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Twitter Sentiment Analysis Using Machine Learning For Product Evaluation 使用机器学习进行产品评估的Twitter情感分析
2020 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2020-02-01 DOI: 10.1109/ICICT48043.2020.9112381
N. Yadav, Omkar Kudale, Srishti Gupta, A. Rao, Ajitkumar Shitole
{"title":"Twitter Sentiment Analysis Using Machine Learning For Product Evaluation","authors":"N. Yadav, Omkar Kudale, Srishti Gupta, A. Rao, Ajitkumar Shitole","doi":"10.1109/ICICT48043.2020.9112381","DOIUrl":"https://doi.org/10.1109/ICICT48043.2020.9112381","url":null,"abstract":"Twitter, a micro-running a blog website, is a massive repository of public opinions expressed in the direction of numerous humans, offerings, companies, merchandise, etc. Sentiment evaluation is the system of analyzing one's public evaluations. Sentiment analysis whilst combined with twitter offers beneficial insights into what's expressed on Twitter. The big availability of online evaluations and postings in social media gives invaluable feedback for groups to make better knowledgeable choices in guidance their marketing techniques towards user's pastimes and alternatives. Sentiment evaluation is, therefore, vital for determining the general public's opinion toward selected services or products. This paper emphasizes the different techniques utilized for classifying the product critiques (which can be within the form of tweets) according to critiques expressed in tweets to analyze whether or not the massive behavior is positive, negative or neutral and use of that analysis for the evaluation of product market. Data used in this look at our online product critiques gathered from twitter and used to rank the satisfactory classifier for sentiments.","PeriodicalId":408134,"journal":{"name":"2020 International Conference on Inventive Computation Technologies (ICICT)","volume":"14 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120848318","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: 14
A Study on Human Behavior based Color Psychology using K-means Clustering 基于k -均值聚类的人类行为色彩心理学研究
2020 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2020-02-01 DOI: 10.1109/ICICT48043.2020.9112442
Kanagaraj, P. Anjana, S. Bavatarani, D. Kumar
{"title":"A Study on Human Behavior based Color Psychology using K-means Clustering","authors":"Kanagaraj, P. Anjana, S. Bavatarani, D. Kumar","doi":"10.1109/ICICT48043.2020.9112442","DOIUrl":"https://doi.org/10.1109/ICICT48043.2020.9112442","url":null,"abstract":"Now-a-days the technology growth and the development of day to day life is varying and increasing vast in its features. There is no separate rising movement or any awareness or any other special learning logos for the connectivity between colors and human emotions that pave an emerging innovative study and get high step productive knowledge in it. The path from colors to emotional intelligence behavior not only shifts its actions but also gives a cohesive image determination on human civilization from human to gentleman. There also arises a technical aspect of sentimental analysis and opinion mining regarding human behavior. The purpose of this paper is to connect and produce a relation between color and human emotions that illustrate the whole meaning of what is life and to promote the originality of the behavior evolving in it. This brings innovative ideas to expose the ground- breaking technology in various fashions regardless to life matching emotions. Here gives a clear cut idea of what defines color technology into human emotion wheel in different moods of recognition.","PeriodicalId":408134,"journal":{"name":"2020 International Conference on Inventive Computation Technologies (ICICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121309871","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
Design of Ultra Wide Band Antenna for Bluetooth and Wi-Fi Applications 用于蓝牙和Wi-Fi应用的超宽带天线设计
2020 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2020-02-01 DOI: 10.1109/ICICT48043.2020.9112428
V. Rekha, P. Prathasaradhi, SK. Khanjuma, N. Chandana, M. Priyanka
{"title":"Design of Ultra Wide Band Antenna for Bluetooth and Wi-Fi Applications","authors":"V. Rekha, P. Prathasaradhi, SK. Khanjuma, N. Chandana, M. Priyanka","doi":"10.1109/ICICT48043.2020.9112428","DOIUrl":"https://doi.org/10.1109/ICICT48043.2020.9112428","url":null,"abstract":"The Ultra-Wide Band (UWB) antenna is designed by using the singularity enlargement method (SEM) has been presented. Dual band notches are received through introducing deflection slots inside the ground plane structure. This antenna is designed on basis of parameters having dielectric substrate thickness 1.54mm and dielectric steady of 2.2. The advanced Matrix Pencil (MP) approach is used in the impulse response performances of the designed notch band of this antenna to comprehend the band notches, the operating bands and the rejected bands. The proposed antenna is used for the Bluetooth, Wi-Fi applications inside the wireless communication devices. The overall performance metrics like Return Loss (S -parameter), Voltage Standing Wave Ratio, gain and performance of the designed antenna are provided in this paper.","PeriodicalId":408134,"journal":{"name":"2020 International Conference on Inventive Computation Technologies (ICICT)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127147472","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
Rice Grain Classification using Image Processing & Machine Learning Techniques 使用图像处理和机器学习技术的稻米分类
2020 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2020-02-01 DOI: 10.1109/ICICT48043.2020.9112418
Biren Arora, Nimisha Bhagat, Saritha L R, Sonali Arcot
