Special Issue "Integrated Artificial Intelligence in Data Science"

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

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editors

Prof. Dr. Jerry Chun-Wei Lin
E-Mail Website
Guest Editor
Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway
Interests: AI and machine learning; data analytics; optimization; soft computing
Special Issues, Collections and Topics in MDPI journals
Dr. Stefania Tomasiello
E-Mail
Guest Editor
Institute of Computer Science, University of Tartu, Narva mnt 18, 50090 Tartu, Estonia
Interests: soft computing; machine learning; dynamical systems and control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gautam Srivastava
E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, Brandon University, Brandon, MB R7A 6A9, Canada
Interests: blockchain technology; cryptography; big data; data mining; social networks; security and privacy; anonymity; graphs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is an emerging research topic since it can be used to solve high-complexity problems and find optimized solutions in many applications and domains. Thus, it has the potential to create a better society. The benefits of AI in science, medicine, technology, and the social sciences have already been shown. Data science, also referred to as pattern analytics and mining, can be used to retrieve useful and meaningful information from databases, which helps to efficiently make decisions and build strategies in different domains. In particular, as a result of the exponential growth of data in recent years, the dual concept of big data and AI has given rise to many research topics, such as scale-up behavior from classical algorithms.

In addition, a recent challenge is the integration of multiple AI technologies, emerging from different fields (e.g., vision, security, control, bioinformatics), in order to develop efficient and robust systems that interact in the real world. In spite of the tremendous progress in core AI technologies in recent years, the integration of such capabilities into larger systems that are reliable, transparent, and maintainable is still in its infancy. There are numerous open issues both from a theoretical and practical perspective.

Topics of interest include, but are not restricted to, the following areas:

  • Data analytics using AI techniques;
  • Evolutionary computation in big datasets;
  • Data-driven AI systems;
  • Machine learning algorithms;
  • Fuzzy modeling and uncertain systems;
  • Data reduction techniques;
  • Deep-learning algorithms in big datasets;
  • Information granularity in high-dimensional data;
  • Pattern mining by machine learning and optimization techniques;
  • Neural network data analytics and prediction;
  • AI-based applications in data science.
Prof. Jerry Chun-Wei Lin
Dr. Stefania Tomasiello
Dr. Gautam Srivastava
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 papers will be 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 2000 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

  • AI
  • data-driven analytics
  • machine learning
  • optimization
  • deep learning

Published Papers (6 papers)

