Advances in Intelligent Data Analysis and Its Applications, Volume II

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: 15 June 2024 | Viewed by 3860

Special Issue Editors

Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
Interests: data mining; granular computing; intelligent decision making
Special Issues, Collections and Topics in MDPI journals
College of Artificial Intelligence, Southwest University, Chongqing 400715, China
Interests: data mining; cognitive computation; granular computing
Special Issues, Collections and Topics in MDPI journals
National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China
Interests: Markov jump systems; stochastic systems; event-triggered schemes; filtering design; controller design; cyber-attacks; time-delay; robust control
Special Issues, Collections and Topics in MDPI journals
School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
Interests: data mining; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid expansion of cloud computing, the Internet of Things (IoT), and the industrial Internet has given rise to a plethora of intricate data analysis tasks within the framework of societal and economic development. In grappling with these multifaceted challenges, the central role of computational intelligence becomes evident, encompassing the utilization of expansive models and the employment of cognitive analysis techniques.

Within the context of addressing data analysis dilemmas, a fundamental quandary surfaces: the effective management, modeling, and processing of the extensive and heterogeneous datasets acquired through the adoption of these emergent technologies. Consequently, there exists an imperative to delve into efficacious models and methodologies that leverage the potential of computational intelligence for the facilitation of intelligent data analysis and applications. In the contemporary milieu, a diverse cohort of scholars and practitioners has collectively woven a rich fabric of intelligent data analysis and applications from a myriad of vantage points. These encompass disciplines spanning data mining, machine learning, natural language processing, granular computing, social networks, machine vision, cognitive computation, and other hybrid paradigms.

Given the inundation of intricate data in the tangible world, the exploration of intelligent data analysis and applications assumes paramount significance across an array of scenarios in the epoch of big data. Such undertakings not only serve to confront immediate challenges but also to enrich the tapestry of the computer science and engineering community, propelling us toward a future characterized by enhanced data literacy and technological advancement.

The inaugural volume of the Special Issue "Advances in Intelligent Data Analysis and its Applications" has been successful, featuring a collection of high-quality papers. Building upon this initial achievement, the objective of this Special Issue is to continue gathering recent advancements in the field of intelligent data analysis and exploring their practical applications across a spectrum of real-world domains. These domains encompass finance, medical diagnosis, business intelligence, engineering, environmental science, and more. We invite submissions of original research contributions, substantially extended renditions of conference papers, and comprehensive review articles. The topics of interest span a broad spectrum and include, but are not limited to, the following areas:

  • Intelligent data mining algorithms and their practical applications;
  • Utilizing machine learning techniques for intelligent data analysis;
  • Advancements in natural language processing for data analysis;
  • Intelligent granular computing models and their real-world use cases;
  • Applying intelligent data analysis to glean insights from social networks;
  • Harnessing machine vision for data analysis and interpretation;
  • Innovations in hybrid models that combine cognitive computation and intelligent data analysis.

Dr. Chao Zhang
Dr. Wentao Li
Dr. Huiyan Zhang
Dr. Tao Zhan
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. Electronics 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 2400 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

  • data mining
  • data analysis
  • cloud computing
  • machine learning

Related Special Issue

Published Papers (6 papers)

