Pub Date : 2022-12-01DOI: 10.1109/ICECA55336.2022.10009358
A. V. Kumar, G. Vyshnavi, P. Harshini, Papareddy Sushanth Reddy
A quick growth of information sharing and transferring has been observed recently. Everything is getting digitized, and everyone requires a simple and straightforward method. As a result, the number of fake documents generated for job applications is increasing, and checking all the documents manually is not a good option as it takes a lot of time. Therefore, digitizing documents is becoming an increasingly popular option for companies and individuals as it is the most secure and least time-consuming way of verifying documents. As Blockchain is a decentralized system that guarantees the protection of data kept in it, this article proposes a solution based on Federated Blockchain technology that allows specific organizations to submit candidates' original documents. It validates the student's submitted document hash value by comparing the existing cryptographic hash in the Blockchain. SHA-512 is used to generate the hash values for the documents. This technique is incredibly efficient, consumes less time, and is less expensive to execute all types of verifications.
{"title":"A Blockchain-based Document Verification Model in Freshers Hiring Process","authors":"A. V. Kumar, G. Vyshnavi, P. Harshini, Papareddy Sushanth Reddy","doi":"10.1109/ICECA55336.2022.10009358","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009358","url":null,"abstract":"A quick growth of information sharing and transferring has been observed recently. Everything is getting digitized, and everyone requires a simple and straightforward method. As a result, the number of fake documents generated for job applications is increasing, and checking all the documents manually is not a good option as it takes a lot of time. Therefore, digitizing documents is becoming an increasingly popular option for companies and individuals as it is the most secure and least time-consuming way of verifying documents. As Blockchain is a decentralized system that guarantees the protection of data kept in it, this article proposes a solution based on Federated Blockchain technology that allows specific organizations to submit candidates' original documents. It validates the student's submitted document hash value by comparing the existing cryptographic hash in the Blockchain. SHA-512 is used to generate the hash values for the documents. This technique is incredibly efficient, consumes less time, and is less expensive to execute all types of verifications.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"274 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":"134450384","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/ICECA55336.2022.10009542
Chen Ping
The system captures live images in real time through cameras and superimposes 2D pictures or 3D models of clothing on the images of table tennis players combined with somatosensory detection devices, to realize the fusion of virtual clothing and athletes' postures, and successfully obtain depth images through the Unity3D virtual reality platform. Data and bone and iliac tracking data and preprocessed the data to obtain a method of customizing user poses in Unity3D combined with Kinect somatosensory devices. Develop a set of independent property rights, easy to operate, stable performance, and can be used directly the “Intelligent Table Tennis Special Technical and Tactical Video Analysis System” that is practiced in table tennis and can be put into the market.
{"title":"Intelligent Attitude Analysis Algorithm based on Computer Somatosensory Technology","authors":"Chen Ping","doi":"10.1109/ICECA55336.2022.10009542","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009542","url":null,"abstract":"The system captures live images in real time through cameras and superimposes 2D pictures or 3D models of clothing on the images of table tennis players combined with somatosensory detection devices, to realize the fusion of virtual clothing and athletes' postures, and successfully obtain depth images through the Unity3D virtual reality platform. Data and bone and iliac tracking data and preprocessed the data to obtain a method of customizing user poses in Unity3D combined with Kinect somatosensory devices. Develop a set of independent property rights, easy to operate, stable performance, and can be used directly the “Intelligent Table Tennis Special Technical and Tactical Video Analysis System” that is practiced in table tennis and can be put into the market.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"159 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":"133140884","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/ICECA55336.2022.10009370
Jerin Joel J, Jefina Jacklin J, R. J. Bharathi, Augustine E, Ajay Sairam N
Patients with epilepsy experience difficulties throughout their lives and need to be careful in managing their condition. Seizures can cause serious injury or be life-threatening when crossing heavy machinery (such as when driving a car). Electroencephalographic signals are used to determine the brain's actions during seizures and as a result of complex skin preparation techniques. This model offers a portable design that integrates data from a set of AgCl electrodes with a reference electrode. The model includes an instrumentation amplifier, low-noise and high-impedance filters. The experimental results show that the system could implement the acquisition and store the EEG signals efficiently.
