Pub Date : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988084
S. Dhanalakshmi, P. K. Rao, A. Reddy, Koteswaramma Dodda, K. Priya, Muniyandy Elangovan
Nowadays, many companies sell their products and services on social media; they can get ideas directly from the end-users on these social media. Manually reading each text is time-consuming, so by analyzing the emotions of all the text, companies can roughly know how many positive or negative users there are on a particular topic. It also helps estimate the effect of organizations' social promoting systems by distinguishing the public view of an item or item-related occasion. A large portion of the exploration done as such far centers around getting profound properties by examining the syntactic and dress properties of plainly communicated words, outlines, and other extraordinary images. Propose a Convolutional Neural Network (CNN) to deal with opinion investigation of item surveys utilizing profound learning. Dissimilar to conventional DL methods, profound learning models don't depend on highlight extraction. These highlights are advanced straightforwardly during the preparation series. The fundamental thought of this assignment is to use convolutional brain organizations to prepare and arrange feeling classes in item surveys. It can utilize this CNN model to foresee the opinion of audits of new items.
{"title":"Sentiment Analysis and Classification Using Convolutional Neural Network Architecture","authors":"S. Dhanalakshmi, P. K. Rao, A. Reddy, Koteswaramma Dodda, K. Priya, Muniyandy Elangovan","doi":"10.1109/ICTACS56270.2022.9988084","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988084","url":null,"abstract":"Nowadays, many companies sell their products and services on social media; they can get ideas directly from the end-users on these social media. Manually reading each text is time-consuming, so by analyzing the emotions of all the text, companies can roughly know how many positive or negative users there are on a particular topic. It also helps estimate the effect of organizations' social promoting systems by distinguishing the public view of an item or item-related occasion. A large portion of the exploration done as such far centers around getting profound properties by examining the syntactic and dress properties of plainly communicated words, outlines, and other extraordinary images. Propose a Convolutional Neural Network (CNN) to deal with opinion investigation of item surveys utilizing profound learning. Dissimilar to conventional DL methods, profound learning models don't depend on highlight extraction. These highlights are advanced straightforwardly during the preparation series. The fundamental thought of this assignment is to use convolutional brain organizations to prepare and arrange feeling classes in item surveys. It can utilize this CNN model to foresee the opinion of audits of new items.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126744558","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9987922
Saloni Parekh, A. Deshpande, N. Prasanth
A file system is a collection of rules that govern how files are labelled, maintained, and accessed from a storage medium. Initially, there were a lot of different file systems within different servers and machines. Various file operations are allowed only between the files present in the same operating systems. When we have files in different operating systems these operations cannot be performed as the file system does not allow it. The virtual file system becomes an abstract overlay over a more tangible file system, which allows heterogeneous file transfer among different Operating Systems. Although the features offered by both the File Systems are similar, the Virtual File System provides us with an environment, wherein the Files can be accessed by any Operating System type and File System type. This paper focuses on comparing the time it takes to complete the different file operations like creating, reading, writing, deleting a file, etc. using the VFS and a traditional FS. We believe this study would help in better understanding the benefits of using a VFS.
{"title":"Time Administration of Virtual File System Operations","authors":"Saloni Parekh, A. Deshpande, N. Prasanth","doi":"10.1109/ICTACS56270.2022.9987922","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987922","url":null,"abstract":"A file system is a collection of rules that govern how files are labelled, maintained, and accessed from a storage medium. Initially, there were a lot of different file systems within different servers and machines. Various file operations are allowed only between the files present in the same operating systems. When we have files in different operating systems these operations cannot be performed as the file system does not allow it. The virtual file system becomes an abstract overlay over a more tangible file system, which allows heterogeneous file transfer among different Operating Systems. Although the features offered by both the File Systems are similar, the Virtual File System provides us with an environment, wherein the Files can be accessed by any Operating System type and File System type. This paper focuses on comparing the time it takes to complete the different file operations like creating, reading, writing, deleting a file, etc. using the VFS and a traditional FS. We believe this study would help in better understanding the benefits of using a VFS.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124150297","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9987887
A. Chaturvedi, T. V. Kumar, A. Srivastava, Surendra Kumar Shukla, Shubhranshu Vikram Singh, Anjum Parvez
Data collection from sensitive places must be done remotely due to laborious communication protocols. In this paper, the deployment of wireless sensor networks is investigated on the battlefield in terms of specific parameters such as node power, latency, and network survival. It investigates the simulated environment at three different levels while using a network simulator. Continuous data gathering and significant power consumption are necessary during the early deployment phase of wireless sensor networks. The wiring stage is then where power usage is highlighted. The transmit phase is when delay estimation is lastly carried out.
