Developing a Virtual Assistant with Machine Learning and Natural Language Processing for Enhanced User Interaction
Keywords:
virtual assistants; Natural language processing; Machine learning; Privacy-preserving; Real-world Applications; User-Centric Systems; Voice-Activated Smart Speakers.
Abstract
From personal job management to commercial operations, virtual assistants are essential. Machine learning (ML) and natural language processing (NLP) are used in this paper to improve virtual assistant systems. The suggested approach starts with ML algorithms for robust task automation and predictive modeling. The virtual assistant can anticipate user demands, automate repetitive processes, and make proactive suggestions by studying user behavior and historical data, improving productivity and user experience. NLP also lets the virtual assistant understand and respond to natural language requests. The assistant can accurately grasp user intent and reply contextually using sentiment analysis, entity recognition, and semantic comprehension. The framework also handles privacy and data security issues by using privacy-preserving ML algorithms and complying with data protection laws. User feedback and ongoing learning allow the virtual assistant to develop over time. Through testing and evaluation, the suggested framework proves its accuracy, efficiency, and user satisfaction. The upgraded virtual assistant system is used in customer service, healthcare, education, and smart home automation. This research advances virtual assistant technology by using ML and NLP to construct intelligent, adaptable, and user-centric systems that meet different user needs in a digital environment.
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Published
2024-12-28
How to Cite
K. Lachimipriya, G. Regina Manicka Rajam, B. Vaidianathan, & R.L. Shyja. (2024). Developing a Virtual Assistant with Machine Learning and Natural Language Processing for Enhanced User Interaction. CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES, 5(6), 629-642. Retrieved from https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/707
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Articles
