Editorial

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3 pages, 182 KiB

Open AccessEditorial

Knowledge Engineering and Data Mining

by Agnieszka Konys and Agnieszka Nowak-Brzezińska

Electronics 2023, 12(4), 927; https://doi.org/10.3390/electronics12040927 - 13 Feb 2023

Cited by 1 | Viewed by 1223

Abstract

Knowledge engineering and data mining are the two biggest pillars of modern intelligent systems [...] Full article

(This article belongs to the Special Issue Knowledge Engineering and Data Mining)

Research

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17 pages, 1941 KiB

Open AccessArticle

Knowledge Mining of Interactions between Drugs from the Extensive Literature with a Novel Graph-Convolutional-Network-Based Method

by Xingjian Xu, Fanjun Meng and Lijun Sun

Electronics 2023, 12(2), 311; https://doi.org/10.3390/electronics12020311 - 07 Jan 2023

Cited by 2 | Viewed by 1155

Abstract

Interactions between drugs can occur when two or more drugs are used for the same patient. This may result in changes in the drug’s pharmacological activity, some of which are beneficial and some of which are harmful. Thus, identifying possible drug–drug interactions (DDIs) [...] Read more.

Interactions between drugs can occur when two or more drugs are used for the same patient. This may result in changes in the drug’s pharmacological activity, some of which are beneficial and some of which are harmful. Thus, identifying possible drug–drug interactions (DDIs) has always been a crucial research topic in the field of clinical pharmacology. As clinical trials are time-consuming and expensive, current approaches for predicting DDIs are mainly based on knowledge mining from the literature using computational methods. However, since the literature contain a large amount of unrelated information, the task of identifying drug interactions with high confidence has become challenging. Thus, here, we present a novel graph-convolutional-network-based method called DDINN to detect potential DDIs. Combining cBiLSTM, graph convolutional networks and weight-rebalanced dependency matrix, DDINN is able to extract both contexture and syntactic information efficiently from the extensive biomedical literature. At last, we compare our DDINN with some other state-of-the-art models, and it is proved that our work is more effective. In addition, the ablation experiments demonstrate the advantages of DDINN’s optimization techniques as well. Full article

(This article belongs to the Special Issue Knowledge Engineering and Data Mining)

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25 pages, 9149 KiB

Open AccessArticle

An Ontology-Based Approach for Knowledge Acquisition: An Example of Sustainable Supplier Selection Domain Corpus

by Agnieszka Konys

Electronics 2022, 11(23), 4012; https://doi.org/10.3390/electronics11234012 - 03 Dec 2022

Cited by 4 | Viewed by 1643

Abstract

Selecting the right supplier is a critical decision in sustainable supply chain management. Sustainable supplier selection plays an important role in achieving a balance between the three pillars of a sustainable supply chain: economic, environmental, and social. One of the most crucial aspects [...] Read more.

Selecting the right supplier is a critical decision in sustainable supply chain management. Sustainable supplier selection plays an important role in achieving a balance between the three pillars of a sustainable supply chain: economic, environmental, and social. One of the most crucial aspects of running a business in this regard is sustainable supplier selection, and, to this end, an accurate and reliable approach is required. Therefore, the main contribution of this paper is to propose and implement an ontology-based approach for knowledge acquisition from the text for a sustainable supplier selection domain. This approach is dedicated to acquiring complex relationships from texts and coding these in the form of rules. The expected outcome is to enrich the existing domain ontology by these rules to obtain higher relational expressiveness, make reasoning, and produce new knowledge. Full article

(This article belongs to the Special Issue Knowledge Engineering and Data Mining)

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23 pages, 5735 KiB

Open AccessArticle

Classification of Task Types in Software Development Projects

by Włodzimierz Wysocki, Ireneusz Miciuła and Marcin Mastalerz

Electronics 2022, 11(22), 3827; https://doi.org/10.3390/electronics11223827 - 21 Nov 2022

Cited by 4 | Viewed by 1897

Abstract

Managing software development processes is still a serious challenge and offers the possibility of introducing improvements that will reduce the resources needed to successfully complete projects. The article presents the original concept of classification of types of project tasks, which will allow for [...] Read more.

