Advances in Intelligent Data Analysis and Its Applications

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 28521

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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,

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 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.

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Published Papers (29 papers)

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Editorial

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8 pages, 165 KiB  
Editorial
Recent Advances in Intelligent Data Analysis and Its Applications
Electronics 2024, 13(1), 226; https://doi.org/10.3390/electronics13010226 - 04 Jan 2024
Viewed by 621
Abstract
In the current rapidly evolving technological landscape, marked by transformative advancements such as cloud computing, the Internet of Things (IoT), and industrial internet, the complexity of data analysis tasks is escalating across the socio-economic spectrum [...] Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)

Research

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28 pages, 23093 KiB  
Article
A Study on the High Reliability Audio Target Frequency Generator for Electronics Industry
Electronics 2023, 12(24), 4918; https://doi.org/10.3390/electronics12244918 - 06 Dec 2023
Viewed by 631
Abstract
The frequency synthesizer performs a simple function of generating a desired frequency by manipulating a reference frequency signal, but stable and precise frequency generation is essential for reliable operation in mechanical equipment such as communication, control, surveillance, medical, and commercial fields. Frequency synthesis, [...] Read more.
The frequency synthesizer performs a simple function of generating a desired frequency by manipulating a reference frequency signal, but stable and precise frequency generation is essential for reliable operation in mechanical equipment such as communication, control, surveillance, medical, and commercial fields. Frequency synthesis, which is commonly used in various contexts, has been used in analog and digital methods or hybrid methods. Especially in the field of communication, a precise frequency synthesizer is required for each frequency band, from very low-frequency AF (audio frequency) to high-frequency microwaves. The purpose of this paper is to design and implement a highly reliable frequency synthesizer for application to a railway track circuit systems using AF frequency only with the logic circuit of an FPGA (field programmable gate array) without using a microprocessor. Therefore, the development trend of analog, digital, and hybrid frequency synthesizers is examined, and a method for precise frequency synthesizer generation on the basis of the digital method is suggested. In this paper, the generated frequency generated by applying the digital frequency synthesizer using the ultra-precision algorithm completed by many trials and errors shows the performance of generating the target frequency with an accuracy of more than 99.999% and a resolution of mHz, which is much higher than the resolution of 5 Hz in the previous study. This highly precise AF-class frequency synthesizer contributes greatly to the safe operation and operation of braking and signaling systems when used in transportation equipment such as railways and subways. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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18 pages, 695 KiB  
Article
A Neighborhood-Similarity-Based Imputation Algorithm for Healthcare Data Sets: A Comparative Study
Electronics 2023, 12(23), 4809; https://doi.org/10.3390/electronics12234809 - 28 Nov 2023
Viewed by 671
Abstract
The increasing computerisation of medical services has highlighted inconsistencies in the way in which patients’ historic medical data were recorded. Differences in process and practice between medical services and facilities have led to many incomplete and inaccurate medical histories being recorded. To create [...] Read more.
The increasing computerisation of medical services has highlighted inconsistencies in the way in which patients’ historic medical data were recorded. Differences in process and practice between medical services and facilities have led to many incomplete and inaccurate medical histories being recorded. To create a single point of truth going forward, it is necessary to correct these inconsistencies. A common way to do this has been to use imputation techniques to predict missing data values based on the known values in the data set. In this paper, we propose a neighborhood similarity measure-based imputation technique and analyze its achieved prediction accuracy in comparison with a number of traditional imputation methods using both an incomplete anonymized diabetes medical data set and a number of simulated data sets as the sources of our data. The aim is to determine whether any improvement could be made in the accuracy of predicting a diabetes diagnosis using the known outcomes of the diabetes patients’ data set. The obtained results have proven the effectiveness of our proposed approach compared to other state-of-the-art single-pass imputation techniques. Full article
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15 pages, 4166 KiB  
Article
Applying Image Analysis to Build a Lightweight System for Blind Obstacles Detecting of Intelligent Wheelchairs
Electronics 2023, 12(21), 4472; https://doi.org/10.3390/electronics12214472 - 31 Oct 2023
Viewed by 779
Abstract
Intelligent wheelchair blind spot obstacle detection is an important issue for semi-enclosed special environments in elderly communities. However, the LiDAR- and 3D-point-cloud-based solutions are expensive, complex to deploy, and require significant computing resources and time. This paper proposed an improved YOLOV5 lightweight obstacle [...] Read more.
