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Advances in Mathematical Methods, Machine Learning and Deep Learning Based Applications, 2nd Edition

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 9177

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Prof. Dr. Heui Seok Lim

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Department of Computer Science and Engineering, Human-inspired AI Computing Research Center, Korea University, Seoul 13557, Korea
Interests: AI; educational data mining; NLP; learning science
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Dr. Sanghyuk Lee

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Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Xi’an 215123, China
Interests: control theory; data analysis; fuzzy set theory; robust controller design; energy optimization
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Prof. Dr. Yeongwook Yang

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Division of Computer Engineering, Hanshin University, Osan-si 447-791, Korea
Interests: recommender systems; applied machine learning; information filtering system; educational data mining
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Prof. Dr. Imatitikua Aiyanyo

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Department of Computer Science and Engineering, Human-inspired AI Computing Research Center, Korea University, Seoul 13557, Korea
Interests: computer science and engineering
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Special Issue Information

Dear Colleagues,

Recent advances in artificial intelligence, particularly machine learning, have shifted fields of study from purely theoretical approaches to fully applied industrial research, not only in computer science but in almost every other conceivable domain as well.

This Special Issue invites papers that address challenges using novel approaches which incorporate not only theoretical mathematical aspects but also machine learning techniques and deep learning approaches. Methodologies such as quantitative, qualitative, hybrid, and action research are all welcome. Our goal is to integrate various methodologies from other disciplines and assess how they are evaluated. Contributions on both theoretical and practical models are welcome. The selection criteria will be based on formal and technical soundness, experimental support, and the relevance of the contribution.

Prof. Dr. Heui Seok Lim
Dr. Sanghyuk Lee
Prof. Dr. Yeongwook Yang
Prof. Dr. Imatitikua Aiyanyo
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

artificial intelligence
natural language processing
data mining and learning analytics
recommender systems
cybersecurity
blockchain
artificial neural networks
machine learning
statistical and optimization methods
evaluation of artificial intelligence
adaptive, or personalized systems intelligent tutoring systems
virtual reality and dialog systems model applications in several domains like education, finance etc.

Published Papers (7 papers)

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Research

20 pages, 1547 KiB

Open AccessArticle

A Novel Variant of LSTM Stock Prediction Method Incorporating Attention Mechanism

by Shuai Sang and Lu Li

Mathematics 2024, 12(7), 945; https://doi.org/10.3390/math12070945 - 22 Mar 2024

Viewed by 458

Abstract

Long Short-Term Memory (LSTM) is an effective method for stock price prediction. However, due to the nonlinear and highly random nature of stock price fluctuations over time, LSTM exhibits poor stability and is prone to overfitting, resulting in low prediction accuracy. To address this issue, this paper proposes a novel variant of LSTM that couples the forget gate and input gate in the LSTM structure, and adds a “simple” forget gate to the long-term cell state. In order to enhance the generalization ability and robustness of the variant LSTM, the paper introduces an attention mechanism and combines it with the variant LSTM, presenting the Attention Mechanism Variant LSTM (AMV-LSTM) model along with the corresponding backpropagation algorithm. The parameters in AMV-LSTM are updated using the Adam gradient descent method. Experimental results demonstrate that the variant LSTM alleviates the instability and overfitting issues of LSTM, effectively improving prediction accuracy. AMV-LSTM further enhances accuracy compared to the variant LSTM, and compared to AM-LSTM, it exhibits superior generalization ability, accuracy, and convergence capability. Full article

(This article belongs to the Special Issue Advances in Mathematical Methods, Machine Learning and Deep Learning Based Applications, 2nd Edition)

