HUMAIRA NISHAT

@cvr.ac.in

Professor
CVR College of Engineering

HUMAIRA NISHAT


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https://researchid.co/humaira.nishat

EDUCATION

Electronics and Communication Engineering

RESEARCH, TEACHING, or OTHER INTERESTS

Engineering, Electrical and Electronic Engineering, Signal Processing

FUTURE PROJECTS

Communcation between Electric Vehicles and Battery Stations


Applications Invited

Drone Based Waste Management System


Applications Invited
12

Scopus Publications

102

Scholar Citations

5

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • Sensitivity-enhanced machine learning–assisted terahertz Plasmonic biosensor using hybrid 2D materials for tuberculosis detection
    K. Rejini, Humaira Nishat, P. Manikandan, P. Ashok, William Ochen
    Sensing and Bio Sensing Research, 2026
    This work introduces, a machine learning-assisted terahertz metasurface biosensor that integrates graphene, gold, MXene and phosphorene within a single hybrid architecture for tuberculosis detection. Unlike conventional single- or dual-material plasmonic sensors, the proposed design exploits multi-material plasmonic hybridization to simultaneously achieve high sensitivity, angular robustness and tunable electromagnetic response. Finite element method (FEM) simulations in COMSOL Multiphysics are used to systematically optimize the sensor by varying the graphene chemical potential (0.1–0.9 eV), incident angle (0°–80°) and geometric parameters. The optimized design achieves a maximum sensitivity of 1000 GHz/RIU, figure of merit (FOM) of 14.286 RIU −1 , quality factor of 10.014 and a detection limit of 0.022 RIU, indicating superior performance compared with conventional THz biosensors. Electromagnetic field analysis reveals strong field confinement and hybridized plasmonic modes within the 0.4–1.8 THz range, with a peak absorption of 76.935% at 80° incidence. A linear resonance frequency–refractive index relationship (R 2 = 0.98098) confirms reliable quantitative sensing. Furthermore, the incorporation of machine learning–assisted analysis, yielding an R 2 exceeding 90%, enhances predictive accuracy and robustness. The proposed architecture demonstrates high angular stability, compactness and tunability, establishing its novelty and suitability for point-of-care TB diagnostics and real-time biomedical sensing.
  • Optimized Dual-Attention Convolutional Neural Networks for Hybrid Beamforming and High-Precision Channel Estimation in 5G Massive MIMO Wireless Communications Systems
    Sandeep Prabhu, Humaira Nishat, Shreenidhi Krishnamurthy Subramaniyan, Harishchander Anandaram, Shargunam Selvam
    Internet Technology Letters, 2025
    Beamforming and channel estimation are fundamental components of 5G massive MIMO (multiple‐input–multiple‐output) systems, particularly in the millimeter‐wave (mmWave) spectrum, where high‐frequency transmissions are susceptible to path loss and signal degradation. The growing demand for ultrareliable low‐latency communication (URLLC) and high‐quality services necessitates advanced, adaptive techniques to manage the highly dynamic nature of mmWave channels. This study proposes a novel framework that integrates dual‐attention convolutional neural networks (DSCN‐PAN) with reformed poplar optimization (RePO) to enhance beamforming accuracy and channel estimation efficiency in 5G massive MIMO systems. Compared to conventional methods, the proposed model demonstrates significant performance gains, including over 90% improvement in spectral efficiency, 99.41% beam alignment precision, a 99.5% enhancement in Channel State Information (CSI) estimation, and a 99.2% reduction in bit error rate (BER). The DSCN‐PAN‐RePO architecture effectively supports dynamic and complex communication environments, offering a scalable and energy‐efficient solution for next‐generation wireless networks.
  • Drone Detection in Restricted Areas Using Deep Learning
    , Humaira Nishat, , Shakeel Ahmed, , , Devarapally Akanksha, , and
    Jurnal Kejuruteraan, 2025
    As the use of drones becomes more widespread, the corresponding rise in drone-related intrusions poses a growing threat to public safety and privacy. Traditional anti-drone systems typically rely on radio-frequency sensors for drone tracking. This paper investigates the fusion of deep learning-based detection algorithms with surveillance cameras within the framework of radio-frequency anti-drone systems. The primary aim is to assess the efficacy of contemporary models and training methodologies in achieving precise and real-time drone detection. One of the central challenges addressed in this paper is the detection of small drones at extended distances, coupled with the demand for real-time performance. To overcome the scarcity of small drone datasets, the research team constructed a real-world dataset for comprehensive evaluations. Various iterations of the YOLO (You Only Look Once) models were compared using this dataset, with specific modifications implemented to enhance small object detection. Additionally, the image sources are diversified for training, incorporating bird images to mitigate false positives and enhance model robustness. Among the YOLO models tested, YOLOv5 exhibited superior precision, recall, and F1 score. The work delves into the impact of additional detection layers on precision, recall, and F1 score, revealing trade-offs between these metrics. The inclusion of bird images in the background training process demonstrated improvements in accuracy and recall, underlining the importance of diverse training data. An intriguing finding was observed when excluding extremely small drones and birds from the analysis, resulting in heightened precision but diminished recall. This highlights the delicate balance required in optimizing detection algorithms for different scenarios. The paper also acknowledges the need for further investigation into the generalizability of the proposed approach across various drone types.
