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

• Published in: 2024 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
• Language: English
• 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
• DOI: 10.1109/ICAC2N63387.2024.10895837

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.