Intelligent wheelchair blind spot obstacle detection is an important issue for semi-enclosed special environments in elderly communities. However, the LiDAR- and 3D-point-cloud-based solutions are expensive, complex to deploy, and require significant computing resources and time. This paper proposed an improved YOLOV5 lightweight obstacle detection model, named GC-YOLO, and built an obstacle dataset that consists of incomplete target images captured in the blind spot view of the smart wheelchair. The feature extraction operations are simplified in the backbone and neck sections of GC-YOLO. The backbone network uses GhostConv in the GhostNet network to replace the ordinary convolution in the original feature extraction network, reducing the model size. Meanwhile, the CoordAttention is applied, aiming to reduce the loss of location information caused by GhostConv. Further, the neck stem section uses a combination module of the lighter SE Attention module and the GhostConv module to enhance the feature extraction capability. The experimental results show that the proposed GC-YOLO outperforms the YOLO5 in terms of model parameters, GFLOPS and F1. Compared with the YOLO5, the number of model parameters and GFLOPS are reduced by 38% and 49.7%, respectively. Additionally, the F1 of the proposed GC-YOLO is improved by 10% on the PASCAL VOC dataset. Moreover, the proposed GC-YOLO achieved mAP of 90% on the custom dataset. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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16 pages, 3520 KiB  
Article
Stratified Sampling-Based Deep Learning Approach to Increase Prediction Accuracy of Unbalanced Dataset
Electronics 2023, 12(21), 4423; https://doi.org/10.3390/electronics12214423 - 27 Oct 2023
Cited by 1 | Viewed by 1395
Abstract
Due to the imbalanced nature of datasets, classifying unbalanced data classes and drawing accurate predictions is still a challenging task. Sampling procedures, along with machine learning and deep learning algorithms, are a boon for solving this kind of challenging task. This study’s objective [...] Read more.
Due to the imbalanced nature of datasets, classifying unbalanced data classes and drawing accurate predictions is still a challenging task. Sampling procedures, along with machine learning and deep learning algorithms, are a boon for solving this kind of challenging task. This study’s objective is to use sampling-based machine learning and deep learning approaches to automate the recognition of rotting trees from a forest dataset. Method/Approach: The proposed approach successfully predicted the dead tree in the forest. Seven of the twenty-one features are computed using the wrapper approach. This research work presents a novel method for determining the state of decay of the tree. The process of classifying the tree’s state of decay is connected to the issue of unequal class distribution. When classes to be predicted are uneven, this frequently hides poor performance in minority classes. Using stratified sampling procedures, the required samples for precise categorization are prepared. Stratified sampling approaches are employed to generate the necessary samples for accurate prediction, and the precise samples with computed features are input into a deep learning neural network. Finding: The multi-layer feed-forward classifier produces the greatest results in terms of classification accuracy (91%). Novelty/Improvement: Correct samples are necessary for correct classification in machine learning approaches. In the present study, stratified samples were considered while deciding which samples to use as deep neural network input. It suggests that the proposed algorithm could accurately determine whether the tree has decayed or not. Full article
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22 pages, 1375 KiB  
Article
Comparison of Selected Machine Learning Algorithms in the Analysis of Mental Health Indicators
Electronics 2023, 12(21), 4407; https://doi.org/10.3390/electronics12214407 - 25 Oct 2023
Cited by 1 | Viewed by 971
Abstract
Machine learning is increasingly being used to solve clinical problems in diagnosis, therapy and care. Aim: the main aim of the study was to investigate how the selected machine learning algorithms deal with the problem of determining a virtual mental health index. Material [...] Read more.
Machine learning is increasingly being used to solve clinical problems in diagnosis, therapy and care. Aim: the main aim of the study was to investigate how the selected machine learning algorithms deal with the problem of determining a virtual mental health index. Material and Methods: a number of machine learning models based on Stochastic Dual Coordinate Ascent, limited-memory Broyden–Fletcher–Goldfarb–Shanno, Online Gradient Descent, etc., were built based on a clinical dataset and compared based on criteria in the form of learning time, running time during use and regression accuracy. Results: the algorithm with the highest accuracy was Stochastic Dual Coordinate Ascent, but although its performance was high, it had significantly longer training and prediction times. The fastest algorithm looking at learning and prediction time, but slightly less accurate, was the limited-memory Broyden–Fletcher–Goldfarb–Shanno. The same data set was also analyzed automatically using ML.NET. Findings from the study can be used to build larger systems that automate early mental health diagnosis and help differentiate the use of individual algorithms depending on the purpose of the system. Full article
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18 pages, 867 KiB  
Article
A Multiscale Neighbor-Aware Attention Network for Collaborative Filtering
Electronics 2023, 12(20), 4372; https://doi.org/10.3390/electronics12204372 - 22 Oct 2023
Viewed by 659
Abstract
Most recommender systems rely on user and item attributes or their interaction records to find similar neighbors for collaborative filtering. Existing methods focus on developing collaborative signals from only one type of neighbors and ignore the unique contributions of different types of neighbor [...] Read more.
