Gaurav Singal

Gaurav Singal (SMIEEE) is an Assistant Professor in Computer Science Engineering Department at NSUT Delhi. He obtained his PhD and M. Tech. in the Computer Science Engineering department from Malaviya National Institute of Technology, Jaipur, India. He received research grants from the Department of Science and Technology, Uttar Pradesh on women security and the Department of Biotechnology on Assistive devices. He is actively working in research and teaching for the last 10 years and published a number of reputed conferences and journals. He is a member of the scientific society IEEE and ACM. He is certified as an NVIDIA Deep learning institute ambassador and UIPath RPA Advanced Developer. His research interests include Wireless and Ad hoc Networking, Internet of Things, Edge computing, Applied Deep learning and Reinforcement learning.

EDUCATION

BTech, MTech, PhD

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence, Computer Networks and Communications

Scopus Publications

Hazardous Object Detection for Visually Impaired People Using Edge Device
Ujjwal Kadam, Riti Kushwaha, Arvind Meena, Chaudhary Abuzar, Ujjwal, Gaurav Singal, and Madhushi Verma
Springer Science and Business Media LLC



RETRACTION: Optimization Using Internet of Agent Based Stacked Sparse Autoencoder Model for Heart Disease Prediction(Expert Systems, (e13359), 10.1111/exsy.13359)
Wiley
RETRACTION: V. Baviskar, M. Verma, P. Chatterjee, G. Singal and T. R. Gadekallu, “,” Expert Systems (Early View): , https://doi.org/10.1111/exsy.13359. The above article, published online on 10 June 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor‐in‐Chief, David Camacho; and John Wiley & Sons Ltd. The article was submitted as part of a guest‐edited special issue. Following publication, it has come to the attention of the journal that parts of the methods in the article lack sufficient detail such that the research cannot be reproduced. A relevant discussion and discrimination for different cardiovascular diseases is missing. The editors have therefore decided to retract this article. The authors disagree with the retraction.


Towards Intelligent Decision Making for Charging Scheduling in Rechargeable Wireless Sensor Networks
Abhinav Tomar, Raj Anwit, Piyush Nawnath Raut, and Gaurav Singal
Springer Science and Business Media LLC



DDoS attack traffic classification in SDN using deep learning
Nisha Ahuja, Debajyoti Mukhopadhyay, and Gaurav Singal
Springer Science and Business Media LLC



A New QoS Optimization in IoT-Smart Agriculture Using Rapid-Adaption-Based Nature-Inspired Approach
Shailendra Pratap Singh, Gaurav Dhiman, Sapna Juneja, Wattana Viriyasitavat, Gaurav Singal, Neeraj Kumar, and Prashant Johri
Institute of Electrical and Electronics Engineers (IEEE)
The rapid growth of the Internet of Things (IoT) in the early 21st century has introduced complexities in delivering various services, including Quality-of-Service (QoS) management for smart agriculture sensors. Selecting optimal IoT nodes considering QoS parameters, such as energy consumption, latency, and network coverage area has become challenging. In response, this research proposes an extended form of differential evolution (DE) that incorporates a rapid adaptation approach using optimization-based design. By leveraging dynamic information from IoT devices, the proposed approach enhances exploration and exploitation capabilities, allowing for adaptive adjustment of algorithm parameters and strategies. Additionally, a novel fitness function for energy harvesting in IoT-based applications is introduced. The effectiveness of the proposed algorithm is evaluated in IoT-based applications and an IoT-service framework, with comparative analysis against state-of-the-art algorithms. The results demonstrate that the proposed approach achieves superior performance in energy harvesting QoS, delay, service cost, and maximum coverage area in the IoT-service network. This research contributes to the IoT field by offering an advanced DE algorithm that addresses limitations, providing valuable insights for QoS management in IoT-based services, particularly in the context of smart agriculture sensors.


Ensemble-Based Botnet Attack Detection and Classification Using Machine Learning Algorithms on NBaIoT Dataset
Mamta Rawat, Avneet Singh Bedi, Balvinder Singh, Sneha Gupta, Gaurav Singal, and Preeti Kaur
IEEE
Through botnet assaults, Mirai and BASHLITE present serious risks in the context of the Internet of Things (loT). A reliable detection and classification model is the need of the hour. This article presents a solution to the problem by suggesting an ensemble-based algorithm to detect and classify different categories of Mirai and BASHLITE attacks. We have used the NBaloT, a heterogeneous dataset, that purposefully com-promised on many devices, and exposes subtle attack categories. Interestingly, certain devices resist Mirai infections, improving our analysis. By utilizing important data types and various time-based window sizes, our approach seeks to create an effective model for identifying and classifying IoT botnet assaults to strengthen the IoT ecosystem against new attacks.


