Pub Date : 2022-12-01DOI: 10.1109/ICECA55336.2022.10009323
Kalyanpu Jagadeeshwar, V. S. S. P. Raju Gottumukkala, B. Srinivasarao, Pala Mahesh Kumar, N. Krishna, P. Pavan Kumar
Recently, COVID-19 is spreading rapidly and fast detection of COVID-19 can save millions of lives. Further, the COVID-19 can be detected easily from computed tomography (CT) images using artificial intelligence methods. However, the performance of these application and methods are reduced due to noises presented in the CT images, which degrading the performance of overall systems. Therefore, this article is focused on implementation of an innovative method for quickly processing CT images of low quality, which enhances the contrast using fuzzy logic. This method makes use of tuned fuzzy intensification operators and is intended to speed up the processing time. Extensive experiments were carried out to test the processing capacity of the method that was proposed, and the results obtained demonstrated that it was capable of filtering a variety of images that had become degraded.
{"title":"Medical Image Contrast Enhancement using Tuned Fuzzy Logic Intensification for COVID-19 Detection Applications","authors":"Kalyanpu Jagadeeshwar, V. S. S. P. Raju Gottumukkala, B. Srinivasarao, Pala Mahesh Kumar, N. Krishna, P. Pavan Kumar","doi":"10.1109/ICECA55336.2022.10009323","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009323","url":null,"abstract":"Recently, COVID-19 is spreading rapidly and fast detection of COVID-19 can save millions of lives. Further, the COVID-19 can be detected easily from computed tomography (CT) images using artificial intelligence methods. However, the performance of these application and methods are reduced due to noises presented in the CT images, which degrading the performance of overall systems. Therefore, this article is focused on implementation of an innovative method for quickly processing CT images of low quality, which enhances the contrast using fuzzy logic. This method makes use of tuned fuzzy intensification operators and is intended to speed up the processing time. Extensive experiments were carried out to test the processing capacity of the method that was proposed, and the results obtained demonstrated that it was capable of filtering a variety of images that had become degraded.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116942849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/ICECA55336.2022.10009127
Kishori Shekokar, Shweta Dour
In modern medicine it is challenging task to detect neurological disorders. In generic way to identify and understand abnormalities in electrical activities of the brain is difficult task. It is very important to bring down to utilize use of traditional diagnostic systems in right time. One of the most common and catastrophic neurological diseases which affects almost all age group diseases is epilepsy. Seizures are described as electrical efficiency of the brain which are unforeseen. It may diversify behaviors, like loss of memory, consciousness, and temporary loss of breath and jerky movements. Classification of Electroencephalogram (EEG) segments is required for purpose of identification of epileptic seizures. The main motive of this study is to present the efficient intelligent model to detect seizures based on noisy EEG data using deep learning techniques. In this paper, for noisy EEG signal analysis, Gaussian noise has been added to two datasets and convolutional neural network model is applied to determine epileptic seizures. Maximum 100 % accuracy is achieved in proposed methodology.
{"title":"Identification of Epileptic Seizures using CNN on Noisy EEG Signals","authors":"Kishori Shekokar, Shweta Dour","doi":"10.1109/ICECA55336.2022.10009127","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009127","url":null,"abstract":"In modern medicine it is challenging task to detect neurological disorders. In generic way to identify and understand abnormalities in electrical activities of the brain is difficult task. It is very important to bring down to utilize use of traditional diagnostic systems in right time. One of the most common and catastrophic neurological diseases which affects almost all age group diseases is epilepsy. Seizures are described as electrical efficiency of the brain which are unforeseen. It may diversify behaviors, like loss of memory, consciousness, and temporary loss of breath and jerky movements. Classification of Electroencephalogram (EEG) segments is required for purpose of identification of epileptic seizures. The main motive of this study is to present the efficient intelligent model to detect seizures based on noisy EEG data using deep learning techniques. In this paper, for noisy EEG signal analysis, Gaussian noise has been added to two datasets and convolutional neural network model is applied to determine epileptic seizures. Maximum 100 % accuracy is achieved in proposed methodology.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125048794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/ICECA55336.2022.10009557
R. S., D. Singh, Shubham Kar
Many expectations placed on students by society have made stress a part of their academic lives. Youth are susceptible to the issues brought on by academic stress since they are going through a phase of transitions in both aspects i.e personal and social. Academic stress has been shown to lower academic achievement and lower motivation toward academics. Therefore, it becomes crucial to develop appropriate and effective intervention options. In recent times, due to COVID, the utilization of online health blogs and sites recommending health, exercise, and yoga has been significantly increased. The blog will provide solution to a problem and then provide precautions to common people but they lack the dynamics to suggest yoga that can be done any person or a personalized yoga by considering their health condition and not a static article. This research work intends to develop an AI model to predict the possible practices a student can do to alleviate their problem by considering their BPM, blood pressure (both systole and diastole), sleep time and some questions related to stress. The proposed stress prediction model has achieved an accuracy of 94.4% and the yoga pose recommendation system has achieved an accuracy of 97.3%.
