Unmanned Aerial Vehicles, Software Defined Networks, Cloud Computing
15
Scopus Publications
154
Scholar Citations
7
Scholar h-index
6
Scholar i10-index
Scopus Publications
Statistical Motion Prediction of Rogue UAVs for Efficient Pursuit and Capture Using a Hybrid Kalman-Particle Filter Framework Mohd. Abuzar Sayeed, Mohd. Asim Sayeed, Tanveer Ahmed, Rohit Verma IEEE Access, 2026 The growing rogue or intruder aerial incursions demand a robust yet stable interception mechanism that can estimate the UAV state considering the measurement inaccuracies and process noise. To estimate, track and capture rogue aerial nodes, navigating through 2D way points with inherent Gaussian process noise, the study introduces a hybrid tracking and estimation framework, integrating Kalman and Particle filters. The integration facilitates tracking and predicting both linear and abrupt non linear maneuvers along the rogue trajectory. The rogue aerial motion is modeled through a random burst process using a random acceleration, while the interceptor UAV employs a pursuit-evasion control. The evasion control in turn is driven by the probabilistic state estimates received from the Hybrid filter. Accuracy, energy efficiency and interception success rate of the proposed framework is measured across extensive Monte Carlo simulations (10,000 trials), under varied noise intensities. Comparative analysis against single-filter methods suggest that the Hybrid filter improves trajectory, achieves better estimation accuracy and high interception probability. Under an unpredictable and dynamic aerial environment, the suggested framework allows for adaptive, real-time pursuit and capture of rogue UAVs.
Risk-Sensitive Stochastic Control for Robust UAV-Assisted Free-Space Quantum Key Distribution under Atmospheric Turbulence Mohd. Asim Sayeed, Mohd. Abuzar Sayeed, Tanveer Ahmed, Anuj Kumar Bharti, Rohit Verma IEEE Access, 2026 Unmanned aerial vehicles (UAVs) enable rapidly deployable secure communication, while quantum key distribution (QKD) ensures information-theoretic security over free-space optical channels. In UAV-assisted QKD, however, atmospheric turbulence and mobility-induced misalignment introduce stochastic channel fluctuations that cause intermittent secrecy degradation. Existing trajectory optimization methods are largely deterministic or risk-neutral and fail to address rare but severe deep-fading events. We develop a risk-sensitive stochastic control framework for robust UAV-assisted free-space QKD under atmospheric uncertainty. The UAV motion is modeled as a continuous-time stochastic dynamical system, and the quantum channel transmissivity is characterized by mobility-dependent log-normal fading, yielding a stochastic secret key rate process. The exponential performance functional simultaneously penalizes the propulsion energy and secrecy outage, resulting in a nonlinear risk-sensitive Hamilton-Jacobi-Bellman equation. Simulation results show that there is a significant reduction in secrecy outage probability and key rate variation when atmospheric turbulence exists, but there is only a relatively minor rise in the energy consumption for propulsion, achieving a robustness-efficiency tradeoff for secure aerial quantum communication.
Enhancing Wind Speed and Power Forecasting Accuracy Using Machine Learning Techniques: A Case Study of Jaisalmer Wind Park, India Raja Owais Ahmad, Aparna Unni, Manjeet Singh, Ravi Sharma, Mohd Abuzar Sayeed, Sunil K Singla 2025 IEEE 4th Industrial Electronics Society Annual on Line Conference Oncon 2025, 2025 This paper presents a detailed investigation into the effectiveness of various machine learning (ML) techniques for forecasting wind speed and power, specifically focusing on the Jaisalmer Wind Park located in Rajasthan, India. The study evaluates linear regression (LR), decision tree regression (DTR), Gaussian process regression (GPR), and artificial neural networks (ANNs) using data from January 2018 to April 2024. The methodologies applied include comprehensive data preprocessing, parameter optimization, and performance evaluation metrics such as mean absolute percentage error and coefficient of determination. The findings aim to enhance the predictive accuracy of wind speed and power forecasts, thereby supporting the integration of renewable energy into the power grid and advancing sustainable energy practices. The research utilizes MATLAB's regression tools for analysis, emphasizing the potential of ML techniques to improve the operational efficiency of wind farms and facilitate a transition towards more sustainable energy systems
Artificial Neural Network Model for Automated Medical Diagnosis Shambhavi Mishra, Tanveer Ahmed, Mohd. Abuzar Sayeed, Umesh Gupta Soft Computing Techniques in Connected Healthcare Systems, 2023 Deep learning, a subfield of machine learning, utilizes artificial neural networks to discern patterns and features within data. It can learn and make decisions autonomously, without explicit programming. In medical diagnosis, deep learning holds the potential to transform disease detection and diagnosis. For example, it can swiftly and accurately analyze vast amounts of data, particularly beneficial in imaging studies or genetic analyses. Deep learning algorithms can also evaluate medical images, such as CT or MRI scans, to discover patterns indicative of specific diseases. Likewise, they can assess genetic data to identify abnormalities that may suggest a predisposition to certain conditions. However, a challenge in using deep learning for medical diagnosis is the necessity for high-quality data. Effective deep learning algorithms require training on extensive and diverse datasets representative of real-world scenarios, which can be problematic in medicine due to data limitations or difficulty in acquisition. Privacy and security concerns surrounding medical data may also hinder deep learning algorithm usage for analysis. Despite these challenges, deep learning's potential in medical diagnosis is widely acknowledged. This book chapter offers a systematic review of deep learning technologies in medical diagnosis, specifically examining performance, privacy, and data availability aspects. Furthermore, this article presents several guidelines to inform future research in deep learning and medical diagnosis.
