Dr Nagender Aneja

Verified email at gmail.com

Asst. Prof. Digital Science
Universiti Brunei Darussalam



                                                     

https://researchid.co/naneja

Dr Nagender Aneja is working as Asst. Prof. and Programme Leader (Computer science) at School of Digital Science, Universiti Brunei Darussalam. He did his PhD in Computer Engineering from J.C. Bose University of Science and Technology, YMCA and M.E. Computer Technology and Applications from Delhi College of Engineering. He is currently working in area of Deep Learning. He has 20+ years of experience that includes five years of Industry Experience at CPA Global for Microsoft Patent Research Services. He has done several process innovations including developing automation tools for patent analysis at CPA Global and developed expert directory for Universiti Brunei Darussalam. He has been awarded Brunei ICT Award 2016 and two patents from USPTO. He is also founder and developer of ResearchID.co.

Please visit https://naneja.github.io/ for more information

EDUCATION

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

RESEARCH INTERESTS

Deep Learning
Computer Vision
Machine Learning

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
22

Scopus Publications

270

Scholar Citations

10

Scholar h-index

11

Scholar i10-index

Scopus Publications

  • Transfer learning for cancer diagnosis in histopathological images
    Sandhya Aneja, Nagender Aneja, Pg Emeroylariffion Abas, and Abdul Ghani Naim

    IAES International Journal of Artificial Intelligence, ISSN: 20894872, eISSN: 22528938, Pages: 129-136, Published: March 2022 Institute of Advanced Engineering and Science
    Transfer learning allows us to exploit knowledge gained from one task to assist in solving another but relevant task. In modern computer vision research, the question is which architecture performs better for a given dataset. In this paper, we compare the performance of 14 pre-trained ImageNet models on the histopathologic cancer detection dataset, where each model has been configured as naive model, feature extractor model, or fine-tuned model. Densenet161 has been shown to have high precision whilst Resnet101 has a high recall. A high precision model is suitable to be used when follow-up examination cost is high, whilst low precision but a high recall/sensitivity model can be used when the cost of follow-up examination is low. Results also show that transfer learning helps to converge a model faster.

  • An Inspection of MANET'S Scenario using AODV, DSDV and DSR Routing Protocols
    Sandeep Singh, Shalini Bhaskar Bajaj, Khushboo Tripathi, and Nagendra Aneja

    Proceedings of 2nd International Conference on Innovative Practices in Technology and Management, ICIPTM 2022, Pages: 707-712, Published: 2022 IEEE
    In this paper the Mobile Ad Hoc Network (MANET) was considered for analyzing the performance of Destination Sequenced Distance Vector (DSDV) of Proactive class and Ad Hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing Protocol (DSR) of Reactive class. The protocols were simulated using the NS-2 (Network Simulator 2.35) package on Linux 12.04. The paper focuses on performance parameters e.g. Packet size, Speed, Packet rate, Transmission Control Protocol (TCP) types and Number of Packets and energy in the network. Simulation results shows that DSR gives better performance as compared to AODV and DSDV. The results were compared for inspection of packet delivery rate, % Lost packets, throughput and Jitter on varying Packet size, TCP types, and the number of packets in queue by changing packet size. The implementation study can further extend by applying artificial algorithms in MANET for enhancing the better results in presence of any type of attacks too.

  • Device fingerprinting using deep convolutional neural networks
    Sandhya Aneja, Nagender Aneja, Bharat Bhargava, and Rajarshi Roy Chowdhury

    International Journal of Communication Networks and Distributed Systems, ISSN: 17543916, eISSN: 17543924, Pages: 171-198, Published: 2022 Inderscience Publishers
    Device fingerprinting is a problem of identifying a network device using network traffic data to secure against cyber-attacks. Automated device classification from a large set of network traffic features space is challenging for the devices connected in the cyberspace. 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. Neural networks are the universal function approximation which learn abstract, highlevel, nonlinear representation of training data. Deep convolution neural network is used on images of inter-arrival time signature for device fingerprinting of 58 non-IoT devices of 5-11 types. To evaluate the performance, we compared ResNet-50 layer and basic CNN-5 layer architectures. We observed that device type identification models perform better than device identification. We also found that when deep learning models are attacked over device signature, the models identify the change in signature, and classify the device in the wrong class thereby the classification performance of the models degrades. The performance of the models to detect the attacks are significantly different from each other though both models indicate the system under attack.

