Faisal Saeed

183 papers receiving 2.9k citations

Hit Papers

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases 2024 · 81 citations
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Peers

Faisal Saeed
Comparison fields: 5 of 184
  • Signal Processing 389
  • Health Informatics 49
  • Computer Networks and Communications 657
  • Artificial Intelligence 901
  • Health Information Management 116
Replace Janmenjoy Nayak with:
Janmenjoy Nayak India
Mukesh Prasad Australia
Valentina Emilia Bălaş Romania
Moez Krichen Tunisia
M. A. Alsalem Malaysia
Rubén González Crespo Spain
Abdu Gumaei Saudi Arabia
Tarik A. Rashid Iraq
Panagiotis Pintelas Greece
Bighnaraj Naik India
Faisal Saeed relative to Janmenjoy Nayak India Janmenjoy Nayak's profile →
Citations per field
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Janmenjoy Nayak · 1×
Citations per year

Countries citing papers authored by Faisal Saeed

Since Specialization
Citations

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

Fields of papers citing papers by Faisal Saeed

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Faisal Saeed, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Faisal Saeed Line = papers co-authored together Faisal Saeed links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20241
3 20240
4 20247
5
Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases
Hit paper breakdown →
202481
6 20232
7 20231
8 202363
9 20233
10 202310
11 202310
12 20225
13 202230
14 202253
15 202275
16 202147
17 202034
18 201934
19 20187
20 20139

About Faisal Saeed

Faisal Saeed is a scholar working on Signal Processing, Computational Theory and Mathematics, Artificial Intelligence, Computer Networks and Communications and Information Systems, having authored 196 papers that have together received 3.0k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (28 papers), Network Security and Intrusion Detection (21 papers), Advanced Malware Detection Techniques (20 papers), Spam and Phishing Detection (16 papers), Anomaly Detection Techniques and Applications (16 papers), Video Surveillance and Tracking Methods (11 papers), Machine Learning in Bioinformatics (10 papers) and Analytical Chemistry and Chromatography (9 papers). The work is most often cited by research in Signal Processing (389 citations), Health Informatics (49 citations), Computer Networks and Communications (657 citations), Artificial Intelligence (901 citations) and Health Information Management (116 citations). Faisal Saeed has collaborated with scholars based in Saudi Arabia, Malaysia and United Kingdom. Frequent co-authors include Mohammed Al-Sarem, Fuad A. Ghaleb, Anand Paul, Tawfik Al-Hadhrami, Sultan Noman Qasem, Naomie Salim, Bander Ali Saleh Al‐rimy, Hyuncheol Seo, Maged Nasser and Abdul Rehman. Their work appears in journals such as Sensors, IEEE Access, Computers, materials & continua/Computers, materials & continua (Print), Applied Sciences and PeerJ Computer Science.

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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