Riya Jain

Thapar Institute of Engineering & Technology

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Co-Authors

Amardeep Kumar

New York University

Uma Sushmitha Gunturi

International Bluegrass Music Association

Gauri Shanker Gupta

Birla Institute of Technology, Mesra

Rajiv Ratn Shah

Indraprastha Institute of Information Technology Delhi

Isabelle G. Lee

New York University

Debanjan Mahata

Indraprastha Institute of Information Technology Delhi

Rakesh Gosangi

New York University

Laiba Mehnaz

University of Washington

Anish Acharya

The University of Texas at Austin

Shweta Shrivastava

Aryabhatta Knowledge University

Cooperative Institutions

Indraprastha Institute of Information Technology Delhi

Indian Institute of Technology Delhi

The University of Texas at Austin

New York University

Bloomberg (United States)

University of Washington

Virginia Tech

Moody's Corporation (United States)

Thapar Institute of Engineering & Technology

Amity University

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ISVM for Face Recognition

International Conference on Computational Intelligence and Communication Networks (2010)

Deepti Sisodia Shweta Shrivastava Riya Jain

The similarity of human faces, unpredictable variations and aging are the crucial obstacles in face recognition. To handle this if large set of training images are used then computational complexity will get increase as images are rather high dimension but if training set kept small, performance decreases. Since both classification and feature information are necessary for a recognition system DCT is used to lower the computational complexity and SVM for classification. Since SVM is a popular classification tool but the main disadvantage of SVM is its large memory requirement and computation time to deal with large data set. Therefore we have used incremental learning approach i.e. ISVM to avoid large training time and memory consumption for face recognition. The biggest advantage of using the proposed technique is that it not only decreases the training time and updating time but also improves the classification accuracy rate up to 100%. Experiments are performed on ORL face database and results has proved that not only the training time used by the ISVM is very less compared to SVM but also the recognition rate raised to 100%. Obtained results have presented accurate face recognition system using the proposed approach..

Feature (linguistics)

Similarity (geometry)

Training set

10.1109/cicn.2010.109

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Citations (22)

Remote Sensing Image Classification using Deep Learning

Shreya Gupta Rakesh Dwivedi Vivek Kumar Riya Jain Shruti Jain

Remote sensing is the technology used for extracting information about the earth surface with the help of sensors installed on the satellites. The remote sensing technology captures and analyzes the sensed data which also helps in planning and monitoring the use of regional data. In our work, we used deep learning to analyze remotely sensed data. This paper aims to identify buildings from the images and provide the result using one of the deep neural networks. Various models of Convolutional Neural Networks (CNNs) help in rapid and accurate analysis of remote sensing data. The significance of the work lies in training the model correctly without overfitting. The work achieves 72.97% accurate model.

Overfitting

Contextual image classification

Earth observation

10.1109/smart52563.2021.9676295

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Citations (2)

GupShup: Summarizing Open-Domain Code-Switched Conversations

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)

Laiba Mehnaz Debanjan Mahata Rakesh Gosangi Uma Sushmitha Gunturi Riya Jain

Laiba Mehnaz, Debanjan Mahata, Rakesh Gosangi, Uma Sushmitha Gunturi, Riya Jain, Gauri Gupta, Amardeep Kumar, Isabelle G. Lee, Anish Acharya, Rajiv Ratn Shah. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.

Code (set theory)

Open domain

10.18653/v1/2021.emnlp-main.499

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Citations (11)

Resolving Autism Spectrum Disorder (ASD) through Brain Topologies using fMRI Dataset with Multi-Layer Perceptron (MLP)

Psychiatry Research Neuroimaging (2024)

Jainy Sachdeva Riyaansh Mittal Jiya Mehta Riya Jain Anmol Ranjan

Multilayer perceptron

Perceptron

10.1016/j.pscychresns.2024.111858

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Citations (0)

Human Computer Interaction – Hand Gesture Recognition

Advanced Journal of Graduate Research (2021)

