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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 14612

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Special Issue Editors

Prof. Dr. Kawa Nazemi

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Head of Human-Computer Interaction and Visual Analytics, Faculty of Media, Darmstadt University of Applied Sciences, 64807 Darmstadt, Germany
Interests: artificial intelligence; visual analytics; human–computer interaction; data analytics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Egils Ginters

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Department of Modelling and Simulation, Riga Technical University, Kalku Street 1, LV-1658 Riga, Latvia
Interests: modeling and simulation technologies; logistics information systems; sociotechnical systems dynamics; acceptance and sustainability; visual analytics; mathematical modeling
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Dr. Michael Bažant

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Faculty of Electrical Engineering and Informatics Department of Software Technologies, University of Pardubice, 532 10 Pardubice II, Czech Republic
Interests: delays; goods distribution; order processing; road vehicles; warehousing

Special Issue Information

Dear Colleagues,

With the recent rise in cutting-edge technologies such as artificial intelligence, new and unsolved societal economic challenges can be solved sophisticatedly. Disciplines such as visual analytics, simulation, data analytics, natural language processing, and image and video processing combined with the human in the interaction loop have led to new systems, methods, concepts, and architectural designs that help us to face societal challenges, e.g., climate, mobility, sustainability, smart city. Furthermore, these approaches lead to predicting likely future scenarios in the economy. These enhancements enable gathering the market relevance of upcoming technologies, analyzing the competitors and other forthcoming competitive technologies, and analyzing new markets for technologies. Thus, the aspect of sustainability plays an increasing role, even in market positioning or in the development and deployment of new technologies.

In this Special Issue, we are interested in systems, system architectures, computational techniques, methods and models, and literature reviews in the areas of analytical decision making, collaborative work, sustainability, economy, simulation, object monitoring, and behaviour detection.

From a methodological point of view, the focus is on combining technological approaches from various disciplines to provide new ways and methods for analyzing data and models and enabling novel approaches for solving societal and economic challenges. On the practical side, we are looking for algorithms, software, prototypes, and demonstrators of decision-making support with the human-in-the-loop in various application fields. Topics of interest include but are not limited to the following:

Visual analytics and information visualization
Artificial intelligence and machine learning
Simulation and modeling for digital twins
Collaboration systems and collaborative work
Data analytics and natural language processing
Analytical systems for mobility, transportation, and traffic
Analytical systems for sustainability and environment
Analytical systems for corporate foresight
Analytical systems for smart manufacturing
Technologies for smart city, virtual, and augmented reality applications
Quantum and high-performance computing use
Digital wallet and blockchain synergy
Fault diagnosis in cyber-physical systems
Predictive maintenance in Industry 4.0
Object monitoring and behaviour detection

Prof. Dr. Kawa Nazemi
Prof. Dr. Egils Ginters
Dr. Michael Bažant
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. Electronics 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 2400 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

visual analytics
information visualization
artificial intelligence
machine learning
data analytics
analytical systems

Published Papers (9 papers)

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Research

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19 pages, 1278 KiB

Open AccessArticle

Campania Crea—A Collaborative Platform to Co-Create Open Data and Scaffold Information Visualization within the Campania Region

by Salvatore Avella, Angela Cocchiarella, Dario Fonzo, Carmela Luciano, Giuseppina Palmieri, Maria Angela Pellegrino and Vittorio Scarano

Electronics 2023, 12(11), 2409; https://doi.org/10.3390/electronics12112409 - 25 May 2023

Viewed by 910

Abstract

Open government data, as open data, are published to let interested stakeholders exploit data and create value out of them, but limited technical skills are a crucial barrier. Moreover, data silos within any public agency behave as a further obstacle in enabling collaboration between different working groups. This paper investigates the acceptance level of a collaborative platform to co-create, analyze, and visualize open government data within an Italian Regional Public Administration—the Campania region. This investigation first requires retracing and documenting the organizational changes applied to the Campania Region in moving from a siloed structure to a more horizontal and collaborative one. Second, it introduces the technical and technological contribution provided by the proposal of a Social Platform on Open Data (SPOD) as a regional public administration back-office, i.e., an internal platform, co-designed with public agency delegates and referred to as Campania Crea. Finally, it reports on the training session moderated by the University of Salerno to evaluate the acceptance rate of the proposed platform in real settings by involving 54 public agency members in actively using Campania Crea to co-create, analyze, and visualize open government data. The After Scenario Questionnaire was used to assess the acceptance level and attitude in using Campania Crea to report task-based results and the Technology Acceptance Model as an overall assessment of the platform acceptance level. As a result, Campania Crea supports regional public administration members in accomplishing their daily tasks concerning co-creation, analysis, and visualization of open data who positively accepted Campania Crea as a back-office tool. However, further effort should be invested in raising awareness and developing skills concerning open government data management. Full article