{"title":"Low Cost Analog EEG amplifier for Healthcare Applications","authors":"Jerin Joel J, Jefina Jacklin J, R. J. Bharathi, Augustine E, Ajay Sairam N","doi":"10.1109/ICECA55336.2022.10009370","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009370","url":null,"abstract":"Patients with epilepsy experience difficulties throughout their lives and need to be careful in managing their condition. Seizures can cause serious injury or be life-threatening when crossing heavy machinery (such as when driving a car). Electroencephalographic signals are used to determine the brain's actions during seizures and as a result of complex skin preparation techniques. This model offers a portable design that integrates data from a set of AgCl electrodes with a reference electrode. The model includes an instrumentation amplifier, low-noise and high-impedance filters. The experimental results show that the system could implement the acquisition and store the EEG signals efficiently.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"3 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":"122398631","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/ICECA55336.2022.10009192
J. Amalraj, B. Suchitra, Harshad Naranbhai Prajapati, M. Shrimali, R. Gnanakumaran, N. Girdharwal
The epidemic of coronavirus disease 2019 (COVID-19) has caused an ever-growing demand for treatment, testing, and diagnosis. Chest x-rays are a fast and low-cost test that can detect COVID19 but chest imaging is not a first-line test for COVID19 because of lower diagnosis performance and confounding with other viral pneumonia. Current studies using deep learning (DL) might assist in overcoming these issues as convolution neural networks (CNN) have illustrated higher performance of COVID19 diagnoses at the earlier phase. This study develops a new Firefly Optimization with Bidirectional Gated Recurrent Unit (FFO-BGRU) for COVID19 diagnoses on Chest Radiographs. The main intention of the FFO-BGRU technique lies in the recognition and classification of COVID-19 on Chest X-ray images. At the initial stage, the presented FFO-BGRU technique applies Wiener filtering (WF) technique for noise removal process. Followed, the hyperparameter tuning process takes place by using FFO algorithm and SqueezeNet architecture is applied for feature extraction. Lastly, the BGRU model is applied for COVID19 recognition and classification. A wide range of simulations were performed to demonstrate the betterment of the FFO-BGRU model. The comprehensive comparison study highlighted the improved outcomes of the FFO-BGRU algorithm over other recent approaches.
{"title":"Firefly Optimization with Bidirectional Gated Recurrent Unit for COVID-19 Diagnosis on Chest Radiographs","authors":"J. Amalraj, B. Suchitra, Harshad Naranbhai Prajapati, M. Shrimali, R. Gnanakumaran, N. Girdharwal","doi":"10.1109/ICECA55336.2022.10009192","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009192","url":null,"abstract":"The epidemic of coronavirus disease 2019 (COVID-19) has caused an ever-growing demand for treatment, testing, and diagnosis. Chest x-rays are a fast and low-cost test that can detect COVID19 but chest imaging is not a first-line test for COVID19 because of lower diagnosis performance and confounding with other viral pneumonia. Current studies using deep learning (DL) might assist in overcoming these issues as convolution neural networks (CNN) have illustrated higher performance of COVID19 diagnoses at the earlier phase. This study develops a new Firefly Optimization with Bidirectional Gated Recurrent Unit (FFO-BGRU) for COVID19 diagnoses on Chest Radiographs. The main intention of the FFO-BGRU technique lies in the recognition and classification of COVID-19 on Chest X-ray images. At the initial stage, the presented FFO-BGRU technique applies Wiener filtering (WF) technique for noise removal process. Followed, the hyperparameter tuning process takes place by using FFO algorithm and SqueezeNet architecture is applied for feature extraction. Lastly, the BGRU model is applied for COVID19 recognition and classification. A wide range of simulations were performed to demonstrate the betterment of the FFO-BGRU model. The comprehensive comparison study highlighted the improved outcomes of the FFO-BGRU algorithm over other recent approaches.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"11 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":"127945262","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/ICECA55336.2022.10009128
ChandraRekha Rayapureddy, G. Jayalakshmi, Bade Kranthi Priya, Divyasri Munugumati
Object detection is a very difficult task in many applications. Presently many authors are trying to develop new research applications to find the objects in Images and videos. In images, static objects are identified and in videos, dynamic objects are identified which are called moving objects. Deep Learning and Artificial intelligence playa major role in finding the objects in Images and also in Videos. So many existing methods are developed for the detection of objects from various sources. In real-time applications, obj ect detection can be used to find malicious objects also. In this paper, an ensemble model is developed to find the accurate objects from the given inputs. The ensemble model is the combination of YOLOV3 (You Only Look Once) and a Convolutional neural network (CNN). The dataset used in this paper is COCO-2017 collected from online sources. The performance of the proposed approach is analyzed by comparing it with the several existing approaches.
