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Advances in Artificial Intelligence, Machine Learning and Deep Learning Application
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Advances in Artificial Intelligence, Machine Learning and Deep Learning Application

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 95477

Special Issue Editors

Dr. Muhammad Salman Haleem

E-Mail Website
Guest Editor
Advanced Biomedical Signal Processing and Intelligent eHealth Lab, School of Engineering, University of Warwick, Library Rd, Coventry CV4 7AL, UK
Interests: artificial intelligence; machine learning; medical signal and image processing; text mining; big data analytics
Prof. Dr. Liangxiu Han

E-Mail Website
Guest Editor
School of Computing, Mathematics & Digital Technology, Manchester Metropolitan University, Manchester M15 6BH, UK
Interests: big data/machine learning; artificial intelligence; parallel and distributed computing
Special Issues, Collections and Topics in MDPI journals
Dr. Ernesto Iadanza

E-Mail Website
Guest Editor
Advanced Biomedical Signal Processing and Intelligent eHealth Lab, School of Engineering, University of Warwick, Library Rd, Coventry CV4 7AL, UK
Interests: clinical engineering; clinical decision support systems; hospital engineering; artificial intelligence; machine learning; deep learning
Prof. Dr. Baihua Li

E-Mail Website
Guest Editor
Department of Computer Science, Loughborough University, Loughborough LE11 3TU, UK
Interests: AI; machine learning; computer vision; deep learning pattern recognition; robotics and HCI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advances in modern technology over the recent decade, the involvement of artificial intelligence (AI) has been pivotal in enhancing the effectiveness and efficiency in many systems and in all fields of knowledge, including medical diagnosis, healthcare, vehicular technologies, agriculture, policing, industrial manufacturing, finance, sports, and many other domains. The purpose of this Special Issue is to report on the advances in state-of-the-art research on artificial intelligence and machine learning applications. This includes the design and development of novel algorithms based on machine learning and deep learning for application to data acquired from recently emerged devices, sensors, and equipment for the automatic prediction and detection of patterns of interest. This also includes implementation of novel AI and big data technologies for extracting relevant information from unstructured data with enhanced performance across different sectors. The advances in the state-of-the-art for addressing real-world AI applications are of great interest.

Manuscripts are required to show significant improvements in a variety of learning methods, problem conceptualization, data collecting and processing, and feature engineering through critical comparisons with existing methodologies.

The research domains may involve (but are not limited to):

  • Medical diagnosis
  • Personalized health monitoring and management
  • Assistive technologies
  • Clinical engineering
  • Health technology assessment
  • Robotics
  • Agricultural technologies
  • Autonomous vehicular technologies
  • Intelligence led policing
  • Industrial manufacturing

Dr. Muhammad Salman Haleem
Prof. Dr. Liangxiu Han
Dr. Ernesto Iadanza
Prof. Dr. Baihua Li
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

  • artificial intelligence
  • machine learning
  • deep learning
  • computer vision
  • signal and image processing
  • text mining
  • big data systems
  • time series analysis

Published Papers (39 papers)

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Editorial

Jump to: Research, Review

5 pages, 174 KiB  
Editorial
Advances in Artificial Intelligence, Machine Learning and Deep Learning Applications
by Muhammad Salman Haleem
Electronics 2023, 12(18), 3780; https://doi.org/10.3390/electronics12183780 - 07 Sep 2023
Viewed by 1694
Abstract
Recent advances in the field of artificial intelligence (AI) have been pivotal in enhancing the effectiveness and efficiency of many systems and in all fields of knowledge, including medical diagnosis [...] Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)

