Special Issue "Computational Intelligence, Soft Computing and Communication Networks for Applied 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: 20 April 2020.

Special Issue Editor

Guest Editor
Prof. Dr. Jason K. Levy

Disaster Preparedness and Emergency Management, University of Hawaii, Kapolei, HI 96707, USA
Website | E-Mail
Interests: disaster risk governance; sustainable hazard mitigation; stochastic and statistical hydrology; sociohydrology; fluvial and marine disasters; global climate change, computational intelligence for water management; hydrologic resilience; process-based modeling of coupled human–water systems; inundation; economics of water resources management; drought

Special Issue Information

Dear Colleagues,

Based on their ability to capture the uncertainty, complexity, and stochastic nature of the underlying physical and sociopolitical processes, recent advances in artificial and computational intelligence have transformed the modeling and management of healthcare, environmental systems, and many fields in the applied sciences. Computational intelligence and soft computing approaches not only process large amounts of information historical data and/or data acquired via interaction with the environment but also continually learn through the consequences of action–result combinations. All aspects of communication systems and networks and computational intelligence will be considered in this Special Issue. Artificial intelligence and soft computing paradigms often leverage nature-inspired computational methodologies, including artificial neural networks (ANNs), fuzzy sets, and evolutionary algorithms (EA), including genetic algorithms (EA/GAs) and their hybridizations, such as neuro-fuzzy computing and neo-fuzzy systems. These systems have produced valuable, timely, robust, high-quality, and human-competitive results that have contributed to artificial intelligence research breakthroughs ranging from deep learning to genetic programming. Powerful computational intelligence and soft computing paradigms have recently been uncovered in numerous branches of soft systems science, including neural networks, swarm intelligence, expert systems, evolutionary computing, fuzzy systems, and artificial immune systems.

Prof. Dr. Jason K. Levy
Guest Editor

Manuscript Submission Information

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Keywords

  • Soft, mobile cloud-based computing for social networks
  • Data mining and big data analytics for applied science and engineering
  • Fuzzy system theory in health and environmental applications
  • Socioenvironmental data analytical approaches using computational methods
  • Deep learning and machine learning algorithms for industrial applications
  • Intelligent techniques for smart surveillance and security in public health systems
  • Crowd computing-assisted access control and digital rights management
  • Evolutionary algorithms for data analysis and recommendations
  • Crowd intelligence and computing paradigms
  • Computer vision and image processing and pattern recognition technologies healthcare
  • Parallel and distributed computing for smart healthcare services
  • Autonomous systems and industrial processes optimization
  • Extreme and intelligent manufacturing
  • Wireless and optical communications and networking
  • Parallel and distributed computing
  • Cloud computing and networks
  • Networked control systems and information security
  • Speech/image/video processing and communications 
  • Green computing and Internet of Things

Published Papers (4 papers)