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Research

13 pages, 2044 KiB  
Article
Deep Neural Network Confidence Calibration from Stochastic Weight Averaging
Electronics 2024, 13(3), 503; https://doi.org/10.3390/electronics13030503 - 25 Jan 2024
Viewed by 461
Abstract
Overconfidence in deep neural networks (DNN) reduces the model’s generalization performance and increases its risk. The deep ensemble method improves model robustness and generalization of the model by combining prediction results from multiple DNNs. However, training multiple DNNs for model averaging is a [...] Read more.
Overconfidence in deep neural networks (DNN) reduces the model’s generalization performance and increases its risk. The deep ensemble method improves model robustness and generalization of the model by combining prediction results from multiple DNNs. However, training multiple DNNs for model averaging is a time-consuming and resource-intensive process. Moreover, combining multiple base learners (also called inducers) is hard to master, and any wrong choice may result in lower prediction accuracy than from a single inducer. We propose an approximation method for deep ensembles that can obtain ensembles of multiple DNNs without any additional costs. Specifically, multiple local optimal parameters generated during the training phase are sampled and saved by using an intelligent strategy. We use cycle learning rates starting at 75% of the training process and save the weights associated with the minimum learning rate in every iteration. Saved sets of the multiple model parameters are used as weights for a new model to perform forward propagation during the testing phase. Experiments on benchmarks of two different modalities, static images and dynamic videos, show that our method not only reduces the calibration error of the model but also improves the accuracy of the model. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications, Volume II)
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17 pages, 3838 KiB  
Article
Visual Analysis Method for Traffic Trajectory with Dynamic Topic Movement Patterns Based on the Improved Markov Decision Process
Electronics 2024, 13(3), 467; https://doi.org/10.3390/electronics13030467 - 23 Jan 2024
Viewed by 435
Abstract
The visual analysis of trajectory topics is helpful for mining potential trajectory patterns, but the traditional visual analysis method ignores the evolution of the temporal coherence of the topic. In this paper, a novel visual analysis method for dynamic topic analysis of traffic [...] Read more.
The visual analysis of trajectory topics is helpful for mining potential trajectory patterns, but the traditional visual analysis method ignores the evolution of the temporal coherence of the topic. In this paper, a novel visual analysis method for dynamic topic analysis of traffic trajectory is proposed, which is used to explore and analyze the traffic trajectory topic and evolution. Firstly, the spatial information is integrated into trajectory words, calculating the dynamic trajectory topic model based on dynamic analysis modeling and, consequently, correlating the evolution of the trajectory topic between adjacent time slices. Secondly, in the trajectory topic, a representative trajectory sequence is generated to overcome the problem of the trajectory topic model not considering the word order, based on the improved Markov Decision Process. Subsequently, a set of meaningful visual codes is designed to analyze the trajectory topic and its evolution through the parallel window visual model from a spatial-temporal perspective. Finally, a case evaluation shows that the proposed method is effective in analyzing potential trajectory movement patterns. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications, Volume II)
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13 pages, 670 KiB  
Article
RoCS: Knowledge Graph Embedding Based on Joint Cosine Similarity
Electronics 2024, 13(1), 147; https://doi.org/10.3390/electronics13010147 - 28 Dec 2023
Cited by 1 | Viewed by 555
Abstract
Knowledge graphs usually have many missing links, and predicting the relationships between entities has become a hot research topic in recent years. Knowledge graph embedding research maps entities and relations to a low-dimensional continuous space representation to predict links between entities. The present [...] Read more.
Knowledge graphs usually have many missing links, and predicting the relationships between entities has become a hot research topic in recent years. Knowledge graph embedding research maps entities and relations to a low-dimensional continuous space representation to predict links between entities. The present research shows that the key to the knowledge graph embedding approach is the design of scoring functions. According to the scoring function, knowledge graph embedding methods can be classified into dot product models and distance models. We find that the triple scores obtained using the dot product model or the distance model were unbounded, which leads to large variance. In this paper, we propose RotatE Cosine Similarity (RoCS), a method to compute the joint cosine similarity of complex vectors as a scoring function to make the triple scores bounded. Our approach combines the rotational properties of the complex vector embedding model RotatE to model complex relational patterns. The experimental results demonstrate that the newly introduced RoCS yields substantial enhancements compared to RotatE across various knowledge graph benchmarks, improving up to 4.0% in hits at 1 (Hits@1) on WN18RR and improving up to 3.3% in Hits@1 on FB15K-237. Meanwhile, our method achieves some new state-of-the-art (SOTA), including Hits@3 of 95.6%, Hits@10 of 96.4% on WN18, and mean reciprocal rank (MRR) of 48.9% and Hits@1 of 44.5% on WN18RR. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications, Volume II)