Research

Jump to: Editorial, Review

20 pages, 1319 KiB  
Article
Deep-Learning-Driven Techniques for Real-Time Multimodal Health and Physical Data Synthesis
by Muhammad Salman Haleem, Audrey Ekuban, Alessio Antonini, Silvio Pagliara, Leandro Pecchia and Carlo Allocca
Electronics 2023, 12(9), 1989; https://doi.org/10.3390/electronics12091989 - 25 Apr 2023
Cited by 3 | Viewed by 1749
Abstract
With the advent of Artificial Intelligence for healthcare, data synthesis methods present crucial benefits in facilitating the fast development of AI models while protecting data subjects and bypassing the need to engage with the complexity of data sharing and processing agreements. Existing technologies [...] Read more.
With the advent of Artificial Intelligence for healthcare, data synthesis methods present crucial benefits in facilitating the fast development of AI models while protecting data subjects and bypassing the need to engage with the complexity of data sharing and processing agreements. Existing technologies focus on synthesising real-time physiological and physical records based on regular time intervals. Real health data are, however, characterised by irregularities and multimodal variables that are still hard to reproduce, preserving the correlation across time and different dimensions. This paper presents two novel techniques for synthetic data generation of real-time multimodal electronic health and physical records, (a) the Temporally Correlated Multimodal Generative Adversarial Network and (b) the Document Sequence Generator. The paper illustrates the need and use of these techniques through a real use case, the H2020 GATEKEEPER project of AI for healthcare. Furthermore, the paper presents the evaluation for both individual cases and a discussion about the comparability between techniques and their potential applications of synthetic data at the different stages of the software development life-cycle. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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15 pages, 110814 KiB  
Article
Image Style Transfer Based on Dynamic Convolutional Manifold Alignment of Halo Attention
by Ke Li, Degang Yang and Yan Ma
Electronics 2023, 12(8), 1881; https://doi.org/10.3390/electronics12081881 - 16 Apr 2023
Cited by 1 | Viewed by 1094
Abstract
The objective of image style transfer is to render an image with artistic features of a style reference while preserving the details of the content image. With the development of deep learning, many arbitrary style transfer methods have emerged. From the recent arbitrary [...] Read more.
The objective of image style transfer is to render an image with artistic features of a style reference while preserving the details of the content image. With the development of deep learning, many arbitrary style transfer methods have emerged. From the recent arbitrary style transfer algorithms, it has been found that the images generated suffer from the problem of poorly stylized quality. To solve this problem, we propose an arbitrary style transfer algorithm based on halo attention dynamic convolutional manifold alignment. First, the features of the content image and style image are extracted by a pre-trained VGG encoder. Then, the features are extracted by halo attention and dynamic convolution, and then the content feature space and style feature space are aligned by attention operations and spatial perception interpolation. The output is achieved through dynamic convolution and halo attention. During this process, multi-level loss functions are used, and total variation loss is introduced to eliminate noise. The manifold alignment process is then repeated three times. Finally, the pre-trained VGG decoder is used to output the stylized image. The experimental results show that our proposed method can generate high-quality stylized images, achieving values of 33.861, 2.516, and 3.602 for ArtFID, style loss, and content loss, respectively. A qualitative comparison with existing algorithms showed that it achieved good results. In future work, we will aim to make the model lightweight. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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13 pages, 615 KiB  
Article
2s-GATCN: Two-Stream Graph Attentional Convolutional Networks for Skeleton-Based Action Recognition
by Shu-Bo Zhou, Ran-Ran Chen, Xue-Qin Jiang and Feng Pan
Electronics 2023, 12(7), 1711; https://doi.org/10.3390/electronics12071711 - 04 Apr 2023
Cited by 3 | Viewed by 1400
Abstract
As human actions can be characterized by the trajectories of skeleton joints, skeleton-based action recognition techniques have gained increasing attention in the field of intelligent recognition and behavior analysis. With the emergence of large datasets, graph convolutional network (GCN) approaches have been widely [...] Read more.
As human actions can be characterized by the trajectories of skeleton joints, skeleton-based action recognition techniques have gained increasing attention in the field of intelligent recognition and behavior analysis. With the emergence of large datasets, graph convolutional network (GCN) approaches have been widely applied for skeleton-based action recognition and have achieved remarkable performances. In this paper, a novel GCN-based approach is proposed by introducing a convolutional block attention module (CBAM)-based graph attention block to compute the semantic correlations between any two vertices. By considering semantic correlations, our model can effectively identify the most discriminative vertex connections associated with specific actions, even when the two vertices are physically unconnected. Experimental results demonstrate that the proposed model is effective and outperforms existing methods. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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23 pages, 839 KiB  
Article
Evaluating Explainable Artificial Intelligence Methods Based on Feature Elimination: A Functionality-Grounded Approach
by Ghada Elkhawaga, Omar Elzeki, Mervat Abuelkheir and Manfred Reichert
Electronics 2023, 12(7), 1670; https://doi.org/10.3390/electronics12071670 - 31 Mar 2023
Cited by 4 | Viewed by 2838
Abstract
Although predictions based on machine learning are reaching unprecedented levels of accuracy, understanding the underlying mechanisms of a machine learning model is far from trivial. Therefore, explaining machine learning outcomes is gaining more interest with an increasing need to understand, trust, justify, and [...] Read more.
Although predictions based on machine learning are reaching unprecedented levels of accuracy, understanding the underlying mechanisms of a machine learning model is far from trivial. Therefore, explaining machine learning outcomes is gaining more interest with an increasing need to understand, trust, justify, and improve both the predictions and the prediction process. This, in turn, necessitates providing mechanisms to evaluate explainability methods as well as to measure their ability to fulfill their designated tasks. In this paper, we introduce a technique to extract the most important features from a data perspective. We propose metrics to quantify the ability of an explainability method to convey and communicate the underlying concepts available in the data. Furthermore, we evaluate the ability of an eXplainable Artificial Intelligence (XAI) method to reason about the reliance of a Machine Learning (ML) model on the extracted features. Through experiments, we further, prove that our approach enables differentiating explainability methods independent of the underlying experimental settings. The proposed metrics can be used to functionally evaluate the extent to which an explainability method is able to extract the patterns discovered by a machine learning model. Our approach provides a means to quantitatively differentiate global explainability methods in order to deepen user trust not only in the predictions generated but also in their explanations. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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13 pages, 579 KiB  
Article
Sequence Segmentation Attention Network for Skeleton-Based Action Recognition
by Yujie Zhang and Haibin Cai
Electronics 2023, 12(7), 1549; https://doi.org/10.3390/electronics12071549 - 25 Mar 2023
Cited by 2 | Viewed by 1131
Abstract
With skeleton-based action recognition, it is crucial to recognize the dependencies among joints. However, the current methods are not able to capture the relativity of the various joints among the frames, which is extremely helpful because various parts of the body are moving [...] Read more.
With skeleton-based action recognition, it is crucial to recognize the dependencies among joints. However, the current methods are not able to capture the relativity of the various joints among the frames, which is extremely helpful because various parts of the body are moving at the same time. In order to solve this problem, a new sequence segmentation attention network (SSAN) is presented. The successive frames are encoded in each of the segments that make up the skeleton sequence. Then, we provide a self-attention block that may record the associated information among various joints in successive frames. In order to better recognize comparable behavior, a model of external segment action attention is employed to acquire the deep interrelation information among parts. Compared with the most advanced approaches, we have shown that the proposed method performs better on NTU RGB+D and NTU RGB+D 120. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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30 pages, 12366 KiB  
Article
A Freehand 3D Ultrasound Reconstruction Method Based on Deep Learning
by Xin Chen, Houjin Chen, Yahui Peng, Liu Liu and Chang Huang
Electronics 2023, 12(7), 1527; https://doi.org/10.3390/electronics12071527 - 23 Mar 2023
Cited by 2 | Viewed by 2711
Abstract
In the medical field, 3D ultrasound reconstruction can visualize the internal structure of patients, which is very important for doctors to carry out correct analyses and diagnoses. Furthermore, medical 3D ultrasound images have been widely used in clinical disease diagnosis because they can [...] Read more.
In the medical field, 3D ultrasound reconstruction can visualize the internal structure of patients, which is very important for doctors to carry out correct analyses and diagnoses. Furthermore, medical 3D ultrasound images have been widely used in clinical disease diagnosis because they can more intuitively display the characteristics and spatial location information of the target. The traditional way to obtain 3D ultrasonic images is to use a 3D ultrasonic probe directly. Although freehand 3D ultrasound reconstruction is still in the research stage, a lot of research has recently been conducted on the freehand ultrasound reconstruction method based on wireless ultrasonic probe. In this paper, a wireless linear array probe is used to build a freehand acousto-optic positioning 3D ultrasonic imaging system. B-scan is considered the brightness scan. It is used for producing a 2D cross-section of the eye and its orbit. This system is used to collect and construct multiple 2D B-scans datasets for experiments. According to the experimental results, a freehand 3D ultrasonic reconstruction method based on depth learning is proposed, which is called sequence prediction reconstruction based on acoustic optical localization (SPRAO). SPRAO is an ultrasound reconstruction system which cannot be put into medical clinical use now. Compared with 3D reconstruction using a 3D ultrasound probe, SPRAO not only has a controllable scanning area, but also has a low cost. SPRAO solves some of the problems in the existing algorithms. Firstly, a 60 frames per second (FPS) B-scan sequence can be synthesized using a 12 FPS wireless ultrasonic probe through 2–3 acquisitions. It not only effectively reduces the requirement for the output frame rate of the ultrasonic probe, but also increases the moving speed of the wireless probe. Secondly, SPRAO analyzes the B-scans through speckle decorrelation to calibrate the acousto-optic auxiliary positioning information, while other algorithms have no solution to the cumulative error of the external auxiliary positioning device. Finally, long short-term memory (LSTM) is used to predict the spatial position and attitude of B-scans, and the calculation of pose deviation and speckle decorrelation is integrated into a 3D convolutional neural network (3DCNN). Prepare for real-time 3D reconstruction under the premise of accurate spatial pose of B-scans. At the end of this paper, SPRAO is compared with linear motion, IMU, speckle decorrelation, CNN and other methods. From the experimental results, it can be observed that the spatial pose deviation of B-scans output using SPRAO is the best of these methods. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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18 pages, 3116 KiB  
