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Electronics
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Applied Data Mining
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Applied Data Mining

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 9713

Special Issue Editor

Prof. Dr. Grzegorz Dudek

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Czestochowa University of Technology, 42-201 Częstochowa, Poland
Interests: machine learning; data mining; artificial intelligence; pattern recognition; evolutionary computation and their application to classification; regression; forecasting and optimization problems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data mining as a process of extracting patterns and knowledge from large amounts of data is one of the most exciting fields in computer science and statistics. Over the past few decades, data mining has become an entrenched part of everyday life and has been successfully used to solve practical problems. Data mining techniques have been widely applied to problems in industry, science, engineering, business, finance, medicine, biology, and many other domains. Application success has led to an explosion in demand for novel data mining technologies addressed for different types of data, such as time series, sequences, streams, text and hypertext, spatial, spatiotemporal, and multimedia data, visual data, biological data, etc.

This Special Issue focuses on applied work addressing real-world problems and systems demonstrating the tangible impact in their respective domains. Application papers are expected describing designs and implementations of solutions and systems for practical tasks in data mining, data analytics, data science, and applied machine learning in a diverse range of fields and problems. Papers should report substantive results on a wide range of data mining technics, discussing problems and methods, critical comparisons with existing techniques, and interpretation of results. Specific attention will be given to recently developed data mining methods. Potential topics include but are not limited to data mining applications for:

  • Financial data analysis;
  • Retail industry;
  • Telecommunication industry;
  • Power systems;
  • Scientific and statistical applications;
  • Software engineering and computer system analysis;
  • Medicine and healthcare;
  •  Biology.

Prof. Dr. Grzegorz Dudek
Guest Editor

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

  • data mining
  • data analytics
  • data science
  • applied machine learning

Published Papers (3 papers)

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Research

15 pages, 1064 KiB  
Article
Improved Heart Disease Prediction Using Particle Swarm Optimization Based Stacked Sparse Autoencoder
by Ibomoiye Domor Mienye and Yanxia Sun
Electronics 2021, 10(19), 2347; https://doi.org/10.3390/electronics10192347 - 25 Sep 2021
Cited by 48 | Viewed by 5269
Abstract
Heart disease is the leading cause of death globally. The most common type of heart disease is coronary heart disease, which occurs when there is a build-up of plaque inside the arteries that supply blood to the heart, making blood circulation difficult. The [...] Read more.
Heart disease is the leading cause of death globally. The most common type of heart disease is coronary heart disease, which occurs when there is a build-up of plaque inside the arteries that supply blood to the heart, making blood circulation difficult. The prediction of heart disease is a challenge in clinical machine learning. Early detection of people at risk of the disease is vital in preventing its progression. This paper proposes a deep learning approach to achieve improved prediction of heart disease. An enhanced stacked sparse autoencoder network (SSAE) is developed to achieve efficient feature learning. The network consists of multiple sparse autoencoders and a softmax classifier. Additionally, in deep learning models, the algorithm’s parameters need to be optimized appropriately to obtain efficient performance. Hence, we propose a particle swarm optimization (PSO) based technique to tune the parameters of the stacked sparse autoencoder. The optimization by the PSO improves the feature learning and classification performance of the SSAE. Meanwhile, the multilayer architecture of autoencoders usually leads to internal covariate shift, a problem that affects the generalization ability of the network; hence, batch normalization is introduced to prevent this problem. The experimental results show that the proposed method effectively predicts heart disease by obtaining a classification accuracy of 0.973 and 0.961 on the Framingham and Cleveland heart disease datasets, respectively, thereby outperforming other machine learning methods and similar studies. Full article
(This article belongs to the Special Issue Applied Data Mining)
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14 pages, 2344 KiB  
Article
D-GENE-Based Discovery of Frequent Occupational Diseases among Female Home-Based Workers
by Muhammad Yasir, Ayesha Ashraf, Muhammad Umar Chaudhry, Farhad Hassan, Jee-Hyong Lee, Michał Jasiński, Zbigniew Leonowicz and Elżbieta Jasińska
Electronics 2021, 10(11), 1230; https://doi.org/10.3390/electronics10111230 - 21 May 2021
Cited by 1 | Viewed by 1948
Abstract
A considerable fraction of the female workforce worldwide is making ends meet by doing various jobs informally at home or in nearby places, rather than at employers’ premises. The contribution of these female home-based workers (FHBWs) is significant to the country’s economic growth. [...] Read more.
A considerable fraction of the female workforce worldwide is making ends meet by doing various jobs informally at home or in nearby places, rather than at employers’ premises. The contribution of these female home-based workers (FHBWs) is significant to the country’s economic growth. FHBWs are often confronted with numerous occupational diseases due to a lack of awareness of occupational safety and health measures, and unhealthy living and working conditions. The informality of FHBWs prevents them from getting proper healthcare, safety, and other dispensations enjoyed by formal employees. Despite their undeniable importance, health issues of FHBWs are still overlooked. This study is an attempt to discover the frequent co-occurring occupational diseases encountered by FHBWs in Punjab, a province of Pakistan. Frequent itemset mining (FIM) or co-occurrence grouping is a technique of data science that identifies the associations among different entities in the data. Based on FIM, the D-GENE algorithm is applied in this study to efficiently discover frequent co-occurring diseases in the data obtained from the Punjab Home-based Workers Survey (2016). The far-reaching goal of the study is to bring awareness of the occupational health issues and safety risks to the health authorities as well as to the FHBWs. Full article
(This article belongs to the Special Issue Applied Data Mining)
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19 pages, 1065 KiB  
Article
Partitioning Power Grid for the Design of the Zonal Energy Market While Preserving Control Area Constraints
by Marcin Blachnik, Karol Wawrzyniak and Marcin Jakubek
Electronics 2021, 10(5), 610; https://doi.org/10.3390/electronics10050610 - 5 Mar 2021
Cited by 1 | Viewed by 1461
Abstract
The use of a zonal structure for energy markets across the globe is expanding; however the debate on how to effectively partition the grid into bidding zones is still open for discussion. One of the factors that needs to be addressed in the [...] Read more.
The use of a zonal structure for energy markets across the globe is expanding; however the debate on how to effectively partition the grid into bidding zones is still open for discussion. One of the factors that needs to be addressed in the process of bidding zones’ delimitation is the transmission system operators control areas. Merging parts of different control areas into one bidding zone can lead to multiple problems, ranging from political, through grid security concerns, to reserve control issues. To address it, this paper presents a novel grid partitioning method aimed at bidding zones delimitation that is based on clustering the power grid using an extended version of the standard agglomerative clustering. The proposed solution adds additional clustering rules when constructing the dendrogram in order to take into account the control areas. The algorithm is applied to the data which represents the locational marginal prices obtained from optimal power flow analysis. Full article
(This article belongs to the Special Issue Applied Data Mining)
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