Managing software development processes is still a serious challenge and offers the possibility of introducing improvements that will reduce the resources needed to successfully complete projects. The article presents the original concept of classification of types of project tasks, which will allow for more beneficial use of the collected data in management support systems in the IT industry. The currently used agile management methods—described in the article—and the fact that changes during the course of projects are inevitable, were the inspiration for creating sets of tasks that occur in software development. Thanks to statistics for generating tasks and aggregating results in an iterative and incremental way, the analysis is more accurate and allows planning the further course of work in the project, selecting the optimal number of employees in task teams, and identifying bottlenecks that may decide on faster completion of the project with success. The use of data from actual software projects in the IT industry made it possible to classify the types of tasks and the necessary values for further work planning, depending on the nature of the planned software development project. Full article

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16 pages, 2081 KiB

Open AccessArticle

Optimising Health Emergency Resource Management from Multi-Model Databases

by Juan C. Arias, Juan J. Cubillas and Maria I. Ramos

Electronics 2022, 11(21), 3602; https://doi.org/10.3390/electronics11213602 - 04 Nov 2022

Cited by 2 | Viewed by 1485

Abstract

The health care sector is one of the most sensitive sectors in our society, and it is believed that the application of specific and detailed database creation and design techniques can improve the quality of patient care. In this sense, better management of [...] Read more.

The health care sector is one of the most sensitive sectors in our society, and it is believed that the application of specific and detailed database creation and design techniques can improve the quality of patient care. In this sense, better management of emergency resources should be achieved. The development of a methodology to manage and integrate a set of data from multiple sources into a centralised database, which ensures a high quality emergency health service, is a challenge. The high level of interrelation between all of the variables related to patient care will allow one to analyse and make the right strategic decisions about the type of care that will be needed in the future, efficiently managing the resources involved in such care. An optimised database was designed that integrated and related all aspects that directly and indirectly affected the emergency care provided in the province of Jaén (city of Jaén, Andalusia, Spain) over the last eight years. Health, social, economic, environmental, and geographical information related to each of these emergency services was stored and related. Linear and nonlinear regression algorithms were used: support vector machine (SVM) with linear kernel and generated linear model (GLM), and the nonlinear SVM with Gaussian kernel. Predictive models of emergency demand were generated with a success rate of over 90%. Full article

(This article belongs to the Special Issue Knowledge Engineering and Data Mining)

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11 pages, 3472 KiB

Open AccessArticle

Design of Automatic Correction System for UAV’s Smoke Trajectory Angle Based on KNN Algorithm

by Pao-Yuan Chao, Wei-Chih Hsu and Wei-You Chen

Electronics 2022, 11(21), 3587; https://doi.org/10.3390/electronics11213587 - 03 Nov 2022

Cited by 1 | Viewed by 1013

Abstract

Unmanned aerial vehicles (UAVs) have evolved with the progress of science and technology in recent years. They combine high-tech, such as information and communications technology, mechanical power, remote control, and electric power storage. In the past, drones could be flown only via remote [...] Read more.

Unmanned aerial vehicles (UAVs) have evolved with the progress of science and technology in recent years. They combine high-tech, such as information and communications technology, mechanical power, remote control, and electric power storage. In the past, drones could be flown only via remote control, and the mounted cameras captured images from the air. Now, UAVs integrate new technologies such as 5G, AI, and IoT in Taiwan. They have a great application value in a high-altitude data acquisition, entertainment performances (such as night light shows and UAV shows with smoke), agriculture, and 3D modeling. UAVs are susceptible to the natural wind when spraying smoke into the air, which leads to a smoke track offset. This study developed an autocorrect system for UAV smoke tracing. An AI model was used to calculate smoke tube angle corrections so that smoke tube angles could be immediately corrected when smoke is sprayed. This led to smoke tracks being consistent with flight tracks. Full article

(This article belongs to the Special Issue Knowledge Engineering and Data Mining)

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17 pages, 2416 KiB

Open AccessArticle

Prediction of Offshore Wave at East Coast of Malaysia—A Comparative Study

by Mohammad Azad and Md. Alhaz Uddin

Electronics 2022, 11(16), 2527; https://doi.org/10.3390/electronics11162527 - 12 Aug 2022

Cited by 1 | Viewed by 1334

Abstract

Exploration of oil and gas in the offshore regions is increasing due to global energy demand. The weather in offshore areas is truly unpredictable due to the sparsity and unreliability of metocean data. Offshore structures may be affected by critical marine environments (severe [...] Read more.

