Browsing by Author "Usha, D."

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    End-to-end monitoring of cloud resources using trust
    (2019) Usha, D.; Chandrasekaran, K.
    Cloud computing is the fast-growing technology in the current era. With the growth, it brings with it multiple issues like privacy, security. Trusting the service provider and his resources is the major drawback for the user. In this paper, we have proposed a trust model which builds an end-to-end trust between the service provider resources and user. Our model calculates trust based on four parameters, namely utilization, saturation, failure, and availability. Our model builds a strong trust decision which enables the user to trust the resources of the service provider. � Springer Nature Singapore Pte Ltd. 2019.
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    Intelligent Data Mining for Collaborative Information Seeking
    (Springer Nature, 2020) Kumar, A.; Chandrasekaran, K.; Shukla, A.; Usha, D.
    World Wide Web (WWW) contains different kinds of information whether it be social, educational, historical, sports, news, financial, weather, technology, politics etc. Most of the people spend time on the internet to access data for information seeking purposes. Information provided on the web is available in different formats like in text format, image format or video format, and they can be accessed through different access interfaces. Accessing information from such a large place i.e. World Wide Web through so many websites would become a very cumbersome process, therefore, in this paper, we present a new method which will produce information based on the user input using appropriate keywords. The data will be retrieved from the internet using Data Mining approach without the need for rules and training of pages. The main focus will be to extract or retrieve data of a person like educational qualifications, gender, contact information, contributions in his work, his/her social nature, etc. The query to be searched on the platform or model should have meaningful keywords attached to it best describing the person or else data of some different person might be fetched. © 2020, Springer Nature Switzerland AG.
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    Logistic regression based DFS for Trip Advising Software (ASCEND)
    (2019) Thomas, E.; Byju, A.; Chandrasekaran, K.; Usha, D.
    Graphs have played a pivotal role in the field of computer science and has been an efficient method for representing and modeling abstractions in various fields. They can be used to represent several real life models. Several domains in today's world use the concept of graphs extensively such as GPS Navigation systems, Computer networks, WebCrawler, Social Networking websites, peer to peer networking, medical and biological field, neural networks etc. Taking into account the numerous applications of the concept of graphs in today's world, graph searching becomes inevitably significant. In this scenario it is important to note that several graph searching algorithms that were proposed to give exhaustive searches doesn't provide the most satisfying outcome in terms of asymptotic time complexity. Through this paper we intend to highlight the significance of machine learning as a useful tool that can be incorporated in various graph searching algorithms that can reduce its complexity. We classify the existing graph searching techniques as subsets or modifications of two major conventional graph searching algorithms namely BFS(Breadth First Search) and DFS(Depth First Search) and suggest the application of logistic regression to improve their performance. It is confounding that only few research papers explore the application of machine learning to the aforementioned graph searching algorithms. Hence, it is evident that there exists scope for future research on this topic and we aim to suggest directions for the same. � 2019 IEEE.
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    Logistic regression based DFS for Trip Advising Software (ASCEND)
    (Institute of Electrical and Electronics Engineers Inc., 2019) Thomas, E.; Byju, A.; Chandrasekaran, K.; Usha, D.
    Graphs have played a pivotal role in the field of computer science and has been an efficient method for representing and modeling abstractions in various fields. They can be used to represent several real life models. Several domains in today's world use the concept of graphs extensively such as GPS Navigation systems, Computer networks, WebCrawler, Social Networking websites, peer to peer networking, medical and biological field, neural networks etc. Taking into account the numerous applications of the concept of graphs in today's world, graph searching becomes inevitably significant. In this scenario it is important to note that several graph searching algorithms that were proposed to give exhaustive searches doesn't provide the most satisfying outcome in terms of asymptotic time complexity. Through this paper we intend to highlight the significance of machine learning as a useful tool that can be incorporated in various graph searching algorithms that can reduce its complexity. We classify the existing graph searching techniques as subsets or modifications of two major conventional graph searching algorithms namely BFS(Breadth First Search) and DFS(Depth First Search) and suggest the application of logistic regression to improve their performance. It is confounding that only few research papers explore the application of machine learning to the aforementioned graph searching algorithms. Hence, it is evident that there exists scope for future research on this topic and we aim to suggest directions for the same. © 2019 IEEE.
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    Role of Activation Functions and Order of Input Sequences in Question Answering
    (2020) Chenna, Keshava, B.S.; Sumukha, P.K.; Chandrasekaran, K.; Usha, D.
    This paper describes a solution for the Question Answering problem in Natural Language Processing using LSTMs. We perform an analysis on the effect of choice of activation functions in the final layer of LSTM cell on the accuracy. Facebook Research�s bAbI dataset is used for our experiments. We also propose an alternative solution, which exploits the language structure and order of words in the English language, i.e. reversing the order of paragraph will introduce many short-term dependencies between the textual data and the initial tokens of a question. This method improves the accuracy in more than half of the tasks by more than 30% over the current state of the art. Our contributions in this paper are improving the accuracy of most of the Q&A tasks by reversing the order of words in the query and the story sections. Also, we have provided a comparison of different activation functions and their respective accuracies with respect to all the 20 different NLP tasks. � 2020, Springer Nature Singapore Pte Ltd.