  • Optimizing live video streaming: Integrating 5G, IoT, and cloud computing with machine learning
    L. Srinivasan, Humaira Nishat, S. Shargunam, Deepak Kumar Nayak, K. Janani
    Internet Technology Letters, 2025
    In this research, we optimize live video broadcast performance by incorporating advanced technologies such as 5G, the Internet of Things (IoT), and cloud computing. Our approach utilizes the Random Forest classifier to categorize data, achieving a 99% precision rate. A comparative study demonstrates that our proposed technique outperforms RCNN and Mask‐RCNN methods in optimizing video streaming efficacy. We show that our method efficiently enhances video streaming quality by integrating machine learning technologies. The combination of 5G, IoT, and cloud computing creates a robust environment for delivering optimized Live video streaming to users. This research underscores the importance of leveraging cutting‐edge technology to address optimization challenges in modern video streaming systems, focusing on the real‐time optimization of video streams in contemporary technological environments.
  • Efficient IoT Based Smart Grid Stability Prediction Using Simple Efficient Metapath Aggregated Network and Pufferfish Optimization Algorithm
    Porchelvi N, Prabakaran P, Krishnakumar R, Vijayakumar K, Humaira Nishat, Arun Kumar U
    2025 International Conference on Computing Technologies and Data Communication Icctdc 2025, 2025
    Internet of Things (IoT)-based Smart Grid (SG) stability prediction leverages interconnected sensors and devices to monitor grid conditions, analyze data and forecast potential instabilities, ensuring efficient power distribution and enhancing the grid's ability to respond to fluctuations in demand or supply. The system encounters difficulties in managing extensive IoT sensor data and maintaining accuracy standards and fast data processing for prompt instability predictions. To overcome these drawbacks, this paper proposes a hybrid approach for IoT based SG stability prediction. The process begins by gathering data from SG dataset, which is then passed through a pre-processing phase. Regularized Bias- Aware Ensemble Kalman Filter (RBAEKF) is employed to clean and remove the missing value in the input data. The pre-processed output was fed to SEMAN the data enters the prediction phase, to enhance the accuracy of predictions. The stability of IoT -based SG, both stable and unstable conditions, is successfully predicted and classified by using SEMAN. The weight parameter of SEMAN is optimized using POA. The SEMAN-POA technique is implemented in MATLAB and evaluated using various performance metrics, including accuracy, precision, recall, Fl-score, specificity and Root Mean Squared Error (RMSE). The results show that the SEMAN- POA method outperforms existing approaches, such as Artificial Neural Network (ANN), Multi-Layer Perceptron- Extreme Learning Machine- Particle Swarm Optimization (MLP-ELM-PSO), Symbiotic Organism Search- Multiheaded Self Attention Long Short-Term Memory (SOS-MHSA- LSTM), Levenberg-Marquardt (LM) optimization and Dipper Throated Optimization Algorithm-Gradient Boosting (DTO- GB). The proposed method, achieving 98.5% accuracy, 98.7% recall, and 98.4 % precision, proves to be the most efficient and reliable approach for SG stability prediction.
  • Development of Authentication Framework Using GAIT for IoT Communication Models
    Humaira Nishat, T.S. Balaji, S. Shargunam, R. Sasikala, K Rani
    2024 International Conference on Optimization Computing and Wireless Communication Icocwc 2024, 2024
    The data collected by the authorised sensor devices is transferred to the cloud for safe storage. Communication occurs via a secure key management system designed for a decentralised network. Subsequently, it is important to guarantee that only authorised individuals are able to get this data from the cloud. Ensuring user authentication is of utmost importance in resource-constrained IoT devices due to the large number of users accessing sensitive data. The current user authentication methods suffer from accuracy, error rate, and implementation time problems. A novel solution is offered to tackle these challenges by introducing a sophisticated stacked ensemble authentication architecture that utilises cellphones for IoT systems. The suggested authentication approach utilises gait analysis to authenticate individuals by recognising their unique patterns of human movement. A smartphone equipped with an accelerometer and gyroscope sensor is used to gather gait characteristics. The proposed system has two components: preprocessors and classifiers. The preprocessor performs noise reduction and feature extraction. Subsequently, stacked ensemble machine learning classification models, including base and meta classifiers, are used to classify the characteristics, such as linear acceleration, rotation rate, and gravity, for the purpose of user authentication. The accuracy and error rate of the outcomes are compared with the schemes that already exist. The observation indicates that the suggested framework attains a 95% accuracy rate, a lower error rate, and optimised execution time.
  • Retracted: Development of Authentication Framework Using GAIT for IoT Communication Models (2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024 DOI: 10.1109/ICOCWC60930.2024.10470660)
    Humaira Nishat, T.S. Balaji, S. Shargunam, R. Sasikala, K Rani
