[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117899-en":3,"doc-seo-117899-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117899,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning Assisted Optimization and Its Application to Hybrid Dielectric Resonator Antenna Design - Abstract","Machine learning assisted optimization (MLAO) accelerates antenna design by replacing time-intensive traditional methods with data-driven models. Model accountability is evaluated through accuracy metrics, while machine learning techniques such as Gaussian Process Regression, artificial neural networks, and support vector machines are used to predict reflection coefficient more efficiently. The study optimizes a Hybrid Dielectric Resonator Antenna using multiple regression models, achieving best performance with random forest regression at 97% accuracy. It further validates the approach by comparison with conventional antenna design methods.","Original scientific paper  \nMACHINE LEARNING ASSISTED OPTIMIZATION AND ITS APPLICATION TO HYBRID DIELECTRIC RESONATOR ANTENNA DESIGN  \nPinku Ranjan1, Harshit Gupta1, Swati Yadav2, Anand Sharma3  \n1ABV-Indian Institute of Information Technology and Management (IIITM), Gwalior,  \nMadhya Pradesh, India  \n2Department of Electronics & Communication Engineering, College of engineering Roorkee(COER), Roorkee, Uttrakhand, India 3Department of Electronics & Communication Engineering, Motilal Nehru National Institute of Technology Allahabad, India  \nAbstract. Machine learning assisted optimization (MLAO) has become very important for improving the antenna design process because it consumes much less time than the traditional methods. These models' accountability can be checked by the accuracy metrics, which tell about the correctness of the predicted result. Machine learning (ML) methods, such as Gaussian Process Regression, Artificial Neural Networks (ANNs), and Support Vector Machine (SVM), are used to simulate the antenna model to predict the reflection coefficient faster. This paper presents the optimization of Hybrid Dielectric Resonator Antenna (DRA) using machine learning models. Several regression models are applied to the dataset for optimization, and the best results are obtained using a random forest regression model with the accuracy of 97%. Additionally, the effectiveness of machine learning based antenna design is demonstrated through comparison with conventional design methods.  \nKey words: Dielectric Resonator Antenna, Machine Learning, Gaussian Process  \nRegression, ANNs, SVM  \n1. INTRODUCTION  \nAntenna design optimization is a topic that has received a lot of attention in previous few years. That is because methodologies of conventional antenna design are comprehensive and do not have any guarantee of producing effective results because of the complications of the latest antennas fabrication and execution necessities [1]-[3] . Despite the fact that design automation via optimization goes with conventional approaches of antenna design, optimization of antenna designs has many problems [3]-[5] . The significant issues cover the  \nReceived May 18, 2022; revised July 27, 2022; accepted August 31, 2022  \nCorresponding author: Pinku Ranjan  \nABV-Indian Institute of Information Technology and Management (IIITM), Gwalior, Madhya Pradesh , India [E-mail: pinkuranjan@iiitm.ac.in](E-mail: pinkuranjan@iiitm.ac.in)  \nefficiency and optimization capacity of accessible techniques to address a wide extent of antenna design issues thinking about the developing details of current antennas. The methods presented in this report can have an effect on the upcoming development of antennas for an abundance of applications. The frequencies which are in the microwave range of their measurements of current (I) and voltage (V) become very difficult [6]-[8] .  \nAt higher frequencies, we do not measure current or voltage. It is preferred to measure power. As it goes to higher microwave frequencies, it is hard to carry out the short circuit and open circuit for the AC signals over the broad bandwidth. To control this problem, at the microwave range, we use S parameters. S parameters are stated in terms of incident and reflected traveling waves. They are easy to use in the analysis. S parameters can simply be measured using network analyzers the acceptances ofthe use of such parameters have been growing rapidly [8] . S-parameters are a complex matrix that shows Reflection/Transmission characteristics (Phase/Amplitude) in the frequency domain. There are various parameters on which this parameter depends, such as Frequency Bandwidth, Return Loss and Radiation Pattern [9]-[11] .  \nSome academics have concentrated on this issue and forecast antenna performance using various ML techniques in the open literature. Sharma [et.al. suggested using LASSO](et.al. suggested using LASSO) (Least Absolute Shrinkage and Selection Operator), ANN, and KNN approache","cbCaib5cS58eowzL","https://ap.wps.com/l/cbCaib5cS58eowzL","pdf",665855,1,12,"English","en",105,"# Abstract\n# Introduction\n## Antenna design optimization and challenges\n## Use of S-parameters for microwave analysis\n## Prior work using machine learning\n# Background\n## Artificial Neural Network\n## Support Vector Machine","[{\"question\":\"What is the main goal of this paper?\",\"answer\":\"To optimize a Hybrid Dielectric Resonator Antenna design using machine learning based optimization methods and to demonstrate faster, effective design compared with conventional techniques.\"},{\"question\":\"Which machine learning models are used for antenna prediction and optimization?\",\"answer\":\"The paper uses Gaussian Process Regression, Artificial Neural Networks (ANNs), Support Vector Machine (SVM), and applies several regression models including random forest regression for optimization.\"},{\"question\":\"How is the performance of the machine learning models evaluated?\",\"answer\":\"Performance is assessed using accuracy metrics that reflect how correctly the predicted reflection coefficient and antenna outcomes match the desired results.\"}]","Machine Learning Assisted Optimization and Its Application to Hybrid Dielectric Resonator Antenna Design - 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