Most recommender systems rely on user and item attributes or their interaction records to find similar neighbors for collaborative filtering. Existing methods focus on developing collaborative signals from only one type of neighbors and ignore the unique contributions of different types of neighbor views. This paper proposes a multiscale neighbor-aware attention network for collaborative filtering (MSNAN). First, attribute-view neighbor embedding is modeled to extract the features of different types of neighbors with co-occurrence attributes, and interaction-view neighbor embedding is leveraged to describe the fine-grained neighborhood behaviors of ratings. Then, a matched attention network is used to identify different contributions of multiscale neighbors and capture multiple types of collaborative signals for overcoming sparse recommendations. Finally, we make the rating prediction by a joint learning of multi-task loss and verify the positive effect of the proposed MSNAN on three datasets. Compared with traditional methods, the experimental results of the proposed MSNAN not only improve the accuracy in MAE and RMSE indexes, but also solve the problem of poor performance for recommendation in sparse data scenarios. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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16 pages, 2511 KiB  
Article
Machine Learning for Energy-Efficient Fluid Bed Dryer Pharmaceutical Machines
Electronics 2023, 12(20), 4325; https://doi.org/10.3390/electronics12204325 - 18 Oct 2023
Viewed by 871
Abstract
The pharmaceutical industry is facing significant economic challenges due to measures aimed at containing healthcare costs and evolving healthcare regulations. In this context, pharmaceutical laboratories seek to extend the lifespan of their machinery, particularly fluid bed dryers, which play a crucial role in [...] Read more.
The pharmaceutical industry is facing significant economic challenges due to measures aimed at containing healthcare costs and evolving healthcare regulations. In this context, pharmaceutical laboratories seek to extend the lifespan of their machinery, particularly fluid bed dryers, which play a crucial role in the drug production process. Older fluid bed dryers, lacking advanced sensors for real-time temperature optimization, rely on fixed-time deterministic approaches controlled by operators. To address these limitations, a groundbreaking approach taking into account Exploration Data Analysis (EDA) and a Catboost machine-learning model is presented. This research aims to analyze and enhance a drug production process on a large scale, showcasing how AI algorithms can revolutionize the manufacturing industry. The Catboost model effectively reduces preheating phase time, resulting in significant energy savings. By continuously monitoring critical parameters, a paradigm shift from the conventional fixed-time models is achieved. It has been shown that the model is able to predict on average a reduction of 50.45% of the preheating process duration and up to 59.68% in some cases. Likewise, the energy consumption of the fluid bed dryer for the preheating process could be reduced on average by 50.48% and up to 59.76%, which would result on average in around 3.120 kWh energy consumption savings per year. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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17 pages, 1196 KiB  
Article
A Network Intrusion Detection Model Based on BiLSTM with Multi-Head Attention Mechanism
Electronics 2023, 12(19), 4170; https://doi.org/10.3390/electronics12194170 - 08 Oct 2023
Viewed by 1121
Abstract
A network intrusion detection tool can identify and detect potential malicious activities or attacks by monitoring network traffic and system logs. The data within intrusion detection networks possesses characteristics that include a high degree of feature dimension and an unbalanced distribution across categories. [...] Read more.
A network intrusion detection tool can identify and detect potential malicious activities or attacks by monitoring network traffic and system logs. The data within intrusion detection networks possesses characteristics that include a high degree of feature dimension and an unbalanced distribution across categories. Currently, the actual detection accuracy of some detection models is relatively low. To solve these problems, we propose a network intrusion detection model based on multi-head attention and BiLSTM (Bidirectional Long Short-Term Memory), which can introduce different attention weights for each vector in the feature vector that strengthen the relationship between some vectors and the detection attack type. The model also utilizes the advantage that BiLSTM can capture long-distance dependency relationships to obtain a higher detection accuracy. This model combined the advantages of the two models, adding a dropout layer between the two models to improve the detection accuracy while preventing training overfitting. Through experimental analysis, the network intrusion detection model that utilizes multi-head attention and BilSTM achieved an accuracy of 98.29%, 95.19%, and 99.08% on the KDDCUP99, NSLKDD, and CICIDS2017 datasets, respectively. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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26 pages, 3290 KiB  
Article
ADQE: Obtain Better Deep Learning Models by Evaluating the Augmented Data Quality Using Information Entropy
Electronics 2023, 12(19), 4077; https://doi.org/10.3390/electronics12194077 - 28 Sep 2023
Viewed by 641
Abstract
Data augmentation, as a common technique in deep learning training, is primarily used to mitigate overfitting problems, especially with small-scale datasets. However, it is difficult for us to evaluate whether the augmented dataset truly benefits the performance of the model. If the training [...] Read more.
Data augmentation, as a common technique in deep learning training, is primarily used to mitigate overfitting problems, especially with small-scale datasets. However, it is difficult for us to evaluate whether the augmented dataset truly benefits the performance of the model. If the training model is relied upon in each case to validate the quality of the data augmentation and the dataset, it will take a lot of time and resources. This article proposes a simple and practical approach to evaluate the quality of data augmentation for image classification tasks, enriching the theoretical research on data augmentation quality evaluation. Based on the information entropy, multiple dimensional metrics for data quality augmentation are established, including diversity, class balance, and task relevance. Additionally, a comprehensive data augmentation quality fusion metric is proposed. Experimental results on the CIFAR-10 and CUB-200 datasets show that our method maintains optimal performance in a variety of scenarios. The cosine similarity between the score of our method and the precision of model is up to 99.9%. A rigorous evaluation of data augmentation quality is necessary to guide the improvement of DL model performance. The quality standards and evaluation defined in this article can be utilized by researchers to train high-performance DL models in situations where data are limited. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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15 pages, 3178 KiB  
Article
An Improved Spatio-Temporally Smoothed Coherence Factor Combined with Delay Multiply and Sum Beamformer
Electronics 2023, 12(18), 3902; https://doi.org/10.3390/electronics12183902 - 15 Sep 2023
Viewed by 546
Abstract
Delay multiply and sum beamforming (DMAS) is a non-linear method used in ultrasound imaging which offers superior performance to conventional delay and sum beamforming (DAS). While the combination of DMAS and coherence factor (CF) can further improve single plane-wave imaging lateral resolution, by [...] Read more.