  • Collaborative adversary nodes learning on the logs of IoT devices in an IoT network
    Sandhya Aneja, Melanie Ang Xuan En, and Nagender Aneja

    2022 14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022, Pages: 231-235, Published: 2022 IEEE
    Artificial Intelligence (AI) development has encouraged many new research areas, including AI-enabled Internet of Things (IoT) network. AI analytics and intelligent paradigms greatly improve learning efficiency and accuracy. Applying these learning paradigms to network scenarios provide technical advantages of new networking solutions. In this paper, we propose an improved approach for IoT security from data perspective. 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. We define network events as a sequence of the time series packets of protocols captured in the log. We have considered different packets TCP packets, UDP packets, and HTTP packets in the network log to make the algorithm robust. The distributed IoT devices can collaborate to cripple our world which is extending to Internet of Intelligence. The time series packets are converted into structured data by removing noise and adding timestamps. The resulting data set is trained by RNN and can detect the node pairs collaborating with each other. We used the BLEU score to evaluate the model performance. Our results show that the predicting performance of the AdLIoTLog model trained by our method degrades by 3-4% in the presence of attack in comparison to the scenario when the network is not under attack. AdLIoTLog can detect adversaries because when adversaries are present the model gets duped by the collaborative events and therefore predicts the next event with a biased event rather than a benign event. We conclude that AI can provision ubiquitous learning for the new generation of Internet of Things.

  • Packet-level and IEEE 802.11 MAC frame-level network traffic traces data of the D-Link IoT devices
    Rajarshi Roy Chowdhury, Sandhya Aneja, Nagender Aneja, and Pg Emeroylariffion Abas

    Data in Brief, eISSN: 23523409, Published: August 2021 Elsevier BV
    With the growth of wireless network technology-based devices, identifying the communication behaviour of wireless connectivity enabled devices, e.g. Internet of Things (IoT) devices, is one of the vital aspects, in managing and securing IoT networks. Initially, devices use frames to connect to the access point on the local area network and then, use packets of typical communication protocols through the access point to communicate over the Internet. Toward this goal, network packet and IEEE 802.11 media access control (MAC) frame analysis may assist in managing IoT networks efficiently, and allow investigation of inclusive behaviour of IoT devices. This paper presents network traffic traces data of D-Link IoT devices from packet and frame levels. Data collection experiment has been conducted in the Network Systems and Signal Processing (NSSP) laboratory at Universiti Brunei Darussalam (UBD). All the required devices, such as IoT devices, workstation, smartphone, laptop, USB Ethernet adapter, and USB WiFi adapter, have been configured accordingly, to capture and store network traffic traces of the 14 IoT devices in the laboratory. These IoT devices were from the same manufacture (D-Link) with different types, such as camera, home-hub, door-window sensor, and smart-plug.

  • Recent Advances in Ad-Hoc Social Networking: Key Techniques and Future Research Directions
    Nagender Aneja and Sapna Gambhir

    Wireless Personal Communications, ISSN: 09296212, eISSN: 1572834X, Volume: 117, Pages: 1735-1753, Published: April 2021 Springer Science and Business Media LLC
    Ad-hoc Social Networks are formed by groups of nodes, designating a similarity of interests. The network establishes a two-layer hierarchical structure that comprises communication within-group and joining with other groups. 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. Future directions include multi-hop social network applications that can be useful for users even in airplane mode and notifying over MANET when a user of profile with similar interest is nearby.

