S. Hashem

1.2k citations
27 papers · 784 · h-index 10

Impact in

Papers in

S. Hashem

26 papers receiving 724 citations

Peers

S. Hashem
Comparison fields: 5 of 106
  • Artificial Intelligence 404
  • Management Science and Operations Research 92
  • Computer Vision and Pattern Recognition 144
  • Signal Processing 74
  • Statistics and Probability 48
Replace Liyong Zhang with:
Liyong Zhang China
Anthony Quinn Ireland
Arnaud Martin France
Guoqiang Wang China
Pei-Yi Hao Taiwan
K. Mehrotra United States
Hongjun Wang China
Olga Kosheleva United States
Marco Cococcioni Italy
Václav Šmídl Czechia
S. Hashem relative to Liyong Zhang China Liyong Zhang's profile →
Citations per field
00.5×1.5×
Liyong Zhang · 1×
Citations per year

Countries citing papers authored by S. Hashem

Since Specialization
Citations

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

Fields of papers citing papers by S. Hashem

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 11 scholars most cited alongside S. Hashem, 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 S. Hashem Line = papers co-authored together S. Hashem links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1997344
2 1995129
3 200369
4 200736
5 199535
6 200231
7 199027
8 199613
9 200211
10 200210
11 20078
12 19928
13 19948
14 20057
15 20207
16
Guideline Model for Digital Forensic Investigation
20076
17
A novel approach to modeling and diagnosing the cardiovascular system
19956
18 19965
19
Teams Responsibilities for Digital Forensic Process
20074
20 20034

About S. Hashem

S. Hashem is a scholar working on Artificial Intelligence, Control and Systems Engineering, Computer Vision and Pattern Recognition, Management Science and Operations Research and Signal Processing, having authored 27 papers that have together received 784 indexed citations. Recurring topics across this work include Neural Networks and Applications (9 papers), Fault Detection and Control Systems (7 papers), Face and Expression Recognition (4 papers), Digital and Cyber Forensics (3 papers), Advanced Malware Detection Techniques (3 papers), Advanced Statistical Process Monitoring (2 papers), Radiation Detection and Scintillator Technologies (2 papers) and Forecasting Techniques and Applications (2 papers). The work is most often cited by research in Artificial Intelligence (404 citations), Management Science and Operations Research (92 citations), Computer Vision and Pattern Recognition (144 citations), Signal Processing (74 citations) and Statistics and Probability (48 citations). S. Hashem has collaborated with scholars based in United States and Egypt. Frequent co-authors include B.W. Schmeiser, Amir F. Atiya, Bruce W. Schmeiser, Hatem A. Fayed, R. T. Kouzes, Paul E. Keller, Lars J. Kangas, Yuehwern Yih, Athanassios N. Avramidis and Mohamed Gamal El‐Din. Their work appears in journals such as ACM Transactions on Mathematical Software, IEEE Transactions on Nuclear Science, Neural Networks, Connection Science and Pattern Recognition.

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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