Multi-disciplinary Trends in Artificial Intelligence

This book constitutes the refereed proceedings of the 15th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2022, held online on November 1719, 2022. The 14 full papers and 5 short papers presented were carefully reviewed and selected from 42 submissions.

Inhalt
Computing Nash Equilibrium among Crops in Real World Agriculture Domain.- Evolutionary Feature Weighting Optimization and Majority Voting Ensemble Learning for Curriculum Recommendation in the Higher Education.- Fuzzy Soft Relations-Based Rough Soft Sets Classified by Overlaps of Successor Classes with Measurement Issues.- Helmet Detection System for Motorcycle Riders with Explainable Artificial Intelligence Using Convolutional Neural Network and Grad-CAM.- Hierarchical Human Activity Recognition based on Smartwatch Sensors using Branch Convolutional Neural Networks.- Improving Predictive Model to Prevent Students' Dropout in Higher Education Using Majority Voting and Data Mining Techniques.- LCIM: Mining Low Cost High Utility Itemsets.- MaxFEM: Mining Maximal Frequent Episodes in Complex Event Sequences.- Method for Image-based Preliminary Assessment of Car Park for the Disabled and the Elderly using Convolutional Neural Networks and Transfer Learning.- Multi-resolution CNN for Lower Limb Movement Recognition based on Wearable Sensors.- News Feed: A Multiagent-based Push Notification System.- News Feed: A Multiagent-based Push Notification System.- Recognizing Driver Activities using Deep Learning Approaches based on Smartphone Sensors.- Sentence-Level Sentiment Analysis for Student Feedback Relevant to Teaching Process Assessment.- Sentiment Analysis of Local Tourism in Thailand from YouTube Comments by using BiLSTM.- Stable Coalitions of Buyers in Real World Agriculture Domain.- The Analysis of Explainable AI via Notion of Congruence.- Using Ensemble Machine Learning Methods to Forecast Particulate Matter (PM2.5) in Bangkok, Thailand.- Wearable Fall Detection Based on Motion Signals using Hybrid Deep Residual Neural Network.