    2024 International Conference on Optimization Computing and Wireless Communication Icocwc 2024, 2024
    The data collected by the authorised sensor devices is transferred to the cloud for safe storage. Communication occurs via a secure key management system designed for a decentralised network. Subsequently, it is important to guarantee that only authorised individuals are able to get this data from the cloud. Ensuring user authentication is of utmost importance in resource-constrained IoT devices due to the large number of users accessing sensitive data. The current user authentication methods suffer from accuracy, error rate, and implementation time problems. A novel solution is offered to tackle these challenges by introducing a sophisticated stacked ensemble authentication architecture that utilises cellphones for IoT systems. The suggested authentication approach utilises gait analysis to authenticate individuals by recognising their unique patterns of human movement. A smartphone equipped with an accelerometer and gyroscope sensor is used to gather gait characteristics. The proposed system has two components: preprocessors and classifiers. The preprocessor performs noise reduction and feature extraction. Subsequently, stacked ensemble machine learning classification models, including base and meta classifiers, are used to classify the characteristics, such as linear acceleration, rotation rate, and gravity, for the purpose of user authentication. The accuracy and error rate of the outcomes are compared with the schemes that already exist. The observation indicates that the suggested framework attains a 95% accuracy rate, a lower error rate, and optimised execution time.
  • Earlier Detection of Pancreatic Cancer Using Neural Network Based Optimization Technique
    K. S. Chandragupta Mauryan, Humaira Nishat, U. Arunkumar, P. Megaladevi
    7th International Conference on Electronics Communication and Aerospace Technology Iceca 2023 Proceedings, 2023
    Medical healthcare systems are being extensively studied, giving computer technology lots of space to innovate. The most important medical research is predicting cancer, which may take numerous forms and affect many body parts. One of the most common fatal diseases is pancreatic cancer, which cannot be cured once identified and is sometimes unexpected since it is placed in the abdomen beyond the stomach. CT and MRI often give CAD, quantitative evaluations, and automated pancreatic cancer segmentation. These cancer classification methods might identify, predict, and assist personalized medicine to cure cancer without malignant invasions. Flying Squirrel optimization segments, extracts, and classifies features. CNN coupled with Frog Leap optimization. The proposed approach uses frog leap optimization to identify picture normalcy and abnormality. The suggested method minimizes errors for correct classification. Segmenting the aberrant picture with an adaptive flying squirrel algorithm determines cancer size and severity. The more efficient CNN-FLFS method predicts pancreatic cancer with 99% accuracy.
  • T-Slot Patch Antenna Design for 5G Applications in Sub-6 GHz Band with Radome Analysis
    Husna Naairah, Lakshminarayana Pollayi, N Srujana Vahini, D Ramakrishna, Humaira Nishat
    2023 IEEE Microwaves Antennas and Propagation Conference Mapcon 2023, 2023
    This paper introduces a compact dual-polarized patch antenna designed specifically for Sub-6 GHz 5G base station arrays. The antenna’s compactness is achieved through a T-slot patch design, and it features a unique feeding structure with connecting vias. By incorporating a reflector with side walls, the antenna’s directivity and port isolation are enhanced. The study also includes an investigation into radome effects, revealing how material and placement height impact antenna performance. This antenna supports simultaneous transmission and reception in two polarizations, ensuring effective signal propagation. With a wide bandwidth of about 0.44 GHz and a high gain of around 7.6 dB, it provides reliable connectivity for Sub-6 GHz 5G networks.
  • A Machine Learning based Accurate Localization Technique for 5G Networks
    Praveen Chakkravarthy S, Humaira Nishat, Deepa B, Ramya P, Pon Bharathi A
    Proceedings of the 3rd International Conference on Artificial Intelligence and Smart Energy Icais 2023, 2023
    To cater the needs of network scalability and improved performance, 5G networks are set to achieve accurate localization in Indoor/Outdoor environment. This capability can be imparted in the network by training it to behave like a Real Dynamic Network (RDN). The proposed Accurate localization algorithm enable network nodes with self learning capability based on local observations. The decision making of the network is clearly autonomous and due to its self-learning capability, it behaves like a Heterogeneous network. With Ultra-Wide Band communication, the following measurements include Time of Arrival (TOA), Channel State Information (CSI) and Time Difference of Arrival (TDOA) are calculated for the network to justify the accuracy of the proposed algorithm. The Q learning model enhances the decision-making capability of nodes and base stations, which in turn enhance the localization of the proposed network. Simulation results prove that the Q learning model outperforms conventional approaches in terms of matching the performance requirements of 5G networks.
  • A modified multi-hop routing protocol for wireless sensor networks using PSO technique
    International Journal of Advanced Science and Technology, 2020
  • A modified TDMA traffic model for vehicular Ad-Hoc networks
    International Journal of Recent Technology and Engineering, 2019