{"title":"Yoga Recommendation System for the Mental Well-Being of Students using Machine Learning","authors":"R. S., D. Singh, Shubham Kar","doi":"10.1109/ICECA55336.2022.10009557","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009557","url":null,"abstract":"Many expectations placed on students by society have made stress a part of their academic lives. Youth are susceptible to the issues brought on by academic stress since they are going through a phase of transitions in both aspects i.e personal and social. Academic stress has been shown to lower academic achievement and lower motivation toward academics. Therefore, it becomes crucial to develop appropriate and effective intervention options. In recent times, due to COVID, the utilization of online health blogs and sites recommending health, exercise, and yoga has been significantly increased. The blog will provide solution to a problem and then provide precautions to common people but they lack the dynamics to suggest yoga that can be done any person or a personalized yoga by considering their health condition and not a static article. This research work intends to develop an AI model to predict the possible practices a student can do to alleviate their problem by considering their BPM, blood pressure (both systole and diastole), sleep time and some questions related to stress. The proposed stress prediction model has achieved an accuracy of 94.4% and the yoga pose recommendation system has achieved an accuracy of 97.3%.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"158 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123438765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/ICECA55336.2022.10009256
Venkata Kotam Raju Poranki, B. Rao
The reliable diagnosis of diabetic retinopathy (DR) has long been a source of concern for researchers. Due to fluctuating glucose levels, the blood vessels in the retina are more vulnerable to aberrant metabolism. These variances result in lesions or retinal damage, which are then referred to as DR collectively. The signs of DR are often difficult for the current eye healthcare procedures to diagnose. Building an artificial intelligence-assisted automated DR classification (AI-ADRC) system is an excellent way to reduce the pressure of incorrect diagnoses as a result. This article is focused on performance evaluation of DR classification methods, which includes machine learning models, deep learning models, feature extraction, and feature selection methods. The problems presented in state-of-art AI-ADRC systems are addressed, which will help to develop the novel AI-ADRC model. Further, the deep learning-based AI-ADRC models are resulted in superior performance as compared to machine learning based AI-ADRC models using various datasets.
{"title":"Performance Evaluation of AI Assisted Automotive Diabetic Retinopathy Classification Systems","authors":"Venkata Kotam Raju Poranki, B. Rao","doi":"10.1109/ICECA55336.2022.10009256","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009256","url":null,"abstract":"The reliable diagnosis of diabetic retinopathy (DR) has long been a source of concern for researchers. Due to fluctuating glucose levels, the blood vessels in the retina are more vulnerable to aberrant metabolism. These variances result in lesions or retinal damage, which are then referred to as DR collectively. The signs of DR are often difficult for the current eye healthcare procedures to diagnose. Building an artificial intelligence-assisted automated DR classification (AI-ADRC) system is an excellent way to reduce the pressure of incorrect diagnoses as a result. This article is focused on performance evaluation of DR classification methods, which includes machine learning models, deep learning models, feature extraction, and feature selection methods. The problems presented in state-of-art AI-ADRC systems are addressed, which will help to develop the novel AI-ADRC model. Further, the deep learning-based AI-ADRC models are resulted in superior performance as compared to machine learning based AI-ADRC models using various datasets.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115338610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/ICECA55336.2022.10009485
Vasudha Mahajan, Jaspreet Singh
Internet security is of the utmost importance to both consumers and organizations due to the massive risk of attack by malicious hackers. In order to increase internet security implementation of a honeypot system is one way. These honey pots are deployed by businesses and government agencies to identify the extent of network intrusions. A honeypot may be used as a reliable security forensic tool to reduce the risk of intrusions and by exposing potential system security gaps. This comprehensive study gives a concise overview of honeypots and attacks, showing honeypot as an efficient strategy for boosting internet security and a safeguard to our systems. This paper intends to test the honeypot system against syn flooding attack using a pentbox honeypot, allowing to record intrusion attempts and to analyze the attack effectiveness. Honeypot works as a cutting-edge security monitoring tool to reduce the risk of attacks on computer networks and are helpful in revealing important information about possible system security flaws.