Depression Detection by Using Wearable Sensors
Vishal Shrivastav, Giriraj, Sudhanshu Tiwari, Gaurav Singal, Riti Kushwaha, Tulika Tewari, and Ankur Gupta
IEEE
This research introduces an innovative approach to depression detection utilizing wearable health sensor systems integrated with machine learning models. By integrating self-reported PHQ-9 results and physiological measurements from wearable sensors, a comprehensive dataset was assembled. Evaluation of supervised and semi-supervised ML techniques displayed notable success, with Hybrid Supervised and Semi-Supervised Approach achieving a remarkable 98% accuracy in detecting Major Depressive Disorder (MDD), marking significant advancement in mental health technology. The study highlights the potential of wearable devices in proactive mental health monitoring and intervention. Insights from exploratory data analysis (EDA) informed algorithmic development, enhancing accuracy and robustness in depression detection. This research aims to make mental health support more accessible and helpful. Our results show that this wearable system could be useful in real-life situations for detecting depression.


HydroPro: An IoT-Based Smart Water Bottle
Aadi Nirbhavane, Riti Kushwaha, Gaurav Singal, Abhinav Tomar, Vipin Pal, and Ankur Gupta
IEEE
Water is a necessity for the correct functioning of the human body. In the modern era, most people have hectic schedules, making it difficult for them to remember to drink enough water and track their water consumption. This problem of not being able to track the daily water intake becomes even more pronounced for people of older age groups. In addition, people on medications also require strict water intake to dilute the toxins that the medicines introduce in their body. We present our product - HydroPro, a smart water bottle aimed at encouraging our target audiences to better their hydration habits. Our product gives a more personalized water tracking facility to the user wherein we take the user's environment parameters into account (namely temperature and humidity) to notify the user whether they have consumed enough water. The objective is to enable effective management of daily water requirements and to ensure users drink good quality water. The result shows the working effectiveness of smart water bottle system which is also tested and validated on mobile application.


StockLens - Analysis and News Sentiment for Smart Investing
Avi Vaswani, Pravesh Gupta, Vijay Kumar Bohat, Abhinav Tomar, and Gaurav Singal
IEEE
This research presents an intra-day stock prediction model based on financial news analysis and sentiment aiming for intelligent investing. Nowadays, the intra-day market is constantly fluctuating, and there is a difficulty in recognizing the market trend. Addressing this, our research aims to develop a predictive framework that extracts actionable insights from news data. We process financial news articles, headlines, and video data from different platforms through techniques such as YOLO for the text detection and Tesseract OCR for text extraction. We then apply sentiment analysis on it using FinBERT and add a custom credibility assessment to further refine predictions for better results. The innovation in our approach is to not only consider traditional textual data but also include video data, and besides this, we take into consideration a few other very critical factors in the model: publisher credibility, market analysis-volatility index, sector-wise analysis, and many more. The developed stock prediction model offers a strategic advantage in navigating the complexities of the stock market, enabling investors to capitalize on emerging opportunities and mitigate risks effectively.


A Solar Power-Enabled and LoRa-Based Smart Irrigation and Monitoring System
Abhishek Jha, Kartik Goyal, Nalin Semwal, Gaurav Singal, and Abhinav Tomar
Springer Nature Singapore



Rediscovering Minutiae Matching Through One Shot Learning's Siamese Framework in Poor Quality Footprint Images
Riti Kushwaha, Gaurav Singal, and Neeta Nain
Institute of Electrical and Electronics Engineers (IEEE)
Footprint biometrics is one of the emerging techniques, which can be utilized in different security systems. A human footprint has unique traits which is sufficient to recognize any person. Existing work evaluates the shape features and texture features but very few authors have explored minutiae features, hence this article provides a study based on minutiae features. The current State-of-the-art methods utilize machine learning techniques, which suffer from low accuracy in case of poor-quality of data. These machine learning techniques provide approx 97% accuracy while using good quality images but are not able to perform well when we use poor quality images. We have proposed a minutiae matching system based on deep learning techniques which is able to handle samples with adequate noise. We have used Convolution Neural Network for the feature extraction. It uses two different ridge flow estimation methods, i.e., ConvNet-based and dictionary-based. Furthermore, fingerprint-matching metrics are used for footprint feature evaluation. We initially employed a contrastive-based loss function, resulting in an accuracy of 56%. Subsequently, we adapted our approach by implementing a distance-based loss function, which improved the accuracy to 66%.