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19 pages, 4497 KiB

Open AccessArticle

A Study of Advancing Ultralow-Power 3D Integrated Circuits with TEI-LP Technology and AI-Enhanced PID Autotuning

by Sangmin Jeon, Hyunseok Kwak and Woojoo Lee

Mathematics 2024, 12(4), 543; https://doi.org/10.3390/math12040543 - 09 Feb 2024

Viewed by 608

Abstract

The 3D integrated circuit (3D-IC) is garnering significant attention from academia and industry as a key technology leading the post-Moore era, offering new levels of efficiency, power, performance, and form-factor advantages to the semiconductor industry. However, thermal management in 3D-ICs presents a critical challenge that must be overcome to ensure prosperity for this technology. Unlike traditional thermal management solutions that perceive heat generation in 3D-ICs negatively and aim to eliminate it, this paper proposes, for the first time, a thermal management method that positively utilizes heat to achieve low-power operation in 3D-ICs. This approach is based on a novel discovery that circuits can reduce power consumption at higher temperatures by leveraging the temperature effect inversion (TEI) phenomenon in ultralow-voltage (ULV) operating circuits, a characteristic of low-power techniques (TEI-LP techniques). Along with a detailed explanation of this discovery, this paper introduces new thermal management technologies for practical application in 3D-ICs. Furthermore, to achieve optimal energy efficiency with the proposed technology, we develop a temperature controller essential for this purpose. The developed controller is a deep learning-based PID autotuner. This paper proves the theoretical validity of the AI control algorithm designed for this purpose and demonstrates the functional correctness and power-saving effectiveness of the developed controller through intensively conducted simulations. Full article

(This article belongs to the Special Issue Advances in Mathematical Methods, Machine Learning and Deep Learning Based Applications, 2nd Edition)

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16 pages, 2437 KiB

Open AccessArticle

Improved Algorithm of Partial Transmit Sequence Based on Discrete Particle Swarm Optimization

by Hongmei Wang, Yunbo Chen, Jiahui Dai, Shiyin Li, Faguang Wang and Minghui Min

Mathematics 2024, 12(1), 80; https://doi.org/10.3390/math12010080 - 25 Dec 2023

Viewed by 668

Abstract

Orthogonal frequency division multiplexing (OFDM) in 5G has many advantages; however, one of the disadvantages is that the superposition of a large number of subcarriers leads to a high peak-to-average power ratio (PAPR) of the transmit signal. A high PAPR results in high-power amplifier distortion and performance degradation. The partial transmit sequence (PTS) algorithm is commonly used for PAPR reduction. It enumerates all combinations of phase factors, weighs the signal using each phase factor combination, and finds the set of phase factors that minimizes the PAPR value of the OFDM signal. The advantage of the PTS is that it determines the optimal solution through enumeration; however, its major drawback is the higher complexity caused by the use of enumeration. Some studies have introduced the discrete particle swarm optimization (DPSO) algorithm instead of enumeration to determine the optimal solution of the PTS algorithm. As an excellent optimization method, the DPSO algorithm represents each individual as a solution during the optimization. Through iterative updates of the initial population, individuals in the population continuously move closer to the optimal solution. This approach significantly reduces complexity compared with the exhaustive enumeration used in the traditional PTS algorithm. However, the disadvantage of the general DPSO algorithm is that it can result in premature and early convergence, which leads to degradation of the PAPR reduction performance. In this study, we propose an improved method based on the general DPSO-based PTS algorithm, and the improved algorithm MDPSO-PTS adopts dynamic time-varying learning factors, which can find the optimal combination of phase factors more efficiently. The MDPSO-PTS algorithm expands the search space when seeking the optimal combination of phase factors. This avoids the drawback of premature convergence commonly observed in general DPSO-PTS algorithms, preventing early consideration of local optima as global optima. A comparative simulation of the improved MDPSO-PTS algorithm with the general DPSO-PTS algorithm shows that the improved algorithm has stronger PAPR reduction, whereas the complexity remains basically unchanged. A comparative simulation with the traditional PTS algorithm shows a significant reduction in complexity, with only a slight, acceptable loss of reduction performance. Full article

