Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Naik, D.Has files: No

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    Performance evaluation of web browsers in Android
    (2013) Harsha Prabha, E.; Piraviperumal, D.; Naik, D.; Kamath S․, S.; Prasad, G.
    In this day and age, smart phones are fast becoming ubiquitous. They have evolved from their traditional use of solely being a device for communication between people, to a multipurpose device. With the advent of Android smart phones, the number of people accessing the Internet through their mobile phones is on a steep rise. Hence, web browsers play a major role in providing a highly enjoyable browsing experience for its users. As such, the objective of this paper is to analyze the performance of five major mobile web browsers available in the Android platform. In this paper, we present the results of a study conducted based on several parameters that assess these mobile browsers' functionalities. Based on this evaluation, we also propose the best among these browsers to further enrich user experience of mobile web browsing along with utmost performance. © 2013 Springer Science+Business Media New York.
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    Vulnerability analysis on virtualized environment using FPVA
    (2013) Jayaraju, L.; Naik, D.; Mohan, B.R.
    Virtualization is rapidly gaining acceptance as a fundamental building block in enterprise data centers and is the core component of cloud computing platforms. It is most known for improving efficiency and ease of management. While this technology is meant to enhance the security of computer systems, some recent attacks show that virtual machine technology has much vulnerability and becomes exposed to security threats. In this paper, we focus on Vulnerability Analysis on Virtualized Environment using First Principles Vulnerability Analysis (FPVA) methodology. This paper analyses the security of Interrupt Descriptor Table on Xen hypervisor and presents outcome of analysis. This paper aids other enthusiasts in better analysing the Security aspects of Xen system using their own programs for security vulnerabilities. © 2013 IEEE.
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    Parallelizing doolittle algorithm using TBB
    (Institute of Electrical and Electronics Engineers Inc., 2014) Sah, S.K.; Naik, D.
    This paper presents a different approach for parallelizing the Doolittle Algorithm with the help of Intel Threading Building Blocks (TBB) allowing the users to utilize the power of multiple cores present in the modern CPUs. Parallel Doolittle Algorithm (PDA) has been divided into 3 parts: Decomposing the data, Parallely processing the data, finally Composing the data. Using the PDA we can solve the linear system of equations in considerably lesser amount time as compare to Serial Doolittle Algorithm (SDA). The PDA has been implemented in C++ using TBB library which makes it highly efficient, cross-platform compatible, and scalable. The efficiency of PDA over SDA has been verified by comparing the running time on different order of matrices. Experiments proved that PDA outperformed SDA by utilizing all the cores present in the CPU. © 2014 IEEE.
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    Retrieve the similar matching images using reduced SIFT with CED algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2014) Bandaru, R.; Naik, D.
    The local feature descriptor called SIFT, is one of the most widely used descriptors. The keypoints found with RSIFT and describe them in a standard way, which makes them invariant to the size changes, rotation, position, scale, and so on. These are quite powerful features and are used in a variety of tasks. This local feature SIFT descriptor gives potential key points, which are extracted from the image. If there are many such key points, a lot of computation time will be required for the matching key points, and some cases one key point matches more than once. For these reasons, here we have tried to reduce the key points in order to cluster the number of key points. The reduced SIFT with Canny Edge Detection (CED) algorithm can easily identify and trace the specified image from large the Database images as much fast as possible. © 2014 IEEE.
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    Secure optimal routing protocol in MANETs
    (Institute of Electrical and Electronics Engineers Inc., 2014) Bhuvaneswari, M.; Naik, D.
    MANET consists of the number of mobile nodes that form a wireless communication. MANETs are self configuring network where the nodes can move freely and randomly. It can dynamically join the network and leave the network at any time. It is widely used in industrial and commercial applications like vehicular communication, agricultural needs and disaster management, etc. There are studies based on routing protocols in MANETs to improve their quality and efficiency of the protocols. However, there may lot of research about routing protocols, always it lacks in security. The proposed algorithm uses public key cryptographic technique to make the protocol more secure. Cryptography provides more secure and optimal transactions. © 2014 IEEE.
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    Image segmentation using encoder-decoder architecture and region consistency activation
    (Institute of Electrical and Electronics Engineers Inc., 2016) Naik, D.; Jaidhar, C.D.
    An Encoder-Decoder Neural Network Architecture is combined with a novel strategy to improve global label consistency, to come with an improved image segmentation model. Label Distribution predictions extracted from the SegNet Network is investigated and used in the project for image labeling. An algorithm called Region Consistency Activation (RCA) to improve the global label consistency is implemented. RCA is based on a novel transformation between Ultra metric Contour Map (UCM) and the Probability of Regions Consistency (PRC). These algorithms were rigorously tested on the CamVid dataset. Superior performances were achieved compared with the state-of-the-art methods on this dataset. © 2016 IEEE.
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    A Novel Approach for Video Captioning Based on Semantic Cross Embedding and Skip-Connection
    (Springer Science and Business Media Deutschland GmbH, 2021) Radarapu, R.; Bandari, N.; Muthyam, S.; Naik, D.
    Video Captioning is the task of describing the content of a video in simple natural language. Encoder-Decoder architecture is the most widely used architecture for this task. Recent works exploit the use of 3D Convolutional Neural Networks (CNNs), Transformers or by changing the structure of basic Long Short-Term Memory (LSTM) units used in Encoder-Decoder to improve the performance. In this paper, we propose the use of a sentence vector to improve the performance of the Encoder-Decoder model. This sentence vector acts as an intermediary between the video space and the text space. Thus, it is referred to as semantic cross embedding that bridges the two vector spaces, in this paper. The sentence vector is generated from the video and is used by the Decoder, along with previously generated words to generate a suitable description. We also employ the use of a skip-connection in the Encoder part of the model. Skip-connection is usually employed to tackle the vanishing gradients problem in deep neural networks. However, our experiments show that a two-layer LSTM with a skip-connection performs better than the Bidirectional LSTM, for our model. Also, the use of a sentence vector improves performance considerably. All our experiments are performed on the MSVD dataset. © 2021, Springer Nature Singapore Pte Ltd.
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    COVID-19 Prediction Using Chest X-rays Images
    (Institute of Electrical and Electronics Engineers Inc., 2021) Kumar, A.; Sharma, N.; Naik, D.
    Understanding covid-19 became very important since large scale vaccination of this was not possible. Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Till now in various fields, great success has been achieved using convolutional neural networks(CNNs) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. The proposed research work has performed transfer learning using deep learning models like Resnet50 and VGG16 and compare their performance with a newly developed CNN based model. Resnet50 and VGG16 are state of the art models and have been used extensively. A comparative analysis with them will give us an idea of how good our model is. Also, this research work develops a CNN model as it is expected to perform really good on image classification related problems. The proposed research work has used kaggle radiography dataset for training, validating and testing. Moreover, this research work has used another x-ray images dataset which have been created from two different sources. The result shows that the CNN model developed by us outperforms VGG16 and Resnet50 model. © 2021 IEEE.
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    Attention based Image Captioning using Depth-wise Separable Convolution
    (Institute of Electrical and Electronics Engineers Inc., 2021) Mallick, V.R.; Naik, D.
    Automatically generating descriptions for an image has been one of the trending topics in the field of Computer Vision. This is due to the fact that various real-life applications like self-driving cars, Google image search, etc. are dependent on it. The backbone of this work is the encoder-decoder architecture of deep learning. The basic image captioning model has CNN as an encoder and RNN as a decoder. Various deep CNNs like VGG-16 and VGG-19, ResNet, Inception have been explored but despite the comparatively better performance, Xception is not that familiar in this field. Again for the decoder, GRU is not been used much, despite being comparatively faster than LSTM. Keeping these things in mind, and being attracted by the accuracy of Xception and efficiency of GRU, we propose an architecture for image captioning task with Xception as encoder and GRU as decoder with an attention mechanism. © 2021 IEEE.
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    Comparitive Study of GRU and LSTM Cells Based Video Captioning Models
    (Institute of Electrical and Electronics Engineers Inc., 2021) Maru, H.; Chandana, T.S.S.; Naik, D.
    Video Captioning task involves generating descriptive text for the events and objects in the videos. It mainly involves taking a video, which is nothing but a sequence of frames, as data from the user and giving a single or multiple sentences (sequence of words) to the user. A lot of research has been done in the area of video captioning. Most of this work is based on using Long Short Term Memory (LSTM) units for avoiding the vanishing gradients problem. In this work, we purpose to implement a video captioning model using Gated Recurrent Units(GRU's), attention mechanism and word embeddings and compare the functionalities and results with traditional models that use LSTM's or Recurrent Neural Networks(RNN's). We train and test our model on the standard MSVD (Microsoft Research Video Description Corpus) dataset. We use a wide range of performance metrics like BLEU score, METEOR score, ROUGE-1, ROUGE-2 and ROUGE-L to evaluate the performance. © 2021 IEEE.