Lightweight Intrusion Detection System for IoT Environment through Compression Techniques
Tulika Tewari, Mamta Rawat, Animesh Malviya, and Gaurav Singal
IEEE
Internet of Things has been widely adopted technology and making secure IoT systems is a necessity. Machine-and deep-learning-based intrusion detection systems have been extensively used to detect anomalies in network traffic. However, deploying such models in resource-constrained environments is cumbersome since these models consume huge memory space. Compression Techniques are implemented on such models to make them suitable for deployment in the IoT ecosystem. In this paper, we present a hybrid CNN-BiLSTM based model for classifying network attacks using the CICIoT2023 dataset. For feature selection and reduction, we utilized Permutation Feature extraction and Principle Component Analysis. To make our model lightweight, we implemented three compression techniques- Knowledge Distillation, weight pruning, and post-training quantization and compared the model performance with all three techniques. We achieved the most reduced model size via Knowledge distillation, reducing the original size by $\\mathbf{9 4. 7 6 \\%}$, followed by quantization, reducing size by $\\mathbf{8 9. 5 2 \\%}$. Post-training quantization showed a high accuracy of $\\mathbf{9 1. 5 0 \\%}$. Through this work, we explore the optimal compression technique to be adopted while designing lightweight Deep learning-based IDS solutions for IoT networks.


The Internet of Trust: Securing and Transparent Supply Chains with Blockchain and IoT
Gaurav Singal, Riti Kushwaha, Ashish Sharma, and Tulika Tewari
IEEE
In an era where digital commerce continues to burgeon, the conventional supply chain confronts challenges of inefficiency, fraud, and a dearth of transparency. Blockchain, renowned for its decentralized and immutable ledger, emerges as a compelling solution to transform the e-commerce supply chain landscape. In conventional supply chain practices marked by centralized databases and manual record-keeping, inefficiencies and a dearth of transparency are widespread. Challenges stemming from cumbersome paperwork, delayed information flow, and vulnerability to errors hindered effective coordination, traceability, and overall operational efficiency. Blockchain and IoT can elevate e-commerce supply chains by ensuring transparent, secure, and automated data-sharing, fostering efficiency and trust throughout the entire process. We have presented an Ethereum-based blockchain and Internet of Things e-commerce platform in this paper. We describe in detail how to put in place a system that stores a product’s journey’s details on an immutable distributed ledger. This allows for secure tracing of a product’s origin. The prototype of the system is deployed and tested on Hardhat testing framework. Traditional databases and cloud storages have been replaced by blockchain and IPFS. IPFS is used as the decentralized and distributed storage layer, facilitating the secure and efficient storage of credentials. Furthermore, the results of our experiments and security and scalability analyses demonstrate the viability of our suggested model.


Efficient Heart Disease Prediction Using Hybrid Deep Learning Classification Models
Vaishali Baviskar, Madhushi Verma, Pradeep Chatterjee, and Gaurav Singal
Elsevier BV



QC-SANE: Robust Control in DRL Using Quantile Critic With Spiking Actor and Normalized Ensemble
Surbhi Gupta, Gaurav Singal, Deepak Garg, and Sarangapani Jagannathan
Institute of Electrical and Electronics Engineers (IEEE)
Recently introduced deep reinforcement learning (DRL) techniques in discrete-time have resulted in significant advances in online games, robotics, and so on. Inspired from recent developments, we have proposed an approach referred to as Quantile Critic with Spiking Actor and Normalized Ensemble (QC_SANE) for continuous control problems, which uses quantile loss to train critic and a spiking neural network (NN) to train an ensemble of actors. The NN does an internal normalization using a scaled exponential linear unit (SELU) activation function and ensures robustness. The empirical study on multijoint dynamics with contact (MuJoCo)-based environments shows improved training and test results than the state-of-the-art approach: population coded spiking actor network (PopSAN).