Article
RSP-DST: Revisable State Prediction for Dialogue State Tracking
by Qianyu Li, Wensheng Zhang, Mengxing Huang, Siling Feng and Yuanyuan Wu
Electronics 2023, 12(6), 1494; https://doi.org/10.3390/electronics12061494 - 22 Mar 2023
Cited by 1 | Viewed by 1198
Abstract
Task-oriented dialogue systems depend on dialogue state tracking to keep track of the intentions of users in the course of conversations. Although recent models in dialogue state tracking exhibit good performance, the errors in predicting the value of each slot at the current [...] Read more.
Task-oriented dialogue systems depend on dialogue state tracking to keep track of the intentions of users in the course of conversations. Although recent models in dialogue state tracking exhibit good performance, the errors in predicting the value of each slot at the current dialogue turn of these models are easily carried over to the next turn, and unlikely to be revised in the next turn, resulting in error propagation. In this paper, we propose a revisable state prediction for dialogue state tracking, which constructs a two-stage slot value prediction process composed of an original prediction and a revising prediction. The original prediction process jointly models the previous dialogue state and dialogue context to predict the original dialogue state of the current dialogue turn. Then, in order to avoid the errors existing in the original dialogue state continuing to the next dialogue turn, a revising prediction process utilizes the dialogue context to revise errors, alleviating the error propagation. Experiments are conducted on MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.4 and results indicate that our model outperforms previous state-of-the-art works, achieving new state-of-the-art performances with 56.35, 58.09, and 75.65% joint goal accuracy, respectively, which has a significant improvement (2.15, 1.73, and 2.03%) over the previous best results. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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13 pages, 1749 KiB  
Article
Low Complexity Speech Enhancement Network Based on Frame-Level Swin Transformer
by Weiqi Jiang, Chengli Sun, Feilong Chen, Yan Leng, Qiaosheng Guo, Jiayi Sun and Jiankun Peng
Electronics 2023, 12(6), 1330; https://doi.org/10.3390/electronics12061330 - 10 Mar 2023
Cited by 3 | Viewed by 1752
Abstract
In recent years, Transformer has shown great performance in speech enhancement by applying multi-head self-attention to capture long-term dependencies effectively. However, the computation of Transformer is quadratic with the input speech spectrograms, which makes it computationally expensive for practical use. In this paper, [...] Read more.
In recent years, Transformer has shown great performance in speech enhancement by applying multi-head self-attention to capture long-term dependencies effectively. However, the computation of Transformer is quadratic with the input speech spectrograms, which makes it computationally expensive for practical use. In this paper, we propose a low complexity hierarchical frame-level Swin Transformer network (FLSTN) for speech enhancement. FLSTN takes several consecutive frames as a local window and restricts self-attention within it, reducing the complexity to linear with spectrogram size. A shifted window mechanism enhances information exchange between adjacent windows, so that window-based local attention becomes disguised global attention. The hierarchical structure allows FLSTN to learn speech features at different scales. Moreover, we designed the band merging layer and the band expanding layer for decreasing and increasing the spatial resolution of feature maps, respectively. We tested FLSTN on both 16 kHz wide-band speech and 48 kHz full-band speech. Experimental results demonstrate that FLSTN can handle speech with different bandwidths well. With very few multiply–accumulate operations (MACs), FLSTN not only has a significant advantage in computational complexity but also achieves comparable objective speech quality metrics with current state-of-the-art (SOTA) models. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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15 pages, 3338 KiB  
Article
Application of Feature Pyramid Network and Feature Fusion Single Shot Multibox Detector for Real-Time Prostate Capsule Detection
by Shixiao Wu, Xinghuan Wang and Chengcheng Guo
Electronics 2023, 12(4), 1060; https://doi.org/10.3390/electronics12041060 - 20 Feb 2023
Cited by 4 | Viewed by 1188
Abstract
In the process of feature propagation, the low-level convolution layers of the forward feature propagation network lack semantic information, and information loss occurs when fine-grained information is transferred to higher-level convolution; therefore, multi-stage feature fusion networks are needed to solve the interaction between [...] Read more.
In the process of feature propagation, the low-level convolution layers of the forward feature propagation network lack semantic information, and information loss occurs when fine-grained information is transferred to higher-level convolution; therefore, multi-stage feature fusion networks are needed to solve the interaction between low-level convolution layers and high-level convolution layers. Based on a two-way feature feedback network and feature fusion mechanism, we created a new object detection network called Feature Pyramid Network (FPN)-based Feature Fusion Single Shot Multibox Detector (FFSSD). A bottom-up and top-down architecture with lateral connections enhances the detector’s ability to extract features, then high-level multi-scale semantic feature maps are utilized to generate a feature pyramid network. The results show that the proposed network the mAP for prostate capsule image detection reaches 83.58%, providing real-time detection ability. The context interaction mechanism can transfer high-level semantic information to low-level convolution, and the resulting convolution after low-level and high-level fusion contains richer location and semantic information. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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20 pages, 2843 KiB  
Article
FESSD: Feature Enhancement Single Shot MultiBox Detector Algorithm for Remote Sensing Image Target Detection
by Jianxin Guo, Zhen Wang and Shanwen Zhang
Electronics 2023, 12(4), 946; https://doi.org/10.3390/electronics12040946 - 14 Feb 2023
Cited by 1 | Viewed by 1391
Abstract
Automatic target detection of remote sensing images (RSI) plays an important role in military surveillance and disaster monitoring. The core task of RSI target detection is to judge the target categories and precise location. However, the existing target detection algorithms have limited accuracy [...] Read more.
Automatic target detection of remote sensing images (RSI) plays an important role in military surveillance and disaster monitoring. The core task of RSI target detection is to judge the target categories and precise location. However, the existing target detection algorithms have limited accuracy and weak generalization capability for RSI with complex backgrounds. This study presents a novel feature enhancement single shot multibox detector (FESSD) algorithm for remote sensing target detection to achieve accurate detection of different categories targets. The FESSD introduces feature enhancement module and attention mechanism into the convolution neural networks (CNN) model, which can effectively enhance the feature extraction ability and nonlinear relationship between different convolution features. Specifically, the feature enhancement module is used to extract the multi-scale feature information and enhance the model nonlinear learning ability; the self-learning attention mechanism (SAM) is used to expand the convolution kernel local receptive field, which makes the model extract more valuable features. In addition, the nonlinear relationship between different convolution features is enhanced using the feature pyramid attention mechanism (PAM). The experimental results show that the mAP value of the proposed method reaches 81.9% and 81.2% on SD-RSI and DIOR datasets, which is superior to other compared state-of-the-art methods. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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12 pages, 2106 KiB  
Article
Improved MLP Energy Meter Fault Diagnosis Method Based on DBN
by Chaochun Zhong, Yang Jiang, Limin Wang, Jiayan Chen, Juan Zhou, Tao Hong and Fan Zheng
Electronics 2023, 12(4), 932; https://doi.org/10.3390/electronics12040932 - 13 Feb 2023
Cited by 2 | Viewed by 1187
Abstract
In order to effectively utilize the large amount of high-dimensionality historical data generated by energy meters during operation, this paper proposes a DBN-MLP fusion neural network method for multi-dimensional analysis and fault-type diagnosis of smart energy meter fault data. In this paper, we [...] Read more.
In order to effectively utilize the large amount of high-dimensionality historical data generated by energy meters during operation, this paper proposes a DBN-MLP fusion neural network method for multi-dimensional analysis and fault-type diagnosis of smart energy meter fault data. In this paper, we first use DBN to strengthen the feature extraction ability of the network and solve the problem of many kinds of feature data and high dimensionality of historical data. After that, the processed feature information is input into the MLP neural network, and the strong processing ability of MLP for nonlinear numbers is used to solve the problem of weak correlation among data in the historical data set and improve the accuracy rate of faults diagnosis. The final results show that the DBN-MLP method used in this paper can effectively reduce the number of training iterations to reduce the training time and improve the accuracy of diagnosis. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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14 pages, 4322 KiB  
Article
Static Gesture Recognition Algorithm Based on Improved YOLOv5s
by Shengwang Wu, Zhongmin Li, Shiji Li, Qiang Liu and Weiyu Wu
Electronics 2023, 12(3), 596; https://doi.org/10.3390/electronics12030596 - 25 Jan 2023
Cited by 5 | Viewed by 2393
Abstract
With the government’s increasing support for the virtual reality (VR)/augmented reality (AR) industry, it has developed rapidly in recent years. Gesture recognition, as an important human-computer interaction method in VR/AR technology, is widely used in the field of virtual reality. The current static [...] Read more.
