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Intelligent Computing for Big Data
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Intelligent Computing for Big Data

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 (31 August 2022) | Viewed by 24904

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Wei Wang

E-Mail Website
Guest Editor
Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
Interests: data and knowledge engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ka Lok Man

E-Mail Website1 Website2
Guest Editor
School of Advanced Technology, Xi’an Jiaotong Liverpool University, Suzhou 215123, China
Interests: AI; IoT; big data analytics; IC designs; WSN
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research on big data processing and analytics has achieved considerable success in the past decades. Nevertheless, the promise of allowing the extraction of valuable information and trustworthy knowledge from a tremendous amount of data of various forms and modalities has yet come true. Recent advances in artificial intelligence research have the potential to move the current big data research one step further. A number of AI techniques, especially deep learning, has achieved breakthroughs in many applications relating to natural language processing, image and video data processing, and multi-modal data fusion. However, there are still many challenges in intelligent computing with big data, most of which arise due to the nature of big data, for example, noise (social media data), various modalities (Internet of Things data), and unlabelled or limited amounts of labelled data.

This Special Issue will consist of selected excellent papers from the 2021 4th International Conference on Computing and Big Data (ICCBD 2021), which will be held in China, on 27-29 November 2021. Contributors will be invited to submit and present papers in a wide variety of areas from concepts to applications. Related submissions outside the conference are also very welcome.

The goal of this Special Issue is to solicit high-quality, original research papers on the following topics, but not limited to them:

  • Intelligent computing from Internet of Things data;
  • Intelligent computing from social media data;
  • Intelligent computing for unlabelled big data;
  • Deep learning in big data applications;
  • Knowledge graph in big data systems;
  • Search, retrieval, recommendation and summarisation in large knowledge graphs and linked data;
  • Big data applications in government, healthcare, bioinformatics, and business;
  • Privacy preserving in big data analytics;
  • Evaluation methods for big data analytics;
  • Security, trust and privacy in big data;
  • Big Data modelling, storage, indexing, searching and querying;
  • Cloud technologies for big data and intelligent computing;
  • Blockchain technologies for big data and intelligent computing.

Prof. Dr. Wei Wang
Dr. Ka Lok Man
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.

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Published Papers (6 papers)

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Editorial

Jump to: Research

2 pages, 155 KiB  
Editorial
Special Issue on Intelligent Computing for Big Data
by Wei Wang and Ka Lok Man
Appl. Sci. 2022, 12(21), 11106; https://doi.org/10.3390/app122111106 - 2 Nov 2022
Viewed by 1116
Abstract
Passion for a classic research area of computer science, artificial intelligence (AI), has experienced new momentum in recent years [...] Full article
(This article belongs to the Special Issue Intelligent Computing for Big Data)

