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4 pages, 181 KiB

Open AccessEditorial

Editorial Note to the Special Issue: “Trends and Applications in Information Systems and Technologies”

by Galina Ilieva and George A. Tsihrintzis

Electronics 2023, 12(22), 4663; https://doi.org/10.3390/electronics12224663 - 15 Nov 2023

Viewed by 909

Abstract

In today’s fast-paced and competitive market, information technologies play a significant role in reshaping business models and enhancing company performance [...] Full article

(This article belongs to the Special Issue Trends and Applications in Information Systems and Technologies)

Research

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20 pages, 7872 KiB

Open AccessArticle

Oracles Integration in Blockchain-Based Platform for Smart Crop Production Data Exchange

by Ivan Popchev, Irina Radeva and Lyubka Doukovska

Electronics 2023, 12(10), 2244; https://doi.org/10.3390/electronics12102244 - 15 May 2023

Cited by 8 | Viewed by 2230

Abstract

Blockchain oracles are an intermediary designed to connect external non-deterministic information and real-world data to the blockchain digital infrastructure. The variety of proposed solutions and purposes are of great variety and suggest that it is necessary to take into account different features of [...] Read more.

Blockchain oracles are an intermediary designed to connect external non-deterministic information and real-world data to the blockchain digital infrastructure. The variety of proposed solutions and purposes are of great variety and suggest that it is necessary to take into account different features of the process and specifically define the required functionalities. The purpose of this paper is to present the integration of oracles into an EOSIO blockchain-based platform for smart crop production data exchange by smart contracts. The functions of two oracles are presented. Their integration is described at the design level and at the implementation of the smart contracts. The design level is illustrated by workflow diagrams of internal processes between oracle applications and the blockchain smart contract and by external processes in the oracles’ smart contracts. The implementation level is illustrated by oracle application configuration files and elements of C++ smart contracts, such as constant and variable declarations, multi-index tables, internal contract functions, and actions called by other contracts and external programs. As results of the oracles’ operation, a report on the detected emergency failures and an estimate of the cost of ram resource are presented. Full article

(This article belongs to the Special Issue Trends and Applications in Information Systems and Technologies)

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17 pages, 3240 KiB

Open AccessArticle

IoT Data Sharing Platform in Web 3.0 Using Blockchain Technology

by Abdul Razzaq, Ahmed B. Altamimi, Abdulrahman Alreshidi, Shahbaz Ahmed Khan Ghayyur, Wilayat Khan and Mohammad Alsaffar

Electronics 2023, 12(5), 1233; https://doi.org/10.3390/electronics12051233 - 4 Mar 2023

Cited by 9 | Viewed by 3579

Abstract

As Internet of Things (IoT)-based systems become more prevalent in the era of data-driven intelligence, they are prone to some unprecedented challenges in terms of data security and systems scalability in an era of context-sensitive data. The current advances in IoT-driven data sensing [...] Read more.

As Internet of Things (IoT)-based systems become more prevalent in the era of data-driven intelligence, they are prone to some unprecedented challenges in terms of data security and systems scalability in an era of context-sensitive data. The current advances in IoT-driven data sensing and sharing rely on third-party sources of information (TTPs) that gather data from one party, then transmit it to the other. As a result of TTPs’ involvement, such IoT systems suffer from many issues including but not limited to security, transparency, trust, and immutability as a result of the involvement of the company. Moreover, a multitude of technical impediments, such as the computation and storage poverty of IoTs, privacy concerns, and energy efficiency, enhances the challenges for IoTs. To address these issues of IoT security, we propose a blockchain-enabled open IoT data-sharing framework based on the potential of the interplanetary file system (IPFS). We have used a case study-based approach to evaluate the proposed solution. It is submitted that the proposed scenario is implemented by building smart contracts in Solidity and deploying them on the local Ethereum test network, using the Solidity programming language. With the implementation of smart contracts on the blockchain for access roles in IoT data sensing, the proposed solution advocates for a blockchain-based approach to data security for IoT systems that makes use of smart contracts for access roles. Full article

