2017 20th International Conference of Computer and Information Technology (ICCIT)最新文献

{"title":"A performance analysis of a typical server running on a cloud","authors":"T. Tanni, M. S. Hasan","doi":"10.1109/ICCITECHN.2017.8281783","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2017.8281783","url":null,"abstract":"In recent years, Cloud computing has become very popular from individuals to big enterprises because of the reduced cost, minimal management effort etc. Cloud providers like Amazon, Google are now offering resources for web deployment, storage, servers etc. However, due to the varying load and cost, evaluating the performance of task scheduling policies in these real Cloud environments is very challenging. In this paper, the performance of Earliest Deadline First (EDF) scheduling policy has been investigated using CloudSim and the hardware configuration of Amazon Web Services (AWS) and Google Cloud Platform (GCP) with the time-out of a web and FTP servers. In addition, a comparison between space-shared and time-shared task provisioning policies have been examined which shows that the average execution time can be minimised by using space-shared policy in both AWS and GCP.","PeriodicalId":350374,"journal":{"name":"2017 20th International Conference of Computer and Information Technology (ICCIT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126079195","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 application of machine learning to detect abusive Bengali text","authors":"S. C. Eshan, M. S. Hasan","doi":"10.1109/ICCITECHN.2017.8281787","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2017.8281787","url":null,"abstract":"Bengali abusive text detection can be useful to prevent cyberbullying and online harassment as these types of crimes are increasing rapidly in Bangladesh. Machine learning approach can be useful to keep the system always updated with the new types of approaches used by the abusers. This paper investigates machine learning algorithms e.g. Random Forest, Multinomial Naïve Bayes, Support Vector Machine (SVM) with Linear, Radial Basis Function (RBF), Polynomial and Sigmoid kernel and have compared with unigram, bigram and trigram based CountVectorizer and TfidfVectorizer features. The results show that SVM Linear kernel performs the best with trigram TfidfVectorizer features.","PeriodicalId":350374,"journal":{"name":"2017 20th International Conference of Computer and Information Technology (ICCIT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127676056","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 application of pre-trained CNN for image classification","authors":"Abdullah, M. S. Hasan","doi":"10.1109/ICCITECHN.2017.8281779","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2017.8281779","url":null,"abstract":"Image Classification is a branch of computer vision where images are classified into categories. This is a very important topic in today's context as large databases of images are becoming very common. Images can be classified as supervised or unsupervised techniques. This paper investigates supervised classification and evaluates performances of two classifiers as well as two feature extraction techniques. The classifiers used are Linear Support Vector Machine (SVM) and Quadratic SVM. The classifiers are trained and tested with features extracted using Bag of Words and pre-trained Convolution Neural Network (CNN), namely AlexNet. It has been observed that the classifiers are able to classify images with very high accuracy when trained with features from CNN. The image categories consisted of Binocular, Motorbikes, Watches, Airplanes, and Faces, which are taken from Caltech 265 image archive.","PeriodicalId":350374,"journal":{"name":"2017 20th International Conference of Computer and Information Technology (ICCIT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116088365","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":"Single image super-resolution using back-propagation neural networks","authors":"M. S. Hasan, Salman Taseen Haque","doi":"10.1109/ICCITECHN.2017.8281778","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2017.8281778","url":null,"abstract":"There are several existing mathematical algorithms for colour image upscaling like Nearest Neighbour, Bicubic and Bilinear. This paper firstly investigates the performances of these three and it has been found that Bicubic performs the best in terms of structural similarity and execution time. A Bicubic with backpropagation based ANN method has been proposed to improve the results. Bicubic with ANN shows 6.5% higher SSIM, 6.9% higher PSNR, 8.7% higher SNR and 30.23% lower MSE values than pure Bicubic. The results of Bicubic with ANN are also compared with state of the art super-resolution techniques like SRCNN. Bicubic with ANN produces 1.48% higher SSIM and 3.44% higher PSNR than SRCNN.","PeriodicalId":350374,"journal":{"name":"2017 20th International Conference of Computer and Information Technology (ICCIT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122078874","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":"Security