With the government’s increasing support for the virtual reality (VR)/augmented reality (AR) industry, it has developed rapidly in recent years. Gesture recognition, as an important human-computer interaction method in VR/AR technology, is widely used in the field of virtual reality. The current static gesture recognition technology has several shortcomings, such as low recognition accuracy and low recognition speed. A static gesture recognition algorithm based on improved YOLOv5s is proposed to address these issues. The content-aware re-assembly of features (CARAFE) is used to replace the nearest neighbor up-sampling method in YOLOv5s to make full use of the semantic information in the feature map and improve the recognition accuracy of the model for gesture regions. The adaptive spatial feature fusion (ASFF) method is introduced to filter out useless information and retain useful information for efficient feature fusion. The bottleneck transformer method is initially introduced into the gesture recognition task, reducing the number of model parameters and increasing the accuracy while accelerating the inference speed. The improved algorithm achieved an mAP(mean average precision) of 96.8%, a 3.1% improvement in average accuracy compared with the original YOLOv5s algorithm; the confidence level of the actual detection results was higher than the original algorithm. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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15 pages, 4599 KiB  
Article
GBH-YOLOv5: Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5 for PV Panel Defect Detection
by Longlong Li, Zhifeng Wang and Tingting Zhang
Electronics 2023, 12(3), 561; https://doi.org/10.3390/electronics12030561 - 21 Jan 2023
Cited by 31 | Viewed by 5014
Abstract
Photovoltaic (PV) panel surface-defect detection technology is crucial for the PV industry to perform smart maintenance. Using computer vision technology to detect PV panel surface defects can ensure better accuracy while reducing the workload of traditional worker field inspections. However, multiple tiny defects [...] Read more.
Photovoltaic (PV) panel surface-defect detection technology is crucial for the PV industry to perform smart maintenance. Using computer vision technology to detect PV panel surface defects can ensure better accuracy while reducing the workload of traditional worker field inspections. However, multiple tiny defects on the PV panel surface and the high similarity between different defects make it challenging to accurately identify and detect such defects. This paper proposes an approach named Ghost convolution with BottleneckCSP and a tiny target prediction head incorporating YOLOv5 (GBH-YOLOv5) for PV panel defect detection. To ensure better accuracy on multiscale targets, the BottleneckCSP module is introduced to add a prediction head for tiny target detection to alleviate tiny defect misses, using Ghost convolution to improve the model inference speed and reduce the number of parameters. First, the original image is compressed and cropped to enlarge the defect size physically. Then, the processed images are input into GBH-YOLOv5, and the depth features are extracted through network processing based on Ghost convolution, the application of the BottleneckCSP module, and the prediction head of tiny targets. Finally, the extracted features are classified by a Feature Pyramid Network (FPN) and a Path Aggregation Network (PAN) structure. Meanwhile, we compare our method with state-of-the-art methods to verify the effectiveness of the proposed method. The proposed PV panel surface-defect detection network improves the mAP performance by at least 27.8%. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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10 pages, 1938 KiB  
Article
A Method for Calculating the Derivative of Activation Functions Based on Piecewise Linear Approximation
by Xuan Liao, Tong Zhou, Longlong Zhang, Xiang Hu and Yuanxi Peng
Electronics 2023, 12(2), 267; https://doi.org/10.3390/electronics12020267 - 04 Jan 2023
Cited by 2 | Viewed by 1668
Abstract
Nonlinear functions are widely used as activation functions in artificial neural networks, which have a great impact on the fitting ability of artificial neural networks. Due to the complexity of the activation function, the computation of the activation function and its derivative requires [...] Read more.
Nonlinear functions are widely used as activation functions in artificial neural networks, which have a great impact on the fitting ability of artificial neural networks. Due to the complexity of the activation function, the computation of the activation function and its derivative requires a lot of computing resources and time during training. In order to improve the computational efficiency of the derivatives of the activation function in the back-propagation of artificial neural networks, this paper proposes a method based on piecewise linear approximation method to calculate the derivative of the activation function. This method is hardware-friendly and universal, it can efficiently compute various nonlinear activation functions in the field of neural network hardware accelerators. In this paper, we use least squares to improve a piecewise linear approximation calculation method that can control the absolute error and get less number of segments or smaller average error, which means fewer hardware resources are required. We use this method to perform a segmented linear approximation to the original or derivative function of the activation function. Both types of activation functions are substituted into a multilayer perceptron for binary classification experiments to verify the effectiveness of the proposed method. Experimental results show that the same or even slightly higher classification accuracy can be achieved by using this method, and the computation time of the back-propagation is reduced by 4–6% compared to the direct calculation of the derivative directly from the function expression using the operator encapsulated in PyTorch. This shows that the proposed method provides an efficient solution of nonlinear activation functions for hardware acceleration of neural networks. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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20 pages, 1550 KiB  
Article
Anomaly-PTG: A Time Series Data-Anomaly-Detection Transformer Framework in Multiple Scenarios
by Gang Li, Zeyu Yang, Honglin Wan and Min Li
Electronics 2022, 11(23), 3955; https://doi.org/10.3390/electronics11233955 - 29 Nov 2022
Cited by 2 | Viewed by 3725
Abstract
In actual scenarios, industrial and cloud computing platforms usually need to monitor equipment and traffic anomalies through multivariable time series data. However, the existing anomaly detection methods can not capture the long-distance temporal correlations of data and the potential relationships between features simultaneously, [...] Read more.
In actual scenarios, industrial and cloud computing platforms usually need to monitor equipment and traffic anomalies through multivariable time series data. However, the existing anomaly detection methods can not capture the long-distance temporal correlations of data and the potential relationships between features simultaneously, and only have high detection accuracy for specific time sequence anomaly detection scenarios without good generalization ability. This paper proposes a time-series anomaly-detection framework for multiple scenarios, Anomaly-PTG (anomaly parallel transformer GRU), given the above limitations. The model uses the parallel transformer GRU as the information extraction module of the model to learn the long-distance correlation between timestamps and the global feature relationship of multivariate time series, which enhances the ability to extract hidden information from time series data. After extracting the information, the model learns the sequential representation of the data, conducts the sequential modeling, and transmits the data to the full connection layer for prediction. At the same time, it also uses the autoencoder to learn the potential representation of the data and reconstruct the data. The two are optimally combined to form an anomaly detection module of the model. The module combines timestamp prediction with time series data reconstruction, improving the detection rate of rare anomalies and detection accuracy. By using three public datasets of physical devices and one dataset of network traffic intrusion detection, the model’s effectiveness was verified, and the model’s generalization ability and strong robustness were demonstrated. Compared with the most advanced method, the average F1 value of the Anomaly-PTG model on four datasets was increased by 2.2%, and the F1 value on each dataset was over 94%. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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14 pages, 1633 KiB  
Article
N-Beats as an EHG Signal Forecasting Method for Labour Prediction in Full Term Pregnancy
by Thierry Rock Jossou, Zakaria Tahori, Godwin Houdji, Daton Medenou, Abdelali Lasfar, Fréjus Sanya, Mêtowanou Héribert Ahouandjinou, Silvio M. Pagliara, Muhammad Salman Haleem and Aziz Et-Tahir
Electronics 2022, 11(22), 3739; https://doi.org/10.3390/electronics11223739 - 15 Nov 2022
Cited by 3 | Viewed by 1907
Abstract
The early prediction of onset labour is critical for avoiding the risk of death due to pregnancy delay. Low-income countries often struggle to deliver timely service to pregnant women due to a lack of infrastructure and healthcare facilities, resulting in pregnancy complications and, [...] Read more.
The early prediction of onset labour is critical for avoiding the risk of death due to pregnancy delay. Low-income countries often struggle to deliver timely service to pregnant women due to a lack of infrastructure and healthcare facilities, resulting in pregnancy complications and, eventually, death. In this regard, several artificial-intelligence-based methods have been proposed based on the detection of contractions using electrohysterogram (EHG) signals. However, the forecasting of pregnancy contractions based on real-time EHG signals is a challenging task. This study proposes a novel model based on neural basis expansion analysis for interpretable time series (N-BEATS) which predicts labour based on EHG forecasting and contraction classification over a given time horizon. The publicly available TPEHG database of Physiobank was exploited in order to train and test the model, where signals from full-term pregnant women and signals recorded after 26 weeks of gestation were collected. For these signals, the 30 most commonly used classification parameters in the literature were calculated, and principal component analysis (PCA) was utilized to select the 15 most representative parameters (all the domains combined). The results show that neural basis expansion analysis for interpretable time series (N-BEATS) forecasting can forecast EHG signals through training after few iterations. Similarly, the forecasting signal’s duration is determined by the length of the recordings. We then deployed XG-Boost, which achieved the classification accuracy of 99 percent, outperforming the state-of-the-art approaches using a number of classification features greater than or equal to 15. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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16 pages, 857 KiB  
Article
Poisonous Plants Species Prediction Using a Convolutional Neural Network and Support Vector Machine Hybrid Model
by Talal H. Noor, Ayman Noor and Mahmoud Elmezain
Electronics 2022, 11(22), 3690; https://doi.org/10.3390/electronics11223690 - 11 Nov 2022
Cited by 3 | Viewed by 2121
Abstract
The total number of discovered plant species is increasing yearly worldwide. Plant species differ from one region to another. Some of these discovered plant species are beneficial while others might be poisonous. Computer vision techniques can be an effective way to classify plant [...] Read more.
