2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)最新文献

Removing Haze Influence from Remote Sensing Images Captured with Airborne Visible/ Infrared imaging Spectrometer by Cascaded Fusion of DCP, GF, LCC with AHE DCP、GF、LCC与AHE级联融合去除机载可见光/红外成像光谱仪遥感图像中的雾霾影响

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397060

R. Gound, Sudeep D. Thepade

引用次数: 1

An Unsupervised Learning of Impedance Plethysmograph for Perceiving Cardiac Events : (Unsupervised Learning of Impedance Plethysmograph) 阻抗性脉搏波的无监督学习(阻抗性脉搏波的无监督学习)

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397149

N. Agham, U. Chaskar, Prachi Samarth

{"title":"An Unsupervised Learning of Impedance Plethysmograph for Perceiving Cardiac Events : (Unsupervised Learning of Impedance Plethysmograph)","authors":"N. Agham, U. Chaskar, Prachi Samarth","doi":"10.1109/ICCCIS51004.2021.9397149","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397149","url":null,"abstract":"Nowadays, unsupervised learning presents a new approach to analyze various hidden patterns inside of medical data. Still, it is a great challenge to apply unsupervised learning and produce valuable data, especially to the cardiac system. This paper has proposed an advanced model for the derivation of cardiac hemodynamic parameters from the first derivative impedance signal. This study aims to analyze the plethysmographic wave by non-invasive measurement of electrical impedance of limb. The proposed model is based on unsupervised learning of morphological features of impedance plethysmography (IPG). We conducted and compared the performance evaluation of three clustering techniques on recorded impedance data to perceive the cardiac cycle characteristics. The findings can potentially assist in determining several vital health care variables like blood pressure, arterial stiffness and respiration rate. The proposed model was tested on a recorded IPG dataset and it achieved DB index and Dunn index of 0.13 and 0.87 in agglomerative clustering for an optimal number of clusters.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121321870","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}

引用次数: 1

Vertical Tunneling Based Dual-material Double-gate TFET 基于垂直隧道的双材料双栅TFET

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/icccis51004.2021.9397208

Km. Sucheta Singh, Satyendra Kumar, K. Nigam

{"title":"Vertical Tunneling Based Dual-material Double-gate TFET","authors":"Km. Sucheta Singh, Satyendra Kumar, K. Nigam","doi":"10.1109/icccis51004.2021.9397208","DOIUrl":"https://doi.org/10.1109/icccis51004.2021.9397208","url":null,"abstract":"To enhance current driving capability, switching ratio and subthreshold swing, a novel device structure vertical tunneling based dual-material double-gate tunnel field-effect transistor (VTDMDG-TFET) is designed in presented work. The device optimization of proposed device in terms of work-function engineering, gate oxide material, gate length, output characteristics and Si-thickness is done by the authors. Moreover, dual-gate approach is used in this work for enhancement in ON-state current of VTDMDG-TFET. Further, gate terminal of VTDMDG-TFET is comprised of two metal gates, namely auxiliary gate and tunnel gate. Use of dual-material at gate terminal makes the presented device structure adequate in terms of high ON-current, optimized subthreshold-swing as well as high switching ratio. To get the better electrostatic control of the gate higher dielectric constant material HfO2 is used as the gate dielectric material oxide, which enhances the ON-current of the presented structure. VTDMDG-TFET is also found better in terms of improved transconductance. The improved transconductance of the device makes VTDMDG-TFET a better choice for analog/RF and linearity performances. This leads to the TFET applications in the field of energy harvesting, biosensing, ultra-low power RF circuits.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124898072","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}

引用次数: 2

IOT enabled Book Publishing Platform 支持物联网的图书出版平台

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397084

Preetam Yadav, Deepali Kamthania

引用次数: 0

Multiclass and Multilabel Classification of Human Cell Components Using Transfer Learning of InceptionV3 Model 基于InceptionV3模型迁移学习的人体细胞成分多类多标签分类