Aqua Clear: Efficient Water Management for home water supply
Shrey Rastogi, Shivam Sharma, Suraj Kumar, and Gaurav Singal
ACM
The quality of water resources available across the world have been degrading much faster than ever before. Therefore, judicious use of water resources has become an important topic as these resources get evermore scarce. Moreover, efficient treatment and utilization of these resources domestically is another factor to be considered. Our proposed IoT-based system aims to enhance the water utilization in households as in preventing excess use of Reverse Osmosis (RO) and prevent highly contaminated water from entering the house water-storage. The system uses sensors which measure physical characteristics of water – its temperature and Total Dissolved Solids (TDS) which is gathered by the microcontroller and displayed to the user on a smartphone. Based on the value of TDS of water, the system automatically controls the water filter system by directing unfiltered water to the suitable filter (RO or Non-RO) as well as controlling the water-storage tank system.


QEST: Quantized and Efficient Scene Text Detector Using Deep Learning
Kanak Manjari, Madhushi Verma, Gaurav Singal, and Suyel Namasudra
Association for Computing Machinery (ACM)
Scene text detection is complicated and one of the most challenging tasks due to different environmental restrictions, such as illuminations, lighting conditions, tiny and curved texts, and many more. Most of the works on scene text detection have overlooked the primary goal of increasing model accuracy and efficiency, resulting in heavy-weight models that require more processing resources. A novel lightweight model has been developed in this article to improve the accuracy and efficiency of scene text detection. The proposed model relies on ResNet50 and MobileNetV2 as backbones with quantization used to make the resulting model lightweight. During quantization, the precision has been changed from float32 to float16 and int8 for making the model lightweight. In terms of inference time and Floating-Point Operations Per Second, the proposed method outperforms the state-of-the-art techniques by around 30–100 times. Here, well-known datasets, i.e., ICDAR2015 and ICDAR2019, have been utilized for training and testing to validate the performance of the proposed model. Finally, the findings and discussion indicate that the proposed model is more efficient than the existing schemes.


RoadWay: lane detection for autonomous driving vehicles via deep learning
Gaurav Singal, Himanshu Singhal, Riti Kushwaha, Venkataramana Veeramsetty, Tapas Badal, and Sonu Lamba
Springer Science and Business Media LLC



NayanCom - A Smart Patient Communication System
Aryaman Sharma, Harshit Gupta, Tabishi Singh, Gaurav Singal, and Riti Kushwaha
IEEE
In the face of increasing cases of temporary and permanent motion and speech disabilities, the project NayanCom aims to solve the human computer interaction (HCI) problem for people in this demographic. The proposed and implemented approach uses the blinking actions of a user to allow easier communication with their caretakers and medical services for a better patient experience. The project uses computer vision to detect the face and eye blink action of a patient, following which it is processed and fed into the action handler, thus setting up a non-vocal communication medium. Along with the communication aspect, a low cost wireless patient vital monitoring system was also developed wherein the patient’s essential vitals are tracked and reported in real time.


Enhancing Class Monitoring Through Video Analytics and Deep Learning Models
Riti Kushwaha, Himanshu Rawat, Suman Bando, Aman Kumar Dass, Lakshya Panwar, Divya Srivastava, and Gaurav Singal
IEEE
This research paper introduces an innovative solution for enhancing class monitoring by integrating video analytics and advanced deep-learning models for human activity recognition. Through a comparative analysis of two implementation methods, Deep Neural Network (DNN) models emerge as the more efficient and scalable choice, revolutionizing the approach to class monitoring in educational and security contexts. The study evaluates four key deep-learning models, including ConvLSTM, LRCN, and CNN+GRU, highlighting their superior performance over traditional feature-based approaches. Notably, the accuracy metrics for these models are as follows: ConvLSTM achieved an accuracy of 0.62, LRCN exhibited exceptional accuracy at 0.94, and GRU + Convolution Model demonstrated robust accuracy of 0.92. The ultimate goal is the development of an automated monitoring system leveraging deep learning to detect and classify human activities, with significant implications for education and security. These DNN models, known for their cost-effectiveness and adaptability, can be swiftly deployed to monitor classrooms and examination rooms, promptly identifying and reporting suspicious activity. Rigorous performance evaluation using diverse metrics underlines the strengths and weaknesses of these models, marking a significant advancement in automated monitoring systems’ capabilities.


