Dr Nagender Aneja

Dr Nagender Aneja

Asst. Prof. School of Digital Science, Programme Leader (Computer Science) and Founder ResearchID.co

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

About me

Posts

More Control Flow Tools

5 minute read

Published:

This lesson covers An Informal Introduction to Python 3.10.5, https://docs.python.org/3/tutorial/introduction.html

An Informal Introduction to Python

6 minute read

Published:

This lesson covers An Informal Introduction to Python 3.10.5, https://docs.python.org/3/tutorial/introduction.html

R Commands

1 minute read

Published:

This lesson is on basic commands of R

Visualization

1 minute read

Published:

This post covers Multivariate Visualization.

Visualization

3 minute read

Published:

This post covers Bivariate Visualization.

Data Gathering

1 minute read

Published:

This post covers Data Gathering.

Data Wrangling

1 minute read

Published:

This post covers Data Wrangling.

Python - Pandas

1 minute read

Published:

This post covers Introduction to Pandas.

t-Tests

12 minute read

Published:

This post covers t-Tests.

Outliers

1 minute read

Published:

This post covers detecting outliers.

Python - Introduction

4 minute read

Published:

This post covers Introduction to Python Programming.

Projects

less than 1 minute read

Published:

This page covers projects completed by Students.

Twitter

less than 1 minute read

Published:

Help Cmd

less than 1 minute read

Published:

This lesson is for help.

Range and Quartiles

2 minute read

Published:

This lesson covers Range and Quartiles.

Skewed Distribution

3 minute read

Published:

This lesson covers Introduction to Skewed Distribution.

Linear Regression

22 minute read

Published:

This lesson covers Linear Regression.

L99 Agile Intro

8 minute read

Published:

permalink: /dt/agile-intro

L98 Agile

12 minute read

Published:

permalink: /dt/agile

L17 Critical factors

13 minute read

Published:

permalink: /digital-transformation/critical-factors

L16 Blockchain

17 minute read

Published:

permalink: /digital-transformation/blockchain

L15 Cloud computing

13 minute read

Published:

permalink: /digital-transformation/cloud-computing

L14 Mobile computing

5 minute read

Published:

permalink: /digital-transformation/mobile-computing

L13 Reality

3 minute read

Published:

permalink: /digital-transformation/reality-technologies

L12 Iot

9 minute read

Published:

permalink: /digital-transformation/iot

L11 Big data

8 minute read

Published:

permalink: /digital-transformation/bigdata

L02 Building ai

30 minute read

Published:

permalink: /ai-for-everyone/building-ai

L01 Introduction

43 minute read

Published:

ai-for-everyone/what-is-ai

ANOVA

7 minute read

Published:

This post explains ANOVA.

t Table

less than 1 minute read

Published:

This post provides t Table.

Slides

less than 1 minute read

Published:

This post explains creating slides using Jupyter Notebook.

XML

8 minute read

Published:

This lesson covers XML

Normal Distribution

3 minute read

Published:

This post covers Normal Distribution from https://www.mathsisfun.com/data/standard-normal-distribution.html and https://www.mathsisfun.com/data/standard-deviation.html

Z Table

less than 1 minute read

Published:

This post provides Z Table.

Flask-Cookie-Session

1 minute read

Published:

This post covers Cookie and Session in Flask.

Flask-Cookie

less than 1 minute read

Published:

This post covers cookie in Flask!

Flask

1 minute read

Published:

This post covers forms in Flask using Calculator App!

Flask

less than 1 minute read

Published:

This post covers introduction to Flask!

Machine Learning

less than 1 minute read

Published:

This lesson is on Machine Learning notes.

Image Classification

13 minute read

Published:

This post covers image classification, nearest neighbor classifier, k-nearest neighbor classifier and validation sets from cs231n.

Mean Median Mode

6 minute read

Published:

This post covers Introduction to descriptive statistics.

Descriptive Statistics

15 minute read

Published:

This lesson is from Introduction to Descriptive Statistics by Jackie Nicholas, Mathematics Learning Centre, University of Sydney

Python-Libraries

less than 1 minute read

Published:

This post covers Introduction to some other python libraries.