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397165

Yadavendra, S. Chand

{"title":"Multiclass and Multilabel Classification of Human Cell Components Using Transfer Learning of InceptionV3 Model","authors":"Yadavendra, S. Chand","doi":"10.1109/ICCCIS51004.2021.9397165","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397165","url":null,"abstract":"Here we are finding 28 different predefined proteins of human cells with a transfer learning of inceptionV3 as the base model. This base model is pre-trained on the imagenet dataset. We add some layers in the base model and trained resulting models for our dataset and find the human cell proteins in a given sample. For this, we used a human cell atlas dataset provided by the Human Protein Atlas (HPA) community. This is a multilabel and multiclass problem in which one sample image can have more than one protein. We trained the given model on the training dataset by using the best hyperparameter of deep learning then tested the trained model on test data. We have found the efficiency of the resulting model in terms of precision, recall, f1-score, micro average, macro average, weighted average, sampled average, and accuracy. The accuracy of the given resulted model is 95.96%. On the basis of the above parameters, we analyzed the performance of the mentioned model, hence we choose the best hyperparameter according to the performance matrices in case of multi labels and multiclass problems.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125563405","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}

引用次数: 2

Security Perspectives of Various IoT Cloud Platforms: A Review & Case Study 各种物联网云平台的安全展望:回顾与案例研究

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397207

P. Mishra, Sachin Kumar, Umang Garg, E. Pilli, R. Joshi

引用次数: 3

Impact of Time Domain Features & Inertial Sensors on Activity Recognition using Randomized Selection 时域特征和惯性传感器对随机选择活动识别的影响

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397213

S. Chaurasia, S. Reddy

{"title":"Impact of Time Domain Features & Inertial Sensors on Activity Recognition using Randomized Selection","authors":"S. Chaurasia, S. Reddy","doi":"10.1109/ICCCIS51004.2021.9397213","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397213","url":null,"abstract":"Activity performed by the user is one of the major components of context sensing. Now a day’s users are carrying Smartphones or wearable devices with them always. The device is fully equipped with the latest sensors, thus smart devices are prominently used in activity detection. The detection of activity is mainly dependent on three things-1- the sensors used for data collection, 2- the various features extracted from the raw data and 3-Machine Learning model used for training and testing. Researchers are using different sensors and extracting more numbers of features for getting better accuracy. However, feature dimensions are dependent on time of execution. Thus, an optimization is required between number of features used and its execution time. It is also required to find out the impact of different sensors on its accuracy and execution time. In this paper we have tried to discover the trade-off between number of features & sensor used with its accuracy and execution time. The evaluation of proposed work has been done by using publicly available dataset on UCI machine learning repository. Random selection methodology is used for selecting features and 5 popular machine learning algorithms is used to compare the results. The evaluation result shows that gyroscope helps in increasing accuracy if it is used along with accelerometer. We also conclude that features have significant effect on accuracy and execution time, and from various ML models Random forest & K nearest neighbor classifiers are providing better accuracy in most of the cases of Activity Recognition.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116411561","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}

引用次数: 1

Voice Recognition Based Security System Using Convolutional Neural Network

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397151

P. Chandankhede, Abhijit S. Titarmare, Sarang Chauhvan

引用次数: 8

A Parsimonious and Practical Approach to Detecting Offensive Speech 一种简约实用的冒犯性言语检测方法

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397140

H. Khan, Frances Yu, A. Sinha, S. Gokhale

{"title":"A Parsimonious and Practical Approach to Detecting Offensive Speech","authors":"H. Khan, Frances Yu, A. Sinha, S. Gokhale","doi":"10.1109/ICCCIS51004.2021.9397140","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397140","url":null,"abstract":"With the proliferation of hateful and offensive speech on social media platforms such as Twitter, machine learning approaches to detect such toxic content have gained prominence. Despite these advances, real-time detection of such speech, while it is being shared on these platforms, remains a challenge for two reasons. First, these approaches train complex models on a plethora of features, which calls into question their computational efficiency for real-time deployment. Moreover, they require sizeable, manually annotated data sets from the same context, and annotating large data sets is extremely time-consuming, error-prone and cumbersome. This paper proposes a parsimonious and practical approach for the detection of offensive speech that alleviates these challenges. The approach is parsimonious because through a comprehensive evaluation of commonly used machine learning models (Logistic Regression, Random Forest, Neural Networks) on two public domain data sets it demonstrates that a simple Logistic Regression model trained on unigrams with frequency counts can detect hate speech with high accuracy of over 90%. It is practical because it demonstrates how an existing labeled training data set can be used to train models that can detect offensive content from a completely unknown data set with moderate accuracy. Based on these findings, the paper offers guidance on the characteristics that may be desirable in benchmark training data sets for offensive speech detection.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128206452","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}

引用次数: 3

A Survey on the Progressing 5G(NR) Modern Technologies and their Challenges 发展中的5G(NR)现代技术及其挑战综述

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397090

Prasanna C Gadge, Sonali Swetapadma Panda, Prabhat Kumar

引用次数: 0