Efficient deployment with throughput maximization for uavs communication networks Mohd Abuzar Sayeed, Rajesh Kumar, Vishal Sharma, Mohd Asim Sayeed Sensors Switzerland, 2020 The article presents a throughput maximization approach for UAV assisted ground networks. Throughput maximization involves minimizing delay and packet loss through UAV trajectory optimization, reinforcing the congested nodes and transmission channels. The aggressive reinforcement policy is achieved by characterizing nodes, links, and overall topology through delay, loss, throughput, and distance. A position-aware graph neural network (GNN) is used for characterization, prediction, and dynamic UAV trajectory enhancement. To establish correctness, the proposed approach is validated against optimized link state routing (OLSR) driven UAV assisted ground networks. The proposed approach considerably outperforms the classical approach by demonstrating significant gains in throughput and packet delivery ratio with notable decrements in delay and packet loss. The performance analysis of the proposed approach against software-defined UAVs (U-S) and UAVs as base stations (U-B) verifies the consistency and gains in average throughput while minimizing delay and packet loss. The scalability test of the proposed approach is performed by varying data rates and the number of UAVs.
Safeguarding unmanned aerial systems: An approach for identifying malicious aerial nodes Mohd Abuzar Sayeed, Rajesh Kumar, Vishal Sharma Iet Communications, 2020 : The coordination between aerial and ground nodes has enhanced the versatility and quality of the traditional networks. The application of aerial systems in mission-critical operations, as well as civilian applications, brings in the context of safeguarding unmanned aerial systems (UAS) from malicious attackers. This study discusses the threats and attacks mounted on UAS, alongside the challenges introduced by the unmanned aerial vehicle (UAV) network structure itself. A framework for safeguarding UAS against malicious attackers and recovering the rogue UAVs is proposed in the study. The proposed framework enforces a dynamic conceptual grid-based layout over the actual geographical deployment. The dynamically shuffling grid ascertains the security of transmission channels, as every time the grid is shuffled periodically or based on abnormal behaviour, the safety paradigm is reinitiated. Public key cryptographic algorithms are deployed for securing the communication links. Neural networks-based predictions are used for detecting abnormality in behavioural, statistical, and mobility patterns. Principal component analysis based on multivariate statistical analysis is used for detecting outliers in the aerial network environment. The behaviour prediction and outlier detection algorithms significantly improve the overall performance of the network and provide immunity against the intruders with reduced false positives, high accuracy, and better detection rate.
Securing mobile agent's information in ad-hoc network Jasleen Kaur, Sharad Saxena, Mohd Abuzar Sayeed Proceedings of the 5th International Conference on Confluence 2014 the Next Generation Information Technology Summit, 2014
RECENT SCHOLAR PUBLICATIONS
Statistical Motion Prediction of Rogue UAVs for Efficient Pursuit and Capture Using a Hybrid Kalman-Particle Filter Framework MA Sayeed, MA Sayeed, T Ahmed, R Verma IEEE Access , 2026 2026
Check for updates Improving Health Outcomes Through Transfer Learning and LSTM-Driven Air Quality Prediction R Kumar, J Singh, MA Sayeed Intelligent Systems Design and Applications: Machine Learning Solutions … , 2024 2024
Mimicking the mind’s eye: AI-driven methodologies for Rorschach-inspired image interpretation A Pranav, A Jain, A Dubey, MM Ali, M Raj, MA Sayeed International Conference On Innovative Computing And Communication, 249-260 , 2024 2024 Citations: 3
Artificial neural network model for automated medical diagnosis S Mishra, T Ahmed, MA Sayeed, U Gupta Soft Computing Techniques in Connected Healthcare Systems, 34-54 , 2023 2023 Citations: 7
Improving Health Outcomes Through Transfer Learning and LSTM-Driven Air Quality Prediction R Kumar, J Singh, MA Sayeed International Conference on Intelligent Systems Design and Applications, 430-439 , 2023 2023
