Expression of Concern for: Applying Machine Learning Techniques to Increase Real-Time Data Analysis Accuracy

• Published in: 2023 International Conference on Emerging Research in Computational Science (ICERCS) p. 1
• Main Authors: Naval, Preeti, T R, Mahesh, Kumar, Ajay, Dhingra, Lovish, V, Janakiraman, Banchhor, Chitrakant O.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 07-12-2023
• DOI: 10.1109/ICERCS57948.2023.10704769

facts analysis examines, transforms, and models record sets to uncover valuable data and help make higher selections. Machine mastering techniques allow computer systems to automatically learn from records to make predictions primarily based on previously discovered patterns. By using these strategies, businesses can grow actual-time records analysis accuracy and see tendencies faster, allowing them to promptly respond to adjustments inside the surroundings. This paper will speak about using system studying techniques for actual-time statistics evaluation and the benefits of using such strategies. Mainly, techniques that include supervised studying, unsupervised learning, and deep studying will be discussed. Moreover, use instances in which system mastering may be applied, including economic fraud detection software programs, digital advertising and marketing, and predictive preservation, will be explored. Eventually, the outcomes of using system mastering strategies for data analysis, including statistics privateness and safety issues, can be mentioned.