Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering
Laith Abualigah,Amir H. Gandomi,Mohamed Abd Elaziz,Husam Al Hamad,Mahmoud Omari,Mohammad Alshinwan,Ahmad M. Khasawneh +6 more
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This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures, its advantages and disadvantages, and recommends potential future research paths.Abstract:
This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) algorithms are recognized as promising swarm intelligence methods due to their successful ability to solve machine learning problems, especially text clustering problems. This paper reviews all of the relevant literature on meta-heuristic-based text clustering applications, including many variants, such as basic, modified, hybridized, and multi-objective methods. As well, the main procedures of text clustering and critical discussions are given. Hence, this review reports its advantages and disadvantages and recommends potential future research paths. The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm.read more
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Advanced metaheuristic optimization techniques in applications of deep neural networks: a review
Mohamed Abd Elaziz,Mohamed Abd Elaziz,Abdelghani Dahou,Laith Abualigah,Liyang Yu,Mohammad Alshinwan,Ahmad M. Khasawneh,Songfeng Lu +7 more
TL;DR: A comprehensive survey of the recent optimization methods employed to enhance DNNs performance on various tasks is presented and the importance of optimization methods in generating the optimal hyper-parameters and structures of Dnns in taking into consideration massive-scale data is analyzed.
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Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit
Hong Fan,Wu Du,Abdelghani Dahou,Ahmed A. Ewees,Dalia Yousri,Mohamed Abd Elaziz,Ammar H. Elsheikh,Laith Abualigah,Mohammed A. A. Al-qaness +8 more
TL;DR: An efficient model to detect and classify toxicity in social media from user-generated content using the Bidirectional Encoder Representations from Transformers (BERT) is built and the results showed that the proposed model can efficiently classify and analyze toxic tweets.
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Meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: a comprehensive survey, applications, comparative analysis, and results
Laith Abualigah,Mohamed Abd Elaziz,Ahmad M. Khasawneh,Mohammad Alshinwan,Rehab Ali Ibrahim,Mohammed A. A. Al-qaness,Seyedali Mirjalili,Putra Sumari,Amir H. Gandomi +8 more
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Machine Learning Technologies for Big Data Analytics
TL;DR: This research presents a meta-modelling system that automates the very labor-intensive and therefore time-heavy and expensive and expensive process of manually cataloging and cataloging data to identify patterns in data.
Journal ArticleDOI
Efficient text document clustering approach using multi-search Arithmetic Optimization Algorithm
Laith Abualigah,Khaled H. Almotairi,Mohammed A. A. Al-qaness,Ahmed A. Ewees,Dalia Yousri,Mohamed Abd Elaziz,Mohammad-Hossein Nadimi-Shahraki +6 more
TL;DR: The Improved AOA (IAOA) as mentioned in this paper is an improved version of AOA for text document clustering problem, which introduces an integration between opposition-based learning (OBL) and Levy flight distribution (LFD) to tackle the limitations of the traditional AOA.
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