Artificial Intelligence Chatbot Advisory System
Chidi Ukamaka Betrand,
Oluchukwu Uzoamaka Ekwealor,
Chinazo Juliet Onyema
Issue:
Volume 12, Issue 1, February 2023
Pages:
1-9
Received:
17 February 2023
Accepted:
3 March 2023
Published:
21 March 2023
Abstract: A chatbot is an intelligent agent that aims at providing a better, easier way to handle activities through smartphones or PCs by simulating the interaction between humans and machines. Chatbots can be deployed on various platforms such as social media applications, web applications, or websites. This project is designed to simulate communication between user and system using natural language processing with python programming and also to provide easy access to information that they would traditionally have to seek through confrontation or handbooks, simply by sending a text message. The motivation behind this work is to have a more direct, automatic way of getting information, to overcome the pitfalls of manual book searching and physical meetings. These existing methods are not very efficient and are usually time-wasting. Analysis of existing methods and related acts enabled the requirements of the specifications to be gathered, and this initiated the design and implementation of the project. The project was developed using the Agile methodology. Artificial Intelligence technology and modern internet technological tools which included NLTK-model (natural processing algorithm model), Sentiment Analyzer model, and Python programming language, respectively. This system was tested for accuracy, and human-interaction likeness. It is deployed on the Telegram messaging app, through Telegram API keys obtained on Botfather. The system effectively responds to queries and on time.
Abstract: A chatbot is an intelligent agent that aims at providing a better, easier way to handle activities through smartphones or PCs by simulating the interaction between humans and machines. Chatbots can be deployed on various platforms such as social media applications, web applications, or websites. This project is designed to simulate communication be...
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Applying the Self-Organizing Map in the Classification of 195 Countries Using 32 Attributes
Issue:
Volume 12, Issue 1, February 2023
Pages:
10-25
Received:
18 February 2023
Accepted:
16 March 2023
Published:
28 March 2023
Abstract: Many organizations such as World Bank, UN, Wikipedia and others have tried to classify countries as under-developed, developing, developed and highly developed countries based on certain criteria but these criteria aren’t robust enough. In most cases, they used one to three criteria. This research classified 195 countries using 32 attributes (features/ criteria) with the self-organizing map (SOM) algorithm. This is a robust classification because 32 features are considered for the classification. SOM is an unsupervised learning algorithm which reduces high dimensional data to 2 dimensions. The SOM classifies the 195 countries into 5 categories, implying that it is possible to classify countries with SOM algorithm. There is no benchmark to measure the accuracy of the SOM algorithm because most classifications are based on at most three criteria which are not robust enough, but comparing the results of the SOM algorithm with these weak classifications still show the flawlessness of the SOM algorithm. This research will help scientist, students, lecturers, teachers, organizations and countries to have a robust knowledge about the state of their countries from an unbiased position and will also help organizations and countries to make concrete decisions about business establishment in viable places all over the world. The key limitation is the reliability of the data and the number of attributes, which could be increased in future researches for better results.
Abstract: Many organizations such as World Bank, UN, Wikipedia and others have tried to classify countries as under-developed, developing, developed and highly developed countries based on certain criteria but these criteria aren’t robust enough. In most cases, they used one to three criteria. This research classified 195 countries using 32 attributes (featu...
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