Riya Jain Muskan Jain Roopal Jain Suman Madan

The creation of intelligent and natural interfaces between users and computer systems has received a lot of attention. Several modes of knowledge like visual, audio, and pen can be used individually or in combination have been proposed in support of this endeavour. Human communication relies heavily on the use of gestures to communicate information. Gesture recognition is a subject of science and language innovation that focuses on numerically quantifying human gestures. It is possible for people to communicate properly with machines using gesture recognition without the use of any mechanical devices. Hand gestures are a form of nonverbal communication that can be applied to several fields, including deaf-mute communication, robot control, human–computer interaction (HCI), home automation, and medical applications. Many different methods have been used in hand gesture research papers, including those focused on instrumented sensor technology and computer vision. To put it another way, the hand sign may be categorized under a variety of headings, including stance and motion, dynamic and static, or a combination of the two. This paper provides an extensive study on hand gesture methods and explores their applications.

Wired glove

10.21467/ajgr.11.1.1-9

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Citations (7)

Hybrid Feature Extraction Approach for Handwritten Character Classification Using Feed-forward Neural Network Techniques

International Journal of Computer Applications (2018)

Riya Jain Gunjan Singh Pankaj Sharma

Automatic character recognition is one of the most discovered and crucial application areas of pattern recognition field.Due to the increasing demand of machine processing of handwritten characters, this area of pattern recognition is gaining a lot of attention of researchers.Despite of a great deal of efforts done so far in this direction, still a lot is required to get done.In this work, we are focusing our attention on handwritten Hindi characters.Automatic recognition of Handwritten Hindi characters is complex due to the cursive nature and high level of similarity (such as presence of header line, vertical bar, etc.) in the structure of characters.Hybrid feature extraction approach is followed to extract meaningful features from the collected handwritten characters data.A comparative study of performance analysis of selected neural networks models is performed.Results indicate that the Radial basis function network model perform with 99.09% recognition accuracy.

Feature (linguistics)

10.5120/ijca2018916599

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Citations (0)

Intrusion Detection System via Classical SVM and Quantum SVM

Ananya Upadhyay Ruchir Namjoshi Riya Jain Jaideep Patel M. Gayathri

Whether as a result of cybersecurity systems' weakness or their absence, we have observed an increase in computer breaches across our communication networks globally in recent years. As a result, an intrusion detection system (IDS) is required. An IDS is a piece of software that keeps an eye out for malicious attempts aimed at stealing or censoring data or altering network protocols on a single computer or a network of computers. The majority of IDS approaches have evolved through time, but they are still unable to address the dynamic and intricate nature of cyberattacks on computer networks. Therefore, effective techniques are needed, which can lead to a greater detection rate. In order to determine which system is superior, this study compares the IDSs of the Classical SVM with the Quantum SVM.

Censoring (clinical trials)

10.1002/9781394167401.ch26

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Citations (0)

GupShup: An Annotated Corpus for Abstractive Summarization of Open-Domain Code-Switched Conversations

arXiv (Cornell University) (2021)

Laiba Mehnaz Debanjan Mahata Rakesh Gosangi Uma Sushmitha Gunturi Riya Jain

Code-switching is the communication phenomenon where speakers switch between different languages during a conversation. With the widespread adoption of conversational agents and chat platforms, code-switching has become an integral part of written conversations in many multi-lingual communities worldwide. This makes it essential to develop techniques for summarizing and understanding these conversations. Towards this objective, we introduce abstractive summarization of Hindi-English code-switched conversations and develop the first code-switched conversation summarization dataset - GupShup, which contains over 6,831 conversations in Hindi-English and their corresponding human-annotated summaries in English and Hindi-English. We present a detailed account of the entire data collection and annotation processes. We analyze the dataset using various code-switching statistics. We train state-of-the-art abstractive summarization models and report their performances using both automated metrics and human evaluation. Our results show that multi-lingual mBART and multi-view seq2seq models obtain the best performances on the new dataset

Code (set theory)

Open domain

10.48550/arxiv.2104.08578

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Citations (8)

Learning Embedded Representation of the Stock Correlation Matrix Using Graph Machine Learning

Bhaskarjit Sarmah Nayana Nair Riya Jain Dhagash Mehta Stefano Pasquali

Matrix representation

Representation

10.1109/cifer62890.2024.10772849

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Citations (1)