Algorithmic Approaches, Practical Implementations and Future Research Directions in Machine Learning

Machine learning has become a disruptive force that is advancing technology and changing industries. With an emphasis on algorithmic techniques, real-world applications, and important future research avenues, this study examines the rapidly changing field of machine learning. It explores the fundame...

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Published in2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N) pp. 121 - 126
Main Authors Sharma, Vaishali, Sharma, Deepank, Kumar Punia, Sanjeev
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.12.2024
Subjects
Algorithmic Approaches
Cross-Domain Collaboration
Ethical AI
Explainable AI
Finance
Force
Future Research Directions
Industries
Learning (artificial intelligence)
Machine Learning
Machine learning algorithms
Medical services
Multimodal AI
Practical Implementations
Privacy-Preserving Learning
Quantum Machine Learning
Reviews
Robustness
Semi-Supervised Learning
Semisupervised learning
Transportation
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Summary:Machine learning has become a disruptive force that is advancing technology and changing industries. With an emphasis on algorithmic techniques, real-world applications, and important future research avenues, this study examines the rapidly changing field of machine learning. It explores the fundamentals of supervised, unsupervised, and semi-supervised learning algorithms and highlights how they are used in a variety of fields, including autonomous systems, healthcare, and finance. The report highlights the potential of several important future research directions, such as explainable AI, robust and privacy-preserving learning, quantum machine learning, and multimodal AI, to overcome present constraints and open up hitherto unheard-of possibilities. These multidisciplinary research avenues emphasize the value of interdisciplinary cooperation in addressing difficult problems and guaranteeing the creation of morally sound and significant AI systems. In order to help academics and practitioners who want to progress the subject, this review attempts to give a thorough grasp of the present situation and potential future direction of machine learning.
DOI:10.1109/ICAC2N63387.2024.10895837