(This article belongs to the Special Issue Trends and Applications in Information Systems and Technologies)

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16 pages, 2273 KiB

Open AccessArticle

Improving Multi-Class Motor Imagery EEG Classification Using Overlapping Sliding Window and Deep Learning Model

by Jeonghee Hwang, Soyoung Park and Jeonghee Chi

Electronics 2023, 12(5), 1186; https://doi.org/10.3390/electronics12051186 - 1 Mar 2023

Cited by 15 | Viewed by 3220

Abstract

Motor imagery (MI) electroencephalography (EEG) signals are widely used in BCI systems. MI tasks are performed by imagining doing a specific task and classifying MI through EEG signal processing. However, it is a challenging task to classify EEG signals accurately. In this study, [...] Read more.

Motor imagery (MI) electroencephalography (EEG) signals are widely used in BCI systems. MI tasks are performed by imagining doing a specific task and classifying MI through EEG signal processing. However, it is a challenging task to classify EEG signals accurately. In this study, we propose a LSTM-based classification framework to enhance classification accuracy of four-class MI signals. To obtain time-varying data of EEG signals, a sliding window technique is used, and an overlapping-band-based FBCSP is applied to extract the subject-specific spatial features. Experimental results on BCI competition IV dataset 2a showed an average accuracy of 97% and kappa value of 0.95 in all subjects. It is demonstrated that the proposed method outperforms the existing algorithms for classifying the four-class MI EEG, and it also illustrates the robustness on the variability of inter-trial and inter-session of MI data. Furthermore, the extended experimental results for channel selection showed the best performance of classification accuracy when using all twenty-two channels by the proposed method, but an average kappa value of 0.93 was achieved with only seven channels. Full article

(This article belongs to the Special Issue Trends and Applications in Information Systems and Technologies)

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20 pages, 4454 KiB

Open AccessArticle

An Innovative Tool to Measure Employee Performance through Customer Satisfaction: Pilot Research Using eWOM, VR, and AR Technologies

by Ioan-David Legman, Manuela Rozalia Gabor and Mihaela Kardos

Electronics 2023, 12(5), 1158; https://doi.org/10.3390/electronics12051158 - 27 Feb 2023

Cited by 1 | Viewed by 3594

Abstract

Recent research reflects the assessment of customer satisfaction from different perspectives, an important aspect in all sectors that must be expressed in measurable parameters of organization performance. By reviewing the literature, we noticed the lack of a specific indicator to quantify the tripartite [...] Read more.

Recent research reflects the assessment of customer satisfaction from different perspectives, an important aspect in all sectors that must be expressed in measurable parameters of organization performance. By reviewing the literature, we noticed the lack of a specific indicator to quantify the tripartite relation: customer satisfaction—employee performance—company performance. Therefore, based on Six Sigma and Lean Six Sigma methods, the paper introduces an innovative measurement tool named the Spc indicator (The Assessment System of Employee Performance according to Customer Satisfaction) and the related implementation methodology (named ITA). The aim of the paper is to implement an innovative tool to improve the efficiency of employee performance assessment systems in relation to company performance in services and industry sectors through customer satisfaction assessment. By using AR and VR as implementation technologies, our present results extend and compare the results from other pilot research made by authors in the e-commerce sector. The results point out that mystery shoppers and electronic word-of-mouth (eWOM) applied in e-commerce are more efficient than AR and VR technologies applied in services and industry, as reflected in the company’s performance. Furthermore, customer–employee interactions and communications with eWOM in e-commerce are more efficient than WOM used in services and industry. This paper contains both theoretical and practical contributions by offering a new, short-time innovative tool for the continuous improvement of the company with applications in different fields. Full article

(This article belongs to the Special Issue Trends and Applications in Information Systems and Technologies)

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29 pages, 1423 KiB

Open AccessArticle

Handling Class Imbalance and Class Overlap in Machine Learning Applications for Undeclared Work Prediction

by Eleni Alogogianni and Maria Virvou

Electronics 2023, 12(4), 913; https://doi.org/10.3390/electronics12040913 - 11 Feb 2023

Cited by 4 | Viewed by 2146

Abstract

Undeclared work is a composite socioeconomic matter severely affecting the welfare of workers, legitimate companies, and the state by issuing unfair competition in the labour market and causing considerable state revenue losses by tax evasion. Labour inspectorates are tasked to deal effectively with [...] Read more.

