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Advances in Machine Learning Applied to Intelligent Systems and Data Analytics

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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: closed (30 September 2023) | Viewed by 12846

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Special Issue Editors

Dr. Linlin You

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Guest Editor

School of Intelligent System Engineering, Sun Yat-sen University, Guangzhou, China
Interests: urban big data; multi-source heterogeneous data fusion; machine learning; federated learning
Special Issues, Collections and Topics in MDPI journals

Dr. Ivan Lee

E-Mail Website
Guest Editor

School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5001, Australia
Interests: smart sensors; multimedia systems; data analytics
Special Issues, Collections and Topics in MDPI journals

Dr. Zhicong Chen

E-Mail Website
Guest Editor

College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
Interests: Intelligent monitoring and fault diagnosis; machine learning; wireless sensors networks; photovoltaic systems; structural health monitoring

Special Issue Information

Dear Colleagues,

Advancements in machine learning (ML) are driving the development of autonomous and intelligent systems (AIS) in various domains, e.g., smart cities, transportation, healthcare, the economy, the environment, etc. Based on analytical models built from data samples, valuable insights can be mined and utilized to assist the decision-making and service delivery processes of AIS. To continuously and consistently elevate the levels of intelligence and automation in AIS, i.e., to be more independent of humans, advanced ML, e.g., deep learning, reinforcement learning, meta-learning, etc., are required to support both supervised and unsupervised analytical tasks via more accurate, robust, and self-interpretable models. Moreover, since the exploration of big data diversifies the data sources, which tend to be more isolated due to the engagement of laws and regulations about data protection and user privacy, the working paradigm of AIS and data analytics is shifting from being centralized to distributed, through which multi-end resources are managed to learn and consume interknowledge in a collaborative and privacy-preserving manner. Driven by these emerging demands, novel solutions that are not limited to learning theories, algorithms, mechanisms, frameworks, systems, and services are required to impel the applications of advanced ML in AIS and data analytics.

The aim of this Special Issue is to provide a forum for researchers to present their original contributions describing their experience and approaches toward a wide range of machine learning techniques applied to intelligent systems and data analytics. Submissions showcasing the latest developments in theoretical analysis, numerical experiments, practical applications, and data analytics are welcome.

Dr. Linlin You
Dr. Ivan Lee
Dr. Zhicong Chen
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

machine learning
autonomous and intelligent systems
AI-driven data analytics
computing theory
deep learning
distributed learning
meta-learning
reinforcement learning
supervised deep learning
unsupervised deep learning
novel learning applications

Published Papers (7 papers)

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Research

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21 pages, 5443 KiB

Open AccessArticle

A Federated Personal Mobility Service in Autonomous Transportation Systems

by Weitao Jian, Kunxu Chen, Junshu He, Sifan Wu, Hongli Li and Ming Cai

Mathematics 2023, 11(12), 2693; https://0-doi-org.brum.beds.ac.uk/10.3390/math11122693 - 14 Jun 2023

Cited by 1 | Viewed by 911

Abstract

A personal mobility service (PMS) is developed to support personalized travel options for users in autonomous transportation systems (ATS), based on a macro-system state and micro-user behavior. However, this functionality necessitates processing and transmitting vast amounts of data, raising concerns about user privacy protection during data processing and transmission within the PMS. Furthermore, the PMS must be maintained and perform well, while preserving privacy. Therefore, we propose a novel federated PMS, denoted as a FPMS. Specifically, the FPMS can serve users’ personal mobility needs by facilitating the collaboration between the physical and information domains. Then, a common framework for FPMS architectures, which captures the features of ATSs, is proposed and discussed from both physical and logical perspectives, which include both the logical architecture and physical architecture; and we present the key algorithms for the FPMS, in conjunction with a artificial neural network (ANN). Additionally, in static estimation scenarios, the FPMS demonstrated a similar accuracy for three different models compared to the traditional PMS, while reducing the computing time by approximately 60% and communication resource consumption by approximately 85%. Full article

(This article belongs to the Special Issue Advances in Machine Learning Applied to Intelligent Systems and Data Analytics)