Exploration of oil and gas in the offshore regions is increasing due to global energy demand. The weather in offshore areas is truly unpredictable due to the sparsity and unreliability of metocean data. Offshore structures may be affected by critical marine environments (severe storms, cyclones, etc.) during oil and gas exploration. In the interest of public safety, fast decisions must be made about whether to proceed or cancel oil and gas exploration, based on offshore wave estimates and anticipated wind speed provided by the Meteorological Department. In this paper, using the metocean data, the offshore wave height and period are predicted from the wind speed by three state-of-the-art machine learning algorithms (Artificial Neural Network, Support Vector Machine, and Random Forest). Such data has been acquired from satellite altimetry and calibrated and corrected by Fugro OCEANOR. The performance of the considered algorithms is compared by various metrics such as mean squared error, root mean squared error, mean absolute error, and coefficient of determination. The experimental results show that the Random Forest algorithm performs best for the prediction of wave period and the Artificial Neural Network algorithm performs best for the prediction of wave height. Full article

(This article belongs to the Special Issue Knowledge Engineering and Data Mining)

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12 pages, 5800 KiB

Open AccessArticle

A Computational Tool for Detection of Soft Tissue Landmarks and Cephalometric Analysis

by Mohammad Azad, Said Elaiwat and Mohammad Khursheed Alam

Electronics 2022, 11(15), 2408; https://doi.org/10.3390/electronics11152408 - 02 Aug 2022

Cited by 1 | Viewed by 1762

Abstract

In facial aesthetics, soft tissue landmark recognition and linear and angular measurement play a critical role in treatment planning. Visual identification and judgment by hand are time-consuming and prone to errors. As a result, user-friendly software solutions are required to assist healthcare practitioners [...] Read more.

In facial aesthetics, soft tissue landmark recognition and linear and angular measurement play a critical role in treatment planning. Visual identification and judgment by hand are time-consuming and prone to errors. As a result, user-friendly software solutions are required to assist healthcare practitioners in improving treatment planning. Our first goal in this paper is to create a computational tool that may be used to identify and save critical landmarks from patient X-ray pictures. The second goal is to create automated software that can assess the soft tissue facial profiles of patients in both linear and angular directions using the landmarks that have been identified. To boost the contrast, we employ gamma correction and a client-server web-based model to display the input images. Furthermore, we use the client-side to record landmarks in pictures and save the annotated landmarks to the database. The linear and angular measurements from the recorded landmarks are then calculated computationally and displayed to the user. Annotation and validation of 13 soft tissue landmarks were completed. The results reveal that our software accurately locates landmarks with a maximum deviation of 1.5 mm to 5 mm for the majority of landmarks. Furthermore, the linear and angular measurement variances across users are not large, indicating that the procedure is reliable. Full article

(This article belongs to the Special Issue Knowledge Engineering and Data Mining)

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21 pages, 1175 KiB

Open AccessArticle

Acquiring, Analyzing and Interpreting Knowledge Data for Sustainable Engineering Education: An Experimental Study Using YouTube

by Zoe Kanetaki, Constantinos Stergiou, Georgios Bekas, Sébastien Jacques, Christos Troussas, Cleo Sgouropoulou and Abdeldjalil Ouahabi

Electronics 2022, 11(14), 2210; https://doi.org/10.3390/electronics11142210 - 14 Jul 2022

Cited by 24 | Viewed by 2256

Abstract

With the immersion of a plethora of technological tools in the early post-COVID-19 era in university education, instructors around the world have been at the forefront of implementing hybrid learning spaces for knowledge delivery. The purpose of this experimental study is not only [...] Read more.

With the immersion of a plethora of technological tools in the early post-COVID-19 era in university education, instructors around the world have been at the forefront of implementing hybrid learning spaces for knowledge delivery. The purpose of this experimental study is not only to divert the primary use of a YouTube channel into a tool to support asynchronous teaching; it also aims to provide feedback to instructors and suggest steps and actions to implement in their teaching modules to ensure students’ access to new knowledge while promoting their engagement and satisfaction, regardless of the learning environment, i.e., face-to-face, distance and hybrid. Learners’ viewing habits were analyzed in depth from the channel’s 37 instructional videos, all of which were related to the completion of a computer-aided mechanical design course. By analyzing and interpreting data directly from YouTube channel reports, six variables were identified and tested to quantify the lack of statistically significant changes in learners’ viewing habits. Two time periods were specifically studied: 2020–2021, when instruction was delivered exclusively via distance education, and 2021–2022, in a hybrid learning mode. The results of both parametric and non-parametric statistical tests showed that “Number of views” and “Number of unique viewers” are the two variables that behave the same regardless of the two time periods studied, demonstrating the relevance of the proposed concept for asynchronous instructional support regardless of the learning environment. Finally, a forthcoming instructor’s manual for learning CAD has been developed, integrating the proposed methodology into a sustainable academic educational process. Full article

(This article belongs to the Special Issue Knowledge Engineering and Data Mining)

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22 pages, 3167 KiB

Open AccessArticle

An RG-FLAT-CRF Model for Named Entity Recognition of Chinese Electronic Clinical Records

by Jiakang Li, Ruixia Liu, Changfang Chen, Shuwang Zhou, Xiaoyi Shang and Yinglong Wang

Electronics 2022, 11(8), 1282; https://doi.org/10.3390/electronics11081282 - 18 Apr 2022

Cited by 7 | Viewed by 2116

Abstract

The goal of Clinical Named Entity Recognition (CNER) is to identify clinical terms from medical records, which is of great importance for subsequent clinical research. Most of the current Chinese CNER models use a single set of features that do not consider the [...] Read more.

The goal of Clinical Named Entity Recognition (CNER) is to identify clinical terms from medical records, which is of great importance for subsequent clinical research. Most of the current Chinese CNER models use a single set of features that do not consider the linguistic characteristics of the Chinese language, e.g., they do not use both word and character features, and they lack morphological information and specialized lexical information on Chinese characters in the medical field. We propose a RoBerta Glyce-Flat Lattice Transformer-CRF (RG-FLAT-CRF) model to address this problem. The model uses a convolutional neural network to discern the morphological information hidden in Chinese characters, and a pre-trained model to obtain vectors with medical features. The different vectors are stitched together to form a multi-feature vector. To use lexical information and avoid the problem of word separation errors, the model uses a lattice structure to add lexical information associated with each word, which can be used to avoid the problem of word separation errors. The RG-FLAT-CRF model scored 95.61%, 85.17%, and 91.2% for F1 on the CCKS 2017, 2019, and 2020 datasets, respectively. We used statistical tests to compare with other models. The results show that most p-values less than 0.05 are statistically significant. Full article

(This article belongs to the Special Issue Knowledge Engineering and Data Mining)

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13 pages, 378 KiB

Open AccessArticle

Aggregation of Rankings Using Metaheuristics in Recommendation Systems

by Michał Bałchanowski and Urszula Boryczka

Electronics 2022, 11(3), 369; https://doi.org/10.3390/electronics11030369 - 26 Jan 2022

Cited by 7 | Viewed by 3061

Abstract

Recommendation systems are a powerful tool that is an integral part of a great many websites. Most often, recommendations are presented in the form of a list that is generated by using various recommendation methods. Typically, however, these methods do not generate identical [...] Read more.

Recommendation systems are a powerful tool that is an integral part of a great many websites. Most often, recommendations are presented in the form of a list that is generated by using various recommendation methods. Typically, however, these methods do not generate identical recommendations, and their effectiveness varies between users. In order to solve this problem, the application of aggregation techniques was suggested, the aim of which is to combine several lists into one, which, in theory, should improve the overall quality of the generated recommendations. For this reason, we suggest using the Differential Evolution algorithm, the aim of which will be to aggregate individual lists generated by the recommendation algorithms and to create a single list that will be fine-tuned to the user’s preferences. Additionally, based on our previous research, we present suggestions to speed up this process. Full article

(This article belongs to the Special Issue Knowledge Engineering and Data Mining)

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17 pages, 1590 KiB

Open AccessArticle

Evaluation of Feature Selection Methods on Psychosocial Education Data Using Additive Ratio Assessment

by Fitriani Muttakin, Jui-Tang Wang, Mulyanto Mulyanto and Jenq-Shiou Leu

Electronics 2022, 11(1), 114; https://doi.org/10.3390/electronics11010114 - 30 Dec 2021

Cited by 5 | Viewed by 1988

Abstract

Artificial intelligence, particularly machine learning, is the fastest-growing research trend in educational fields. Machine learning shows an impressive performance in many prediction models, including psychosocial education. The capability of machine learning to discover hidden patterns in large datasets encourages researchers to invent data [...] Read more.