{"title":"Energy Optimization in Wireless Sensor Networks","authors":"A. Chaturvedi, T. V. Kumar, A. Srivastava, Surendra Kumar Shukla, Shubhranshu Vikram Singh, Anjum Parvez","doi":"10.1109/ICTACS56270.2022.9987887","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987887","url":null,"abstract":"Data collection from sensitive places must be done remotely due to laborious communication protocols. In this paper, the deployment of wireless sensor networks is investigated on the battlefield in terms of specific parameters such as node power, latency, and network survival. It investigates the simulated environment at three different levels while using a network simulator. Continuous data gathering and significant power consumption are necessary during the early deployment phase of wireless sensor networks. The wiring stage is then where power usage is highlighted. The transmit phase is when delay estimation is lastly carried out.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123192279","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9987774
Abhishek Bhola, S. Athithan, Shashank Singh, S. Mittal, Yogesh Kumar Sharma, Jagjit Singh Dhatterwal
Sentiment analysis is specifically a text mining technique that utilizes natural language processing to computerize the process of analyzing text that aims to determine the sentiment expressed. The fundamental purpose of sentiment analysis is to get valuable insights that lead to all-around development in specific domains. The fantastic applications of sentimental analysis include monitoring social media, management of customer support, and customer reviews research. One of the major pitfalls in sentiment analysis is word ambiguity. To overcome this drawback, a proposed hybrid framework presented in this work is capable of dealing with such ambiguity issues. The considered evaluation parameters are accuracy, F1 score and time taken. The proposed hybrid framework utilizes Convolutional Bi-directional Long short-term memory network (ConvBiLSTM) with Bidirectional Encoder representations from Transformer (BERT) tokeniser on the given dataset and outperform other methodologies with 95.10% accuracy.
{"title":"Hybrid Framework for Sentiment Analysis Using ConvBiLSTM and BERT","authors":"Abhishek Bhola, S. Athithan, Shashank Singh, S. Mittal, Yogesh Kumar Sharma, Jagjit Singh Dhatterwal","doi":"10.1109/ICTACS56270.2022.9987774","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987774","url":null,"abstract":"Sentiment analysis is specifically a text mining technique that utilizes natural language processing to computerize the process of analyzing text that aims to determine the sentiment expressed. The fundamental purpose of sentiment analysis is to get valuable insights that lead to all-around development in specific domains. The fantastic applications of sentimental analysis include monitoring social media, management of customer support, and customer reviews research. One of the major pitfalls in sentiment analysis is word ambiguity. To overcome this drawback, a proposed hybrid framework presented in this work is capable of dealing with such ambiguity issues. The considered evaluation parameters are accuracy, F1 score and time taken. The proposed hybrid framework utilizes Convolutional Bi-directional Long short-term memory network (ConvBiLSTM) with Bidirectional Encoder representations from Transformer (BERT) tokeniser on the given dataset and outperform other methodologies with 95.10% accuracy.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123113083","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988583
Naga Rajesh A, K. A. Sunitha
An electrocardiogram (ECG)can identify any cardiac activity abnormalities. The electrical signal produced when the heart muscles contract and relax, or the ECG, is contaminated with power line and instrument noise during recording. Wavelet algorithms can be used to denoise the ECG signal. For a successful denoised ECG, it is crucial to adjust the wavelet decomposition level. In this study, wavelet transformation technique is used to simulate noisy synthetic ECGs and denoise them. At each stage of decomposition, the Mean Square Error (MSE) between the clean synthetic ECG and denoised synthetic ECG is computed. According to the examination of MSEs, the level of wavelet decomposition can be optimized to produce an efficient denoised ECG output.