Undeclared work is a composite socioeconomic matter severely affecting the welfare of workers, legitimate companies, and the state by issuing unfair competition in the labour market and causing considerable state revenue losses by tax evasion. Labour inspectorates are tasked to deal effectively with this issue but usually lack adequate resources and proper tools, yet they own large volumes of past inspection data that, if aptly processed through innovative machine learning techniques, may produce understandable insights into the extent and prevailing patterns of undeclared work and efficient tools to address it. Such datasets are typically imbalanced regarding undeclared work, and contain overlapping inspection discoveries, two issues that impede the learning process. This research points to the problems of class imbalance and class overlap in this domain and applies combinations of data engineering techniques to address them using a dataset of 16.7 K actual labour inspections. Three associative classification algorithms are employed, and multiple classifiers are built and assessed for their predictability and interpretability. The study indicates the overall benefits for the inspection authorities when integrating machine learning methods in targeting undeclared work and proves considerable prediction performance improvement when following data engineering approaches to address the class imbalance and class overlap issues. Full article

(This article belongs to the Special Issue Trends and Applications in Information Systems and Technologies)

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17 pages, 1486 KiB

Open AccessArticle

Investigating Trace Equivalences in Information Networks

by Run Li, Jinzhao Wu and Wujie Hu

Electronics 2023, 12(4), 865; https://doi.org/10.3390/electronics12040865 - 8 Feb 2023

Viewed by 1229

Abstract

Equivalences are widely used and have achieved much success in concurrent systems. Meanwhile, information networks are ubiquitous for representing many complex systems and have similar characteristics and properties to concurrent systems such that they both can be described by graphs. In order to [...] Read more.

Equivalences are widely used and have achieved much success in concurrent systems. Meanwhile, information networks are ubiquitous for representing many complex systems and have similar characteristics and properties to concurrent systems such that they both can be described by graphs. In order to simplify information networks, we introduce equivalence to information networks, specifically leveraging the trace equivalence to reduce the complexity of these networks. In this paper, we first define the concept of trace and trace equivalence in information networks, drawing on the similar concept of concurrent systems. We then propose a computational method for determining whether two nodes are trace equivalent in an information network. With the help of this method, we derive trace-equivalent networks from original networks. Experiments show that we are able to reduce the number of nodes in the ACM and DBLP datasets by at most 65.21% and 46.68%, respectively. Running the PathSim algorithm on the original and derived networks, the mean error is 0.0728 in ACM and 0.0446 in DBLP. Overall, the results indicate that the derived networks have fewer nodes and edges than the original networks, yet still capture the same or similar information. By using trace equivalence, we are able to simplify information networks and improve their efficiency while preserving most of their informational content. Full article

(This article belongs to the Special Issue Trends and Applications in Information Systems and Technologies)

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15 pages, 323 KiB

Open AccessArticle

ICT Penetration and Insurance Sector Development: Evidence from the 10 New EU Member States

by Yilmaz Bayar, Dan Constantin Danuletiu, Adina Elena Danuletiu and Marius Dan Gavriletea

Electronics 2023, 12(4), 823; https://doi.org/10.3390/electronics12040823 - 6 Feb 2023

Cited by 3 | Viewed by 3170

Abstract

The insurance sector provides protection to individuals and businesses against many types of risks and also promotes economic growth, being an important source of long-term capital. Analyzing factors that facilitate insurance sector development is important for both individuals and the entire economy. The [...] Read more.