Artificial intelligence, particularly machine learning, is the fastest-growing research trend in educational fields. Machine learning shows an impressive performance in many prediction models, including psychosocial education. The capability of machine learning to discover hidden patterns in large datasets encourages researchers to invent data with high-dimensional features. In contrast, not all features are needed by machine learning, and in many cases, high-dimensional features decrease the performance of machine learning. The feature selection method is one of the appropriate approaches to reducing the features to ensure machine learning works efficiently. Various selection methods have been proposed, but research to determine the essential subset feature in psychosocial education has not been established thus far. This research investigated and proposed methods to determine the best feature selection method in the domain of psychosocial education. We used a multi-criteria decision system (MCDM) approach with Additive Ratio Assessment (ARAS) to rank seven feature selection methods. The proposed model evaluated the best feature selection method using nine criteria from the performance metrics provided by machine learning. The experimental results showed that the ARAS is promising for evaluating and recommending the best feature selection method for psychosocial education data using the teacher’s psychosocial risk levels dataset. Full article

(This article belongs to the Special Issue Knowledge Engineering and Data Mining)

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27 pages, 3025 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Integration Strategy and Tool between Formal Ontology and Graph Database Technology

by Stefano Ferilli

Electronics 2021, 10(21), 2616; https://doi.org/10.3390/electronics10212616 - 26 Oct 2021

Cited by 22 | Viewed by 2785

Abstract

Ontologies, and especially formal ones, have traditionally been investigated as a means to formalize an application domain so as to carry out automated reasoning on it. The union of the terminological part of an ontology and the corresponding assertional part is known as [...] Read more.

Ontologies, and especially formal ones, have traditionally been investigated as a means to formalize an application domain so as to carry out automated reasoning on it. The union of the terminological part of an ontology and the corresponding assertional part is known as a Knowledge Graph. On the other hand, database technology has often focused on the optimal organization of data so as to boost efficiency in their storage, management and retrieval. Graph databases are a recent technology specifically focusing on element-driven data browsing rather than on batch processing. While the complementarity and connections between these technologies are patent and intuitive, little exists to bring them to full integration and cooperation. This paper aims at bridging this gap, by proposing an intermediate format that can be easily mapped onto the formal ontology on one hand, so as to allow complex reasoning, and onto the graph database on the other, so as to benefit from efficient data handling. Full article

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22 pages, 614 KiB

Open AccessArticle

Credit Decision Support Based on Real Set of Cash Loans Using Integrated Machine Learning Algorithms

by Paweł Ziemba, Jarosław Becker, Aneta Becker, Aleksandra Radomska-Zalas, Mateusz Pawluk and Dariusz Wierzba

Electronics 2021, 10(17), 2099; https://doi.org/10.3390/electronics10172099 - 30 Aug 2021

Cited by 11 | Viewed by 2319

Abstract

One of the important research problems in the context of financial institutions is the assessment of credit risk and the decision to whether grant or refuse a loan. Recently, machine learning based methods are increasingly employed to solve such problems. However, the selection [...] Read more.