MNIST-NN

2 minute read

Published:

This post covers introduction to Neural Network using MNIST dataset.

MNIST-CNN

5 minute read

Published:

This post covers introduction to Convolution Neural Network using MNIST dataset.

HTML Forms

1 minute read

Published:

This post covers introduction to HTML Forms.

Datasets

less than 1 minute read

Published:

This lesson covers possible datasets to be used for projects.

Missing data

less than 1 minute read

Published:

https://app.datacamp.com/learn/courses/dealing-with-missing-data-in-r

Geospatial

1 minute read

Published:

This lesson is from datacamp Visualizing Geospatial data in R and https://eriqande.github.io/rep-res-eeb-2017/map-making-in-R.html

Shiny

5 minute read

Published:

This lesson is from datacamp Introduction to Shiny

Plotly

8 minute read

Published:

This lesson is from datacamp Introduction to Plotly

dplyr

6 minute read

Published:

This lesson is from datacamp Introduction to Tidyverse

HTML Fundamentals

5 minute read

Published:

This lesson covers HTML Fundamentals.

CSS Fundamentals

6 minute read

Published:

This lesson covers CSS Fundamentals

Maths and Statistics

3 minute read

Published:

This post covers certain formulas useful for Deep Learning.

patents

Anti-fraud computer implemented method for financial card transaction

U.S. Patent Publication, 2015

The patent describes a safe card that that by default is in a blocked state so that no transcation can be made. The safe card can be activated for a limited time by an authorized user for the transaction.

Recommended citation: Aneja, N., and Aneja, S. "Anti-fraud computer implemented method for financial card transaction." U.S. Patent Publication 2015/0339,657, filed May 23, 2014, published Nov 26, 2015. https://patentimages.storage.googleapis.com/06/22/8e/674c30a57d0fe8/US20150339657A1.pdf

Method and System for Ad-hoc Social Networking and Profile Matching

U.S. Issued Patent, 2019

The patent describes a method to create a social network based on dynamic interests of users and using ad-hoc networking. A user of mobile device can identify another user who is close by and has similar interests. Dynamic interests of the users can be extracted or identified based on mobile usage, browsing history, places traveled, and other user actions. Users are notified if another user who is similar more than a threshold is present nearby. The system also facilitates communication between users by way of chatting, file sharing, image sharing etc.

Recommended citation: Aneja, N., and Gambhir, S. "Method and system for Ad-hoc Social Networking and Profile Matching." U.S. Patent 10,264,609, filed Dec 28, 2015, issued April 16, 2019. https://patentimages.storage.googleapis.com/0f/63/b5/a0d9ef4311bf34/US10264609B2.pdf

Instant messaging for mobile device with offline and online mode

U.S. Issued Patent, 2020

The discloosure presents unified instant messaging application/service that enables to concurrently send messages via online mode using internet and Offline or Airplane mode

Recommended citation: Aneja, S., Aneja. N., and Petra, I. "Instant messaging for mobile device with offline and online mode." U.S. Patent 10,834,035, filed Dec 28, 2015, issued Nov 10, 2020. https://patentimages.storage.googleapis.com/58/d4/f7/58ebe5626a9362/US10834035.pdf

portfolio

Behavior-Cloning

Published:

Implementation of convolutional neural network to clone driving behavior using Keras. The model outputs a steering angle to an autonomous vehicle.

NL-Augmenter

Published:

NL-Augmenter 🦎 → 🐍 A Framework for Task-Sensitive Natural Language Augmentation

publications

Reliable Ad-hoc On-demand Distance Vector Routing Protocol

Published in International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies (ICNICONSMCL06), 2006

The paper presents a co-operative security scheme called Reliable Ad hoc On-demand Distance Vector (RAODV) routing protocol based on local monitoring has been proposed to solve the problem of attack by malicious node as well as selfish behavior

Recommended citation: S. Khurana, N. Gupta and N. Aneja, "Reliable Ad-hoc On-demand Distance Vector Routing Protocol," International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies (ICNICONSMCL06), Morne, Mauritius, 2006, pp. 98-98, doi: 10.1109/ICNICONSMCL.2006.183. https://ieeexplore.ieee.org/document/1628344

Minimum Exposed Path to the Attack (MEPA) in Mobile Ad Hoc Network (MANET)

Published in Sixth International Conference on Networking (ICN07), 2007

The paper presents algorithm to determine a path which is farthest from the endangered nodes in mobile ad-hoc social network.

