2018 Eleventh International Conference on Contemporary Computing (IC3)最新文献_第4页

Automated Sign Language Interpreter 自动手语翻译

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530658

Hardik Rewari, Vishal Dixit, Dhroov Batra, N. Hema

引用次数: 11

Greening the Cloud Through Power-Aware Virtual Machine Allocation 通过功耗感知的虚拟机分配来绿化云

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530625

Kashav Ajmera, T. K. Tewari

引用次数: 6

Direct Perceptive Routing Protocol for Opportunistic Networks 机会网络的直接感知路由协议

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530597

D. Sharma, Gurmehr Sohi, Hitesh Dhankhar, Mayank Yadav

引用次数: 3

On-Demand Energy Provisioning in Wireless Sensor Networks with Capacity-Constrained Mobile Chargers 容量受限移动充电器无线传感器网络的按需能量供应

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530654

Abhinav Tomar, Amar Kaswan, P. K. Jana

{"title":"On-Demand Energy Provisioning in Wireless Sensor Networks with Capacity-Constrained Mobile Chargers","authors":"Abhinav Tomar, Amar Kaswan, P. K. Jana","doi":"10.1109/IC3.2018.8530654","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530654","url":null,"abstract":"The limited energy of the sensor nodes (SNs) has always been a major hindrance for wireless sensor networks (WSNs). Wireless charging of the SNs is a promising alternative to solve the energy constraint problem in WSNs. The charging paradigm with such rechargeable SNs is known as wireless rechargeable sensor network (WRSN). While plenty research efforts have been made to improve the charging performance in a WRSN, little has been done to address the scheduling problem of the MCs having limited capacity. By scheduling problem of the MCs, we want to mean that how efficiently the capacity of multiple MCs can be utilized so that minimum number of MCs are used in order to fulfill the energy demands of the SNs. To fill this research lacuna, we propose three novel on-demand charging schemes which are termed as Pcharge, Bcharge, and Fcharge in this paper. The effectiveness of the proposed schemes is validated by simulation results which reveal that the proposed schemes can achieve promising performance in terms of tour length and capacity utilization of the MCs as compared to a state-of-the-art scheme.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115083513","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

Sentiment Analysis of Tweets Using Machine Learning Approach 基于机器学习方法的推文情感分析

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530517

Megha Rathi, Aditya Malik, D. Varshney, Rachita Sharma, Sarthak Mendiratta

{"title":"Sentiment Analysis of Tweets Using Machine Learning Approach","authors":"Megha Rathi, Aditya Malik, D. Varshney, Rachita Sharma, Sarthak Mendiratta","doi":"10.1109/IC3.2018.8530517","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530517","url":null,"abstract":"Microblogging websites like Twitter and Facebook, in this new era, is loaded with opinions and data. One of the most widely used micro-blogging site, Twitter, is where people share their ideas in the form of tweets and therefore it becomes one of the best sources for sentimental analysis. Opinions can be widely grouped into three categories good for positive, bad for negative and neutral and the process of analyzing differences of opinions and grouping them in all these categories is known as Sentiment Analysis. Data mining is basically used to uncover relevant information from web pages especially from the social networking sites. Merging data mining with other fields like text mining, NLP and computational intelligence we are able to classify tweets as good, bad or neutral. The main emphasis of this research is on the classification of emotions of tweets' data gathered from Twitter. In the past, researchers were using existing machine learning techniques for sentiment analysis but the results showed that existing machine learning techniques were not providing better results of sentiment classification. In order to improve classification results in the domain of sentiment analysis, we are using ensemble machine learning techniques for increasing the efficiency and reliability of proposed approach. For the same, we are merging Support Vector Machine with Decision Tree and experimental results prove that our proposed approach is providing better classification results in terms of f-measure and accuracy in contrast to individual classifiers.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123814730","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: 91

Optimised Prediction Model for Stock Market Trend Analysis 股票市场趋势分析的优化预测模型

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530457

Devpriya Soni, Sparsh Agarwal, Tushar Agarwel, Pooshan Arora, Kopal Gupta

{"title":"Optimised Prediction Model for Stock Market Trend Analysis","authors":"Devpriya Soni, Sparsh Agarwal, Tushar Agarwel, Pooshan Arora, Kopal Gupta","doi":"10.1109/IC3.2018.8530457","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530457","url":null,"abstract":"The main objective of this work is to add to the academic understanding of stock market analysis using some well defined algorithms and machine learning techniques. Stock price forecasting is a popular and important topic in financial studies and at academic levels. Share Market is not a neat place for analyzing since there are no significant rules to estimate or predict the price of share in the share market. Many a method like technical analysis, fundamental analysis, time series analysis and statistical analysis, etc. have been used in an attempt to analyze the share trends in the market but none of these methods have so far proved to be a universal approach for acceptance as a prediction tool. The intricacy while analyzing market trends is that they have a dependency on a number of external factors some of which are not under one's control. The goal of this work is to analyze stock market trends using some machine learning and nature inspired techniques, these were first studied and then implemented (a few of them used in this paper are Decision Tree, PSO, Black-Hole, Naïve Bayes.) After analyzing the trends with the help of standard techniques, we then proposed an entirely new approach to analyze stock market indices over which accuracy is calculated and compared over different techniques and algorithms. We outline the design of the proposed model with its salient features and customizable parameters. We finally tested our model on the one year of Nifty stock index dataset at real time where we analyzed the values on the basis of data from the past days for three months.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129896394","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

