Submit to Special Issue Submit Abstract to Special Issue Review for Electronics Edit a Special Issue

Journal Browser

Special Issue "Advances in Intelligent Data Analysis and Its Applications"

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Published Papers

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 30 September 2023 | Viewed by 2967

Share This Special Issue

Special Issue Editors

Dr. Chao Zhang

E-Mail Website
Guest Editor

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

Dr. Wentao Li

E-Mail Website
Guest Editor

College of Artificial Intelligence, Southwest University, Chongqing 400715, China
Interests: data mining; cognitive computation; granular computing

Dr. Huiyan Zhang

E-Mail Website
Guest Editor

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

Dr. Tao Zhan

E-Mail Website
Guest Editor

School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
Interests: data mining; fuzzy set; control system

Special Issue Information

Dear Colleagues,

With the rapid growth of cloud computing, the Internet of Things, and the industrial internet, various complicated data analysis tasks subsequently emerged in the development of social economy. Among the known problem-solving procedures of data analysis issues, one of the key challenges is how to manage, model, and process numerous acquired data. Thus, it is imperative to explore efficient models and methods for intelligent data analysis and applications. Currently, many scholars and practitioners have put forward a series of intelligent data analyses and applications from various perspectives, such as data mining, machine learning, natural language processing, granular computing, social networks, machine vision, cognitive computation, and other hybrid models. Aiming at numerous complicated data in the real world, investigating intelligent data analyses and applications are significant to diverse scenarios in the era of big data, so as to further enrich the community of computer science and engineering.

The goal of this Special Issue is to collect recent developments in the area of intelligent data analysis and how can they be applied to various real-world issues, such as finance, medical diagnosis, business intelligence, engineering, environmental science, etc. Original research work, significantly extended versions of conference papers, and review papers are welcome. Topics of interest include, but are not limited to, the following:

Intelligent data mining algorithms and applications;
Machine learning for intelligent data analysis;
Natural language processing methods;
Intelligent granular computing models;
Intelligent data analysis in social networks;
Machine vision-based data analysis;
Hybrid models of 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 2000 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.

Published Papers (6 papers)

Download All Papers

Research

Open AccessArticle

Flight Delay Prediction Model Based on Lightweight Network ECA-MobileNetV3

and

Electronics 2023, 12(6), 1434; https://doi.org/10.3390/electronics12061434 - 17 Mar 2023

Viewed by 345

Abstract

In exploring the flight delay problem, traditional deep learning algorithms suffer from low accuracy and extreme computational complexity; therefore, the deep flight delay prediction algorithm is difficult to directly deploy to the mobile terminal. In this paper, a flight delay prediction model based on the lightweight network ECA-MobileNetV3 algorithm is proposed. The algorithm first preprocesses the data with real flight information and weather information. Then, in order to increase the accuracy of the model without increasing the computational complexity too much, feature extraction is performed using the lightweight ECA-MobileNetV3 algorithm with the addition of the Efficient Channel Attention mechanism. Finally, the flight delay classification prediction level is output via a Softmax classifier. In the experiments of single airport and airport cluster datasets, the optimal accuracy of the ECA-MobileNetV3 algorithm is 98.97% and 96.81%, the number of parameters is 0.33 million and 0.55 million, and the computational volume is 32.80 million and 60.44 million, respectively, which are better than the performance of the MobileNetV3 algorithm under the same conditions. The improved model can achieve a better balance between accuracy and computational complexity, which is more conducive mobility. Full article

(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)

► Show Figures

Figure 1

Open AccessArticle

Machine Learning-Based Prediction of Orphan Genes and Analysis of Different Hybrid Features of Monocot and Eudicot Plants

and

Electronics 2023, 12(6), 1433; https://doi.org/10.3390/electronics12061433 - 17 Mar 2023

Viewed by 303

Abstract

Orphan genes (OGs) may evolve from noncoding sequences or be derived from older coding material. Some shares of OGs are present in all sequenced genomes, participating in the biochemical and physiological pathways of many species, while many of them may be associated with the response to environmental stresses and species-specific traits or regulatory patterns. However, identifying OGs is a laborious and time-consuming task. This paper presents an automated predictor, XGBoost-A2OGs (identification of OGs for angiosperm based on XGBoost), used to identify OGs for seven angiosperm species based on hybrid features and XGBoost. The precision and accuracy of the proposed model based on fivefold cross-validation and independent testing reached 0.90 and 0.91, respectively, outperforming other classifiers in cross-species validation via other models, namely, Random Forest, AdaBoost, GBDT, and SVM. Furthermore, by analyzing and subdividing the hybrid features into five sets, it was proven that different hybrid feature sets influenced the prediction performance of OGs involving eudicot and monocot groups. Finally, testing of small-scale empirical datasets of each species separately based on optimal hybrid features revealed that the proposed model performed better for eudicot groups than for monocot groups. Full article

(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)

► Show Figures

Figure 1

Open AccessArticle

UAV Abnormal State Detection Model Based on Timestamp Slice and Multi-Separable CNN

and

Electronics 2023, 12(6), 1299; https://doi.org/10.3390/electronics12061299 - 08 Mar 2023