{"title":"Optimization of Wavelet Decomposition Level for Synthetic ECG Signal Denoising Analysis","authors":"Naga Rajesh A, K. A. Sunitha","doi":"10.1109/ICTACS56270.2022.9988583","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988583","url":null,"abstract":"An electrocardiogram (ECG)can identify any cardiac activity abnormalities. The electrical signal produced when the heart muscles contract and relax, or the ECG, is contaminated with power line and instrument noise during recording. Wavelet algorithms can be used to denoise the ECG signal. For a successful denoised ECG, it is crucial to adjust the wavelet decomposition level. In this study, wavelet transformation technique is used to simulate noisy synthetic ECGs and denoise them. At each stage of decomposition, the Mean Square Error (MSE) between the clean synthetic ECG and denoised synthetic ECG is computed. According to the examination of MSEs, the level of wavelet decomposition can be optimized to produce an efficient denoised ECG output.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129513440","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988577
B. Jhansi, M. Ramesh, A. Deepak, P. R. Karthikeyan
The aim of this analysis is to identify the textural alterations due to incidence of COVID-19 in lung CT scan images using GLCM matrix in comparison with GLRLM. Materials and Methods: Sample size is calculated using G power analysis and a total of 176 sample sizes are acquired for this novel texture analysis using parameters like effect size (0.3), standard error rate (0.05), maximum rate (0.8) and allocation rate (N2/N1=1). For this analysis the required CT images are collected from Github. For group 1 a total of 94 sample images are taken and for group 2 a total of 82 sample images are taken. For analyzing the textural alterations of CT scan lung images, comparison between Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) is carried out for this analysis. In the process of evaluation of classifiers 10-fold cross validation is performed. Normal and COVID subjects are classified using Random forest, K-NN, Logistic regression classifiers for better classification. Results and Discussion: Due to incidence of COVID in lunge tissues it is observed that textural alterations are formed in lung CT scan images. From the acquired features values of GLCM and GLRLM it is observed that GLCM is statistically significant than the GLRLM. Contrast, homogeneity and sum of average features are statistically significant (0.0001) in identifying normal and COVID subjects. The mean value of homogeneity for healthy controls is (0.215) and for COVID subjects it is (0.327) such that normal subjects have a gentle surface of the lung and COVID subjects have rough surface and significance value is (p<0.05). GLCM has acquired precision (0.931), F1-score (0.928), Recall (0.929), AUC (0.981), Classification Accuracy (0.929) are obtained using random forest classifiers. From the above values it is observed that COVID subjects have textural variations than the normal subjects. Conclusion: From this analysis it is observed that GLCM provides significantly better classification in differentiating the COVID and normal subjects than GLRLM.
