In today's networked environment, massive volume of data being generated, gathered and stored in databases across the world. This trend is growing very fast, year after year. Today it is normal to find databases with terabytes of data, in which vital information and knowledge is hidden. The unseen information in such databases is not feasible to mine without efficient mining techniques for extracting information. In past years many algorithms are created to extract knowledge from large sets of data. There are many different methodologies to approach data mining: classification, clustering, association rule, etc. Classification is the most conventional technique to analyse the large data sets. Classification can help identify intrusions, as well as for discovering new and unknown types of intrusions. For classification, feature selection provides an efficient mechanism to analyse the dataset. We are trying to analyse the NSL-KDD cup 99, dataset using various classification algorithms. Primary experiments are performed in WEKA environment. The accuracy of the various algorithms is also calculated. A feature selection method has been implemented to provide improved accuracy. The main objective of this analysis is to deliver the broad analysis feature selection methods for NSL-KDD intrusion detection dataset.
Abstract
A company's customer service is very important to its success. It can help boost revenue and retain customers. As digital technology has increased the demand for 24-hour support, businesses are now turning to AI chatbots to provide better and more personalized service. Artificial intelligence (AI) chatbots can help businesses improve the customer experience and reduce the workload of their customer service agents. The paper presents the development and implementation of a chatbot utilizing NLP and AI techniques. It aims to provide efficient and personalized responses to customers' inquiries. The research process involved gathering and analyzing data, developing the chatbot's framework, and carrying out the study. Its architecture and framework were built with the help of NLP and AI. This feature allows the chatbot to respond to users' natural language queries. Its features were also designed to help customers navigate through various tasks and provide recommendations. The chatbot was well-received by its users and was able to provide effective and efficient customer service. The findings of the study indicate that the potential of AI and NLP in enhancing the experience of customers is immense.
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