Dr. Nagender Aneja

Verified email at ieee.org

Researcher, Institute of Applied Data Analytics
Universiti Brunei Darussalam



                                             

rid108
http://researchid.co/naneja

I am a Deep Learning and Patent Professional. I recently developed projects in Histopathologic Cancer Detection, (ranked 65th among 1157 - top 6%) ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection, https://github.com/naneja/isic2018 Automated Plant Species Identification, https://github.com/naneja/plants Device Fingerprinting to identify network devices using deep learning https://github.com/naneja/device-fingerprinting Applied CNN-LSTM for Image Captioning, Identified Key facial points. Most of my work is available at https://github.com/naneja

I am also the founder and developer of https://researchid.co

EDUCATION

Ph.D. Computer Engineering
M.E. Computer Technology and Applications

RESEARCH INTERESTS

Deep Learning
Computer Vision

FUTURE PROJECTS

DEEP LEARNING TRAINING WITH LIMITED DATA

Deep learning needs lots of data for training; however, in some industrial applications, the significant amount of data may not be available, limiting the deep learning approach. Modern techniques like transfer learning and generative adversarial networks show some hope to solve this challenge. The objective of the project is to propose new techniques for deep learning training.


Applications Invited
Remote Research Collaboration

DEEP LEARNING SECURITY

Deep-learning networks are susceptible to butterfly effect wherein small alterations in the input data can point to drastically distinctive outcomes, making the deep learning network inherently volatile. Thus, the output of deep learning network may be controlled by altering its input or by adding noise. Research has shown that it is possible to fool the deep learning network by adding an imperceptible amount of noise in the input.


Applications Invited
Remote Research Collaboration

GENERATIVE ADVERSARIAL NETWORKS - REVERSE IMAGE CAPTIONING - TEXT TO IMAGE AND SCALING GAN TRAINING WITH BATCH SIZE

Generative Adversarial Networks may have potential to solve the text-to-image problem, but there are challenges in using GANs for NLP. Image classification have got benefitted with large mini-batches and one of the open question the question https://distill.pub/2019/gan-open-problems/#batchsize is if they can also help to scale GANs


Applications Invited
Remote Research Collaboration
11

Scopus Publications

120

Google Scholar Citations

7

Google Scholar h-index

4

Google Scholar i10-index

Scopus Publications

RECENT SCHOLAR PUBLICATIONS

  • Transfer Learning using CNN for Handwritten Devanagari Character Recognition
    N Aneja, S Aneja
    arXiv preprint arXiv:1909.08774 2019

  • Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases
    AK Jaiswal, I Panshin, D Shulkin, N Aneja, S Abramov
    arXiv preprint arXiv:1906.09587 2019

  • Method and system for ad-hoc social networking and profile matching
    N Aneja, S Gambhir
    US Patent 10,264,609 2019

  • IoT Device Fingerprint using Deep Learning
    S Aneja, N Aneja, MS Islam
    2018 IEEE International Conference on Internet of Things and Intelligence 2018

  • Options and challenges in Providing Universal Access
    M Aledhari, N Aneja, AK Bashir, R Bennett, J Bielby, HA Hicks, R Johnson, ...
    Retrieved from Internet Initiative IEEE: https://internetinitiative. ieee 2018

  • Profile-Based Ad Hoc Social Networking Using Wi-Fi Direct on the Top of Android
    N Aneja, S Gambhir
    Mobile Information Systems 2018 2018

  • Piecewise Maximal Similarity for Ad-hoc Social Networks
    S Gambhir, N Aneja, LC De Silva
    Wireless Personal Communications, 1-11 2017

  • Social Profile Aware AODV Protocol for Ad-Hoc Social Networks
    N Aneja, S Gambhir
    Wireless Personal Communications, 1-22 2017

  • Security and Privacy: Challenges and Defending Solutions for NoSQL Data Stores
    S Aneja, N Aneja
    NoSQLDatabase for Storage and Retrieval of Data in Cloud, 237-250 2017

  • Options and Challenges in Providing Universal Access
    P Mantri, M Aledhari, HA Hicks, R Nighot, N Aneja, S Mandal, AK Bashir, ...
    IEEE Internet Technology Policy Community White Paper 2017

