Dr. Nagender Aneja


Senior Manager, Office of AVC (Innovation and Enterprise) and Researcher, Institute of Applied Data Analytics



I did PhD in Computer Engineering and M.E in Computer Technology and Applications. My current research interests include Artificial Intelligence, in particular, I am working in Deep Learning, Computer Vision, Deep Reinforcement Learning, and Natural Language Processing.

I recently participated in Histopathologic Cancer Detection research competition, https://www.kaggle.com/naneja and was ranked 65th among 1157 - top 6%. My other research projects include ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection; Automated Plant Species Recognition, http://github.com/naneja/plants Device Fingerprinting for Access Control, https://github.com/naneja/device-fingerprinting

I have also been granted a US Patent http://innovation.ubd.edu.bn/pats/U54-G-US.pdf

I developed Expert Directory (https://expert.ubd.edu.bn) for Universiti Brunei Darussalam where faculty members can self-register and create and update profile. The system has been designed to fetch the list of publications and citation information automatically so that profile is automatically updated without human intervention.

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


Ph.D. Computer Engineering
M.E. Computer Technology and Applications
Master of Management Studies
M.Sc. Mathematics
B.Sc. Physics, Chemistry, Mathematics


Deep Learning, Computer Vision, Deep Reinforcement Learning, Natural Language Processing


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
for 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
for 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
for Remote Research Collaboration

Scopus Publications


Google Scholar Citations


Google Scholar h-index


Google Scholar i10-index

Scopus Publications


1. Amit Kumar Jaiswal, Ivan Panshin, Dimitrij Shulkin, Nagender Aneja, and Samuel Abramov, "Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases," https://s1155026040.github.io/mvd-2019-cvpr-workshop/

2. Nagender Aneja, Sapna Gambhir, "Profile-Based Ad Hoc Social Networking Using Wi-Fi Direct on the Top of Android", Mobile Information Systems, 2018, http://hindawi.com/journals/misy/2018/9469536/ (SCIE Indexed)

3. Nagender Aneja, Sapna Gambhir, "Social Profile Aware AODV Protocol for Ad-hoc Social Networks", Wireless Personal Communications, 2017, http://link.springer.com/article/10.1007/s11277-017-4718-x (SCIE Indexed)

4. Sapna Gambhir, Nagender Aneja, Liyanage Chandratilake De Silva "Piecewise Maximal Similarity for Ad-hoc Social Networks", Wireless Personal Communications, 2017, http://link.springer.com/article/10.1007/s11277-017-4683-4 (SCIE Indexed)


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, http://kaggle.com/naneja
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