Dr. Nagender Aneja


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



I am a Deep Learning, Computer Vision and Patent Professional and have a passion for the applications in Medical Imaging. I participated in Histopathologic Cancer Detection, https://www.kaggle.com/naneja [achieved the rank of 65th among 1157 (top 6%)] and ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection, https://github.com/naneja/isic2018 . I also developed a prototype for Automated Plant Species Identification, https://github.com/naneja/plants and implemented Device Fingerprinting to identify network devices using deep learning https://github.com/naneja/device-fingerprinting I also did a project to apply CNN-LSTM for Image Captioning.

I have 26 Research Publications including 11 Scopus-indexed and three SCIE Indexed. One of my US Patents has also been allowed in the area of ad-hoc social networking.

I developed Autobot that does web scrapping from online public Patents Databases to create the desired Prior Art Search Report and developed Expert Directory (http://expert.ubd.edu.bn) for Universiti Brunei Darussalam where faculty members can self-register and update their individual 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 https://expert.ubd.edu.bn/nagender.aneja The expert system is also available at https://researchid.co for public use freely.

I have also expertise in writing patent claims and responding to the patent examiner on the patent objections for US Patent Applications.


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
Applications of Computer Vision in Medical Imaging


Scopus Publications


Google Scholar Citations


Google Scholar h-index


Google Scholar i10-index

Scopus Publications


1. 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)

2. 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)

3. 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)


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