Recommended citation: S. Khurana, N. Gupta and N. Aneja, "Minimum Exposed Path to the Attack (MEPA) in Mobile Ad Hoc Network (MANET)," Sixth International Conference on Networking (ICN07), Martinique, 2007, pp. 16-16, doi: 10.1109/ICN.2007.57. https://ieeexplore.ieee.org/document/4196209

Various Issues in Ad-hoc Social Networks ( ASN )

Published in National Conference on Recent Trends in Computer Science and Information Technology (RTCSIT), 2012, Echelon Institute of Technology, 2012

The paper presents research challenges in ad-hoc social network.

Recommended citation: Aneja, N. and Gambhir, S. (2012) "Various Issues in Ad-hoc Social Networks", National Conference on Recent Trends in Computer Science and Information Technology (RTCSIT), 2012, Echelon Institute of Technology Faridabad Haryana India, 6-9

Ad-hoc Social Network: A Comprehensive Survey

Published in International Journal of Scientific and Engineering Research, 2014

The paper presents research challenges in ad-hoc social network.

Recommended citation: Gambhir, S. and Aneja, N. (2013) International Journal of Scientific and Engineering Research, Vol 4, Issue 8, August 2013

Geo-Social Profile Matching Algorithm for Dynamic Interests in Ad-Hoc Social Network

Published in Social Networking, 2014

The paper presents a profile-matching algorithm for automatically building a user profile based on dynamic GPS (Global Positing System) location and browsing history of users.

Recommended citation: Aneja, N. and Gambhir, S. (2014) Geo-Social Profile Matching Algorithm for Dynamic Interests in Ad-Hoc Social Network. Social Networking, 3, 240-247. doi: 10.4236/sn.2014.35029 https://scirp.org/journal/PaperInformation.aspx?PaperID=51108

Geo-Social Semantic Profile Matching Algorithm for Dynamic Interests in Ad-hoc Social Network

Published in IEEE International Conference on Computational Intelligence & Communication Technology, 2015

This paper provides an algorithm to semantically match users profiles based on geographic location and dynamic interests.

Recommended citation: N. Aneja and S. Gambhir, "Geo-Social Semantic Profile Matching Algorithm for Dynamic Interests in Ad-hoc Social Network," 2015 IEEE International Conference on Computational Intelligence and Communication Technology, Ghaziabad, 2015, pp. 354-358, doi: 10.1109/CICT.2015.50. https://ieeexplore.ieee.org/document/7078725

Need of ad-hoc social network based on users dynamic interests

Published in International Conference on Soft Computing Techniques and Implementations (ICSCTI), 2015

This paper presents survey results for need of ad-hoc social network. Results indicate that users prefer 75 percent of average profile similarity to connect nearby users.

Recommended citation: S. Gambhir, N. Aneja and S. Mangla, "Need of ad-hoc social network based on users dynamic interests," 2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI), Faridabad, 2015, pp. 52-56, doi: 10.1109/ICSCTI.2015.7489562. https://ieeexplore.ieee.org/document/7489562

Software design for social profile matching algorithm to create ad-hoc social network on top of Android

Published in 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 2016

This paper proposes software design and implementation of profile matching algorithm to create ad-hoc social network on top of Android.

Recommended citation: S. Gambhir, S. Mangla and N. Aneja, "Software design for social profile matching algorithm to create ad-hoc social network on top of Android," 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, 2016, pp. 3450-3454. https://ieeexplore.ieee.org/document/7724906

Middleware Architecture for Ad-hoc Social Network

Published in Wireless Personal Communications, 2016

The paper presents middleware architecture for Ad-hoc Social Network that provides software developers a platform for developing mobile apps that enable social connections. The proposed architecture is a software suite that includes an application layer, transport layer, ad-hoc social layer and ad-hoc communication layer.