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22 pages, 1213 KiB

Open AccessArticle

ICMFed: An Incremental and Cost-Efficient Mechanism of Federated Meta-Learning for Driver Distraction Detection

by Zihan Guo, Linlin You, Sheng Liu, Junshu He and Bingran Zuo

Mathematics 2023, 11(8), 1867; https://0-doi-org.brum.beds.ac.uk/10.3390/math11081867 - 14 Apr 2023

Cited by 2 | Viewed by 1305

Abstract

Driver distraction detection (3D) is essential in improving the efficiency and safety of transportation systems. Considering the requirements for user privacy and the phenomenon of data growth in real-world scenarios, existing methods are insufficient to address four emerging challenges, i.e., data accumulation, communication optimization, data heterogeneity, and device heterogeneity. This paper presents an incremental and cost-efficient mechanism based on federated meta-learning, called ICMFed, to support the tasks of 3D by addressing the four challenges. In particular, it designs a temporal factor associated with local training batches to stabilize the local model training, introduces gradient filters of each model layer to optimize the client–server interaction, implements a normalized weight vector to enhance the global model aggregation process, and supports rapid personalization for each user by adapting the learned global meta-model. According to the evaluation made based on the standard dataset, ICMFed can outperform three baselines in training two common models (i.e., DenseNet and EfficientNet) with average accuracy improved by about 141.42%, training time saved by about 54.80%, communication cost reduced by about 54.94%, and service quality improved by about 96.86%. Full article

(This article belongs to the Special Issue Advances in Machine Learning Applied to Intelligent Systems and Data Analytics)

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22 pages, 4485 KiB

Open AccessArticle

Personalized Movie Recommendations Based on a Multi-Feature Attention Mechanism with Neural Networks

by Saisai Yu, Ming Guo, Xiangyong Chen, Jianlong Qiu and Jianqiang Sun

Mathematics 2023, 11(6), 1355; https://0-doi-org.brum.beds.ac.uk/10.3390/math11061355 - 10 Mar 2023

Cited by 3 | Viewed by 2915

Abstract

With the rapid growth of the Internet, a wealth of movie resources are readily available on the major search engines. Still, it is unlikely that users will be able to find precisely the movies they are more interested in any time soon. Traditional recommendation algorithms, such as collaborative filtering recommendation algorithms only use the user’s rating information of the movie, without using the attribute information of the user and the movie, which has the problem of inaccurate recommendations. In order to achieve personalized accurate movie recommendations, a movie recommendation algorithm based on a multi-feature attention mechanism with deep neural networks and convolutional neural networks is proposed. In order to make the predicted movie ratings more accurate, user attribute information and movie attribute information are added, user network and movie network are presented to learn user features and movie features, respectively, and a feature attention mechanism is proposed so that different parts contribute differently to movie ratings. Text features are also extracted using convolutional neural networks, in which an attention mechanism is added to make the extracted text features more accurate, and finally, personalized movie accurate recommendations are achieved. The experimental results verify the effectiveness of the algorithm. The user attribute features and movie attribute features have a good effect on the rating, the feature attention mechanism makes the features distinguish the degree of importance to the rating, and the convolutional neural network adding the attention mechanism makes the extracted text features more effective and achieves high accuracy in MSE, MAE, MAPE, R², and RMSE indexes. Full article

(This article belongs to the Special Issue Advances in Machine Learning Applied to Intelligent Systems and Data Analytics)

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18 pages, 1220 KiB

Open AccessArticle

Latin Matchings and Ordered Designs OD(n−1, n, 2n−1)

by Kai Jin, Taikun Zhu, Zhaoquan Gu and Xiaoming Sun

Mathematics 2022, 10(24), 4703; https://0-doi-org.brum.beds.ac.uk/10.3390/math10244703 - 11 Dec 2022

Viewed by 929

Abstract

This paper revisits a combinatorial structure called the large set of ordered design (

L O D

). Among others, we introduce a novel structure called Latin matching and prove that a Latin matching of order n leads to an [...] Read more.