{"title":"Performance Analysis of Honeypots Against Flooding Attack","authors":"Vasudha Mahajan, Jaspreet Singh","doi":"10.1109/ICECA55336.2022.10009485","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009485","url":null,"abstract":"Internet security is of the utmost importance to both consumers and organizations due to the massive risk of attack by malicious hackers. In order to increase internet security implementation of a honeypot system is one way. These honey pots are deployed by businesses and government agencies to identify the extent of network intrusions. A honeypot may be used as a reliable security forensic tool to reduce the risk of intrusions and by exposing potential system security gaps. This comprehensive study gives a concise overview of honeypots and attacks, showing honeypot as an efficient strategy for boosting internet security and a safeguard to our systems. This paper intends to test the honeypot system against syn flooding attack using a pentbox honeypot, allowing to record intrusion attempts and to analyze the attack effectiveness. Honeypot works as a cutting-edge security monitoring tool to reduce the risk of attacks on computer networks and are helpful in revealing important information about possible system security flaws.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116530491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/ICECA55336.2022.10009535
G. Jayesh, Gurpreet Singh, Rinki Mishra, Bajarang Prasad Mishra
Open banking is a new data-sharing paradigm that may help new firms get rapid loan approvals and higher investment returns. Most clients are hesitant to embrace open banking because they fear sharing data with third-party suppliers. This research work has presented a blockchain-based self-sovereign identification system architecture for open banking. The proposed architecture offers a secure communication network between users and third-party service providers and let individuals manage their identities and data. Comparing the BBM model to current work shows and analyses its advantages.
{"title":"Management and Access Control Framework for Open Banking Eco System by using Block Chain Technology","authors":"G. Jayesh, Gurpreet Singh, Rinki Mishra, Bajarang Prasad Mishra","doi":"10.1109/ICECA55336.2022.10009535","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009535","url":null,"abstract":"Open banking is a new data-sharing paradigm that may help new firms get rapid loan approvals and higher investment returns. Most clients are hesitant to embrace open banking because they fear sharing data with third-party suppliers. This research work has presented a blockchain-based self-sovereign identification system architecture for open banking. The proposed architecture offers a secure communication network between users and third-party service providers and let individuals manage their identities and data. Comparing the BBM model to current work shows and analyses its advantages.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116721887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/ICECA55336.2022.10009325
Prakash U, Anila A, Swetha C, Vigneshwaran K, K. N
One of the most significant issues in the telecom industry is jumping of customer to another network called customer churn. It has a direct impact on the revenue of the business, particularly in the telecom sector. As a result, businesses are attempting to develop strategies for anticipating customer turnover. Therefore, it is crucial to identify the factors that influence customer churn. Our paper demonstrates how to identify customer attrition effectively in the telecom sector. Our article includes a churn ANN model, which helps telecom businesses manage the individuals who are willing to churn, as well as some practical data analysis, which can be used to draw conclusions from the data. This prediction model with a high accuracy score can be created using neural networks, machine learning algorithms, artificial intelligence and other technologies.
{"title":"A Survey on Artificial Intelligence in Telecommunication for Churn Prediction","authors":"Prakash U, Anila A, Swetha C, Vigneshwaran K, K. N","doi":"10.1109/ICECA55336.2022.10009325","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009325","url":null,"abstract":"One of the most significant issues in the telecom industry is jumping of customer to another network called customer churn. It has a direct impact on the revenue of the business, particularly in the telecom sector. As a result, businesses are attempting to develop strategies for anticipating customer turnover. Therefore, it is crucial to identify the factors that influence customer churn. Our paper demonstrates how to identify customer attrition effectively in the telecom sector. Our article includes a churn ANN model, which helps telecom businesses manage the individuals who are willing to churn, as well as some practical data analysis, which can be used to draw conclusions from the data. This prediction model with a high accuracy score can be created using neural networks, machine learning algorithms, artificial intelligence and other technologies.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117285849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/ICECA55336.2022.10009390
C. Jayaramulu, B. Venkateswarlu
Outlier identification is one of the trending research projects, which is used to detect the normal (important) and abnormal (abusive, unimportant, attack) content presented in the data. So, the automatic outlier detection plays the major role in various applications. However, the conventional methods are failed to provide the maximum accuracy, efficiency due to ineffective classification. Therefore, this work focused on implementation of deep learning-based outlier tool network (DLOT-Net). Initially, Outlier Detection Datasets (ODDS) is considered for simulations, which is preprocessed to remove the missed symbols. Then, the deep learning convolutional neural network (DLCNN) model trained with the preprocessed dataset. During the training process, DLCNN model creates the memory of outliers. Then, for every random test sample, the DLCNN model identifies the normal and abnormal attributes presented in the data using probability comparisons. The simulations conducted on ODDS dataset shows that, the proposed DLOT-Net resulted in superior objective performance as compared to several other outlier detection methods.
