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Advances in Artificial Intelligence: Models, Optimization, and Machine Learning, 3rd...
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Advances in Artificial Intelligence: Models, Optimization, and Machine Learning, 3rd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 10 May 2025 | Viewed by 1543

Special Issue Editors

Prof. Dr. Florin Leon

E-Mail Website
Guest Editor
Faculty of Automatic Control and Computer Engineering, “Gheorghe Asachi” Technical University of Iași, 700050 Iași, Romania
Interests: artificial intelligence; machine learning; multiagent systems; software design
Special Issues, Collections and Topics in MDPI journals
Dr. Mircea Hulea

E-Mail Website
Guest Editor
Faculty of Automatic Control and Computer Engineering, “Gheorghe Asachi” Technical University of Iași, 700050 Iași, Romania
Interests: spiking neural networks; artificial intelligence; embedded systems; optical wireless communication
Special Issues, Collections and Topics in MDPI journals
Dr. Marius Gavrilescu

E-Mail Website
Guest Editor
Faculty of Automatic Control and Computer Engineering, “Gheorghe Asachi” Technical University of Iași, 700050 Iași, Romania
Interests: machine learning; computer graphics; data analytics; gaming engines; physics simulations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, artificial intelligence is an integral part of scientific progress. Various methods have been used to solve problems that were considered challenging until now. AI has the potential to offer tools for learning, knowledge discovery, and decision-making that can outperform human abilities and can be used in a large number of application domains.

This Special Issue, as a follow-up to the successful first edition and second edition, will focus on recent theoretical and computational studies of artificial intelligence, with an attention on models, optimization, and machine learning.

Topics include, but are not limited to, the following:

  1. Deep learning and classic machine learning algorithms;
  2. Neural modeling, architectures, and learning algorithms;
  3. Generative and conversational models;
  4. Neuro-symbolic models;
  5. Explainable artificial intelligence models;
  6. Neural-network-based reasoning;
  7. Spiking neural networks: theory and applications;
  8. Hebbian learning and other biologically plausible neural models;
  9. Optical neural networks;
  10. Biologically inspired optimization algorithms;
  11. Algorithms for autonomous driving;
  12. Reinforcement learning and deep reinforcement learning;
  13. Probabilistic models and Bayesian reasoning;
  14. Adaptive systems;
  15. Intelligent agents and multiagent systems.

Prof. Dr. Florin Leon
Dr. Mircea Hulea
Dr. Marius Gavrilescu
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

  • neural networks
  • deep learning
  • machine learning
  • generative and conversational models
  • neuro-symbolic models
  • biologically inspired optimization
  • optimization algorithms
  • Bayesian networks
  • reinforcement learning
  • multiagent systems

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue polices can be found here.

Related Special Issue

Published Papers (3 papers)

