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Bio-Inspired Computing and Applications in Sensor Network
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Bio-Inspired Computing and Applications in Sensor Network

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 10939

Special Issue Editors

Prof. Dr. Ajith Abraham

E-Mail Website
Guest Editor
Machine Intelligence Research Labs (MIR Labs), 3rd Street NW, P.O. Box 2259, Auburn, WA 98071, USA
Interests: artificial intelligence; data mining; big data analytics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Artūras Kaklauskas

E-Mail Website
Guest Editor
Department of Construction Economics and Property Management, Vilniaus Gedimino Technikos Universitetas, Vilnius, Lithuania
Interests: civil engineering; multiple criteria; decision making; construction engineering and management; decision support systems
Dr. Ana M. Madureira

E-Mail Website
Guest Editor
School of Engineering, Polytechnic of Porto (ISEP/IPP), 4200-072 Porto, Portugal
Interests: artificial intelligence; metaheuristics; user modeling; dynamic scheduling; data science
Special Issues, Collections and Topics in MDPI journals
Dr. Laura Garcia-Hernandez

E-Mail Website
Guest Editor
Department of Rural Engineering, University of Córdoba, Av. de Medina Azahara, 5, 14071 Córdoba, Spain
Interests: UA-FLP; evolutionary algorithms; engineering education; project management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

IBICA'21 is the 12th International Conference on Innovations in Bio-Inspired Computing and Applications. The aim of IBICA is to provide a platform for world research leaders and practitioners, to discuss the “full spectrum” of current theoretical developments, emerging technologies, and innovative applications of Bio-inspired Computing. Bio-inspired Computing is currently one of the most exciting research areas, and it is continuously demonstrating exceptional strength in solving complex real life problems. The main driving force of the conference is to further explore the intriguing potential of Bio-inspired Computing.  This special issue will focus on this field and collect the conference extensions or other papers under this topic. Topic areas include, but are not limited to, the following: Topic areas include, but are not limited to, the following:

Bio-inspired Computing for Cloud Computing

Bio-inspired Computing for Data Mining and Knowledge Discovery

Bio-inspired Computing for Wireless/Sensor Networks

Distributed frameworks and middleware for the Internet of Things

Agent-based wireless sensor networks

Context-aware intelligent computing

Virtualization infrastructures for intelligent computing

Prof. Dr. Ajith Abraham
Prof. Dr. Artūras Kaklauskas
Prof. Dr. Ana M. Madureira
Dr. Laura Garcia-Hernandez 
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. Sensors 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 2600 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 (3 papers)

