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Mathematics
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Computational Intelligence and Machine Learning with Applications
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Computational Intelligence and Machine Learning with Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: closed (1 November 2024) | Viewed by 2853

Special Issue Editor

Prof. Dr. Jun Sun

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Jiangnan University, No.1800, Lihu Avenue, Wuxi, China
Interests: machine learning; deep learning; evolutionary computation; swarm intelligence; matrix optimization

Special Issue Information

Dear Colleagues,

This Special Issue titled "Computational Intelligence and Machine Learning with Applications" delves into the intricate synergy of mathematics, computational intelligence, and machine learning, unraveling the transformative impact of these fields in diverse applications.

The topics of interest for publication include,  but are not limited to, neural networks, machine learning, fuzzy logic and fuzzy systems, evolutionary computation, evolutionary learning, swarm intelligence, applications in image processing, computer vision, modelling of complex systems, and more.

All interested researchers are kindly invited to contribute to this Special Issue with their original research articles, short communications, and review articles.

Prof. Dr. Jun Sun
Guest Editor

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
  • fuzzy logic and fuzzy systems
  • evolutionary computation
  • machine learning
  • deep learning
  • particle swarm optimization
  • computer vision and image processing
  • natural language processing
  • modelling of complex systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

14 pages, 1309 KiB  
Article
Combined Keyword Spotting and Localization Network Based on Multi-Task Learning
by Jungbeom Ko, Hyunchul Kim and Jungsuk Kim
Mathematics 2024, 12(21), 3309; https://doi.org/10.3390/math12213309 - 22 Oct 2024
Viewed by 877
Abstract
The advent of voice assistance technology and its integration into smart devices has facilitated many useful services, such as texting and application execution. However, most assistive technologies lack the capability to enable the system to act as a human who can localize the [...] Read more.
The advent of voice assistance technology and its integration into smart devices has facilitated many useful services, such as texting and application execution. However, most assistive technologies lack the capability to enable the system to act as a human who can localize the speaker and selectively spot meaningful keywords. Because keyword spotting (KWS) and sound source localization (SSL) are essential and must operate in real time, the efficiency of a neural network model is crucial for memory and computation. In this paper, a single neural network model for KWS and SSL is proposed to overcome the limitations of sequential KWS and SSL, which require more memory and inference time. The proposed model uses multi-task learning to utilize the limited resources of the device efficiently. A shared encoder is used as the initial layer to extract common features from the multichannel audio data. Subsequently, the task-specific parallel layers utilize these features for KWS and SSL. The proposed model was evaluated on a synthetic dataset with multiple speakers, and a 7-module shared encoder structure was identified as optimal in terms of accuracy, direction of arrival (DOA) accuracy, DOA error, and latency. It achieved a KWS accuracy of 94.51%, DOA error of 12.397°, and DOA accuracy of 89.86%. Consequently, the proposed model requires significantly less memory owing to the shared network architecture, which enhances the inference time without compromising KWS accuracy, DOA error, and DOA accuracy. Full article
(This article belongs to the Special Issue Computational Intelligence and Machine Learning with Applications)
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25 pages, 2821 KiB  
Article
Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems
by Jia Tian, Xingqin Zhang, Shuangqing Zheng, Zhiyong Liu and Changshu Zhan
Mathematics 2024, 12(9), 1301; https://doi.org/10.3390/math12091301 - 25 Apr 2024
Cited by 1 | Viewed by 1119
Abstract
In the realm of automated industry and smart production, the deployment of fault warning systems is crucial for ensuring equipment reliability and enhancing operational efficiency. Although there are a multitude of existing methodologies for fault warning, the proficiency of these systems in processing [...] Read more.
In the realm of automated industry and smart production, the deployment of fault warning systems is crucial for ensuring equipment reliability and enhancing operational efficiency. Although there are a multitude of existing methodologies for fault warning, the proficiency of these systems in processing and analysing data is increasingly challenged by the progression of industrial apparatus and the escalating magnitude and intricacy of the data involved. To address these challenges, this research outlines an innovative fault warning methodology that combines a bi-directional long short-term memory (Bi-LSTM) network with an enhanced hunter–prey optimisation (EHPO) algorithm. The Bi-LSTM network is strategically utilised to outline complex temporal patterns in machinery operational data, while the EHPO algorithm is employed to meticulously fine-tune the hyperparameters of the Bi-LSTM, aiming to enhance the accuracy and generalisability of fault warning. The EHPO algorithm, building upon the foundational hunter–prey optimisation (HPO) framework, introduces an advanced population initialisation process, integrates a range of strategic exploration methodologies, and strengthens its search paradigms through the incorporation of the differential evolution (DE) algorithm. This comprehensive enhancement aims to boost the global search efficiency and accelerate the convergence speed of the algorithm. Empirical analyses, conducted using datasets from real-world industrial scenarios, have validated the improved warning performance of this proposed methodology against some benchmark techniques, as evidenced by superior metrics such as root mean square error (RMSE) and mean absolute error (MAE), albeit with a slight increase in computational resource requirements. This study not only proposes a novel paradigm for fault warning within complex industrial frameworks but also contributes to the discourse on hyperparameter optimisation within the field of machine learning algorithms. Full article
(This article belongs to the Special Issue Computational Intelligence and Machine Learning with Applications)
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