The total number of discovered plant species is increasing yearly worldwide. Plant species differ from one region to another. Some of these discovered plant species are beneficial while others might be poisonous. Computer vision techniques can be an effective way to classify plant species and predict their poisonous status. However, the lack of comprehensive datasets that include not only plant images but also plant species’ scientific names, description, poisonous status, and local name make the issue of poisonous plants species prediction a very challenging issue. In this paper, we propose a hybrid model relying on transformers models in conjunction with support vector machine for plant species classification and poisonous status prediction. First, six different Convolutional Neural Network (CNN) architectures are used to determine which produces the best results. Second, the features are extracted using six different CNNs and then optimized and employed to Support Vector Machine (SVM) for testing. To prove the feasibility and benefits of our proposed approach, we used a real case study namely, plant species discovered in the Arabian Peninsula. We have gathered a dataset that contains 2500 images of 50 different Arabic plant species and includes plants images, plant species scientific name, description, local name, and poisonous status. This study on the types of Arabic plants species will help in the reduction of the number of poisonous plants victims and their negative impact on the individual and society. The results of our experiments for the CNN approach in conjunction SVM are favorable where the classifier scored 0.92, 0.94, and 0.95 in accuracy, precision, and F1-Score respectively. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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25 pages, 1861 KiB  
Article
A Federated Learning Framework Based on Incremental Weighting and Diversity Selection for Internet of Vehicles
by Yuan Lei, Shir Li Wang, Minghui Zhong, Meixia Wang and Theam Foo Ng
Electronics 2022, 11(22), 3668; https://doi.org/10.3390/electronics11223668 - 09 Nov 2022
Cited by 9 | Viewed by 2158
Abstract
With the rapid increase of data, centralized machine learning can no longer meet the application requirements of the Internet of Vehicles (IoV). On the one hand, both car owners and regulators pay more attention to data privacy and are unwilling to share data, [...] Read more.
With the rapid increase of data, centralized machine learning can no longer meet the application requirements of the Internet of Vehicles (IoV). On the one hand, both car owners and regulators pay more attention to data privacy and are unwilling to share data, which forms the isolated data island challenge. On the other hand, the incremental data generated in IoV are massive and diverse. All these issues have brought challenges of data increment and data diversity. The current common federated learning or incremental learning frameworks cannot effectively integrate incremental data with existing machine learning (ML) models. Therefore, this paper proposes a Federated Learning Framework Based on Incremental Weighting and Diversity Selection for IoV (Fed-IW&DS). In Fed-IW&DS, a vehicle diversity selection algorithm was proposed, which uses a variety of performance indicators to calculate diversity scores, effectively reducing homogeneous computing. Also, it proposes a vehicle federated incremental algorithm that uses an improved arctangent curve as the decay function, to realize the rapid fusion of incremental data with existing ML models. Moreover, we have carried out several sets of experiments to test the validity of the proposed Fed-IW&DS framework’s performance. The experimental results show that, under the same global communication round and similar computing time, the Fed-IW&DS framework has significantly improved performance in all aspects compared to the frameworks FED-AVG, FED-SGD, FED-prox & the decay functions linear, square curve and arc tangent. Specifically, the Fed-IW&DS framework improves the Acc (accuracy), loss (loss), and Matthews correlation coefficient (MCC) by approximately 32%, 83%, and 66%, respectively. This result shows that Fed-IW&DS is a more reliable solution than the common frameworks of federated learning, and it can effectively deal with the dynamic incremental data in the IoV scenario. Our findings should make a significant contribution to the field of federated learning. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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19 pages, 6743 KiB  
Article
An Aircraft Trajectory Prediction Method Based on Trajectory Clustering and a Spatiotemporal Feature Network
by You Wu, Hongyi Yu, Jianping Du, Bo Liu and Wanting Yu
Electronics 2022, 11(21), 3453; https://doi.org/10.3390/electronics11213453 - 25 Oct 2022
Cited by 6 | Viewed by 1951
Abstract
The maneuvering characteristics and range of motion of real aircraft are highly uncertain, which significantly increases the difficulty of trajectory prediction. To solve the problem that high-speed maneuvers and excessive trajectories in airspace cause a decrease in prediction accuracy and to find out [...] Read more.
The maneuvering characteristics and range of motion of real aircraft are highly uncertain, which significantly increases the difficulty of trajectory prediction. To solve the problem that high-speed maneuvers and excessive trajectories in airspace cause a decrease in prediction accuracy and to find out the laws of motion hidden in a large number of real trajectories, this paper proposes a deep learning algorithm based on trajectory clustering and spatiotemporal feature extraction, which aims to better describe the regularity of aircraft movement for higher prediction accuracy. First, the abnormal trajectories in the public dataset of automatic dependent surveillance–broadcast (ADS-B) were analyzed, and to ensure the uniform sampling of trajectory data, the cleaning and interpolation of the trajectory data were performed. Then, the Hausdorff distance was used to measure the similarity between the trajectories, K-Medoids was used for clustering, and the corresponding prediction model was established according to the clustering results. Finally, a trajectory spatiotemporal feature extraction network was constructed based on a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network, and a joint attention mechanism was used to obtain the important features of the trajectory points. A large number of actual trajectory prediction experiments showed that the proposed method is more accurate than existing algorithms based on BP, LSTM, and CNN–LSTM models. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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20 pages, 4418 KiB  
Article
Improved YOLOv3 Model for Workpiece Stud Leakage Detection
by Peichao Cong, Kunfeng Lv, Hao Feng and Jiachao Zhou
Electronics 2022, 11(21), 3430; https://doi.org/10.3390/electronics11213430 - 23 Oct 2022
Cited by 5 | Viewed by 1470
Abstract
In this study, a deep convolutional neural network based on an improved You only look once version 3 (YOLOv3) is proposed to improve the accuracy and real-time detection of small targets in complex backgrounds when detecting leaky weld studs on an automotive workpiece. [...] Read more.
In this study, a deep convolutional neural network based on an improved You only look once version 3 (YOLOv3) is proposed to improve the accuracy and real-time detection of small targets in complex backgrounds when detecting leaky weld studs on an automotive workpiece. To predict stud locations, the prediction layer of the model increases from three layers to four layers. An image pyramid structure obtains stud feature maps at different scales, and shallow feature fusion at multiple scales obtains stud contour details. Focal loss is added to the loss function to solve the imbalanced sample problem. The reduced weight of simple background classes allows the algorithm to focus on foreground classes, reducing the number of missed weld studs. Moreover, K-medians algorithm replaces the original K-means clustering to improve model robustness. Finally, an image dataset of car body workpiece studs is built for model training and testing. The results reveal that the average detection accuracy of the improved YOLOv3 model is 80.42%, which is higher than the results of Faster R-CNN, single-shot multi-box detector (SSD), and YOLOv3. The detection time per image is just 0.32 s (62.8% and 23.8% faster than SSD and Faster R-CNN, respectively), fulfilling the requirement for stud leakage detection in real-world working environments. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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18 pages, 1809 KiB  
Article
LASNet: A Light-Weight Asymmetric Spatial Feature Network for Real-Time Semantic Segmentation
by Yu Chen, Weida Zhan, Yichun Jiang, Depeng Zhu, Renzhong Guo and Xiaoyu Xu
Electronics 2022, 11(19), 3238; https://doi.org/10.3390/electronics11193238 - 09 Oct 2022
Cited by 2 | Viewed by 1457
Abstract
In recent years, deep learning models have achieved great success in the field of semantic segmentation, which achieve satisfactory performance by introducing a large number of parameters. However, this achievement usually leads to high computational complexity, which seriously limits the deployment of semantic [...] Read more.
In recent years, deep learning models have achieved great success in the field of semantic segmentation, which achieve satisfactory performance by introducing a large number of parameters. However, this achievement usually leads to high computational complexity, which seriously limits the deployment of semantic segmented applications on mobile devices with limited computing and storage resources. To address this problem, we propose a lightweight asymmetric spatial feature network (LASNet) for real-time semantic segmentation. We consider the network parameters, inference speed, and performance to design the structure of LASNet, which can make the LASNet applied to embedded devices and mobile devices better. In the encoding part of LASNet, we propose the LAS module, which retains and utilize spatial information. This module uses a combination of asymmetric convolution, group convolution, and dual-stream structure to reduce the number of network parameters and maintain strong feature extraction ability. In the decoding part of LASNet, we propose the multivariate concatenate module to reuse the shallow features, which can improve the segmentation accuracy and maintain a high inference speed. Our network attains precise real-time segmentation results in a wide range of experiments. Without additional processing and pre-training, LASNet achieves 70.99% mIoU and 110.93 FPS inference speed in the CityScapes dataset with only 0.8 M model parameters. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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15 pages, 2117 KiB  
Article
A Rumor Detection Method Based on Multimodal Feature Fusion by a Joining Aggregation Structure
by Nanjiang Zhong, Guomin Zhou, Weijie Ding and Jiawen Zhang
Electronics 2022, 11(19), 3200; https://doi.org/10.3390/electronics11193200 - 06 Oct 2022
Cited by 2 | Viewed by 1564
Abstract
Online rumors spread rapidly through social media, which is a great threat to public safety. Existing solutions are mainly based on content features or propagation structures for rumor detection. However, due to the variety of strategies in creating rumors, only considering certain features [...] Read more.