在许多应用中，目标检测是一项非常困难的任务。目前，许多作者正在尝试开发新的研究应用程序来寻找图像和视频中的对象。在图像中，静态对象被识别，而在视频中，动态对象被识别，称为运动对象。深度学习和人工智能在寻找图像和视频中的物体方面发挥着重要作用。因此，为了检测来自不同来源的物体，开发了许多现有的方法。在实时应用中，对象检测也可用于发现恶意对象。本文建立了一个集成模型，从给定的输入中找到精确的目标。该集成模型是YOLOV3 (You Only Look Once)和卷积神经网络(CNN)的结合。本文使用的数据集为COCO-2017，收集自网络资源。通过与现有几种方法的比较，分析了该方法的性能。
{"title":"Ensemble Learning Model for Object Detection in Image and Videos","authors":"ChandraRekha Rayapureddy, G. Jayalakshmi, Bade Kranthi Priya, Divyasri Munugumati","doi":"10.1109/ICECA55336.2022.10009128","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009128","url":null,"abstract":"Object detection is a very difficult task in many applications. Presently many authors are trying to develop new research applications to find the objects in Images and videos. In images, static objects are identified and in videos, dynamic objects are identified which are called moving objects. Deep Learning and Artificial intelligence playa major role in finding the objects in Images and also in Videos. So many existing methods are developed for the detection of objects from various sources. In real-time applications, obj ect detection can be used to find malicious objects also. In this paper, an ensemble model is developed to find the accurate objects from the given inputs. The ensemble model is the combination of YOLOV3 (You Only Look Once) and a Convolutional neural network (CNN). The dataset used in this paper is COCO-2017 collected from online sources. The performance of the proposed approach is analyzed by comparing it with the several existing approaches.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"26 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":"121345694","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/ICECA55336.2022.10009427
A. Sangeetha Devi, A. Shanmugapriya, A. Kalaivani
Real-world knowledge is represented by a knowledge graph that provides assistance for various applications built on the basis of artificial intelligence. Awareness of the neighbourhood is obtained from the individuals and relationships of the Knowledge Graph. High-dimensional data analysis is a difficult task in many applications and this article discusses the dimensionality by specifying a limited collection of features that implies high-dimensional data without visible or substantial data loss. An unsupervised learning approach based on learning that uses the neural network principle and learns the features using the graph. The Positive Feature Selection approach using the Neural Network (PFSNN) approach in this paper defines features using a graph where the classification is carried out by the NN process and analyses the output of the proposed system. The efficiency of the PFSNN is evaluated by contrasting it with existing classification methods and using different datasets. Performance is measured using the classification performance metrics and it is defined from the observation that the proposed PFSNN algorithm has the best outcome.
