Khaled Rasheed

3.5k total citations · 2 hit papers
91 papers, 2.0k citations indexed

About

Khaled Rasheed is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Khaled Rasheed has authored 91 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Artificial Intelligence, 17 papers in Computational Theory and Mathematics and 16 papers in Computer Vision and Pattern Recognition. Recurrent topics in Khaled Rasheed's work include Advanced Multi-Objective Optimization Algorithms (15 papers), Evolutionary Algorithms and Applications (13 papers) and Metaheuristic Optimization Algorithms Research (13 papers). Khaled Rasheed is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (15 papers), Evolutionary Algorithms and Applications (13 papers) and Metaheuristic Optimization Algorithms Research (13 papers). Khaled Rasheed collaborates with scholars based in United States, Iran and Netherlands. Khaled Rasheed's co-authors include Hamid R. Arabnia, Mohammadreza Iman, Bo Qian, Abolfazl Farahani, Haym Hirsh, Dongsheng Che, Qi Liu, Doyle Knight, Andrew Gelsey and Dongsheng Che and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Sensors.

In The Last Decade

Khaled Rasheed

81 papers receiving 1.9k citations

Hit Papers

A Review of Deep Transfer Learning and Recent Advanc... 2021 2026 2022 2024 2023 2021 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Khaled Rasheed United States 21 683 308 273 238 190 91 2.0k
Jing Zhao China 20 848 1.2× 383 1.2× 593 2.2× 86 0.4× 127 0.7× 128 2.5k
Marc Parizeau Canada 22 831 1.2× 320 1.0× 542 2.0× 150 0.6× 101 0.5× 52 2.4k
Sibylle D. Müller Switzerland 8 1.1k 1.7× 622 2.0× 196 0.7× 92 0.4× 97 0.5× 11 2.2k
Sumit Singh Chauhan India 3 596 0.9× 223 0.7× 257 0.9× 96 0.4× 112 0.6× 6 2.8k
Madana Srinivas India 7 685 1.0× 246 0.8× 156 0.6× 70 0.3× 80 0.4× 15 1.9k
Marc-André Gardner Canada 8 429 0.6× 175 0.6× 282 1.0× 134 0.6× 67 0.4× 10 1.7k
Adam Słowik Poland 23 1.0k 1.5× 373 1.2× 257 0.9× 109 0.5× 78 0.4× 116 2.5k
Afshin Rostamizadeh United States 21 1.5k 2.2× 151 0.5× 800 2.9× 155 0.7× 206 1.1× 34 2.6k
Farhad Pourpanah China 20 1.3k 1.9× 167 0.5× 627 2.3× 87 0.4× 94 0.5× 37 2.9k
S.R. Safavian United States 4 1.0k 1.5× 150 0.5× 357 1.3× 197 0.8× 80 0.4× 11 2.7k

Countries citing papers authored by Khaled Rasheed

Since Specialization
Citations

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

Fields of papers citing papers by Khaled Rasheed

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Khaled Rasheed

This figure shows the co-authorship network connecting the top 25 collaborators of Khaled Rasheed. A scholar is included among the top collaborators of Khaled Rasheed 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 Khaled Rasheed. Khaled Rasheed 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.
Smith, B., Khaled Rasheed, Ali Missaoui, et al.. (2025). Utility of Domain Adaptation for Biomass Yield Forecasting. AgriEngineering. 7(7). 237–237. 1 indexed citations
2.
Li, Guoming, et al.. (2025). Identifying mating events of group-housed broiler breeders via bio-inspired deep learning models. Poultry Science. 104(7). 105126–105126.
3.
Li, Guoming, Lilong Chai, Ramesh Bahadur Bist, et al.. (2024). Zero-shot image segmentation for monitoring thermal conditions of individual cage-free laying hens. Computers and Electronics in Agriculture. 226. 109436–109436. 6 indexed citations
4.
Bettinger, Pete, et al.. (2024). Associations between forest harvest scheduling and artificial intelligence. The International Forestry Review. 26(4). 387–397.
5.
Iman, Mohammadreza, Hamid R. Arabnia, & Khaled Rasheed. (2023). A Review of Deep Transfer Learning and Recent Advancements. SHILAP Revista de lepidopterología. 11(2). 40–40. 337 indexed citations breakdown →
6.
Huang, Liang‐Chin, Wayland Yeung, Aarya Venkat, et al.. (2020). Quantitative Structure–Mutation–Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction. BMC Bioinformatics. 21(1). 520–520. 10 indexed citations
7.
McSkimming, Daniel, Khaled Rasheed, & Natarajan Kannan. (2017). Classifying kinase conformations using a machine learning approach. BMC Bioinformatics. 18(1). 86–86. 31 indexed citations
8.
Rasheed, Khaled, et al.. (2016). Harnessing Crowds and Experts for Semantic Annotation of the Qur'an.. International Semantic Web Conference. 1 indexed citations
9.
Che, Dongsheng, et al.. (2011). Decision Tree and Ensemble Learning Algorithms with Their Applications in Bioinformatics. Advances in experimental medicine and biology. 696. 191–199. 159 indexed citations
10.
Oliwa, Tomasz & Khaled Rasheed. (2010). A Machine Learning Approach for Sensitivity Inference in Genetic Algorithms.. 36–41. 1 indexed citations
11.
Rasheed, Khaled, et al.. (2010). Application of Machine Learning Algorithms for Binning Metagenomic Data.. 68–74.
12.
Rasheed, Khaled, et al.. (2009). Using Genetic Algorithms for Simultaneous Noise Removal and Feature Selection in Classification and Regression problems.. International Conference on Artificial Intelligence. 304–310. 1 indexed citations
13.
Rasheed, Khaled, et al.. (2008). Enhancing the Quality of Noisy Training Data Using a Genetic Algorithm and Prototype Selection.. International Conference on Artificial Intelligence. 821–827. 5 indexed citations
14.
Rasheed, Khaled, et al.. (2008). Temporal Difference Learning for Nondeterministic Board Games.. International Conference on Artificial Intelligence. 800–806. 3 indexed citations
15.
Rasheed, Khaled, et al.. (2008). Path Normalcy Analysis Using Nearest Neighbor Outlier Detection.. International Conference on Artificial Intelligence. 85(2). 776–783. 1 indexed citations
16.
Qu, Junfeng, Hamid R. Arabnia, Yinglei Song, Khaled Rasheed, & Jack E. Houston. (2007). Time Series Similarity Matching with a New Distance Measure.. 183–189. 1 indexed citations
17.
Rasheed, Khaled, et al.. (2004). Using Machine Learning Techniques for Stylometry.. International Conference on Artificial Intelligence. 218. 897–903. 17 indexed citations
18.
Rasheed, Khaled & Haym Hirsh. (2000). Informed operators: speeding up genetic-algorithm-based design optimization using reduced models. Genetic and Evolutionary Computation Conference. 11(12). 628–635. 42 indexed citations
19.
Steinberg, Louis & Khaled Rasheed. (1999). Optimization by searching a tree of populations. Genetic and Evolutionary Computation Conference. 1723–1730. 1 indexed citations
20.
Rasheed, Khaled & Haym Hirsh. (1997). Using Case Based Learning to Improve Genetic Algorithm Based Design Optimization.. 513–520. 9 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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