Towards Sustainable Economic Zones with UAV-assisted Real-time Energy Replenishment in Wireless Rechargeable Sensor Networks
Riya Goyal, Abhinav Tomar, and Gaurav Singal
IEEE
An economic zone is bound to have continuous monitoring and control by an autonomous surveillance system for better production competency and security. Wireless Rechargeable Sensor Networks (WRSNs) have gained popularity to provide reliable and sustainable energy supply for continual network operations. In economic zones, numerous sensor nodes are deployed for diverse activities that require continuous energy supply. However, the integration of WRSNs with UAVs has increased the efficiency of recharging the sensor nodes deployed in areas that are difficult to reach by mobile chargers. Nevertheless, UAVs’ limited battery life limits their performance and range which requires efficient deployment of charging stations. This paper proposes an integration of the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithms to address the charging scheduling problem through efficient deployment of charging stations and finding the optimal charging scheduling path for UAVs. Simulation results demonstrate that the proposed scheme produces a shorter planning path, leading to a longer network lifespan than alternative approaches.


Structural analysis using MPU6050 gyroscope
Rhythm Narula, Pulkit Barwal, and Gaurav Singal
IEEE
Many buildings are sensitive to earthquake and hurricanes, and it is easy to generate large deflection and dynamic response even through daily motion of a huge crowd. There are also structures with inappropriate distributions of strength and stiffness which performs poorly in case of any seismic activity and are one of the major causes of collapses leading to loss of lives. With these issues, there is a growing demand for the exploration of different methodologies to detect the prime areas in the structures that require improvement and repair, and with these early detection techniques, we can take suitable action that can, in turn, save a lot of lives. Previous research in this field mainly revolved around checking concrete strength and rust which was insufficient. Other devices which exist are expensive and they has to be equipped while constructing the building. In this paper we have summarised our study on finding the structural strength of buildings with the help of MPU6050 gyroscope. The MPU6050 gyroscope is a 3-axis gyroscope and a 3-axis accelerometer. The sensor measures the angular rotation from its axis and transfers it to the smartphone using a Bluetooth module.


Path Texture Detection for Visually Impaired using Transfer Learning
Kanak Manjari, Animesh, Madhushi Verma, and Gaurav Singal
IEEE
The computer vision technique known as object detection is used to determine the location of an object in an image or video. Recent advances in a number of areas, including computer vision, speech recognition, and natural language processing have been made using deep learning approaches. Convolution neural networks (CNNs), which have several layers, are used to detect objects. A deep learning model that uses CNN to identify various surface regions has been suggested. The study of tracks, walkways, playgrounds, highways, and muddy areas is a pertinent and significant aspect of object detection for Visually Impaired (VI) individuals. In addition to adding indirect vision to the blind individual, this model recognizes surface regions and alerts them using text-to-sound conversion API. In this study, transfer learning was carried out utilizing Faster RCNN (Region Based Convolutional Neural Networks) with Inception V2 and ResNet50 model weights. The accuracy of both trained models was the same, while Faster RCNN Inception V2 had the lowest loss (0.02).


A Transfer Learning Based Approach for Human Foot Image Classification
Riti Kushwaha, Gaurav Singal, and Neeta Nain
IEEE
A human footprint has all the properties like fingerprint which are required for authentication. It has all the desirable properties including uniqueness, universality, and invariability, yet not very widely used for authentication purpose. Hence exploring this novel biometric system is very important. The article explores the singularity available in a footprint image, it is further utilized while performing footprint classification. The main application of footprint classification is to reduce the sample size while performing the authentication. Four different singularities are manually marked on the collected dataset, furthermore, it is trained using different CNN models VGGNet16 and ResNet. A VGGNet-16 model has 16 convolution layer of uniform architecture,with 138 million parameters per layer, while a ResNet50 has 152 layers which is 8×8 times deeper than VGGNet-16. These models are tested on poor-quality dataset (most of the time we are not able to collect clear footprint images), it provides the accuracy of 80%.


Jal Setu: Smart Water Tank for Homes and Public Places
Divyanshu Rao, Aryan Sharma, Hrishikesh Shah, Gaurav Singal, Riti Kushwaha, and Vandana Bhatia
IEEE
The primary vision behind this paper is to develop a one-stop solution from the consumer end to the supplier of water in cities. The solution is being enabled through a kit that can be installed in the water tanks. Besides this, the consumer and the water supplying authorities can monitor the functionalities and reports remotely. The kit is built using various sensors which measure the most crucial parameters to determine the quality of water that is being loaded onto the tank. The sensors used here are powerful to measure Turbidity, pH, Temperature, and quantity of the water. All these powerful sensors have been interfaced with the microcontroller unit which can transmit the data remotely.