{"title":"Feature Selection and Classification using a Positive Learning Approach Focused on Graph and Neural Network","authors":"A. Sangeetha Devi, A. Shanmugapriya, A. Kalaivani","doi":"10.1109/ICECA55336.2022.10009427","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009427","url":null,"abstract":"Real-world knowledge is represented by a knowledge graph that provides assistance for various applications built on the basis of artificial intelligence. Awareness of the neighbourhood is obtained from the individuals and relationships of the Knowledge Graph. High-dimensional data analysis is a difficult task in many applications and this article discusses the dimensionality by specifying a limited collection of features that implies high-dimensional data without visible or substantial data loss. An unsupervised learning approach based on learning that uses the neural network principle and learns the features using the graph. The Positive Feature Selection approach using the Neural Network (PFSNN) approach in this paper defines features using a graph where the classification is carried out by the NN process and analyses the output of the proposed system. The efficiency of the PFSNN is evaluated by contrasting it with existing classification methods and using different datasets. Performance is measured using the classification performance metrics and it is defined from the observation that the proposed PFSNN algorithm has the best outcome.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"182 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":"114144479","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/ICECA55336.2022.10009365
Komal T, K. S, Meenalakshmi S, M. R
On the subject of a decentralized open healthcare network, healthcare blockchains provide a novel way to information depository, transaction execution, along with trust building for data integration and sharing. In spite of that, problems relating to safety and solitude are the major focusing concerns in terms of working with technology like blockchain for sectors like healthcare. However, blockchain technology for healthcare has acquired widespread interest and recognition in business, administration, and education. This composition targets on the safety protocols and solitude concerns for the exchange of healthcare information via blockchain and examines the threats and obligation more in detail. Privacy, as well as methodologies and technical solutions. This research study initially reviews the prerequisites and the required security and privacy features to install a healthy blockchain and share electronic health data. Furthermore, this research study confers about the function involved in blockchain, including anonymous signatures, attribute-based encryption, proof-of-nothing, and verification techniques to secure smart contracts. This research study is finally concluded by discussing about other potential use cases in medical sector. This study will provide technical and insightful information on healthcare management system using blockchain technology to efficiently deal with the safety protocols, privacy concepts, threats, prerequisite as well as developments for healthcare professionals, healthcare providers, policy makers and health service developers.
{"title":"Blockchain based Effective Ledger and Decentralization in Healthcare System","authors":"Komal T, K. S, Meenalakshmi S, M. R","doi":"10.1109/ICECA55336.2022.10009365","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009365","url":null,"abstract":"On the subject of a decentralized open healthcare network, healthcare blockchains provide a novel way to information depository, transaction execution, along with trust building for data integration and sharing. In spite of that, problems relating to safety and solitude are the major focusing concerns in terms of working with technology like blockchain for sectors like healthcare. However, blockchain technology for healthcare has acquired widespread interest and recognition in business, administration, and education. This composition targets on the safety protocols and solitude concerns for the exchange of healthcare information via blockchain and examines the threats and obligation more in detail. Privacy, as well as methodologies and technical solutions. This research study initially reviews the prerequisites and the required security and privacy features to install a healthy blockchain and share electronic health data. Furthermore, this research study confers about the function involved in blockchain, including anonymous signatures, attribute-based encryption, proof-of-nothing, and verification techniques to secure smart contracts. This research study is finally concluded by discussing about other potential use cases in medical sector. This study will provide technical and insightful information on healthcare management system using blockchain technology to efficiently deal with the safety protocols, privacy concepts, threats, prerequisite as well as developments for healthcare professionals, healthcare providers, policy makers and health service developers.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"1245 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":"127434049","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/ICECA55336.2022.10009389
Hari Krishna Marrapu, B. Maram, Smritilekha Das, T. Daniya
Artificial intelligence and machine learning have the power to revolutionize the healthcare industry and unlock a world of amazing potential. But unless all interested parties possess rudimentary knowledge of healthcare and machine learning fundamentals and principles, it is not able to fully utilize the capabilities of these technologies. This present literature survey work analyzes the research which implemented the machine learning algorithms in the detection of pneumonia. Furthermore, it is increasingly obvious that AI systems will not substantially replace human clinicians in patient care but rather support them. Human physicians may eventually gravitate toward duties and work arrangements that make use of particularly human abilities like empathy, persuasion, and big-picture integration. Those healthcare professionals who refuse to collaborate with artificial intelligence may end up being the only ones to lose their professions in the future.
