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Electronics
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Deep Learning for Big Data Processing
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Deep Learning for Big Data Processing

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

Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 8211

Special Issue Editors

Prof. Dr. Qingli Li

E-Mail Website
Guest Editor
Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200050, China
Interests: optical imaging; medical imaging; image processing; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yan Wang

E-Mail Website
Guest Editor
School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
Interests: computer vision;medical imaging;machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The fast-growing data exhibit heterogenous, large-scale, multi-task and multi-source natures. Traditional data processing techniques have several limitations in processing large amounts of complex data. Deep learning has been a ubiquitous tool in various research fields, such as natural language processing, computer vision, biomedical engineering and informatics. On one hand, it plays an important role in big data analytic and solutions. On the other hand, it usually requires large scale fine annotated datasets for training. To reduce human labor of labeling data, numerous efforts have been played in recent years, where weakly supervised and self-supervised methods are emerging. Moreover, more people are trying to give insights on the dimensional decomposability and algorithmic transparency of deep learning methods, which offer interpretability for understanding what deep networks have learned from the big data.

This Special Issue aims to study 1) how to build supervised/weakly supervised/self-supervised deep learning models, which leverage large variety, large velocity and large veracity representation learning, 2) how to conduct rigorous empirical investigation of different deep learning methods across a variety of tasks, including, but not limited to, recognition, detection, biomedical imaging, biomedical signal processing and analysis, 3) how to improve the interpretability of deep learning algorithms with regard to human-understandable justifications or insights about the inner workings, and 4) how to create large-scale datasets for algorithms research and real world applications development.

Prof. Dr. Qingli Li
Prof. Dr. Yan Wang
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

  • deep networks
  • deep learning architectures/losses in self/semi/weakly supervised learning
  • CNN/RNN/transformer
  • interpretable deep learning
  • large-scale datasets

Published Papers (4 papers)