Recommended citation: Aneja, N., and Gambhir, S. (2016), "Middleware Architecture for Ad-hoc Social Network" Research Journal of Applied Sciences, Engineering and Technology https://maxwellsci.com/jp/mspabstract.php?jid=RJASET&doi=rjaset.13.3342

Internet of Things (IOT) Security Best Practices

Published in IEEE, 2017

The purpose of this paper is to present a set of well-investigated Internet of Things (IoT) security guidelines and best practices that others can use as a basis for future standards, certifications, laws, policies and/or product ratings.

Recommended citation: Glenn A. Fink, Mohammed Aledhari, Jared Bielby, Rajesh Nighot, Sukanya Mandal, Nagender Aneja, Chris Hrivnak, Lucian Cristache, "Internet of Things (IOT) Security Best Practices" IEEE Internet Technology Policy Community White Paper https://internetinitiative.ieee.org/images/files/resources/white_papers/internet_of_things_may_2017.pdf

Protecting Internet Traffic: Security Challenges and Solutions

Published in IEEE, 2017

The white paper presents a set of well investigated internet traffic security guidelines and best practices which others can use as a basis for future standards, certifications, laws, policies and/or product ratings.

Recommended citation: Mohammed Aledhari, Sukanya Mandal, Nagender Aneja, Mikael Dautrey, Rajesh Nighot, Prasad Mantri, Jared Bielby, "Protecting Internet Traffic: Security Challenges and Solutions", IEEE Internet Technology Policy Community White Paper https://internetinitiative.ieee.org/images/files/resources/white_papers/protecting_internet_traffic_may_2017.pdf

Security and Privacy: Challenges and Defending Solutions for NoSQL Data Stores

Published in NoSQL Database for Storage and Retrieval of Data in Cloud, 2017

The chapter discusses security of traditional database systems using an example of PostgreSQL database system. It explains RBAC and its variations with an example of PostgreSQL and describes the basic RBAC model in context of MongoDB.

Recommended citation: Aneja S, Aneja N. "Security and Privacy: Challenges and Defending Solutions for NoSQL Data Stores", book chapter in NoSQL Database for Storage and Retrieval of Data in Cloud, 237-250., Taylor & Francis Group, CRC Press 2017, Print ISBN: 978-1-4987-8436-8, eBook ISBN: 978-1-4987-8437-5 https://www.taylorfrancis.com/books/e/9781315155579/chapters/10.1201/9781315155579-13

Social Profile Aware AODV Protocol for Ad-Hoc Social Networks

Published in Wireless Personal Communications, 2017

The paper proposes contextual social profile aware routing protocol that allows users to use social networking applications using social routing protocol.

Recommended citation: Aneja, N., Gambhir, S. Social Profile Aware AODV Protocol for Ad-Hoc Social Networks. Wireless Pers Commun 97, 4161–4182 (2017). https://doi.org/10.1007/s11277-017-4718-x https://link.springer.com/article/10.1007%2Fs11277-017-4718-x

Piecewise Maximal Similarity for Ad-hoc Social Networks

Published in Wireless Personal Communications, 2017

The paper proposes Piecewise Maximal Similarity metric to match users profiles. The proposed metric is more effective to measure similarity than cosine similarity based on computations on real data.

Recommended citation: Gambhir, S., Aneja, N. and De Silva, L.C. Piecewise Maximal Similarity for Ad-hoc Social Networks. Wireless Pers Commun 97, 3519–3529 (2017). https://doi.org/10.1007/s11277-017-4683-4 https://link.springer.com/article/10.1007/s11277-017-4683-4

Options and Challenges in Providing Universal Access

Published in IEEE, 2018

The white paper presents obstacles and challenges towards achieving universal Internet access.