{"title":"Evaluating Textural Changes of Lung in CT Images using GLCM in Comparison with GLRLM","authors":"B. Jhansi, M. Ramesh, A. Deepak, P. R. Karthikeyan","doi":"10.1109/ICTACS56270.2022.9988577","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988577","url":null,"abstract":"The aim of this analysis is to identify the textural alterations due to incidence of COVID-19 in lung CT scan images using GLCM matrix in comparison with GLRLM. Materials and Methods: Sample size is calculated using G power analysis and a total of 176 sample sizes are acquired for this novel texture analysis using parameters like effect size (0.3), standard error rate (0.05), maximum rate (0.8) and allocation rate (N2/N1=1). For this analysis the required CT images are collected from Github. For group 1 a total of 94 sample images are taken and for group 2 a total of 82 sample images are taken. For analyzing the textural alterations of CT scan lung images, comparison between Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) is carried out for this analysis. In the process of evaluation of classifiers 10-fold cross validation is performed. Normal and COVID subjects are classified using Random forest, K-NN, Logistic regression classifiers for better classification. Results and Discussion: Due to incidence of COVID in lunge tissues it is observed that textural alterations are formed in lung CT scan images. From the acquired features values of GLCM and GLRLM it is observed that GLCM is statistically significant than the GLRLM. Contrast, homogeneity and sum of average features are statistically significant (0.0001) in identifying normal and COVID subjects. The mean value of homogeneity for healthy controls is (0.215) and for COVID subjects it is (0.327) such that normal subjects have a gentle surface of the lung and COVID subjects have rough surface and significance value is (p<0.05). GLCM has acquired precision (0.931), F1-score (0.928), Recall (0.929), AUC (0.981), Classification Accuracy (0.929) are obtained using random forest classifiers. From the above values it is observed that COVID subjects have textural variations than the normal subjects. Conclusion: From this analysis it is observed that GLCM provides significantly better classification in differentiating the COVID and normal subjects than GLRLM.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130615621","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988115
P. R. Sai, S. Marjorie
This research is a study on the electronic conductivity of Pure and doped Cadmium oxide thin films. The synthesis is carried out using an innovative dip coating technique by introducing Nickel and Cobalt as dopants. The thin films were deposited on the glass substrate using an innovative dip coating technique. The change in resistance of the doped and undoped samples is analyzed for a change in frequency. The sample sizes of Pure and Nickel and Cobalt doped Cadmium oxide thin films were 202 each with the total sample size as 606. This was calculated using the clincalc calculator by keeping the pretest power as 80 % while maintaining the error correction at 0.05. The Cobalt and Nickel dopants cause a reduction in the resistance for a frequency of 5 KHz, the resistance of Pure Cadmium oxide is -120 ohm and is reduced to -211.1 ohm ad - 322.3 ohm for cobalt and Nickel doped Cadmium oxides respectively. It is seen from the various experiments conducted on Cadmium oxide thin films that by adding dopants the resistivity is decreased. The significance of pure Cadmium oxide and doped Cadmium oxide are 0.001 and 1.000 respectively. The p value is taken to be less than 0.05, and the same can be observed from the SPSS results.
{"title":"Performance Analysis of the Conductivity of Pure Cadmium Oxide in Comparison with the Doped Cadmium Oxide using a Low Cost Technique","authors":"P. R. Sai, S. Marjorie","doi":"10.1109/ICTACS56270.2022.9988115","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988115","url":null,"abstract":"This research is a study on the electronic conductivity of Pure and doped Cadmium oxide thin films. The synthesis is carried out using an innovative dip coating technique by introducing Nickel and Cobalt as dopants. The thin films were deposited on the glass substrate using an innovative dip coating technique. The change in resistance of the doped and undoped samples is analyzed for a change in frequency. The sample sizes of Pure and Nickel and Cobalt doped Cadmium oxide thin films were 202 each with the total sample size as 606. This was calculated using the clincalc calculator by keeping the pretest power as 80 % while maintaining the error correction at 0.05. The Cobalt and Nickel dopants cause a reduction in the resistance for a frequency of 5 KHz, the resistance of Pure Cadmium oxide is -120 ohm and is reduced to -211.1 ohm ad - 322.3 ohm for cobalt and Nickel doped Cadmium oxides respectively. It is seen from the various experiments conducted on Cadmium oxide thin films that by adding dopants the resistivity is decreased. The significance of pure Cadmium oxide and doped Cadmium oxide are 0.001 and 1.000 respectively. The p value is taken to be less than 0.05, and the same can be observed from the SPSS results.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129442529","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988505
K. Sudhakar, Boussaadi Smail, T. S. Reddy, S. Shitharth, Diwakar Ramanuj Tripathi, M. Fahlevi
In today's technology-driven world, a user profile is a virtual representation of each user, containing various user information such as personal, interest and preference data. These profiles are the result of a user profiling process and are essential to personalizing the service. As the amount of information available on the Internet increases and the number of different users, customization becomes a priority. Due to the large amount of information available on the Internet, referral systems that aim to provide relevant information to users are becoming increasingly important and popular. Various methods, methodologies and algorithms have been proposed in the literature for the user analysis process. Creating automated user profiles is a big challenge in creating adaptive customized applications. In this work proposed the method, Long Short-Term Architecture (LSTM) is User profile is an important issue for both information and service customization. Based on the original information, the user's topic preference and text emotional features into attention information and combines various formats and LSTM (Long Short Term Memory) models to describe and predict the elements of informal community clients. At last, the trial consequences of different gatherings show that the concern-based LSTM model proposed can accomplish improved results than the right now regularly involved strategies in recognizing client character qualities, and the model has great speculation, which implies that it has this capacity.