  • Systematic patent review of nanoparticles in drug delivery and cancer therapy in the last decade
    Nur Umairah Ali Hazis, Nagender Aneja, Rajan Rajabalaya, and Sheba Rani David

    Recent Advances in Drug Delivery and Formulation, ISSN: 26673878, eISSN: 26673886, Pages: 59-74, Published: 2021 Bentham Science Publishers Ltd.
    Background: The application of nanotechnology has been considered a powerful platform in improving the current situation in drug delivery and cancer therapy, especially in targeting the desired site of action. Objective: 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. Methods: The patents related to the nanoparticle-based inventions utilized in drug delivery and cancer treatment from 2010 onwards were browsed in databases like USPTO, WIPO, Google Patents, and Free Patents Online. After conducting numerous screening processes, a total of 40 patents were included in the patent analysis. See the PRISMA checklist 2020 checklist. Results: Amongst the selected patents, an overview of various types of nanoparticles is presented in this paper, including polymeric, metallic, silica, lipid-based nanoparticles, quantum dots, carbon nanotubes, and albumin-based nanomedicines. Conclusion: Nanomedicines advantages include improvements in terms of drug delivery, bioavailability, solubility, penetration, and stability of drugs. It is concluded that the utilization of nanoparticles in medicines is essential in the pursuit of better clinical practice.

  • Detecting Fake News with Machine Learning
    Nagender Aneja and Sandhya Aneja

    Lecture Notes in Networks and Systems, ISSN: 23673370, eISSN: 23673389, Volume: 175, Pages: 53-64, Published: 2021 Springer International Publishing
    Fake news is intentionally written to influence individuals and their belief system. Detection of fake news has become extremely important since it is impacting society and politics negatively. Most existing works have used supervised learning but given importance to the words used in the dataset. The approach may work well when the dataset is huge and covers a wide domain. However, getting the labeled dataset of fake news is a challenging problem. Additionally, the algorithms are trained after the news has already been disseminated. In contrast, this research gives importance to content-based prediction based on language statistical features. Our assumption of using language statistical features is relevant since the fake news is written to impact human psychology. A pattern in the language features can predict whether the news is fake or not. We extracted 43 features that include Parts of Speech and Sentiment Analysis and shown that AdaBoost gave accuracy and F-score close to 1 when using 43 features. Results also show that the top ten features instead of all 43 features give the accuracy of 0.85 and F-Score of 0.87.

  • Network Traffic Analysis based IoT Device Identification
    Rajarshi Roy Chowdhury, Sandhya Aneja, Nagender Aneja, and Emeroylariffion Abas

    ACM International Conference Proceeding Series, Pages: 79-89, Published: 22 August 2020 ACM
    Device identification is the process of identifying a device on Internet without using its assigned network or other credentials. The sharp rise of usage in Internet of Things (IoT) devices has imposed new challenges in device identification due to a wide variety of devices, protocols and control interfaces. In a network, conventional IoT devices identify each other by utilizing IP or MAC addresses, which are prone to spoofing. Moreover, IoT devices are low power devices with minimal embedded security solution. To mitigate the issue in IoT devices, fingerprint (DFP) for device identification can be used. DFP identifies a device by using implicit identifiers, such as network traffic (or packets), radio signal, which a device used for its communication over the network. These identifiers are closely related to the device hardware and software features. In this paper, we exploit TCP/IP packet header features to create a device fingerprint utilizing device originated network packets. We present a set of three metrics which separate some features from a packet which contribute actively for device identification. To evaluate our approach, we used publicly accessible two datasets. We observed the accuracy of device genre classification 99.37% and 83.35% of accuracy in the identification of an individual device from IoT Sentinel dataset. However, using UNSW dataset device type identification accuracy reached up to 97.78%.

  • Neural Machine Translation model for University Email Application
    Sandhya Aneja, Siti Nur Afikah Bte Abdul Mazid, and Nagender Aneja

    ACM International Conference Proceeding Series, Pages: 74-79, Published: 11 July 2020 ACM
    Machine translation has many applications such as news translation, email translation, official letter translation etc. Commercial translators, e.g. Google Translation lags in regional vocabulary and are unable to learn the bilingual text in the source and target languages within the input. In this paper, a regional vocabulary-based application-oriented Neural Machine Translation (NMT) model is proposed over the data set of emails used at the University for communication over a period of three years. A state-of-the-art Sequence-to-Sequence Neural Network for ML → EN (Malay to English) and EN → ML (English to Malay) translations is compared with Google Translate using Gated Recurrent Unit Recurrent Neural Network machine translation model with attention decoder. The low BLEU score of Google Translation in comparison to our model indicates that the application based regional models are better. The low BLEU score of English to Malay of our model and Google Translation indicates that the Malay Language has complex language features corresponding to English.