This paper revisits a combinatorial structure called the large set of ordered design (

L O D

). Among others, we introduce a novel structure called Latin matching and prove that a Latin matching of order n leads to an

L O D (n - 1, n, 2 n - 1)

; thus, we obtain constructions for

L O D (1, 2, 3)

L O D (2, 3, 5)

, and

L O D (4, 5, 9)

. Moreover, we show that constructing a Latin matching of order n is at least as hard as constructing a Steiner system

S (n - 2, n - 1, 2 n - 2)

; therefore, the order of a Latin matching must be prime. We also show some applications in multiagent systems. Full article

(This article belongs to the Special Issue Advances in Machine Learning Applied to Intelligent Systems and Data Analytics)

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18 pages, 637 KiB

Open AccessArticle

Subgraph Adaptive Structure-Aware Graph Contrastive Learning

by Zhikui Chen, Yin Peng, Shuo Yu, Chen Cao and Feng Xia

Mathematics 2022, 10(17), 3047; https://0-doi-org.brum.beds.ac.uk/10.3390/math10173047 - 24 Aug 2022

Cited by 1 | Viewed by 1832

Abstract

Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases. Full article

(This article belongs to the Special Issue Advances in Machine Learning Applied to Intelligent Systems and Data Analytics)

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19 pages, 3258 KiB

Open AccessArticle

Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction

by Haohao Qu, Sheng Liu, Jun Li, Yuren Zhou and Rui Liu

Mathematics 2022, 10(12), 2039; https://0-doi-org.brum.beds.ac.uk/10.3390/math10122039 - 12 Jun 2022

Cited by 7 | Viewed by 1549

Abstract

Parking occupancy prediction (POP) plays a vital role in many parking-related smart services for better parking management. However, an issue hinders its mass deployment: many parking facilities cannot collect enough data to feed data-hungry machine learning models. To tackle the challenges in small-sample POP, we propose an approach named Adaptation and Learning to Learn (ALL) by adopting the capability of advanced deep learning and federated learning. ALL integrates two novel ideas: (1) Adaptation: by leveraging the Asynchronous Advantage Actor-Critic (A3C) reinforcement learning technique, an auto-selector module is implemented, which can group and select data-scarce parks automatically as supporting sources to enable the knowledge adaptation in model training; and (2) Learning to learn: by applying federated meta-learning on selected supporting sources, a meta-learner module is designed, which can train a high-performance local prediction model in a collaborative and privacy-preserving manner. Results of an evaluation with 42 parking lots in two Chinese cities (Shenzhen and Guangzhou) show that, compared to state-of-the-art baselines: (1) the auto-selector can reduce the model variance by about 17.8%; (2) the meta-learner can train a converged model 10

^{2}

× faster; and (3) finally, ALL can boost the forecasting performance by about 29.8%. Through the integration of advanced machine learning methods, i.e., reinforcement learning, meta-learning, and federated learning, the proposed approach ALL represents a significant step forward in solving small-sample issues in parking occupancy prediction. Full article

(This article belongs to the Special Issue Advances in Machine Learning Applied to Intelligent Systems and Data Analytics)

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Review

Jump to: Research

22 pages, 6732 KiB

Open AccessReview

Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms

by Tuo Sun, Shihao Zhu, Ruochen Hao, Bo Sun and Jiemin Xie

Mathematics 2022, 10(14), 2544; https://0-doi-org.brum.beds.ac.uk/10.3390/math10142544 - 21 Jul 2022

Cited by 4 | Viewed by 2351

Abstract

A great challenge for intelligent transportation systems (ITS) is missing traffic data. Traffic data are input from various transportation applications. In the past few decades, several methods for traffic temporal data imputation have been proposed. A key issue is that temporal information collected by neighbor detectors can make traffic missing data imputation more accurate. This review analyzes traffic temporal data imputation methods. Research methods, missing patterns, assumptions, imputation styles, application conditions, limitations, and public datasets are reviewed. Then, five representative methods are tested under different missing patterns and missing ratios. California performance measurement system (PeMS) data including traffic volume and speed are selected to conduct the test. Probabilistic principal component analysis performs the best under the most conditions. Full article

(This article belongs to the Special Issue Advances in Machine Learning Applied to Intelligent Systems and Data Analytics)

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