{"title":"Rice Grain Classification using Image Processing & Machine Learning Techniques","authors":"Biren Arora, Nimisha Bhagat, Saritha L R, Sonali Arcot","doi":"10.1109/ICICT48043.2020.9112418","DOIUrl":"https://doi.org/10.1109/ICICT48043.2020.9112418","url":null,"abstract":"Rice Grain Classification becomes very important as there are multiple rice grain types available in the market today. Classifying rice grains as per rice types manually is not feasible nor efficient. Classification can be a really tedious task when it comes to doing it manually instead of automatically. This would consume a lot of efforts as well as a lot of time would be wasted. There is a need for an intelligent and smart system which can overcome this difficulty by automating this process. It should be able to identify and classify individual rice grains according to the respective type automatically. The collection of data set should be the primary process. This includes extraction of various parameters of individual rice grains like major axis, minor axis, eccentricity, length, breadth, just to name a few. The system will utilize this information to train the computer. Each rice grain or image would be allocated to its respective class. Classes used in this project are surti kolam, idli rice, long grain basmati and boiled rice. Any rice sample that has been encountered in the system will be first classified and then will be segregated into its respective class. This would keep the entire system organized and segregated. Managing and keeping a track of different rice types is important and its proper classification in an industrial environment becomes crucial. Automating the system would encourage the industry to have future scope for its implementation according to the changes required as per the industry requirements.","PeriodicalId":408134,"journal":{"name":"2020 International Conference on Inventive Computation Technologies (ICICT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124876095","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
Prediction of Survival Rate from Non-Small Cell Lung Cancer using Improved Random Forest 利用改进的随机森林预测非小细胞肺癌生存率
2020 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2020-02-01 DOI: 10.1109/ICICT48043.2020.9112558
Pranamita Nanda, N. Duraipandian
{"title":"Prediction of Survival Rate from Non-Small Cell Lung Cancer using Improved Random Forest","authors":"Pranamita Nanda, N. Duraipandian","doi":"10.1109/ICICT48043.2020.9112558","DOIUrl":"https://doi.org/10.1109/ICICT48043.2020.9112558","url":null,"abstract":"The major advantage of survival rate prediction is to help patients by giving a better understanding about the success rate of his treatment. In case of lung cancer it is difficult to determine which feature should be used in order to determine this information. In this paper a number of algorithms are applied to the data set to classify the survival rate of Non -Small Cell lung cancer patients along with our proposed method Improved Random forest. The key data features used in these algorithms are overall treatment time, stages, total tumor dose, gender and age. The predictive power of various algorithms are compared. The results show that among the four individual models developed, Improved Random Forest is the most accurate one with an accuracy of 98%. Hence this work provides an effective and powerful approach to predict survival rate of NSCLC patients.","PeriodicalId":408134,"journal":{"name":"2020 International Conference on Inventive Computation Technologies (ICICT)","volume":"241 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115221131","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
Optimizing the Role of Orchestrator for Integration Aneka PaaS with AWS Cloud 优化集成Aneka PaaS与AWS云的协调器角色
2020 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2020-02-01 DOI: 10.1109/ICICT48043.2020.9112522
K. Ramesh, P. Renjith, S. Sasikumar
{"title":"Optimizing the Role of Orchestrator for Integration Aneka PaaS with AWS Cloud","authors":"K. Ramesh, P. Renjith, S. Sasikumar","doi":"10.1109/ICICT48043.2020.9112522","DOIUrl":"https://doi.org/10.1109/ICICT48043.2020.9112522","url":null,"abstract":"The adoption of Hybrid cloud in enterprises has grown rapidly every year. Hybrid cloud solution implements the better of Private and Public cloud while proving that running of multiple application in different environment to provide segregation of personal useful data into a private environment from the raw meta data to reside on the cost-effective public cloud. This paper covers the deployment of hybrid cloud application with the use of Aneka PaaS (Platform as a Service) computing and Amazon Web Services Elastic Cloud Computing (AWS EC2) cloud service. The deployed application is automated using Orchestra- tor- an automation tool that allows EC2 resources to be deployed that enables uninterrupted running off application without having to take down the application during the situations of low compute resources. Aneka allows for the monitoring of resources to allow Optimization and billing for respective on demand resource usage. The proposed Orchestrator flow algorithm allows the application to load balance computing resources between the private and public cloud.","PeriodicalId":408134,"journal":{"name":"2020 International Conference on Inventive Computation Technologies (ICICT)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116062378","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
Heart Disease Prediction Using Deep Neural Network 基于深度神经网络的心脏病预测
2020 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2020-02-01 DOI: 10.1109/ICICT48043.2020.9112443
P. Ramprakash, R. Sarumathi, R. Mowriya, S. Nithyavishnupriya