{"title":"DLOT-Net: A Deep Learning Tool For Outlier Identification","authors":"C. Jayaramulu, B. Venkateswarlu","doi":"10.1109/ICECA55336.2022.10009390","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009390","url":null,"abstract":"Outlier identification is one of the trending research projects, which is used to detect the normal (important) and abnormal (abusive, unimportant, attack) content presented in the data. So, the automatic outlier detection plays the major role in various applications. However, the conventional methods are failed to provide the maximum accuracy, efficiency due to ineffective classification. Therefore, this work focused on implementation of deep learning-based outlier tool network (DLOT-Net). Initially, Outlier Detection Datasets (ODDS) is considered for simulations, which is preprocessed to remove the missed symbols. Then, the deep learning convolutional neural network (DLCNN) model trained with the preprocessed dataset. During the training process, DLCNN model creates the memory of outliers. Then, for every random test sample, the DLCNN model identifies the normal and abnormal attributes presented in the data using probability comparisons. The simulations conducted on ODDS dataset shows that, the proposed DLOT-Net resulted in superior objective performance as compared to several other outlier detection methods.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129040653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/ICECA55336.2022.10009371
M. Harshini, Jeethu Philip, I. Haritha, Shruti Patil
This research proposes a ground breaking method for examining the condition of sewage pipes. The underground sewage piping system in cities is a vital form of common infrastructure because it helps to ensure a safe atmosphere. One of the most commonly used sewer inspection process which uses CCTV systems, has a weak performance. A camera is installed on one side of the pipe or on some other unit, and video is recorded within the pipes and sent off-line to an engineer to classify possible faults. The machine-controlled detection and testing of the location of divergences within the internal structure is the subject of this project with the help of Open Source computer vision Library techniques. Many steps are used in the machine-controlled inspection technique, including normalize RGB, Background Subtraction, Canny edge detection, Arc Detect, contours High-light, time Convert, and circular Mask, which involves segmenting the image into Mathematical choices and the defected unit field. Recognizing and classifying defects found within the pipe of an area unit using computation perception techniques helped reconcile image processing. The method of detection is both fast and automatic.
{"title":"Sewage Pipeline Fault Detection using Image Processing","authors":"M. Harshini, Jeethu Philip, I. Haritha, Shruti Patil","doi":"10.1109/ICECA55336.2022.10009371","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009371","url":null,"abstract":"This research proposes a ground breaking method for examining the condition of sewage pipes. The underground sewage piping system in cities is a vital form of common infrastructure because it helps to ensure a safe atmosphere. One of the most commonly used sewer inspection process which uses CCTV systems, has a weak performance. A camera is installed on one side of the pipe or on some other unit, and video is recorded within the pipes and sent off-line to an engineer to classify possible faults. The machine-controlled detection and testing of the location of divergences within the internal structure is the subject of this project with the help of Open Source computer vision Library techniques. Many steps are used in the machine-controlled inspection technique, including normalize RGB, Background Subtraction, Canny edge detection, Arc Detect, contours High-light, time Convert, and circular Mask, which involves segmenting the image into Mathematical choices and the defected unit field. Recognizing and classifying defects found within the pipe of an area unit using computation perception techniques helped reconcile image processing. The method of detection is both fast and automatic.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123911700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/ICECA55336.2022.10009507
R. Amrutha, R. Gayathri
In recent years, the development of compact multiband antennas has attracted attention in the field of communications. To meet the above requirements, the concept of fractals is used in antenna design. The concept of fractals plays an important role in the design of Microstrip antennas using fractal geometries and has a wide range of applications in science and technology. An interesting step in antenna design is the fractal structure, which is the main determinant of the antenna's effective resonant frequency. Iterated function systems (IFS) are considered an efficient modeling technique among various fractal generation and modeling methods due to their conceptual simplicity and computational efficiency. This article presents a comprehensive survey of research work in the field of Iterative functional systems of fractal antenna.
{"title":"Investigation on Mathematical Modeling of Fractal Geometry using IFS for Microstrip Antenna","authors":"R. Amrutha, R. Gayathri","doi":"10.1109/ICECA55336.2022.10009507","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009507","url":null,"abstract":"In recent years, the development of compact multiband antennas has attracted attention in the field of communications. To meet the above requirements, the concept of fractals is used in antenna design. The concept of fractals plays an important role in the design of Microstrip antennas using fractal geometries and has a wide range of applications in science and technology. An interesting step in antenna design is the fractal structure, which is the main determinant of the antenna's effective resonant frequency. Iterated function systems (IFS) are considered an efficient modeling technique among various fractal generation and modeling methods due to their conceptual simplicity and computational efficiency. This article presents a comprehensive survey of research work in the field of Iterative functional systems of fractal antenna.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123372576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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