Recommended citation: Prasad Mantri, Mohammed Aledhari, Helen Anne Hicks, Rajesh Nighot, Nagender Aneja, Sukanya Mandal, Ali Kashif Bashir, Jay Wack, Jared Bielby, "Options and Challenges in Providing Universal Access", IEEE Internet Technology Policy Community White Paper https://internetinitiative.ieee.org/images/files/resources/white_papers/OPTIONS-AND-CHALLENGES-IN-PROVIDING-UNIVERSAL-ACCESS-v180418.pdf

IoT Device Fingerprint using Deep Learning

Published in IEEE International Conference on Internet of Things and Intelligence System (IOTAIS), 2018

This work proposes identifying IoT devices based on Inter-Arrival-Time of Packets and improves efficiency to identify a device by feeding 100 IATs in the form of a graph to CNN

Recommended citation: S. Aneja, N. Aneja and M. S. Islam (2018). " IoT Device Fingerprint using Deep Learning." IEEE International Conference on Internet of Things and Intelligence System (IOTAIS), Bali, 2018, pp. 174-179, doi: 10.1109/IOTAIS.2018.8600824. https://ieeexplore.ieee.org/abstract/document/8600824

Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases

Published in Towards Causal, Explainable and Universal Medical Visual Diagnosis, CVPR Workshop, 2019

The proposed model trained with a semi-supervised learning approach by using pseudo labels on PCam-level significantly leads to better performances to strong CNN baseline on the AUC metric.

Recommended citation: Jaiswal, A. K., Panshin, I., Shulkin, D., Aneja, N., & Abramov, S. (2019). " Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases." arXiv preprint arXiv:1906.09587. https://s1155026040.github.io/mvd-2019-cvpr-workshop/

Transfer Learning using CNN for Handwritten Devanagari Character Recognition

Published in IEEE International Conference on Advances in Information Technology (ICAIT), 2019

This paper presents an analysis of pre-trained models to recognize handwritten Devanagari alphabets using transfer learning for Deep Convolution Neural Network (DCNN).

Recommended citation: N. Aneja and S. Aneja, "Transfer Learning using CNN for Handwritten Devanagari Character Recognition," 2019 1st International Conference on Advances in Information Technology (ICAIT), Chikmagalur, India, 2019, pp. 293-296, c. https://ieeexplore.ieee.org/document/8987286

Neural Machine Translation model for University Email Application

Published in International Conference on Natural Language Processing (ICNLP 2020), 2020

A regional vocabulary-based application-oriented Neural Machine Translation (NMT) model is proposed over the data set of emails.

Recommended citation: Sandhya Aneja, Siti Nur Afikah Bte Abdul Mazid, Nagender Aneja, Neural Machine Translation model for University Email Application, International Conference on Natural Language Processing (ICNLP 2020), July 11-13, 2020, https://arxiv.org/abs/2007.16011 https://arxiv.org/abs/2007.16011

Network Traffic Analysis based IoT Device Identification

Published in 4th International Conference on Big Data and Internet of Things, BDIOT 2020, Singapore, 2020

TCP/IP packet header features to fingerprint a device for device identification based on its communication pattern.

Recommended citation: Chowdhury R., Aneja S., Aneja N., and Abas E. Network Traffic Analysis based IoT Device Identification. In Proceedings of the 2020 the 4th International Conference on Big Data and Internet of Things (BDIOT 2020). Association for Computing Machinery, New York, NY, USA, 79–89. DOI:https://doi.org/10.1145/3421537.3421545 https://dl.acm.org/doi/abs/10.1145/3421537.3421545

Recent Advances in Ad-Hoc Social Networking: Key Techniques and Future Research Directions

Published in Wireless Personal Communications, 2020

This paper presents survey and future directions in four areas of establishing ad-hoc social network using mobile ad-hoc social network (MANET) that includes architecture or implementation features, Profile Management of users, Similarity Metric, and Routing Protocols. The survey presents the need to provide social applications over MANET, optimizing profile matching algorithms of users, and context aware routing protocols.