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Research

24 pages, 1458 KiB  
Article
Intelligent Risk Prediction System in IoT-Based Supply Chain Management in Logistics Sector
by Ahmed Alzahrani and Muhammad Zubair Asghar
Electronics 2023, 12(13), 2760; https://doi.org/10.3390/electronics12132760 - 21 Jun 2023
Cited by 6 | Viewed by 1513
Abstract
The Internet of Things (IoT) has resulted in substantial advances in the logistics sector, particularly in logistics storage management, communication systems, service quality, and supply chain management. The goal of this study is to create an intelligent supply chain (SC) management system that [...] Read more.
The Internet of Things (IoT) has resulted in substantial advances in the logistics sector, particularly in logistics storage management, communication systems, service quality, and supply chain management. The goal of this study is to create an intelligent supply chain (SC) management system that provides decision support to SC managers in order to achieve effective Internet of Things (IOT)-based logistics. Current research on predicting risks in shipping operations in the logistics sector during natural disasters has produced a variety of unexpected findings utilizing machine learning (ML) algorithms and traditional feature-encoding approaches. This has prompted a variety of concerns regarding the research’s validity. These previous attempts, like many others before them, used deep neural models to gain features without requiring the user to maintain track of all of the sequence information. This paper offers a hybrid deep learning (DL) approach, convolutional neural network (CNN) + bidirectional gating recurrent unit (BiGRU), to lessen the impact of natural disasters on shipping operations by addressing the question, “Can goods be shipped from a source location to a destination?”. The suggested DL methodology is divided into four stages: data collection, de-noising or pre-processing, feature extraction, and prediction. When compared to the baseline work, the proposed CNN + BiGRU achieved an accuracy of up to 94%. Full article
(This article belongs to the Special Issue Deep Learning for Big Data Processing)
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12 pages, 6869 KiB  
Article
License Plate Detection with Attention-Guided Dual Feature Pyramid Networks in Complex Environments
by Yu-Jie Xiong, Yong-Bin Gao, Jun-Qing Zhang and Jian-Xin Ren
Electronics 2022, 11(23), 3895; https://doi.org/10.3390/electronics11233895 - 25 Nov 2022
Viewed by 1284
Abstract
License plate detection plays a significant role in intelligent transportation systems. Convolutional neural networks have shown a remarkable performance and made significant progress for the detection task. Despite these outstanding achievements, license plate detection in complex environments is still a challenging task, due [...] Read more.
License plate detection plays a significant role in intelligent transportation systems. Convolutional neural networks have shown a remarkable performance and made significant progress for the detection task. Despite these outstanding achievements, license plate detection in complex environments is still a challenging task, due to the noisy background, unpredictable environments and varying shapes and sizes of the license plates. In order to improve the performance of license plate detection in complex environments, we propose a novel approach using an attention-guided dual feature pyramid and a cascaded positioning strategy. At first, the original features of images are extracted by the residual network. In order to make sure that each feature map contains higher- and lower-level semantic information, we utilize a bottom-up and a top-down pathway, respectively. Meanwhile, the proposed attention-guided dual feature pyramid network is used to receive the extracted features for a multilevel feature fusion. Our proposed attention-guided modules contain both spatial and channel attention. Attention-guided modules deduce the attention weights according to channel and spatial dimensions and multiply the calculated result with the input to obtain the refined feature maps. Then, a region proposal network is used to generate the candidate regions for the license plates. Finally, a cascaded positioning network is utilized to obtain the final locations of the license plates. To validate the effectiveness of the proposed method, we conducted a series of experiments on different public datasets. Experiments on AOLP and CCPD validated the effectiveness of our proposed method. Full article
(This article belongs to the Special Issue Deep Learning for Big Data Processing)
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17 pages, 5544 KiB  
Article
Graph-Embedded Online Learning for Cell Detection and Tumour Proportion Score Estimation
by Jinhao Chen, Yuang Zhu and Zhao Chen
Electronics 2022, 11(10), 1642; https://doi.org/10.3390/electronics11101642 - 21 May 2022
Viewed by 1794
Abstract
Cell detection in microscopy images can provide useful clinical information. Most methods based on deep learning for cell detection are fully supervised. Without enough labelled samples, the accuracy of these methods would drop rapidly. To handle limited annotations and massive unlabelled data, semi-supervised [...] Read more.
Cell detection in microscopy images can provide useful clinical information. Most methods based on deep learning for cell detection are fully supervised. Without enough labelled samples, the accuracy of these methods would drop rapidly. To handle limited annotations and massive unlabelled data, semi-supervised learning methods have been developed. However, many of these are trained off-line, and are unable to process new incoming data to meet the needs of clinical diagnosis. Therefore, we propose a novel graph-embedded online learning network (GeoNet) for cell detection. It can locate and classify cells with dot annotations, saving considerable manpower. Trained by both historical data and reliable new samples, the online network can predict nuclear locations for upcoming new images while being optimized. To be more easily adapted to open data, it engages dynamic graph regularization and learns the inherent nonlinear structures of cells. Moreover, GeoNet can be applied to downstream tasks such as quantitative estimation of tumour proportion score (TPS), which is a useful indicator for lung squamous cell carcinoma treatment and prognostics. Experimental results for five large datasets with great variability in cell type and morphology validate the effectiveness and generalizability of the proposed method. For the lung squamous cell carcinoma (LUSC) dataset, the detection F1-scores of GeoNet for negative and positive tumour cells are 0.734 and 0.769, respectively, and the relative error of GeoNet for TPS estimation is 11.1%. Full article
(This article belongs to the Special Issue Deep Learning for Big Data Processing)
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24 pages, 7338 KiB  
Article
Unsupervised and Self-Supervised Tensor Train for Change Detection in Multitemporal Hyperspectral Images
by Muhammad Sohail, Haonan Wu, Zhao Chen and Guohua Liu
Electronics 2022, 11(9), 1486; https://doi.org/10.3390/electronics11091486 - 6 May 2022
Cited by 3 | Viewed by 2557
Abstract
Remote sensing change detection (CD) using multitemporal hyperspectral images (HSIs) provides detailed information on spectral–spatial changes and is useful in a variety of applications such as environmental monitoring, urban planning, and disaster detection. However, the high dimensionality and low spatial resolution of HSIs [...] Read more.
Remote sensing change detection (CD) using multitemporal hyperspectral images (HSIs) provides detailed information on spectral–spatial changes and is useful in a variety of applications such as environmental monitoring, urban planning, and disaster detection. However, the high dimensionality and low spatial resolution of HSIs do not only lead to expensive computation but also bring about inter-class homogeneity and inner-class heterogeneity. Meanwhile, labeled samples are difficult to obtain in reality as field investigation is expensive, which limits the application of supervised CD methods. In this paper, two algorithms for CD based on the tensor train (TT) decomposition are proposed and are called the unsupervised tensor train (UTT) and self-supervised tensor train (STT). TT uses a well-balanced matricization strategy to capture global correlations from tensors and can therefore effectively extract low-rank discriminative features, so the curse of the dimensionality and spectral variability of HSIs can be overcome. In addition, the two proposed methods are based on unsupervised and self-supervised learning, where no manual annotations are needed. Meanwhile, the ket-augmentation (KA) scheme is used to transform the low-order tensor into a high-order tensor while keeping the total number of entries the same. Therefore, high-order features with richer texture can be extracted without increasing computational complexity. Experimental results on four benchmark datasets show that the proposed methods outperformed their tensor counterpart, the tucker decomposition (TD), the higher-order singular value decomposition (HOSVD), and some other state-of-the-art approaches. For the Yancheng dataset, OA and KAPPA of UTT reached as high as 98.11% and 0.9536, respectively, while OA and KAPPA of STT were at 98.20% and 0.9561, respectively. Full article
(This article belongs to the Special Issue Deep Learning for Big Data Processing)
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