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Mathematics
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Computational Intelligence and Nature-Inspired Computing for Data Analytics
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Computational Intelligence and Nature-Inspired Computing for Data Analytics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5524

Special Issue Editors

Prof. Dr. Absalom El Shamir Ezugwu

E-Mail Website
Guest Editor
Unit for Data Science and Computing, North-West University, Potchefstroom 2520, South Africa
Interests: swarm intelligence; machine learning; nature-inspired computing; global optimization; combinatorial optimization; scheduling; data mining
Prof. Dr. Amir H. Gandomi

grade E-Mail Website
Guest Editor
1. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
2. University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary
Interests: data analytics; machine learning; evolutionary computation; engineering optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational intelligence and nature-inspired computing generally deal with the theory, design, application and development of biologically and linguistically motivated computational paradigms. Moreover, over the past few decades, computational intelligence and nature-inspired computing research have provided a handy and comprehensive tool for solving complex real-world optimization problems by mimicking nature-inspired processes and artificial models such as swarm intelligence, artificial immune systems, membrane computing, cognitive computing and neural and evolutionary computations.

In this Special Issue, we aim to promote discussions around recent efforts and advances in applying computational intelligence and nature-inspired computing approaches to practical optimization problems in data analytics research relating to discovering, interpreting and communicating meaningful patterns in big data. Specific areas of interest include novel technical contributions focusing on: large language models (such as GPT: generative pre-trained transformer 3 & 4 and BERT: bidirectional encoder representations from transformers); extreme learning machines; predictive maintenance machine learning; logistics and delivery; unmanned aerial vehicle; autopilot and self-driving vehicles; renewable energy; fraud detection; drug design; security; marketing and digital advertising; etc. We also encourage submissions that explore the presentation of solid mathematical theory or proof to address the advancement of computational intelligence algorithms' performance superiority assertions from data analytics perspectives.

Prof. Dr. Absalom El Shamir Ezugwu
Prof. Dr. Amir H. Gandomi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • swarm intelligence
  • particle swarm optimization
  • evolutionary programming
  • evolution strategies
  • artificial bee colony
  • ant colony optimization
  • artificial immune systems
  • genetic algorithm
  • genetic programming
  • memetic algorithms
  • descriptive analytics
  • evolutionary computation
  • machine learning
  • deep learning
  • feature selection
  • decision support systems
  • clustering
  • scheduling
  • fuzzy systems
  • ChatGPT
  • GPT-3
  • GPT-4
  • BERT
  • data science
  • self-driving vehicles
  • artificial general intelligence
  • drones
  • renewable energy
  • diagnostic analytics
  • predictive analytics
  • prescriptive analytics
  • neural networks
  • cellular automata

Published Papers (2 papers)

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19 pages, 5962 KiB  
Article
Differentiating Chat Generative Pretrained Transformer from Humans: Detecting ChatGPT-Generated Text and Human Text Using Machine Learning
by Iyad Katib, Fatmah Y. Assiri, Hesham A. Abdushkour, Diaa Hamed and Mahmoud Ragab
Mathematics 2023, 11(15), 3400; https://doi.org/10.3390/math11153400 - 03 Aug 2023
Viewed by 3605
Abstract
Recently, the identification of human text and ChatGPT-generated text has become a hot research topic. The current study presents a Tunicate Swarm Algorithm with Long Short-Term Memory Recurrent Neural Network (TSA-LSTMRNN) model to detect both human as well as ChatGPT-generated text. The purpose [...] Read more.
Recently, the identification of human text and ChatGPT-generated text has become a hot research topic. The current study presents a Tunicate Swarm Algorithm with Long Short-Term Memory Recurrent Neural Network (TSA-LSTMRNN) model to detect both human as well as ChatGPT-generated text. The purpose of the proposed TSA-LSTMRNN method is to investigate the model’s decision and detect the presence of any particular pattern. In addition to this, the TSA-LSTMRNN technique focuses on designing Term Frequency–Inverse Document Frequency (TF-IDF), word embedding, and count vectorizers for the feature extraction process. For the detection and classification processes, the LSTMRNN model is used. Finally, the TSA is employed for selecting the parameters for the LSTMRNN approach, which enables improved detection performance. The simulation performance of the proposed TSA-LSTMRNN technique was investigated on benchmark databases, and the outcome demonstrated the advantage of the TSA-LSTMRNN system over other recent methods with a maximum accuracy of 93.17% and 93.83% on human- and ChatGPT-generated datasets, respectively. Full article
(This article belongs to the Special Issue Computational Intelligence and Nature-Inspired Computing for Data Analytics)
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15 pages, 1663 KiB  
Article
Reinforcement Learning Recommendation Algorithm Based on Label Value Distribution
by Zhida Guo, Jingyuan Fu and Peng Sun
Mathematics 2023, 11(13), 2895; https://doi.org/10.3390/math11132895 - 28 Jun 2023
Viewed by 1438
Abstract
Reinforcement learning is an important machine learning method and has become a hot popular research direction topic at present in recent years. The combination of reinforcement learning and a recommendation system, is a very important application scenario and application, and has always received [...] Read more.
Reinforcement learning is an important machine learning method and has become a hot popular research direction topic at present in recent years. The combination of reinforcement learning and a recommendation system, is a very important application scenario and application, and has always received close attention from researchers in all sectors of society. In this paper, we first propose a feature engineering method based on label distribution learning, which analyzes historical behavior is analyzed and constructs, whereby feature vectors are constructed for users and products via label distribution learning. Then, a recommendation algorithm based on value distribution reinforcement learning is proposed. We first designed the stochastic process of the recommendation process, described the user’s state in the interaction process (by including the information on their explicit state and implicit state), and dynamically generated product recommendations through user feedback. Next, by studying hybrid recommendation strategies, we combined the user’s dynamic and static information to fully utilize their information and achieve high-quality recommendation algorithms. Finally, the algorithm was designed and validated, and various relevant baseline models were compared to demonstrate the effectiveness of the algorithm in this study. With this study, we actually tested the remarkable advantages of relevant design models based on nonlinear expectations compared to other homogeneous individual models. The use of recommendation systems with nonlinear expectations has considerably increased the accuracy, data utilization, robustness, model convergence speed, and stability of the systems. In this study, we incorporated the idea of nonlinear expectations into the design and implementation process of recommendation systems. The main practical value of the improved recommendation model is that its performance is more accurate than that of other recommendation models at the same level of computing power level. Moreover, due to the higher amount of information that the enhanced model contains, it provides theoretical support and the basis for an algorithm that can be used to achieve high-quality recommendation services, and it has many application prospects. Full article
(This article belongs to the Special Issue Computational Intelligence and Nature-Inspired Computing for Data Analytics)
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