Recommended citation: Aneja, N., Gambhir, S. Recent Advances in Ad-Hoc Social Networking: Key Techniques and Future Research Directions. Wireless Pers Commun (2020). https://doi.org/10.1007/s11277-020-07942-7 https://link.springer.com/article/10.1007/s11277-020-07942-7

Detecting Fake News with Machine Learning

Published in International Conference on Deep Learning, Artificial Intelligence and Robotics, (ICDLAIR), 2021

This paper provides algorithm to detect Fake News and in particular the paper proposes most important language features that can determine if the news content is fake or real.

Recommended citation: Aneja N., Aneja S. (2021) Detecting Fake News with Machine Learning. Conference Proceedings of ICDLAIR2019. ICDLAIR 2019. Lecture Notes in Networks and Systems, vol 175. Springer, Cham. https://doi.org/10.1007/978-3-030-67187-7_7 https://link.springer.com/chapter/10.1007/978-3-030-67187-7_7

Packet-level and IEEE 802.11 MAC frame-level network traffic traces data of the D-Link IoT devices

Published in Data in Brief, 2021

This paper presents network traffic traces data of D-Link IoT devices from packet and frame levels.

Recommended citation: Rajarshi Roy Chowdhury, Sandhya Aneja, Nagender Aneja, and Pg Emeroylariffion Abas (2021). " Packet-level and IEEE 802.11 MAC frame-level network traffic traces data of the D-Link IoT devices." Data in Brief, Volume 37, Aug 2021, 107208, ISSN 2352-3409, doi: 10.1109/IOTAIS.2018.8600824. https://www.sciencedirect.com/science/article/pii/S2352340921004923

Device Fingerprinting using Deep Convolutional Neural Networks

Published in International Journal of Communication Networks and Distributed Systems, 2021

In this work, the idea is to define a device-specific unique fingerprint by analysing solely inter-arrival time of packets as a feature to identify a device.

Recommended citation: Sandhya Aneja, Nagender Aneja, Bharat K. Bhargava, Rajarshi Roy Chowdhury, "Device Fingerprinting using Deep Convolutional Neural Networks", International Journal of Communication Networks and Distributed Systems. https://doi.org/10.1504/IJCNDS.2022.10041894. https://doi.org/10.1504/IJCNDS.2022.10041894

Systematic Patent Review of Nanoparticles in Drug Delivery and Cancer Therapy in the Last Decade

Published in Recent Advances in Drug Delivery and Formulation, 2021

The main objective of the patent review is to survey and review patents from the past ten years that are related to the two particular areas of nanomedicines.

Recommended citation: Ali Hazis Umairah Nur, Aneja Nagender, Rajabalaya Rajan, David Rani Sheba, "Systematic Patent Review of Nanoparticles in Drug Delivery and Cancer Therapy in the Last Decade", Recent Advances in Drug Delivery and Formulation 2021; 15(1). https://doi.org/10.2174/1872211314666210521105534. https://www.eurekaselect.com/193475

NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation

Published in arXiv, 2021

In this paper, we present NL-Augmenter, a new participatory Python-based natural language augmentation framework which supports the creation of both transformations (modifications to the data) and filters (data splits according to specific features).

