Haya Alaskar

1.2k total citations
36 papers, 676 citations indexed

About

Haya Alaskar is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Biomedical Engineering. According to data from OpenAlex, Haya Alaskar has authored 36 papers receiving a total of 676 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 8 papers in Computer Vision and Pattern Recognition and 8 papers in Biomedical Engineering. Recurrent topics in Haya Alaskar's work include Anomaly Detection Techniques and Applications (6 papers), AI in cancer detection (3 papers) and Deception detection and forensic psychology (3 papers). Haya Alaskar is often cited by papers focused on Anomaly Detection Techniques and Applications (6 papers), AI in cancer detection (3 papers) and Deception detection and forensic psychology (3 papers). Haya Alaskar collaborates with scholars based in Saudi Arabia, United Kingdom and United Arab Emirates. Haya Alaskar's co-authors include Abir Hussain, Dhiya Al‐Jumeily, Thavavel Vaiyapuri, Panos Liatsis, Abir Hussain, Wasiq Khan, Ahmed A. Abd El‐Latif, Bassem Abd-El-Atty, Abdullah M. Iliyasu and Adel Binbusayyis and has published in prestigious journals such as IEEE Access, Sensors and Remote Sensing.

In The Last Decade

Haya Alaskar

33 papers receiving 642 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Haya Alaskar Saudi Arabia 15 192 109 108 93 85 36 676
Fawaz Waselallah Alsaade Saudi Arabia 14 245 1.3× 106 1.0× 108 1.0× 39 0.4× 95 1.1× 51 822
Muhammad Nazir Pakistan 21 369 1.9× 452 4.1× 44 0.4× 64 0.7× 212 2.5× 53 1.0k
Smita Sharma India 17 142 0.7× 79 0.7× 162 1.5× 61 0.7× 39 0.5× 71 652
Marwa Obayya Saudi Arabia 15 283 1.5× 172 1.6× 62 0.6× 57 0.6× 191 2.2× 78 725
Håvard D. Johansen Norway 13 408 2.1× 424 3.9× 137 1.3× 69 0.7× 311 3.7× 54 1.1k
Ishfaq Yaseen Saudi Arabia 14 254 1.3× 92 0.8× 171 1.6× 28 0.3× 93 1.1× 95 629
Chun-Rong Huang Taiwan 21 161 0.8× 645 5.9× 21 0.2× 39 0.4× 92 1.1× 90 1.2k
Guanghui Yue China 19 269 1.4× 517 4.7× 50 0.5× 78 0.8× 241 2.8× 87 1.0k
Daniel Sierra-Sosa United States 15 380 2.0× 168 1.5× 168 1.6× 72 0.8× 114 1.3× 52 1.0k
Hamidullah Binol United States 12 166 0.9× 123 1.1× 35 0.3× 30 0.3× 77 0.9× 33 467

Countries citing papers authored by Haya Alaskar

Since Specialization
Citations

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

Fields of papers citing papers by Haya Alaskar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Haya Alaskar

This figure shows the co-authorship network connecting the top 25 collaborators of Haya Alaskar. A scholar is included among the top collaborators of Haya Alaskar 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 Haya Alaskar. Haya Alaskar 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.
Alaskar, Haya, Soliman A. Mahmoud, Ayad Turky, et al.. (2024). Explainable Machine Learning Model for Alzheimer Detection Using Genetic Data: A Genome-Wide Association Study Approach. IEEE Access. 12. 95091–95105. 6 indexed citations
2.
Waleed, Jumana, et al.. (2024). Intelligent Gesture-Enhanced Blockchain Voting: A New Era of Secure and Accessible E-Voting. IEEE Access. 12. 144055–144068. 2 indexed citations
3.
Hamed, Dhafar, et al.. (2024). Image dataset of healthy and infected fig leaves with Ficus leaf worm. Data in Brief. 53. 109958–109958. 2 indexed citations
4.
Mahmmod, Basheera M., Marwah Abdulrazzaq Naser, Muntadher Alsabah, et al.. (2024). Patient Monitoring System Based on Internet of Things: A Review and Related Challenges With Open Research Issues. IEEE Access. 12. 132444–132479. 24 indexed citations
5.
Alaskar, Haya, et al.. (2024). Optimising Delivery Routes Under Real-World Constraints: A Comparative Study of Ant Colony, Particle Swarm and Genetic Algorithms. International Journal of Advanced Computer Science and Applications. 15(10).
6.
Alaskar, Haya, et al.. (2024). A Lightweight Image Encryption Algorithm Based on Secure Key Generation. IEEE Access. 12. 95871–95883. 3 indexed citations
7.
Alaskar, Haya. (2023). Hybrid Metaheuristics with Deep Learning Enabled Automated Deception Detection and Classification of Facial Expressions. Computers, materials & continua/Computers, materials & continua (Print). 75(3). 5433–5449.
8.
Binbusayyis, Adel, et al.. (2022). An investigation and comparison of machine learning approaches for intrusion detection in IoMT network. The Journal of Supercomputing. 78(15). 17403–17422. 51 indexed citations
9.
Vaiyapuri, Thavavel, et al.. (2022). Cat Swarm Optimization-Based Computer-Aided Diagnosis Model for Lung Cancer Classification in Computed Tomography Images. Applied Sciences. 12(11). 5491–5491. 22 indexed citations
10.
Khalil, Shuker Mahmood, et al.. (2021). Fuzzy Logical Algebra and Study of the Effectiveness of Medications for COVID-19. Mathematics. 9(22). 2838–2838. 11 indexed citations
11.
Vaiyapuri, Thavavel, et al.. (2021). Deep Learning Approaches for Intrusion Detection in IIoT Networks – Opportunities and Future Directions. International Journal of Advanced Computer Science and Applications. 12(4). 27 indexed citations
12.
Abd-El-Atty, Bassem, Abdullah M. Iliyasu, Haya Alaskar, & Ahmed A. Abd El‐Latif. (2020). A Robust Quasi-Quantum Walks-based Steganography Protocol for Secure Transmission of Images on Cloud-based E-healthcare Platforms. Sensors. 20(11). 3108–3108. 64 indexed citations
13.
Vaiyapuri, Thavavel & Haya Alaskar. (2020). Whale Optimization for Wavelet-Based Unsupervised Medical Image Segmentation: Application to CT and MR Images. International Journal of Computational Intelligence Systems. 13(1). 941–941. 11 indexed citations
14.
Alaskar, Haya, et al.. (2020). Pretrained Convolutional Neural Networks for Cancer Genome Classification. 19. 1–5. 3 indexed citations
15.
Alaskar, Haya. (2019). High Predictive Performance of Dynamic Neural Network Models for Forecasting Financial Time Series. International Journal of Advanced Computer Science and Applications. 10(12). 3 indexed citations
16.
17.
Alaskar, Haya & Abir Hussain. (2018). Prediction of Parkinson Disease Using Gait Signals. 23–26. 11 indexed citations
18.
Alaskar, Haya. (2018). Deep Learning-Based Model Architecture for Time-Frequency Images Analysis. International Journal of Advanced Computer Science and Applications. 9(12). 18 indexed citations
19.
Hussain, Abir, et al.. (2018). Predicting Freezing of Gait in Parkinsons Disease Patients Using Machine Learning. 1–8. 29 indexed citations
20.
Hussain, Abir, Dhiya Al‐Jumeily, Haya Alaskar, & Naeem Radi. (2015). Regularized dynamic self-organized neural network inspired by the immune algorithm for financial time series prediction. Neurocomputing. 188. 23–30. 27 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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