Recent Developments in Data Science and Knowledge Discovery

A special issue of Applied System Innovation (ISSN 2571-5577). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 835

Special Issue Editor


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Guest Editor
Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH 44115, USA
Interests: human-centered systems; machine learning; data science; distributed computing; blockchain
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Special Issue Information

Dear Colleagues,

The Special Issue aims to highlight the latest advancements and emerging trends in the fields of data science and knowledge discovery. This issue brings together a collection of high-quality research papers, reviews, and case studies that explore innovative methodologies, tools, and applications driving the field forward.

Methodologies: We are inviting examinations of techniques for managing, processing, and analyzing large-scale datasets, which includes advancements in distributed computing, cloud-based analytics, and real-time data processing frameworks; explorations of new methods for extracting meaningful patterns and insights from complex datasets, including clustering, association rule mining, and sequential pattern mining; and papers that explore advancements in supervised, unsupervised, and reinforcement learning, with applications ranging from natural language processing to image recognition and beyond.

Topics include improvements in algorithm efficiency, scalability, and interpretability, as well as the development of hybrid models that combine different machine-learning approaches for enhanced performance.

Tools: We welcome introductions to new tools, platforms, and frameworks that facilitate data science workflows; an emphasis on open-source solutions and platforms that enhance collaboration, reproducibility, and scalability in data science projects; and reviews of integrated development environments, data visualization tools, and data management systems that support efficient data analysis and knowledge discovery.

Applications: We are interested in showcasing how data science and knowledge discovery techniques are being applied across various disciplines; papers that illustrate the impact of data-driven approaches in fields such as environmental science, education, and public policy; and innovative applications that demonstrate the transformative potential of data science in solving real-world problems and improving societal outcomes.

Prof. Dr. Wenbing Zhao
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. Applied System Innovation 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 1400 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

  • big data analytics
  • data mining
  • knowledge discovery
  • natural language processing
  • image recognition
  • predictive analytics
  • anomaly detection
  • decision-making

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Published Papers (1 paper)

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Research

13 pages, 44634 KiB  
Article
Predictive and Explainable Machine Learning Models for Endocrine, Nutritional, and Metabolic Mortality in Italy Using Geolocalized Pollution Data
by Donato Romano, Michele Magarelli, Pierfrancesco Novielli, Domenico Diacono, Pierpaolo Di Bitonto, Nicola Amoroso, Alfonso Monaco, Roberto Bellotti and Sabina Tangaro
Appl. Syst. Innov. 2025, 8(2), 48; https://doi.org/10.3390/asi8020048 - 1 Apr 2025
Viewed by 352
Abstract
This study investigated the predictive performance of three regression models—Gradient Boosting (GB), Random Forest (RF), and XGBoost—in forecasting mortality due to endocrine, nutritional, and metabolic diseases across Italian provinces. Utilizing a dataset encompassing air pollution metrics and socio-economic indices, the models were trained [...] Read more.
This study investigated the predictive performance of three regression models—Gradient Boosting (GB), Random Forest (RF), and XGBoost—in forecasting mortality due to endocrine, nutritional, and metabolic diseases across Italian provinces. Utilizing a dataset encompassing air pollution metrics and socio-economic indices, the models were trained and tested to evaluate their accuracy and robustness. Performance was assessed using metrics such as coefficient of determination (r2), mean absolute error (MAE), and root mean squared error (RMSE), revealing that GB outperformed both RF and XGB, offering superior predictive accuracy and model stability (r2 = 0.55, MAE = 0.17, and RMSE = 0.05). To further interpret the results, SHAP (SHapley Additive exPlanations) analysis was applied to the best-performing model to identify the most influential features driving mortality predictions. The analysis highlighted the critical roles of specific pollutants, including benzene and socio-economic factors such as life quality and instruction, in influencing mortality rates. These findings underscore the interplay between environmental and socio-economic determinants in health outcomes and provide actionable insights for policymakers aiming to reduce health disparities and mitigate risk factors. By combining advanced machine learning techniques with explainability tools, this research demonstrates the potential for data-driven approaches to inform public health strategies and promote targeted interventions in the context of complex environmental and social determinants of health. Full article
(This article belongs to the Special Issue Recent Developments in Data Science and Knowledge Discovery)
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