(This article belongs to the Special Issue Visual Analytics, Simulation, and Decision-Making Technologies)

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24 pages, 3686 KiB

Open AccessArticle

A Method for Predicting the Visual Attention Area in Real-Time Using Evolving Neuro-Fuzzy Models

by Rab Nawaz Jadoon, Aqsa Nadeem, Jawad Shafi, Muhammad Usman Khan, Mohammed ELAffendi, Sajid Shah and Gauhar Ali

Electronics 2023, 12(10), 2243; https://doi.org/10.3390/electronics12102243 - 15 May 2023

Viewed by 962

Abstract

This research paper presents the prediction of the visual attention area on a visual display using an evolving rule-based fuzzy model: evolving Takagi–Sugeno (eTS). The evolving fuzzy model is feasible for predicting the visual attention area because of its non-iterative, recursive, online, and real-time nature. Visual attention area prediction through a web camera is a problem that requires online adaptive systems with higher accuracy and greater performance. The proposed approach using an evolving fuzzy model to predict the eye-gaze attention area on a visual display in an ambient environment (to provide further services) mimics the human cognitive process and its flexibility to generate fuzzy rules without any prior knowledge. The proposed Visual Attention Area Prediction using Evolving Neuro-Fuzzy Systems (VAAPeNFS) approach can quickly generate compact fuzzy rules from new data. Numerical experiments conducted in a simulated environment further validate the performance and accuracy of the proposed model. To validate the model, the forecasting results of the eTS model are compared with DeTS and ANFIS. The result shows high accuracy, transparency and flexibility achieved by applying the evolving online versions compared to other offline techniques. The proposed approach significantly reduces the computational overhead, which makes it suitable for any sort of AmI application. Thus, using this approach, we achieve reusability, robustness, and scalability with better performance with high accuracy. Full article

(This article belongs to the Special Issue Visual Analytics, Simulation, and Decision-Making Technologies)

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22 pages, 561 KiB

Open AccessArticle

Recommending Words Using a Bayesian Network

by Pedro Santos, Matilde Pato, Nuno Datia, José Sobral, Noel Leitão, Manuel Ramos Ferreira and Nuno Gomes

Electronics 2023, 12(10), 2218; https://doi.org/10.3390/electronics12102218 - 12 May 2023

Cited by 1 | Viewed by 980

Abstract

Asset management involves the coordinated activities of an organisation to derive value from assets, which may include physical assets. It encompasses activities related to design, construction, installation, operation, maintenance, renewal, and asset disposal. Asset management ensures the coordination of all activities, resources, and data related to physical assets. Recording and monitoring all maintenance activities is a key part of asset management, often done using work orders (WOs). Technicians typically create WOs using “free text”, which can result in missing or ungrammatical words, making it difficult to identify trends and analyse information. To standardise the terminology used for the same asset maintenance operation, this paper proposes a method that suggests words to technicians as they complete WOs. The word suggestion algorithm is based on past maintenance records, and a Bayesian network-based recommender system adapts to present needs verified by technicians using implicit user feedback. Implementing this system aims to normalise the terms used by technicians when filling in a WO. The corpus for this work comes from asset management records collected in a health facility in Portugal operated by a private company. Full article

(This article belongs to the Special Issue Visual Analytics, Simulation, and Decision-Making Technologies)

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19 pages, 4451 KiB

Open AccessArticle

Quantifying the Simulation–Reality Gap for Deep Learning-Based Drone Detection

by Tamara Regina Dieter, Andreas Weinmann, Stefan Jäger and Eva Brucherseifer

Electronics 2023, 12(10), 2197; https://doi.org/10.3390/electronics12102197 - 11 May 2023