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Research

25 pages, 6195 KiB  
Article
Deep Neural Networks Applied to Stock Market Sentiment Analysis
by Filipe Correia, Ana Maria Madureira and Jorge Bernardino
Sensors 2022, 22(12), 4409; https://doi.org/10.3390/s22124409 - 10 Jun 2022
Cited by 10 | Viewed by 4559
Abstract
The volume of data is growing exponentially and becoming more valuable to organizations that collect it, from e-commerce data, shipping, audio and video logs, text messages, internet search queries, stock market activity, financial transactions, the Internet of Things, and various other sources. The [...] Read more.
The volume of data is growing exponentially and becoming more valuable to organizations that collect it, from e-commerce data, shipping, audio and video logs, text messages, internet search queries, stock market activity, financial transactions, the Internet of Things, and various other sources. The major challenges are related with the way to extract insights from such a rich data environment and whether Deep Learning can be successful with Big Data. To get some insight on these topics, social network data are employed as a case study on how sentiments can affect decisions in stock market environments. In this paper, we propose a generalized Deep Learning-based classification framework for Stock Market Sentiment Analysis. This work comprises the study, the development, and implementation of an automatic classification system based on Deep Learning and the validation of its adequacy and efficiency in any scenario, particularly Stock Market Sentiment Analysis. Distinct datasets and several Deep Learning approaches with different layers and embedded techniques are used, and their performances are evaluated. These developments show how Deep Learning reacts to distinct contexts. The results also give context on how different techniques with different parameter combinations react to certain types of data. Convolution obtained the best results when dealing with complex data inputs, and long short-term layers kept a memory of data, allowing inputs which are not as common to still be considered for decisions. The models that resulted from Stock Market Sentiment Analysis datasets were applied with some success to real-life problems. The best models reached accuracies of 73% in training and 69% in certain test datasets. In a simulation, a model was able to provide a Return on Investment of 4.4%. The results contribute to understanding how to process Big Data efficiently using Deep Learning and specialized hardware techniques. Full article
(This article belongs to the Special Issue Bio-Inspired Computing and Applications in Sensor Network)
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18 pages, 331 KiB  
Article
Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization
by Anu Bajaj, Ajith Abraham, Saroj Ratnoo and Lubna Abdelkareim Gabralla
Sensors 2022, 22(12), 4374; https://doi.org/10.3390/s22124374 - 9 Jun 2022
Cited by 12 | Viewed by 3059
Abstract
The emerging areas of IoT and sensor networks bring lots of software applications on a daily basis. To keep up with the ever-changing expectations of clients and the competitive market, the software must be updated. The changes may cause unintended consequences, necessitating retesting, [...] Read more.
The emerging areas of IoT and sensor networks bring lots of software applications on a daily basis. To keep up with the ever-changing expectations of clients and the competitive market, the software must be updated. The changes may cause unintended consequences, necessitating retesting, i.e., regression testing, before being released. The efficiency and efficacy of regression testing techniques can be improved with the use of optimization approaches. This paper proposes an improved quantum-behaved particle swarm optimization approach for regression testing. The algorithm is improved by employing a fix-up mechanism to perform perturbation for the combinatorial TCP problem. Second, the dynamic contraction-expansion coefficient is used to accelerate the convergence. It is followed by an adaptive test case selection strategy to choose the modification-revealing test cases. Finally, the superfluous test cases are removed. Furthermore, the algorithm’s robustness is analyzed for fault as well as statement coverage. The empirical results reveal that the proposed algorithm performs better than the Genetic Algorithm, Bat Algorithm, Grey Wolf Optimization, Particle Swarm Optimization and its variants for prioritizing test cases. The findings show that inclusivity, test selection percentage and cost reduction percentages are higher in the case of fault coverage compared to statement coverage but at the cost of high fault detection loss (approx. 7%) at the test case reduction stage. Full article
(This article belongs to the Special Issue Bio-Inspired Computing and Applications in Sensor Network)
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37 pages, 8379 KiB  
Article
Investigating a Dual-Channel Network in a Sustainable Closed-Loop Supply Chain Considering Energy Sources and Consumption Tax
by Mehran Gharye Mirzaei, Fariba Goodarzian, Saeid Maddah, Ajith Abraham and Lubna Abdelkareim Gabralla
Sensors 2022, 22(9), 3547; https://doi.org/10.3390/s22093547 - 6 May 2022
Cited by 13 | Viewed by 2356
Abstract
This paper proposes a dual-channel network of a sustainable Closed-Loop Supply Chain (CLSC) for rice considering energy sources and consumption tax. A Mixed Integer Linear Programming (MILP) model is formulated for optimizing the total cost, the amount of pollutants, and the number of [...] Read more.
This paper proposes a dual-channel network of a sustainable Closed-Loop Supply Chain (CLSC) for rice considering energy sources and consumption tax. A Mixed Integer Linear Programming (MILP) model is formulated for optimizing the total cost, the amount of pollutants, and the number of job opportunities created in the proposed supply chain network under the uncertainty of cost, supply, and demand. In addition, to deal with uncertainty, fuzzy logic is used. Moreover, four multi-objective metaheuristic algorithms are employed to solve the model, which include a novel multi-objective version of the recently proposed metaheuristic algorithm known as Multi-Objective Reptile Search Optimizer (MORSO), Multi-Objective Simulated Annealing (MOSA), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Grey Wolf (MOGWO). All the algorithms are evaluated using LP-metric in small sizes and their results and performance are compared based on criteria such as Max Spread (MS), Spread of Non-Dominance Solution (SNS), the number of Pareto solutions (NPS), Mean Ideal Distance (MID), and CPU time. In addition, to achieve better results, the parameters of all algorithms are tuned by the Taguchi method. The programmed model is implemented using a real case study in Iran to confirm its accuracy and efficiency. To further evaluate the current model, some key parameters are subject to sensitivity analysis. Empirical results indicate that MORSO performed very well and by constructing solar panel sites and producing energy out of rice waste up to 19% of electricity can be saved. Full article
(This article belongs to the Special Issue Bio-Inspired Computing and Applications in Sensor Network)
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