Delay multiply and sum beamforming (DMAS) is a non-linear method used in ultrasound imaging which offers superior performance to conventional delay and sum beamforming (DAS). While the combination of DMAS and coherence factor (CF) can further improve single plane-wave imaging lateral resolution, by using CF to weight the DMAS output, the main lobe width and aberration effects can be suppressed, which will improve the disadvantage of low lateral resolution when imaging with a single plane-wave. However, in low signal-to-noise ratio (SNR) environments, the speckle variance of the image increases, and there are black area artifacts around high echo objects. To improve the quality of the scatter without significantly reducing the lateral resolution of the DMAS-CF, this paper proposes an adaptive spatio-temporally smoothed coherence factor (GSTS-CF) combined with delay multiply and sum beamformer (DMAS + GSTS-CF), which uses the generalized coherence factor (GCF) as a local coherence detection tool to adaptively determine the subarray length to obtain an improved adaptive spatio-temporally smoothed factor, and uses this factor to weight the output of DMAS. The simulation and experimental data show that the proposed method improves lateral resolution (20 mm depth) by 86.87% compared to DAS, 52.13% compared to DMAS, 15.84% compared to DMAS + STS-CF, and has a full width at half maxima (FWHM) similar to DMAS-CF. The proposed method improves the speckle signal-to-noise ratio (sSNR) by 87.85% (simulation) and 77.84% (in carotid) compared to DMAS-CF, 20.37% (simulation) and 40.74% (in carotid) compared to DMAS, 15.03% (simulation) and 13.46% (in carotid) compared to DMAS + STS-CF, and has sSNR and scatter variance similar to DAS. This indicates that the method improves scatter quality (lower scatter variance and higher sSNR) without significantly reducing lateral resolution. Full article
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13 pages, 787 KiB  
Article
Underwater AUV Navigation Dataset in Natural Scenarios
Electronics 2023, 12(18), 3788; https://doi.org/10.3390/electronics12183788 - 07 Sep 2023
Viewed by 1099
Abstract
Autonomous underwater vehicles (AUVs) are extensively utilized in various autonomous underwater missions, encompassing ocean environment monitoring, underwater searching, and geological exploration. Owing to their profound underwater capabilities and robust autonomy, AUVs have emerged as indispensable instruments. Nevertheless, AUVs encounter several constraints in the [...] Read more.
Autonomous underwater vehicles (AUVs) are extensively utilized in various autonomous underwater missions, encompassing ocean environment monitoring, underwater searching, and geological exploration. Owing to their profound underwater capabilities and robust autonomy, AUVs have emerged as indispensable instruments. Nevertheless, AUVs encounter several constraints in the domain of underwater navigation, primarily stemming from the cost-intensive nature of inertial navigation devices and Doppler velocity logs, which impede the acquisition of navigation data. Underwater simultaneous localization and mapping (SLAM) techniques, along with other navigation approaches reliant on perceptual sensors like vision and sonar, are employed to augment the precision of self-positioning. Particularly within the realm of machine learning, the utilization of extensive datasets for training purposes plays a pivotal role in enhancing algorithmic performance. However, it is common for data obtained exclusively from inertial sensors, a Doppler Velocity Log (DVL), and depth sensors in underwater environments to not be publicly accessible. This research paper introduces an underwater navigation dataset derived from a controllable AUV that is equipped with high-precision fiber-optic inertial sensors, a DVL, and depth sensors. The dataset underwent rigorous testing through numerical calculations and optimization-based algorithms, with the evaluation of various algorithms being based on both the actual surfacing position and the calculated position. Full article
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16 pages, 511 KiB  
Article
Local-Aware Hierarchical Attention for Sequential Recommendation
Electronics 2023, 12(18), 3742; https://doi.org/10.3390/electronics12183742 - 05 Sep 2023
Viewed by 544
Abstract
Modeling the dynamic preferences of users is a challenging and essential task in a recommendation system. Taking inspiration from the successful use of self-attention mechanisms in tasks within natural language processing, several approaches have initially explored integrating self-attention into sequential recommendation, demonstrating promising [...] Read more.