RECENT SCHOLAR PUBLICATIONS

  • Sensitivity-enhanced machine learning–assisted terahertz Plasmonic biosensor using hybrid 2D materials for tuberculosis detection
    K Rejini, H Nishat, P Manikandan, P Ashok, W Ochen
    Sensing and Bio-Sensing Research, 100964 , 2026
    2026
    Citations: 1
  • Optimized Dual‐Attention Convolutional Neural Networks for Hybrid Beamforming and High‐Precision Channel Estimation in 5G Massive MIMO Wireless Communications Systems
    S Prabhu, H Nishat, S Krishnamurthy Subramaniyan, H Anandaram, ...
    Internet Technology Letters 8 (6), e70129 , 2025
    2025
  • Communication between Electric Vehicles and Battery Stations using CAN Protocol
    H Nishat, S Ahmed
    CVR Journal of Science and Technology 28 (1), 36-42 , 2025
    2025
  • Optimizing live video streaming: Integrating 5G, IoT, and cloud computing with machine learning
    L Srinivasan, H Nishat, S Shargunam, DK Nayak, K Janani
    Internet Technology Letters 8 (2), e556 , 2025
    2025
    Citations: 2
  • DRONE DETECTION IN RESTRICTED AREAS USING DEEP LEARNING
    H NISHAT, SH AHMED, D AKANKSHA
    JURNAL KEJURUTERAAN 37 (2), 609-616 , 2025
    2025
  • The smart enhancement of near field sensing range for terahertz antenna in 6G wireless communication systems
    MM Kamruzzaman, Y Trabelsi, H Nishat, R Perinbaraj, P Ashok, R Mekala
    Optical and Quantum Electronics 56 (9), 1452 , 2024
    2024
    Citations: 10
  • IR Sensor based Smart Parking System
    H Nishat, S Ahmed
    CVR Journal of Science and Technology 26 (1), 27-31 , 2024
    2024
    Citations: 1
  • Retracted: Development of Authentication Framework Using GAIT for IoT Communication Models
    H Nishat, TS Balaji, S Shargunam, R Sasikala, K Rani
    2024 International Conference on Optimization Computing and Wireless … , 2024
    2024
    Citations: 1
  • T-Slot Patch Antenna Design for 5G Applications in Sub-6 GHz Band with Radome Analysis
    H Naairah, L Pollayi, NS Vahini, D Ramakrishna, H Nishat
    2023 IEEE Microwaves, Antennas, and Propagation Conference (MAPCON), 1-5 , 2023
    2023
    Citations: 1
  • Earlier Detection of Pancreatic Cancer Using Neural Network Based Optimization Technique
    KSC Mauryan, H Nishat, U Arunkumar, P Megaladevi
    2023 7th International Conference on Electronics, Communication and … , 2023
    2023
    Citations: 4
  • A machine learning based accurate localization technique for 5G networks
    P Chakkravarthy, H Nishat
    2023 Third International Conference on Artificial Intelligence and Smart … , 2023
    2023
    Citations: 4
  • IoT Based Smart Power Management in Public Areas along with Public Traffic Monitoring
    V Shilpa, H Nishat
    CVR Journal of Science and Technology 23 (1), 65-69 , 2023
    2023
  • An SDN Framework for VANET
    S Ahmed, H Nishat
    CVR Journal of Science and Technology 20 (1), 67-71 , 2021
    2021
  • A Modified Multi-Hop Routing Protocol for Wireless Sensor Networks Using PSO Technique
    SA Humaira Nishat
    SCOPUS Indexed International Journal of Advanced Science and Technology … , 2020
    2020
  • A Modified TDMA Traffic Model for Vehicular Ad-Hoc Networks
    SA Humaira Nishat
    SCOPUS Indexed International Journal of Recent Technology and Engineering 7 … , 2019
    2019
  • A Cluster-Based TDMA Traffic Model for VANETs
    SA Humaira Nishat
    AICTE Sponsored International Conference in Computing and Communication … , 2018
    2018
  • A Study on Non Orthogonal Multiple Access (NOMA) for 5G
    RPD Humaira Nishat
    UGC Approved International Journal on Computational Engineering Research 8 … , 2018
    2018
  • BER analysis of digital modulation schemes using LabVIEW
    RP Devi, H Nishat
    CVR Journal of Science and Technology 13, 41-45 , 2017
    2017
    Citations: 3
  • Design and Implementation of Secure and Efficient CASER Protocol for Wireless Sensor Networks
    GN Humaira Nishat
    International Journal of Modern Trends in Engineering and Research 4 (Issue … , 2017
    2017
  • Performance Evaluation of Digital Modulation Schemes BPSK, QPSK QAM
    R Devi, H Nishat
    Int. J. Eng. Tech 3 (2), 71-74 , 2017
    2017
    Citations: 7