  • Transfer Learning using CNN for Handwritten Devanagari Character Recognition
    Nagender Aneja and Sandhya Aneja

    1st IEEE International Conference on Advances in Information Technology, ICAIT 2019 - Proceedings, Pages: 293-296, Published: July 2019 IEEE
    This paper presents an analysis of pre-trained models to recognize handwritten Devanagari alphabets using transfer learning for Deep Convolution Neural Network(DCNN). This research implements AlexNet, DenseNet, Vgg, and Inception ConvNet as a fixed feature extractor. We implemented 15 epochs for each of AlexNet, DenseNet 121, DenseNet 201, Vgg 11, Vgg 16, Vgg 19, and Inception V3.Results show that Inception V3 performs better in terms of accuracy achieving 99% accuracy with average epoch time 16.3 minutes while AlexNet performs fastest with 2.2 minutes per epoch and achieving 98%accuracy.

  • IoT Device fingerprint using deep learning
    Sandhya Aneja, Nagender Aneja, and Md Shohidul Islam

    Proceedings - 2018 IEEE International Conference on Internet of Things and Intelligence System, IOTAIS 2018, Pages: 174-179, Published: 3 January 2019 IEEE
    Device Fingerprinting (DFP) is the identification of a device without using its network or other assigned identities including IP address, Medium Access Control (MAC) address, or International Mobile Equipment Identity (IMEI) number. DFP identifies a device using information from the packets which the device uses to communicate over the network. Packets are received at a router and processed to extract the information. In this paper, we worked on the DFP using Inter Arrival Time (IAT). IAT is the time interval between the two consecutive packets received. This has been observed that the IAT is unique for a device because of different hardware and the software used for the device. The existing work on the DFP uses the statistical techniques to analyze the IAT and to further generate the information using which a device can be identified uniquely. This work presents a novel idea of DFP by plotting graphs of IAT for packets with each graph plotting 100 IATs and subsequently processing the resulting graphs for the identification of the device. This approach improves the efficiency to identify a device DFP due to achieved benchmark of the deep learning libraries in the image processing. We configured Raspberry Pi to work as a router and installed our packet sniffer application on the Raspberry Pi. The packet sniffer application captured the packet information from the connected devices in a log file. We connected two Apple devices iPad4 and iPhone 7 Plus to the router and created IAT graphs for these two devices. We used Convolution Neural Network (CNN) to identify the devices and observed the accuracy of 86.7%.

  • Profile-based Ad hoc social networking using Wi-Fi direct on the top of android
    Nagender Aneja and Sapna Gambhir

    Mobile Information Systems, ISSN: 1574017X, eISSN: 1875905X, Volume: 2018, Published: 2018 Hindawi Limited
    Ad hoc social networks have become popular to support novel applications related to location-based mobile services that are of great importance to users and businesses. Unlike traditional social services using a centralized server to fetch location, ad hoc social network services support infrastructure-less real-time social networking. It allows users to collaborate and share views anytime anywhere. However, current ad hoc social network applications either are not available without rooting the mobile phones or do not filter the nearby users based on common interests without a centralized server. This paper presents an architecture and implementation of social networks on commercially available mobile devices that allow broadcasting name and a limited number of keywords representing users’ interests without any connection in a nearby region to facilitate matching of interests. The broadcasting region creates a digital aura and is limited by the Wi-Fi region that is around 200 meters. The application connects users to form a group based on their profile or interests using the peer-to-peer communication mode without using any centralized networking or profile-matching infrastructure. The peer-to-peer group can be used for private communication when the network is not available.

