Recent Advances and Applications of Machine Learning in Sensor-Based Business and Industry
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 2555
Special Issue Editor
Interests: AI; machine learning; information systems; software quality; mobile computing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In an era of unprecedented technological innovation, machine learning has emerged as a transformative force that is reshaping the landscape of business and industry worldwide. Sensors play a critical role in a wide range of industries. As organizations leverage sensor technologies to develop innovative solutions and seek to harness the power of data, artificial intelligence and advanced analytics, the integration of machine learning has become critical to driving efficiencies, improving decision-making and unlocking new opportunities for growth and innovation.
Thus, it is our pleasure to invite you to contribute to a Special Issue of our esteemed journal dedicated to the recent advances in and applications of machine learning in sensor-based business and industry. This Special Issue aims to showcase cutting-edge research, methods, methodologies and real-world applications that highlight the recent applications and solutions in a range of sectors such as agriculture, energy management, environmental monitoring, defense and security, financial services, healthcare, manufacturing and industrial processes, smart buildings, smart cities, transportation and logistics, retail, etc..
Contributors to this Special Issue are invited to explore the many dimensions of machine learning, including its role in predictive analytics, pattern recognition, natural language processing, signal processing and reinforcement learning. We encourage submissions that not only elucidate theoretical advances, but also provide practical insights into the deployment and implementation of machine learning solutions in real-world business and industrial settings.
By bringing together diverse perspectives and expertise, we aim to create a comprehensive collection of articles that not only showcase the state of the art in machine learning, but also provide valuable insights for practitioners, researchers and policy makers navigating the evolving landscape of technology-driven business transformation.
We look forward to receiving your contributions and believe that this Special Issue will make a significant contribution to the ongoing discourse on the dynamic intersection of sensing technologies with machine learning methods and systems.
Dr. Paweł Weichbroth
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. Sensors 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 2600 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
- sensing technology
- machine learning
- deep learning
- natural language processing
- signal processing
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