CT, QI, and AP according to the requirements of different applications in different environments, the QoE-aware strategy is able to achieve the lowest energy consumption and the highest data timeliness. © 2023, Chinese institute of Electrical Engineering. All rights reserved.

关键词： Fog

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