  • Piecewise maximal Similarity for Ad-hoc Social Networks
    Sapna Gambhir, Nagender Aneja, and Liyanage Chandratilake De Silva

    Wireless Personal Communications, ISSN: 09296212, eISSN: 1572834X, Pages: 3519-3529, Published: 1 December 2017 Springer Science and Business Media LLC
    Computing Profile Similarity is a fundamental requirement in the area of Social Networks to suggest similar social connections that have high chance of being accepted as actual connection. Representing and measuring similarity appropriately is a pursuit of many researchers. Cosine similarity is a widely used metric that is simple and effective. This paper provides analysis of cosine similarity for social profiles and proposes a novel method to compute Piecewise Maximal Similarity between profiles. The proposed metric is 6% more effective to measure similarity than cosine similarity based on computations on real data.

  • Social Profile Aware AODV Protocol for Ad-Hoc Social Networks
    Nagender Aneja and Sapna Gambhir

    Wireless Personal Communications, ISSN: 09296212, eISSN: 1572834X, Pages: 4161-4182, Published: 1 December 2017 Springer Science and Business Media LLC
    Ad-hoc social networks are required to strengthen local communication between people. Mobile ad-hoc social networks have emerged as self-configuring and self-organizing social networks to facilitate interactions among different mobile users without Internet. Contextual routing based on social patterns has been proposed and advantageous for ad-hoc social networks. Social profile aware routing protocol proposed in this paper allows users to use social networking applications using social routing protocol. The protocol has been implemented on network simulator ns-2 and is also available as a patch file for other researchers. Results indicate protocol has low overhead with 64 nodes. Results have been presented for packet delivery ratio, and average end-to-end delay. The need of multi-hop social network was also studied and observed that probability of nodes being connected at mult-hop increases with increment of number of nodes and geographical area.

  • Security and privacy: Challenges and defending solutions for nosql data stores
    Sandhya Aneja and Nagender Aneja

    NoSQL: Database for Storage and Retrieval of Data in Cloud, Pages: 237-250, Published: 1 January 2017 Chapman and Hall/CRC
    Relational database management systems (RDBMSs) have traditionally been used to store and manage data from Internet, Intranet, or Desktop applications in order to serve multiusers systems. RDBMS has also been known to provide flexible services with a wide range of scalability. In traditional RDBMSs, role-based access control (RBAC) models have been implemented in commercial products like Oracle, MySQL, and PostgreSQL and many more with some variations from each other. Privacy is an important factor for data stores in addition to the security. 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. The chapter also explains the procedure to create user, roles, and functionalities provided in MongoDB for user authentication and access control. It explores possible modifications proposed for basic RBAC.

  • Software design for social profile matching algorithm to create ad-hoc social network on top of Android
    Proceedings of the 10th INDIACom; 2016 3rd International Conference on Computing for Sustainable Global Development, INDIACom 2016, Pages: 3450-3454, Published: 27 October 2016

  • Need of ad-hoc social network based on users' dynamic interests
    Sapna Gambhir, Nagender Aneja, and Samridhi Mangla

    International Conference on Soft Computing Techniques and Implementations, ICSCTI 2015, Pages: 52-56, Published: 10 June 2016 IEEE
    Ad-hoc social network (ASN) is a location based network that makes use of ad-hoc network to connect interested users socially. Ad-hoc social network is a combination of social network that maintains profile and interests of users, and ad-hoc network that helps to connect nearby users without centralized access point. A survey was conducted to know users' perception and preferences for ASN. This paper presents survey and results for need of ad-hoc social network. Results also indicate that in users prefer 75% of average profile similarity to connect nearby users.

  • Geo-social semantic profile matching algorithm for dynamic interests in Ad-hoc social network
    Nagender Aneja and Sapna Gambhir

    Proceedings - 2015 IEEE International Conference on Computational Intelligence and Communication Technology, CICT 2015, Pages: 354-358, Published: 1 April 2015 IEEE
    Ad-hoc Social Network (ASN) allows users to create social network connections using wireless ad hoc network. Various techniques have been proposed to create ASN by matching profiles of users. In order to create meaningful ASN, there is a need to dynamically set and match user profile based on changing user's interests. This paper provides an algorithm to semantically match users profiles based on geographic location and dynamic interests.

