2022 IEEE Silchar Subsection Conference (SILCON)最新文献_第6页

Flush Mountable Multi-Octave Band Circularly Polarized Printed Antenna for Missile and Satellite Applications 用于导弹和卫星的嵌入式多倍频带圆极化印刷天线

2022 IEEE Silchar Subsection Conference (SILCON) Pub Date : 2022-11-04 DOI: 10.1109/SILCON55242.2022.10028849

Roja Vadlamudi, D. Sriram Kumar

引用次数: 0

Common-Mode Voltage Reduction Of Nine Switch Converter Fed Dual Three-Phase Load System Using Reverse Carrier Scheme 采用反向载波方案的九开关变换器馈电双三相负载系统共模降压

2022 IEEE Silchar Subsection Conference (SILCON) Pub Date : 2022-11-04 DOI: 10.1109/SILCON55242.2022.10028978

Ashish A. Dongre, J. Mishra, Ravi Kumar Majji

引用次数: 0

Two new 3D distance measures for IFSs and their applications in pattern classification and pathological diagnosis 两种新的ifs三维距离测量方法及其在模式分类和病理诊断中的应用

2022 IEEE Silchar Subsection Conference (SILCON) Pub Date : 2022-11-04 DOI: 10.1109/SILCON55242.2022.10028888

Anjali Patel, Subhankarkumar Jana, J. Mahanta

引用次数: 1

Design of a Robust Discrete Time Sliding Mode Controller With Dynamic Switching Gain 具有动态切换增益的鲁棒离散时间滑模控制器设计

2022 IEEE Silchar Subsection Conference (SILCON) Pub Date : 2022-11-04 DOI: 10.1109/SILCON55242.2022.10028973

S. R. Shiledar, G. Malwatkar

引用次数: 0

ECG-CCNet: Cardiovascular(Cardiac) and COVID‑19 Disease Classification Using Deep Convolutional Neural Network Learning Pipeline Approaches From Electrocardiography(ECG)- A Study ECG- ccnet:使用心电图(ECG)的深度卷积神经网络学习管道方法进行心血管(心脏)和COVID - 19疾病分类-一项研究

2022 IEEE Silchar Subsection Conference (SILCON) Pub Date : 2022-11-04 DOI: 10.1109/SILCON55242.2022.10028792

Yogesh H. Bhosale, K. Sridhar Patnaik

{"title":"ECG-CCNet: Cardiovascular(Cardiac) and COVID‑19 Disease Classification Using Deep Convolutional Neural Network Learning Pipeline Approaches From Electrocardiography(ECG)- A Study","authors":"Yogesh H. Bhosale, K. Sridhar Patnaik","doi":"10.1109/SILCON55242.2022.10028792","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028792","url":null,"abstract":"Experimental studies demonstrate that COVID-19 illness affects the cardiovascular as well as the pulmonary / lung tract. The limits of existing COVID-19 diagnostic procedures have been revealed. In contrast, to present diagnoses, such as low-sensitivity conventional RT-PCR testing and costly healthcare scanning equipment, implementing additional approaches for COVID-19 illness assessment would be advantageous for COVID-19 epidemic management. Furthermore, problems generated by COVID-19 on the cardiovascular tract must be detected rapidly and precisely using ECG. Considering the numerous advantages of electrocardiogram (ECG) functionalities, the proposed study offers a novel pipeline termed ECG-CCNet for examining the feasibility of employing ECG pulses to diagnose COVID-19. This study is a two-phase transfer learning (TL) approach is suggested for the prognosis of COVID-19 disorder, which includes feature mining utilizing DCNNs models and ensemble pipelining using ECG tracing imageries generated from ECG signals of COVID-19 diseased sufferers relying on the anomalies induced by COVID-19 pathogen on cardiovascular structures. A complete classification performance of 93.5% accuracy, 87% recall, 87.03% F1-score, 95.66% specificity, 87.16% precision, and 95.33% AUC attained by abnormal heartbeats, COVID-19, myocardial, and normal/healthy classification. This experiment is considered a high possibility for speeding up the diagnostic and treatments of COVID-19 individuals, reducing practitioners' efforts, and improving epidemic containment by utilizing ECG data.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132540811","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: 4

A Clustering-based model for the Generation of Diversified Recommendations 基于聚类的多样化推荐生成模型

2022 IEEE Silchar Subsection Conference (SILCON) Pub Date : 2022-11-04 DOI: 10.1109/SILCON55242.2022.10028791

Vinnakota Saran Chaitanya, Mahesh Mohan, P. S. Thilagam

引用次数: 0

SEC-EnD: Stacked Ensemble Correlation-based Feature Selection Method for Emotion Detection SEC-EnD:基于堆叠集成相关的情感检测特征选择方法

2022 IEEE Silchar Subsection Conference (SILCON) Pub Date : 2022-11-04 DOI: 10.1109/SILCON55242.2022.10028868

Sricheta Parui, D. Samanta, N. Chakravorty

{"title":"SEC-EnD: Stacked Ensemble Correlation-based Feature Selection Method for Emotion Detection","authors":"Sricheta Parui, D. Samanta, N. Chakravorty","doi":"10.1109/SILCON55242.2022.10028868","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028868","url":null,"abstract":"Because the brain signal is so complex, choosing the right features from the EEG signal has become a major area of research and is still important if you want to receive reliable findings. Due to the extremely high dimensionality of EEG data, redundant features and the dimensionality curse are frequently present, and the classification method frequently fails to produce the intended results. We have chosen an optimum feature set for emotion recognition using certain well-known feature selection techniques to address these issues. But most of them are unstable in nature, and for different training subsets, different types of feature selection methods work best. In this research, we provide SEC-END, a stacked ensemble correlation-based feature selection method for emotion detection that can combine and stack different feature selection methods to find the best features. This method has a 3-level feature selection technique, which helps to finetune the selection of features. After integrating the entire feature set, we took the features that each approach had in common and used the union quorum method to eliminate the features that were unnecessary. Then, using four classifiers-Random Forest, Decision Tree, SVM, and KNN-we tested the feature set. Regardless of the training set and classifier, the feature subset is outperforming other state-of-the-art techniques for all the dimensions. The experiment is also carried out with different sizes of windows for EEG signals so that the optimum window size can be recognized and used for further experiments.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130793871","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

