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Trends in Computational and Cognitive Engineering
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Special Issue "Trends in Computational and Cognitive Engineering"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: 1 July 2023 | Viewed by 2814

Special Issue Editors

Dr. Anirban Bandyopadhyay

E-Mail Website
Guest Editor
International Center for Materials and Nanoarchitectronics (MANA), Research Center for Advanced Measurement and Characterization (RCAMC), National Institute for Materials Science (NIMS), 1-2-1 Sengen, Main Bldg, Tsukuba 815, Japan
Interests: bioinformatics; information theory; modelling human brain; dielectric resonance of biomaterials; proteins; neuron; organic jelly-based neuromorphic device; artificial brain; molecular robots for drug delivery
Special Issues, Collections and Topics in MDPI journals
Dr. Kanad Ray

E-Mail Website1 Website2
Guest Editor
Dept. of Physics, Amity School of Applied Sciences, Amity University Rajasthan, Kant Kalwar, NH-11C, Jaipur, India
Interests: cognition; antenna; bioelectromagnetics; plasmonics; applied physics; consciousness
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

TCCE (Trends in Computation and Cognitive Engineering, https://www.tcce.info/) focuses on experimental, theoretical, and application-based computational and cognitive engineering. Computational and cognitive engineering not only consists of computational and mathematical methods commonly used in all fields of science, engineering, technology, and industry, but also analyzes diseases and behavioral disorders. The TCCE conference series, organized by IIOIR (www.iioir.org), will be insightful and fascinating for those interested in learning about computational intelligence and cognitive engineering that explores the dynamics of exponentially increasing knowledge in core and related fields. This Special Issue is to be published by the journal, Information, a publication of MDPI, and will cover the above topics and all special features covered in the TCCE conference series. Enthusiasts should submit their manuscripts here in this journal or the dedicated website for TCCE. All publications passing through this Special Issue will be eligible for a 30% discount.

Authors of invited papers should be aware that the final submitted manuscript must provide a minimum of 50% new content and not exceed 30% copy/paste from the Proceeding paper.

Dr. Anirban Bandyopadhyay
Dr. Kanad Ray
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. Information is an international peer-reviewed open access monthly 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 1600 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

  • artificial intelligence and soft computing
  • cognitive science and computational biology
  • IoT and data analytics
  • network and security
  • signal processing
  • computer vision & rhythm engineering

Published Papers (4 papers)

