2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT)最新文献

{"title":"Design and Development of trust management scheme for the internet of things based on the optimization algorithm","authors":"Shilpa V. Shankhpal, Brahmananda S.H.","doi":"10.1109/CSNT48778.2020.9115784","DOIUrl":"https://doi.org/10.1109/CSNT48778.2020.9115784","url":null,"abstract":"Nowadays a very fast-growing trend known as IoT is emerging very rapidly. IoT system becomes a very necessary part & almost amongst all applications in our day-to-day life for educated people or non-educated people. Various real-time applications such as smart healthcare system in the medical field, smart home application for daily life, intelligent retail shop management, a smart agricultural application for the farming function or smart hand-on application for construction the site, the intelligent driving system for the vehicle and so on. As the Internet of Things (IoT) is spreading like a viral network all over the world very rapidly hence it is more vulnerable in security, malfunctioning of a system. Moreover, lots of internal or external attacks and threats are there to hack the system data and malfunctioning the system working. With the help of IoT, an object can communicate with each other without moving here and there, hence data reliability is more important here to provide excellent end-to- end services. Hence trust management endures a very powerful role for accurate and trustworthy data transfer for data computation, and for providing quality data services between the different users while maintaining the participant’s privacy and data security. It must be noted that securing the IoT system with Trust provides a stable solution for protecting the system from external threats but the literature review reveals that no well- structured trust management system framework is developed till now which will help the user to resolve security and threat issue and maintain the IoT services properly working. As in the trust system trust values of nodes should be measured with the help of parameters that are based on the node’s functional values of an application. The computation part of the node’s trustworthiness is a very difficult task, as all nodes provide different services in the IoT system. Also, the trust system will provide 100% trustworthiness is also a very major challenge because of the dynamic IoT system environment. So herewith considering all these issues in mind we had done a literature review on the IoT system and their complexities come while in the functioning of the network system. Also the various types of trust management models we had explored with their pros & cons. Finally, we had proposed our trust management model based on the optimization algorithm. In the end, we had concluded that if our proposed algorithm will be implemented in the future it will provide fast processing than the already existing system.","PeriodicalId":131745,"journal":{"name":"2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127083507","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}

{"title":"An Analysis of Image Segmentation Methods for Brain Tumour Detection on MRI Images","authors":"Anurag Goswami, M. Dixit","doi":"10.1109/CSNT48778.2020.9115791","DOIUrl":"https://doi.org/10.1109/CSNT48778.2020.9115791","url":null,"abstract":"MRI scans have been very helpful in the study of the diagnosis or segmentation of brain tumors in recent years. The brain tumor may be detected due to MRI scans. The MRI image is shown in the nervous system to create abnormal tissue growth or blood blocks. The first step in diagnosing the brain tumor is to control the brain structure, which symmetrically and asymmetrically identifies abnormalities. The next step is segmentation based on morphological operations (Fuzzy transformation). In this post, we explored different methods for MRI image identification and brain tumor segmentation. Precise tumor removal is important for brain tumors because of the complex brain structure. Some parameters for extracting features such as configuration, form, dimensions and image position are considered. With respect to the results retrieved from extract features the process of tumor classification is performed. This paper offers a number of techniques for the prediction of brain tumors.","PeriodicalId":131745,"journal":{"name":"2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126624175","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}

{"title":"Evolution of 5G Wireless Network in IoT","authors":"Saurabh Arora, Nikhil Sharma, B. Bhushan, I. Kaushik, A. Ahmad","doi":"10.1109/CSNT48778.2020.9115773","DOIUrl":"https://doi.org/10.1109/CSNT48778.2020.9115773","url":null,"abstract":"The fundamental feature of internet of things (IoT) is to integrate the sensing technology along with the technology of radio frequency identification and port these technology on the devices produced now-a-days. As of now the IoT has been facing many issues like poor security measures, high maintenance cost and least reliable. So, there is a need of new emerging technology like 5G which could resolve all these issues. 5G has the capability to introduce features like ultra-dense network support, high reliability with world class security measure. Which make the production of new devices with advance technology highly cost effective. So, in this paper we are going to discuss about what were the IoT based devices were facing before the introduction of emerging technology like 5G and how will be able to resolve out all those issues. Lastly, we will discuss how 5G along with IoT could bring a revolution in the existing technology.","PeriodicalId":131745,"journal":{"name":"2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116128601","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}

