2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)最新文献_第5页

Design and Simulation of Elliptical Micro strip Patch Antenna with Coaxial Probe Feeding for Satellites Applications Using Matlab 卫星用同轴探头馈电椭圆微带贴片天线的设计与仿真

2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2020-10-07 DOI: 10.1109/I-SMAC49090.2020.9243472

V. Prakasam, N. Reddy

{"title":"Design and Simulation of Elliptical Micro strip Patch Antenna with Coaxial Probe Feeding for Satellites Applications Using Matlab","authors":"V. Prakasam, N. Reddy","doi":"10.1109/I-SMAC49090.2020.9243472","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243472","url":null,"abstract":"This paper uses coaxial probe feed method to present, design, and simulate elliptical microstrip patch antenna at ISM band. This paper processes an innovative elliptical microstrip patch (MSPA) antenna at standard ISM frequency band ranges from 2.4 GHz to 2.5 GHz. The planned and simulated EMSPA operating frequency is 2.4 GHz to 2.5 GHz and 4.2, 4.4, 4.6 & 4.8 FR4 substrate, this selected frequency increases efficiency in terms of S11 and reasonable gain value. In this study, coaxial probes feed the proposed antenna fixed on an FR-4 substrate material which has 4.2, 4.4, 4.6 & 4.8 dielectric constant, substratum thickness is 6.6 mm. The intension of the proposed antenna is that to determine the higher gain, less S11 at different operating frequencies that are 2.35 GHz, 2.4 GHz, 2.45GHz and 2.5 GHz, which is the ISM band range. The high-performance systems such as rockets, ships, missiles and satellites use elliptical microstrip patch antennas. Antennas with optimal measurements of elliptical microstrip patches act as circularly polarized wave radiators. Various simulation antenna design software is available, such as FEKO, IE3D, CST, HFSS, Antenna Magus and MATLAB. Here, using MATLAB simulation software tool, the EMSPA is designed and simulated and also estimate the performance characteristics, such as s-parameter, vswr, EH fields, radiation pattern, current distribution, gain, elevation and azimuthal radiation pattern.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114827329","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

Performance Analysis of Self Adaptive Equalizers using EPLMS Algorithm 基于EPLMS算法的自适应均衡器性能分析

2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2020-10-07 DOI: 10.1109/I-SMAC49090.2020.9243512

Shwetha N, M. Priyatham

引用次数: 10

DB-Scan Algorithm based Colon Cancer Detection And Stratification Analysis 基于DB-Scan算法的结肠癌检测与分层分析

2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2020-10-07 DOI: 10.1109/I-SMAC49090.2020.9243531

Gundlapalle Raiesh, Boda Saroia, Manian Dhivya, A. B. Gurulakshmi

{"title":"DB-Scan Algorithm based Colon Cancer Detection And Stratification Analysis","authors":"Gundlapalle Raiesh, Boda Saroia, Manian Dhivya, A. B. Gurulakshmi","doi":"10.1109/I-SMAC49090.2020.9243531","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243531","url":null,"abstract":"Histopathological examination of tissue models is basic for the conclusion and reviewing of colon malignancy. In any case, the technique is subjective and prompts imperative intra/bury spectator distinction in the examination as it predominantly relies upon the graphical evaluation of histopathologists. Thus, a tried and true PC supported technique, which can naturally group harmful and ordinary colon tests are required; however, automating this strategy is demanding because of the nearness of exceptions. In this paper, a productive technique for identifying colon disease from biopsy tests which comprise of four imperative stages. DB-SCAN estimation to distinguish colon tumor from biopsy tests is presented in this paper. In the proposed approach, from the outset, the colon biopsy tests are preprocessed using DB-SCAN configuration to make a set of redundant localities in which groups or clusters are formed. At that point, the exceptions inside the bunched areas are created as a tree structure in light of the choice tree in which the anomalies are hubs, and the connection between hubs are delivered based on data about exceptions. At that point, entropy-based exception score calculation will be done on every hub of the tree. The Information picks up technique is utilized to figure the score for the exceptions. At long last, score based grouping is accomplished to order the ordinary or harmful cells. Experimental trials exhibit, the proposed strategy has better outcomes contrasted to existing strategies. It furthermore acclaims that the proposed procedure is adequate for the colon tumor identification process. The proposed strategy is executed on Matlab working platform and the investigations exhibit that the proposed technique has high accomplished high grouping precision contrasted and different strategies.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125385971","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: 8

IoT System Accomplishment using BlockChain in Validating and Data Security with Cloud 利用区块链实现物联网系统验证和云数据安全

2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2020-10-07 DOI: 10.1109/I-SMAC49090.2020.9243412

J. K. Solomon Doss, S. Kamalakkannan

引用次数: 4

Depression and Impaired Mental Health Analysis from Social Media Platforms using Predictive Modelling Techniques 使用预测建模技术分析社交媒体平台上的抑郁和心理健康受损

2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2020-10-07 DOI: 10.1109/I-SMAC49090.2020.9243334