Modeling the dynamic preferences of users is a challenging and essential task in a recommendation system. Taking inspiration from the successful use of self-attention mechanisms in tasks within natural language processing, several approaches have initially explored integrating self-attention into sequential recommendation, demonstrating promising results. However, existing methods have overlooked the intrinsic structure of sequences, failed to simultaneously consider the local fluctuation and global stability of users’ interests, and lacked user information. To address these limitations, we propose LHASRec (Local-Aware Hierarchical Attention for Sequential Recommendation), a model that divides a user’s historical interaction sequences into multiple sessions based on a certain time interval and computes the weight values for each session. Subsequently, the calculated weight values are combined with the user’s historical interaction sequences to obtain a weighted user interaction sequence. This approach can effectively reflect the local fluctuation of the user’s interest, capture the user’s particular preference, and at the same time, consider the user’s general preference to achieve global stability. Additionally, we employ Stochastic Shared Embeddings (SSE) as a regularization technique to mitigate the overfitting issue resulting from the incorporation of user information. We conduct extensive experiments, showing that our method outperforms other competitive baselines on sparse and dense datasets and different evaluation metrics. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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21 pages, 9026 KiB  
Article
An Off-Line Error Compensation Method for Absolute Positioning Accuracy of Industrial Robots Based on Differential Evolution and Deep Belief Networks
Electronics 2023, 12(17), 3718; https://doi.org/10.3390/electronics12173718 - 02 Sep 2023
Viewed by 757
Abstract
Industrial robots have been increasingly used in the field of intelligent manufacturing. The low absolute positioning accuracy of industrial robots is one of the difficulties in their application. In this paper, an accuracy compensation algorithm for the absolute positioning of industrial robots is [...] Read more.
Industrial robots have been increasingly used in the field of intelligent manufacturing. The low absolute positioning accuracy of industrial robots is one of the difficulties in their application. In this paper, an accuracy compensation algorithm for the absolute positioning of industrial robots is proposed based on deep belief networks using an off-line compensation method. A differential evolution algorithm is presented to optimize the networks. Combined with the evidence theory, a position error mapping model is proposed to realize the absolute positioning accuracy compensation of industrial robots. Experiments were conducted using a laser tracker AT901-B on an industrial robot KR6_R700 sixx_CR. The absolute position error of the end of the robot was reduced from 0.469 mm to 0.084 mm, improving the accuracy by 82.14% after the compensation. Experimental results demonstrated that the proposed compensation algorithm could improve the absolute positioning accuracy of industrial robots, as well as its potential uses for precise operational tasks. Full article
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18 pages, 3687 KiB  
Article
A Data-Driven Approach Using Enhanced Bayesian-LSTM Deep Neural Networks for Picks Wear State Recognition
Electronics 2023, 12(17), 3593; https://doi.org/10.3390/electronics12173593 - 25 Aug 2023
Viewed by 819
Abstract
Picks are key components for the mechanized excavation of coal by mining machinery, with their wear state directly influencing the efficiency of the mining equipment. In response to the difficulty of determining the overall wear state of picks during coal-mining production, a data-driven [...] Read more.
Picks are key components for the mechanized excavation of coal by mining machinery, with their wear state directly influencing the efficiency of the mining equipment. In response to the difficulty of determining the overall wear state of picks during coal-mining production, a data-driven wear state identification model for picks has been constructed through the enhanced optimization of Long Short-Term Memory (LSTM) networks via Bayesian algorithms. Initially, a mechanical model of pick and coal-rock interaction is established through theoretical analysis, where the stress characteristic of the pick is analyzed, and the wear mechanism of the pick is preliminarily revealed. A method is proposed that categorizes the overall wear state of picks into three types based on the statistical relation of the actual wear amount and the limited wear amount. Subsequently, the vibration signals of the cutting drum from a bolter miner that contain the wear information of picks are decomposed and denoised using wavelet packet decomposition, with the standard deviation of wavelet packet coefficients from decomposed signal nodes selected as the feature signals. These feature signals are normalized and then used to construct a feature matrix representing the vibration signals. Finally, this constructed feature matrix and classification labels are fed into the Bayesian-LSTM network for training, thus resulting in the picks wear state identification model. To validate the effectiveness of the Bayesian-LSTM deep learning algorithm in identifying the overall picks wear state of mining machinery, vibration signals from the X, Y, and Z axes of the cutting drum from a bolter miner at the C coal mine in Shaanxi, China, are collected, effectively processed, and then input into deep LSTM and Back-Propagation (BP) neural networks respectively for comparison. The results showed that the Bayesian-LSTM network achieved a recognition accuracy of 98.33% for picks wear state, showing a clear advantage over LSTM, BP network models, thus providing important references for the identification of picks wear state based on deep learning algorithms. This method only requires the processing and analysis of the equipment parameters automatically collected from bolter miners or other mining equipment, offering the advantages of simplicity, low cost, and high accuracy, and providing a basis for a proper picks replacement strategy. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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15 pages, 448 KiB  
Article
Improving Question Answering over Knowledge Graphs with a Chunked Learning Network
Electronics 2023, 12(15), 3363; https://doi.org/10.3390/electronics12153363 - 06 Aug 2023
Cited by 1 | Viewed by 1345
Abstract
The objective of knowledge graph question answering is to assist users in answering questions by utilizing the information stored within the graph. Users are not required to comprehend the underlying data structure. This is a difficult task because, on the one hand, correctly [...] Read more.