A Novel Distributed Machine Learning Framework for Semi-Supervised Detection of Botnet Attacks 僵尸网络攻击半监督检测的分布式机器学习框架

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530562

Gagandeep Kaur

{"title":"A Novel Distributed Machine Learning Framework for Semi-Supervised Detection of Botnet Attacks","authors":"Gagandeep Kaur","doi":"10.1109/IC3.2018.8530562","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530562","url":null,"abstract":"In today's Internet world where everything is interconnected, the misuse of the shared communication channels and the service providers by the malicious users requires all time monitoring. Amongst the various methods being adopted by network attackers, like Distributed Denial of Service (DDoS) attacks, spams, phishing attacks, etc. botnet threats are increasing day-by-day. Detecting botnet attacks is a challenging task. Firstly, botnets are difficult to detect because of stealthy nature of Command & Control protocol. Secondly, different types of bots have varied characteristics and combined with large size of the network traffic their detection becomes a very challenging task. Lastly, network traffic is unlabeled and classification techniques like decision trees cannot be used directly. Moreover with the success of distributed frameworks like Hadoop and Apache Spark it is feasible to handle very large data. In this paper we have used distributed framework to apply semi-supervised machine learning techniques of KMeans clustering for labeling a large dataset and decision trees as classifiers. High accuracy was achieved in prediction of the classes. Novelty of our work is labeling of unlabeled network traffic and classification using efficient distributed frameworks.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129210751","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: 9

Enhancing RansomwareElite App for Detection of Ransomware in Android Applications 增强RansomwareElite应用程序检测勒索软件在Android应用程序

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530614

Shivangi, Gautam Sharma, Anubhav Johri, Akshita, Anurag Goel, Anuradha Gupta

{"title":"Enhancing RansomwareElite App for Detection of Ransomware in Android Applications","authors":"Shivangi, Gautam Sharma, Anubhav Johri, Akshita, Anurag Goel, Anuradha Gupta","doi":"10.1109/IC3.2018.8530614","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530614","url":null,"abstract":"As the number of android applications (apps) available in the market are increasing rapidly, various types of security attacks using the android apps are also increasing with the same pace. The ransomware attack is one of these kind of security attacks in which the attackers locks the user's phone, encrypts user's data or blocks the user's access to their own data and threatens the user to pay a ransom to gain the access back. This cyber-threat is terrorizing the world from many years as it performs mimicry attacks i. e. combination of encryption & locking attacks. Android devices are more prone to these ransomware attacks compared to Windows and IOS devices. RansomwareElite is an android application which detects the presence of ransomware in the apps installed on an android device by checking the presence of any threatening text in app code or by verifying the permissions requested by the app from the user. In this paper, we focused on improving the performance of RansomwareElite app by extending its features. Now, the RansomwareElite app also searches the presence of any threatening image or file containing threatening text by analyzing the Android Package Kit(APK) file of android app. Moreover, it also detects some specific methods and classes in the code of the APK which could be used for locking the device and checks some specific permissions requested, for uninstalled apps now. Further, it maintains a database on the online server for the records of all the suspicious and ransomware apps detected by RansomwareElite. We have tested RansomwareElite with 9 Test Apps which are manually created based on the features of ransomware family and on 48 android devices. After analyzing the test results, we have found that the performance of RansomwareElite is improved after incorporating the new features and RansomwareElite app detects the presence of ransomware in installed as well as uninstalled apps present on an android's device in an efficient manner.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123210861","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: 3

Influence Indexing of Developers, Repositories, Technologies and Programming Languages on Social Coding Community GitHub 影响索引的开发人员，存储库，技术和编程语言对社会编码社区GitHub

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530644

R. Bana, Anuja Arora

引用次数: 10

Fractional Discrete Cosine Transform Based Approach for Compensating the Effect of Light Variations for Robust Face Recognition 基于分数阶离散余弦变换的鲁棒人脸识别光变化补偿方法

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530612

V. P. Vishwakarma

{"title":"Fractional Discrete Cosine Transform Based Approach for Compensating the Effect of Light Variations for Robust Face Recognition","authors":"V. P. Vishwakarma","doi":"10.1109/IC3.2018.8530612","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530612","url":null,"abstract":"This paper presents a novel approach of compensating the effect of light variations using fractional discrete Cosine transform (FrDCT) to efficiently solve the problem of robust person identification using face images under varying light conditions. Illumination variations are due to non-frontal lighting source with varying distribution and positions, varying ambient lighting, along with 3D shape of human face. The appearance of any object image can be characterized by illumination and reflectance generated by the object in which illumination changes slowly compared to the reflectance. Hence illumination variations mainly correspond to low frequency band of the face image. Fractional discrete Cosine transform (FrDCT) which is a generalized representation of discrete Cosine transform (DCT), has been used to process the illumination variations in the present approach. FrDcttransform the input image into $f$-domain at an angle from the input domain axis and it provides the flexibility to vary the value of the angle. In f-domain, a fuzzy filter has been employed to compensate the impact of light variations while preserving the facial features which lie in low frequency band. Percentage error rate has been used as performance metric and it is compared on Yale face database with existing state-of-art techniques of illumination normalization. The performance achieved on benchmark database clearly establishes the efficacy of the proposed approach of reducing the effect of light variations.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131553986","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