analysis of IEEE 802.21 standard in software defined wireless networking","authors":"Asma Islam Swapna, Nazrul Islam","doi":"10.1109/ICCITECHN.2017.8281843","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2017.8281843","url":null,"abstract":"Software Defined Networking (SDN) is the best choice in establishing a software controlled inter-domain network. Convergence of different Wireless link technologies bring the mobile users to choose the network being in any geographical location. IEEE 802.21 is such a standard for exchanging networking information for connecting with the network being at any region in the world. Integrated with SDN wireless network this functionality of IEEE 802.21 standard can discover programmable network services with profound resource utilization. However, the information exchange should circulate through a reliable source. Hence, the security analysis of IEEE 802.21 Media Independent Handover (MIH) mechanism for Software Defined Wireless Network (SDWN) is the primary concern of this research work. This study, conducts architectural and functional analysis of MIH integrated with SDWN interface for mobility management of the wireless nodes. The outcome specifies a possible integration with future deployment opportunities in information exchange of IEEE 802.21 MIH for programmable network devices.","PeriodicalId":350374,"journal":{"name":"2017 20th International Conference of Computer and Information Technology (ICCIT)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125064799","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":"Secure domain name service in software defined network","authors":"Vishal Gupta, Shrey Shah, Shantanu Shrivastava","doi":"10.1109/ICCITECHN.2017.8281791","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2017.8281791","url":null,"abstract":"Domain Name Service (DNS) is an important service generally used by other application layer protocols of TCP/IP protocol stack. These protocols use DNS to translate human readable web address to machine readable IP address which is then used by other protocols of network stack for communication between computers over the network. The correctness of DNS translation cannot be compromised as it may lead to unsecure transactions with in the network. Because of this, DNS is generally a soft target for attackers and is vulnerable to different security threats including DNS spoofing, DNS cache poisoning, etc. Many solutions for such threats are proposed for traditional IP network. In this paper we talk about security loops in DNS and propose a solution for it in Software Defined Network (SDN) environment.","PeriodicalId":350374,"journal":{"name":"2017 20th International Conference of Computer and Information Technology (ICCIT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122937376","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 feature based method for real time vehicle detection and classification from on-road videos","authors":"Md. Shamim Reza Sajib, S. M. Tareeq","doi":"10.1109/ICCITECHN.2017.8281786","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2017.8281786","url":null,"abstract":"Vision Based vehicle detection and classification has become an active area of research for intelligent transportation system. But this task is very difficult and challenging due to the dynamic condition of roads. Many solutions have been given by the researchers to overcome these challenges. Some of them are giving good performance but computationally highly expensive and fail in some circumstances. In the proposed method, a feature based cost effective detection and classification method is proposed that is suitable for real time applications, provide satisfactory accuracy and computationally cheap. The proposed method uses haar-like features of the images and AdaBoost classifier for detection which provides a very fast detection rate with high accuracy. To reduce inconsiderable false positive rate generated by this method, we propose to use two virtual detection lines (VDL) that reduces the false positive rate. In order to predict the class of a vehicle, a feature based method is proposed. HOG, SIFT, SURF all are well represented feature for image classification. The existing feature based vehicle classification methods lack accuracy because of using those features inefficiently. In order to reduce those lacking, we propose to use bag of visual words (BOVW) model for classification. BOVW model also needs a lower computation time and resources. As the proposed method aims to be implemented in real time, we propose to use SURF feature for BOVW which is faster to compute and well described for recognizing an object. The BOVW then used for identifying the vehicle's class by multi class SVM classifier. Error correcting output code (ECOC) framework is used to achieve multi class prediction with SVM. Extensive experiments have been carried out on different traffic data of varying environments to evaluate the detection and classification performance of the proposed method. Experiment results demonstrate