Munif Alotaibi

1.2k total citations · 1 hit paper
32 papers, 736 citations indexed

About

Munif Alotaibi is a scholar working on Information Systems, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Munif Alotaibi has authored 32 papers receiving a total of 736 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Information Systems, 11 papers in Computer Vision and Pattern Recognition and 10 papers in Artificial Intelligence. Recurrent topics in Munif Alotaibi's work include Advanced Malware Detection Techniques (8 papers), Blockchain Technology Applications and Security (5 papers) and Gait Recognition and Analysis (5 papers). Munif Alotaibi is often cited by papers focused on Advanced Malware Detection Techniques (8 papers), Blockchain Technology Applications and Security (5 papers) and Gait Recognition and Analysis (5 papers). Munif Alotaibi collaborates with scholars based in Saudi Arabia, Kazakhstan and United States. Munif Alotaibi's co-authors include Bandar Alotaibi, Ausif Mahmood, Abdul Razaque, Muder Almiani, Salim Hariri, Aziz Alotaibi, Turki M. Alanazi, Mohamed Esmail Karar, Yaser Jararweh and Ammar Oad and has published in prestigious journals such as IEEE Access, Sensors and Future Generation Computer Systems.

In The Last Decade

Munif Alotaibi

29 papers receiving 709 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.

Deep learning for unmanned aerial vehicles detection: A review

2024 73 citations Turki M. Alanazi, Bandar Alotaibi et al. Computer Science Review profile →

Peers

Munif Alotaibi

CVPR

AI

BE

IS

CNC

Brett J. Borghetti United States

Jiangbo Qian China

Miguel Ángel Bautista Spain

Med Salim Bouhlel Tunisia

Sira Yongchareon New Zealand

Junhuai Li China

Aznul Qalid Md Sabri Malaysia

Suliman A. Alsuhibany Saudi Arabia

Brett J. Borghetti United States

Munif Alotaibi

153 ×0.6

CVPR

329 ×1.3

AI

77 ×0.5

BE

51 ×0.4

IS

220 ×1.9

CNC

Citations per year, relative to Munif Alotaibi Munif Alotaibi (= 1×) peers Brett J. Borghetti

Countries citing papers authored by Munif Alotaibi

Since Specialization

Citations

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

Fields of papers citing papers by Munif Alotaibi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Munif Alotaibi. 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 Munif Alotaibi. The network helps show where Munif Alotaibi may publish in the future.

Co-authorship network of co-authors of Munif Alotaibi

This figure shows the co-authorship network connecting the top 25 collaborators of Munif Alotaibi. A scholar is included among the top collaborators of Munif Alotaibi 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 Munif Alotaibi. Munif Alotaibi 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

1.

Alanazi, Turki M., et al.. (2024). Deep learning for unmanned aerial vehicles detection: A review. Computer Science Review. 51. 100614–100614. 73 indexed citations breakdown →

2.

Гарванова, Магдалена, Ivan Garvanov, Vladimir Jotsov, et al.. (2023). A Data-Science Approach for Creation of a Comprehensive Model to Assess the Impact of Mobile Technologies on Humans. Applied Sciences. 13(6). 3600–3600. 2 indexed citations

3.

Razaque, Abdul, Bandar Alotaibi, Munif Alotaibi, et al.. (2022). Clickbait Detection Using Deep Recurrent Neural Network. Applied Sciences. 12(1). 504–504. 12 indexed citations

4.

Alanazi, Turki M., Bandar Alotaibi, Munif Alotaibi, et al.. (2022). Detection of Unauthorized Unmanned Aerial Vehicles Using YOLOv5 and Transfer Learning. Electronics. 11(17). 2669–2669. 25 indexed citations

5.

Razaque, Abdul, Yaser Jararweh, Aziz Alotaibi, et al.. (2022). A blockchain-enabled framework for securing connected consumer electronics against wireless attacks. Simulation Modelling Practice and Theory. 121. 102652–102652. 5 indexed citations

6.

Razaque, Abdul, et al.. (2022). Big Data Handling Approach for Unauthorized Cloud Computing Access. Electronics. 11(1). 137–137. 14 indexed citations

7.

Razaque, Abdul, Munif Alotaibi, Bandar Alotaibi, et al.. (2022). Anomaly Detection Paradigm for Multivariate Time Series Data Mining for Healthcare. Applied Sciences. 12(17). 8902–8902. 8 indexed citations

8.

Razaque, Abdul, Yaser Jararweh, Bandar Alotaibi, Munif Alotaibi, & Muder Almiani. (2022). Hybrid energy-efficient algorithm for efficient Internet of Things deployment. Sustainable Computing Informatics and Systems. 35. 100715–100715. 7 indexed citations

9.

Oad, Ammar, et al.. (2021). Blockchain-Enabled Transaction Scanning Method for Money Laundering Detection. Electronics. 10(15). 1766–1766. 15 indexed citations

10.

Razaque, Abdul, Bandar Alotaibi, Munif Alotaibi, et al.. (2021). Blockchain-Enabled Deep Recurrent Neural Network Model for Clickbait Detection. IEEE Access. 10. 3144–3163. 15 indexed citations

11.

Razaque, Abdul, Yaser Jararweh, Bandar Alotaibi, et al.. (2021). Energy-efficient and secure mobile fog-based cloud for the Internet of Things. Future Generation Computer Systems. 127. 1–13. 36 indexed citations

12.

Razaque, Abdul, et al.. (2021). Comparative Analysis of Digital Signature and Elliptic Curve Digital Signature Algorithms for the Validation of QR Code Vulnerabilities. 4. 1–7.

13.

Razaque, Abdul, et al.. (2021). Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification. Sensors. 21(13). 4431–4431. 54 indexed citations

14.

Alotaibi, Munif, Bandar Alotaibi, & Abdul Razaque. (2021). A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media. Electronics. 10(21). 2664–2664. 47 indexed citations

15.

Almiani, Muder, et al.. (2021). An Efficient Data-Balancing Cyber-Physical System Paradigm for Quality-of-Service (QoS) Provision over Fog Computing. Applied Sciences. 12(1). 246–246. 2 indexed citations

16.

Alotaibi, Bandar & Munif Alotaibi. (2020). A Hybrid Deep ResNet and Inception Model for Hyperspectral Image Classification. PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science. 88(6). 463–476. 33 indexed citations

17.

Alotaibi, Munif & Ausif Mahmood. (2017). Improved gait recognition based on specialized deep convolutional neural network. Computer Vision and Image Understanding. 164. 103–110. 109 indexed citations

18.

Alotaibi, Munif & Ausif Mahmood. (2017). Reducing covariate factors of gait recognition using feature selection and dictionary-based sparse coding. Signal Image and Video Processing. 11(6). 1131–1138. 11 indexed citations

19.

Alotaibi, Munif & Ausif Mahmood. (2016). Reduction of Gait Covariate Factors Using Feature Selection and Sparse Dictionary Learning. 337–340. 3 indexed citations

20.

Elleithy, Khaled, et al.. (2014). A new hardware quantum-based encryption algorithm. 1–5. 1 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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