Online rumors spread rapidly through social media, which is a great threat to public safety. Existing solutions are mainly based on content features or propagation structures for rumor detection. However, due to the variety of strategies in creating rumors, only considering certain features cannot achieve good enough detection results. In addition, existing works only consider the rumor propagation structure and ignore the aggregation structures of rumors, which cannot provide enough discriminative features (especially in the early days of rumors, when the structure of the propagation is incomplete). To solve these problems, this paper proposes a rumor detection method with multimodal feature fusion and enhances the feature representation of the rumor propagation network by adding aggregation features. More specifically, we built a graph model of the propagation structure as well as the aggregation structure. Next, by utilizing the BERT pre-training model and the bidirectional graph convolutional network, we captured the features of text content, propagation structure, and aggregation structure, respectively. Finally, the multimodal features were aggregated based on the attention mechanism, and the final result was obtained through the MLP classifier. Experiments on real-world datasets show that our model outperforms state-of-the-art approaches. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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14 pages, 3278 KiB  
Article
Specific Emitter Identification Based on Self-Supervised Contrast Learning
by Bo Liu, Hongyi Yu, Jianping Du, You Wu, Yongbin Li, Zhaorui Zhu and Zhenyu Wang
Electronics 2022, 11(18), 2907; https://doi.org/10.3390/electronics11182907 - 14 Sep 2022
Cited by 4 | Viewed by 1563
Abstract
The current deep learning (DL)-based Specific Emitter Identification (SEI) methods rely heavily on the training of massive labeled data during the training process. However, the lack of labeled data in a real application would lead to a decrease in the method’s identification performance. [...] Read more.
The current deep learning (DL)-based Specific Emitter Identification (SEI) methods rely heavily on the training of massive labeled data during the training process. However, the lack of labeled data in a real application would lead to a decrease in the method’s identification performance. In this paper, we propose a self-supervised method via contrast learning (SSCL), which is used to extract fingerprint features from unlabeled data. The proposed method uses large amounts of unlabeled data to constitute positive and negative pairs by designing a composition of data augmentation operations for emitter signals. Then, the pairs would be input into the neural network (NN) for feature extraction, and a contrastive loss function is introduced to drive the network to measure the similarity among data. Finally, the identification model can be completed by fixing the parameters of the feature extraction network and fine-tuning with few labeled data. The simulation experiment result shows that, after being fine-tuned, the proposed method can effectively extract fingerprint features. When the SNR is 20 dB, the identification accuracy reaches 94.45%, which is better than the current mainstream DL approaches. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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20 pages, 4411 KiB  
Article
Sarcasm Detection over Social Media Platforms Using Hybrid Auto-Encoder-Based Model
by Dilip Kumar Sharma, Bhuvanesh Singh, Saurabh Agarwal, Hyunsung Kim and Raj Sharma
Electronics 2022, 11(18), 2844; https://doi.org/10.3390/electronics11182844 - 08 Sep 2022
Cited by 15 | Viewed by 4153
Abstract
Sarcasm is a language phrase that conveys the polar opposite of what is being said, generally something highly unpleasant to offend or mock somebody. Sarcasm is widely used on social media platforms every day. Because sarcasm may change the meaning of a statement, [...] Read more.
Sarcasm is a language phrase that conveys the polar opposite of what is being said, generally something highly unpleasant to offend or mock somebody. Sarcasm is widely used on social media platforms every day. Because sarcasm may change the meaning of a statement, the opinion analysis procedure is prone to errors. Concerns about the integrity of analytics have grown as the usage of automated social media analysis tools has expanded. According to preliminary research, sarcastic statements alone have significantly reduced the accuracy of automatic sentiment analysis. Sarcastic phrases also impact automatic fake news detection leading to false positives. Various individual natural language processing techniques have been proposed earlier, but each has textual context and proximity limitations. They cannot handle diverse content types. In this research paper, we propose a novel hybrid sentence embedding-based technique using an autoencoder. The framework proposes using sentence embedding from long short term memory-autoencoder, bidirectional encoder representation transformer, and universal sentence encoder. The text over images is also considered to handle multimedia content such as images and videos. The final framework is designed after the ablation study of various hybrid fusions of models. The proposed model is verified on three diverse real-world social media datasets—Self-Annotated Reddit Corpus (SARC), headlines dataset, and Twitter dataset. The accuracy of 83.92%, 90.8%, and 92.80% is achieved. The accuracy metric values are better than previous state-of-art frameworks. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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12 pages, 4188 KiB  
Article
A Granulation Tissue Detection Model to Track Chronic Wound Healing in DM Foot Ulcers
by Angela Shin-Yu Lien, Chen-Yao Lai, Jyh-Da Wei, Hui-Mei Yang, Jiun-Ting Yeh and Hao-Chih Tai
Electronics 2022, 11(16), 2617; https://doi.org/10.3390/electronics11162617 - 20 Aug 2022
Cited by 2 | Viewed by 5860
Abstract
Diabetes mellitus (DM) foot ulcer is a chronic wound and is highly related to the mortality and morbidity of infection, and might induce sepsis and foot amputation, especially during the isolation stage of the COVID-19 pandemic. Visual observation when changing dressings is the [...] Read more.
Diabetes mellitus (DM) foot ulcer is a chronic wound and is highly related to the mortality and morbidity of infection, and might induce sepsis and foot amputation, especially during the isolation stage of the COVID-19 pandemic. Visual observation when changing dressings is the most common and traditional method of detecting wound healing. The formation of granulation tissues plays an important role in wound healing. In the complex pathophysiology of excess and unhealthy granulation induced by infection, oxygen supply may explain the wound healing process in DM patients with multiple complicated wounds. Thus, advanced and useful tools to observe the condition of wound healing are very important for DM patients with extremities ulcers. For this purpose, we developed an artificial intelligence (AI) detection model to identify the growth of granulation tissue of the wound bed. We recruited 100 patients to provide 219 images of wounds at different healing stages from 2 hospitals. This was performed to understand the wound images of inconsistent size, and to allow self-inspection on mobile devices, having limited computing resources. We segmented those images into 32 × 32 blocks and used a reduced ResNet-18 model to test them individually. Furthermore, we conducted a learning method of active learning to improve the efficiency of model training. Experimental results reveal that our model can identify the region of granulation tissue with an Intersection-over-Union (IOU) rate higher than 0.5 compared to the ground truth. Multiple cross-repetitive validations also confirm that the detection results of our model may serve as an auxiliary indicator for assessing the progress of wound healing. The preliminary findings may help to identify the granulation tissue of patients with DM foot ulcer, which may lead to better long-term home care during the COVID-19 pandemic. The current limit of our model is an IOU of about 0.6. If more actual data are available, the IOU is expected to improve. We can continue to use the currently established active learning process for subsequent training. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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22 pages, 6321 KiB  
Article
A New Perspective on Traffic Flow Prediction: A Graph Spatial-Temporal Network with Complex Network Information
by Zhiqiu Hu, Fengjing Shao and Rencheng Sun
Electronics 2022, 11(15), 2432; https://doi.org/10.3390/electronics11152432 - 04 Aug 2022
Cited by 6 | Viewed by 2341
Abstract
Traffic flow prediction provides support for travel management, vehicle scheduling, and intelligent transportation system construction. In this work, a graph space–time network (GSTNCNI), incorporating complex network feature information, is proposed to predict future highway traffic flow time series. Firstly, a traffic complex network [...] Read more.
Traffic flow prediction provides support for travel management, vehicle scheduling, and intelligent transportation system construction. In this work, a graph space–time network (GSTNCNI), incorporating complex network feature information, is proposed to predict future highway traffic flow time series. Firstly, a traffic complex network model using traffic big data is established, the topological features of traffic road networks are then analyzed using complex network theory, and finally, the topological features are combined with graph neural networks to explore the roles played by the topological features of 97 traffic network nodes. Consequently, six complex network properties are discussed, namely, degree centrality, clustering coefficient, closeness centrality, betweenness centrality, point intensity, and shortest average path length. This study improves the graph convolutional neural network based on the above six complex network properties and proposes a graph spatial–temporal network consisting of a combination of several complex network properties. By comparison with existing baselines containing graph convolutional neural networks, it is verified that GSTNCNI possesses high traffic flow prediction accuracy and robustness. In addition, ablation experiments are conducted for six different complex network features to verify the effect of different complex network features on the model’s prediction accuracy. Experimental analysis indicates that the model with combined multiple complex network features has a higher prediction accuracy, and its performance is improved by 31.46% on average, compared with the model containing only one complex network feature. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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15 pages, 5949 KiB  
Article
Evaluating Intelligent Methods for Detecting COVID-19 Fake News on Social Media Platforms
by Hosam Alhakami, Wajdi Alhakami, Abdullah Baz, Mohd Faizan, Mohd Waris Khan and Alka Agrawal
Electronics 2022, 11(15), 2417; https://doi.org/10.3390/electronics11152417 - 03 Aug 2022
Cited by 8 | Viewed by 3078
Abstract
The advent of Internet-based technology has made daily life much easy than earlier days. The exponential rise in the popularity of social media platforms has not only connected people from faraway places, but has also increased communication among humans. However, in several instances, [...] Read more.
