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147 results on '"Venkataraman, Revathi"'

1. Incentivized Third Party Collateralization for Stablecoins

Author

Das, Souradeep and Venkataraman, Revathi

Subjects
Computer Science - Computer Science and Game Theory, Computer Science - Cryptography and Security
Abstract

Stablecoins, which are primarily intended to function as a global reserve of value are insubstantial in their design and present many failure points. The primary mechanism to enable these coins to hold on to a fixed value is by backing them with collateral. Fiat collateralized stablecoins require users to trust a centralized entity, which breaks the total concept of decentralization. Crypto collateralized stablecoins have issues involving high collateral requirements and introduces risks of auto-liquidation. In this paper we aim to propose an alternative architecture for the creation of a functional and secure stablecoin.

Published
2023

3. A Combined Attribute Extraction Method for Detecting Postpartum Depression Using Social Media

Author

Gopalakrishnan, Abinaya, Gururajan, Raj, Venkataraman, Revathi, Zhou, Xujuan, Chan, Ka Ching, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Li, Yan, editor, Huang, Zhisheng, editor, Sharma, Manik, editor, Chen, Lu, editor, and Zhou, Rui, editor

Published
2023
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8. Emerging Applications in Healthcare and Their Implications to Academia and Practice

Author

Gururajan, Raj, Tao, Xiaohui, Li, Yuefeng, Zhou, Xujuan, Elangovan, Soman, Chennakesavan, Srinivas Kondalsamy, Venkataraman, Revathi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhang, Wenjie, editor, Zou, Lei, editor, Maamar, Zakaria, editor, and Chen, Lu, editor

Published
2021
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9. Proof of Policy (PoP): A New Attribute-Based Blockchain Consensus Protocol

Author

Mythili, R., Venkataraman, Revathi, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Singh, Vijendra, editor, Asari, Vijayan K., editor, Kumar, Sanjay, editor, and Patel, R. B., editor

Published
2021
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10. A hardware-based multi-factor authentication - An initiative to secure web applications.

Author

Sornalakshmi, K., Venkataraman, Revathi, Kavitha, V., Shamunesh, P., Raja, Ashwin, and Khemka, Charu

Subjects
*MULTI-factor authentication, *WEB-based user interfaces, *COMPUTER software security, *CYBERTERRORISM, *SECURITY systems
Abstract

Hardware-based multi-factor authentication is a broad and extensive security solution approach to overcome the impact of cyber data compromises that are happening these days in applications using cutting-edge technologies. Our proposed work handles the high-end security modules by developing frameworks in parallel to the IoT-based security visualization to make the system's security an unbreakable unit. This work depicts the idea of prototyping a hardware key, termed in this paper as "AppSec Key" that handles the MFA/2FA checkpoints into a hardware level making it impossible for hackers to bypass it online using the tools. The visualization of IoT setup to the software security is a new revolution to the complication of mishandling data online. This work proposes sequence-based authentication that adds an extra protection layer. The overall approaches to tackle the existing cyber threats are been discussed with many real-world instances. [ABSTRACT FROM AUTHOR]

Published
2024
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20. A survey of autonomous monitoring systems in mental health.

Author

Gopalakrishnan, Abinaya, Gururajan, Raj, Zhou, Xujuan, Venkataraman, Revathi, Chan, Ka Ching, and Higgins, Niall

Subjects
DEEP learning, MACHINE learning, MENTAL health, ARTIFICIAL intelligence, VIRTUAL communications, EMOTIONAL state
Abstract

Smartphones and personal sensing technologies have made collecting data continuously and in real time feasible. The promise of pervasive sensing technologies in the realm of mental health has recently garnered increased attention. Using Artificial Intelligence methods, it is possible to forecast a person's emotional state based on contextual information such as their current location, movement patterns, and so on. As a result, conditions like anxiety, stress, depression, and others might be tracked automatically and in real‐time. The objective of this research was to survey the state‐of‐the‐art autonomous psychological health monitoring (APHM) approaches, including those that make use of sensor data, virtual chatbot communication, and artificial intelligence methods like Machine learning and deep learning algorithms. We discussed the main processing phases of APHM from the sensing layer to the application layer and an observation taxonomy deals with various observation devices, observation duration, and phenomena related to APHM. Our goal in this study includes research works pertaining to working of APHM to predict the various mental disorders and difficulties encountered by researchers working in this sector and potential application for future clinical use highlighted. This article is categorized under:Technologies > Machine LearningTechnologies > PredictionApplication Areas > Health Care [ABSTRACT FROM AUTHOR]

Published
2024
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26. Predicting remaining useful life of turbofan engines using degradation signal based echo state network.