The objective of knowledge graph question answering is to assist users in answering questions by utilizing the information stored within the graph. Users are not required to comprehend the underlying data structure. This is a difficult task because, on the one hand, correctly understanding the semantics of a problem is difficult for machines. On the other hand, the growing knowledge graph will inevitably lead to information retrieval errors. Specifically, the question-answering task has three difficulties: word abbreviation, object complement, and entity ambiguity. An object complement means that different entities share the same predicate, and entity ambiguity means that words have different meanings in different contexts. To solve these problems, we propose a novel method named the Chunked Learning Network. It uses different models according to different scenarios to obtain a vector representation of the topic entity and relation in the question. The answer entity representation that yields the closest fact triplet, according to a joint distance metric, is returned as the answer. For sentences with an object complement, we use dependency parsing to construct dependency relationships between words to obtain more accurate vector representations. Experiments demonstrate the effectiveness of our method. Full article
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21 pages, 11492 KiB  
Article
Centrifugal Navigation-Based Emotion Computation Framework of Bilingual Short Texts with Emoji Symbols
Electronics 2023, 12(15), 3332; https://doi.org/10.3390/electronics12153332 - 03 Aug 2023
Viewed by 587
Abstract
Heterogeneous corpora including Chinese, English, and emoji symbols are increasing on platforms. Previous sentiment analysis models are unable to calculate emotional scores of heterogeneous corpora. They also struggle to effectively fuse emotional tendencies of these corpora with the emotional fluctuation, generating low accuracy [...] Read more.
Heterogeneous corpora including Chinese, English, and emoji symbols are increasing on platforms. Previous sentiment analysis models are unable to calculate emotional scores of heterogeneous corpora. They also struggle to effectively fuse emotional tendencies of these corpora with the emotional fluctuation, generating low accuracy of tendency prediction and score calculation. For these problems, this paper proposes a Centrifugal Navigation-Based Emotional Computation framework (CNEC). CNEC adopts Emotional Orientation of Related Words (EORW) to calculate scores of unknown Chinese/English words and emoji symbols. In EORW, t neighbor words of the predicted sample from one element in the short text are selected from a sentiment dictionary according to spatial distance, and related words are extracted using the emotional dominance principle from the t neighbor words. Emotional scores of related words are fused to calculate scores of the predicted sample. Furthermore, CNEC utilizes Centrifugal Navigation-Based Emotional Fusion (CNEF) to achieve the emotional fusion of heterogeneous corpora. In CNEF, how the emotional fluctuation occurs is illustrated by the trigger angle of centrifugal motion in physical theory. In light of the corresponding relationship between the trigger angle and conditions of the emotional fluctuation, the fluctuation position is determined. Lastly, emotional fusion with emotional fluctuation is carried out by a CNEF function, which considers the fluctuation position as a significant position. Experiments demonstrate that the proposed CNEC effectively computes emotional scores for bilingual short texts with emojis on the Weibo dataset collected. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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23 pages, 4967 KiB  
Article
An Enhancement Method in Few-Shot Scenarios for Intrusion Detection in Smart Home Environments
Electronics 2023, 12(15), 3304; https://doi.org/10.3390/electronics12153304 - 31 Jul 2023
Cited by 1 | Viewed by 662
Abstract
Different devices in the smart home environment are subject to different levels of attack. Devices with lower attack frequencies confront difficulties in collecting attack data, which restricts the ability to train intrusion detection models. Therefore, this paper presents a novel method called EM-FEDE [...] Read more.
Different devices in the smart home environment are subject to different levels of attack. Devices with lower attack frequencies confront difficulties in collecting attack data, which restricts the ability to train intrusion detection models. Therefore, this paper presents a novel method called EM-FEDE (enhancement method based on feature enhancement and data enhancement) to generate adequate training data for expanding few-shot datasets. Training intrusion detection models with an expanded dataset can enhance detection performance. Firstly, the EM-FEDE method adaptively extends the features by analyzing the historical intrusion detection records of smart homes, achieving format alignment of device data. Secondly, the EM-FEDE method performs data cleaning operations to reduce noise and redundancy and uses a random sampling mechanism to ensure the diversity of the few-shot data obtained by sampling. Finally, the processed sampling data is used as the input to the CWGAN, and the loss between the generated and real data is calculated using the Wasserstein distance. Based on this loss, the CWGAN is adjusted. Finally, the generator outputs effectively generated data. According to the experimental findings, the accuracy of J48, Random Forest, Bagging, PART, KStar, KNN, MLP, and CNN has been enhanced by 21.9%, 6.2%, 19.4%, 9.2%, 6.3%, 7%, 3.4%, and 5.9%, respectively, when compared to the original dataset, along with the optimal generation sample ratio of each algorithm. The experimental findings demonstrate the effectiveness of the EM-FEDE approach in completing sparse data. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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16 pages, 3030 KiB  
Article
A Network Clustering Algorithm for Protein Complex Detection Fused with Power-Law Distribution Characteristic
Electronics 2023, 12(14), 3007; https://doi.org/10.3390/electronics12143007 - 08 Jul 2023
Viewed by 798
Abstract
Network clustering for mining protein complexes from protein–protein interaction (PPI) networks has emerged as a prominent research area in data mining and bioinformatics. Accurately identifying complexes plays a crucial role in comprehending cellular organization and functionality. Network characteristics are often useful in enhancing [...] Read more.
