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Mathematics
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Artificial Intelligence, Pattern Recognition and Data Learning with Applications in...
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Artificial Intelligence, Pattern Recognition and Data Learning with Applications in Engineering and Science

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 5449

Special Issue Editors

Dr. Pradeep Kumar Mallick

E-Mail Website1 Website2
Guest Editor
School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIIT), Deemed to be University, Bhuvaneswar 751024, India
Interests: data mining; image processing; machine learning; pattern recognition; mathematical modeling
Prof. Dr. Arun Kumar Ray

E-Mail Website1 Website2
Guest Editor
School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIIT), Deemed to be University, Bhuvaneswar 751024, India
Interests: image processing; signal processing; computer network/ADSP
Prof. Dr. Gyoo-Soo Chae

E-Mail Website1 Website2
Guest Editor
Division of Smart IT Engineering, Baekseok University, Cheonan-si 31065, Korea
Interests: signal processing; antennas; IoT; multimedia communication; telecommunication networks

Special Issue Information

Dear Colleagues,

The foundation of machine learning is found in computer science and mathematics. When machine learning (ML) methods are discussed, probability and statistics are the tools that will help you to understand their behaviors the best. With this technique, automatic data learning, performance enhancement, and prediction are all feasible. The sophisticated set of algorithms known as machine learning enables us to automatically spot patterns in data. Patterns serve as a unique identity for everything in the current digital era. Algorithms can be used to detect patterns visually or statistically. Pattern recognition is the process of locating and examining data patterns. Items are assigned to a class during the recognition or categorization process.

This Special Issue will focus on the topics of recent advancements in different areas of pattern recognition and artificial intelligence (AI), such as statistical, structural, and syntactic pattern recognition, signal processing, image processing, feature extraction and selection, machine learning, data mining, neural networks, computer vision, multimedia systems, information retrieval, etc. We warmly welcome original papers in aspects of statistics and mathematics aligned to AI/ML, and pattern recognition problems, in the context of engineering, science, and technology. Advanced statistical and mathematical principles and properties behind artificial intelligence, pattern recognition, and data learning are particularly of interest to the Special Issue.

Dr. Pradeep Kumar Mallick
Prof. Dr. Arun Kumar Ray
Prof. Dr. Gyoo-Soo Chae
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

  • AI/ML modeling
  • bio-inspired computation and developments
  • pattern recognition
  • statistical machine learning
  • signal processing
  • clustering and classification
  • data acquisition
  • data mining
  • data normalization
  • feature extraction and selection
  • pre-processing strategies
  • sensor fusion

Published Papers (2 papers)

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Research

20 pages, 6462 KiB  
Article
Urban Traffic Flow Congestion Prediction Based on a Data-Driven Model
by Kai Zhang, Zixuan Chu, Jiping Xing, Honggang Zhang and Qixiu Cheng
Mathematics 2023, 11(19), 4075; https://doi.org/10.3390/math11194075 - 26 Sep 2023
Cited by 1 | Viewed by 2066
Abstract
Intelligent transportation systems need to realize accurate traffic congestion prediction. The spatio-temporal features of traffic flow are essential to analyze and predict congestion. Our study proposes a data-driven model to predict the traffic congested flow. Firstly, the traffic zone/grid method is used to [...] Read more.
Intelligent transportation systems need to realize accurate traffic congestion prediction. The spatio-temporal features of traffic flow are essential to analyze and predict congestion. Our study proposes a data-driven model to predict the traffic congested flow. Firstly, the traffic zone/grid method is used to store the local area roads’ average speed of the vehicles. Second, the discrete snapshot set is proposed to characterize traffic flow’s spatial and temporal features over a continuous period. Third, the evolution of traffic congested flow in various time dimensions (weekly days, weekend days, and one week) is examined by transforming the global urban transportation network into traffic zones. Finally, the data-driven model is constructed to predict urban road traffic congestion by using the extracted spatio-temporal characteristics of traffic zones’ traffic flow, the snapshot set of which serves as inputs for this model. The model adopts the convolutional LSTM network to learn the temporal and local spatial features of traffic flow, while utilizing a convolutional neural network to effectively capture the global spatial features inherent in traffic flow. The numerical experiments are conducted on two cities’ transportation networks, and the results demonstrate that the performance of the proposed model outperforms traditional traffic flow prediction models. Full article
(This article belongs to the Special Issue Artificial Intelligence, Pattern Recognition and Data Learning with Applications in Engineering and Science)
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21 pages, 7867 KiB  
Article
A Tracklet-before-Clustering Initialization Strategy Based on Hierarchical KLT Tracklet Association for Coherent Motion Filtering Enhancement
by Sami Abdulla Mohsen Saleh, A. Halim Kadarman, Shahrel Azmin Suandi, Sanaa A. A. Ghaleb, Waheed A. H. M. Ghanem, Solehuddin Shuib and Qusay Shihab Hamad
Mathematics 2023, 11(5), 1075; https://doi.org/10.3390/math11051075 - 21 Feb 2023
Cited by 3 | Viewed by 1491
Abstract
Coherent motions depict the individuals’ collective movements in widely existing moving crowds in physical, biological, and other systems. In recent years, similarity-based clustering algorithms, particularly the Coherent Filtering (CF) clustering approach, have accomplished wide-scale popularity and acceptance in the field of coherent motion [...] Read more.
Coherent motions depict the individuals’ collective movements in widely existing moving crowds in physical, biological, and other systems. In recent years, similarity-based clustering algorithms, particularly the Coherent Filtering (CF) clustering approach, have accomplished wide-scale popularity and acceptance in the field of coherent motion detection. In this work, a tracklet-before-clustering initialization strategy is introduced to enhance coherent motion detection. Moreover, a Hierarchical Tracklet Association (HTA) algorithm is proposed to address the disconnected KLT tracklets problem of the input motion feature, thereby making proper trajectories repair to optimize the CF performance of the moving crowd clustering. The experimental results showed that the proposed method is effective and capable of extracting significant motion patterns taken from crowd scenes. Quantitative evaluation methods, such as Purity, Normalized Mutual Information Index (NMI), Rand Index (RI), and F-measure (Fm), were conducted on real-world data using a huge number of video clips. This work has established a key, initial step toward achieving rich pattern recognition. Full article
(This article belongs to the Special Issue Artificial Intelligence, Pattern Recognition and Data Learning with Applications in Engineering and Science)
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