MOST CITED SCHOLAR PUBLICATIONS

  • Performance evaluation of on demand routing protocols AODV and modified AODV (R-AODV) in MANETS
    H Nishat, V Krishna, DDS Rao, S Ahmed
    International Journal of Distributed and Parallel Systems (IJDPS) Vol 2 , 2011
    2011
    Citations: 30
  • Energy Efficient Routing Protocols for Mobile Ad Hoc Networks
    DSR Humaira Nishat
    International Journal of Computer Applications 26 (Issue 2), 1-4 , 2011
    2011
    Citations: 18
  • Performance evaluation of routing protocols in MANETs
    H Nishat, S Pothalaiah, DS Rao
    International Journal of Wireless & Mobile Networks (IJWMN) 3 (2), 67-75 , 2011
    2011
    Citations: 12
  • The smart enhancement of near field sensing range for terahertz antenna in 6G wireless communication systems
    MM Kamruzzaman, Y Trabelsi, H Nishat, R Perinbaraj, P Ashok, R Mekala
    Optical and Quantum Electronics 56 (9), 1452 , 2024
    2024
    Citations: 10
  • Performance Evaluation of Digital Modulation Schemes BPSK, QPSK QAM
    R Devi, H Nishat
    Int. J. Eng. Tech 3 (2), 71-74 , 2017
    2017
    Citations: 7
  • Earlier Detection of Pancreatic Cancer Using Neural Network Based Optimization Technique
    KSC Mauryan, H Nishat, U Arunkumar, P Megaladevi
    2023 7th International Conference on Electronics, Communication and … , 2023
    2023
    Citations: 4
  • A machine learning based accurate localization technique for 5G networks
    P Chakkravarthy, H Nishat
    2023 Third International Conference on Artificial Intelligence and Smart … , 2023
    2023
    Citations: 4
  • BER analysis of digital modulation schemes using LabVIEW
    RP Devi, H Nishat
    CVR Journal of Science and Technology 13, 41-45 , 2017
    2017
    Citations: 3
  • Energy efficient dynamic route discovery protocol for mobile ad hoc networks
    H Nishat, D Sreenivasa Rao
    International Journal of Computer Applications 56 (7), 44-47 , 2012
    2012
    Citations: 3
  • Optimizing live video streaming: Integrating 5G, IoT, and cloud computing with machine learning
    L Srinivasan, H Nishat, S Shargunam, DK Nayak, K Janani
    Internet Technology Letters 8 (2), e556 , 2025
    2025
    Citations: 2
  • Energy aware qos on-demand routing protocols for manets
    H Nishat, DS Rao
    International Journal Of Computer Applications 23 (8), 12-17 , 2011
    2011
    Citations: 2
  • Sensitivity-enhanced machine learning–assisted terahertz Plasmonic biosensor using hybrid 2D materials for tuberculosis detection
    K Rejini, H Nishat, P Manikandan, P Ashok, W Ochen
    Sensing and Bio-Sensing Research, 100964 , 2026
    2026
    Citations: 1
  • IR Sensor based Smart Parking System
    H Nishat, S Ahmed
    CVR Journal of Science and Technology 26 (1), 27-31 , 2024
    2024
    Citations: 1
  • Retracted: Development of Authentication Framework Using GAIT for IoT Communication Models
    H Nishat, TS Balaji, S Shargunam, R Sasikala, K Rani