Network clustering for mining protein complexes from protein–protein interaction (PPI) networks has emerged as a prominent research area in data mining and bioinformatics. Accurately identifying complexes plays a crucial role in comprehending cellular organization and functionality. Network characteristics are often useful in enhancing the performance of protein complex detection methods. Many protein complex detection algorithms have been proposed, primarily focusing on local micro-topological structure metrics while overlooking the potential power-law distribution characteristic of community sizes at the macro global level. The effective use of this distribution characteristic information may be beneficial for mining protein complexes. This paper proposes a network clustering algorithm for protein complex detection fused with power-law distribution characteristic. The clustering algorithm constructs a cluster generation model based on scale-free power-law distribution to generate a cluster with a dense center and relatively sparse periphery. Following the cluster generation model, a candidate cluster is obtained. From a global perspective, the number distribution of clusters of varying sizes is taken into account. If the candidate cluster aligns with the constraints defined by the power-law distribution function of community sizes, it is designated as the final cluster; otherwise, it is discarded. To assess the prediction performance of the proposed algorithm, the gold standard complex sets CYC2008 and MIPS are employed as benchmarks. The algorithm is compared to DPClus, IPCA, SEGC, Core, SR-MCL, and ELF-DPC in terms of F-measure and Accuracy on several widely used protein–protein interaction networks. The experimental results show that the algorithm can effectively detect protein complexes and is superior to other comparative algorithms. This study further enriches the connection between analyzing complex network topology features and mining network function modules, thereby significantly contributing to the improvement of protein complex detection performance. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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11 pages, 630 KiB  
Article
Graph Convolution Network over Dependency Structure Improve Knowledge Base Question Answering
Electronics 2023, 12(12), 2675; https://doi.org/10.3390/electronics12122675 - 14 Jun 2023
Cited by 2 | Viewed by 625
Abstract
Knowledge base question answering (KBQA) can be divided into two types according to the type of complexity: questions with constraints and questions with multiple hops of relationships. Previous work on knowledge base question answering have mostly focused on entities and relations. In a [...] Read more.
Knowledge base question answering (KBQA) can be divided into two types according to the type of complexity: questions with constraints and questions with multiple hops of relationships. Previous work on knowledge base question answering have mostly focused on entities and relations. In a multihop question, it is insufficient to focus solely on topic entities and their relations since the relation between words also contains some important information. In addition, because the question contains constraints or multiple relationships, the information is difficult to capture, or the constraints are missed. In this paper, we applied a dependency structure to questions that capture relation information (e.g., constraint) between the words in question through a graph convolution network. The captured relation information is integrated into the question for re-encoding, and the information is used to generate and rank query graphs. Compared with existing sequence models and query graph generation models, our approach achieves a 0.8–3% improvement on two benchmark datasets. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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20 pages, 1614 KiB  
Article
A Collaborative Multi-Granularity Architecture for Multi-Source IoT Sensor Data in Air Quality Evaluations
Electronics 2023, 12(11), 2380; https://doi.org/10.3390/electronics12112380 - 24 May 2023
Cited by 2 | Viewed by 826
Abstract
Air pollution (AP) is a significant environmental issue that poses a potential threat to human health. Its adverse effects on human health are diverse, ranging from sensory discomfort to acute physiological reactions. As such, air quality evaluation (AQE) serves as a crucial process [...] Read more.
Air pollution (AP) is a significant environmental issue that poses a potential threat to human health. Its adverse effects on human health are diverse, ranging from sensory discomfort to acute physiological reactions. As such, air quality evaluation (AQE) serves as a crucial process that involves the collection of samples from the environment and their analysis to measure AP levels. With the proliferation of Internet of Things (IoT) devices and sensors, real-time and continuous measurement of air pollutants in urban environments has become possible. However, the data obtained from multiple sources of IoT sensors can be uncertain and inaccurate, posing challenges in effectively utilizing and fusing this data. Meanwhile, differences in opinions among decision-makers regarding AQE can affect the outcome of the final decision. To tackle these challenges, this paper systematically investigates a novel multi-attribute group decision-making (MAGDM) approach based on hesitant trapezoidal fuzzy (HTrF) information and discusses its application to AQE. First, by combining HTrF sets (HTrFSs) with multi-granulation rough sets (MGRSs), a new rough set model, named HTrF MGRSs, on a two-universe model is proposed. Second, the definition and property of the presented model are studied. Third, a decision-making approach based on the background of AQE is constructed via utilizing decision-making index sets (DMISs). Lastly, the validity and feasibility of the constructed approach are demonstrated via a case study conducted in the AQE setting using experimental and comparative analyses. The outcomes of the experiment demonstrate that the presented architecture owns the ability to handle multi-source IoT sensor data (MSIoTSD), providing a sensible conclusion for AQE. In summary, the MAGDM method presented in this article is a promising scheme for solving decision-making problems, where HTrFSs possess excellent information description capabilities and can adequately describe indecision and uncertainty information. Meanwhile, MGRSs serve as an outstanding information fusion tool that can improve the quality and level of decision-making. DMISs are better able to analyze and evaluate information and reduce the impact of disagreement on decision outcomes. The proposed architecture, therefore, provides a viable solution for MSIoTSD facing uncertainty or hesitancy in the AQE environment. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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13 pages, 808 KiB  
Article
A Variable Structure Multiple-Model Estimation Algorithm Aided by Center Scaling
Electronics 2023, 12(10), 2257; https://doi.org/10.3390/electronics12102257 - 16 May 2023
Viewed by 742
Abstract
The accuracy for target tracking using a conventional interacting multiple-model algorithm (IMM) is limited. In this paper, a new variable structure of interacting multiple-model (VSIMM) algorithm aided by center scaling (VSIMM-CS) is proposed to solve this problem. The novel VSIMM-CS has two main [...] Read more.