The insurance sector provides protection to individuals and businesses against many types of risks and also promotes economic growth, being an important source of long-term capital. Analyzing factors that facilitate insurance sector development is important for both individuals and the entire economy. The purpose of this study is to investigate the relationship between information and communication technologies (ICT) represented by mobile cellular subscriptions per 100 people and individuals using the Internet (% of population) and insurance sector development represented by insurance company assets to GDP (%). Using data from 10 new member states of the European Union for the period 2000–2020, this study reveals a mutual interaction between ICT penetration indicators and insurance sector development. Furthermore, a regression analysis reveals that Internet penetration has a significant positive influence on insurance sector growth. Specifically, at the country level, the results indicate the existence of bidirectional causality between mobile cellular subscriptions and the insurance sector in Latvia, Poland, and Slovakia, and unidirectional causality between insurance and mobile cellular subscriptions in Estonia and Hungary. Full article

(This article belongs to the Special Issue Trends and Applications in Information Systems and Technologies)

24 pages, 6451 KiB

Open AccessArticle

An Extreme Value Analysis-Based Systemic Approach in Healthcare Information Systems: The Case of Dietary Intake

by Dimitrios P. Panagoulias, Dionisios N. Sotiropoulos and George A. Tsihrintzis

Electronics 2023, 12(1), 204; https://doi.org/10.3390/electronics12010204 - 31 Dec 2022

Cited by 3 | Viewed by 1809

Abstract

Biomarkers are measurements of biological variables that can determine a state of health. They consist of measuring a single variable or a combination of variables related to the state of health that these variables represent. Biomarkers can provide an early warning of a [...] Read more.

Biomarkers are measurements of biological variables that can determine a state of health. They consist of measuring a single variable or a combination of variables related to the state of health that these variables represent. Biomarkers can provide an early warning of a health problem in relation to an individual patient or group of patients, and thus trigger actions and lead to interventions. Nutritional biomarkers measure the biological consequences of one’s diet. In our recent work, we have used machine learning to predict weight, metabolic syndrome and blood pressure, using blood-exam-based biomarkers. In the current work, we use extreme value theory to examine the significance of outliers in health data, with a focus on diet and the standard biochemistry profile. Specifically, we show that, using extreme value analysis and by applying a systemisation of the process, health trends can be predicted, and thus, health interventions can be (at least partially) automated. For that purpose, public access datasets have been used, which were retrieved from the National Health and Nutrition Examination Survey. The NHANES is a program of studies designed to assess the health and nutritional status of the population in the United States. In total, about 70,000 datapoints were analysed, covering about a decade’s worth of observations. Full article

(This article belongs to the Special Issue Trends and Applications in Information Systems and Technologies)

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13 pages, 1537 KiB

Open AccessArticle

A Framework for Smart Home System with Voice Control Using NLP Methods

by Yuliy Iliev and Galina Ilieva

Electronics 2023, 12(1), 116; https://doi.org/10.3390/electronics12010116 - 27 Dec 2022

Cited by 15 | Viewed by 7465

Abstract

The proliferation of information technologies and the emergence of ubiquitous computing have quickly transformed electronic devices from isolated islands of data and control into interconnected parts of intelligent systems. These network-based systems have advanced features, including Internet of Things (IoT) sensors and actuators, [...] Read more.

The proliferation of information technologies and the emergence of ubiquitous computing have quickly transformed electronic devices from isolated islands of data and control into interconnected parts of intelligent systems. These network-based systems have advanced features, including Internet of Things (IoT) sensors and actuators, multiple connectivity options and multimodal user interfaces, and they also enable remote monitoring and management. In order to develop a human machine interface of smart home systems with speech recognition, we propose a new IoT-fog-cloud framework using natural language processing (NLP) methods. The new methodology adds utterance to command transformation to the existing cloud-based speech-to-text and text-to-speech services. This approach is flexible and can be easily adapted for different types of automation systems and consumer electronics as well as to almost every non-tonal language not currently supported by online platforms for intent detection and classification. The proposed framework has been employed in the development of prototypes of voice user interface extension of existing smart security system via new service for speech intent recognition. Tests on the system were carried out and the obtained results show the effectiveness of the new voice communication option. The speech-based interface is reliable; it facilitates customers and improves their experience with smart home devices. Full article