Phishing Website Detection Using LGBM Classifier With URL-Based Lexical Features 基于url词法特征的LGBM分类器网络钓鱼网站检测

2022 IEEE Silchar Subsection Conference (SILCON) Pub Date : 2022-11-04 DOI: 10.1109/SILCON55242.2022.10028793

Thahira A, Ansamma John

{"title":"Phishing Website Detection Using LGBM Classifier With URL-Based Lexical Features","authors":"Thahira A, Ansamma John","doi":"10.1109/SILCON55242.2022.10028793","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028793","url":null,"abstract":"People have become increasingly reliant on the internet for their everyday activities, and the majority of internet users are uninformed of the phishing attack, which is caused by the careless or unaware nature of humans, resulting in financial loss as well identity theft. Phishing detection is not a new research area; multiple efforts from various perspectives have been conducted in this domain and machine learning-based approaches have gained popularity among existing works. Machine learning-based model used a large number of features including external service-based features for phishing website detection, but these features are time-consuming and not suitable for real-time detection. This research proposes a fast and lightweight phishing website detection model based on URL-based lexical features, as well as a novel dataset. The experimental result shows that the parameter-tuned light gradient boosting machine (LGBM) classifier outperforms the random forest classifier, and the Pearson correlation algorithm outranks the other feature selection methods. The proposed approach out performs most of the existing method interms of accuracy with newly created dataset as well as the other two benchmark datasets.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"366 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115981233","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

A Novel Centrality-based Measure for Election Network Analysis 一种新的基于中心性的选举网络分析方法

2022 IEEE Silchar Subsection Conference (SILCON) Pub Date : 2022-11-04 DOI: 10.1109/SILCON55242.2022.10028812

Amartya Chakraborty, Nikhil Badyal, Aman Sharma, N. Mukherjee

{"title":"A Novel Centrality-based Measure for Election Network Analysis","authors":"Amartya Chakraborty, Nikhil Badyal, Aman Sharma, N. Mukherjee","doi":"10.1109/SILCON55242.2022.10028812","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028812","url":null,"abstract":"With the worldwide adaptation of the internet, social network platforms have been rapidly engaging a vast global population. Such networks are subject to user engagement and interaction on various topics - social, political, economic, etc. The present work gathers such interaction between Twitter users over a period of 6 weeks, where the topic of interaction was restricted to the West Bengal state assembly election. The conversion of the acquired data to a node adjacency list was performed on the basis of the mention relationship between users. The network analysis was performed using a novel measure based on only the eigen-vector centrality of the users of the network, termed the NetworkPresenceFactor(NPF). The results reveal that network users affiliated with Party_1 had the most influence and presence over the dynamic network, followed by those affiliated with Party_2 and Party_3. The analysis of party-wise network influence also reveals a similar scenario. The weekly trends of presence reveal that Party_1 had a constantly reducing presence that was almost twice that of Party_2, while Party_3 started off with a minimum influence that peaked during week 6. On tallying, the results of our experiment differ from the ground truth. The authors conclude that the consideration of network structure alone (as in our work) is not sufficient in effective analysis of the network and the inclusion of the context of user interaction is essential for proper network analysis.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123218898","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

An Efficient Dielectric Resonator Antenna for Dual-Band Dual-Polarized RFEH Applications in Smart City Environment 智慧城市环境下双频双极化RFEH应用的高效介质谐振器天线

2022 IEEE Silchar Subsection Conference (SILCON) Pub Date : 2022-11-04 DOI: 10.1109/SILCON55242.2022.10028861

D. Surender, Md. Ahsan Halimi, T. Khan, F. Talukdar

{"title":"An Efficient Dielectric Resonator Antenna for Dual-Band Dual-Polarized RFEH Applications in Smart City Environment","authors":"D. Surender, Md. Ahsan Halimi, T. Khan, F. Talukdar","doi":"10.1109/SILCON55242.2022.10028861","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028861","url":null,"abstract":"This work presents a dual-polarized dielectric resonator antenna (DRA) operating over two frequency bands that cover LTE1800 and LTE2500 bands for radio frequency energy harvesting (RFEH) applications. An Eccostock HIK K10 material with a dielectric constant of 10 is used to create a DRA. The dimension of DRA is 9×16.1×5.5 mm3 placed over the 1.6 mm thick FR4 substrate (εr=4.4). The area of the substrate material is optimized as 60×50 mm2. A Bow-tie-shaped slot is created into the ground plane to help bring down the resonant frequency to 1.95 GHz and also helps in increasing the reflection coefficient performance. One more resonance is observed by placing a reflecting plane having an equal dimension to the antenna substrate at a gap of 20 mm from the antenna ground plane. In addition to this, an improvement in the antenna gain is also observed with a reflecting surface. The reflecting plane helps to increase the gain value. The maximum possible gain value is 8.36 dB at 2.6 GHz frequency. The proposed antenna exhibits omnidirectional radiation properties, thus it is suitable to harvest ambient EM energies available at LTE bands.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126038070","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: 0