{"title":"An Efficient Storage in the cloud &Secure HER Retrieval by using HECC","authors":"P. Kumari, Neetesh Gupta","doi":"10.1109/CSNT48778.2020.9115749","DOIUrl":"https://doi.org/10.1109/CSNT48778.2020.9115749","url":null,"abstract":"The sector of healthcare generates lot of data related to the health of patients, where large number of people enrols in hospital. It contains a large image and document data that needs to be processed over a different entity. Since the data is in the isolated form, unmanaged manner there is a need for the personal health records system that will ultimately holds up the factors affecting the data and provides a better managing capability. Indexing, secured transmission, Data minimization is always an issue which deals with encryption and decryption algorithms with different key sizes. With existing algorithm, time consumption to store and retrieve stored data is high as it involves MD5 algorithm. This paper gives an overall explanation of the storage of data in cloud where huge data plays a major role. Hence, an proposed algorithm containing compress storage, security along with fast as well as secure access is also presented in this paper. Improvement of Security and storage is done by Hyper-elliptic curve cryptography (HECC) with Asymmetric encryption technique, Hash function using secure Hashing algorithm (SHA-2) followed by Discrete Wavelet Transform (DWT) compression to minimize cloud space.","PeriodicalId":131745,"journal":{"name":"2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128432640","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}

{"title":"High Dimensional Data Processing in Privacy Preserving Data Mining","authors":"M. Rathi, A. Rajavat","doi":"10.1109/CSNT48778.2020.9115771","DOIUrl":"https://doi.org/10.1109/CSNT48778.2020.9115771","url":null,"abstract":"In business intelligence data is an essential feature in decision making. An incomplete or lake of information can damage the entire project ideas. Therefore sometimes different business dimensions are collaborating their sensitive and personal data for enhancing decisional ability. During this, the dataset is significantly growing in dimensions. Therefore it is much intense to find a method by which the higher dimensional data can be handled. This paper contributes two key directions of the PPDM (privacy-preserving data mining), first a survey conducted on the various PPDM models to understand the working and requirements of the PPDM systems. In addition to an experimental comparative study among PCA, k-PCA and Correlation coefficient based feature selection or dimensionality reduction is conducted. On the basis of experimental observations, the PCA and k-PCA feature selection techniques are degrading the classification accuracy as compared to correlation coefficient based classification. Therefore, in further system design and implementation, the correlation coefficient is used to handling a huge quantity of data dimensions.","PeriodicalId":131745,"journal":{"name":"2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124658933","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}

{"title":"Hand Written Digit Recognition using Machine Learning","authors":"R. Sethi, I. Kaushik","doi":"10.1109/CSNT48778.2020.9115746","DOIUrl":"https://doi.org/10.1109/CSNT48778.2020.9115746","url":null,"abstract":"Hand-written character and digit recognition have been one of the most exigent and engrossing field of pattern recognition and image processing. The main aim of this paper is to demonstrate and represent the work which is related to hand-written digit recognition. The hand-written digit recognition is a very exigent task. In this recognition task, the numbers are not accurately written or scripted as they differ in shape or size; due to which the feature extraction and segmentation of hand-written numerical script is arduous. The vertical and horizontal projections methods are used for the purpose of segmentation in the proposed work. SVM is applied for recognition and classification, while Convex hull algorithm is applied for feature extraction.","PeriodicalId":131745,"journal":{"name":"2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121796253","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}

