2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT)最新文献

A Compact MIMO Antenna with High Isolation and Gain-Bandwidth Product for Wireless Personal Communication 一种用于无线个人通信的高隔离高增益带宽的小型MIMO天线产品

2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2022-04-23 DOI: 10.1109/CSNT54456.2022.9787672

A. Rani, Bhawna Sharma, N. Saluja, Geetanjali Singla, T. Sabapathy

{"title":"A Compact MIMO Antenna with High Isolation and Gain-Bandwidth Product for Wireless Personal Communication","authors":"A. Rani, Bhawna Sharma, N. Saluja, Geetanjali Singla, T. Sabapathy","doi":"10.1109/CSNT54456.2022.9787672","DOIUrl":"https://doi.org/10.1109/CSNT54456.2022.9787672","url":null,"abstract":"The multiple-input-multiple-output (MIMO) system consisting of multiple antenna elements faces issue of isolation performance between antenna elements. In this paper, isolation is improved by inserting a thin parasitic slit between antennas on the upper side and a plus-shaped stub in the ground plane. The proposed MIMO antenna consists of two bevelled circular antennas with a common partial ground. It is designed on FR-4 substrate having 12.5 mm x 24 mm x 1.6 mm dimensions. Bevelling is applied at the bottom of a circular patch to attain wideband performance. This antenna achieved bandwidth from 5.22 GHz to 14.89 GHz and offers isolation better than 15 dB in most of the operational bands while isolation more than 20 dB in the entire X-band. The Envelope Correlation Coefficient (ECC) of the antenna is less than 0.017 and DG is more than 9.9175. It offers an acceptable gain between 1.12 to 3 dBi and radiation efficiency between 57 to 75 %. The simulation and measurement results show good agreement. The designed MIMO antenna is compatible with WLAN (5.2 GHz to 5.8 GHz), upper C-band (5.22 GHz to 8 GHz), X-band (8 GHz to12 GHz) and lower Ku-band (12 GHz to 14.89 GHz) applications.","PeriodicalId":398594,"journal":{"name":"2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121030129","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

Sub-Terahertz MIMO Array Antenna for Future Wireless Applications 未来无线应用的亚太赫兹MIMO阵列天线

2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2022-04-23 DOI: 10.1109/CSNT54456.2022.9787576

S. Moghe, R. Yadav, L. Malviya

引用次数: 3

Blockchain Based Spectrum Sensing for Secured Cognitive Radio wireless networks 基于区块链的安全认知无线电无线网络频谱感知

2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2022-04-23 DOI: 10.1109/CSNT54456.2022.9787585

V. Roopa, Himansu Shekhar Pradhan

引用次数: 6

Implementing a Robust IoT Solution to Monitor Prone Position using Machine Learning Techniques 使用机器学习技术实现强大的物联网解决方案来监测俯卧位置

2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2022-04-23 DOI: 10.1109/CSNT54456.2022.9787604

Semagn Shifere Belay, T. Prince, Tizazu Bayih Beyene, Walelegne Yirdew Marew, Andebet Dessiewu Kebede

{"title":"Implementing a Robust IoT Solution to Monitor Prone Position using Machine Learning Techniques","authors":"Semagn Shifere Belay, T. Prince, Tizazu Bayih Beyene, Walelegne Yirdew Marew, Andebet Dessiewu Kebede","doi":"10.1109/CSNT54456.2022.9787604","DOIUrl":"https://doi.org/10.1109/CSNT54456.2022.9787604","url":null,"abstract":"It is essential to monitor sleeping patterns and position in order to learn about one's health state in order to enhance it and minimize sleep apnea. The use of a bed-mounted seismometer device to track the heart and respiratory patterns, as well as physical movement and attitude, while sleeping is discussed in this paper. The Internet of Things (IoT) and its gadgets and systems are becoming a part of everyday life. They are primarily used to track everyday tasks, particularly those that are relevant to individual health and wellness. As a matter of fact, and it's becoming increasingly important throughout the COVID-19 outbreak. A intelligent tracking and warning Internet of Things system known \"NEF\" was updated in this work to detect on-bed movement patterns, such as the prone position, using a variety of neural network models. On-bed movement patterns were recorded from seven different participants. The models derived using deep network were the most accurate when just the prone and supine postures were included. Random forest, on the other hand, produced the greatest total proportion of properly analysis refers to a set. Beddings such as pillows and blankets are expected to be investigated in greater depth in the future.","PeriodicalId":398594,"journal":{"name":"2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122941855","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

