A theoretical framework for human-centric cyber-physical production systems in industry 5.0: Enabling resilient, autonomous, and adaptive manufacturing

Akash Abaji Kadam, Srinivas Reddy Kosna, Supriya Akash Kadam — Fri, 28 Mar 2025 00:00:00 -0500

This paper intends to put forward a conceptual framework for the development of Cyber-Physical Production Systems (CPPS), which integrates AI, ML, and IoT technologies with human-centric design principles. Industry 5.0 is focused on a human-centric, resilient, and adaptive manufacturing system where harmony in human-machine collaboration exists. The proposed framework addresses some of the challenges facing currently trending ecologies of manufacturing. In other words, it lets CPPS automatically adjust to changes in the environment and keep learning from real-time data and interactions between people in production settings, making the whole setting more resilient and effective. At the core of this framework is the inclusion of self-learning algorithms and adaptive system architectures that accommodate intuitive human-machine interfaces that support collaborative decision-making and operational flexibility. The paper also includes conceptual implementation scenarios in order to show precisely how this envisioned CPPS framework dynamically copes with these operational challenges and sustains itself at optimal performance to support human roles in complex manufacturing processes. The proposed framework further has the ambition to lay foundation for future empirical research and enable the development of Industry 5.0-aligned smart manufacturing systems in a manner that pays due attention to technological innovation and human well-being. The outcome of our findings underlines the human-centered CPPS for the revolutionary effects it could bring to sustainable and agile manufacturing solutions for the next industrial revolution. Critical challenges such as sustainability, operational disruptions, and collaborative decision-making are addressed, and actionable insights are given for further empirical research and industrial applications.

Pneumonia detection on chest X-ray images using segmentation technique and modified AlexNet-ML classifiers

Devchand J Chaudhari, Latesh Malik, Laxmikant Malphedwar, Prashant Jawade — Fri, 28 Mar 2025 00:00:00 -0500

Worldwide, pneumonia is a serious health issue, and detecting pneumonia quickly and accurately is necessary for improving patient outcomes. This method seeks to save human labor, increase diagnostic efficiency, and eventually enhance patient outcomes in the healthcare system by automating the pneumonia detection process. This study aims to develop a robust pneumonia detection system using modified deep learning techniques followed by different classifiers such as kNN, DT, RF, and SVM applied to chest X-ray images. In the planned model, the images are processed beforehand to enhance the quality and clarity of the image for accurate prediction and are segmented utilizing the GrabCut algorithm to eliminate unwanted portions from the image. In this work, features are extracted utilizing a modified AlexNet architecture, and pneumonia disease is classified into pneumonia and normal images using different classifiers. The proposed method achieved optimal performance based on the testing accuracy, testing sensitivity, and testing specificity of 96%, 95.59%, and 96.15%, respectively, using the chest X-ray image dataset. According to the testing results, the most effective approach among the current methods with the highest accuracy was the combination of modified AlexNet models and the SVM classifier.

Review of Computer Engineering Research

A theoretical framework for human-centric cyber-physical production systems in industry 5.0: Enabling resilient, autonomous, and adaptive manufacturing

Pneumonia detection on chest X-ray images using segmentation technique and modified AlexNet-ML classifiers