  • Internet of Things (IoT) Security Best Practices
    GA Fink, M Aledhari, J Bielby, R Nighot, S Mandal, N Aneja, C Hrivnak, ...
    IEEE Internet Technology Policy Community White Paper 2017

  • Protecting Internet Traffic: Security Challenges and Solutions
    M Aledhari, S Mandal, N Aneja, M Dautrey, R Nighot, P Mantri, J Bielby
    IEEE Internet Technology Policy Community White Paper 2017

  • Options and Challenges in Providing Universal Access to the Internet: Part Two
    IJBI Helen Anne Hicks (North Park University), Rajesh Nighot (IEEE ...
    Newsletter 2017 2017

  • Middleware Architecture for Ad-hoc Social Network
    N Aneja, S Gambhir
    Research Journal of Applied Sciences, Engineering and Technology 13 (9), 690-695 2016

  • Software Design for Social Profile Matching Algorithm to create Ad-hoc social network on top of Android
    S Gambhir, S Mangla, N Aneja
    IEEE International Conference on Computing for Sustainable Global Development 2016

  • Anti-fraud computer implemented method for financial card transaction
    N Aneja, S Aneja
    US Patent App. 2015/0/339,657 2015

  • Geo-Social Semantic Profile Matching Algorithm for Dynamic Interests in Ad-hoc Social Network
    N Aneja, S Gambhir
    IEEE International Conference on Computational Intelligence & Communication 2015

  • Need of Ad-hoc social network based on Users’ Dynamic Interests
    S Gambhir, N Aneja, S Mangla
    IEEE International Conference on Soft Computing Techniques & Implementation 2015

  • Method and System for Profile Matching in Social Networks
    N Aneja, S Gambhir
    IN Patent App. 3105/DEL/2,015 2015

  • Geo-Social Profile Matching Algorithm for Dynamic Interests in Ad-Hoc Social Network
    N Aneja, S Gambhir
    Social Networking 3 (5), 240-247 2014

MOST CITED SCHOLAR PUBLICATIONS

  • Reliable ad-hoc on-demand distance vector routing protocol
    S Khurana, N Gupta, N Aneja
    IEEE International Conference on Networking, International Conference on 2006
    Citations: 38

  • Method and system for ad-hoc social networking and profile matching
    N Aneja, S Gambhir
    US Patent 10,264,609 2019
    Citations: 20

  • Minimum exposed path to the attack (MEPA) in mobile ad hoc network (MANET)
    S Khurana, N Gupta, N Aneja
    Sixth International Conference on Networking (ICN'07), 16-16 2007
    Citations: 11

  • Ad-hoc Social Network: A Comprehensive Survey
    S Gambhir, N Aneja
    International Journal of Scientific & Engineering Research 4 (8) 2013
    Citations: 10

  • Geo-Social Profile Matching Algorithm for Dynamic Interests in Ad-Hoc Social Network
    N Aneja, S Gambhir
    Social Networking 3 (5), 240-247 2014
    Citations: 9

  • Various Issues in Ad-hoc Social Networks
    N Aneja, S Gambhir
    National Conference on Recent Trends in Computer Science and Information 2012
    Citations: 8

  • Geo-Social Semantic Profile Matching Algorithm for Dynamic Interests in Ad-hoc Social Network
    N Aneja, S Gambhir
    IEEE International Conference on Computational Intelligence & Communication 2015
    Citations: 7

  • IoT Device Fingerprint using Deep Learning
    S Aneja, N Aneja, MS Islam
    2018 IEEE International Conference on Internet of Things and Intelligence 2018
    Citations: 4

  • Piecewise Maximal Similarity for Ad-hoc Social Networks
    S Gambhir, N Aneja, LC De Silva
    Wireless Personal Communications, 1-11 2017
    Citations: 3

  • Need of Ad-hoc social network based on Users’ Dynamic Interests
    S Gambhir, N Aneja, S Mangla
    IEEE International Conference on Soft Computing Techniques & Implementation 2015
    Citations: 3

  • Profile-Based Ad Hoc Social Networking Using Wi-Fi Direct on the Top of Android
    N Aneja, S Gambhir
    Mobile Information Systems 2018 2018
    Citations: 2

