Real-World Applications of Machine Learning Techniques

Dear Colleagues,

We invite researchers and practitioners to submit their original research papers to this Special Issue, titled "Real-World Applications of Machine Learning Techniques", where our aim is to foster collaboration on and discussion of cutting-edge machine learning techniques and their impact on real-world domains, with a special emphasis on medicine and education.

Topics of Interest:

We welcome submissions on a wide range of topics related to machine learning, including, but not limited to:

Machine Learning in Medicine:

- Disease diagnosis and prognosis;

- Drug discovery and personalized medicine;

- Healthcare management and optimization;

- Natural language processing in healthcare.

Machine Learning in Education:

- Intelligent tutoring systems;

- Adaptive learning platforms;

- Educational data mining;

- Personalized and gamified learning;

- Educational networks.

Machine Learning in other Domains:

- Manufacturing and industry;

- Robotics and automation.

Dr. Dimitrios Karapiperis
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. Information is an international peer-reviewed open access monthly 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 1600 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.

• machine learning
• medicine
• education

Title: Unsupervised Learning in NBA Injury Recovery: Advanced Data Mining to Decode Recovery Durations and Economic Impacts
Authors: G. Papageorgiou, V. Sarlis and Christos Tjortjis*
Affiliation: -
Abstract: This study utilizes advanced data mining and machine learning to examine player injuries in the National Basketball Association (NBA) from 2000-01 to 2022-23. Analyzing a dataset of 2,296 players, including performance, injury records, and financial data, the research investigates rela-tionships between injury types, recovery durations, and socioeconomic impacts. Our methodology involves data collection, engineering, and mining, applying techniques like DBSCAN, isolation forest, and Z-score for anomaly detection, along with the Apriori algorithm for association rule mining. Anomaly detection revealed 189 anomalies (1.04% of cases), highlighting unusual recovery durations and factors influencing recovery beyond physical healing. Association rule mining in-dicated shorter recovery times for lower extremity injuries and a 95% confidence level for quick returns from 'Rest' injuries, affirming NBA's treatment and rest policies. Additionally, economic factors were observed, with players in lower salary brackets experiencing shorter recoveries, pointing to financial influences on recovery decisions.

Title: Enhancing Distance Education through Exploitation of Student Networks: Unleashing the Power of Connectivity
Authors: Rozita Tsoni1, Evgenia Paxinou1, Aris Gkoulalas-Divanis2, Dimitrios Karapiperis3, Dimitrios Kalles1 & Vassilios S. Verykios1
Affiliation: 1 Hellenic Open University, Patras, GREECE 2 Merative, Healthcare, Dublin, IRELAND 3 International Hellenic University, Thermi, GREECE
Abstract: Distance Learning has become the new standard, especially after the pandemic and due to the technological advances, that are incorporated into the teaching procedure. At the same time, the augmented use of the internet has blurred the borders between distance and conventional learning. Students interact mainly through LMSs, leaving their digital traces that can be leveraged to improve the educational process. This work aims to propose a model that can capture the students' behaviors based on the clickstream data associated with the discussion forum and additionally to suggest interpretable patterns that will support education administrators and tutors in the decision-making process. To achieve our goal, we use Social Network Analysis (SNA) as networks represent complex interactions in a meaningful and easily interpretable way. Moreover, simple or complex network metrics are becoming available to provide valuable insights into the students’ social interaction. This study concludes that by leveraging the imprint of these actions in an LMS and using metrics of SNA, differences can be spotted in the communication patterns that go beyond simple participation recording. Although HITS and PageRank algorithms were created with completely different targeting, it is shown that they can also reveal methodological features in students’ communication approaches.