Hayat Ullah

1.1k total citations
23 papers, 667 citations indexed

About

Hayat Ullah is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Safety, Risk, Reliability and Quality. According to data from OpenAlex, Hayat Ullah has authored 23 papers receiving a total of 667 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 10 papers in Artificial Intelligence and 2 papers in Safety, Risk, Reliability and Quality. Recurrent topics in Hayat Ullah's work include Video Surveillance and Tracking Methods (7 papers), Image Enhancement Techniques (6 papers) and Anomaly Detection Techniques and Applications (6 papers). Hayat Ullah is often cited by papers focused on Video Surveillance and Tracking Methods (7 papers), Image Enhancement Techniques (6 papers) and Anomaly Detection Techniques and Applications (6 papers). Hayat Ullah collaborates with scholars based in United States, South Korea and Pakistan. Hayat Ullah's co-authors include Muhammad Zubair Asghar, Arslan Munir, Khan Muhammad, Victor Hugo C. de Albuquerque, Daniyal Alghazzawi, Omaimah Bamasag, Abdu Gumaei, Mabrook Al‐Rakhami, Asad Masood Khattak and Muhammad Sajjad and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Transactions on Image Processing.

In The Last Decade

Hayat Ullah

23 papers receiving 641 citations

Peers

Hayat Ullah

CVPR

CNC

Lijun He China

Minas Dasygenis Greece

S. P. Raja India

Akey Sungheetha India

Vinh Truong Hoang Vietnam

Azizi Abdullah Malaysia

Minh Nguyen New Zealand

Norman Haas United States

Sheshang Degadwala India

K. Sakthidasan Sankaran India

Lijun He China

Hayat Ullah

237 ×0.8

CVPR

127 ×0.7

175 ×1.2

206 ×2.3

CNC

29 ×0.5

Citations per year, relative to Hayat Ullah Hayat Ullah (= 1×) peers Lijun He

Countries citing papers authored by Hayat Ullah

Since Specialization

Citations

This map shows the geographic impact of Hayat Ullah's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hayat Ullah with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hayat Ullah more than expected).

Fields of papers citing papers by Hayat Ullah

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hayat Ullah. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hayat Ullah. The network helps show where Hayat Ullah may publish in the future.

Co-authorship network of co-authors of Hayat Ullah

This figure shows the co-authorship network connecting the top 25 collaborators of Hayat Ullah. A scholar is included among the top collaborators of Hayat Ullah based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hayat Ullah. Hayat Ullah is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Alghazzawi, Daniyal, et al.. (2025). Explainable AI-based suicidal and non-suicidal ideations detection from social media text with enhanced ensemble technique. Scientific Reports. 15(1). 1111–1111. 3 indexed citations

Ullah, Hayat, et al.. (2025). LiteCOD: Lightweight Camouflaged Object Detection via Holistic Understanding of Local-Global Features and Multi-Scale Fusion. AI. 6(9). 197–197. 1 indexed citations

Munir, Arslan, et al.. (2024). H-QNN: A Hybrid Quantum–Classical Neural Network for Improved Binary Image Classification. SHILAP Revista de lepidopterología. 5(3). 1462–1481. 6 indexed citations

Ullah, Hayat & Arslan Munir. (2023). A 3DCNN-Based Knowledge Distillation Framework for Human Activity Recognition. Journal of Imaging. 9(4). 82–82. 3 indexed citations

Ullah, Hayat & Arslan Munir. (2023). Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework. Journal of Imaging. 9(7). 130–130. 26 indexed citations

Ullah, Hayat, et al.. (2023). ViT-ReT: Vision and Recurrent Transformer Neural Networks for Human Activity Recognition in Videos. IEEE Access. 11. 72227–72249. 43 indexed citations

Ullah, Hayat, et al.. (2023). Cascaded Deep Reinforcement Learning-Based Multi-Revolution Low-Thrust Spacecraft Orbit-Transfer. IEEE Access. 11. 82894–82911. 6 indexed citations

Muhammad, Khan, Hayat Ullah, Salman Khan, Mohammad Hijji, & Jaime Lloret. (2022). Efficient Fire Segmentation for Internet-of-Things-Assisted Intelligent Transportation Systems. IEEE Transactions on Intelligent Transportation Systems. 24(11). 13141–13150. 10 indexed citations

Muhammad, Khan, Tanveer Hussain, Hayat Ullah, et al.. (2022). Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks. IEEE Transactions on Intelligent Transportation Systems. 23(12). 22694–22715. 93 indexed citations

10.

Munir, Arslan, et al.. (2022). A Spatial AI-Based Agricultural Robotic Platform for Wheat Detection and Collision Avoidance. SHILAP Revista de lepidopterología. 3(3). 719–738. 7 indexed citations

11.

Ullah, Hayat, et al.. (2022). Perceptual quality assessment of panoramic stitched contents for immersive applications: a prospective survey. Virtual Reality & Intelligent Hardware. 4(3). 223–246. 4 indexed citations

12.

Muhammad, Khan, Hayat Ullah, Zulfiqar Ahmad Khan, et al.. (2022). WEENet: An Intelligent System for Diagnosing COVID-19 and Lung Cancer in IoMT Environments. Frontiers in Oncology. 11. 811355–811355. 10 indexed citations

13.

Shabir, Muhammad, et al.. (2022). Novel Bipolar Soft Rough-Set Approximations and Their Application in Solving Decision-Making Problems. International Journal of Fuzzy Logic and Intelligent Systems. 22(3). 303–324. 2 indexed citations

14.

Muhammad, Khan, Hayat Ullah, Mohammad S. Obaidat, et al.. (2021). AI-Driven Salient Soccer Events Recognition Framework for Next-Generation IoT-Enabled Environments. IEEE Internet of Things Journal. 10(3). 2202–2214. 17 indexed citations

15.

Alghazzawi, Daniyal, Omaimah Bamasag, Hayat Ullah, & Muhammad Zubair Asghar. (2021). Efficient Detection of DDoS Attacks Using a Hybrid Deep Learning Model with Improved Feature Selection. Applied Sciences. 11(24). 11634–11634. 91 indexed citations

16.

Ullah, Hayat, et al.. (2021). Comparative study for machine learning classifier recommendation to predict political affiliation based on online reviews. CAAI Transactions on Intelligence Technology. 6(3). 251–264. 16 indexed citations

17.

Ullah, Hayat, Khan Muhammad, Saeed Anwar, et al.. (2021). Light-DehazeNet: A Novel Lightweight CNN Architecture for Single Image Dehazing. IEEE Transactions on Image Processing. 30. 8968–8982. 115 indexed citations

18.

Ullah, Hayat, et al.. (2020). Automatic 360° Mono-Stereo Panorama Generation Using a Cost-Effective Multi-Camera System. Sensors. 20(11). 3097–3097. 16 indexed citations

19.

Ullah, Hayat, et al.. (2020). DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment. Sensors. 20(22). 6457–6457. 15 indexed citations

20.

Ullah, Hayat & Irfan Mehmood. (2019). Real-Time Video Dehazing for Industrial Image Processing. 1–6. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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