{"title":"A Review on Early Prediction of Pneumonia Using Deep Learning, Convolutional Neural Network and X-Ray Images","authors":"Hari Krishna Marrapu, B. Maram, Smritilekha Das, T. Daniya","doi":"10.1109/ICECA55336.2022.10009389","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009389","url":null,"abstract":"Artificial intelligence and machine learning have the power to revolutionize the healthcare industry and unlock a world of amazing potential. But unless all interested parties possess rudimentary knowledge of healthcare and machine learning fundamentals and principles, it is not able to fully utilize the capabilities of these technologies. This present literature survey work analyzes the research which implemented the machine learning algorithms in the detection of pneumonia. Furthermore, it is increasingly obvious that AI systems will not substantially replace human clinicians in patient care but rather support them. Human physicians may eventually gravitate toward duties and work arrangements that make use of particularly human abilities like empathy, persuasion, and big-picture integration. Those healthcare professionals who refuse to collaborate with artificial intelligence may end up being the only ones to lose their professions in the future.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"29 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":"127559643","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/ICECA55336.2022.10009239
Amogh A. Dyavangoudar, Avneesh Sharma, A. Saharia, Y. Ismail, A. Bourdine, M. Tiwari
This research work proposes a photonic crystal fiber in which the air holes are structured in the shape of spiral. The material selected for background is fused silica. Operating wavelength is selected as 1550 nm. Hence, the refractive index of fused silica (1.444) is computed using Sellmeier coefficients. Finite Element Analysis is used for modal analysis and mode propagation in the fiber is studied. TE0,1, HE2,1, and TE0,1 are identified. Further OAM mode propagation is studied and 2 OAM modes have successfully been shown to propagate. Multiple fiber parameters are further studied for TE0,1 mode. Wavelength dependent effective refractive index is studied using Sellmeier coefficients and is observed to be monotonically decreasing with wavelength. In the wavelength range from 0.5-2.0µm, an all-anomalous dispersion is achieved for the PCF with values ranging from ~209 ps(km-nm)−1 to ~42 ps(km-nm)−1. Imaginary refractive index dependent confinement loss is also studied. With values ranging from 1.87×10−11 m2 at 0.5µm to 2.31×10−11 m2 at 2µm, the effective area of the PCF is found to be increasing with wavelength. Nonlinearity is vastly influenced by effective area due to their inverse nature. Hence, nonlinearity is observed to be increasing in the wavelength window. Highest and lowest values achieved are 18.8 (Wkm)−1 and 3.79 (Wkm)−1
{"title":"Orbital Angular Momentum Mode Propagation in a Hexagonal-Ring Core Spiral PCF","authors":"Amogh A. Dyavangoudar, Avneesh Sharma, A. Saharia, Y. Ismail, A. Bourdine, M. Tiwari","doi":"10.1109/ICECA55336.2022.10009239","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009239","url":null,"abstract":"This research work proposes a photonic crystal fiber in which the air holes are structured in the shape of spiral. The material selected for background is fused silica. Operating wavelength is selected as 1550 nm. Hence, the refractive index of fused silica (1.444) is computed using Sellmeier coefficients. Finite Element Analysis is used for modal analysis and mode propagation in the fiber is studied. TE0,1, HE2,1, and TE0,1 are identified. Further OAM mode propagation is studied and 2 OAM modes have successfully been shown to propagate. Multiple fiber parameters are further studied for TE0,1 mode. Wavelength dependent effective refractive index is studied using Sellmeier coefficients and is observed to be monotonically decreasing with wavelength. In the wavelength range from 0.5-2.0µm, an all-anomalous dispersion is achieved for the PCF with values ranging from ~209 ps(km-nm)−1 to ~42 ps(km-nm)−1. Imaginary refractive index dependent confinement loss is also studied. With values ranging from 1.87×10−11 m2 at 0.5µm to 2.31×10−11 m2 at 2µm, the effective area of the PCF is found to be increasing with wavelength. Nonlinearity is vastly influenced by effective area due to their inverse nature. Hence, nonlinearity is observed to be increasing in the wavelength window. Highest and lowest values achieved are 18.8 (Wkm)−1 and 3.79 (Wkm)−1","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"7 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":"125759377","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/ICECA55336.2022.10009178
