Pub Date : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744205
Sreelekshmi P. S., Jobymol Jacob
The effect of InGaN and p-GaN back barrier layers on the electrical characteristics of a p-GaN gate based enhancement mode HEMT is investigated in this work. A comparative analysis on the performance of back barrier based HEMT, with that of a conventional device is performed. The impact of mole fraction and doping of back barrier on device behaviour is analysed. The scope of back barrier design on device threshold voltage engineering is highlighted. The insertion of back barrier layer helps to increase carrier confinement and can be suitably adopted as a solution for short channel effects found in scaled devices.
{"title":"Performance Evaluation of p-GaN Gate Enhancement Mode HEMT with Back Barriers","authors":"Sreelekshmi P. S., Jobymol Jacob","doi":"10.1109/ICITIIT54346.2022.9744205","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744205","url":null,"abstract":"The effect of InGaN and p-GaN back barrier layers on the electrical characteristics of a p-GaN gate based enhancement mode HEMT is investigated in this work. A comparative analysis on the performance of back barrier based HEMT, with that of a conventional device is performed. The impact of mole fraction and doping of back barrier on device behaviour is analysed. The scope of back barrier design on device threshold voltage engineering is highlighted. The insertion of back barrier layer helps to increase carrier confinement and can be suitably adopted as a solution for short channel effects found in scaled devices.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127657962","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744233
Balaji T.K., Annushree Bablani, Sreeja Sr
Since the COVID-19 outbreak, considering the people’s opinion has been perceived as the most crucial challenge for the government to combat the pandemic, such as implementing a national lockdown, instituting a quarantine procedure, providing health services, and more. Furthermore, the government made many critical decisions based on public opinion to combat coronavirus. Opinion mining or sentiment analysis has arisen as a method for mining people’s views on several issues using machine learning techniques. With the support of machine learning methods, this paper extracted the Indian people’s opinions on vaccines through Twitter tweets. More than four lakh vaccine-related tweets from May 04 to May 11, 2021, and from Aug 13 to Aug 21, 2021, were analyzed using state-of-the-art machine learning and deep learning approaches. The BERT and RoBERTa models produced promising results compared to other models on the collected twitter dataset.
{"title":"Opinion mining on COVID-19 vaccines in India using deep and machine learning approaches","authors":"Balaji T.K., Annushree Bablani, Sreeja Sr","doi":"10.1109/ICITIIT54346.2022.9744233","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744233","url":null,"abstract":"Since the COVID-19 outbreak, considering the people’s opinion has been perceived as the most crucial challenge for the government to combat the pandemic, such as implementing a national lockdown, instituting a quarantine procedure, providing health services, and more. Furthermore, the government made many critical decisions based on public opinion to combat coronavirus. Opinion mining or sentiment analysis has arisen as a method for mining people’s views on several issues using machine learning techniques. With the support of machine learning methods, this paper extracted the Indian people’s opinions on vaccines through Twitter tweets. More than four lakh vaccine-related tweets from May 04 to May 11, 2021, and from Aug 13 to Aug 21, 2021, were analyzed using state-of-the-art machine learning and deep learning approaches. The BERT and RoBERTa models produced promising results compared to other models on the collected twitter dataset.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132458464","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744184
S. S, K. T, Sayantan Bhattacharjee, Durri Shahwar, K. S. Sekhar Reddy
India is on track to become the world’s diabetes capital thus demanding accurate diagnosis of Diabetic retinopathy from optical coherence tomography (OCT) retinal images. Accurate and faster diagnosis is difficult as it depends on quality of image, operator handling and also the growing number of patients. In this paper we propose the use of quantum transfer learning model to accomplish diagnosis of Diabetic Retinopathy. Quantum Transfer Learning (QTL), is a hybrid combination of classical transfer learning and quantum computing. Unlike classical computers, quantum computers provide faster computation and better accuracy. The concept of QTL is mainly used where the dataset size is limited. The QTL model, diagnostically significant image features are extracted with Resnet18 Convolutional Neural NEtwork (CNN) model, which is reduced to 4-bit feature vector to be encoded as qubit and is finally classified by utilizing Variational Quantum Circuit (VQC). The proposed model gave a better accuracy than existing state of the art methods in terms of high accuracy despite with a smaller set of images in the training phase.