The advent of Internet-based technology has made daily life much easy than earlier days. The exponential rise in the popularity of social media platforms has not only connected people from faraway places, but has also increased communication among humans. However, in several instances, social media platforms have also been utilized for unethical and criminal activities. The propagation of fake news on social media during the ongoing COVID-19 pandemic has deteriorated the mental and physical health of people. Therefore, to control the flow of fake news regarding the novel coronavirus, several studies have been undertaken to automatically detect the fake news about COVID-19 using various intelligent techniques. However, different studies have shown different results on the performance of the predicting models. In this paper, we have evaluated several machine learning and deep learning models for the automatic detection of fake news regarding COVID-19. The experiments were carried out on two publicly available datasets, and the results were assessed using several evaluation metrics. The traditional machine learning models produced better results than the deep learning models in predicting fake news. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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18 pages, 16227 KiB  
Article
An Intelligent Method for Detecting Surface Defects in Aluminium Profiles Based on the Improved YOLOv5 Algorithm
by Teng Wang, Jianhuan Su, Chuan Xu and Yinguang Zhang
Electronics 2022, 11(15), 2304; https://doi.org/10.3390/electronics11152304 - 23 Jul 2022
Cited by 16 | Viewed by 3168
Abstract
In response to problems such as low recognition rate, random distribution of defects and large-scale differences in the detection of surface defects of aluminum profiles by other state-of-the-art algorithms, this paper proposes an improved MS-YOLOv5 model based on the YOLOv5 algorithm. First, a [...] Read more.
In response to problems such as low recognition rate, random distribution of defects and large-scale differences in the detection of surface defects of aluminum profiles by other state-of-the-art algorithms, this paper proposes an improved MS-YOLOv5 model based on the YOLOv5 algorithm. First, a PE-Neck structure is proposed to replace the neck part of the original algorithm in order to enhance the model’s ability to extract and locate defects at different scales. Secondly, a multi-streamnet is proposed as the first detection head of the algorithm to increase the model’s ability to identify distributed random defects. Meanwhile, to overcome the problem of inadequate industrial defect samples, the training set is enhanced by geometric variations and image-processing techniques. Experiments show that the proposed MS-YOLOv5 model has the best mean average precision (mAP) compared to the mainstream target-detection algorithm for detecting surface defects in aluminium profiles, whereas the average single image recognition time is within 19.1FPS, meeting the real-time requirements of industrial inspection. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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16 pages, 4222 KiB  
Article
A Spatio-Temporal Feature Trajectory Clustering Algorithm Based on Deep Learning
by Xintai He, Qing Li, Runze Wang and Kun Chen
Electronics 2022, 11(15), 2283; https://doi.org/10.3390/electronics11152283 - 22 Jul 2022
Viewed by 1517
Abstract
The trajectory data of aircraft, ships, and so on, can be analyzed to obtain valuable information. Clustering is the basic technology of trajectory analysis, and the feature extraction process is one of the decisive factors for clustering performance. Trajectory features can be divided [...] Read more.
The trajectory data of aircraft, ships, and so on, can be analyzed to obtain valuable information. Clustering is the basic technology of trajectory analysis, and the feature extraction process is one of the decisive factors for clustering performance. Trajectory features can be divided into two categories: spatial features and temporal features. In mainstream algorithms, spatial features are represented by latitude and longitude coordinates. However, such algorithms are only suitable for trajectories where spatial features are tightly coupled with latitude and longitude. When the same types of trajectories are in different latitude and longitude ranges or there are transformations such as rotation, scaling, and so on, this kind of algorithm is infeasible. Therefore, this paper proposes a spatio-temporal feature trajectory clustering algorithm based on deep learning. In this algorithm, the extraction process of the trajectory spatial shape feature is designed based on image matching technology, and the extracted spatial features are combined with the trajectory temporal features to improve the clustering performance. The experimental results on simulated and real datasets show that the algorithm can effectively extract the trajectory spatial shape features and that the clustering effect of the fused spatio-temporal feature is better than that of a single feature. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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21 pages, 6904 KiB  
Article
Digital Twin Based Network Latency Prediction in Vehicular Networks
by Yanfang Fu, Dengdeng Guo, Qiang Li, Liangxin Liu, Shaochun Qu and Wei Xiang
Electronics 2022, 11(14), 2217; https://doi.org/10.3390/electronics11142217 - 15 Jul 2022
Cited by 2 | Viewed by 1730
Abstract
Network latency is a crucial factor affecting the quality of communications networks due to the irregularity of vehicular traffic. To address the problem of performance degradation or instability caused by latency in vehicular networks, this paper proposes a time delay prediction algorithm, in [...] Read more.
Network latency is a crucial factor affecting the quality of communications networks due to the irregularity of vehicular traffic. To address the problem of performance degradation or instability caused by latency in vehicular networks, this paper proposes a time delay prediction algorithm, in which digital twin technology is employed to obtain a large quantity of actual time delay data for vehicular networks and to verify autocorrelation. Subsequently, to meet the prediction conditions of the ARMA time series model, two neural networks, i.e., Radial basis function (RBF) and Elman networks, were employed to construct a time delay prediction model. The experimental results show that the average relative error of the RBF is 7.6%, whereas that of the Elman-NN is 14.2%. This indicates that the RBF has a better prediction performance, and a better real-time performance than the Elman-NN. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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19 pages, 4111 KiB  
Article
An Appearance Defect Detection Method for Cigarettes Based on C-CenterNet
by Hongyu Liu, Guowu Yuan, Lei Yang, Kunxiao Liu and Hao Zhou
Electronics 2022, 11(14), 2182; https://doi.org/10.3390/electronics11142182 - 12 Jul 2022
Cited by 10 | Viewed by 2200
Abstract
Due to the poor adaptability of traditional methods in the cigarette detection task on the automatic cigarette production line, it is difficult to accurately identify whether a cigarette has defects and the types of defects; thus, a cigarette appearance defect detection method based [...] Read more.
Due to the poor adaptability of traditional methods in the cigarette detection task on the automatic cigarette production line, it is difficult to accurately identify whether a cigarette has defects and the types of defects; thus, a cigarette appearance defect detection method based on C-CenterNet is proposed. This detector uses keypoint estimation to locate center points and regresses all other defect properties. Firstly, Resnet50 is used as the backbone feature extraction network, and the convolutional block attention mechanism (CBAM) is introduced to enhance the network’s ability to extract effective features and reduce the interference of non-target information. At the same time, the feature pyramid network is used to enhance the feature extraction of each layer. Then, deformable convolution is used to replace part of the common convolution to enhance the learning ability of different shape defects. Finally, the activation function ACON (ActivateOrNot) is used instead of the ReLU activation function, and the activation operation of some neurons is adaptively selected to improve the detection accuracy of the network. The experimental results are mainly acquired via the mean Average Precision (mAP). The experimental results show that the mAP of the C-CenterNet model applied in the cigarette appearance defect detection task is 95.01%. Compared with the original CenterNet model, the model’s success rate is increased by 6.14%, so it can meet the requirements of precision and adaptability in cigarette detection tasks on the automatic cigarette production line. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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18 pages, 2227 KiB  
Article
Using Deep Learning Networks to Identify Cyber Attacks on Intrusion Detection for In-Vehicle Networks
by Hsiao-Chung Lin, Ping Wang, Kuo-Ming Chao, Wen-Hui Lin and Jia-Hong Chen
Electronics 2022, 11(14), 2180; https://doi.org/10.3390/electronics11142180 - 12 Jul 2022
Cited by 14 | Viewed by 3165
Abstract
With rapid advancements in in-vehicle network (IVN) technology, the demand for multiple advanced functions and networking in electric vehicles (EVs) has recently increased. To enable various intelligent functions, the electrical system of existing vehicles incorporates a controller area network (CAN) bus system that [...] Read more.
With rapid advancements in in-vehicle network (IVN) technology, the demand for multiple advanced functions and networking in electric vehicles (EVs) has recently increased. To enable various intelligent functions, the electrical system of existing vehicles incorporates a controller area network (CAN) bus system that enables communication among electrical control units (ECUs). In practice, traditional network-based intrusion detection systems (NIDSs) cannot easily identify threats to the CAN bus system. Therefore, it is necessary to develop a new type of NIDS—namely, on-the-move Intrusion Detection System (OMIDS)—to categorise these threats. Accordingly, this paper proposes an intrusion detection model for IVNs, based on the VGG16 classifier deep learning model, to learn attack behaviour characteristics and classify threats. The experimental dataset was provided by the Hacking and Countermeasure Research Lab (HCRL) to validate classification performance for denial of service (DoS), fuzzy attacks, spoofing gear, and RPM in vehicle communications. The proposed classifier’s performance was compared with that of the XBoost ensemble learning scheme to identify threats from in-vehicle networks. In particular, the test cases can detect anomalies in terms of accuracy, precision, recall, and F1-score to ensure detection accuracy and identify false alarm threats. The experimental results show that the classification accuracy of the dataset for HCRL Car-Hacking by the VGG16 and XBoost classifiers (n = 50) reached 97.8241% and 99.9995% for the 5-subcategory classification results on the testing data, respectively. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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19 pages, 3297 KiB  
Article
Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization
by Saad Gadal, Rania Mokhtar, Maha Abdelhaq, Raed Alsaqour, Elmustafa Sayed Ali and Rashid Saeed
Electronics 2022, 11(14), 2158; https://doi.org/10.3390/electronics11142158 - 10 Jul 2022
Cited by 22 | Viewed by 2960
Abstract
Recently, artificial intelligence (AI) techniques have been used to describe the characteristics of information, as they help in the process of data mining (DM) to analyze data and reveal rules and patterns. In DM, anomaly detection is an important area that helps discover [...] Read more.
