Application of Machine Learning in Data Analysis and Process
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 1776
Special Issue Editor
Special Issue Information
Dear Colleagues,
The application of machine learning (ML) in data analysis and data processing has transformed the way insights are extracted and decisions are made from complex datasets. ML techniques, including predictive analytics, anomaly detection, natural language processing, image analysis, recommendation systems, and process optimization, offer valuable capabilities that complement traditional statistical modeling approaches.
ML algorithms enhance predictive analytics by leveraging historical data to make accurate forecasts and support proactive decision making. Anomaly detection powered by ML enables the identification of outliers and patterns indicative of fraud, network intrusions, and quality control issues. ML's integration with natural language processing facilitates sentiment analysis, text classification, and information extraction from unstructured text sources.
In image analysis, ML algorithms excel at tasks such as object recognition, facial recognition, and content moderation, with applications across healthcare, security, and multimedia content management. ML-based recommendation systems provide personalized suggestions, enhancing user experiences and engagement. Additionally, ML optimization techniques improve process efficiency by identifying patterns and optimizing workflows, resource allocation, and cost reduction.
The strengths of ML lie in its ability to handle complex datasets, process unstructured data types, and automate decision-making processes. However, ongoing research is needed to address challenges related to interpretability, fairness, scalability, and adapting to new data sources.
In summary, the integration of ML with data analysis and processing offers a powerful approach to extract insights, make accurate predictions, and optimize processes. ML complements traditional statistical modeling techniques, expanding their capabilities and enabling advanced analyses. The continuous advancement of ML holds immense potential for unlocking deeper insights, driving data-driven decision making, and addressing the challenges posed by complex and ever-growing datasets.
Dr. Kenan Matawie
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 Sciences 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.
