Neda Abdelhamid

1.4k total citations · 1 hit paper
22 papers, 928 citations indexed

About

Neda Abdelhamid is a scholar working on Artificial Intelligence, Information Systems and Cognitive Neuroscience. According to data from OpenAlex, Neda Abdelhamid has authored 22 papers receiving a total of 928 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 13 papers in Information Systems and 4 papers in Cognitive Neuroscience. Recurrent topics in Neda Abdelhamid's work include Text and Document Classification Technologies (8 papers), Data Mining Algorithms and Applications (8 papers) and Spam and Phishing Detection (5 papers). Neda Abdelhamid is often cited by papers focused on Text and Document Classification Technologies (8 papers), Data Mining Algorithms and Applications (8 papers) and Spam and Phishing Detection (5 papers). Neda Abdelhamid collaborates with scholars based in New Zealand, United Kingdom and United Arab Emirates. Neda Abdelhamid's co-authors include Fadi Thabtah, Aladdin Ayesh, Michael P. Thompson, David Peebles, Li Zhang, Hussein Abdel-Jaber, Wael Hadi, Samad Ahmadi, Seyed Reza Shahamiri and Sayan Kumar Ray and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Computers & Security.

In The Last Decade

Neda Abdelhamid

22 papers receiving 868 citations

Hit Papers

Phishing detection based Associative Classification data ... 2014 2026 2018 2022 2014 50 100 150 200

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Neda Abdelhamid New Zealand 14 475 466 176 123 108 22 928
Juan A. Lara Spain 15 297 0.6× 358 0.8× 86 0.5× 179 1.5× 62 0.6× 61 1.0k
Wael Hadi Jordan 14 292 0.6× 338 0.7× 56 0.3× 71 0.6× 19 0.2× 61 596
Zbigniew W. Raś United States 17 365 0.8× 528 1.1× 162 0.9× 106 0.9× 16 0.1× 113 951
Sudhir Dhage India 13 164 0.3× 226 0.5× 126 0.7× 175 1.4× 83 0.8× 60 554
Naveen Ashish United States 15 515 1.1× 511 1.1× 164 0.9× 428 3.5× 28 0.3× 46 1.0k
Sani Muhamad Isa Indonesia 14 142 0.3× 190 0.4× 73 0.4× 63 0.5× 25 0.2× 111 672
Giuseppe Polese Italy 20 373 0.8× 508 1.1× 118 0.7× 186 1.5× 12 0.1× 89 983
Stephen H. Bach United States 12 203 0.4× 1.1k 2.4× 97 0.6× 92 0.7× 22 0.2× 32 1.4k
Muhammad Usman Pakistan 17 144 0.3× 244 0.5× 110 0.6× 47 0.4× 83 0.8× 59 863

Countries citing papers authored by Neda Abdelhamid

Since Specialization
Citations

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

Fields of papers citing papers by Neda Abdelhamid

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Neda Abdelhamid

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

All Works

20 of 20 papers shown
1.
Abdelhamid, Neda, et al.. (2024). Managerial insights for AI/ML implementation: a playbook for successful organizational integration. SHILAP Revista de lepidopterología. 4(1). 7 indexed citations
2.
Abdelhamid, Neda, et al.. (2023). Assessing Autistic Traits in Toddlers Using a Data-Driven Approach with DSM-5 Mapping. Bioengineering. 10(10). 1131–1131. 5 indexed citations
3.
Thabtah, Fadi, et al.. (2022). Autism screening: an unsupervised machine learning approach. Health Information Science and Systems. 10(1). 26–26. 12 indexed citations
4.
Shahamiri, Seyed Reza, Fadi Thabtah, & Neda Abdelhamid. (2021). A new classification system for autism based on machine learning of artificial intelligence. Technology and Health Care. 30(3). 605–622. 17 indexed citations
5.
Abdelhamid, Neda, et al.. (2020). Data Imbalance in Autism Pre-Diagnosis Classification Systems: An Experimental Study. Journal of Information & Knowledge Management. 19(1). 2040014–2040014. 12 indexed citations
6.
Thabtah, Fadi, et al.. (2020). Exploring feature selection and classification methods for predicting heart disease. Digital Health. 6. 1345269337–1345269337. 135 indexed citations
7.
Thabtah, Fadi, Neda Abdelhamid, & David Peebles. (2019). A machine learning autism classification based on logistic regression analysis. Health Information Science and Systems. 7(1). 12–12. 89 indexed citations
8.
Abdelhamid, Neda, et al.. (2019). Forecasting Models Based on Data Analytics for Predicting Rice Price Volatility: A Case Study of the Sri Lankan Rice Market. Journal of Information & Knowledge Management. 18(1). 1950006–1950006. 5 indexed citations
9.
Thabtah, Fadi, et al.. (2019). Patient Discharge Classification Using Machine Learning Techniques. Annals of Data Science. 8(4). 755–767. 10 indexed citations
10.
Thabtah, Fadi, Li Zhang, & Neda Abdelhamid. (2019). NBA Game Result Prediction Using Feature Analysis and Machine Learning. Annals of Data Science. 6(1). 103–116. 77 indexed citations
11.
Thabtah, Fadi, et al.. (2018). A visualization cybersecurity method based on features' dissimilarity. Computers & Security. 77. 289–303. 13 indexed citations
12.
Abdelhamid, Neda, Fadi Thabtah, & Hussein Abdel-Jaber. (2017). Phishing detection: A recent intelligent machine learning comparison based on models content and features. 72–77. 61 indexed citations
13.
Thabtah, Fadi & Neda Abdelhamid. (2016). Deriving Correlated Sets of Website Features for Phishing Detection: A Computational Intelligence Approach. Journal of Information & Knowledge Management. 15(4). 1650042–1650042. 24 indexed citations
14.
Abdelhamid, Neda, et al.. (2016). Associative Classification Common Research Challenges. 9. 432–437. 10 indexed citations
15.
Abdelhamid, Neda & Fadi Thabtah. (2014). Associative Classification Approaches: Review and Comparison. Journal of Information & Knowledge Management. 13(3). 1450027–1450027. 65 indexed citations
16.
Abdelhamid, Neda, Aladdin Ayesh, & Fadi Thabtah. (2014). Phishing detection based Associative Classification data mining. Expert Systems with Applications. 41(13). 5948–5959. 244 indexed citations breakdown →
17.
Abdelhamid, Neda. (2014). Multi-label rules for phishing classification. Applied Computing and Informatics. 11(1). 29–46. 39 indexed citations
18.
Abdelhamid, Neda, Aladdin Ayesh, & Wael Hadi. (2014). Multi-Label Rules Algorithm Based Associative Classification. Parallel Processing Letters. 24(1). 1450001–1450001. 16 indexed citations
19.
Abdelhamid, Neda, Aladdin Ayesh, & Fadi Thabtah. (2012). An experimental study of three different rule ranking formulas in associative classification. DMU Open Research Archive (De Montfort University). 795–800. 6 indexed citations
20.
Thabtah, Fadi, et al.. (2011). PREDICTION PHASE IN ASSOCIATIVE CLASSIFICATION MINING. International Journal of Software Engineering and Knowledge Engineering. 21(6). 855–876. 21 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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