Forecasting health impacts of air pollution with deep learning models R Kumar, J Singh, MA Sayeed International Conference on Advanced Computing and Intelligent Technologies … , 2023 2023 Citations: 3
Data leakage detection and prevention using cloud computing V Singh, M Raj, I Gupta, MA Sayeed Sustainable Computing: Transforming Industry 4.0 to Society 5.0, 159-169 , 2023 2023 Citations: 7
Efficient deployment with throughput maximization for UAVs communication networks MA Sayeed, R Kumar, V Sharma, MA Sayeed Sensors 20 (22), 6680 , 2020 2020 Citations: 18
Safeguarding unmanned aerial systems: An approach for identifying malicious aerial nodes MA Sayeed, R Kumar, V Sharma IET Communications 14 (17), 3000-3012 , 2020 2020 Citations: 14
Efficient data management and control over WSNs using SDN‐enabled aerial networks MA Sayeed, R Kumar, V Sharma International Journal of Communication Systems 33 (1), e4170 , 2020 2020 Citations: 22
An efficient mobility model for improving transmissions in multi-UAVs enabled WSNs MA Sayeed, R Kumar Drones 2 (3), 31 , 2018 2018 Citations: 31
An SDN-based secure mobility model for UAV-ground communications R Kumar, MA Sayeed, V Sharma, I You International Symposium on Mobile Internet Security, 169-179 , 2017 2017 Citations: 15
Intrusion detection system based on Software Defined Network firewall MA Sayeed, MA Sayeed, S Saxena 2015 1st International Conference on Next Generation Computing Technologies … , 2015 2015 Citations: 28
Securing mobile agent's information in ad-hoc network J Kaur, S Saxena, MA Sayeed 2014 5th International Conference-Confluence The Next Generation Information … , 2014 2014 Citations: 6
MOST CITED SCHOLAR PUBLICATIONS
An efficient mobility model for improving transmissions in multi-UAVs enabled WSNs MA Sayeed, R Kumar Drones 2 (3), 31 , 2018 2018 Citations: 31
Intrusion detection system based on Software Defined Network firewall MA Sayeed, MA Sayeed, S Saxena 2015 1st International Conference on Next Generation Computing Technologies … , 2015 2015 Citations: 28
Efficient data management and control over WSNs using SDN‐enabled aerial networks MA Sayeed, R Kumar, V Sharma International Journal of Communication Systems 33 (1), e4170 , 2020 2020 Citations: 22
Efficient deployment with throughput maximization for UAVs communication networks MA Sayeed, R Kumar, V Sharma, MA Sayeed Sensors 20 (22), 6680 , 2020 2020 Citations: 18
An SDN-based secure mobility model for UAV-ground communications R Kumar, MA Sayeed, V Sharma, I You International Symposium on Mobile Internet Security, 169-179 , 2017 2017 Citations: 15
Safeguarding unmanned aerial systems: An approach for identifying malicious aerial nodes MA Sayeed, R Kumar, V Sharma IET Communications 14 (17), 3000-3012 , 2020 2020 Citations: 14
Artificial neural network model for automated medical diagnosis S Mishra, T Ahmed, MA Sayeed, U Gupta Soft Computing Techniques in Connected Healthcare Systems, 34-54 , 2023 2023 Citations: 7
Data leakage detection and prevention using cloud computing V Singh, M Raj, I Gupta, MA Sayeed Sustainable Computing: Transforming Industry 4.0 to Society 5.0, 159-169 , 2023 2023 Citations: 7
Securing mobile agent's information in ad-hoc network J Kaur, S Saxena, MA Sayeed 2014 5th International Conference-Confluence The Next Generation Information … , 2014 2014 Citations: 6
Mimicking the mind’s eye: AI-driven methodologies for Rorschach-inspired image interpretation A Pranav, A Jain, A Dubey, MM Ali, M Raj, MA Sayeed International Conference On Innovative Computing And Communication, 249-260 , 2024 2024 Citations: 3
Forecasting health impacts of air pollution with deep learning models R Kumar, J Singh, MA Sayeed International Conference on Advanced Computing and Intelligent Technologies … , 2023 2023 Citations: 3
Statistical Motion Prediction of Rogue UAVs for Efficient Pursuit and Capture Using a Hybrid Kalman-Particle Filter Framework MA Sayeed, MA Sayeed, T Ahmed, R Verma IEEE Access , 2026 2026
Check for updates Improving Health Outcomes Through Transfer Learning and LSTM-Driven Air Quality Prediction R Kumar, J Singh, MA Sayeed Intelligent Systems Design and Applications: Machine Learning Solutions … , 2024 2024
Improving Health Outcomes Through Transfer Learning and LSTM-Driven Air Quality Prediction R Kumar, J Singh, MA Sayeed International Conference on Intelligent Systems Design and Applications, 430-439 , 2023 2023
