Laith Mohammad Abualigah

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 18 papers; trusts 60 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 341 citations into it; #9,147 of 35,345 by trust here, #7,241 by citations
• Computational Theory and Mathematics — 178 citations into it
• Computer Vision and Pattern Recognition — 75 citations into it; #15,712 of 21,575 by trust here, #11,721 by citations
• Information Systems — 36 citations into it
• Signal Processing — 35 citations into it
• Biomedical Engineering — 34 citations into it
• Molecular Biology — 29 citations into it
• Industrial and Manufacturing Engineering — 15 citations into it

Whom Laith Mohammad Abualigah trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Xin-She Yang
2. Seyedali Mirjalili
3. James Kennedy
4. R. Eberhart
5. Amir H. Gandomi
6. Dervis Karaboga
7. Dan Simon
8. Ali Kaveh
9. Seyed Mohammad Mirjalili
10. John R. Koza

Who trusts Laith Mohammad Abualigah overall

1. Absalom El-Shamir Ezugwu
2. Ali Diabat
3. Jeffrey O. Agushaka
4. Ahamad Tajudin Abdul Khader
5. Mohammad Shehab
6. Seyedali Mirjalili
7. Mohammad Hussein Amiri
8. Mohammed Abdulaziz Aide Al-qaness
9. Vahideh Hayyolalam
10. Essam Said Hanandeh

Papers from Laith Mohammad Abualigah's perspective

Ordered by how strongly the researchers Laith Mohammad Abualigah trusts endorse each paper. Same corpus, a different order for every researcher.

1. Genetic Algorithms in Search Optimization and Machine Learning
2. A new optimizer using particle swarm theory
3. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
4. No free lunch theorems for optimization
5. A New Heuristic Optimization Algorithm: Harmony Search
6. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence
7. Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems (Engineering computations)
8. An Efficient Constraint Handling Method for Genetic Algorithms
9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
10. ImageNet classification with deep convolutional neural networks

Their papers

• Salp swarm algorithm: a comprehensive survey (2019) — 378 citations
• Aquila Optimizer: A novel meta-heuristic optimization algorithm (2021) — 1,956 citations
• Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning (2024) — 430 citations
• Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer (2022) — 422 citations
• Prairie Dog Optimization Algorithm (2022) — 517 citations
• Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer (2022) — 816 citations
• A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects (2022) — 848 citations
• Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm (2022) — 315 citations
• K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data (2022) — 2,020 citations
• Machine learning in medical applications: A review of state-of-the-art methods (2022) — 470 citations
• The Arithmetic Optimization Algorithm (2021) — 2,535 citations
• Advances in Sine Cosine Algorithm: A comprehensive survey (2021) — 302 citations
• A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments (2020) — 321 citations
• Moth–flame optimization algorithm: variants and applications (2019) — 295 citations
• Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering (2018) — 475 citations
• Hybrid clustering analysis using improved krill herd algorithm (2018) — 335 citations
• A new feature selection method to improve the document clustering using particle swarm optimization algorithm (2017) — 450 citations
• Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering (2017) — 413 citations