Author

Sharanya, S., Venkataraman, Revathi, and Murali, G.

Abstract

The field of Prognostics and Health Management (PHM) in industries is gaining greater popularity to achieve high reliability by shifting the preventive maintenance to predictive maintenance. Estimation of Remaining Useful Life (RUL) is an effective prognostic measure that forecasts the health state of machine based on degradation modelling and condition monitoring. This article proposes a novel and robust methodology that uses Reduced Affinity Propagation (RAP) clustering technique that extracts representatives from the temporal signals measured through various heterogeneous sensors to predict the RUL using Echo State Network (ESN) with dynamic lateral inhibiting connections. The main advantage of the proposed model is that it does not overlook the features from the degradation signals and also learns the natural mapping among the representative points from the integrated sensor value. This approach is verified using CMAPPS dataset to show hopeful results in predicting the RUL of aircraft turbo fan engine. Also, this methodology can be a deployed as a tool in real time industrial applications to schedule predictive maintenance activities. [ABSTRACT FROM AUTHOR]

Published
2023
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43. A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection

Author

Zhou, Xujuan, Li, Yuefeng, Gururajan, Raj, Bargshady, Ghazal, Tao, Xiaohui, Venkataraman, Revathi, Barua, Prabal D., Kondalsamy-Chennakesavan, Srinivas, Zhou, Xujuan, Li, Yuefeng, Gururajan, Raj, Bargshady, Ghazal, Tao, Xiaohui, Venkataraman, Revathi, Barua, Prabal D., and Kondalsamy-Chennakesavan, Srinivas

Abstract

Breast cancer is reported as one of most common malignancy amongst women in the world. Early detection of this cancer is critical to clinical and epidemiologic for aiding in informing subsequent treatments. This study investigates automated breast cancer prediction using deep learning techniques. A new 19-layer deep convolutional neural network (CNN) model for detecting the benign breast tumors from malignant cancers was proposed and implemented. The experiments on BreaKHis dataset was conducted and K-fold Cross Validation technique are used for the model evaluation. The proposed 19-layer deep CNN based classifiers compared with conventional machine learning classifier, namely Support Vector Machine (SVM) and a state-of-the-art deep learning model, namely GoogLeNet in terms of Accuracy, Area under the Receiver Operating Characteristic (ROC) Curve (AUC), the Classification Mean Absolute Error (MAE), Mean Squared Error (MSE) metrics. The results demonstrate that the proposed new model outperformed the other classifiers. The proposed model achieved an accuracy, AUC, MAE and MSE of 84.5%, 85.7%, 0.082, and 0.043, respectively.

Published
2020

44. A survey on text classification and its applications

Author

Zhou, Xujuan, Gururajan, Raj, Li, Yuefeng, Venkataraman, Revathi, Tao, Xiaohui, Bargshady, Ghazal, Barua, Prabal D., Kondalsamy-Chennakesavan, Srinivas, Zhou, Xujuan, Gururajan, Raj, Li, Yuefeng, Venkataraman, Revathi, Tao, Xiaohui, Bargshady, Ghazal, Barua, Prabal D., and Kondalsamy-Chennakesavan, Srinivas

Abstract

Text classification (a.k.a text categorisation) is an effective and efficient technology for information organisation and management. With the explosion of information resources on the Web and corporate intranets continues to increase, it has being become more and more important and has attracted wide attention from many different research fields. In the literature, many feature selection methods and classification algorithms have been proposed. It also has important applications in the real world. However, the dramatic increase in the availability of massive text data from various sources is creating a number of issues and challenges for text classification such as scalability issues. The purpose of this report is to give an overview of existing text classification technologies for building more reliable text classification applications, to propose a research direction for addressing the challenging problems in text mining.

Published
2020

49. Embryology of Drimys winteri

Author

Bhandari, N. N., Venkataraman, Revathi, BioStor, and Harvard University Botany Libraries

Published
1968

50. A survey on text classification and its applications

Author

Zhou, Xujuan, primary, Gururajan, Raj, additional, Li, Yuefeng, additional, Venkataraman, Revathi, additional, Tao, Xiaohui, additional, Bargshady, Ghazal, additional, Barua, Prabal D., additional, and Kondalsamy-Chennakesavan, Srinivas, additional

Published
2020
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