Lijesh L, G. Saroja
Landslide is a complicated phenomenon related to land movement that cause heavy human loss, ecological imbalance and structural damages. This complicated phenomenon is commonly seen in mountainous regions due to gravitational mass movement and shear strength decrement leading to geological disaster. Here, human activities include excavation, digging, and deforestation; whereas natural calamities includeheavy rainfall, volcanic eruptions and earthquake. Landslide is ranked third among disaster types, as it causes monetary loss to billions of dollars along human loss to millions. Hence, it is compulsory needed to identify landslide to avoid losses in its earlier stage itself. To identify these losses, researchers have established various new methods by utilizing optimization algorithms. The aim of this research is to justify and compare various optimization algorithms for the identification of landslide. The comparison analysis for landslide identification is done with five algorithms, such as Competitive S warm Optimizer (CSO)-basedDeepGenerative Adversarial Network (Deep GAN), Tunicate Swarm Algorithm (TSA)-based deep GAN, Particle Swarm Optimization (PSO) algorithm-based deep GAN, Water Cycle Algorithm (WCA)-based deep GAN, and Water Cycle Particle Swarm Optimization (WCPSO)-based GAN. However, WCPSO is derived by the hybridization of WCA and PSO. From the comparison, the WCPSO exhibits maximum values of accuracy, specificity, and sensitivity with 0.897, 0.857, and 0.915.
{"title":"Comparative study of Landslide Identification using different optimization Algorithms","authors":"Lijesh L, G. Saroja","doi":"10.1109/ICECA55336.2022.10009178","DOIUrl":"https://doi.org/10.1109/ICECA55336.2022.10009178","url":null,"abstract":"Landslide is a complicated phenomenon related to land movement that cause heavy human loss, ecological imbalance and structural damages. This complicated phenomenon is commonly seen in mountainous regions due to gravitational mass movement and shear strength decrement leading to geological disaster. Here, human activities include excavation, digging, and deforestation; whereas natural calamities includeheavy rainfall, volcanic eruptions and earthquake. Landslide is ranked third among disaster types, as it causes monetary loss to billions of dollars along human loss to millions. Hence, it is compulsory needed to identify landslide to avoid losses in its earlier stage itself. To identify these losses, researchers have established various new methods by utilizing optimization algorithms. The aim of this research is to justify and compare various optimization algorithms for the identification of landslide. The comparison analysis for landslide identification is done with five algorithms, such as Competitive S warm Optimizer (CSO)-basedDeepGenerative Adversarial Network (Deep GAN), Tunicate Swarm Algorithm (TSA)-based deep GAN, Particle Swarm Optimization (PSO) algorithm-based deep GAN, Water Cycle Algorithm (WCA)-based deep GAN, and Water Cycle Particle Swarm Optimization (WCPSO)-based GAN. However, WCPSO is derived by the hybridization of WCA and PSO. From the comparison, the WCPSO exhibits maximum values of accuracy, specificity, and sensitivity with 0.897, 0.857, and 0.915.","PeriodicalId":356949,"journal":{"name":"2022 6th International Conference on Electronics, Communication and Aerospace Technology","volume":"44 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":"128125757","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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