RECENT SCHOLAR PUBLICATIONS

Hazardous Object Detection for Visually Impaired People Using Edge Device
U Kadam, R Kushwaha, A Meena, C Abuzar, G Singal, M Verma
SN Computer Science 6 (1), 1-13 2025



The Internet of Trust: Securing and Transparent Supply Chains with Blockchain and IoT
G Singal, R Kushwaha, A Sharma, T Tewari
2024 IEEE International Conference on Advanced Networks and 2024



Lightweight Intrusion Detection System for IoT Environment through Compression Techniques
T Tewari, M Rawat, A Malviya, G Singal
2024 IEEE International Conference on Advanced Networks and 2024



Towards Intelligent Decision Making for Charging Scheduling in Rechargeable Wireless Sensor Networks
A Tomar, R Anwit, PN Raut, G Singal
Journal of Network and Systems Management 32 (4), 86 2024



Ensemble-Based Botnet Attack Detection and Classification Using Machine Learning Algorithms on NBaIoT Dataset
M Rawat, AS Bedi, B Singh, S Gupta, G Singal, P Kaur
2024 IEEE Region 10 Symposium (TENSYMP), 1-6 2024



StockLens-Analysis and News Sentiment for Smart Investing
A Vaswani, P Gupta, VK Bohat, A Tomar, G Singal
2024 IEEE Region 10 Symposium (TENSYMP), 1-6 2024



HydroPro: An IoT-Based Smart Water Bottle
A Nirbhavane, R Kushwaha, G Singal, A Tomar, V Pal, A Gupta
2024 IEEE Region 10 Symposium (TENSYMP), 1-6 2024



Depression Detection by Using Wearable Sensors
V Shrivastav, S Tiwari, G Singal, R Kushwaha, T Tewari, A Gupta
2024 IEEE Region 10 Symposium (TENSYMP), 1-6 2024



Rediscovering Minutiae Matching through One Shot Learning’s Siamese Framework in Poor Quality Footprint Images
R Kushwaha, G Singal, N Nain
IEEE Transactions on Biometrics, Behavior, and Identity Science 2024



DDoS attack traffic classification in SDN using deep learning
N Ahuja, D Mukhopadhyay, G Singal
Personal and Ubiquitous Computing 28 (2), 417-429 2024



Towards Sustainable Economic Zones with UAV-assisted Real-time Energy Replenishment in Wireless Rechargeable Sensor Networks
R Goyal, A Tomar, G Singal
2023 IEEE International Conference on Advanced Networks and 2023



An Approach for Dam Monitoring and Alerting System Using IoT
H Agarwal, Y Jindal, P Singh, G Singal, R Kushwaha
International Conference on Signal, Machines, Automation, and Algorithm, 55-70 2023



IoT-Based Automatic Vehicle Safety Monitoring System in Tunnels
S Agrawal, N Kumar, L Panwar, R Kushwaha, G Singal, V Bhatia
International Conference on Signal, Machines, Automation, and Algorithm, 101-112 2023



Currency Detection for Visually Impaired People using Edge Devices
U Kadam, A Meena, C Abuzar, G Singhal, D Srivastava
2023 International Conference on Modeling, Simulation & Intelligent 2023



Enhancing Class Monitoring Through Video Analytics and Deep Learning Models
R Kushwaha, H Rawat, S Bando, AK Dass, L Panwar, D Srivastava, ...
2023 International Conference on Modeling, Simulation & Intelligent 2023



NayanCom-A Smart Patient Communication System
A Sharma, H Gupta, T Singh, G Singal, R Kushwaha
2023 26th Conference of the Oriental COCOSDA International Committee for the 2023



Efficient Heart Disease Prediction Using Hybrid Deep Learning Classification Models
V Baviskar, M Verma, P Chatterjee, G Singal
IRBM 44 (5), 100786 2023



A new qos optimization in iot-smart agriculture using rapid-adaption-based nature-inspired approach
SP Singh, G Dhiman, S Juneja, W Viriyasitavat, G Singal, N Kumar, ...
IEEE Internet of Things Journal 11 (3), 5417-5426 2023



Aqua Clear: Efficient Water Management for home water supply
S Rastogi, S Sharma, S Kumar, G Singal
Proceedings of the 2023 Fifteenth International Conference on Contemporary 2023



