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ORCIDRakshit Agrawal
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2020 – today
- 2024
[j3]Mahendra Kumar Gourisaria, Rakshit Agrawal, Manoj Sahni, Pradeep Kumar Singh
:
Comparative analysis of audio classification with MFCC and STFT features using machine learning techniques. Discov. Internet Things 4(1): 1 (2024)- 2023
- [j2]Rakshit Agrawal, Hrishita Sarkar, Abhisek Omkar Prasad, Abhaya Kumar Sahoo, Ankit Vidyarthi, Rabindra Kumar Barik:
Exploration of Deep Neural Networks and Effect of Optimizer for Pulmonary Disease Diagnosis. SN Comput. Sci. 4(5): 471 (2023) - 2022
- [c22]Rakshit Agrawal, Jack W. Stokes, Lukas Rist, Ryan Littlefield, Xun Fan, Ken Hollis, Zane Coppedge, Noah Chesterman, Christian Seifert:
Long-Term Study of Honeypots in a Public Cloud. DSN (Supplements) 2022: 1-4 - 2021
- [j1]Mahendra Kumar Gourisaria, Harshvardhan GM, Rakshit Agrawal, Sudhansu Shekhar Patra, Siddharth Swarup Rautaray, Manjusha Pandey:
Arrhythmia Detection Using Deep Belief Network Extracted Features From ECG Signals. Int. J. E Health Medical Commun. 12(6): 1-24 (2021) - 2020
- [c21]Jack W. Stokes, Rakshit Agrawal, Geoff McDonald:
Detection of Malicious Vbscript Using Static and Dynamic Analysis with Recurrent Deep Learning. ICASSP 2020: 2887-2891 - [c20]Adrian Brasoveanu, Megan Moodie, Rakshit Agrawal:
Textual Evidence for the Perfunctoriness of Independent Medical Reviews. KiML@KDD 2020: 1-9
2010 – 2019
- 2019
- [b1]Rakshit Agrawal:
Generalized Learning Models for Structured Data. University of California, Santa Cruz, USA, 2019 - [c19]Mahika Dubey, Alan Peral Ortiz, Rakshit Agrawal, Angus G. Forbes:
Predicting Biker Density at Bikeshare Station Intersections in San Francisco. GHTC 2019: 1-7 - [c18]Sarah Frost, Bryan Tor, Rakshit Agrawal, Angus G. Forbes:
CompostNet: An Image Classifier for Meal Waste. GHTC 2019: 1-4 - [c17]Matthew J. Howard, Rakshit Agrawal:
Predicting Substance Misuse Admission Rates via Recurrent Neural Networks. GHTC 2019: 1-8 - [c16]Abolfazl Zargari Khuzani, Rakshit Agrawal, Najmeh Mashhadi:
Fire detection in video sequences using a machine learning system and a clustered quantitative image marker. GHTC 2019: 1-5 - [c15]Rakshit Agrawal, Jack W. Stokes, Karthik Selvaraj, Mady Marinescu:
Attention in Recurrent Neural Networks for Ransomware Detection. ICASSP 2019: 3222-3226 - [c14]Jack W. Stokes, Rakshit Agrawal, Geoff McDonald, Matthew J. Hausknecht:
ScriptNet: Neural Static Analysis for Malicious JavaScript Detection. MILCOM 2019: 1-8 - [c13]Rakshit Agrawal, Luca de Alfaro:
Learning Edge Properties in Graphs from Path Aggregations. WWW 2019: 15-25 - [i8]Rakshit Agrawal, Luca de Alfaro, David P. Helmbold:
A New Family of Neural Networks Provably Resistant to Adversarial Attacks. CoRR abs/1902.01208 (2019) - [i7]Rakshit Agrawal, Luca de Alfaro, Gabriele Ballarin, Stefano Moret, Massimo Di Pierro, Eugenio Tacchini, Marco L. Della Vedova:
Identifying Fake News from Twitter Sharing Data: A Large-Scale Study. CoRR abs/1902.07207 (2019) - [i6]Rakshit Agrawal, Luca de Alfaro:
Learning Edge Properties in Graphs from Path Aggregations. CoRR abs/1903.04613 (2019) - [i5]Jack W. Stokes, Rakshit Agrawal, Geoff McDonald, Matthew J. Hausknecht:
ScriptNet: Neural Static Analysis for Malicious JavaScript Detection. CoRR abs/1904.01126 (2019) - 2018
- [c12]Suzanne B. da Câmara, Rakshit Agrawal, Katherine Isbister:
Identifying Children's Fidget Object Preferences: Toward Exploring the Impacts of Fidgeting and Fidget-Friendly Tangibles. Conference on Designing Interactive Systems 2018: 301-311 - [c11]Rakshit Agrawal, Anwar Habeeb, Chih-Hsin Hsueh:
Learning User Intent from Action Sequences on Interactive Systems. AAAI Workshops 2018: 59-64 - [c10]Rakshit Agrawal, Jack W. Stokes, Mady Marinescu, Karthik Selvaraj:
Neural Sequential Malware Detection with Parameters. ICASSP 2018: 2656-2660 - [c9]Rakshit Agrawal, Jack W. Stokes, Mady Marinescu, Karthik Selvaraj:
Robust Neural Malware Detection Models for Emulation Sequence Learning. MILCOM 2018: 1-8 - [i4]Jack W. Stokes, Rakshit Agrawal, Geoff McDonald:
Neural Classification of Malicious Scripts: A study with JavaScript and VBScript. CoRR abs/1805.05603 (2018) - [i3]Rakshit Agrawal, Jack W. Stokes, Mady Marinescu, Karthik Selvaraj:
Robust Neural Malware Detection Models for Emulation Sequence Learning. CoRR abs/1806.10741 (2018) - 2017
- [c8]Rakshit Agrawal, Luca de Alfaro, Vassilis Polychronopoulos:
Learning from Graph Neighborhoods Using LSTMs. AAAI Workshops 2017 - [i2]Rakshit Agrawal, Anwar Habeeb, Chih-Hsin Hsueh:
Learning User Intent from Action Sequences on Interactive Systems. CoRR abs/1712.01328 (2017) - 2016
- [c7]Rakshit Agrawal, Luca de Alfaro:
Predicting the quality of user contributions via LSTMs. OpenSym 2016: 19:1-19:10 - [i1]Rakshit Agrawal, Luca de Alfaro, Vassilis Polychronopoulos:
Learning From Graph Neighborhoods Using LSTMs. CoRR abs/1611.06882 (2016) - 2015
- [c6]Rakshit Agrawal, Aaron Springer, Emily Lovell:
QuickResponseHost: Enabling crowdsourced disaster response stations. GHTC 2015: 233-239 - [c5]Rakshit Agrawal:
Collaborative systems with applications for social good. OpenSym Companion 2015: 1:1-1:2 - 2013
- [c4]Rakshit Agrawal, Mridu Atray, Krishna Sundari Sattiraju:
Exploring suitable interfaces for agriculture based smartphone apps in India. APCHI 2013: 280-285 - [c3]Rakshit Agrawal, Mridu Atray, S. Krishna Sundari:
KrishiEkta: integrated knowledge and information distribution system for Indian agriculture. ACM DEV (4) 2013: 19:1-19:2 - 2012
- [c2]Rakshit Agrawal, Mridu Atray, S. Krishna Sundari:
Concept to design a hand-held crop management device (CMD) for farmers. ACM DEV 2012: 17:1 - [c1]Rakshit Agrawal, Mridu Atray, S. Krishna Sundari:
Sensor network architecture for soil and weather data extraction and database generation. SenSys 2012: 337-338
Coauthor Index
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