(This article belongs to the Special Issue Trends and Applications in Information Systems and Technologies)

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17 pages, 11785 KiB

Open AccessArticle

An Incremental Learning Framework for Photovoltaic Production and Load Forecasting in Energy Microgrids

by Elissaios Sarmas, Sofoklis Strompolas, Vangelis Marinakis, Francesca Santori, Marco Antonio Bucarelli and Haris Doukas

Electronics 2022, 11(23), 3962; https://doi.org/10.3390/electronics11233962 - 29 Nov 2022

Cited by 25 | Viewed by 1992

Abstract

Energy management is crucial for various activities in the energy sector, such as effective exploitation of energy resources, reliability in supply, energy conservation, and integrated energy systems. In this context, several machine learning and deep learning models have been developed during the last [...] Read more.

Energy management is crucial for various activities in the energy sector, such as effective exploitation of energy resources, reliability in supply, energy conservation, and integrated energy systems. In this context, several machine learning and deep learning models have been developed during the last decades focusing on energy demand and renewable energy source (RES) production forecasting. However, most forecasting models are trained using batch learning, ingesting all data to build a model in a static fashion. The main drawback of models trained offline is that they tend to mis-calibrate after launch. In this study, we propose a novel, integrated online (or incremental) learning framework that recognizes the dynamic nature of learning environments in energy-related time-series forecasting problems. The proposed paradigm is applied to the problem of energy forecasting, resulting in the construction of models that dynamically adapt to new patterns of streaming data. The evaluation process is realized using a real use case consisting of an energy demand and a RES production forecasting problem. Experimental results indicate that online learning models outperform offline learning models by 8.6% in the case of energy demand and by 11.9% in the case of RES forecasting in terms of mean absolute error (MAE), highlighting the benefits of incremental learning. Full article

(This article belongs to the Special Issue Trends and Applications in Information Systems and Technologies)

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30 pages, 4563 KiB

Open AccessArticle

A Multi-Attribute Decision-Making Approach for the Analysis of Vendor Management Using Novel Complex Picture Fuzzy Hamy Mean Operators

by Abrar Hussain, Kifayat Ullah, Dragan Pamucar and Đorđe Vranješ

Electronics 2022, 11(23), 3841; https://doi.org/10.3390/electronics11233841 - 22 Nov 2022

Cited by 19 | Viewed by 1464

Abstract

Vendor management systems (VMSs) are web-based software packages that can be used to manage businesses. The performance of the VMSs can be assessed using multi-attribute decision-making (MADM) techniques under uncertain situations. This article aims to analyze and assess the performance of VMSs using [...] Read more.

Vendor management systems (VMSs) are web-based software packages that can be used to manage businesses. The performance of the VMSs can be assessed using multi-attribute decision-making (MADM) techniques under uncertain situations. This article aims to analyze and assess the performance of VMSs using MADM techniques, especially when the uncertainty is of complex nature. To achieve the goals, we aim to explore Hany mean (HM) operators in the environment of complex picture fuzzy (CPF) sets (CPFSs). We introduce CPF Hamy mean (CPFHM) and CPF weighted HM (CPFWHM) operators. Moreover, the reliability of the newly proposed HM operators is examined by taking into account the properties of idempotency, monotonicity, and boundedness. A case study of VMS is briefly discussed, and a comprehensive numerical example is carried out to assess VMSs using the MADM technique based on CPFHM operators. The sensitivity analysis and comprehensive comparative analysis of the proposed work are discussed to point out the significance of the newly established results. Full article

(This article belongs to the Special Issue Trends and Applications in Information Systems and Technologies)

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17 pages, 2451 KiB

Open AccessArticle

Adoption of Digital Business Solutions: Designing and Monitoring Critical Success Factors

by Rositsa Doneva and Silvia Gaftandzhieva

Electronics 2022, 11(21), 3494; https://doi.org/10.3390/electronics11213494 - 28 Oct 2022

Cited by 2 | Viewed by 1826

Abstract

The success of a business organization on its path to digital transformation depends on the success of the various stages of the project in order to adopt the selected digital business solutions. The success of this project is determined to a large extent [...] Read more.