    2024 International Conference on Optimization Computing and Wireless … , 2024
    2024
    Citations: 1
  • T-Slot Patch Antenna Design for 5G Applications in Sub-6 GHz Band with Radome Analysis
    H Naairah, L Pollayi, NS Vahini, D Ramakrishna, H Nishat
    2023 IEEE Microwaves, Antennas, and Propagation Conference (MAPCON), 1-5 , 2023
    2023
    Citations: 1
  • An Enhanced Dynamic Multilevel Priority Packet Scheduling Scheme for Wireless Sensor Networks
    S Ahmed, H Nishat
    International Journal of Science Technology and Engineering 1 (Issue 11 … , 2015
    2015
    Citations: 1
  • A New Scalable Hybrid Routing Protocol for Vanets
    AS Humaira Nishat
    International Journal of Engineering Research and Technology 2 (Issue 11 … , 2013
    2013
    Citations: 1
  • A New Dynamic Route Discovery Mechanism for Mobile Ad Hoc Networks
    H Nishat, DDS Rao
    International Journal of Computer Applications and Information Technology … , 2012
    2012
    Citations: 1
  • Optimized Dual‐Attention Convolutional Neural Networks for Hybrid Beamforming and High‐Precision Channel Estimation in 5G Massive MIMO Wireless Communications Systems
    S Prabhu, H Nishat, S Krishnamurthy Subramaniyan, H Anandaram, ...
    Internet Technology Letters 8 (6), e70129 , 2025
    2025
  • Communication between Electric Vehicles and Battery Stations using CAN Protocol
    H Nishat, S Ahmed
    CVR Journal of Science and Technology 28 (1), 36-42 , 2025
    2025

Publications

1. G. Durga Akshaya, Humaira Nishat, “A Novel Approach to Provide Security for Women Using Smart Device”, Juni Khyat ISSN: 2278-4632 (UGC Care Group I Listed Journal) Vol-12 Issue-01 Dec 2022, pp. 1279-1287.
2. Gaurav Sharma, Humaira Nishat, “Outdoor Node Localization in WSN for Industry Application”, UGC Care Approved Journal Samriddhi: A Journal of Physical Sciences, Engineering and Technology, , Issue 4, ISSN: 2229-7111, 2022.
3. Humaira Nishat, Shakeel Ahmed, “A Modified Multi-Hop Routing Protocol for Wireless Sensor Networks Using PSO Technique”, SCOPUS Indexed International Journal of Advanced Science and Technology (IJAST), Volume 29, , ISSN: 2005-4238, March 2020, , Impact factor 0.41 H-index
4. Shakeel Ahmed, Humaira Nishat, “A Modified TDMA Traffic Model for Vehicular Ad-Hoc Networks”, SCOPUS Indexed International Journal of Recent Technology and Engineering (IJRTE), Volume 7, Issue-6S, ISSN: 2277-3878, March 2019, , Impact factor 5.11
5. R. Prameela Devi, Humaira Nishat, “A Study on Non-Orthogonal Multiple Access (NOMA) for 5G”, UGC Approved International Journal on Computational Engineering Research (IJCER), Volume 8, Issue 9, , ISSN (e): 2250-3005. Impact factor 6.41
6. G.Nagarani, Humaira Nishat, “ Design and Implementation of Secure and Efficient CASER Protocol for Wireless Sensor Networks”, UGC Approved International Journal of Modern Trends in Engineering and Research (IJMTER), Volume 4, Iss

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

9 patents published, 1 granted, 1 under examination