that the proposed method achieves a significant improvement in classification of heterogeneous vehicles in terms of accuracy with a considerable execution time as compared to other methods.","PeriodicalId":350374,"journal":{"name":"2017 20th International Conference of Computer and Information Technology (ICCIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129261636","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 comparison between Support Vector Machine (SVM) and bootstrap aggregating technique for recognizing Bangla handwritten characters","authors":"Asish Ghosh, Shyla Afroge","doi":"10.1109/ICCITECHN.2017.8281810","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2017.8281810","url":null,"abstract":"This paper represents the optical character recognition for Bangla handwritten characters using the popular classifier SVM and Bootstrap Aggregating technique. The segmentation process in Bangla is difficult because of complex letters and “Matra (top horizontal line)” in the words. For the feature extraction method there was no particular algorithm found, which was efficient enough, so in this experiment the Hog feature extraction and Binary pixel feature extraction methods were used. Hog features and Binary pixel features were combined for the proposed system. To recognize a character Support Vector Machine (SVM) and Bootstrap Aggregating were used. Experimental results for the SVM classifier and Bootstrap aggregating shows 100% accuracy for trained characters and for random untrained characters, SVM classifier shows accuracy about 89.8% and for the Bootstrap Aggregating method the accuracy is 93%.","PeriodicalId":350374,"journal":{"name":"2017 20th International Conference of Computer and Information Technology (ICCIT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132013658","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":"CrowdsouRS: A crowdsourced reputation system for identifying deceptive online contents","authors":"Masiur Rahman Siddiki, Md. Abu Talha, F. Chowdhury, M. Ferdous","doi":"10.1109/ICCITECHN.2017.8281829","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2017.8281829","url":null,"abstract":"In recent years, accelerated web-based technologies have revolutionized content generation and broadcast mecha-nisms through the Internet. Social media, blogs, e-newspaper, auction sites facilitate the creation and exchange of user-generated contents, which rarely go through any fact-finding mechanism or rigorous editorial process. This has fuelled the creation and publication of fake news in the web. The proliferation of social networks has been exploited to accelerate the distribution and propagation of such fake news at an unprecedented level, creating a major concern for the web. There have been several efforts undertaken to rectify this problem, unfortunately, none seems to be effective to root out this concerning issue. In this paper, we present CrowdsouRS, a Crowd-sourced Reputation System, implemented as a browser extension, for the web that leverages the wisdom of the crowd to identify and tag deceptive online contents. It aggregates reputation scores for a web page from multiple users, which is then visualized in order to help other users to determine if the contents of the web page are deceptive. We have evaluated the usability and effectiveness of CrowdsouRS with a number of users and our evaluations suggest that users find the tool useful in serving its purpose.","PeriodicalId":350374,"journal":{"name":"2017 20th International Conference of Computer and Information Technology (ICCIT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132482890","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":"Efficient route selection in ad hoc on-demand distance vector routing","authors":"M. Uddin, Arnisha Akther, Shamima Parvez, A. Stranieri","doi":"10.1109/ICCITECHN.2017.8281807;","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2017.8281807;","url":null,"abstract":"The protocol diversities of mobile ad hoc have already got hold of the field to a peak of a matured and developed area. Still, the restraint of delay and bandwidth of mobile ad hoc network have kept a little room to draft a routing protocol for the pursuit of providing quality of service. In the paper, we proposed protocol namely Efficient Route Selection in Ad Hoc On-Demand Distance Vector Routing. We select the best path among multiple paths from source to destination using covariance and delay. We consider the delay, link stability and energy to devise a covariance-based metric to discover the most balanced path. We also propose a metric for the selection of a node that acts as a local backup node for the most vulnerable nodes on the selected path. We accomplish our implementation in NS3and it shows the more reliable path and less end to end delay than other counterpart protocols.","PeriodicalId":350374,"journal":{"name":"2017 20th International Conference of Computer and Information Technology (ICCIT)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128934839","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}