Age Prediction Model using Convolutional Neural Network 基于卷积神经网络的年龄预测模型

2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2022-04-23 DOI: 10.1109/CSNT54456.2022.9787602

S. Rathor, Danish Ali, Shradha Gupta, Ritika Singh, Harshita Jaiswal

{"title":"Age Prediction Model using Convolutional Neural Network","authors":"S. Rathor, Danish Ali, Shradha Gupta, Ritika Singh, Harshita Jaiswal","doi":"10.1109/CSNT54456.2022.9787602","DOIUrl":"https://doi.org/10.1109/CSNT54456.2022.9787602","url":null,"abstract":"Age bunch grouping is a perplexing assignment that is utilized to order facial picture into foreordained age classifications. It is a significant undertaking because of its various applications like wellbeing, security, confirmation framework, enlistment, and furthermore in wise social robots. Convolutional Neural Network (CNN) has as of late shown brilliant execution in examining human face pictures. Programmed age arrangement has become pertinent to a rising measure of utilizations, especially since the ascent of social stages and online media. By and by, execution of existing techniques on genuine pictures is still altogether missing, particularly when contrasted with the huge jumps in execution as of late detailed for the connected errand of face acknowledgment. In this paper we show that by learning portrayals using profound convolutional neural network (CNN), a critical expansion in execution can be gotten on these assignments. To this end, we propose a straightforward convolutional net design that can be utilized in an event of predicting age, when how much learning information is restricted. We assess our technique on the new IMDB-WIKI dataset for age assessment and show it to drastically beat present status of-the-craftsmanship strategies.","PeriodicalId":398594,"journal":{"name":"2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128667996","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

Galaxy Shape Categorization Using Convolutional Neural Network Approach 基于卷积神经网络的星系形状分类

2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2022-04-23 DOI: 10.1109/CSNT54456.2022.9787649

Amritesh Nandan, V. Tripathi

引用次数: 0

A Key Escrow Free Anonymous Identity Based Encryption Scheme using Ring Signatures 基于环签名的无密钥托管匿名身份加密方案

2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2022-04-23 DOI: 10.1109/CSNT54456.2022.9787654

Khaleda Afroaz, Y. S. Subba Rao, Rekha N. Rukma

{"title":"A Key Escrow Free Anonymous Identity Based Encryption Scheme using Ring Signatures","authors":"Khaleda Afroaz, Y. S. Subba Rao, Rekha N. Rukma","doi":"10.1109/CSNT54456.2022.9787654","DOIUrl":"https://doi.org/10.1109/CSNT54456.2022.9787654","url":null,"abstract":"Identity-based encryption (IBE) is an encryption scheme that is proposed to eliminate the need for certificates to certify the public keys of receivers in public-key cryptography (PKC). Nevertheless, the key escrow problem is a major problem to deploy identity-based encryption (IBE) in real-time. If the private key generator (PKG) is malicious, then he can decrypt all the messages. In this paper, we proposed an anonymous identity-based encryption scheme using ring signatures to solve this problem. In this process, the identity will get the corresponding private key from the key generation authority (KGA) by signing on masked identity using the ring signature generated by ring authority (RA). The KGA has no way of knowing who received which key. Hence, it solves the key escrow problem. While most existing techniques require certificates to ensure anonymity, which is also a motive for IBE, the proposed scheme will obtain the relevant key anonymously based on the ring signatures. In this paper, a proof of security of the proposed scheme is also presented along with the scheme.","PeriodicalId":398594,"journal":{"name":"2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126273492","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