Recently, artificial intelligence (AI) techniques have been used to describe the characteristics of information, as they help in the process of data mining (DM) to analyze data and reveal rules and patterns. In DM, anomaly detection is an important area that helps discover hidden behavior within the data that is most vulnerable to attack. It also helps detect network intrusion. Algorithms such as hybrid K-mean array and sequential minimal optimization (SMO) rating can be used to improve the accuracy of the anomaly detection rate. This paper presents an anomaly detection model based on the machine learning (ML) technique. ML improves the detection rate, reduces the false-positive alarm rate, and is capable of enhancing the accuracy of intrusion classification. This study used a dataset known as network security-knowledge and data discovery (NSL-KDD) lab to evaluate a proposed hybrid ML technology. K-mean cluster and SMO were used for classification. In the study, the performance of the proposed anomaly detection was tested, and results showed that the use of K-mean and SMO enhances the rate of positive detection besides reducing the rate of false alarms and achieving a high accuracy at the same time. Moreover, the proposed algorithm outperformed recent and close work related to using similar variables and the environment by 14.48% and decreased false alarm probability (FAP) by (12%) in addition to giving a higher accuracy by 97.4%. These outcomes are attributed to the common algorithm providing an appropriate number of detectors to be generated with an acceptable accurate detection and a trivial false alarm probability (FAP). The proposed hybrid algorithm could be considered for anomaly detection in future data mining systems, where processing in real-time is highly likely to be reduced dramatically. The justification is that the hybrid algorithm can provide appropriate detectors numbers that can be generated with an acceptable detection accuracy and trivial FAP. Given to the low FAP, it is highly expected to reduce the time of the preprocessing and processing compared with the other algorithms. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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43 pages, 10793 KiB  
Article
Automatic Classification of Hospital Settings through Artificial Intelligence
by Ernesto Iadanza, Giovanni Benincasa, Isabel Ventisette and Monica Gherardelli
Electronics 2022, 11(11), 1697; https://doi.org/10.3390/electronics11111697 - 26 May 2022
Cited by 3 | Viewed by 2232
Abstract
Modern hospitals have to meet requirements from national and international institutions in order to ensure hygiene, quality and organisational standards. Moreover, a hospital must be flexible and adaptable to new delivery models for healthcare services. Various hospital monitoring tools have been developed over [...] Read more.
Modern hospitals have to meet requirements from national and international institutions in order to ensure hygiene, quality and organisational standards. Moreover, a hospital must be flexible and adaptable to new delivery models for healthcare services. Various hospital monitoring tools have been developed over the years, which allow for a detailed picture of the effectiveness and efficiency of the hospital itself. Many of these systems are based on database management systems (DBMSs), building information modelling (BIM) or geographic information systems (GISs). This work presents an automatic recognition system for hospital settings that integrates these tools. Three alternative proposals were analysed in terms of the construction of the system: the first was based on the use of general models that are present on the cloud for the classification of images; the second consisted of the creation of a customised model and referred to the Clarifai Custom Model service; the third used an object recognition software that was developed by Facebook AI Research combined with a random forest classifier. The obtained results were promising. The customised model almost always classified the photos according to the correct intended use, resulting in a high percentage of confidence of up to 96%. Classification using the third tool was excellent when considering a limited number of hospital settings, with a peak accuracy of higher than 99% and an area under the ROC curve (AUC) of one for specific classes. As expected, increasing the number of room typologies to be discerned negatively affected performance. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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23 pages, 1301 KiB  
Article
Machine Learning Models for Early Prediction of Sepsis on Large Healthcare Datasets
by Javier Enrique Camacho-Cogollo, Isis Bonet, Bladimir Gil and Ernesto Iadanza
Electronics 2022, 11(9), 1507; https://doi.org/10.3390/electronics11091507 - 07 May 2022
Cited by 7 | Viewed by 4468
Abstract
Sepsis is a highly lethal syndrome with heterogeneous clinical manifestation that can be hard to identify and treat. Early diagnosis and appropriate treatment are critical to reduce mortality and promote survival in suspected cases and improve the outcomes. Several screening prediction systems have [...] Read more.
Sepsis is a highly lethal syndrome with heterogeneous clinical manifestation that can be hard to identify and treat. Early diagnosis and appropriate treatment are critical to reduce mortality and promote survival in suspected cases and improve the outcomes. Several screening prediction systems have been proposed for evaluating the early detection of patient deterioration, but the efficacy is still limited at individual level. The increasing amount and the versatility of healthcare data suggest implementing machine learning techniques to develop models for predicting sepsis. This work presents an experimental study of some machine-learning-based models for sepsis prediction considering vital signs, laboratory test results, and demographics using Medical Information Mart for Intensive Care III (MIMIC-III) (v1.4), a publicly available dataset. The experimental results demonstrate an overall higher performance of machine learning models over the commonly used Sequential Organ Failure Assessment (SOFA) and Quick SOFA (qSOFA) scoring systems at the time of sepsis onset. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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18 pages, 3632 KiB  
Article
Deep-Forest-Based Encrypted Malicious Traffic Detection
by Xueqin Zhang, Min Zhao, Jiyuan Wang, Shuang Li, Yue Zhou and Shinan Zhu
Electronics 2022, 11(7), 977; https://doi.org/10.3390/electronics11070977 - 22 Mar 2022
Cited by 7 | Viewed by 2185
Abstract
The SSL/TLS protocol is widely used in data encryption transmission. Aiming at the problem of detecting SSL/TLS-encrypted malicious traffic with small-scale and unbalanced training data, a deep-forest-based detection method called DF-IDS is proposed in this paper. According to the characteristics of SSL/TSL protocol, [...] Read more.
The SSL/TLS protocol is widely used in data encryption transmission. Aiming at the problem of detecting SSL/TLS-encrypted malicious traffic with small-scale and unbalanced training data, a deep-forest-based detection method called DF-IDS is proposed in this paper. According to the characteristics of SSL/TSL protocol, the network traffic was split into sessions according to the 5-tuple information. Each session was then transformed into a two-dimensional traffic image as the input of a deep-learning classifier. In order to avoid information loss and improve the detection efficiency, the multi-grained cascade forest (gcForest) framework was simplified with only cascade structure, which was named cascade forest (CaForest). By integrating random forest and extra trees in the CaForest framework, an end-to-end high-precision detector for small-scale and unbalanced SSL/TSL encrypted malicious traffic was realized. Compared with other deep-learning-based methods, the experimental results showed that the detection rate of DF-IDS was 6.87% to 29.5% higher than that of other methods on a small-scale and unbalanced dataset. The advantage of DF-IDS was more obvious in the multi-classification case. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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20 pages, 2773 KiB  
Article
Power Forecasting of Regional Wind Farms via Variational Auto-Encoder and Deep Hybrid Transfer Learning
by Mansoor Khan, Muhammad Rashid Naeem, Essam A. Al-Ammar, Wonsuk Ko, Hamsakutty Vettikalladi and Irfan Ahmad
Electronics 2022, 11(2), 206; https://doi.org/10.3390/electronics11020206 - 10 Jan 2022
Cited by 11 | Viewed by 2387
Abstract
Wind power is a sustainable green energy source. Power forecasting via deep learning is essential due to diverse wind behavior and uncertainty in geological and climatic conditions. However, the volatile, nonlinear and intermittent behavior of wind makes it difficult to design reliable forecasting [...] Read more.
Wind power is a sustainable green energy source. Power forecasting via deep learning is essential due to diverse wind behavior and uncertainty in geological and climatic conditions. However, the volatile, nonlinear and intermittent behavior of wind makes it difficult to design reliable forecasting models. This paper introduces a new approach using variational auto-encoding and hybrid transfer learning to forecast wind power for large-scale regional windfarms. Transfer learning is applied to windfarm data collections to boost model training. However, multiregional windfarms consist of different wind and weather conditions, which makes it difficult to apply transfer learning. Therefore, we propose a hybrid transfer learning method consisting of two feature spaces; the first was obtained from an already trained model, while the second, small feature set was obtained from a current windfarm for retraining. Finally, the hybrid transferred neural networks were fine-tuned for different windfarms to achieve precise power forecasting. A comparison with other state-of-the-art approaches revealed that the proposed method outperforms previous techniques, achieving a lower mean absolute error (MAE), i.e., between 0.010 to 0.044, and a lowest root mean square error (RMSE), i.e., between 0.085 to 0.159. The normalized MAE and RMSE was 0.020, and the accuracy losses were less than 5%. The overall performance showed that the proposed hybrid model offers maximum wind power forecasting accuracy with minimal error. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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Review

Jump to: Editorial, Research

34 pages, 6145 KiB  
Review
Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches
by Sadaf Safavi, Mehrdad Jalali and Mahboobeh Houshmand
Electronics 2022, 11(13), 1998; https://doi.org/10.3390/electronics11131998 - 26 Jun 2022
Cited by 6 | Viewed by 3909
Abstract
In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI [...] Read more.
In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI recommendation system combines different information sources and creates numerous research challenges and questions. New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than other methods. Despite all the obvious improvements that have been made recently, this field still does not have an updated and integrated view of the types of methods, their limitations, features, and future prospects. This paper provides a systematic review focusing on recent research on this topic. First, this approach prepares an overall view of the types of recommendation methods, their challenges, and the various influencing factors that can improve model performance in POI recommendations, then it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses. The recently proposed models are categorized according to the method used, the dataset, and the evaluation metrics. It found that these articles give priority to accuracy in comparison with other dimensions of quality. Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Deep Learning Application)
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