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Article
An Automated Path-Focused Test Case Generation with Dynamic Parameterization Using Adaptive Genetic Algorithm (AGA) for Structural Program Testing
by
Manikandan Rajagopal
,
Ramkumar Sivasakthivel
,
Karuppusamy Loganathan
and
Loannis E. Sarris
Information 2023, 14(3), 166; https://doi.org/10.3390/info14030166 - 06 Mar 2023
Viewed by 446
Abstract
Various software engineering paradigms and real-time projects have proved that software testing is the most critical and highly important phase in the SDLC. In general, software testing takes approximately 40–60% of the total effort and time involved in project development. Generating test cases [...] Read more.
Various software engineering paradigms and real-time projects have proved that software testing is the most critical and highly important phase in the SDLC. In general, software testing takes approximately 40–60% of the total effort and time involved in project development. Generating test cases is the most important process in software testing. There are many techniques involved in the automatic generation of these test cases which aim to find a smaller group of cases that could allow for an adequacy level to be achieved which will hence reduce the effort and cost involved in software testing. In the structural testing of a product, the auto-generation of test cases that are path focused in an efficient manner is a challenging process. These are often considered optimization problems and hence search-based methods such as genetic algorithm (GA) and swarm optimizations have been proposed to handle this issue. The significance of the study is to address the optimization problem of automatic test case generation in search-based software engineering. The proposed methodology aims to close the gap of genetic algorithms acquiring local minimum due to poor diversity. Here, dynamic adjustment of cross-over and mutation rate is achieved by calculating the individual measure of similarity and fitness and searching for the more global optimum. The proposed method is applied and experimented on a benchmark of five industrial projects. The results of the experiments have confirmed the efficiency of generating test cases that have optimum path coverage. Full article
(This article belongs to the Special Issue Trends in Computational and Cognitive Engineering)
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Article
Decision Making in Networks: A Model of Awareness Raising
by
Federico Bizzarri
,
Alessandro Giuliani
and
Chiara Mocenni
Information 2023, 14(2), 72; https://doi.org/10.3390/info14020072 - 27 Jan 2023
Viewed by 479
Abstract
This work investigates how interpersonal interactions among individuals could affect the dynamics of awareness raising. Even though previous studies on mathematical models of awareness in the decision making context demonstrate how the level of awareness results from self-observation impinged by optimal decision selections [...] Read more.
This work investigates how interpersonal interactions among individuals could affect the dynamics of awareness raising. Even though previous studies on mathematical models of awareness in the decision making context demonstrate how the level of awareness results from self-observation impinged by optimal decision selections and external uncertainties, an explicit accounting of interaction among individuals is missing. Here we introduce for the first time a theoretical mathematical framework to evaluate the effect on individual awareness exerted by the interaction with neighbor agents. This task is performed by embedding the single agent model into a graph and allowing different agents to interact by means of suitable coupling functions. The presence of the network allows, from a global point of view, the emergence of diffusion mechanisms for which the population tends to reach homogeneous attractors, and, among them, the one with the highest level of awareness. The structural and behavioral patterns, such as the initial levels of awareness and the relative importance the individual assigns to their own state with respect to others’, may drive real actors to stress effective actions increasing individual and global awareness. Full article
(This article belongs to the Special Issue Trends in Computational and Cognitive Engineering)
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Article
Image Geo-Site Estimation Using Convolutional Auto-Encoder and Multi-Label Support Vector Machine
by
Arpit Jain
,
Chaman Verma
,
Neerendra Kumar
,
Maria Simona Raboaca
,
Jyoti Narayan Baliya
and
George Suciu
Information 2023, 14(1), 29; https://doi.org/10.3390/info14010029 - 03 Jan 2023
Cited by 1 | Viewed by 749
Abstract
The estimation of an image geo-site solely based on its contents is a promising task. Compelling image labelling relies heavily on contextual information, which is not as simple as recognizing a single object in an image. An Auto-Encode-based support vector machine approach is [...] Read more.
The estimation of an image geo-site solely based on its contents is a promising task. Compelling image labelling relies heavily on contextual information, which is not as simple as recognizing a single object in an image. An Auto-Encode-based support vector machine approach is proposed in this work to estimate the image geo-site to address the issue of misclassifying the estimations. The proposed method for geo-site estimation is conducted using a dataset consisting of 125 classes of various images captured within 125 countries. The proposed work uses a convolutional Auto-Encode for training and dimensionality reduction. After that, the acquired preprocessed input dataset is further processed by a multi-label support vector machine. The performance assessment of the proposed approach has been accomplished using accuracy, sensitivity, specificity, and F1-score as evaluation parameters. Eventually, the proposed approach for image geo-site estimation presented in this article outperforms Auto-Encode-based K-Nearest Neighbor and Auto-Encode-Random Forest methods. Full article
(This article belongs to the Special Issue Trends in Computational and Cognitive Engineering)
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Article
Mathematical Theory of Conflicts as a Cognitive Control Theory
by
Ekaterina Antipova
and
Sergey Rashkovskiy
Information 2023, 14(1), 1; https://doi.org/10.3390/info14010001 - 21 Dec 2022
Viewed by 588
Abstract
We give a rigorous mathematical definition of conflict, on the basis of which we formulate the mathematical theory of conflicts as a problem of the theory of cognitive control. Possible ways of influencing the conflicting parties on each other are considered and analyzed. [...] Read more.
We give a rigorous mathematical definition of conflict, on the basis of which we formulate the mathematical theory of conflicts as a problem of the theory of cognitive control. Possible ways of influencing the conflicting parties on each other are considered and analyzed. The analysis carried out shows that the control of a conflict situation is fundamentally different from the control of technical objects. So, when controlling technical objects, it is usually possible to directly influence the reason that causes error (deviation) in the system. In a conflict situation, there is often no opportunity to directly influence the opposite side of the conflict. However, each of the conflicting parties has the ability to change its own parameters and, thereby, create a conflict for the opposite side, which is forced to change its parameters to those necessary for the opponent in order to resolve its own conflict. Within the framework of the developed theory, the conflict between the worker and the employer is considered, and this conflict is analyzed from the point of view of the cognitive control theory. Full article
(This article belongs to the Special Issue Trends in Computational and Cognitive Engineering)
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