Vaibhav Jain, Dhruv Chandel, Piyush Garg, D. Vishwakarma

{"title":"Depression and Impaired Mental Health Analysis from Social Media Platforms using Predictive Modelling Techniques","authors":"Vaibhav Jain, Dhruv Chandel, Piyush Garg, D. Vishwakarma","doi":"10.1109/I-SMAC49090.2020.9243334","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243334","url":null,"abstract":"Depression is the leading global disability, and unipolar (as opposed to bipolar) depression is the 10th leading cause of early death, as stated by the World Health Organization (WHO) in 2015. The study aims to build an approach for depression and impaired mental health analysis from social media platforms. Although for Depression analysis and cure. Psyscologists preferred over machines because they are manipulative and precautionary to Human emotions to a greater extent, Machine Learning has an added advantage. It has no emotions; it studies patterns, not face or beauty or other factors. It studies a wide variety of data and then trains to give better predictions. Although it is not 100% reliable nor are the doctors. Moreover, in countries like India where people don't treat Depression as a Chronic Illness or don't even consider it as an illness of any sort, embedding Machine Learning Depression Detection Algorithms in Social Media combined with recommendation systems to treat a Human Mind positively, still being unnoticeable is a Great Boon to humanity The study is assisted by data collected from users after obtaining their consent and applying data preprocessing techniques. Several machine learning is used to analyze the data in the best way possible. A VAPID Technique is developed that performs far better than a classic feed-forward neural network. This study aims to develop a correlation between features and depressed people to observe a continuous pattern. Moreover, the aim is to conclude that social media can be a new exceptional methodology for analyzing depression and analyzing indirect patterns, improving many lives.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130109443","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

Cloud-based Protection and Performance Improvement in the E-Health Management Framework 电子健康管理框架中基于云的保护和性能改进

2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2020-10-07 DOI: 10.1109/I-SMAC49090.2020.9243419

P. Kanehanadevi, D. Selvapandian, Laxmi Raja, R. Dhanapal

引用次数: 2

Design and Implementation of IoT Testbed with Improved Reliability using Conditional Probability Techniques 基于条件概率技术的物联网试验台可靠性改进设计与实现

2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2020-10-07 DOI: 10.1109/I-SMAC49090.2020.9243309

Kayalvizhi Jayavel, Kanagaraj Venusamy, L. G

{"title":"Design and Implementation of IoT Testbed with Improved Reliability using Conditional Probability Techniques","authors":"Kayalvizhi Jayavel, Kanagaraj Venusamy, L. G","doi":"10.1109/I-SMAC49090.2020.9243309","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243309","url":null,"abstract":"Internet of Things (IoT) test beds are widely used by developers predominantly. Off late, Test beds are being used by data analysts, academicians, industrial persons and hardware ardent. The real purpose of test beds, is to achieve accurate testing results, mimicking the real time environment to the extent possible which is otherwise not possible to reproduce using simulators. As predicted by many industrial giants IoT based devices will reach the scale of billions by 2015. The applications and opportunities they create will also be innumerable. This has created a huge demand for such testing grounds, because a system deployed without proper testing may be vulnerable and sometimes disastrous. Thus our research aims to explore the qualities of test beds, the services they offer and how to enhance the performance of test beds. Our test bed framework is designed and developed with open-source boards to achieved heterogeneity, reusability, interoperability and scalability. This framework would like to be addressed as a utility, with “X” as service: data, sensor client, actuator client, and platform. To achieve this, APIs which is platform and language independent has been developed and provides third-party developer support. The APIs developed have shown considerable improvement in terms of data transfer rate, database upload and retrieval, and user responsiveness. Thus, our framework is capable of offering services through our API. And have demonstrated with the help of conditional probability techniques enhancement in performance and reusability, visualized the same in terms of graphs and datasets.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"274 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122153410","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: 5

Simulation based reactive power compensation by using TBSC/TBSR for dynamic load 基于仿真的TBSC/TBSR动态负载无功补偿

2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2020-10-07 DOI: 10.1109/I-SMAC49090.2020.9243473

M. Hans, Manoj Kumar Nigam, Maheshwari D. Mirajkar, Brijesh Patel

引用次数: 0

Detection of Fraudulent Transactions in Credit Card using Machine Learning Algorithms 利用机器学习算法检测信用卡欺诈交易

2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2020-10-07 DOI: 10.1109/I-SMAC49090.2020.9243545

Praveen Kumar Sadineni

{"title":"Detection of Fraudulent Transactions in Credit Card using Machine Learning Algorithms","authors":"Praveen Kumar Sadineni","doi":"10.1109/I-SMAC49090.2020.9243545","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243545","url":null,"abstract":"Today we are living in a digital world where most of the activities performed are online. Fraud transactions are ever growing since the growth of ecommerce applications. Millions of transactions are happening around every second everyday giving us the benefit of enjoying financial services through credit and debit cards. Fraud transactions are allowing illegal users to misuse the money of genuine users causing them financial loss. Accessibility of credit card transactions data, techniques used by the frauds, identifying scams in the bulk data which is getting produced very quickly, imbalanced data are some of the major challenges involved in detecting fraudulent credit card transactions. Hence, we need powerful techniques to identify fraudulent transactions. The current paper deals with various machine learning techniques such as Artificial Neural Network (ANN), Decision Trees, Support Vector Machine (SVM), Logistic Regression and Random Forest to detect fraudulent transactions. Performance analysis of these techniques is done using Accuracy, Precision and False alarm rate metrics. Dataset used to carry out the experiment is taken from Kaggle data repository. The experiment shows that Radom Forest could achieve an accuracy of 99.21%, Decision Tree 98.47%. Logistic Regression 95.55%, SVM 95.16% and ANN 99.92%.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120941111","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: 24

A study of Secure cryptographic based Hardware security module in a cloud environment 云环境下基于安全密码的硬件安全模块研究

2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2020-10-07 DOI: 10.1109/I-SMAC49090.2020.9243328

J. Divya, S. Shivagami

引用次数: 3