2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)最新文献

Sentiment Analysis and Classification on Twitter Spam Account Dataset Twitter垃圾邮件账户数据集的情感分析与分类

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213206

Gaurav N. Shetty, Ashwin Nair, Pradyumna Vishwanath, Ahuja Stuti

{"title":"Sentiment Analysis and Classification on Twitter Spam Account Dataset","authors":"Gaurav N. Shetty, Ashwin Nair, Pradyumna Vishwanath, Ahuja Stuti","doi":"10.1109/ACCTHPA49271.2020.9213206","DOIUrl":"https://doi.org/10.1109/ACCTHPA49271.2020.9213206","url":null,"abstract":"The amount of people using social media is very large and is increasing day by day. The impact of public figures in social media is quite big. Fake accounts are created in social media platforms and are used for various purposes like inflating the follower list of a particular account. These accounts also called spam accounts usually post spam messages which are used for marketing certain products or spreading particular agendas. Such accounts can be dangerous as they may alter a normal user’s perspective on certain topics. These accounts are used to modify and help in creating a fake sense of popularity which can influence political and social situations. In this project, we try to examine some of the existing methods and approaches for fake Twitter accounts detection. We will make use of a public dataset which contains tweets and account information of both Legitimate accounts as well as spam accounts. We make use of account information to create a classifier which can easily classify whether the given account is a fake account or a legitimate account. We also apply sentiment analysis algorithms on the tweet to find patterns among them. We try to analyse the sentiments behind the tweets of different accounts. Comparing our model with the existing model we will improve the features present in our model. In the process of building a better model, we try to reduce overfitting. The final result is an optimum classifier, which can be used to separate a fake account from a list of legitimate accounts.","PeriodicalId":191794,"journal":{"name":"2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)","volume":"34 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123052743","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 Cloud Ranking Protocol For User Service Selection Using Fuzzy Logic 基于模糊逻辑的用户服务选择的高效云排序协议

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213222

Anupama Mishra, A. K. Daniel

引用次数: 0

SAARTHI : Real-Time Monitoring of Patients by Wearable Device SAARTHI:可穿戴设备对患者的实时监测

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213239

R. Priya, A. Vaidya, Mohit Thorat, Vinit Motwani, Chetas Shinde

引用次数: 3

A Deep CNN Technique for Detection of Breast Cancer Using Histopathology Images 利用组织病理学图像检测乳腺癌的深度CNN技术

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213192

Gitanjali Wadhwa, A. Kaur

引用次数: 8

Evaluation of Factors Affecting Compressive Strength of Concrete using Machine Learning 用机器学习评价影响混凝土抗压强度的因素

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213199

A. Jha, Surabhi Adhikari, Surendrabikram Thapa, Abhay Kumar, Arunish Kumar, Sushruti Mishra

引用次数: 9

Computer-Aided Diagnosis of Thyroid Nodule from Ultrasound Images Using Transfer Learning from Deep Convolutional Neural Network Models 基于深度卷积神经网络模型迁移学习的超声图像甲状腺结节计算机辅助诊断

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213210

O. A. Ajilisa, V. Jagathyraj, M. Sabu

{"title":"Computer-Aided Diagnosis of Thyroid Nodule from Ultrasound Images Using Transfer Learning from Deep Convolutional Neural Network Models","authors":"O. A. Ajilisa, V. Jagathyraj, M. Sabu","doi":"10.1109/ACCTHPA49271.2020.9213210","DOIUrl":"https://doi.org/10.1109/ACCTHPA49271.2020.9213210","url":null,"abstract":"Nowadays, thyroid cancer is considered as one of the most common endocrine cancer in the human body. Ultrasonography is the primary imaging modality for the diagnosis of thyroid cancer. Computer-Aided assessment of ultrasound images for differentiating malignant nodules from benign nodule may help the clinicians for their decision making, and it leads to early diagnosis and on-time treatment. The important problem is difficulty in capturing features appropriate for differentiating malignant nodules from benign nodules. In this study, we extensively investigated the feasibility of transfer learning technique for the extraction of high-level features from thyroid ultrasound images. Images are preprocessed to adjust the skewed distribution using a cluster-based sampling technique. Pre-trained convolutional neural network models are fine-tuned with these preprocessed Images for the extraction of high-level semantic features from Images. Then the extracted features are fed into several supervised learning algorithms, and the performance of each model is evaluated. The experimental results recommend the viability of the Inception-v3 network and Xception network for efficiently differentiating malignant thyroid nodules from benign nodules.","PeriodicalId":191794,"journal":{"name":"2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)","volume":"207 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122422883","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

