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AI and Quantum Computing for Big Data Analytics
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AI and Quantum Computing for Big Data Analytics

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Big Data, Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 May 2019) | Viewed by 12548

Special Issue Editors

Dr. Anand Paul

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
Interests: machine learning; digital twinning; self-supervised learning; smart city; data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Awais Ahmad

E-Mail Website
Guest Editor
College of Computer and Information Science, Imam Muhammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia
Interests: data fusion; data integration; big data analytics and wireless sensor network
Special Issues, Collections and Topics in MDPI journals
Dr. Ganeshkumar Pugalendhi

E-Mail Website
Guest Editor
Department of IT, Anna University, Coimbatore, Tamil Nadu, India
Interests: big medical data; big data for smart transportation

Special Issue Information

Dear Colleagues,

Now, sensor data is everywhere and it is important to gain meaningful insights from these data and also to save these data for future analyses. However, it is becoming difficult to apply computing techniques to these big data. With the help of AI (ML/ANN/DL), complex computation problems can be analyzed and done at greater speeds; for example, classification or clustering or prediction methods can be used on these large data sets to perform tasks at incredibly faster paces, especially with high-computing GPUs. We are almost approaching an era where there is no artificial intelligence without big data.

Real-time, rapid analysis are needed. This has propelled AI and machine learning and allowed the transition to a data-first approach. Quantum computing is going to play a vital role in the decades to come, as this computing mechanism can support massive data processing. Self-replicating AI create algorithms to solve complex big data problems quickly with the aid of ML, which could benefit quantum computing technology to leap forward to next BIG THING of 2020.

Prof. Anand Paul
Dr. Awais Ahmad
Dr. Ganeshkumar Pugalendhi
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. Journal of Sensor and Actuator Networks 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

  • big data analytics
  • artificial intelligence
  • quantum computing

Published Papers (2 papers)

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Research

16 pages, 721 KiB  
Article
Decision Making in Power Distribution System Reconfiguration by Blended Biased and Unbiased Weightage Method
by Sachin Gorakh Kamble, Kinhal Vadirajacharya and Udaykumar Vasudeo Patil
J. Sens. Actuator Netw. 2019, 8(2), 20; https://doi.org/10.3390/jsan8020020 - 12 Apr 2019
Cited by 6 | Viewed by 5543
Abstract
The term smart grid (SG) has been used by many government bodies and researchers to refer to the new trend in the power industry of modernizing and automating the existing power system. SGs must utilize assets optimally by making use of the information, [...] Read more.
The term smart grid (SG) has been used by many government bodies and researchers to refer to the new trend in the power industry of modernizing and automating the existing power system. SGs must utilize assets optimally by making use of the information, like equipment capacity, voltage drop, radial network structure, minimizing investment and operating costs, minimizing energy loss and reliability indices, and so on. One way to achieve this is to re-route or reconfigure distribution systems (DSs). Distribution systems are reconfigured to choose a switching combination of branches of the system that optimize certain performance parameters of the power supply, while satisfying some specified constraints. In this paper, a blended biased and unbiased weightage (BBUW) multiple attribute decision-making (MADM) method is proposed for finding the compromised best configuration and compared it with other decision-making methods, such as the weighted sum method (WSM), weighted product method (WPM), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The BBUW method is implemented for two distribution systems, and the result obtained shows a good co-relationship between BBUW and other decision-making methods. Further weights obtained from the BBUW method are used for the WSM, WPM and TOPSIS methods for decision making. Examples of the distribution system are worked out in this paper to demonstrate the validity and effectiveness of the method. Full article
(This article belongs to the Special Issue AI and Quantum Computing for Big Data Analytics)
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31 pages, 11602 KiB  
Article
Chaotic Quantum Double Delta Swarm Algorithm Using Chebyshev Maps: Theoretical Foundations, Performance Analyses and Convergence Issues
by Saptarshi Sengupta, Sanchita Basak and Richard Alan Peters II
J. Sens. Actuator Netw. 2019, 8(1), 9; https://doi.org/10.3390/jsan8010009 - 17 Jan 2019
Cited by 4 | Viewed by 6522
Abstract
The Quantum Double Delta Swarm (QDDS) Algorithm is a networked, fully-connected novel metaheuristic optimization algorithm inspired by the convergence mechanism to the center of potential generated within a single well of a spatially colocated double–delta well setup. It mimics the wave nature of [...] Read more.
The Quantum Double Delta Swarm (QDDS) Algorithm is a networked, fully-connected novel metaheuristic optimization algorithm inspired by the convergence mechanism to the center of potential generated within a single well of a spatially colocated double–delta well setup. It mimics the wave nature of candidate positions in solution spaces and draws upon quantum mechanical interpretations much like other quantum-inspired computational intelligence paradigms. In this work, we introduce a Chebyshev map driven chaotic perturbation in the optimization phase of the algorithm to diversify weights placed on contemporary and historical, socially-optimal agents’ solutions. We follow this up with a characterization of solution quality on a suite of 23 single–objective functions and carry out a comparative analysis with eight other related nature–inspired approaches. By comparing solution quality and successful runs over dynamic solution ranges, insights about the nature of convergence are obtained. A two-tailed t-test establishes the statistical significance of the solution data whereas Cohen’s d and Hedge’s g values provide a measure of effect sizes. We trace the trajectory of the fittest pseudo-agent over all iterations to comment on the dynamics of the system and prove that the proposed algorithm is theoretically globally convergent under the assumptions adopted for proofs of other closely-related random search algorithms. Full article
(This article belongs to the Special Issue AI and Quantum Computing for Big Data Analytics)
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