Cited by 1 | Viewed by 1794

Abstract

The detection of drones or unmanned aerial vehicles is a crucial component in protecting safety-critical infrastructures and maintaining privacy for individuals and organizations. The widespread use of optical sensors for perimeter surveillance has made optical sensors a popular choice for data collection in the context of drone detection. However, efficiently processing the obtained sensor data poses a significant challenge. Even though deep learning-based object detection models have shown promising results, their effectiveness depends on large amounts of annotated training data, which is time consuming and resource intensive to acquire. Therefore, this work investigates the applicability of synthetically generated data obtained through physically realistic simulations based on three-dimensional environments for deep learning-based drone detection. Specifically, we introduce a novel three-dimensional simulation approach built on Unreal Engine and Microsoft AirSim for generating synthetic drone data. Furthermore, we quantify the respective simulation–reality gap and evaluate established techniques for mitigating this gap by systematically exploring different compositions of real and synthetic data. Additionally, we analyze the adaptation of the simulation setup as part of a feedback loop-based training strategy and highlight the benefits of a simulation-based training setup for image-based drone detection, compared to a training strategy relying exclusively on real-world data. Full article

(This article belongs to the Special Issue Visual Analytics, Simulation, and Decision-Making Technologies)

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18 pages, 4375 KiB

Open AccessArticle

Study on Imagery Modeling of Solid Wood Chairs in Big Data

by Le Xu and Younghwan Pan

Electronics 2023, 12(8), 1949; https://doi.org/10.3390/electronics12081949 - 21 Apr 2023

Cited by 1 | Viewed by 1390

Abstract

With the continuous improvement of living quality and aesthetics, people have increasingly higher requirements for furniture products. Excellent solid wood chairs are one of the most representative products in the furniture industry. To enhance space and taste, the design of chairs may significantly impact consumers’ emotional experiences and purchase decisions. This study aims to evaluate how the modeling imagery of solid wood chairs affects consumers’ preferences and emotional experiences. The development of the current era is inseparable from the analysis of big data. Firstly, a representative sample is obtained by multidimensional scaling (MDS), analyzed, and evaluated by factor analysis. Moreover, five groups of adjective vocabulary are selected to describe the modeling imagery of solid wood chairs, such as “balanced and coordinated”, “unique and novel”, “practical and simple”, “quality and detailed”, and “traditional and plain”. Further, the triangular fuzzy theory is applied to analyze and discuss the twelve types of solid wood chairs in the five groups of adjective vocabulary. Then, the study verifies that the differences are significant in the evaluations of the 12 samples in the “unique and novel” and “quality and detailed” groups, and small in the groups of “traditional and plain”, “balanced and coordinated”, and “practical and simple”. Through comprehensive comparisons, five groups with similar modeling imagery are created, and solid wood chairs with different modeling imagery should be placed in suitable spaces. According to the results of this study, the evaluation of the modeling imagery of solid wood chairs cannot solely rely on subjective judgments. However, it can be reasonably refined through data analysis and mathematical algorithms. It can also scientifically and effectively reflect the potential perception needs of consumers on the modeling imagery of solid wood chairs, as well as help to improve the design efficiency of the furniture product development stage. Full article

(This article belongs to the Special Issue Visual Analytics, Simulation, and Decision-Making Technologies)

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18 pages, 1473 KiB

Open AccessArticle

Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System

by Midhad Blazevic, Lennart B. Sina, Cristian A. Secco and Kawa Nazemi

Electronics 2023, 12(7), 1699; https://doi.org/10.3390/electronics12071699 - 03 Apr 2023

Cited by 4 | Viewed by 1382

Abstract

Studies have shown that although having more information improves the quality of decision-making, information overload causes adverse effects on decision quality. Visual analytics and recommendation systems counter this adverse effect on decision-making. Accurately identifying relevant information can reduce the noise during exploration and improve decision-making. These countermeasures also help scientists make correct decisions during research. We present a novel and intuitive approach that supports real-time collaboration. In this paper, we instantiate our approach to scientific writing and propose a system that supports scientists. The proposed system analyzes text as it is being written and recommends similar publications based on the written text through similarity algorithms. By analyzing text as it is being written, it is possible to provide targeted real-time recommendations to improve decision-making during research by finding relevant publications that might not have been otherwise found in the initial research phase. This approach allows the recommendations to evolve throughout the writing process, as recommendations begin on a paragraph-based level and progress throughout the entire written text. This approach yields various possible use cases discussed in our work. Furthermore, the recommendations are presented in a visual analytics system to further improve scientists’ decision-making capabilities. Full article