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Research

Article
An Improved VGG16 Model for Pneumonia Image Classification
Appl. Sci. 2021, 11(23), 11185; https://doi.org/10.3390/app112311185 - 25 Nov 2021
Viewed by 145
Abstract
Image recognition has been applied to many fields, but it is relatively rarely applied to medical images. Recent significant deep learning progress for image recognition has raised strong research interest in medical image recognition. First of all, we found the prediction result using [...] Read more.
Image recognition has been applied to many fields, but it is relatively rarely applied to medical images. Recent significant deep learning progress for image recognition has raised strong research interest in medical image recognition. First of all, we found the prediction result using the VGG16 model on failed pneumonia X-ray images. Thus, this paper proposes IVGG13 (Improved Visual Geometry Group-13), a modified VGG16 model for classification pneumonia X-rays images. Open-source thoracic X-ray images acquired from the Kaggle platform were employed for pneumonia recognition, but only a few data were obtained, and datasets were unbalanced after classification, either of which can result in extremely poor recognition from trained neural network models. Therefore, we applied augmentation pre-processing to compensate for low data volume and poorly balanced datasets. The original datasets without data augmentation were trained using the proposed and some well-known convolutional neural networks, such as LeNet AlexNet, GoogLeNet and VGG16. In the experimental results, the recognition rates and other evaluation criteria, such as precision, recall and f-measure, were evaluated for each model. This process was repeated for augmented and balanced datasets, with greatly improved metrics such as precision, recall and F1-measure. The proposed IVGG13 model produced superior outcomes with the F1-measure compared with the current best practice convolutional neural networks for medical image recognition, confirming data augmentation effectively improved model accuracy. Full article
(This article belongs to the Special Issue Integrated Artificial Intelligence in Data Science)
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Article
SLA-DQTS: SLA Constrained Adaptive Online Task Scheduling Based on DDQN in Cloud Computing
Appl. Sci. 2021, 11(20), 9360; https://doi.org/10.3390/app11209360 - 09 Oct 2021
Viewed by 400
Abstract
Task scheduling is key to performance optimization and resource management in cloud computing systems. Because of its complexity, it has been defined as an NP problem. We introduce an online scheme to solve the problem of task scheduling under a dynamic load in [...] Read more.
Task scheduling is key to performance optimization and resource management in cloud computing systems. Because of its complexity, it has been defined as an NP problem. We introduce an online scheme to solve the problem of task scheduling under a dynamic load in the cloud environment. After analyzing the process, we propose a server level agreement constraint adaptive online task scheduling algorithm based on double deep Q-learning (SLA-DQTS) to reduce the makespan, cost, and average overdue time under the constraints of virtual machine (VM) resources and deadlines. In the algorithm, we prevent the change of the model input dimension with the number of VMs by taking the Gaussian distribution of related parameters as a part of the state space. Through the design of the reward function, the model can be optimized for different goals and task loads. We evaluate the performance of the algorithm by comparing it with three heuristic algorithms (Min-Min, random, and round robin) under different loads. The results show that the algorithm in this paper can achieve similar or better results than the comparison algorithms at a lower cost. Full article
(This article belongs to the Special Issue Integrated Artificial Intelligence in Data Science)
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Article
Variant of Data Particle Geometrical Divide for Imbalanced Data Sets Classification by the Example of Occupancy Detection
Appl. Sci. 2021, 11(11), 4970; https://doi.org/10.3390/app11114970 - 28 May 2021
Cited by 2 | Viewed by 619
Abstract
The history of gravitational classification started in 1977. Over the years, the gravitational approaches have reached many extensions, which were adapted into different classification problems. This article is the next stage of the research concerning the algorithms of creating data particles by their [...] Read more.
The history of gravitational classification started in 1977. Over the years, the gravitational approaches have reached many extensions, which were adapted into different classification problems. This article is the next stage of the research concerning the algorithms of creating data particles by their geometrical divide. In the previous analyses it was established that the Geometrical Divide (GD) method outperforms the algorithm creating the data particles based on classes by a compound of 1 ÷ 1 cardinality. This occurs in the process of balanced data sets classification, in which class centroids are close to each other and the groups of objects, described by different labels, overlap. The purpose of the article was to examine the efficiency of the Geometrical Divide method in the unbalanced data sets classification, by the example of real case-occupancy detecting. In addition, in the paper, the concept of the Unequal Geometrical Divide (UGD) was developed. The evaluation of approaches was conducted on 26 unbalanced data sets-16 with the features of Moons and Circles data sets and 10 created based on real occupancy data set. In the experiment, the GD method and its unbalanced variant (UGD) as well as the 1CT1P approach, were compared. Each method was combined with three data particle mass determination algorithms-n-Mass Model (n-MM), Stochastic Learning Algorithm (SLA) and Bath-update Algorithm (BLA). k-fold cross validation method, precision, recall, F-measure, and number of used data particles were applied in the evaluation process. Obtained results showed that the methods based on geometrical divide outperform the 1CT1P approach in the imbalanced data sets classification. The article’s conclusion describes the observations and indicates the potential directions of further research and development of methods, which concern creating the data particle through its geometrical divide. Full article