  • Verification of forecasts from high resolution numerical weather prediction model
    Nagender Aneja and Thomas George

    Research Journal of Applied Sciences, Engineering and Technology, ISSN: 20407459, eISSN: 20407467, Pages: 1255-1258, Published: 2014 Maxwell Scientific Publication Corp.
    Assessment of forecast quality is a critical component for weather model development as well as evaluating the impact on weather sensitive business applications such as renewable energy forecasting, agriculture, insurance etc. This study presents forecast quality results of a high resolution numerical weather model deployed for the country of Brunei at Universiti Brunei Darussalam. We present the monthly accuracy and probability of detection scores for precipitation as well as accuracy scores for Relative Humidity (RH) and Dew Point Temperature (DPT) for the year 2013.

  • Minimum Exposed Path to the Attack (MEPA) in Mobile Ad hoc Network (MANET)
    Sandhya Khurana, Neelima Gupta, and Nagender Aneja

    Proceedings of the Sixth International Conference on Networking, ICN'07, Pages: 16-20, Published: 2007 IEEE
    Lack of infrastructure, central controlling authority and the properties of wireless links make mobile ad hoc networks (MANETs) vulnerable to attacks. Several protocols have been proposed to make the routing protocols handle attacks in MANETs. These protocols detect the misbehaving nodes and re-route the data packets around them, mostly along the shortest such path. However, no single protocol handles all the attacks. A variant of the problem for routing around misbehaving nodes in ad hoc networks can be stated as: given a set of nodes under the danger of attack, one wishes to determine the path which is farthest from the endangered nodes. The problem does not address the problem of handling attack directly but tries to minimize the impact of attack. The problem also finds its applications in sensor networks. In this paper, we present a simple and efficient algorithm to solve the problem. The algorithm converges in O(d2) time where d is the diameter of the network.

  • Reliable ad-hoc on-demand distance vector routing protocol
    S. Khurana, N. Gupta, and N. Aneja

    Proceedings of the International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies,ICN/ICONS/MCL'06, Volume: 2006, Pages: 98-103, Published: 2006 IEEE
    Mobile Ad hoc Networks’ (MANETs) properties present major vulnerabilities in security. The threats considered in MANETS are due to maliciousness that intentionally disrupt the network by using variety of attacks and due to selfishness of node which do not perform certain operations due to a wish to save power. In this paper, 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. RAODV behaves as AODV in the absence of attack and, detects and isolates misbehaving nodes in the presence of attack. Also it recovers from the attack when a misbehaving node leaves the network or becomes good.

RECENT SCHOLAR PUBLICATIONS

  • Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising
    S Aneja, N Aneja, PE Abas, AG Naim
    IAES International Journal of Artificial Intelligence (IJ-AI) 11 (3) 2022

  • Transfer learning for cancer diagnosis in histopathological images
    S Aneja, N Aneja, PE Abas, AG Naim
    IAES International Journal of Artificial Intelligence (IJ-AI) 11 (1), 129-136 2022

  • An Inspection of MANET'S Scenario using AODV, DSDV and DSR Routing Protocols
    S Singh, SB Bajaj, K Tripathi, N Aneja
    2022 2nd International Conference on Innovative Practices in Technology and 2022

  • Collaborative adversary nodes learning on the logs of IoT devices in an IoT network
    S Aneja, MAX En, N Aneja
    2022 14th International Conference on COMmunication Systems & NETworkS 2022

  • A Comparative Performance Model of Machine Learning Classifiers on Time Series Prediction for Weather Forecasting
    S Sharma, KK Bhatt, R Chabra, N Aneja
    Advances in Information Communication Technology and Computing, 577-587 2022

  • Device fingerprinting using deep convolutional neural networks
    S Aneja, N Aneja, B Bhargava, RR Chowdhury
    International Journal of Communication Networks and Distributed Systems 28 2022

  • NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
    KD Dhole, V Gangal, S Gehrmann, A Gupta, Z Li, S Mahamood, ...
    arXiv preprint arXiv:2112.02721 2021