(This article belongs to the Special Issue Advances in Mathematical Methods, Machine Learning and Deep Learning Based Applications, 2nd Edition)

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9 pages, 456 KiB

Open AccessArticle

Enhancing Machine Translation Quality Estimation via Fine-Grained Error Analysis and Large Language Model

by Dahyun Jung, Chanjun Park, Sugyeong Eo and Heuiseok Lim

Mathematics 2023, 11(19), 4169; https://doi.org/10.3390/math11194169 - 05 Oct 2023

Viewed by 1165

Abstract

Fine-grained error span detection is a sub-task within quality estimation that aims to identify and assess the spans and severity of errors present in translated sentences. In prior quality estimation, the focus has predominantly been on evaluating translations at the sentence and word levels. However, such an approach fails to recognize the severity of specific segments within translated sentences. To the best of our knowledge, this is the first study that concentrates on enhancing models for this fine-grained error span detection task in machine translation. This study introduces a framework that sequentially performs sentence-level error detection, word-level error span extraction, and severity assessment. We present a detailed analysis for each of the methodologies we propose, substantiating the effectiveness of our system, focusing on two language pairs: English-to-German and Chinese-to-English. Our results suggest that task granularity enhances performance and that a prompt-based fine-tuning approach can offer optimal performance in the classification tasks. Furthermore, we demonstrate that employing a large language model to edit the fine-tuned model’s output constitutes a top strategy for achieving robust quality estimation performance. Full article

(This article belongs to the Special Issue Advances in Mathematical Methods, Machine Learning and Deep Learning Based Applications, 2nd Edition)

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25 pages, 692 KiB

Open AccessArticle

Attention-Based 1D CNN-BiLSTM Hybrid Model Enhanced with FastText Word Embedding for Korean Voice Phishing Detection

by Milandu Keith Moussavou Boussougou and Dong-Joo Park

Mathematics 2023, 11(14), 3217; https://doi.org/10.3390/math11143217 - 21 Jul 2023

Cited by 2 | Viewed by 2861

Abstract

In the increasingly complex domain of Korean voice phishing attacks, advanced detection techniques are paramount. Traditional methods have achieved some degree of success. However, they often fail to detect sophisticated voice phishing attacks, highlighting an urgent need for enhanced approaches to improve detection performance. Addressing this, we have designed and implemented a novel artificial neural network (ANN) architecture that successfully combines data-centric and model-centric AI methodologies for detecting Korean voice phishing attacks. This paper presents our unique hybrid architecture, consisting of a 1-dimensional Convolutional Neural Network (1D CNN), a Bidirectional Long Short-Term Memory (BiLSTM), and Hierarchical Attention Networks (HANs). Our evaluations using the real-world KorCCVi v2 dataset demonstrate that the proposed architecture effectively leverages the strengths of CNN and BiLSTM to extract and learn contextually rich features from word embedding vectors. Additionally, implementing word and sentence attention mechanisms from HANs enhances the model’s focus on crucial features, considerably improving detection performance. Achieving an accuracy score of 99.32% and an F1 score of 99.31%, our model surpasses all baseline models we trained, outperforms several existing solutions, and maintains comparable performance to others. The findings of this study underscore the potential of hybrid neural network architectures in improving voice phishing detection in the Korean language and pave the way for future research. This could involve refining and expanding upon this model to tackle increasingly sophisticated voice phishing strategies effectively or utilizing larger datasets. Full article

(This article belongs to the Special Issue Advances in Mathematical Methods, Machine Learning and Deep Learning Based Applications, 2nd Edition)

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16 pages, 8522 KiB

Open AccessArticle

Prediction and Analysis of the Price of Carbon Emission Rights in Shanghai: Under the Background of COVID-19 and the Russia–Ukraine Conflict

by Qing Liu, Huina Jin, Xiang Bai and Jinliang Zhang

Mathematics 2023, 11(14), 3126; https://doi.org/10.3390/math11143126 - 15 Jul 2023