  • Transfer Learning using CNN for Handwritten Devanagari Character Recognition
    N Aneja, S Aneja
    arXiv preprint arXiv:1909.08774 2019
    Citations: 1

  • Options and challenges in Providing Universal Access
    M Aledhari, N Aneja, AK Bashir, R Bennett, J Bielby, HA Hicks, R Johnson, ...
    Retrieved from Internet Initiative IEEE: https://internetinitiative. ieee 2018
    Citations: 1

  • Middleware Architecture for Ad-hoc Social Network
    N Aneja, S Gambhir
    Research Journal of Applied Sciences, Engineering and Technology 13 (9), 690-695 2016
    Citations: 1

  • Software Design for Social Profile Matching Algorithm to create Ad-hoc social network on top of Android
    S Gambhir, S Mangla, N Aneja
    IEEE International Conference on Computing for Sustainable Global Development 2016
    Citations: 1

  • Verification of Forecasts from High Resolution Numerical Weather Prediction Model
    N Aneja, T George
    Research Journal of Applied Sciences, Engineering and Technology 8 (10 2014
    Citations: 1

Publications

Nagender Aneja and Sapna Gambhir, "Profile-based Ad hoc social networking using Wi-Fi direct on the top of android", Mobile Information Systems, 2018, Hindawi

Nagender Aneja and Sapna Gambhir, "Social Profile Aware AODV Protocol for Ad-Hoc Social Networks", Wireless Personal Communications, 2017, Springer

Sapna Gambhir, Nagender Aneja and Liyanage Chandratilake de Silva, "Piecewise Maximal Similarity for Ad-hoc Social Networks", Wireless Personal Communications, 2017, Springer New York LLC

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

Patents
Nagender Aneja and Sapna Gambhir, "Method and System for Ad-Hoc Social Networking and Profile Matching" US 10,264,609 Granted Apr 16, 2019

Histopathologic Cancer Detection
I am participating in this Kaggle competition to create an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans. The data for this competition is a slightly modified version of the PatchCamelyon (PCam) benchmark dataset. Currently, my rank is in the top 6% (65th from 1157).

ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection, http://github.com/naneja/isic2018
The International Skin Imaging Collaboration (ISIC) is an international effort to improve melanoma diagnosis, sponsored by the International Society for Digital Imaging of the Skin (ISDIS). The ISIC Archive contains the largest publicly available collection of quality controlled dermoscopic images of skin lesions. The goal of this challenge is to help participants develop image analysis tools to enable the automated diagnosis of melanoma from dermoscopic images. This challenge is broken into three separate tasks: Task 1: Lesion Segmentation Task 2: Lesion Attribute Detection Task 3: Disease Classification. I participated in Task 3 after the challenge is over and was able to get around 70% accuracy.

Automated Plant Species Recognition, http://github.com/naneja/plants
We created a dataset of 740 images from 11 different plants species and the dataset was divided into 596 images for training the model and 144 images used for testing the model. I implemented transfer learning using Alexnet with PyTorch. The following hyperparameters Arch = ’alexnet’; Batch = 32; Hidden_units = 4096; Epochs = 200; Dropout = 0.5; Learning Rate = 0.01, Optimizer = SGD, Momentum = 0.9 provided best accuracy of 91.7%. We are looking for external funding to develop the project at International Level where we can have a database of medicinal plants from multiple countries.

Device Fingerprinting for Access Control, https://github.com/naneja/device-fingerprinting
Device Fingerprinting (DFP) is a technique to identify devices using Inter-Arrival Time (IAT) of packets and without using any other unique identifier. Our experiments include generating graphs of IATs of 100 and 1000 packets and using Convolutional Neural Network on the generated graphs
to identify a device. We implemented CNN on the IATs graphs for two datasets. The first data set was collected by us for two devices and another dataset is standard public dataset available at http://crawdad.org/gatech/fingerprinting/20140609. We achieved 86% accuracy in the first set and 95% accuracy in the second dataset. Initial results have been published at http://ieeexplore.ieee.org/abstract/document/8600824

STARTUP

Founder and Developer, http://ResearchID.co