Research

Jump to: Editorial

20 pages, 413 KiB  
Article
Proxy Re-Encryption Scheme for Decentralized Storage Networks
by Jia Kan, Jie Zhang, Dawei Liu and Xin Huang
Appl. Sci. 2022, 12(9), 4260; https://doi.org/10.3390/app12094260 - 22 Apr 2022
Cited by 14 | Viewed by 6340
Abstract
Storage is a promising application for permission-less blockchains. Before blockchain, cloud storage was hosted by a trusted service provider. The centralized system controls the permission of the data access. In web3, users own their data. Data must be encrypted in a permission-less decentralized [...] Read more.
Storage is a promising application for permission-less blockchains. Before blockchain, cloud storage was hosted by a trusted service provider. The centralized system controls the permission of the data access. In web3, users own their data. Data must be encrypted in a permission-less decentralized storage network, and the permission control should be pure cryptographic. Proxy re-encryption (PRE) is ideal for cryptographic access control, which allows a proxy to transfer Alice’s ciphertext to Bob with Alice’s authorization. The encrypted data are stored in several copies for redundancy in a permission-less decentralized storage network. The redundancy suffers from the outsourcing attack. The malicious resource provider may fetch the content from others and respond to the verifiers. This harms data integrity security. Thus, proof-of-replication (PoRep) must be applied to convince the user that the storage provider is using dedicated storage. PoRep is an expensive operation that encodes the original content into a replication. Existing PRE schemes cannot satisfy PoRep, as the cryptographic permission granting generates an extra ciphertext. A new ciphertext would result in several expensive replication operations. We searched most of the PRE schemes for the combination of the cryptographic methods to avoid transforming the ciphertext. Therefore, we propose a new PRE scheme. The proposed scheme does not require the proxy to transfer the ciphertext into a new one. It reduces the computation and operation time when allowing a new user to access a file. Furthermore, the PRE scheme is CCA (chosen-ciphertext attack) security and only needs one key pair. Full article
(This article belongs to the Special Issue Intelligent Computing for Big Data)
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14 pages, 2641 KiB  
Article
A Convolution Neural Network-Based Representative Spatio-Temporal Documents Classification for Big Text Data
by Byoungwook Kim, Yeongwook Yang, Ji Su Park and Hong-Jun Jang
Appl. Sci. 2022, 12(8), 3843; https://doi.org/10.3390/app12083843 - 11 Apr 2022
Cited by 4 | Viewed by 1799
Abstract
With the proliferation of mobile devices, the amount of social media users and online news articles are rapidly increasing, and text information online is accumulating as big data. As spatio-temporal information becomes more important, research on extracting spatiotemporal information from online text data [...] Read more.
With the proliferation of mobile devices, the amount of social media users and online news articles are rapidly increasing, and text information online is accumulating as big data. As spatio-temporal information becomes more important, research on extracting spatiotemporal information from online text data and utilizing it for event analysis is being actively conducted. However, if spatiotemporal information that does not describe the core subject of a document is extracted, it is rather difficult to guarantee the accuracy of core event analysis. Therefore, it is important to extract spatiotemporal information that describes the core topic of a document. In this study, spatio-temporal information describing the core topic of a document is defined as ‘representative spatio-temporal information’, and documents containing representative spatiotemporal information are defined as ‘representative spatio-temporal documents’. We proposed a character-level Convolution Neuron Network (CNN)-based document classifier to classify representative spatio-temporal documents. To train the proposed CNN model, 7400 training data were constructed for representative spatio-temporal documents. The experimental results show that the proposed CNN model outperforms traditional machine learning classifiers and existing CNN-based classifiers. Full article
(This article belongs to the Special Issue Intelligent Computing for Big Data)
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22 pages, 3508 KiB  
Article
Efficient Diagnosis of Autism with Optimized Machine Learning Models: An Experimental Analysis on Genetic and Personal Characteristic Datasets
by Maraheb Alsuliman and Heyam H. Al-Baity
Appl. Sci. 2022, 12(8), 3812; https://doi.org/10.3390/app12083812 - 10 Apr 2022
Cited by 9 | Viewed by 3007
Abstract
Early diagnosis of autism is extremely beneficial for patients. Traditional diagnosis approaches have been unable to diagnose autism in a fast and accurate way; rather, there are multiple factors that can be related to identifying the autism disorder. The gene expression (GE) of [...] Read more.