Recommended citation: Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahendiran, Simon Mille, Ashish Srivastava, Samson Tan, Tongshuang Wu, Jascha Sohl-Dickstein, Jinho D. Choi, Eduard Hovy, Ondrej Dusek, Sebastian Ruder, Sajant Anand, Nagender Aneja, Rabin Banjade, Lisa Barthe, Hanna Behnke, Ian Berlot-Attwell, Connor Boyle, Caroline Brun, Marco Antonio Sobrevilla Cabezudo, Samuel Cahyawijaya, Emile Chapuis, Wanxiang Che, Mukund Choudhary, Christian Clauss, Pierre Colombo, Filip Cornell, Gautier Dagan, Mayukh Das, Tanay Dixit, Thomas Dopierre, Paul-Alexis Dray, Suchitra Dubey, Tatiana Ekeinhor, Marco Di Giovanni, Rishabh Gupta, Rishabh Gupta, Louanes Hamla, Sang Han, Fabrice Harel-Canada, Antoine Honore, Ishan Jindal, Przemyslaw K. Joniak, Denis Kleyko, Venelin Kovatchev, Kalpesh Krishna, Ashutosh Kumar, Stefan Langer, Seungjae Ryan Lee, Corey James Levinson, Hualou Liang, Kaizhao Liang, Zhexiong Liu, Andrey Lukyanenko, Vukosi Marivate, Gerard de Melo, Simon Meoni, Maxime Meyer, Afnan Mir, Nafise Sadat Moosavi, Niklas Muennighoff, Timothy Sum Hon Mun, Kenton Murray, Marcin Namysl, Maria Obedkova, Priti Oli, Nivranshu Pasricha, Jan Pfister, Richard Plant, Vinay Prabhu, Vasile Pais, Libo Qin, Shahab Raji, Pawan Kumar Rajpoot, Vikas Raunak, Roy Rinberg, Nicolas Roberts, Juan Diego Rodriguez, Claude Roux, Vasconcellos P. H. S., Ananya B. Sai, Robin M. Schmidt, Thomas Scialom, Tshephisho Sefara, Saqib N. Shamsi, Xudong Shen, Haoyue Shi, Yiwen Shi, Anna Shvets, Nick Siegel, Damien Sileo, Jamie Simon, Chandan Singh, Roman Sitelew, Priyank Soni , Taylor Sorensen, William Soto, Aman Srivastava, KV Aditya Srivatsa, Tony Sun, Mukund Varma T, A Tabassum, Fiona Anting Tan, Ryan Teehan, Mo Tiwari, Marie Tolkiehn, Athena Wang, Zijian Wang, Gloria Wang, Zijie J. Wang, Fuxuan Wei, Bryan Wilie, Genta Indra Winata, Xinyi Wu, Witold Wydmański, Tianbao Xie, Usama Yaseen, M. Yee, Jing Zhang, Yue Zhang, "NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation", https://arxiv.org/abs/2112.02721. https://arxiv.org/abs/2112.02721

Collaborative adversary nodes learning on the logs of IoT devices in an IoT network

Published in 14th International Conference on COMmunication Systems & NETworkS (COMSNETS 2022), 2021

The network traffic of IoT devices can be analyzed using AI techniques. The Adversary Learning (AdLIoTLog) model is proposed using Recurrent Neural Network (RNN) with attention mechanism on sequences of network events in the network traffic.

Recommended citation: Sandhya Aneja, Melanie Ang Xuan En, Nagender Aneja, "Collaborative adversary nodes learning on the logs of IoT devices in an IoT network", 14th International Conference on COMmunication Systems & NETworkS (COMSNETS 2022). https://arxiv.org/abs/2112.12546. https://arxiv.org/abs/2112.12546

research

Deep Learning Papers

Published:

This is a collection of some deep learning projects/papers.

Deep Learning

Published:

This is the collection of posts on Machine Learning, Deep Learning, and Python Programming.

CS Journals

Published:

This is a list of Scopus indexed Journals of Computer Science.

Available Projects

Published:

This post covers projects available for FYP/Research students. Students may also contact with their project proposals.

talks

Introduction to Deep Learning

Published:

Keynote Speech at International Conference (Virtual) Recent Trends in Computing (ICRTC-2021), Compucom Institute of Information Technology & Management, Jaipur, India

teaching

Internet Programming and Development (SS-2201)

Bachelor of Science (Computer Science), Universiti Brunei Darussalam, Digital Science, 2021

Students will learn the latest technologies including Python in the field of Internet Programming.

Workshop on Patents

Bachelor/Master's Degree, Universiti Brunei Darussalam, 2021

Students will learn the Introduction to Patents and Patents Search.

Introduction to Data Analytics (ZD-1201)

Bachelor of Digital Science, Universiti Brunei Darussalam, Digital Science, 2022

Students will learn the latest technologies including Tableau and Python in the field of data analytics.

Digital Transformation

Short Term Course for visiting students of Singapore Management University, School of Digital Science, Universiti Brunei Darussalam, 2022

Students will learn to use digital technologies to create new or modify existing businesses in this short term course.