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Research

20 pages, 7232 KiB  
Article
Generalized Type-2 Fuzzy Approach for Parameter Adaptation in the Whale Optimization Algorithm
by Leticia Amador-Angulo, Oscar Castillo, Patricia Melin and Zong Woo Geem
Mathematics 2024, 12(24), 4031; https://doi.org/10.3390/math12244031 (registering DOI) - 22 Dec 2024
Abstract
An enhanced whale optimization algorithm (WOA) through the implementation of a generalized type-2 fuzzy logic system (GT2FLS) is outlined. The initial idea is to improve the efficacy of the original WOA using a GT2FLS to find the optimal values of the and parameters [...] Read more.
An enhanced whale optimization algorithm (WOA) through the implementation of a generalized type-2 fuzzy logic system (GT2FLS) is outlined. The initial idea is to improve the efficacy of the original WOA using a GT2FLS to find the optimal values of the and parameters of the WOA, for the case of optimizing mathematical functions. In the WOA algorithm, is a variable that affects the new position of the whale in the search space, in this case, affecting the exploration, and is a variable that has an effect on finding the local optima, which is an important factor for the exploration. The efficiency of a fuzzy WOA with a GT2FLS (FWOA-GT2FLS) is highlighted by presenting the excellent results of the case study of the benchmark function optimization. A relevant analysis and comparison with a bio-inspired algorithm based on artificial bees is also presented. Statistical tests and comparisons with other bio-inspired algorithms and the initial WOA, with type-1 FLS (FWOA-T1FLS) and interval type-2 FLS (FWOA-IT2FLS), are presented. For each of the methodologies, the metric for evaluation is the average of the minimum squared errors. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence: Models, Optimization, and Machine Learning, 3rd Edition)
18 pages, 3501 KiB  
Article
GRUvader: Sentiment-Informed Stock Market Prediction
by Akhila Mamillapalli, Bayode Ogunleye, Sonia Timoteo Inacio and Olamilekan Shobayo
Mathematics 2024, 12(23), 3801; https://doi.org/10.3390/math12233801 - 30 Nov 2024
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Abstract
Stock price prediction is challenging due to global economic instability, high volatility, and the complexity of financial markets. Hence, this study compared several machine learning algorithms for stock market prediction and further examined the influence of a sentiment analysis indicator on the prediction [...] Read more.
Stock price prediction is challenging due to global economic instability, high volatility, and the complexity of financial markets. Hence, this study compared several machine learning algorithms for stock market prediction and further examined the influence of a sentiment analysis indicator on the prediction of stock prices. Our results were two-fold. Firstly, we used a lexicon-based sentiment analysis approach to identify sentiment features, thus evidencing the correlation between the sentiment indicator and stock price movement. Secondly, we proposed the use of GRUvader, an optimal gated recurrent unit network, for stock market prediction. Our findings suggest that stand-alone models struggled compared with AI-enhanced models. Thus, our paper makes further recommendations on latter systems. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence: Models, Optimization, and Machine Learning, 3rd Edition)
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40 pages, 3414 KiB  
Article
Investigating the Predominance of Large Language Models in Low-Resource Bangla Language over Transformer Models for Hate Speech Detection: A Comparative Analysis
by Fatema Tuj Johora Faria, Laith H. Baniata and Sangwoo Kang
Mathematics 2024, 12(23), 3687; https://doi.org/10.3390/math12233687 - 25 Nov 2024
Viewed by 554
Abstract
The rise in abusive language on social media is a significant threat to mental health and social cohesion. For Bengali speakers, the need for effective detection is critical. However, current methods fall short in addressing the massive volume of content. Improved techniques are [...] Read more.
The rise in abusive language on social media is a significant threat to mental health and social cohesion. For Bengali speakers, the need for effective detection is critical. However, current methods fall short in addressing the massive volume of content. Improved techniques are urgently needed to combat online hate speech in Bengali. Traditional machine learning techniques, while useful, often require large, linguistically diverse datasets to train models effectively. This paper addresses the urgent need for improved hate speech detection methods in Bengali, aiming to fill the existing research gap. Contextual understanding is crucial in differentiating between harmful speech and benign expressions. Large language models (LLMs) have shown state-of-the-art performance in various natural language tasks due to their extensive training on vast amounts of data. We explore the application of LLMs, specifically GPT-3.5 Turbo and Gemini 1.5 Pro, for Bengali hate speech detection using Zero-Shot and Few-Shot Learning approaches. Unlike conventional methods, Zero-Shot Learning identifies hate speech without task-specific training data, making it highly adaptable to new datasets and languages. Few-Shot Learning, on the other hand, requires minimal labeled examples, allowing for efficient model training with limited resources. Our experimental results show that LLMs outperform traditional approaches. In this study, we evaluate GPT-3.5 Turbo and Gemini 1.5 Pro on multiple datasets. To further enhance our study, we consider the distribution of comments in different datasets and the challenge of class imbalance, which can affect model performance. The BD-SHS dataset consists of 35,197 comments in the training set, 7542 in the validation set, and 7542 in the test set. The Bengali Hate Speech Dataset v1.0 and v2.0 include comments distributed across various hate categories: personal hate (629), political hate (1771), religious hate (502), geopolitical hate (1179), and gender abusive hate (316). The Bengali Hate Dataset comprises 7500 non-hate and 7500 hate comments. GPT-3.5 Turbo achieved impressive results with 97.33%, 98.42%, and 98.53% accuracy. In contrast, Gemini 1.5 Pro showed lower performance across all datasets. Specifically, GPT-3.5 Turbo excelled with significantly higher accuracy compared to Gemini 1.5 Pro. These outcomes highlight a 6.28% increase in accuracy compared to traditional methods, which achieved 92.25%. Our research contributes to the growing body of literature on LLM applications in natural language processing, particularly in the context of low-resource languages. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence: Models, Optimization, and Machine Learning, 3rd Edition)
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