{"title":"Deep Learning for massive MIMO: Challenges and Future prospects","authors":"Vandana Bhatia, M. Tripathy, P. Ranjan","doi":"10.1109/CSNT48778.2020.9115783","DOIUrl":"https://doi.org/10.1109/CSNT48778.2020.9115783","url":null,"abstract":"The wireless networks today are complex, massive and have dynamic capacity demands. Increase in demand resulted into trouble in managing and monitoring the network components. Thus, intelligent data-driven designs and approaches are required so that the 5th generation (5G) of mobile systems can be reformed for enabling self-organizing capabilities. Thus, in the last decade, mathematical models are designed and adapted among modems. This paper presents a comprehensive outline of the emerging research on deep learning-based models for massive MIMO systems. In most of the work, Deep learning models are used for redesigning the conventional communication system. It may involve channel encoding, decoding, detection, recognition, antenna selection, modulation, etc. It will be interesting to comprehend that replacement of the communication system with a profoundly new architecture such as deep learning based autoencoder, convolutional neural network, etc. These Deep learning-based models show promising performance enhancements with a few limitations and can be efficiently used with massive MIMO.","PeriodicalId":131745,"journal":{"name":"2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131170495","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}

{"title":"CSNT 2020 Cover Page","authors":"","doi":"10.1109/csnt48778.2020.9115753","DOIUrl":"https://doi.org/10.1109/csnt48778.2020.9115753","url":null,"abstract":"","PeriodicalId":131745,"journal":{"name":"2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132826067","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}

{"title":"A Multi-classifier Framework for Detecting Spam and Fake Spam Messages in Twitter","authors":"R. Raj, S. Srinivasulu, Aldrin Ashutosh","doi":"10.1109/CSNT48778.2020.9115796","DOIUrl":"https://doi.org/10.1109/CSNT48778.2020.9115796","url":null,"abstract":"Social media plays vital role among the user communities for social gathering, entertainment, communication, sharing knowledge so on. Twitter is one such network to connect millions of users to share information. Nowadays, there are humpteen numbers of users using social media for social engagements. Due to the fact that wide publicity of individuals and products get viral in social media, everyone wish to use social media as a platform to promote their product. Furthermore, large number of people relies on social media contents to take decisions. Twitter is one of the social media platforms to post the media contents by the user. Spammers are illegal users intrude the twitter account and send the duplicate messages to promote advertisement, phishing, scam and personal blogs etc. In this paper, a novel spam detection mechanism is introduced to detect the suspicious users on twitter. The system has been designed such a way that it initially set with semi-supervised at the tweet level and update into supervised level for learning the input tweets to detect the spammers. The proposed system will also identify the type of spammers and will also remove duplicate tweets. We have applied with multi-classifier algorithms like naïve Bayesian, K-Nearest neighbor and Random forest into twitter data set and the performance is compared. The experimental result shows very promising results.","PeriodicalId":131745,"journal":{"name":"2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132592334","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}

{"title":"Wavelet Based Empirical Approach to Mitigate the Effect of Motion Artifacts from EEG Signal","authors":"S. Shukla, Vandana Roy, Anand Prakash","doi":"10.1109/CSNT48778.2020.9115761","DOIUrl":"https://doi.org/10.1109/CSNT48778.2020.9115761","url":null,"abstract":"Physiological signal such as Electroencephalographic (EEG) is often corrupted by artifacts during measurement and processing. These artifacts may corrupt the important topographies and signal information quality. The human health diagnosis needs a strong and feasible biomedical signal. Hence, the elimination of artifacts from physiological signal is a vital step. The Ensemble Empirical Mode Decomposition (EEMD) algorithm is used to convert input single channel EEG signal into a multi-channel EEG signal. This multi-channel EEG signal is further processed with Canonical Correlation Analysis (CCA) algorithm. Finally Discrete Wavelet Transform (DWT) is employed to remove the randomness available in the signal due to remaining artifacts. This technique is tested and evaluated against currently available artifact removal techniques using efficiency matrices such as Del Signal to Noise Ratio (DSNR), Lambda, Root Mean Square Error (RMSE) and Power Spectral Density (PSD) improvement. The improved parameters DSNR and by 28% and 17.81% respectively, pronounce the eligibility of the proposed algorithm to stand on top of currently employed algorithms.","PeriodicalId":131745,"journal":{"name":"2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127857311","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}