{"title":"Web User Profile Generation and Discovery Analysis using LSTM Architecture","authors":"K. Sudhakar, Boussaadi Smail, T. S. Reddy, S. Shitharth, Diwakar Ramanuj Tripathi, M. Fahlevi","doi":"10.1109/ICTACS56270.2022.9988505","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988505","url":null,"abstract":"In today's technology-driven world, a user profile is a virtual representation of each user, containing various user information such as personal, interest and preference data. These profiles are the result of a user profiling process and are essential to personalizing the service. As the amount of information available on the Internet increases and the number of different users, customization becomes a priority. Due to the large amount of information available on the Internet, referral systems that aim to provide relevant information to users are becoming increasingly important and popular. Various methods, methodologies and algorithms have been proposed in the literature for the user analysis process. Creating automated user profiles is a big challenge in creating adaptive customized applications. In this work proposed the method, Long Short-Term Architecture (LSTM) is User profile is an important issue for both information and service customization. Based on the original information, the user's topic preference and text emotional features into attention information and combines various formats and LSTM (Long Short Term Memory) models to describe and predict the elements of informal community clients. At last, the trial consequences of different gatherings show that the concern-based LSTM model proposed can accomplish improved results than the right now regularly involved strategies in recognizing client character qualities, and the model has great speculation, which implies that it has this capacity.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"306 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133160225","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988060
K. R. Reddy, J. C. Robinson Azariah
The aim of the work is to simulate and compare current-voltage characteristics in double gate nanowire field effect transistors (NWFET) made of zirconium titanate (ZrTiO4) and silicon dioxide (SiO2) as gate oxides with thicknesses ranging from 1 nm to 30 nm. Materials and methods: Group 1 and group 2 are ZrTiO4 and SiO2 based double gate NWFET. A total of 294 samples of drain current required for sample size analysis with a pretest power of 80 % and a type I error rate of 0.05. Results: The two groups are analysed using independent sample t tests providing a significance value of 0.0001 (p < 0.05). The drain current of ZrTiO4 based NWFET has a mean of 4.85E-4 A, standard deviation of 7.188E-4 and standard error mean of S.93E-5 whereas the drain current of SiO2 based NWFET has a mean of 1.15E-4 A, standard deviation of 8.014E-4 and standard error mean of 6.61E-6. Conclusion: ZrTiO4(mean = 0.485) nanowire has a higher mean drain current than SiO2 (mean = 0.115). The drain current is altered by varying the oxide thickness of the material. The drain current of ZrTiO4 is significantly better than SiO2.