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27 pages, 8733 KiB  
Article
Improved A-Star Path Planning Algorithm in Obstacle Avoidance for the Fixed-Wing Aircraft
Electronics 2023, 12(24), 5047; https://doi.org/10.3390/electronics12245047 - 18 Dec 2023
Viewed by 466
Abstract
The flight management system is a basic component of avionics for modern airliners. However, the airborne flight management system needs to be improved and relies on imports; path planning is the key to the flight management system. Based on the classical A* algorithm, [...] Read more.
The flight management system is a basic component of avionics for modern airliners. However, the airborne flight management system needs to be improved and relies on imports; path planning is the key to the flight management system. Based on the classical A* algorithm, this paper proposes an improved A* path planning algorithm, which solves the problem of low planning efficiency and following a non-smooth path. In order to solve the problem of the large amount of data calculation and long planning time of the classical A* algorithm, a new data structure called a “value table” is designed to replace the open table and close table of the classical A* algorithm to improve the retrieval efficiency, and the Heap sort algorithm is used to optimize the efficiency of node sorting. Aiming at the problem that the flight trajectory is hard to follow, the trajectory smoothing optimization algorithm combined with turning angle limit is proposed. The gray value in the digital map is added to the A* algorithm, and the calculation methods of gray cost, cumulative cost, and estimated cost are improved, which can better meet the constraints of obstacle avoidance. Through the comparative simulation verification of the algorithm, the improved A* algorithm can significantly reduce the path planning time to 1% compared to the classical A* algorithm; it can be seen that the proposed algorithm improves the efficiency of path planning and the smoother planned path, which has obvious advantages compared to the classical A* algorithm. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications, Volume II)
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14 pages, 2551 KiB  
Article
Joint Overlapping Event Extraction Model via Role Pre-Judgment with Trigger and Context Embeddings
Electronics 2023, 12(22), 4688; https://doi.org/10.3390/electronics12224688 - 18 Nov 2023
Viewed by 667
Abstract
The objective of event extraction is to recognize event triggers and event categories within unstructured text and produce structured event arguments. However, there is a common phenomenon of triggers and arguments of different event types in a sentence that may be the same [...] Read more.
The objective of event extraction is to recognize event triggers and event categories within unstructured text and produce structured event arguments. However, there is a common phenomenon of triggers and arguments of different event types in a sentence that may be the same word elements, which poses new challenges to this task. In this article, a joint learning framework for overlapping event extraction (ROPEE) is proposed. In this framework, a role pre-judgment module is devised prior to argument extraction. It conducts role pre-judgment by leveraging the correlation between event types and roles, as well as trigger embeddings. Experiments on the FewFC show that the proposed model outperforms other baseline models in terms of Trigger Classification, Argument Identification, and Argument Classification by 0.4%, 0.9%, and 0.6%. In scenarios of trigger overlap and argument overlap, the proposed model outperforms the baseline models in terms of Argument Identification and Argument Classification by 0.9%, 1.2%, 0.7%, and 0.6%, respectively, indicating the effectiveness of ROPEE in solving overlapping events. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications, Volume II)
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23 pages, 1650 KiB  
Article
Resolving Agent Conflicts Using Enhanced Uncertainty Modeling Tools for Intelligent Decision Making
Electronics 2023, 12(21), 4547; https://doi.org/10.3390/electronics12214547 - 05 Nov 2023
Viewed by 701
Abstract
Conflict analysis in intelligent decision making has received increasing attention in recent years. However, few researchers have analyzed conflicts by considering trustworthiness from the perspective of common agreement and common opposition. Since L-fuzzy three-way concept lattice is able to describe both the [...] Read more.
Conflict analysis in intelligent decision making has received increasing attention in recent years. However, few researchers have analyzed conflicts by considering trustworthiness from the perspective of common agreement and common opposition. Since L-fuzzy three-way concept lattice is able to describe both the attributes that objects commonly possess and the attributes that objects commonly do not possess, this paper introduces an L-fuzzy three-way concept lattice to capture the issues on which agents commonly agree and the issues which they commonly oppose, and proposes a hybrid conflict analysis model. In order to resolve conflicts identified by the proposed model, we formulate the problem as a knapsack problem and propose a method for selecting the optimal attitude change strategy. This strategy takes into account the associated costs and aims to provide the decision maker with the most favorable decision in terms of resolving conflicts and reaching consensus. To validate the effectiveness and feasibility of the proposed model, a case study is conducted, providing evidence of the model’s efficacy and viability in resolving conflicts. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications, Volume II)
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