Securing 5G Networks through Machine Learning – A Comparative Analysis 通过机器学习保护5G网络-比较分析

2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2022-04-23 DOI: 10.1109/CSNT54456.2022.9787645

Piyush Kulshreshtha, Amit Garg

{"title":"Securing 5G Networks through Machine Learning – A Comparative Analysis","authors":"Piyush Kulshreshtha, Amit Garg","doi":"10.1109/CSNT54456.2022.9787645","DOIUrl":"https://doi.org/10.1109/CSNT54456.2022.9787645","url":null,"abstract":"5G Networks have evolved to support higher speeds and bandwidths, ultra-density, lower latencies, and low TCO. These features are achieved through many new technologies and applications that make the network vulnerable and prone to cyber-attacks. Security being an important criteria for public networks, there is a requirement to update the security mechanisms to detect and prevent these cyber attacks. Detection of network anomalies can always be done through identifying the signature of the attack. However, this requires knowledge of all existing attacks which is a formidable task. The second option is to study the normal behaviour of the network and identify any anomalies as a possible attack that should be further explored. All these options are computation intensive and require time which can leave the network exposed. In order to speed up the identification of anomalies, Machine Learning is a viable option. This paper reviews and explores application of various Machine Learning models for Intrusion Detection. A new dataset – CSE-CIC IDS2018 has been used to model and compare the effectiveness of several machine learning techniques in identification of cyber attacks. The subset of dataset selected has balanced number of flows for normal traffic as well as attacks so binary classification has been applied on it. The dataset was preprocessed and cleaned and then feature selection was carried out to identify 20 relevant features. A number of supervised learning classifiers including Gaussian Naive Bayes, Gradient Boosting, Logistic Regression, Decision Tree, Random Forest and Support Vector Machine were applied on the dataset to check their effectiveness. This paper also presents a comparison of performance of these classifiers for Intrusion Detection in the network using CSE CIC IDS2018 dataset.","PeriodicalId":398594,"journal":{"name":"2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134375972","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

Development of a Hybrid Model using Deep Neural Network and XGBOOST for Mortality Prediction of ICU Patients 基于深度神经网络和XGBOOST的ICU患者死亡率预测混合模型的建立

2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2022-04-23 DOI: 10.1109/CSNT54456.2022.9787564

B. Majhi, Aarti Kashyap

{"title":"Development of a Hybrid Model using Deep Neural Network and XGBOOST for Mortality Prediction of ICU Patients","authors":"B. Majhi, Aarti Kashyap","doi":"10.1109/CSNT54456.2022.9787564","DOIUrl":"https://doi.org/10.1109/CSNT54456.2022.9787564","url":null,"abstract":"With the rapid development of various machine learning techniques, this paper proposes a hybrid model using Deep Neural Network (DNN) and XGBoost (XGB) to predict the ICU patient's mortality accurately. For prediction of mortality Physionet challenge 2012 dataset is used, which consists of 8000 records (set A and B) and 42 features. The outcome of this study reveals whether the ICU patient will survive or dies. Since, the dataset has large number of missing values and also highly imbalanced, makes the classification problem more complex. To circumvent these problems, k-Nearest Neighbor (k-NN) imputation is used to handle the missing value and (Synthetic Minority Oversampling Technique) SMOTE for balancing the dataset. Then, the proposed model is trained using 5-fold cross-validation in 80% of the data. The model is tested with rest 20% of the data and various performance measures such as accuracy, sensitivity, specificity, f1-score, score1 and AUC are obtained. The hybrid model (DNN+XGB) is compared with the Gaussian Support Vector Machine and Multinomial Naive Bayes models and reported to be better predictor with AUC of 75.05%.","PeriodicalId":398594,"journal":{"name":"2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130719484","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

Brain Tumour Detection using Image Processing 基于图像处理的脑肿瘤检测

2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2022-04-23 DOI: 10.1109/CSNT54456.2022.9787643

V. Ugale, S. Pawar, Sheetal Pawar

引用次数: 2