| Architectures and Security of Software Defined Networks for Internet of Things: State-of-the-Art and Challenges |
| Pages: 108-116 (9) | [Full Text] PDF (326 KB) |
| MS Al-kahtani, L Karim |
| Dept. of Computer Engineering, Prince Sattam bin Abdulaziz University, Saudi ArabiaSchool of ICT, Seneca College of Applied Arts and Technology, Toronto, Canada |
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Abstract - Internet of Things (IoT) connects thousands of everyday use objects and devices under the same network. Hence, Software Defined Networking (SDN) is crucial in evolving IoT. SDN provides a programmable and adaptive networking through the use of dedicated central controller that can handle a multitude of different connected devices. Integrating SDN into IoT can enable intelligent routing, simplified data acquisition, transition, and analysis, centralized management of network resources and applications, and dynamic on-demand reconfiguration of the network. At the same time, they pose challenges in terms of performance, interoperability, scalability, reliability and security. In this paper, we present a comprehensive survey on SDN for IoT, i.e., the concepts of SDN-IoT and their impact on each other, several SDN-IoT architectures, security and privacy implications, and other challenges. |
| Index Terms - SDN, IoT, Device to Device Communication, IoT Security and Privacy, Threat model |
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C itation - MS Al-kahtani, L Karim. "Architectures and Security of Software Defined Networks for Internet of Things: State-of-the-Art and Challenges ." International Journal of Computer Engineering and Information Technology 9, no. 6 (2017): 108-116. |
| SSL-QA: Analysis of Semi-Supervised Learning for Question-Answering |
| Pages: 117-119 (3) | [Full Text] PDF (308 KB) |
| P Patel, J Prajapati |
| Assistant Professor, Vadodara Institute of Engineering, IndiaAssistant Professor, Vadodara Institute of Engineering, India |
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Abstract - Open domain natural language question answering (QA) is a process of automatically finding answers to questions searching collections of text files. Question answering (QA) is a long-standing challenge in NLP, and the community has introduced several paradigms and datasets for the task over the past few years. These patterns differ from each other in the type of questions and answers and the size of the training data, from a few hundreds to millions of examples. Context-aware QA paradigm and two most notable types of supervisions are coarse sentence-level and fine-grained span-level. In this paper we analyse different intensive researches in semi-supervised learning for question-answering. |
| Index Terms - SSL-QA, Semi-Supervised, Learning, Question-Answering |
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C itation - P Patel, J Prajapati. "SSL-QA: Analysis of Semi-Supervised Learning for Question-Answering." International Journal of Computer Engineering and Information Technology 9, no. 6 (2017): 117-119. |
| Twitter Sentiment Analysis on Demonetization tweets in India Using R language |
| Pages: 120-126 (7) | [Full Text] PDF (602 KB) |
| K Arun, A Srinagesh, M Ramesh |
| Dept of CSE, RVR & JC College of Engineering, Guntur, India |
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Abstract - In this global village social media is in the front row to interact with people, Twitter is a the ninth largest social networking website in the world, only because of micro blogging people can share information by way of the short message up to 140 characters called tweets, It allows the registered users to search for the latest news on the topics they have an interest, Lakhs of tweets shared daily on a real-time basis by the members, it has more than 328 million active users per month , Twitter is the best source for the sentiment and opinion analysis on product reviews, movie reviews and current issues in the world. In this paper we present the sentiment analysis on the current twitters like Demonetization, Indians and all our the world people are share their opinions in twitter about current news in the country. The sentiment analysis extracts positive and negative opinions from the twitter data set, R studio provides best environment for this twitter sentiment analysis. Access twitter data from Twitter API, data is written into txt files as the input dataset. Sentiment analysis is performed on the input dataset that initially performs data cleaning by removing the stop words, followed by classifying the tweets as positive and negative by polarity of the words. Generate the word cloud. Finally that generates positive and negative word cloud, comparison of positive and negative scores to get the current public pulse and opinion. |
| Index Terms - Twitter Data, Text Mining, Sentiment Analysis, Natural Language Processing, R-Studio |
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C itation - K Arun, A Srinagesh, M Ramesh. "Twitter Sentiment Analysis on Demonetization tweets in India Using R language." International Journal of Computer Engineering and Information Technology 9, no. 6 (2017): 120-126. |