Viewed by 914

Abstract

In the spring of 2022, a new round of epidemic broke out in Shanghai, causing a shock to the Shanghai carbon trading market. Against this background, this paper studied the impact of the new epidemic on the price of Shanghai carbon emission rights and tried to explore the prediction model under the unexpected event. First, because a model based on point value data cannot capture the information hidden in inter-day price fluctuation, based on the interval price of Shanghai carbon emission rights (SHEA) and its influencing factors, an autoregressive conditional interval model with jumping and exogenous variables (ACIXJ) was established to explore the influence of the Russian–Ukrainian conflict and COVID-19 on the interval price of SHEA, respectively. The empirical results show that the conflict between Russia and Ukraine has no obvious influence on the price of SHEA, but COVID-19 led to a decline in the price trend of SHEA over four days before the city was closed, and the volatility changed significantly on the day before the city was closed. The price fluctuation was the strongest within 3 days after the city was closed; In addition, in order to accurately predict the interval data of SHEA against the background of COVID-19, based on the interval data decomposition algorithm (BEMD), a hybrid forecasting model of NDGM-ACIXJ/CNN-LSTM was constructed, in which the discrete gray model of approximate nonhomogeneous exponential series (NDGM) combined with the ACIXJ model is used to predict the high-frequency sub-interval, and the convolution neural network long-term and short-term memory model (CNN-LSTM) is used to predict the low-frequency sub-interval. The empirical results show that the prediction model proposed in this article has higher prediction precision than the reference models (ACIX, ACIXJ, NDGM-ACIXJ, BEMD-ACIX/CNN-LSTM, BEMD-ACIXJ/CNN-LSTM). Full article

(This article belongs to the Special Issue Advances in Mathematical Methods, Machine Learning and Deep Learning Based Applications, 2nd Edition)

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20 pages, 4927 KiB

Open AccessArticle

Genetics Information with Functional Brain Networks for Dementia Classification

by Uttam Khatri, Ji-In Kim and Goo-Rak Kwon

Mathematics 2023, 11(6), 1529; https://doi.org/10.3390/math11061529 - 21 Mar 2023

Cited by 1 | Viewed by 1454

Abstract

Mild cognitive impairment (MCI) precedes the Alzheimer’s disease (AD) continuum, making it crucial for therapeutic care to identify patients with MCI at risk of progression. We aim to create generalized models to identify patients with MCI who advance to AD using high-dimensional-data resting state functional magnetic resonance imaging (rs-fMRI) brain networks and gene expression. Studies that integrate genetic traits with brain imaging for clinical examination are limited, compared with most current research methodologies, employing separate or multi-imaging features for disease prognosis. Healthy controls (HCs) and the two phases of MCI (convertible and stable MCI) along with AD can be effectively diagnosed using genetic markers. The rs-fMRI-based brain functional connectome provides various information regarding brain networks and is utilized in combination with genetic factors to distinguish people with AD from HCs. The most discriminating network nodes are identified using the least absolute shrinkage and selection operator (LASSO). The most common brain areas for nodal detection in patients with AD are the middle temporal, inferior temporal, lingual, hippocampus, amygdala, and middle frontal gyri. The highest degree of discriminative power is demonstrated by the nodal graph metrics. Similarly, we propose an ensemble feature-ranking algorithm for high-dimensional genetic information. We use a multiple-kernel learning support vector machine to efficiently merge multipattern data. Using the suggested technique to distinguish AD from HCs produced combined features with a leave-one-out cross-validation (LOOCV) classification accuracy of 93.07% and area under the curve (AUC) of 95.13%, making it the most state-of-the-art technique in terms of diagnostic accuracy. Therefore, our proposed approach has high accuracy and is clinically relevant and efficient for identifying AD. Full article

(This article belongs to the Special Issue Advances in Mathematical Methods, Machine Learning and Deep Learning Based Applications, 2nd Edition)

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