The accuracy for target tracking using a conventional interacting multiple-model algorithm (IMM) is limited. In this paper, a new variable structure of interacting multiple-model (VSIMM) algorithm aided by center scaling (VSIMM-CS) is proposed to solve this problem. The novel VSIMM-CS has two main steps. Firstly, we estimate the approximate location of the true model. This is aided by the expected-mode augmentation algorithm (EMA), and a new method—namely, the expected model optimization method—is proposed to further enhance the accuracy of EMA. Secondly, we change the original model set to ensure the current true model as the symmetry center of the current model set, and the model set is scaled down by a certain percentage. Considering the symmetry and linearity of the system, the errors produced by symmetrical models can be well offset. Furthermore, narrowing the distance between the true model and the default model is another effective method to reduce the error. The second step is based on two theories: symmetric model set optimization method and proportional reduction optimization method. All proposed theories aim to minimize errors as much as possible, and simulation results highlight the correctness and effectiveness of the proposed methods. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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20 pages, 4442 KiB  
Article
An Accelerator for Semi-Supervised Classification with Granulation Selection
Electronics 2023, 12(10), 2239; https://doi.org/10.3390/electronics12102239 - 15 May 2023
Cited by 1 | Viewed by 767
Abstract
Semi-supervised classification is one of the core methods to deal with incomplete tag information without manual intervention, which has been widely used in various real problems for its excellent performance. However, the existing algorithms need to store all the unlabeled instances and repeatedly [...] Read more.
Semi-supervised classification is one of the core methods to deal with incomplete tag information without manual intervention, which has been widely used in various real problems for its excellent performance. However, the existing algorithms need to store all the unlabeled instances and repeatedly use them in the process of iteration. Thus, the large population size may result in slow execution speed and large memory requirements. Many efforts have been devoted to solving this problem, but mainly focused on supervised classification. Now, we propose an approach to decrease the size of the unlabeled instance set for semi-supervised classification algorithms. In this algorithm, we first divide the unlabeled instance set into several subsets with the information granulation mechanism, then sort the divided subsets according to the contribution to the classifier. Following this order, the subsets that take great classification performance are saved. The proposed algorithm is compared with the state-of-the-art algorithms on 12 real datasets, and experiment results show it could get a similar prediction ability but have the lowest instance storage ratio. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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18 pages, 2001 KiB  
Article
Flight Delay Prediction Model Based on Lightweight Network ECA-MobileNetV3
Electronics 2023, 12(6), 1434; https://doi.org/10.3390/electronics12061434 - 17 Mar 2023
Cited by 3 | Viewed by 1363
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 [...] Read more.
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)
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14 pages, 2772 KiB  
Article
Machine Learning-Based Prediction of Orphan Genes and Analysis of Different Hybrid Features of Monocot and Eudicot Plants
Electronics 2023, 12(6), 1433; https://doi.org/10.3390/electronics12061433 - 17 Mar 2023
Viewed by 1184
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 [...] Read more.
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)
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16 pages, 5222 KiB  
Article
UAV Abnormal State Detection Model Based on Timestamp Slice and Multi-Separable CNN
Electronics 2023, 12(6), 1299; https://doi.org/10.3390/electronics12061299 - 08 Mar 2023
Cited by 3 | Viewed by 1173
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 [...] Read more.
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)
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17 pages, 3501 KiB  
Article
A Context Awareness Hierarchical Attention Network for Next POI Recommendation in IoT Environment
Electronics 2022, 11(23), 3977; https://doi.org/10.3390/electronics11233977 - 30 Nov 2022
Viewed by 1106
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, [...] Read more.
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)
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23 pages, 711 KiB  
Article
Cost-Sensitive Multigranulation Approximation in Decision-Making Applications
Electronics 2022, 11(22), 3801; https://doi.org/10.3390/electronics11223801 - 18 Nov 2022
Viewed by 843
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 [...] Read more.
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)
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25 pages, 4744 KiB  
Article
Relative Knowledge Distance Measure of Intuitionistic Fuzzy Concept
Electronics 2022, 11(20), 3373; https://doi.org/10.3390/electronics11203373 - 19 Oct 2022
Cited by 2 | Viewed by 1015
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 [...] Read more.
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)
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