(This article belongs to the Special Issue Visual Analytics, Simulation, and Decision-Making Technologies)

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16 pages, 2227 KiB

Open AccessArticle

Multivariate Demand Forecasting for Rental Bike Systems Based on an Unobserved Component Model

by Christian Wirtgen, Matthias Kowald, Johannes Luderschmidt and Holger Hünemohr

Electronics 2022, 11(24), 4146; https://doi.org/10.3390/electronics11244146 - 12 Dec 2022

Viewed by 1370

Abstract

Many German cities, municipalities and transport associations are expanding their bike-sharing systems (BSS) to offer citizens a cost-effective and climate-friendly means of transport and an alternative to private motorized transport (PMT). However, operators face the challenge of generating high-quality predictive analyses and time series forecasts. In particular, the prediction of demand is a key component to foster data-driven decisions. To address this problem, an Unobserved Component Model (UCM) has been developed to predict the monthly rentals of a BSS, whereby the station-based BSS VRNnextbike, including over 2000 bikes, 297 stations and 21 municipalities, is employed as an example. The model decomposes the time series into trend, seasonal, cyclical, auto-regressive and irregular components for statistical modeling. Additionally, the model includes exogenous factors such as weather, user behavior (e.g., traveled distance), school holidays and COVID-19 relevant covariates as independent effects to calculate scenario based forecasts. It can be shown that the UCM calculates reasonably accurate forecasts and outperforms classical time series models such as ARIMA(X) or SARIMA(X). Improvements were observed in model quality in terms of AIC/BIC (2.5% to 22%) and a reduction in error metrics from 15% to 45% depending on the considered model. Full article

(This article belongs to the Special Issue Visual Analytics, Simulation, and Decision-Making Technologies)

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42 pages, 20171 KiB

Open AccessFeature PaperArticle

Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories

by Lukas Kaupp, Kawa Nazemi and Bernhard Humm

Electronics 2022, 11(23), 3942; https://doi.org/10.3390/electronics11233942 - 28 Nov 2022

Viewed by 1426

Abstract

Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions. Full article

(This article belongs to the Special Issue Visual Analytics, Simulation, and Decision-Making Technologies)

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14 pages, 392 KiB

Open AccessSystematic Review

Hybrid Forecasting Methods—A Systematic Review

by Lennart B. Sina, Cristian A. Secco, Midhad Blazevic and Kawa Nazemi

Electronics 2023, 12(9), 2019; https://doi.org/10.3390/electronics12092019 - 27 Apr 2023

Cited by 7 | Viewed by 2975

Abstract

Time series forecasting has been performed for decades in both science and industry. The forecasting models have evolved steadily over time. Statistical methods have been used for many years and were later complemented by neural network approaches. Currently, hybrid approaches are increasingly presented, aiming to combine both methods’ advantages. These hybrid forecasting methods could lead to more accurate predictions and enhance and improve visual analytics systems for making decisions or for supporting the decision-making process. In this work, we conducted a systematic literature review using the PRISMA methodology and investigated various hybrid forecasting approaches in detail. The exact procedure for searching and filtering and the databases in which we performed the search were documented and supplemented by a PRISMA flow chart. From a total of 1435 results, we included 21 works in this review through various filtering steps and exclusion criteria. We examined these works in detail and collected the quality of the prediction results. We summarized the error values in a table to investigate whether hybrid forecasting approaches deliver better results. We concluded that all investigated hybrid forecasting methods perform better than individual ones. Based on the results of the PRISMA study, the possible applications of hybrid prediction approaches in visual analytics systems for decision making are discussed and illustrated using an exemplary visualization. Full article

(This article belongs to the Special Issue Visual Analytics, Simulation, and Decision-Making Technologies)

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