  • Packet-level and IEEE 802.11 MAC frame-level network traffic traces data of the D-Link IoT devices
    RR Chowdhury, S Aneja, N Aneja, PE Abas
    Data in Brief 37, 107208 2021

  • Recent Advances in Ad-Hoc Social Networking: Key Techniques and Future Research Directions
    N Aneja, S Gambhir
    Wireless Personal Communications 117 (3), 1735-1753 2021

  • Systematic Patent Review of Nanoparticles in Drug Delivery and Cancer Therapy in the Last Decade
    NU Ali Hazis, N Aneja, R Rajabalaya, SR David
    Recent Advances in Drug Delivery and Formulation: Formerly Recent Patents on 2021

  • Instant messaging for mobile device with offline and online mode
    S Aneja, N Aneja, MI Petra
    US Patent 10,834,035 2020

  • Network traffic analysis based iot device identification
    RR Chowdhury, S Aneja, N Aneja, E Abas
    Proceedings of the 2020 the 4th International Conference on Big Data and 2020

  • Neural Machine Translation model for University Email Application
    S Aneja, S Nur Afikah Bte Abdul Mazid, N Aneja
    2020 2nd Symposium on Signal Processing Systems, 74-79 2020

  • Detecting Fake News with Machine Learning
    N Aneja, S Aneja
    International Conference on Deep Learning, Artificial Intelligence and 2019

  • Transfer Learning using CNN for Handwritten Devanagari Character Recognition
    N Aneja, S Aneja
    2019 IEEE International Conference on Advances in Information Technology (ICAIT) 2019

  • Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases
    AK Jaiswal, I Panshin, D Shulkin, N Aneja, S Abramov
    2019 Towards Causal, Explainable and Universal Medical Visual Diagnosis 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

  • Profile-based ad hoc social networking using Wi-Fi direct on the top of android
    N Aneja, S Gambhir
    Mobile Information Systems 2018 2018

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

MOST CITED SCHOLAR PUBLICATIONS

  • IoT device fingerprint using deep learning
    S Aneja, N Aneja, MS Islam
    2018 IEEE International Conference on Internet of Things and Intelligence 2018
    Citations: 51

  • 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: 46

  • Transfer Learning using CNN for Handwritten Devanagari Character Recognition
    N Aneja, S Aneja
    2019 IEEE International Conference on Advances in Information Technology (ICAIT) 2019
    Citations: 31

  • Method and system for ad-hoc social networking and profile matching
    N Aneja, S Gambhir
    US Patent 10,264,609 2019
    Citations: 27

  • Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases
    AK Jaiswal, I Panshin, D Shulkin, N Aneja, S Abramov
    2019 Towards Causal, Explainable and Universal Medical Visual Diagnosis 2019
    Citations: 15

  • 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: 13

  • Ad-hoc Social Network: A Comprehensive Survey
    S Gambhir, N Aneja
    International Journal of Scientific & Engineering Research 4 (8) 2013
    Citations: 13

  • 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: 11

  • 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

  • Network traffic analysis based iot device identification
    RR Chowdhury, S Aneja, N Aneja, E Abas
    Proceedings of the 2020 the 4th International Conference on Big Data and 2020
    Citations: 10

  • 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: 10

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

  • Piecewise Maximal Similarity for Ad-hoc Social Networks
    S Gambhir, N Aneja, LC De Silva
    Wireless Personal Communications, 1-11 2017
    Citations: 5

  • 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: 5

  • NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
    KD Dhole, V Gangal, S Gehrmann, A Gupta, Z Li, S Mahamood, ...
    arXiv preprint arXiv:2112.02721 2021
    Citations: 3

  • 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: 3

  • Packet-level and IEEE 802.11 MAC frame-level network traffic traces data of the D-Link IoT devices
    RR Chowdhury, S Aneja, N Aneja, PE Abas
    Data in Brief 37, 107208 2021
    Citations: 1

  • Recent Advances in Ad-Hoc Social Networking: Key Techniques and Future Research Directions
    N Aneja, S Gambhir
    Wireless Personal Communications 117 (3), 1735-1753 2021
    Citations: 1

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

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

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