{"title":"Heart Disease Prediction Using Deep Neural Network","authors":"P. Ramprakash, R. Sarumathi, R. Mowriya, S. Nithyavishnupriya","doi":"10.1109/ICICT48043.2020.9112443","DOIUrl":"https://doi.org/10.1109/ICICT48043.2020.9112443","url":null,"abstract":"Healthcare occupies an indispensable part in human lives. The healthcare industry contain large amount of psychiatric data hence machine learning models were used to provide conclusion effectively in the heart disease prediction. The classification of healthy person and non-healthy person can be done reliably by using machine learning methods. We developed a framework in this exploration that can understand the principles of predicting the risk profile of patients with the clinical data parameters. The proposed model is constructed using Deep Neural Network and χ2-statistical model. The problem of under fitting and over fitting is eliminated. This model shows better results on both the testing and training data. DNN and ANN were used to analyse the efficiency of the model which accurately predicts the presence or absence of heart disease.","PeriodicalId":408134,"journal":{"name":"2020 International Conference on Inventive Computation Technologies (ICICT)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126661546","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 Multi-Level Type II Fuzzy Logic based Prediction of Connectivity in a Wireless Sensor Network 基于多级II型模糊逻辑的无线传感器网络连通性预测
2020 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2020-02-01 DOI: 10.1109/ICICT48043.2020.9112497
Anvita, I. Snigdh
{"title":"A Multi-Level Type II Fuzzy Logic based Prediction of Connectivity in a Wireless Sensor Network","authors":"Anvita, I. Snigdh","doi":"10.1109/ICICT48043.2020.9112497","DOIUrl":"https://doi.org/10.1109/ICICT48043.2020.9112497","url":null,"abstract":"Topology, whether fixed or ad hoc is dependent on the availability of a connection between the nodes as well as the stability of the connection. The agricultural monitoring scenario uses inadvertently an ad hoc and randomly places sensor nodes. Therefore, the three factors; availability, stability and connectivity become major parameters to determine the health of the network. Our paper tires to predict the stability and availability of the routes by employing fuzzy inference system at the sink node. During analysis we also observe that the dependence and computations of the aforementioned parameters are multi-faceted and hence one FIS could not accurately interpret the dependence of such factors on the network. Therefore, based on the characteristics of the factors we design a multi-level type I and type II fuzzy system to predict connectivity of a WSN network.","PeriodicalId":408134,"journal":{"name":"2020 International Conference on Inventive Computation Technologies (ICICT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123810722","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
Interleave-Division Multiple Access Systems with Invert Tree Based Interleavers with Unequal Power Sharing Algorithm 基于非等功率共享算法的逆变树交织器的交错多址系统
2020 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2020-02-01 DOI: 10.1109/ICICT48043.2020.9112571
Arpita Patel
{"title":"Interleave-Division Multiple Access Systems with Invert Tree Based Interleavers with Unequal Power Sharing Algorithm","authors":"Arpita Patel","doi":"10.1109/ICICT48043.2020.9112571","DOIUrl":"https://doi.org/10.1109/ICICT48043.2020.9112571","url":null,"abstract":"Power allocation is a major fear for practically coded Interleave division multiple access scheme. Various power allocation algorithms have been proposed for IDMA system. In this paper, the performance improvement of IDMA system using unequal power algorithm with comparison of various interleavers with coded and uncoded IDMA is presented. The simulation result shows that IDMA system gives better performance with invert tree based interleaver (ITBI) in comparison with other interleaver in terms of memory and complexity.","PeriodicalId":408134,"journal":{"name":"2020 International Conference on Inventive Computation Technologies (ICICT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121536253","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
Machine Learning Approach for 5G Hybrid Technologies 5G混合技术的机器学习方法
2020 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2020-02-01 DOI: 10.1109/ICICT48043.2020.9112519
A. Mathews, G. Glan Devadhas
{"title":"Machine Learning Approach for 5G Hybrid Technologies","authors":"A. Mathews, G. Glan Devadhas","doi":"10.1109/ICICT48043.2020.9112519","DOIUrl":"https://doi.org/10.1109/ICICT48043.2020.9112519","url":null,"abstract":"The rapid evolution of mobile communication networks is due to the large increase in the number of users. But higher throughput is not the only criterion to address the fifth generation of cellular networks. It mainly focuses on the redressal of the possible issues of the networks. The mostly found issues are lesser area of coverage, non-linear signal effects and the dispersion which is found to occur during the signal pathway. This work entails on increasing the maximum limit of coverage without signal loss. Through the usage of microcells in the proposed system, maximum limit of coverage is achieved in highly populated areas. The simulations are carried out using software MATLAB 2017a and Opti-System, in which enhanced symbol error rate plot and reduced out of band emissions power performance have been improved. Finally, the conclusion and the future scope of the work has been discussed.","PeriodicalId":408134,"journal":{"name":"2020 International Conference on Inventive Computation Technologies (ICICT)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132033667","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
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