An Interactive Tele-Medicine System via Android Application 基于Android应用的交互式远程医疗系统

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213200

Samsul Arefin Riffat, Fatma Harun, Tanvir Hassan

{"title":"An Interactive Tele-Medicine System via Android Application","authors":"Samsul Arefin Riffat, Fatma Harun, Tanvir Hassan","doi":"10.1109/ACCTHPA49271.2020.9213200","DOIUrl":"https://doi.org/10.1109/ACCTHPA49271.2020.9213200","url":null,"abstract":"The healthcare companies of today are looking at ways to lessen costs and increase patient care. As part of this, Telemedicine is developing as the innovative concept of e-healthcare. The new telemedicine stages are making the health-care industry more elastic and flexible. It is with preparing the patient with the required tools to interconnect with the healthcare providers from the comfort of their homes. Following this trend, in this paper, this paper proposes an interactive android application. Using this application, patients can interact with available doctors via video conferencing. This paper will certainly save time by not going to the hospital and wait for long waiting hours. It is a visionary step in the field of the medicinal sector in Bangladesh, which will not only help the patient but also the doctors as well the economy of the country. This study provided evidence that a robust system for telemedicine can be developed to ensure the welfare of the people of Bangladesh. To develop this project Android Studio, Firebase was used. To provide video calling, DUO has been used.","PeriodicalId":191794,"journal":{"name":"2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130337966","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

1-Bit Full Adder Output Analysis Using Adiabatic ECRL Technique 用绝热ECRL技术分析1位全加法器输出

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213214

B. Pravitha, D. Vishnu, S. Shabeer

引用次数: 1

Web Text Content Credibility Analysis using Max Voting and Stacking Ensemble Classifiers 基于最大投票和堆叠集成分类器的Web文本内容可信度分析

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213234

P. Meel, Puneet Chawla, Sahil Jain, Utkarsh Rai

引用次数: 5

A Comprehensive Study on Convolutional Neural Networks for Chromosome Classification 卷积神经网络在染色体分类中的综合研究

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213238

R. Remya, S. Hariharan, Vishnu Vinod, David John W Fernandez, NM Muhammed Ajmal, C. Gopakumar

{"title":"A Comprehensive Study on Convolutional Neural Networks for Chromosome Classification","authors":"R. Remya, S. Hariharan, Vishnu Vinod, David John W Fernandez, NM Muhammed Ajmal, C. Gopakumar","doi":"10.1109/ACCTHPA49271.2020.9213238","DOIUrl":"https://doi.org/10.1109/ACCTHPA49271.2020.9213238","url":null,"abstract":"Cytogenetics plays significant role in the diagnosis, prognosis and treatment evaluation of genetic disorders through chromosome image analysis technique called karyotyping. Karyotyping is the way by which chromosomes are classified into 24 classes. Digital image processing techniques and machine learning algorithms found its scope in automated karyotyping since they ease or eliminate manual efforts in chromosome classification and its analysis. Even though, researchers were putting great efforts in the design of Automated Karyotyping System (AKS), for the last three decades, a fully automated system is not yet routinely accepted in practice. These days, deepnets exhibit improved performance in computer vision tasks, they are progressively utilized for automating classification tasks as well. Here, two variants of deep Convolutional Neural Networks (CNNs) for chromosome classification are modelled. A preliminary study on the hyperparameters of these models has been conducted. Other state-of-the-art CNN models are experimented and analyzed for chromosome classification. Performance measures of all these CNN deep models are compared to formulate hypotheses on hyperparameters to classify chromosomes efficiently.","PeriodicalId":191794,"journal":{"name":"2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117244732","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