Early diagnosis of autism is extremely beneficial for patients. Traditional diagnosis approaches have been unable to diagnose autism in a fast and accurate way; rather, there are multiple factors that can be related to identifying the autism disorder. The gene expression (GE) of individuals may be one of these factors, in addition to personal and behavioral characteristics (PBC). Machine learning (ML) based on PBC and GE data analytics emphasizes the need to develop accurate prediction models. The quality of prediction relies on the accuracy of the ML model. To improve the accuracy of prediction, optimized feature selection algorithms are applied to solve the high dimensionality problem of the datasets used. Comparing different optimized feature selection methods using bio-inspired algorithms over different types of data can allow for the most accurate model to be identified. Therefore, in this paper, we investigated enhancing the classification process of autism spectrum disorder using 16 proposed optimized ML models (GWO-NB, GWO-SVM, GWO-KNN, GWO-DT, FPA-NB, FPA-KNN, FPA-SVM, FPA-DT, BA-NB, BA-SVM, BA-KNN, BA-DT, ABC-NB, ABC-SVM, ABV-KNN, and ABC-DT). Four bio-inspired algorithms namely, Gray Wolf Optimization (GWO), Flower Pollination Algorithm (FPA), Bat Algorithms (BA), and Artificial Bee Colony (ABC), were employed for optimizing the wrapper feature selection method in order to select the most informative features and to increase the accuracy of the classification models. Five evaluation metrics were used to evaluate the performance of the proposed models: accuracy, F1 score, precision, recall, and area under the curve (AUC). The obtained results demonstrated that the proposed models achieved a good performance as expected, with accuracies of 99.66% and 99.34% obtained by the GWO-SVM model on the PBC and GE datasets, respectively. Full article
(This article belongs to the Special Issue Intelligent Computing for Big Data)
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14 pages, 10115 KiB  
Article
A Smart-Mutual Decentralized System for Long-Term Care
by Hsien-Ming Chou
Appl. Sci. 2022, 12(7), 3664; https://doi.org/10.3390/app12073664 - 6 Apr 2022
Cited by 7 | Viewed by 2439
Abstract
Existing caretakers of long-term care are assigned constrainedly and randomly to taking care of older people, which could lead to issues of shortage of manpower and poor human quality, especially the proportion of older people increases year after year to let long-term care [...] Read more.
Existing caretakers of long-term care are assigned constrainedly and randomly to taking care of older people, which could lead to issues of shortage of manpower and poor human quality, especially the proportion of older people increases year after year to let long-term care become more and more important. In addition, due to different backgrounds, inadequate caregivers may cause older people to suffer from spiritual alienation under the current system. Most of the existing studies present a centralized architecture, but even if technology elements are incorporated, such as cloud center services or expert systems, it is still impossible to solve the above-mentioned challenges. This study moves past the centralized architecture and attempts to use the decentralized architecture with Artificial Intelligence and Blockchain technology to refine the model of providing comprehensive care for older people. Using the proposed mapping mutual clustering algorithm in this study, the positions of caregivers and older people can be changed at any time based on the four main background elements: risk level, physiology, medical record, and demography. In addition, this study uses the proposed long-term care decentralized architecture algorithm to solve the stability of care records with transparency to achieve the effect of continuous tracking. Based on previous records, it can also dynamically change the new matching mode. The main contribution of this research is the proposal of an innovative solution to the problem of mental alienation, insufficient manpower, and the privacy issue. In addition, this study evaluates the proposed method through practical experiments. The corporation features have been offered and evaluated with user perceptions by a one-sample t-test; the proposed algorithm to the research model also has been compared with not putting it into the model through ANOVA analysis to get that all hypotheses are supported. The results reveal a high level of accuracy of the proposed mutual algorithm forecasting and positive user perceptions from the post-study questionnaire. As an emerging research topic, this study undoubtedly provides an important research basis for scholars and experts who are interested in continued related research in the future. Full article
(This article belongs to the Special Issue Intelligent Computing for Big Data)
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17 pages, 3912 KiB  
Article
Dog Behavior Recognition Based on Multimodal Data from a Camera and Wearable Device
by Jinah Kim and Nammee Moon
Appl. Sci. 2022, 12(6), 3199; https://doi.org/10.3390/app12063199 - 21 Mar 2022
Cited by 22 | Viewed by 8693
Abstract
Although various studies on monitoring dog behavior have been conducted, methods that can minimize or compensate data noise are required. This paper proposes multimodal data-based dog behavior recognition that fuses video and sensor data using a camera and a wearable device. The video [...] Read more.
Although various studies on monitoring dog behavior have been conducted, methods that can minimize or compensate data noise are required. This paper proposes multimodal data-based dog behavior recognition that fuses video and sensor data using a camera and a wearable device. The video data represent the moving area of dogs to detect the dogs. The sensor data represent the movement of the dogs and extract features that affect dog behavior recognition. Seven types of behavior recognition were conducted, and the results of the two data types were used to recognize the dog’s behavior through a fusion model based on deep learning. Experimentation determined that, among FasterRCNN, YOLOv3, and YOLOv4, the object detection rate and behavior recognition accuracy were the highest when YOLOv4 was used. In addition, the sensor data showed the best performance when all statistical features were selected. Finally, it was confirmed that the performance of multimodal data-based fusion models was improved over that of single data-based models and that the CNN-LSTM-based model had the best performance. The method presented in this study can be applied for dog treatment or health monitoring, and it is expected to provide a simple way to estimate the amount of activity. Full article
(This article belongs to the Special Issue Intelligent Computing for Big Data)
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