The success of a business organization on its path to digital transformation depends on the success of the various stages of the project in order to adopt the selected digital business solutions. The success of this project is determined to a large extent by identifying and monitoring critical success factors (CSFs) for these stages. Based on the studies in the field of CSFs and consultations with experts from the ICT sector and academia, the paper presents a comprehensive framework for the overall management of CSFs (from CSFs’ design to their monitoring). The framework helps business organizations conceptualize CSFs for different stages of a project for the adoption of a chosen digital business solution. Furthermore, the framework provides practical guidance in the form of a framework, a methodology on what organizations should do to identify, monitor and manage the proper CSFs so that they can take the most advantage of them. The proposed framework has broad practical application and can be used by companies implementing projects for the digitalization of their activity to improve the digital services offered and advance the organization’s efficiency, etc. Currently, aiming to validate the presented framework, it will be applied for the adoption of a customer relationship management (CRM) digitalization solution, based on a cooperation agreement with a national company from the internet and TV service delivery industry. The framework can be further developed and applied to other project types. Full article

(This article belongs to the Special Issue Trends and Applications in Information Systems and Technologies)

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13 pages, 687 KiB

Open AccessArticle

Emotion-Based Literature Book Classification Using Online Reviews

by Elena-Ruxandra Luţan and Costin Bădică

Electronics 2022, 11(20), 3412; https://doi.org/10.3390/electronics11203412 - 21 Oct 2022

Cited by 3 | Viewed by 2327

Abstract

Reading is not only a recreational activity; it also shapes the emotional and cognitive competences of the reader. In this paper, we present a method and tools for the analysis of emotions extracted from online reviews of literature books. We implement a scraper [...] Read more.

Reading is not only a recreational activity; it also shapes the emotional and cognitive competences of the reader. In this paper, we present a method and tools for the analysis of emotions extracted from online reviews of literature books. We implement a scraper to create a new experimental dataset of reviews gathered from Goodreads, a website dedicated to readers that contains a large database of books and readers’ reviews. We propose a system which extracts the emotions from the reviews and associates them with the reviewed book. Afterwards, this information can be used to find similarities between the books based on readers’ impressions. Lastly, we show the experimental setup, consisting of the user interface developed for the proposed system, together with the experimental results. Full article

(This article belongs to the Special Issue Trends and Applications in Information Systems and Technologies)

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20 pages, 639 KiB

Open AccessSystematic Review

Investigating the Factors Influencing the Adoption of Blockchain Technology across Different Countries and Industries: A Systematic Literature Review

by Agostino Marengo and Alessandro Pagano

Electronics 2023, 12(14), 3006; https://doi.org/10.3390/electronics12143006 - 9 Jul 2023

Cited by 14 | Viewed by 5480

Abstract

Despite the reported disruptive nature of blockchain technology in the extant literature, its adoption is slower than its potential. This difference between the technology’s promises and its current adoption has sparked interest in understanding the factors impeding widespread adoption. This systematic literature review [...] Read more.

Despite the reported disruptive nature of blockchain technology in the extant literature, its adoption is slower than its potential. This difference between the technology’s promises and its current adoption has sparked interest in understanding the factors impeding widespread adoption. This systematic literature review (SLR), drawn from 1786 studies published between 2008 and May 2023, seeks to address this gap. Specifically, our research explores the influence of factors and their differences and commonalities on blockchain adoption. The SLR, examining individual and organisational perspectives, identifies 152 unique factors influencing 25 industries across 21 countries. This review also highlights distinct commonalities and variations in these factors across industries and countries. For instance, while regulatory issues and costs were universal concerns, the importance of technical understanding diverged between industries. Furthermore, country-specific factors, including local regulations and cultural aspects, emerged as significantly influenced insights that provide a comprehensive perspective on the dynamics of blockchain adoption, offering valuable guidance to industry practitioners and researchers striving to navigate the complexities of blockchain integration. Full article

(This article belongs to the Special Issue Trends and Applications in Information Systems and Technologies)

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