Structural analysis using MPU6050 gyroscope
R Narula, P Barwal, G Singal
2023 14th International Conference on Computing Communication and Networking 2023

MOST CITED SCHOLAR PUBLICATIONS

Automated DDOS attack detection in software defined networking
N Ahuja, G Singal, D Mukhopadhyay, N Kumar
Journal of Network and Computer Applications 187, 103108 2021
Citations: 208



A survey on assistive technology for visually impaired
K Manjari, M Verma, G Singal
Internet of Things 11, 100188 2020
Citations: 129



Short-term electric power load forecasting using random forest and gated recurrent unit
V Veeramsetty, KR Reddy, M Santhosh, A Mohnot, G Singal
Electrical Engineering 104 (1), 307-329 2022
Citations: 87



IoT Network Traffic Classification Using Machine Learning Algorithms: An Experimental Analysis
R Kumar, M Swarnkar, G Singal, N Kumar
IEEE Internet of Things Journal 9 (2), 989-1008 2021
Citations: 86



DLSDN: Deep Learning for DDOS attack detection in Software Defined Networking
N Ahuja, G Singal, D Mukhopadhyay
Confluence-2021:11th International Conference on Cloud Computing, Data 2021
Citations: 71



DDOS attack SDN dataset
N Ahuja, G Singal, D Mukhopadhyay
Mendeley Data 1 2020
Citations: 70



FCNN-LDA: A Faster Convolution Neural Network model for Leaf Disease identification on Apple's leaf dataset
M Agarwal, RK Kaliyar, G Singal, SK Gupta
2019 12th International Conference on Information & Communication Technology 2019
Citations: 64



Multi-constraints link stable multicast routing protocol in MANETs
G Singal, V Laxmi, MS Gaur, S Todi, V Rao, M Tripathi, R Kushwaha
Ad Hoc Networks 63, 115-128 2017
Citations: 55



Deep reinforcement learning techniques in diversified domains: a survey
S Gupta, G Singal, D Garg
Archives of Computational Methods in Engineering 28 (7), 4715-4754 2021
Citations: 54



Edge device based military vehicle detection and classification from uav
P Gupta, B Pareek, G Singal, DV Rao
Multimedia Tools and Applications 81 (14), 19813-19834 2022
Citations: 53



Coinnet: platform independent application to recognize Indian currency notes using deep learning techniques
V Veeramsetty, G Singal, T Badal
Multimedia Tools and Applications 2020
Citations: 49



Moralism: mobility prediction with link stability based multicast routing protocol in MANETs
G Singal, V Laxmi, MS Gaur, V Rao
Wireless Networks 23 (3), 663-679 2017
Citations: 49



Recognition of Gurmukhi Handwritten City Names Using Deep Learning and Cloud Computing
S Sharma, S Gupta, D Gupta, S Juneja, G Singal, G Dhiman, S Kautish
Scientific Programming 2022 2022
Citations: 46



A Novel Approach for Detection of Counterfeit Indian Currency Notes Using Deep Convolutional Neural Network
SN Kumar, G Singal, S Sirikonda, R Nethravathi
IOP Conference Series: Materials Science and Engineering 981 (2), 022018 2020
Citations: 37



Ascertain the efficient machine learning approach to detect different ARP attacks
N Ahuja, G Singal, D Mukhopadhyay, A Nehra
Computers and Electrical Engineering 99, 107757 2022
Citations: 33



A secure and lightweight anonymous mutual authentication scheme for wearable devices in Medical Internet of Things
A Gupta, M Tripathi, S Muhuri, G Singal, N Kumar
Journal of Information Security and Applications 68, 103259 2022
Citations: 29



DDOS Attack Detection & Prevention in SDN using OpenFlow Statistics
N Ahuja, G Singal
2019 IEEE 9th International Conference on Advanced Computing (IACC), 147-152 2019
Citations: 29



Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models
V Veeramsetty, A Mohnot, G Singal, SR Salkuti
Energies 14 (11), 2981 2021
Citations: 28



Heart rate and blood pressure measurement based on photoplethysmogram signal using fast Fourier transform
A Sharma, RS Tanwar, Y Singh, A Sharma, S Daudra, G Singal, ...
Computers and Electrical Engineering 101, 108057 2022
Citations: 26



Corridor segmentation for automatic robot navigation in indoor environment using edge devices
S Gupta, R Sangeeta, RS Mishra, G Singal, T Badal, D Garg
Computer Networks 178, 107374 2020
Citations: 22