One of the important research problems in the context of financial institutions is the assessment of credit risk and the decision to whether grant or refuse a loan. Recently, machine learning based methods are increasingly employed to solve such problems. However, the selection of appropriate feature selection technique, sampling mechanism, and/or classifiers for credit decision support is very challenging, and can affect the quality of the loan recommendations. To address this challenging task, this article examines the effectiveness of various data science techniques in issue of credit decision support. In particular, processing pipeline was designed, which consists of methods for data resampling, feature discretization, feature selection, and binary classification. We suggest building appropriate decision models leveraging pertinent methods for binary classification, feature selection, as well as data resampling and feature discretization. The selected models’ feasibility analysis was performed through rigorous experiments on real data describing the client’s ability for loan repayment. During experiments, we analyzed the impact of feature selection on the results of binary classification, and the impact of data resampling with feature discretization on the results of feature selection and binary classification. After experimental evaluation, we found that correlation-based feature selection technique and random forest classifier yield the superior performance in solving underlying problem. Full article

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14 pages, 343 KiB

Open AccessArticle

Parallel Tiled Code for Computing General Linear Recurrence Equations

by Włodzimierz Bielecki and Piotr Błaszyński

Electronics 2021, 10(17), 2050; https://doi.org/10.3390/electronics10172050 - 25 Aug 2021

Cited by 1 | Viewed by 1388

Abstract

In this article, we present a technique that allows us to generate parallel tiled code to calculate general linear recursion equations (GLRE). That code deals with multidimensional data and it is computing-intensive. We demonstrate that data dependencies available in an original code computing [...] Read more.

In this article, we present a technique that allows us to generate parallel tiled code to calculate general linear recursion equations (GLRE). That code deals with multidimensional data and it is computing-intensive. We demonstrate that data dependencies available in an original code computing GLREs do not allow us to generate any parallel code because there is only one solution to the time partition constraints built for that program. We show how to transform the original code to another one that exposes dependencies such that there are two linear distinct solutions to the time partition restrictions derived from these dependencies. This allows us to generate parallel 2D tiled code computing GLREs. The wavefront technique is used to achieve parallelism, and the generated code conforms to the OpenMP C/C++ standard. The experiments that we conducted with the resulting parallel 2D tiled code show that this code is much more efficient than the original serial code computing GLREs. Code performance improvement is achieved by allowing parallelism and better locality of the target code. Full article

(This article belongs to the Special Issue Knowledge Engineering and Data Mining)

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Review

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28 pages, 5599 KiB

Open AccessReview

A Systematic Review of the Applications of Multi-Criteria Decision Aid Methods (1977–2022)

by Marcio Pereira Basílio, Valdecy Pereira, Helder Gomes Costa, Marcos Santos and Amartya Ghosh

Electronics 2022, 11(11), 1720; https://doi.org/10.3390/electronics11111720 - 28 May 2022

Cited by 83 | Viewed by 7489

Abstract

Multicriteria methods have gained traction in academia and industry practices for effective decision-making. This systematic review investigates and presents an overview of multi-criteria approaches research conducted over forty-four years. The Web of Science (WoS) and Scopus databases were searched for papers on multi-criteria [...] Read more.

Multicriteria methods have gained traction in academia and industry practices for effective decision-making. This systematic review investigates and presents an overview of multi-criteria approaches research conducted over forty-four years. The Web of Science (WoS) and Scopus databases were searched for papers on multi-criteria methods with titles, abstracts, keywords, and articles from January 1977 to 29 April 2022. Using the R Bibliometrix tool, the bibliographic data was evaluated. According to this bibliometric analysis, in 131 countries over the past forty-four years, 33,201 authors have written 23,494 documents on multi-criteria methods. This area’s scientific output increases by 14.18 percent every year. China has the highest percentage of publications at 18.50 percent, followed by India at 10.62 percent and Iran at 7.75 percent. Islamic Azad University has the most publications with 504, followed by Vilnius Gediminas Technical University with 456 and the National Institute of Technology with 336. Expert Systems with Applications, Sustainability, and the Journal of Cleaner Production are the top journals, accounting for over 4.67 percent of all indexed works. In addition, E. Zavadskas and J. Wang have the most papers in the multi-criteria approaches sector. AHP, followed by TOPSIS, VIKOR, PROMETHEE, and ANP, is the most popular multi-criteria decision-making method among the ten nations with the most publications in this field. The bibliometric literature review method enables researchers to investigate the multi-criteria research area in greater depth than the conventional literature review method. It allows a vast dataset of bibliographic records to be statistically and systematically evaluated, producing insightful insights. This bibliometric study is helpful because it provides an overview of the issue of multi-criteria techniques from the past forty-four years, allowing other academics to use this research as a starting point for their studies. Full article

(This article belongs to the Special Issue Knowledge Engineering and Data Mining)

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