这项工作的目的是模拟和比较由钛酸锆(ZrTiO4)和二氧化硅(SiO2)作为栅极氧化物，厚度从1纳米到30纳米的双栅极纳米线场效应晶体管(NWFET)的电流电压特性。材料和方法:第1组和第2组为基于ZrTiO4和SiO2的双栅nwwfet。样本量分析共需要294个漏极电流样本，预试功率为80%，I型错误率为0.05。结果:两组采用独立样本t检验进行分析，显著性值为0.0001 (p < 0.05)。ZrTiO4基NWFET的漏极电流平均值为4.85E-4 a，标准差为7.1888 e -4，标准误差平均值为S.93E-5; SiO2基NWFET的漏极电流平均值为1.15E-4 a，标准差为8.014E-4，标准误差平均值为6.61E-6。结论:ZrTiO4纳米线(平均0.485)的平均漏极电流高于SiO2纳米线(平均0.115)。漏极电流通过改变材料的氧化物厚度而改变。ZrTiO4的漏极电流明显优于SiO2。
{"title":"Simulation and Comparison of Current-Voltage Characteristics in Double Gate Nanowire FET using ZrTiO4 and SiO2 as Gate Oxide Materials","authors":"K. R. Reddy, J. C. Robinson Azariah","doi":"10.1109/ICTACS56270.2022.9988060","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988060","url":null,"abstract":"The aim of the work is to simulate and compare current-voltage characteristics in double gate nanowire field effect transistors (NWFET) made of zirconium titanate (ZrTiO4) and silicon dioxide (SiO2) as gate oxides with thicknesses ranging from 1 nm to 30 nm. Materials and methods: Group 1 and group 2 are ZrTiO4 and SiO2 based double gate NWFET. A total of 294 samples of drain current required for sample size analysis with a pretest power of 80 % and a type I error rate of 0.05. Results: The two groups are analysed using independent sample t tests providing a significance value of 0.0001 (p < 0.05). The drain current of ZrTiO4 based NWFET has a mean of 4.85E-4 A, standard deviation of 7.188E-4 and standard error mean of S.93E-5 whereas the drain current of SiO2 based NWFET has a mean of 1.15E-4 A, standard deviation of 8.014E-4 and standard error mean of 6.61E-6. Conclusion: ZrTiO4(mean = 0.485) nanowire has a higher mean drain current than SiO2 (mean = 0.115). The drain current is altered by varying the oxide thickness of the material. The drain current of ZrTiO4 is significantly better than SiO2.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116438014","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9987759
J. Jasmine, Saranya Devi M, Vanshika G A, Moni Sruthi T, Thulasi R
Agriculture is a necessary supply of profits and the spine of the Indian economy. Plant production is seriously harmed by a variety of diseases, which, if precisely and appropriately recognized, have the potential to considerably improve health standards and economic growth. Traditional disease detection and categorization methods need a significant amount of time, heavy effort, and frequent farm monitoring. For modern agriculture, an automated image processing-based leaf disease diagnosis approach with soil monitoringtechnology is presented in this work. The proposed approach is ideal for farmers looking to increase their harvests. They can also gain extra from this reliable, non - adverse approach through detecting plant troubles early. The suggested system is made up of an Arduino controller and a GSM alert for disease detection, soil moisture and pH level.
{"title":"An Innovative Approach for Leaf-based Disease Detection in Crops and Soil Analyzer using Machine Learning for Smart Agriculture","authors":"J. Jasmine, Saranya Devi M, Vanshika G A, Moni Sruthi T, Thulasi R","doi":"10.1109/ICTACS56270.2022.9987759","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987759","url":null,"abstract":"Agriculture is a necessary supply of profits and the spine of the Indian economy. Plant production is seriously harmed by a variety of diseases, which, if precisely and appropriately recognized, have the potential to considerably improve health standards and economic growth. Traditional disease detection and categorization methods need a significant amount of time, heavy effort, and frequent farm monitoring. For modern agriculture, an automated image processing-based leaf disease diagnosis approach with soil monitoringtechnology is presented in this work. The proposed approach is ideal for farmers looking to increase their harvests. They can also gain extra from this reliable, non - adverse approach through detecting plant troubles early. The suggested system is made up of an Arduino controller and a GSM alert for disease detection, soil moisture and pH level.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114723607","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：