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Research

Open AccessArticle
Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center
Appl. Sci. 2019, 9(16), 3223; https://doi.org/10.3390/app9163223
Received: 12 July 2019 / Revised: 4 August 2019 / Accepted: 5 August 2019 / Published: 7 August 2019
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Abstract
In data centers, cloud-based services are usually deployed among multiple virtual machines (VMs), and these VMs have data traffic dependencies on each other. However, traffic dependency between VMs has not been fully considered when the services running in the data center are expanded [...] Read more.
In data centers, cloud-based services are usually deployed among multiple virtual machines (VMs), and these VMs have data traffic dependencies on each other. However, traffic dependency between VMs has not been fully considered when the services running in the data center are expanded by creating additional VMs. If highly dependent VMs are placed in different physical machines (PMs), the data traffic increases in the underlying physical network of the data center. To reduce the amount of data traffic in the underlying network and improve the service performance, we propose a traffic-dependency-based strategy for VM placement in software-defined data center (SDDC). The traffic dependencies between the VMs are analyzed by principal component analysis, and highly dependent VMs are grouped by gravity-based clustering. Each group of highly dependent VMs is placed within an appropriate PM based on the Hungarian matching method. This strategy of dependency-based VM placement facilitates reducing data traffic volume of the data center, since the highly dependent VMs are placed within the same PM. The results of the performance evaluation in SDDC testbed indicate that the proposed VM placement method efficiently reduces the amount of data traffic in the underlying network and improves the data center performance. Full article
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Open AccessArticle
Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets
Appl. Sci. 2019, 9(14), 2875; https://doi.org/10.3390/app9142875
Received: 31 May 2019 / Revised: 10 July 2019 / Accepted: 14 July 2019 / Published: 18 July 2019
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Abstract
The isolation and observation of cardiomyocytes serve as the fundamental approach to cardiovascular research. The state-of-the-practice for the isolation and observation relies on manual operation of the entire culture process. Such a manual approach not only incurs high human errors, but also takes [...] Read more.
The isolation and observation of cardiomyocytes serve as the fundamental approach to cardiovascular research. The state-of-the-practice for the isolation and observation relies on manual operation of the entire culture process. Such a manual approach not only incurs high human errors, but also takes a long period of time. This paper proposes a new computer-aided paradigm to automatically, accurately, and efficiently perform the clustering and counting of cardiomyocytes, one of the key procedures for evaluating the success rate of cardiomyocytes isolation and the quality of culture medium. The key challenge of the proposed method lies in the unique, rod-like shape of cardiomyocytes, which has been hardly addressed in literature. Our proposed method employs a novel algorithm inspired by hesitant fuzzy sets and integrates an efficient implementation into the whole process of analyzing cardiomyocytes. The system, along with the data extracted from adult rats’ cardiomyocytes, has been experimentally evaluated with Matlab, showing promising results. The false accept rate (FAR) and the false reject rate (FRR) are as low as 1.46% and 1.97%, respectively. The accuracy rate is up to 98.7%—20% higher than the manual approach—and the processing time is reduced from tens of seconds to 3–5 s—an order of magnitude performance improvement. Full article
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Open AccessArticle
Performance Analysis of Feature Selection Methods in Software Defect Prediction: A Search Method Approach
Appl. Sci. 2019, 9(13), 2764; https://doi.org/10.3390/app9132764
Received: 26 April 2019 / Revised: 10 May 2019 / Accepted: 14 May 2019 / Published: 9 July 2019
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Abstract
Software Defect Prediction (SDP) models are built using software metrics derived from software systems. The quality of SDP models depends largely on the quality of software metrics (dataset) used to build the SDP models. High dimensionality is one of the data quality problems [...] Read more.
Software Defect Prediction (SDP) models are built using software metrics derived from software systems. The quality of SDP models depends largely on the quality of software metrics (dataset) used to build the SDP models. High dimensionality is one of the data quality problems that affect the performance of SDP models. Feature selection (FS) is a proven method for addressing the dimensionality problem. However, the choice of FS method for SDP is still a problem, as most of the empirical studies on FS methods for SDP produce contradictory and inconsistent quality outcomes. Those FS methods behave differently due to different underlining computational characteristics. This could be due to the choices of search methods used in FS because the impact of FS depends on the choice of search method. It is hence imperative to comparatively analyze the FS methods performance based on different search methods in SDP. In this paper, four filter feature ranking (FFR) and fourteen filter feature subset selection (FSS) methods were evaluated using four different classifiers over five software defect datasets obtained from the National Aeronautics and Space Administration (NASA) repository. The experimental analysis showed that the application of FS improves the predictive performance of classifiers and the performance of FS methods can vary across datasets and classifiers. In the FFR methods, Information Gain demonstrated the greatest improvements in the performance of the prediction models. In FSS methods, Consistency Feature Subset Selection based on Best First Search had the best influence on the prediction models. However, prediction models based on FFR proved to be more stable than those based on FSS methods. Hence, we conclude that FS methods improve the performance of SDP models, and that there is no single best FS method, as their performance varied according to datasets and the choice of the prediction model. However, we recommend the use of FFR methods as the prediction models based on FFR are more stable in terms of predictive performance. Full article
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Open AccessArticle
Fuzzy Logic Controller Parameter Optimization Using Metaheuristic Cuckoo Search Algorithm for a Magnetic Levitation System
Appl. Sci. 2019, 9(12), 2458; https://doi.org/10.3390/app9122458
Received: 18 April 2019 / Revised: 2 June 2019 / Accepted: 13 June 2019 / Published: 16 June 2019
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Abstract
The main benefits of fuzzy logic control (FLC) allow a qualitative knowledge of the desired system’s behavior to be included as IF-THEN linguistic rules for the control of dynamical systems where either an analytic model is not available or is too complex due, [...] Read more.
The main benefits of fuzzy logic control (FLC) allow a qualitative knowledge of the desired system’s behavior to be included as IF-THEN linguistic rules for the control of dynamical systems where either an analytic model is not available or is too complex due, for instance, to the presence of nonlinear terms. The computational structure requires the definition of the FLC parameters namely, membership functions (MF) and a rule base (RB) defining the desired control policy. However, the optimization of the FLC parameters is generally carried out by means of a trial and error procedure or, more recently by using metaheuristic nature-inspired algorithms, for instance, particle swarm optimization, genetic algorithms, ant colony optimization, cuckoo search, etc. In this regard, the cuckoo search (CS) algorithm as one of the most promising and relatively recent developed nature-inspired algorithms, has been used to optimize FLC parameters in a limited variety of applications to determine the optimum FLC parameters of only the MF but not to the RB, as an extensive search in the literature has shown. In this paper, an optimization procedure based on the CS algorithm is presented to optimize all the parameters of the FLC, including the RB, and it is applied to a nonlinear magnetic levitation system. Comparative simulation results are provided to validate the features improvement of such an approach which can be extended to other FLC based control systems. Full article
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