Volume 9, Issue 6
This issue shares four studies that show how smart technologies are helping to solve real-world problems in industry, healthcare, education, and AI security. One study uses machine learning to predict work time in factories more accurately, helping improve production planning. Another offers a smart camera-based system to detect falls in elderly people, using deep learning for safer elder care. In education, a card-based game called Value Karuta is introduced to teach business students the idea of customer value in a fun and effective way. Lastly, a study explores how compressed AI models can better resist cyberattacks, making AI systems more secure.
Download Complete Issue
Front Cover
Editorial Board
Editorial
Table of Contents
Utilizing 3D models for the Prediction of Work Man-Hour in Complex Industrial Products using Machine Learning
The integration of machine learning techniques in industrial production has the potential to revolutionize traditional manufacturing processes. In this study, we examine the efficacy of gradient-boosting machine learning models, specifically focusing on feature engineering techniques, applied to a novel dataset with 3D product models pertaining to work moan-hours in metal sheet stamping projects, framed as…
Read More Advanced Fall Analysis for Elderly Monitoring Using Feature Fusion and CNN-LSTM: A Multi-Camera Approach
As society ages, the imbalance between family caregivers and elderly individuals increases, leading to inadequate support for seniors in many regions. This situation has ignited interest in automatic health monitoring systems, particularly in fall detection, due to the significant health risks that falls pose to older adults. This research presents a vision-based fall detection system…
Read More Development and Application of Value Karuta to Understand Value in Lean Management: Initial Small-group Trial in Japan and the UK
This study proposes the Value Karuta (VK), an application of the traditional Japanese card game karuta. Its goal is to contribute to the understanding of value, which is the first principle of lean management. After stating the problems of lean management and the specifications of VK, this paper confirms the validity of the proposal by…
Read More On Adversarial Robustness of Quantized Neural Networks Against Direct Attacks
Deep Neural Networks (DNNs) prove to be susceptible to synthetically generated samples, so-called adversarial examples. Such adversarial examples aim at generating misclassifications by specifically optimizing input data for a matching perturbation. With the increasing use of deep learning on embedded devices and the resulting use of quantization techniques to compress deep neural networks, it is…
Read More