Viewed by 294

Abstract

With the rapid development of UAVs (Unmanned Aerial Vehicles), abnormal state detection has become a critical technology to ensure the flight safety of UAVs. The position and orientation system (POS) data, etc., used to evaluate UAV flight status are from different sensors. The traditional abnormal state detection model ignores the difference of POS data in the frequency domain during feature learning, which leads to the loss of key feature information and limits the further improvement of detection performance. To deal with this and improve UAV flight safety, this paper presents a method for detecting the abnormal state of a UAV based on a timestamp slice and multi-separable convolutional neural network (TS-MSCNN). Firstly, TS-MSCNN divides the POS data reasonably in the time domain by setting a set of specific timestamps and then extracts and fuses the key features to avoid the loss of feature information. Secondly, TS-MSCNN converts these feature data into grayscale images by data reconstruction. Lastly, TS-MSCNN utilizes a multi-separable convolution neural network (MSCNN) to learn key features more effectively. The binary and multi-classification experiments conducted on the real flight data, Air Lab Fault and Anomaly (ALFA), demonstrate that the TS-MSCNN outperforms traditional machine learning (ML) and the latest deep learning methods in terms of accuracy. Full article

(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)

► Show Figures

Figure 1

Open AccessArticle

A Context Awareness Hierarchical Attention Network for Next POI Recommendation in IoT Environment

Xuebo Liu

Jingjing Guo

and

Peng Qiao

Electronics 2022, 11(23), 3977; https://doi.org/10.3390/electronics11233977 - 30 Nov 2022

Viewed by 528

Abstract

The rapid increase in the number of sensors in the Internet of things (IoT) environment has resulted in the continuous generation of massive and rich data in Location-Based Social Networks (LBSN). In LBSN, the next point-of-interest (POI) recommendation has become an important task, which provides the best POI recommendation according to the user’s recent check-in sequences. However, all existing methods for the next POI recommendation only focus on modeling the correlation between POIs based on users’ check-in sequences but ignore the significant fact that the next POI recommendation is a time-subtle recommendation task. In view of the fact that the attention mechanism does not comprehensively consider the influence of the user’s trajectory sequences, time information, social relations and geographic information of Point-of-Interest (POI) in the next POI recommendation field, a Context Geographical-Temporal-Social Awareness Hierarchical Attention Network (CGTS-HAN) model is proposed. The model extracts context information from the user’s trajectory sequences and designs a Geographical-Temporal-Social attention network and a common attention network for learning dynamic user preferences. In particular, a bidirectional LSTM model is used to capture the temporal influence between POIs in a user’s check-in trajectory. Moreover, In the context interaction layer, a feedforward neural network is introduced to capture the interaction between users and context information, which can connect multiple context factors with users. Then an embedded layer is added after the interaction layer, and three types of vectors are established for each POI to represent its sign-in trend so as to solve the heterogeneity problem between context factors. Finally reconstructs the objective function and learns model parameters through a negative sampling algorithm. The experimental results on Foursquare and Yelp real datasets show that the AUC, precision and recall of CGTS-HAN are better than the comparison models, which proves the effectiveness and superiority of CGTS-HAN. Full article

(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)

► Show Figures

Figure 1

Open AccessArticle

Cost-Sensitive Multigranulation Approximation in Decision-Making Applications

and

Electronics 2022, 11(22), 3801; https://doi.org/10.3390/electronics11223801 - 18 Nov 2022

Viewed by 419

Abstract

A multigranulation rough set (MGRS) model is an expansion of the Pawlak rough set, in which the uncertain concept is characterized by optimistic and pessimistic upper/lower approximate boundaries, respectively. However, there is a lack of approximate descriptions of uncertain concepts by existing information granules in MGRS. The approximation sets of rough sets presented by Zhang provide a way to approximately describe knowledge by using existing information granules. Based on the approximation set theory, this paper proposes the cost-sensitive multigranulation approximation of rough sets, i.e., optimistic approximation and pessimistic approximation. Their related properties were further analyzed. Furthermore, a cost-sensitive selection algorithm to optimize the multigranulation approximation was performed. The experimental results show that when multigranulation approximation sets and upper/lower approximation sets are applied to decision-making environments, multigranulation approximation produces the least misclassification costs on each dataset. In particular, misclassification costs are reduced by more than 50% at each granularity on some datasets. Full article

(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)

► Show Figures

Figure 1

Open AccessArticle

Relative Knowledge Distance Measure of Intuitionistic Fuzzy Concept

and

Electronics 2022, 11(20), 3373; https://doi.org/10.3390/electronics11203373 - 19 Oct 2022

Cited by 1 | Viewed by 490

Abstract

Knowledge distance is used to measure the difference between granular spaces, which is an uncertainty measure with strong distinguishing ability in a rough set. However, the current knowledge distance failed to take the relative difference between granular spaces into account under the given perspective of uncertain concepts. To solve this problem, this paper studies the relative knowledge distance of intuitionistic fuzzy concept (IFC). Firstly, a micro-knowledge distance (md) based on information entropy is proposed to measure the difference between intuitionistic fuzzy information granules. Then, based on md, a macro-knowledge distance (MD) with strong distinguishing ability is further constructed, and it is revealed the rule that MD is monotonic with the granularity being finer in multi-granularity spaces. Furthermore, the relative MD is further proposed to analyze the relative differences between different granular spaces from multiple perspectives. Finally, the effectiveness of relative MD is verified by relevant experiments. According to these experiments, the relative MD has successfully measured the differences in granular space from multiple perspectives. Compared with other attribute reduction algorithms, the number of subsets after reduction by our algorithm is in the middle, and the mean-square error value is appropriate. Full article

(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Special Issue "Advances in Intelligent Data Analysis and Its Applications"

Share This Special Issue

Special Issue Editors

Special Issue Information

Published Papers (6 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI