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Machine Learning for Brand Protection: A Review of a Proactive Defense Mechanism
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Author(s): Kutubuddin Sayyad Liyakat Kazi (Baldev Ram Mirdha Institute of Technology, India), Sunita Sunil Shinde (Walchand College of Engineering, India), Priya Mangesh Nerkar (N. K. Orchid College of Engineering and Technology, Solapur, India), Sultanabanu SayyadLiyakat Kazi (Brahmdevdada Mane Institute of Technology, India)and Vahidabegam SayyadLiyakat Kazi (ZPP School, India)
Copyright: 2025
Pages: 46
Source title:
Avoiding Ad Fraud and Supporting Brand Safety: Programmatic Advertising Solutions
Source Author(s)/Editor(s): Muhammad Ibrahim Khan (Iqra University, Pakistan)and Mirza Amin Ul Haq (Dr. Ziauddin University, Pakistan)
DOI: 10.4018/979-8-3693-7041-4.ch007
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Abstract
As ML technology continues to evolve, we can expect even more sophisticated solutions to protect brands and consumers alike. By embracing this exciting technology, brands can proactively address the challenges of counterfeit goods and ensure a brighter future for genuine products. ML is a powerful tool for advertising and brand protection, but its potential for misuse requires careful consideration. By embracing responsible AI/ML practices and investing in proactive defense mechanisms, brands can harness the benefits of ML while mitigating the risks. This will ensure that ML remains a force for good in the evolving world of online advertising and brand management. Here we have the study of Random Forest, Support vector machine, K-Nearest Neighbor, Artificial Neural Networks, Decision Tree, and Natural Language Processing. We studied the said approached from the point of view of Roles of these approaches, benefits, problems and restrictions on brands and advertisement protection.
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