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Intelligent Big Data Processing
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Intelligent Big Data Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 March 2025) | Viewed by 10933

Special Issue Editors

Prof. Dr. Zhongnan Zhang

E-Mail Website
Guest Editor
School of Informatics, Xiamen University, Xiamen 361005, China
Interests: big data analysis; machine learning; bioinformatics
Dr. Tingxi Wen

E-Mail Website
Guest Editor
School of Engineering, Huaqiao University, Quanzhou 36200, China
Interests: edge computing; big data analytics; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of the Internet of things, cloud computing, neural networks and other technologies, information technology and political, economic, military, scientific research, life and other fields continue to cross-integrate, which has generated huge amounts of data beyond any previous era. Various websites, applications, mobile devices, etc., in different fields around the world are generating huge data traffic at all times, which has spurred and promoted the development of related industries relying on data, and has also presented severe challenges for data analysis and processing. In this regard, the intelligent analysis and processing of Big Data has become a research hotspot. Meanwhile, by combining computer science, data analytics, and biology, bioinformatics plays an increasingly important role in improving population health and advancing the healthcare industry. It is a great motivating force for biomedical engineering in the information age as it transforms accumulated data into information and knowledge by deeply mining the biological meaning of massive biomedical information. The Special Issue will publish the latest innovative and high-standard scientific research outcomes with sufficient scientific value in intelligent Big Data processing. These research outcomes describe the scientific research process, progress, and effects; discuss key technologies and problems in the research process; and explain the innovation and feasibility of the results.

Prof. Dr. Zhongnan Zhang
Dr. Tingxi Wen
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. Applied Sciences 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

  • big data
  • intelligent processing
  • internet of things
  • cloud computing
  • bioinformatics
  • biomedical engineering
  • neural network

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Published Papers (3 papers)

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Research

24 pages, 1909 KiB  
Article
Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
by Wangyong Guo, Shijin Liu, Liguo Weng and Xingyu Liang
Appl. Sci. 2025, 15(5), 2435; https://doi.org/10.3390/app15052435 - 24 Feb 2025
Cited by 1 | Viewed by 1648
Abstract
Optimizing short-term load forecasting performance is a challenge due to the non-linearity and randomness of electrical load, as well as the variability of system operating patterns. Existing methods often fail to consider how to effectively combine their complementary advantages and fail to fully [...] Read more.
Optimizing short-term load forecasting performance is a challenge due to the non-linearity and randomness of electrical load, as well as the variability of system operating patterns. Existing methods often fail to consider how to effectively combine their complementary advantages and fail to fully capture the internal information in the load sequence, leading to a decrease in accuracy. To achieve accurate and efficient short-term load forecasting, this study proposes a novel power grid load forecasting model that integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), Multi-Head Self-Attention Mechanism (MHSA), Global Attention Mechanism (GAM), and Channel Attention Mechanism (CAM) to achieve efficient and precise short-term load forecasting. This model aims to address the issue in traditional methods where complex temporal features and important information in power grid load data are not fully captured. Firstly, the CNN module is used to extract high-dimensional spatial features from the load data, and a pooling layer is applied to reduce dimensionality while retaining key information. Then, the Multi-Head Self-Attention mechanism is employed to model the long-range dependencies of the sequence data, enhancing the ability to extract temporal features. Next, the LSTM layer further captures the time dependencies in the load sequence. Subsequently, the Global Attention mechanism helps the model focus more on the most relevant parts of the input sequence, improving the model’s performance and generalization ability. The Channel Attention module is then applied to weight different feature channels, highlighting important information and reducing redundancy. Finally, the flattened output layer produces the forecast results. Experimental validation shows that the proposed CNN-MHSA-LSTM-GAM-CAM model outperforms existing mainstream methods in terms of load forecasting accuracy, providing effective support for the optimized scheduling of smart grids. Full article
(This article belongs to the Special Issue Intelligent Big Data Processing)
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16 pages, 4852 KiB  
Article
Efficient Future Waste Management: A Learning-Based Approach with Deep Neural Networks for Smart System (LADS)
by Ritu Chauhan, Sahil Shighra, Hatim Madkhali, Linh Nguyen and Mukesh Prasad
Appl. Sci. 2023, 13(7), 4140; https://doi.org/10.3390/app13074140 - 24 Mar 2023
Cited by 19 | Viewed by 5037
Abstract
Waste segregation, management, transportation, and disposal must be carefully managed to reduce the danger to patients, the public, and risks to the environment’s health and safety. The previous method of monitoring trash in strategically placed garbage bins is a time-consuming and inefficient method [...] Read more.
Waste segregation, management, transportation, and disposal must be carefully managed to reduce the danger to patients, the public, and risks to the environment’s health and safety. The previous method of monitoring trash in strategically placed garbage bins is a time-consuming and inefficient method that wastes time, human effort, and money, and is also incompatible with smart city needs. So, the goal is to reduce individual decision-making and increase the productivity of the waste categorization process. Using a convolutional neural network (CNN), the study sought to create an image classifier that recognizes items and classifies trash material. This paper provides an overview of trash monitoring methods, garbage disposal strategies, and the technology used in establishing a waste management system. Finally, an efficient system and waste disposal approach is provided that may be employed in the future to improve performance and cost effectiveness. One of the most significant barriers to efficient waste management can now be overcome with the aid of a deep learning technique. The proposed method outperformed the alternative AlexNet, VGG16, and ResNet34 methods. Full article
(This article belongs to the Special Issue Intelligent Big Data Processing)
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15 pages, 15125 KiB  
Article
Synthesizing 3D Gait Data with Personalized Walking Style and Appearance
by Yao Cheng, Guichao Zhang, Sifei Huang, Zexi Wang, Xuan Cheng and Juncong Lin
Appl. Sci. 2023, 13(4), 2084; https://doi.org/10.3390/app13042084 - 6 Feb 2023
Cited by 3 | Viewed by 3212
Abstract
Extracting gait biometrics from videos has been receiving rocketing attention given its applications, such as person re-identification. Although deep learning arises as a promising solution to improve the accuracy of most gait recognition algorithms, the lack of enough training data becomes a bottleneck. [...] Read more.
Extracting gait biometrics from videos has been receiving rocketing attention given its applications, such as person re-identification. Although deep learning arises as a promising solution to improve the accuracy of most gait recognition algorithms, the lack of enough training data becomes a bottleneck. One of the solutions to address data deficiency is to generate synthetic data. However, gait data synthesis is particularly challenging as the inter-subject and intra-subject variations of walking style need to be carefully balanced. In this paper, we propose a complete 3D framework to synthesize unlimited, realistic, and diverse motion data. In addition to walking speed and lighting conditions, we emphasize two key factors: 3D gait motion style and character appearance. Benefiting from its 3D nature, our system can provide various gait-related data, such as accelerometer data and depth map, not limited to silhouettes. We conducted various experiments using the off-the-shelf gait recognition algorithm and draw the following conclusions: (1) the real-to-virtual gap can be closed when adding a small portion of real-world data to a synthetically trained recognizer; (2) the amount of real training data needed to train competitive gait recognition systems can be reduced significantly; (3) the rich variations in gait data are helpful for investigating algorithm performance under different conditions. The synthetic data generator, as well as all experiments, will be made publicly available. Full article
(This article belongs to the Special Issue Intelligent Big Data Processing)
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