Salem Alelyani

2.7k total citations · 2 hit papers
35 papers, 1.7k citations indexed

About

Salem Alelyani is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics. According to data from OpenAlex, Salem Alelyani has authored 35 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 10 papers in Computer Vision and Pattern Recognition and 5 papers in Statistical and Nonlinear Physics. Recurrent topics in Salem Alelyani's work include Face and Expression Recognition (7 papers), Complex Network Analysis Techniques (4 papers) and Online Learning and Analytics (3 papers). Salem Alelyani is often cited by papers focused on Face and Expression Recognition (7 papers), Complex Network Analysis Techniques (4 papers) and Online Learning and Analytics (3 papers). Salem Alelyani collaborates with scholars based in Saudi Arabia, Pakistan and United States. Salem Alelyani's co-authors include Jiliang Tang, Huan Liu, Saeed‐Ul Hassan, Naif Radi Aljohani, Raheel Nawaz, Hajra Waheed, Huan Liu, Julie Hardman, Mohamed Mohana and Nor Azlina Ab. Aziz and has published in prestigious journals such as SHILAP Revista de lepidopterología, Computers in Human Behavior and Sensors.

In The Last Decade

Salem Alelyani

34 papers receiving 1.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Feature selection for classification: A review

2014 560 citations Jiliang Tang, Salem Alelyani et al. CRC Press eBooks profile →
Predicting academic performance of students from VLE big data using deep learning models

2019 337 citations Saeed‐Ul Hassan, Naif Radi Aljohani et al. profile →

Peers

Salem Alelyani

AI

CSA

CVPR

IS

CNC

Muna Al‐Razgan Saudi Arabia

Kwok Tai Chui Hong Kong

Carlos Soares Portugal

Dimitris Kanellopoulos Greece

Giovanni Acampora Italy

Muhammad Umer Pakistan

Sarah Jane Delany Ireland

Radhika M. Pai India

Gjergji Kasneci Germany

Muna Al‐Razgan Saudi Arabia

Salem Alelyani

720 ×1.0

AI

224 ×0.7

CSA

350 ×1.3

CVPR

317 ×1.3

IS

156 ×1.0

CNC

Citations per year, relative to Salem Alelyani Salem Alelyani (= 1×) peers Muna Al‐Razgan

Countries citing papers authored by Salem Alelyani

Since Specialization

Citations

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

Fields of papers citing papers by Salem Alelyani

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Salem Alelyani

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Arunachalam, Rajesh, et al.. (2023). Optimized RNN-based performance prediction of IoT and WSN-oriented smart city application using improved honey badger algorithm. Measurement. 210. 112505–112505. 46 indexed citations

2.

Alsaqer, Mohammed, et al.. (2023). Predicting Location of Tweets Using Machine Learning Approaches. Applied Sciences. 13(5). 3025–3025. 6 indexed citations

3.

Hassan, Saeed‐Ul, Naif Radi Aljohani, Iqra Safder, et al.. (2022). Exploiting tweet sentiments in altmetrics large-scale data. Journal of Information Science. 49(5). 1229–1245. 5 indexed citations

4.

Alelyani, Salem, et al.. (2022). Meeting Challenges of Modern Standard Arabic and Saudi Dialect Identification. 341–351.

5.

Alelyani, Salem. (2021). Stable bagging feature selection on medical data. Journal Of Big Data. 8(1). 51 indexed citations

6.

Aziz, Nor Azlina Ab., et al.. (2021). Comfort and energy consumption optimization in smart homes using bat algorithm with inertia weight. Journal of Building Engineering. 47. 103848–103848. 32 indexed citations

7.

Safder, Iqra, Raheem Sarwar, Saeed‐Ul Hassan, et al.. (2021). Sentiment analysis for Urdu online reviews using deep learning models. Expert Systems. 38(8). 27 indexed citations

8.

Mohana, Mohamed, et al.. (2021). Small-Scale Solar Photovoltaic Power Prediction for Residential Load in Saudi Arabia Using Machine Learning. Energies. 14(20). 6759–6759. 29 indexed citations

9.

Alelyani, Salem, et al.. (2021). Prediction of diabetic retinopathy using machine learning techniques. Journal of Engineering Research. 11(2). 27–37. 1 indexed citations

10.

Alelyani, Salem. (2021). Special Educational Need of the Gifted and Talented Students in Saudi Arabia: A Review Paper. International Journal of Educational Research Review. 6(2). 124–133. 7 indexed citations

11.

Alelyani, Salem. (2021). Detection and Evaluation of Machine Learning Bias. Applied Sciences. 11(14). 6271–6271. 22 indexed citations

12.

Aziz, Nor Azlina Ab., et al.. (2021). Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review. Sensors. 21(15). 5015–5015. 114 indexed citations

13.

Abbas, Ghulam, Muhammad Usman Asad, Jason Gu, et al.. (2020). Multivariable Unconstrained Pattern Search Method for Optimizing Digital PID Controllers Applied to Isolated Forward Converter. Energies. 14(1). 77–77. 9 indexed citations

14.

Tariq, Faisal, Salem Alelyani, Ghulam Abbas, Ayman Qahmash, & Mohammad Rashid Hussain. (2020). Solving Renewables-Integrated Economic Load Dispatch Problem by Variant of Metaheuristic Bat-Inspired Algorithm. Energies. 13(23). 6225–6225. 29 indexed citations

15.

Hussain, Mohammad Rashid, Ayman Qahmash, Salem Alelyani, & Mohammed Alsaqer. (2020). Effective Graph Based Vertex Cover Algorithm Using Multi-document Node Summarizations. 25. 1–6. 1 indexed citations

16.

Alelyani, Salem, Jiliang Tang, & Huan Liu. (2018). Feature Selection for Clustering: A Review. 29–60. 167 indexed citations

17.

Tang, Jiliang, Salem Alelyani, & Huan Liu. (2014). Feature selection for classification: A review. CRC Press eBooks. 37–64. 560 indexed citations breakdown →

18.

Alelyani, Salem & Huan Liu. (2012). Ensemble Feature Selection in Face Recognition: ICMLA 2012 Challenge. 588–591. 2 indexed citations

19.

Alelyani, Salem & Huan Liu. (2012). Supervised Low Rank Matrix Approximation for Stable Feature Selection. 13. 324–329. 2 indexed citations

20.

Alelyani, Salem, Zheng Zhao, & Huan Liu. (2011). A Dilemma in Assessing Stability of Feature Selection Algorithms. 3. 701–707. 29 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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