{"title":"Quantum Transfer Learning for Diagnosis of Diabetic Retinopathy","authors":"S. S, K. T, Sayantan Bhattacharjee, Durri Shahwar, K. S. Sekhar Reddy","doi":"10.1109/ICITIIT54346.2022.9744184","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744184","url":null,"abstract":"India is on track to become the world’s diabetes capital thus demanding accurate diagnosis of Diabetic retinopathy from optical coherence tomography (OCT) retinal images. Accurate and faster diagnosis is difficult as it depends on quality of image, operator handling and also the growing number of patients. In this paper we propose the use of quantum transfer learning model to accomplish diagnosis of Diabetic Retinopathy. Quantum Transfer Learning (QTL), is a hybrid combination of classical transfer learning and quantum computing. Unlike classical computers, quantum computers provide faster computation and better accuracy. The concept of QTL is mainly used where the dataset size is limited. The QTL model, diagnostically significant image features are extracted with Resnet18 Convolutional Neural NEtwork (CNN) model, which is reduced to 4-bit feature vector to be encoded as qubit and is finally classified by utilizing Variational Quantum Circuit (VQC). The proposed model gave a better accuracy than existing state of the art methods in terms of high accuracy despite with a smaller set of images in the training phase.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128751361","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744224
S. S, K. T, Issac K, Sudha M
Quantum computing is the main emerging technology solving complex problems and even though error raised will be high, can be computed using customized algorithms. We propose the use of discrete wavelet transform to decompose the ECG signals followed by computing 2D scalogram to obtain time-frequency features and apply Quanvolutional Neural Network to classify those scalogram images to recognize Arrhythmia. This is the first paper to introduce scalogram and Quanvolutional neural networks. We considered using publicly available physio net MIT-BIH arrhythmia database for our research. The proposed model of hybrid quantum classical model comprising quantum convolutional neural networks for the MIT-BIH arrhythmia database resulting in the precision of 98% and Receiver Operating Curve Score of 100%.
量子计算是解决复杂问题的主要新兴技术，即使产生的误差很高，也可以使用定制算法进行计算。本文提出利用离散小波变换对心电信号进行分解，计算二维尺度图得到时频特征，并应用量子神经网络对尺度图图像进行分类，实现心律失常的识别。本文首次介绍了尺度图和量子神经网络。我们考虑在我们的研究中使用公开的physio net MIT-BIH心律失常数据库。本文提出的由量子卷积神经网络组成的混合量子经典模型用于MIT-BIH心律失常数据库，其准确率为98%，接收者工作曲线评分为100%。
{"title":"Quanvolution Neural Network to Recognize arrhythmia from 2D scaleogram features of ECG signals","authors":"S. S, K. T, Issac K, Sudha M","doi":"10.1109/ICITIIT54346.2022.9744224","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744224","url":null,"abstract":"Quantum computing is the main emerging technology solving complex problems and even though error raised will be high, can be computed using customized algorithms. We propose the use of discrete wavelet transform to decompose the ECG signals followed by computing 2D scalogram to obtain time-frequency features and apply Quanvolutional Neural Network to classify those scalogram images to recognize Arrhythmia. This is the first paper to introduce scalogram and Quanvolutional neural networks. We considered using publicly available physio net MIT-BIH arrhythmia database for our research. The proposed model of hybrid quantum classical model comprising quantum convolutional neural networks for the MIT-BIH arrhythmia database resulting in the precision of 98% and Receiver Operating Curve Score of 100%.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126590861","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744220
K. T, S. S, Tirumalanadhuni Siva Manikumar, T. Dheeraj, A. Sumanth
We expand the idea of transfer learning, generally applied in current machine learning paradigm, to the evolving hybrid neural network contrived out of traditional and quantum components for brain tumor recognition from MRI images. The proposed model is a perfect synergy of traditional classical component and a revolutionary quantum component. The notion of traditional components is to customize a pre-trained network to extract features from brain tumor MRI images, whereas the notion of the quantum components is to employ variational quantum circuit to act as a classifier with learnable parameters. Exhaustive simulation experiments reveals the efficacy of the quantum transfer learning scheme to beat the performance of the conventional classical transfer learning.
{"title":"Brain Tumor Recognition based on Classical to Quantum Transfer Learning","authors":"K. T, S. S, Tirumalanadhuni Siva Manikumar, T. Dheeraj, A. Sumanth","doi":"10.1109/ICITIIT54346.2022.9744220","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744220","url":null,"abstract":"We expand the idea of transfer learning, generally applied in current machine learning paradigm, to the evolving hybrid neural network contrived out of traditional and quantum components for brain tumor recognition from MRI images. The proposed model is a perfect synergy of traditional classical component and a revolutionary quantum component. The notion of traditional components is to customize a pre-trained network to extract features from brain tumor MRI images, whereas the notion of the quantum components is to employ variational quantum circuit to act as a classifier with learnable parameters. Exhaustive simulation experiments reveals the efficacy of the quantum transfer learning scheme to beat the performance of the conventional classical transfer learning.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128478279","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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