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Special Issue "Knowledge Extraction from Data Using Machine Learning"
A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".
Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 37373
Special Issue Editor
Interests: acoustics; architecture; digital signal processing; sound; audio signal processing; acoustic signal processing; acoustic analysis; acoustics and acoustic engineering; sound analysis; noise analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Machine Learning is a field of artificial intelligence that deals with the creation of algorithms and systems capable of extracting new knowledge from input data. It is widely used in disciplines including physics, mathematics, statistics, and mechanics as an alternative to classical data analysis procedures. In many fields, including computer vision, image processing, speech processing, and pattern recognition, thanks to the use of algorithms based on machine learning, we have witnessed a progressive technological evolution that has led to the processing of intelligent machines. Machine learning represents a form of adaptation of the system to the environment through experience, similar to what happens to every living being. This adaptation of the system to the environment through experience is aims to lead to an improvement without relying on continuous human intervention. To achieve this, the system must be able to learn—that is, it must be able to extract useful information on a given problem by examining a series of examples associated with it. The constant increase in the amount of data produced daily and the high growth in the computing capacity of computers are two key factors that have contributed to the development of new data analysis methodologies. Machine learning is used in science to facilitate research on the collection, classification, and correlation of data. Instead, companies are increasingly using these algorithms to extract knowledge from data in order to develop models to support strategic decisions and create value. To obtain this result, it is essential to obtain the key information which makes it possible to create knowledge.
The purpose of this Special Issue is to collect scientific contributions that demonstrate the widespread use of machine-learning-based applications to extract knowledge from data. Therefore, original research articles as well as review articles will be welcome, containing examples of works based on these technologies in the most popular fields: natural sciences, healthcare, medicine, finance, business, and economics.
Dr. Giuseppe Ciaburro
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. Data 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.