Khairil Imran Ghauth

537 total citations
23 papers, 312 citations indexed

About

Khairil Imran Ghauth is a scholar working on Information Systems, Artificial Intelligence and Computer Science Applications. According to data from OpenAlex, Khairil Imran Ghauth has authored 23 papers receiving a total of 312 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Information Systems, 7 papers in Artificial Intelligence and 6 papers in Computer Science Applications. Recurrent topics in Khairil Imran Ghauth's work include Recommender Systems and Techniques (10 papers), Intelligent Tutoring Systems and Adaptive Learning (4 papers) and Online Learning and Analytics (3 papers). Khairil Imran Ghauth is often cited by papers focused on Recommender Systems and Techniques (10 papers), Intelligent Tutoring Systems and Adaptive Learning (4 papers) and Online Learning and Analytics (3 papers). Khairil Imran Ghauth collaborates with scholars based in Malaysia and Pakistan. Khairil Imran Ghauth's co-authors include Nor Aniza Abdullah, Fang-Fang Chua, Choo‐Yee Ting, C. Eswaran and Noramiza Hashim and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and IEEE Access.

In The Last Decade

Khairil Imran Ghauth

19 papers receiving 277 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Khairil Imran Ghauth Malaysia 9 198 148 141 56 35 23 312
Christoph Rensing Germany 10 168 0.8× 159 1.1× 122 0.9× 42 0.8× 49 1.4× 76 361
Jehad Najjar Belgium 9 96 0.5× 203 1.4× 110 0.8× 42 0.8× 27 0.8× 18 293
Jui-Feng Weng Taiwan 10 183 0.9× 123 0.8× 125 0.9× 51 0.9× 17 0.5× 21 308
José A. Cruz-Lemus Spain 11 273 1.4× 114 0.8× 157 1.1× 80 1.4× 14 0.4× 28 433
Jawad Berri Saudi Arabia 10 156 0.8× 78 0.5× 105 0.7× 31 0.6× 67 1.9× 41 297
Wayne Hodgins United States 5 135 0.7× 220 1.5× 159 1.1× 23 0.4× 13 0.4× 8 357
Qintai Hu China 8 140 0.7× 122 0.8× 138 1.0× 57 1.0× 25 0.7× 11 307
Víctor Hugo Menéndez Domínguez Mexico 8 146 0.7× 141 1.0× 86 0.6× 27 0.5× 14 0.4× 56 282
Enric Mayol Spain 9 160 0.8× 90 0.6× 81 0.6× 26 0.5× 9 0.3× 38 268
Rao Shen United States 10 100 0.5× 42 0.3× 105 0.7× 19 0.3× 44 1.3× 15 226

Countries citing papers authored by Khairil Imran Ghauth

Since Specialization
Citations

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

Fields of papers citing papers by Khairil Imran Ghauth

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Khairil Imran Ghauth. 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 Khairil Imran Ghauth. The network helps show where Khairil Imran Ghauth may publish in the future.

Co-authorship network of co-authors of Khairil Imran Ghauth

This figure shows the co-authorship network connecting the top 25 collaborators of Khairil Imran Ghauth. A scholar is included among the top collaborators of Khairil Imran Ghauth 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 Khairil Imran Ghauth. Khairil Imran Ghauth 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.
Ghauth, Khairil Imran, et al.. (2025). Feature Engineering for Phishing Website Detection Using Machine Learning: A Systematic Review. IEEE Access. 13. 192080–192104.
2.
3.
Ghauth, Khairil Imran, et al.. (2024). Optimizing Retail Recommendation via Similarity Measures and Machine Learning Approach. SHILAP Revista de lepidopterología. 8(3). 1192–1192.
4.
Ghauth, Khairil Imran, et al.. (2023). Multi-Label Classification with Deep Learning for Retail Recommendation. SHILAP Revista de lepidopterología. 2(2). 218–232. 4 indexed citations
5.
Hashim, Noramiza, et al.. (2023). Business Category Classification via Indistinctive Satellite Image Analysis Using Deep Learning. International Journal on Advanced Science Engineering and Information Technology. 13(6). 2219–2230. 2 indexed citations
6.
Ghauth, Khairil Imran, et al.. (2022). User Experience Design Using Machine Learning: A Systematic Review. IEEE Access. 10. 51501–51514. 22 indexed citations
7.
Ghauth, Khairil Imran, et al.. (2022). Discriminate2Rec: Negation-based dynamic discriminative interest-based preference learning for semantics-aware content-based recommendation. Expert Systems with Applications. 199. 116988–116988. 9 indexed citations
8.
Ghauth, Khairil Imran, et al.. (2021). Parallel classification and optimization of telco trouble ticket dataset. TELKOMNIKA (Telecommunication Computing Electronics and Control). 19(3). 872–872. 1 indexed citations
9.
Ghauth, Khairil Imran, et al.. (2021). The automated machine learning classification approach on telco trouble ticket dataset. 3 indexed citations
10.
Abdullah, Nor Aniza, et al.. (2020). A Framework for Optimal Worker Selection in Spatial Crowdsourcing Using Bayesian Network. IEEE Access. 8. 120218–120233. 9 indexed citations
11.
Ghauth, Khairil Imran, et al.. (2018). Utilizing Learners' Negative Ratings in Semantic Content-Based Recommender System for e-Learning Forum.. Educational Technology & Society. 21(1). 112–125. 42 indexed citations
12.
Ghauth, Khairil Imran, et al.. (2018). Application of NLP on Big Data Using Hadoop: Case Study Using Trouble Tickets. Advanced Science Letters. 24(10). 7696–7702.
13.
Ghauth, Khairil Imran, et al.. (2017). Adopting Big Data Analytics Strategy in Telecommunication Industry. 57–67. 1 indexed citations
14.
Ghauth, Khairil Imran, et al.. (2012). Collision detection optimization on mobile device for shoot'em up game. Siti Hasmah Digital Library-MMU Institutiona Repository (Multimedia University). 464–468. 3 indexed citations
15.
Ghauth, Khairil Imran & Nor Aniza Abdullah. (2011). The Effect of Incorporating Good Learners' Ratings in e-Learning Content-Based Recommender System.. Educational Technology & Society. 14(2). 248–257. 28 indexed citations
16.
Ghauth, Khairil Imran & Nor Aniza Abdullah. (2010). An Empirical Evaluation Of Learner Performance In E-Learning Recommender Systems And An Adaptive Hypermedia System. Malaysian Journal of Computer Science. 23(3). 141–152. 7 indexed citations
17.
Ghauth, Khairil Imran & Nor Aniza Abdullah. (2010). Measuring learner's performance in e-learning recommender systems. Australasian Journal of Educational Technology. 26(6). 40 indexed citations
18.
Ghauth, Khairil Imran, et al.. (2009). A graph-based web usage mining method considering client side data. Siti Hasmah Digital Library-MMU Institutiona Repository (Multimedia University). 4. 147–153. 12 indexed citations
19.
Ghauth, Khairil Imran & Nor Aniza Abdullah. (2009). Building an E-learning Recommender System Using Vector Space Model and Good Learners Average Rating. Siti Hasmah Digital Library-MMU Institutiona Repository (Multimedia University). 194–196. 36 indexed citations
20.
Ghauth, Khairil Imran, et al.. (2007). Using service-based content adaptation platform to enhance mobile user experience. 552–557. 4 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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