Titlebook: Intelligent Systems and Applications; Proceedings of the 2 Kohei Arai Conference proceedings 2024 The Editor(s) (if applicable) and The Aut

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Patterns in Human Activity Recognition Through Machine Learning Analysis Towards 6G Applications,ch as smartphone-based activity recognition, to reliably identify physical activities has declined. Sixth-generation (6G) systems aim to integrate communication and sensing seamlessly. Human activity recognition is a sensing task with many applications in smart homes, emergency systems, and games. I

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Gamified Chatbot Management Process: A Way to Build Gamified Chatbots,(GCMP), is a process for the development of gamified chatbots, it comprises eight activities, arranged into four steps, emphasizing gamification implementation. This process includes gamification planning, gamification management, updating chatbot content, chatbot behavior implementation, chatbot be

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Mutual Learning for News Classification,es and subsequently exchange knowledge among themselves. This fosters concurrent enhancement of classification and prediction accuracies across all participating agents. Particularly advantageous in resource-constrained environments such as mobile platforms, this dynamic learning approach circumvent

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Automatic Generation of a Large Multiple-Choice Question-Answer Corpus,e informative content. We introduce a new approach for automatically generating a large corpus of health-related content with associated multiple-choice questions using Google’s related questions and ChatGPT, including two new algorithms for generating potential wrong answers. We compare both the qu

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,Multilingual Fake News Detection: A Study on Various Models and Training Scenarios,esearch has explored fake news detection from various perspectives, a notable gap persists—the majority of studies concentrate on the English language. This highlights, the need for more research focusing on other languages, especially considering the scarcity of available non-English fake news data

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,Improving Convolutional End-to-End Memory Networks with BERT for Question Answering,etworks have shown encouraging results in certain reasoning tasks of QA such as the end-to-end memory networks. In this paper, we explore the integration of BERT (Bidirectional Encoder Representations from Transformers) with Convolutional End-to-End Memory Networks to improve QA performance. It is a