(This article belongs to the Special Issue Integrated Artificial Intelligence in Data Science)
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Article
A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization
Appl. Sci. 2021, 11(10), 4575; https://doi.org/10.3390/app11104575 - 17 May 2021
Cited by 1 | Viewed by 493
Abstract
This paper addresses group multi-objective optimization under a new perspective. For each point in the feasible decision set, satisfaction or dissatisfaction from each group member is determined by a multi-criteria ordinal classification approach, based on comparing solutions with a limiting boundary between classes [...] Read more.
This paper addresses group multi-objective optimization under a new perspective. For each point in the feasible decision set, satisfaction or dissatisfaction from each group member is determined by a multi-criteria ordinal classification approach, based on comparing solutions with a limiting boundary between classes “unsatisfactory” and “satisfactory”. The whole group satisfaction can be maximized, finding solutions as close as possible to the ideal consensus. The group moderator is in charge of making the final decision, finding the best compromise between the collective satisfaction and dissatisfaction. Imperfect information on values of objective functions, required and available resources, and decision model parameters are handled by using interval numbers. Two different kinds of multi-criteria decision models are considered: (i) an interval outranking approach and (ii) an interval weighted-sum value function. The proposal is more general than other approaches to group multi-objective optimization since (a) some (even all) objective values may be not the same for different DMs; (b) each group member may consider their own set of objective functions and constraints; (c) objective values may be imprecise or uncertain; (d) imperfect information on resources availability and requirements may be handled; (e) each group member may have their own perception about the availability of resources and the requirement of resources per activity. An important application of the new approach is collective multi-objective project portfolio optimization. This is illustrated by solving a real size group many-objective project portfolio optimization problem using evolutionary computation tools. Full article
(This article belongs to the Special Issue Integrated Artificial Intelligence in Data Science)
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Article
Natural Language Description of Videos for Smart Surveillance
Appl. Sci. 2021, 11(9), 3730; https://doi.org/10.3390/app11093730 - 21 Apr 2021
Cited by 1 | Viewed by 599
Abstract
After the September 11 attacks, security and surveillance measures have changed across the globe. Now, surveillance cameras are installed almost everywhere to monitor video footage. Though quite handy, these cameras produce videos in a massive size and volume. The major challenge faced by [...] Read more.
After the September 11 attacks, security and surveillance measures have changed across the globe. Now, surveillance cameras are installed almost everywhere to monitor video footage. Though quite handy, these cameras produce videos in a massive size and volume. The major challenge faced by security agencies is the effort of analyzing the surveillance video data collected and generated daily. Problems related to these videos are twofold: (1) understanding the contents of video streams, and (2) conversion of the video contents to condensed formats, such as textual interpretations and summaries, to save storage space. In this paper, we have proposed a video description framework on a surveillance dataset. This framework is based on the multitask learning of high-level features (HLFs) using a convolutional neural network (CNN) and natural language generation (NLG) through bidirectional recurrent networks. For each specific task, a parallel pipeline is derived from the base visual geometry group (VGG)-16 model. Tasks include scene recognition, action recognition, object recognition and human face specific feature recognition. Experimental results on the TRECViD, UET Video Surveillance (UETVS) and AGRIINTRUSION datasets depict that the model outperforms state-of-the-art methods by a METEOR (Metric for Evaluation of Translation with Explicit ORdering) score of 33.9%, 34.3%, and 31.2%, respectively. Our results show that our framework has distinct advantages over traditional rule-based models for the recognition and generation of natural language descriptions. Full article
(This article belongs to the Special Issue Integrated Artificial Intelligence in Data Science)
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Article
Improving Monte Carlo Tree Search with Artificial Neural Networks without Heuristics
Appl. Sci. 2021, 11(5), 2056; https://doi.org/10.3390/app11052056 - 25 Feb 2021
Cited by 1 | Viewed by 745
Abstract
Monte Carlo Tree Search is one of the main search methods studied presently. It has demonstrated its efficiency in the resolution of many games such as Go or Settlers of Catan and other different problems. There are several optimizations of Monte Carlo, but [...] Read more.
Monte Carlo Tree Search is one of the main search methods studied presently. It has demonstrated its efficiency in the resolution of many games such as Go or Settlers of Catan and other different problems. There are several optimizations of Monte Carlo, but most of them need heuristics or some domain language at some point, making very difficult its application to other problems. We propose a general and optimized implementation of Monte Carlo Tree Search using neural networks without extra knowledge of the problem. As an example of our proposal, we made use of the Dots and Boxes game. We tested it against other Monte Carlo system which implements specific knowledge for this problem. Our approach improves accuracy, reaching a winning rate of 81% over previous research but the generalization penalizes performance. Full article
(This article belongs to the Special Issue Integrated Artificial Intelligence in Data Science)
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