The manufacturing sector has been significantly influenced by the area of machine learning (ML) within the framework of the sector 4.0 concept. The concept of Industry 4.0 promotes the use of intelligent sensors, gadgets, and machinery in order to facilitate the development of intelligent factories that consistently gather production-related data. Machine learning approaches facilitate the production of practical insights by analyzing the gathered data in order to enhance industrial efficiency while minimizing the need for substantial resource modifications. Moreover, the utilization of machine learning (ML) techniques has facilitated the identification of intricate manufacturing patterns and presents an opportunity for the development of an intelligent decision support system in various manufacturing domains. These domains include intelligent and continuous inspection, predictive maintenance, quality enhancement, process optimization, supply chain management, and task scheduling. Various machine learning (ML) techniques have been employed in diverse manufacturing applications previously. However, numerous unresolved inquiries and difficulties persist in this domain. These include the curation, storage, and comprehension of large datasets, the ability to reason with data to facilitate real-time actionable insights, as well as concerns related to edge computing and the cybersecurity aspects of smart manufacturing. Therefore, the primary objective of this special edition is to assemble a diverse group of scholars who will present the most recent advancements in both the theoretical underpinnings and practical implementations of machine learning (ML) within the context of manufacturing and production systems.

Smart Supply Chain Management and Big Data Analysis Using Machine Learning in Industry 4.0

This study explores the impact of Industry 5.0 on discriminatory behaviors toward migrant employees within organizations. Through semi-structured qualitative interviews with 15 migrant workers in the UK, this research identifies key challenges faced by migrant employees amidst the integration of advanced technologies like AI and robotics in HRM systems. Thematic analysis reveals that while Industry 5.0 has the potential to mitigate human biases, it can also perpetuate existing prejudices if not managed effectively. This study highlights two main themes: the experiences of discrimination and challenges in the context of Industry 5.0, and the role of technology in HRM systems. The findings indicate that automated HR systems can both reduce and increase biases, highlighting the importance of inclusive practices and targeted support programs to help migrant workers adapt to a technologically advanced labor market. This research contributes to the literature by providing insights into the duality of technological advancements in reducing and reinforcing workplace discrimination. 

Technological Advancements and Organizational Discrimination: The Dual Impact of Industry 5.0 on Migrant Workers

Adopting the concept of Industry 4.0 enables manufacturing companies to gain advantages from digital production management, though issues have been identified related to human resources, management and organisation. Problem areas concern workplace ergonomics to the general wellbeing and satisfaction of workers, and social responsibility. Industry 5.0 promotes progress based on human-machine collaboration and sustainable, human-centred value creation. The research model of the degree of technological humanism, which aims to ensure that technological progress improves human wellbeing in manufacturing companies in the transformation towards Industry 5.0, is a demanding area that has not been sufficiently researched or unambiguously defined so far. The presented model can facilitate the digital transition of manufacturing companies towards Industry 5.0, as it contains the components necessary for the transformation of organisational processes, thereby enhancing and accelerating the implementation of smart manufacturing. 

Research Model of the Degree of Technological Humanism in Manufacturing Companies in the Transformation towards Industry 5.0

The industry 5.0 is the new revolution in which transition is going right now from industry 4.0. While industry 4.0 consisted of technological developments in the fields of data analytics, cyber physical systems and huge exposure to Artificial Intelligence, Machine Learning, IoT etc., Industry 5.0 is all about mass customisation or personalization of these existing technologies. While the world is going through this transition the current Human Resource Management of the organizations also has to go through this transition. The present research analyses certain factors affecting the HRM practises during this process. There are hypotheses formed on factors such as better performance, emotional intelligence, automation, and cross disciplinary workforce. The authors also try to propose certain possible and suitable suggestions based on analysis of these hypotheses towards smooth processing of the transition.

Analytical study of HRM practices in industry 5.0

The stock market is an area where the stock prices are not constant; however, they are dynamic, nonlinear and volatile in nature. The prediction of the stock price is a challenging task with the factors such as the financial performance of the organization, politics, unexpected natural and man-made causes, and worldwide economic conditions. In concern to these issues, several analytic techniques are being developed by researchers, financial analysts, and data scientists for exploring the nature of stock market trends a novel approach is known as the Heuristic optimization-based hybrid approach for the pre-processing of data collected from the stocks. The GANN square technique for the prediction of stock price exactly. The data pre-processing involves four stages (i) ignoring the data with missing values of features, (ii) data imputation initialization, (iii) detection and deletion of outliers, and Hybrid Archimedes-based SALP Swarm Algorithm based future selection. Thus the stated approach effectively removes the unwanted noises and features from the dataset. this made it easier for the GANN square technique the prediction of stock prices effectively

Stock Market Price Trend Prediction using an Outlier Data Mining Algorithm

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Analytical study of HRM practices in industry 5.0

Stock Market Price Trend Prediction using an Outlier Data Mining Algorithm