Fath U Min Ullah

2.0k total citations · 1 hit paper
30 papers, 1.4k citations indexed

About

Fath U Min Ullah is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Fath U Min Ullah has authored 30 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 14 papers in Electrical and Electronic Engineering and 12 papers in Computer Vision and Pattern Recognition. Recurrent topics in Fath U Min Ullah's work include Energy Load and Power Forecasting (10 papers), Anomaly Detection Techniques and Applications (9 papers) and Video Surveillance and Tracking Methods (8 papers). Fath U Min Ullah is often cited by papers focused on Energy Load and Power Forecasting (10 papers), Anomaly Detection Techniques and Applications (9 papers) and Video Surveillance and Tracking Methods (8 papers). Fath U Min Ullah collaborates with scholars based in South Korea, United Kingdom and Saudi Arabia. Fath U Min Ullah's co-authors include Sung Wook Baik, Ijaz Ul Haq, Amin Ullah, Khan Muhammad, Mi Young Lee, Noman Khan, Tanveer Hussain, Zulfiqar Ahmad Khan, Seungmin Rho and Waseem Ullah and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Sensors.

In The Last Decade

Fath U Min Ullah

29 papers receiving 1.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A modified YOLOv5 architecture for efficient fire detection in smart cities

2023 83 citations Hikmat Yar, Zulfiqar Ahmad Khan et al. Expert Systems with Applications profile →

Peers

Countries citing papers authored by Fath U Min Ullah

Since Specialization

Citations

This map shows the geographic impact of Fath U Min 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 Fath U Min Ullah with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fath U Min Ullah more than expected).

Fields of papers citing papers by Fath U Min Ullah

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fath U Min 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 Fath U Min Ullah. The network helps show where Fath U Min Ullah may publish in the future.

Co-authorship network of co-authors of Fath U Min Ullah

This figure shows the co-authorship network connecting the top 25 collaborators of Fath U Min Ullah. A scholar is included among the top collaborators of Fath U Min 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 Fath U Min Ullah. Fath U Min 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

#	Work	Indexed citations
1	Improved EfficientNet Architecture for Multi-Grade Brain Tumor Detection 2025 ·Electronics ·(unknown), Fath U Min Ullah, (unknown), (unknown), Tae‐Sun Chung	7
2	EFNet-CSM: EfficientNet with a modified attention mechanism for effective fire detection 2025 ·Knowledge-Based Systems ·Hikmat Yar, Fath U Min Ullah, Zulfiqar Ahmad Khan, (unknown), Sung Wook Baik	0
3	Optimized cross-module attention network and medium-scale dataset for effective fire detection 2024 ·Pattern Recognition ·Zulfiqar Ahmad Khan, Fath U Min Ullah, Hikmat Yar, Waseem Ullah, Noman Khan, (unknown), Sung Wook Baik	6
4	Dual Deep Learning Network for Abnormal Action Detection 2024 ·CLOK (University of Central Lancashire) ·Fath U Min Ullah, Zulfiqar Ahmad Khan, Sung Wook Baik, (unknown), Saeed Anwar, Muhammad Khurram Khan	1
5	Predictive modeling for ubiquitin proteins through advanced machine learning technique 2024 ·Heliyon ·(unknown), Fath U Min Ullah, Seungmin Rho, Mi Young Lee	2
6	A modified YOLOv5 architecture for efficient fire detection in smart cities breakdown → 2023 ·Expert Systems with Applications ·Hikmat Yar, Zulfiqar Ahmad Khan, Fath U Min Ullah, Waseem Ullah, Sung Wook Baik	83
7	TransCNN: Hybrid CNN and transformer mechanism for surveillance anomaly detection 2023 ·Engineering Applications of Artificial Intelligence ·Waseem Ullah, Tanveer Hussain, Fath U Min Ullah, Mi Young Lee, Sung Wook Baik	77
8	CT-NET: A Novel Convolutional Transformer-Based Network for Short-Term Solar Energy Forecasting Using Climatic Information 2023 ·Computer Systems Science and Engineering ·Muhammad Munsif, Fath U Min Ullah, Samee U. Khan, Noman Khan, Sung Wook Baik	16
9	FADS: An Intelligent Fatigue and Age Detection System 2023 ·Mathematics ·Mohammad Hijji, Hikmat Yar, Fath U Min Ullah, Mohammed M. Alwakeel, (unknown), (unknown), Faouzi Alaya Cheikh, Khan Muhammad, Muhammad Sajjad	5
10	AD‐Graph: Weakly Supervised Anomaly Detection Graph Neural Network 2023 ·International Journal of Intelligent Systems ·Waseem Ullah, Tanveer Hussain, Fath U Min Ullah, Khan Muhammad, M. Hassaballah, Joel J. P. C. Rodrigues, Sung Wook Baik, Victor Hugo C. de Albuquerque	6
11	A comprehensive survey on deep facial expression recognition: challenges, applications, and future guidelines 2023 ·Alexandria Engineering Journal ·Muhammad Sajjad, Fath U Min Ullah, Mohib Ullah, (unknown), Faouzi Alaya Cheikh, Mohammad Hijji, Khan Muhammad, Joel J. P. C. Rodrigues	67
12	Sequential attention mechanism for weakly supervised video anomaly detection 2023 ·Expert Systems with Applications ·Waseem Ullah, Fath U Min Ullah, Zulfiqar Ahmad Khan, Sung Wook Baik	35
13	A Hybrid Deep Learning‐Based Network for Photovoltaic Power Forecasting 2022 ·Complexity ·Altaf Hussain, Zulfiqar Ahmad Khan, Tanveer Hussain, Fath U Min Ullah, Seungmin Rho, Sung Wook Baik	30
14	A Comprehensive Review on Vision-Based Violence Detection in Surveillance Videos 2022 ·ACM Computing Surveys ·Fath U Min Ullah, Mohammad S. Obaidat, Amin Ullah, Khan Muhammad, Mohammad Hijji, Sung Wook Baik	47
15	Deep Learning‐Assisted Short‐Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRU 2022 ·Complexity ·Fath U Min Ullah, Amin Ullah, Noman Khan, Mi Young Lee, Seungmin Rho, Sung Wook Baik	26
16	Deep Learning Assists Surveillance Experts: Toward Video Data Prioritization 2022 ·IEEE Transactions on Industrial Informatics ·Tanveer Hussain, Fath U Min Ullah, Samee U. Khan, Amin Ullah, (unknown), Khan Muhammad, Sung Wook Baik, Victor Hugo C. de Albuquerque	4
17	AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting 2021 ·Mathematics ·Noman Khan, Fath U Min Ullah, Ijaz Ul Haq, Samee U. Khan, Mi Young Lee, Sung Wook Baik	43
18	Diving Deep into Short-Term Electricity Load Forecasting: Comparative Analysis and a Novel Framework 2021 ·Mathematics ·Fath U Min Ullah, Noman Khan, Tanveer Hussain, Mi Young Lee, Sung Wook Baik	37
19	Short-Term Prediction of Residential Power Energy Consumption via CNN and Multi-Layer Bi-Directional LSTM Networks 2019 ·IEEE Access ·Fath U Min Ullah, Amin Ullah, Ijaz Ul Haq, Seungmin Rho, Sung Wook Baik	150
20	Raspberry Pi assisted facial expression recognition framework for smart security in law-enforcement services 2018 ·Information Sciences ·Muhammad Sajjad, Mansoor Nasir, Fath U Min Ullah, Khan Muhammad, Arun Kumar Sangaiah, Sung Wook Baik	81

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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