Mohammad Al-Sa’d

588 total citations
10 papers, 409 citations indexed

About

Mohammad Al-Sa’d is a scholar working on Control and Systems Engineering, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, Mohammad Al-Sa’d has authored 10 papers receiving a total of 409 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Control and Systems Engineering, 3 papers in Artificial Intelligence and 3 papers in Biomedical Engineering. Recurrent topics in Mohammad Al-Sa’d's work include Wireless Signal Modulation Classification (2 papers), Neonatal and fetal brain pathology (2 papers) and Neural dynamics and brain function (2 papers). Mohammad Al-Sa’d is often cited by papers focused on Wireless Signal Modulation Classification (2 papers), Neonatal and fetal brain pathology (2 papers) and Neural dynamics and brain function (2 papers). Mohammad Al-Sa’d collaborates with scholars based in Qatar, Finland and Australia. Mohammad Al-Sa’d's co-authors include Amr Mohamed, Abdulla Al‐Ali, Tamer Khattab, Aiman Erbad, B. Boashash, Moncef Gabbouj, Serkan Kıranyaz, Ahmed Ben Said, Khaled A. Harras and Abdeldjalil Aïssa El Bey and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Signal Processing and IEEE Access.

In The Last Decade

Mohammad Al-Sa’d

9 papers receiving 397 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Mohammad Al-Sa’d Qatar	6	241	151	143	79	48	10	409
Maryam Fatemi Sweden	10	141 0.6×	270 1.8×	69 0.5×	55 0.7×	59 1.2×	18	399
Farshad Koohifar United States	6	217 0.9×	44 0.3×	87 0.6×	86 1.1×	64 1.3×	7	336
Ju Hong Yoon South Korea	7	102 0.4×	149 1.0×	304 2.1×	39 0.5×	22 0.5×	14	400
Denis Pomorski France	8	224 0.9×	152 1.0×	93 0.7×	97 1.2×	32 0.7×	20	397
Hendrik Deusch Germany	8	134 0.6×	129 0.9×	112 0.8×	54 0.7×	23 0.5×	11	310
Alessandro Benini Italy	9	187 0.8×	52 0.3×	102 0.7×	151 1.9×	46 1.0×	21	333
Anastasios Vafeiadis Greece	8	107 0.4×	112 0.7×	188 1.3×	38 0.5×	50 1.0×	14	369
Fangwei Zhong China	10	272 1.1×	107 0.7×	445 3.1×	54 0.7×	16 0.3×	19	573
Zachary Omohundro United States	5	336 1.4×	50 0.3×	253 1.8×	65 0.8×	37 0.8×	5	451
Ryusuke Miyamoto Japan	11	139 0.6×	48 0.3×	458 3.2×	148 1.9×	30 0.6×	80	553

Countries citing papers authored by Mohammad Al-Sa’d

Since Specialization

Citations

This map shows the geographic impact of Mohammad Al-Sa’d'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 Mohammad Al-Sa’d with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohammad Al-Sa’d more than expected).

Fields of papers citing papers by Mohammad Al-Sa’d

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mohammad Al-Sa’d. 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 Mohammad Al-Sa’d. The network helps show where Mohammad Al-Sa’d may publish in the future.

Co-authorship network of co-authors of Mohammad Al-Sa’d

This figure shows the co-authorship network connecting the top 25 collaborators of Mohammad Al-Sa’d. A scholar is included among the top collaborators of Mohammad Al-Sa’d 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 Mohammad Al-Sa’d. Mohammad Al-Sa’d is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

Al-Sa’d, Mohammad, et al.. (2024). Real-Time Damage Detection in Fiber Lifting Ropes Using Lightweight Convolutional Neural Networks. IEEE Sensors Journal. 25(4). 7496–7507.

Al-Sa’d, Mohammad, Sampsa Vanhatalo, & Anton Tokariev. (2024). Multiplex dynamic networks in the newborn brain disclose latent links with neurobehavioral phenotypes. Human Brain Mapping. 45(2). e26610–e26610. 2 indexed citations

Al-Sa’d, Mohammad, et al.. (2024). Real-Time Vibration-Based Bearing Fault Diagnosis Under Time-Varying Speed Conditions. Qatar University QSpace (Qatar University). 1–7. 4 indexed citations

Al-Sa’d, Mohammad, et al.. (2022). A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras. Sensors. 22(2). 418–418. 17 indexed citations

Al-Sa’d, Mohammad, B. Boashash, & Moncef Gabbouj. (2021). Design of an Optimal Piece-Wise Spline Wigner-Ville Distribution for TFD Performance Evaluation and Comparison. IEEE Transactions on Signal Processing. 69. 3963–3976. 35 indexed citations

Al-Sa’d, Mohammad, et al.. (2019). DroneRF dataset: A dataset of drones for RF-based detection, classification and identification. SHILAP Revista de lepidopterología. 26. 104313–104313. 103 indexed citations

Al-Sa’d, Mohammad, Abdulla Al‐Ali, Amr Mohamed, Tamer Khattab, & Aiman Erbad. (2019). RF-based drone detection and identification using deep learning approaches: An initiative towards a large open source drone database. Future Generation Computer Systems. 100. 86–97. 205 indexed citations

Al-Sa’d, Mohammad & B. Boashash. (2019). Design and implementation of a multi-sensor newborn EEG seizure and background model with inter-channel field characterization. Digital Signal Processing. 90. 71–99. 3 indexed citations

Boashash, B., Abdeldjalil Aïssa El Bey, & Mohammad Al-Sa’d. (2018). Multisensor Time–Frequency Signal Processing MATLAB package: An analysis tool for multichannel non-stationary data. SoftwareX. 8. 53–58. 11 indexed citations

10.

Said, Ahmed Ben, Mohammad Al-Sa’d, Alaa Awad Abdellatif, et al.. (2018). A Deep Learning Approach for Vital Signs Compression and Energy Efficient Delivery in mhealth Systems. IEEE Access. 6. 33727–33739. 29 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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