Manufacturing Smart Industry Perspective an Overview

In recent years, the application of artificial intelligence and big data analysis has become increasingly common, including in areas related to security and cybersecurity. The use of artificial intelligence and advanced big data analysis tools has enabled new critical perspectives on many different areas of the world around us. The purpose of this volume is to present the perspectives of researchers, experts and scientists, who are concerned with the problem of the distortion of reality by the authors of false narratives. The book focuses primarily on security matters, issues demanded by the international political and economic situation. Disinformation is a tool of hybrid war, shaping the social and economic environment long before the start of the Russian invasion of Ukraine. Since February 2022, the issues of post-truth, fake news, and disinformation have taken on additional gravity.

The concept of personalized learning is not something new in the field of education but with the advent of artificial intelligence and Big Data analytics, new ways of implementing it are being discovered. This paper is focused on these new ways of its application through these technologies. First of all, it explains what personalized learning is and how it differs from traditional teaching methods. Then, it focuses on the artificial intelligence component of personalized learning, and how it can be utilized to make this type of education more effective. Furthermore, the paper discusses the role of Big Data in personalized learning, and how this data should be dealt with appropriately. Finally, the paper discusses some of the arguments against these trends in education.

This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research.

Smart maintenanceis a model created on the concept of Industry 4.0, and uses its advanced technologies, the Internet of Things (IoT), artificial intelligence (AI) and predictive big data analytics (BDA), in order to reduce equipment failures and extend its life, thus reducing maintenance costs, and technological resources are used optimally. By monitoring data in real time, from sensors and IoT devices, the condition of the equipment is predicted, decisions are made automatically, and the operational performance of the equipment (OEE) is improved. Equipment failure prediction is done by analyzing the behavior patterns of equipment entities, which are obtained by machine learning (ML), generated by digital twins (DT) and supported by cloud computing. Today, we are witnessing the full maturity of Industry 4.0, and the emergence of its advanced or future version, Industry 5.0. However, we must state that the creators of the Industry 4.0 model (German Academy of Engineering Sciences) believe that the Industry 5.0 model today, as promoted by its creators, is not a NEW model, but only a somewhat improved version of Industry 4.0 in the areas of: greater human integration into this automation model (operator 4.0/5.0), greater environmental aspects of production (green production) and the application of artificial intelligence (AI), which we are witnessing today in the Industry model as well 4.0. Starting from the above facts, this paper deals with the current level of development of the maintenance model in the context of Industry 4.0, which is the smart maintenance model (SM). System analysis of predictive maintenance model, smart maintenance framework model was performed. Some results of our research in this area are also given at the end of the paper

In this advance era, artificial intelligence (AI) is the basic building block for numerous innovation opportunities. The basic concept behind all artificial intelligence applications is classical machine learning (CML) technique. The intelligent data analysis (IDA) is used to analyze data in advanced reasoning systems. In this paper, our motto is to analyze the data intelligently, i.e., intelligent data analysis process using classical machine learning algorithms based on experimental data set empirical hidden regularities. It is concluded that machine learning process is the basic concept to implement any problem based on artificial intelligence, whereas intelligent data analysis uses classical machine learning concept and helps in deciding the next best available step during data analysis.

The objective of this paper is to research the synergistic interaction between Industrial Internet of Things (IIoT) and the application of Artificial Intelligence (AI) in IIoT ecosystems (AIoT). For achieving this purpose, a method of integrative study of leading scientific publications on the topics ‘Industrial Internet of Things’ and ‘Artificial Internet of Things’ and the PRIZMA flow chart for filtering the list of found documents are applied. The inability of full coverage of the AIoT topic in one article has necessitated the usage of an approach for its content analysis in several thematic groups. The IIoT and AIoT concepts has been explored by various theoretical and practical aspects in many scientific papers published mainly over the past five years, but continues to arouse the interest of researchers. The novelty of the manuscript could be viewed in the attempts of systematizing the key characteristics, advantages, and risks in the practical use of these technologies in the form of smart manufacturing machines, robotic devices, autonomous vehicles, and software for predictive maintenance, and in outlining the possibility for green and sustainable AIoT platforms creation. It is concluded that a key requirement for the implementation of AIoT solutions is the consideration of the various threats and ethical aspects of their design, which contributes to the correct definition of the ‘social value’ of such business initiatives

Big data analytics’ incorporation into customized cancer care is a revolutionary development in medical science. Using knowledge from genomic sequencing, electronic health records (EHRs), clinical trial databases, and social determinants of health, this systematic review investigates the relationship between artificial intelligence (AI), machine learning, and big data analysis in customizing cancer treatments for each patient. The study highlights how AI‐driven prediction models might improve treatment outcomes, reduce side effects, and increase patient safety by synthesizing insights from the last 10 years of academic research. A systematic literature review that follows PRISMA guidelines and includes both quantitative and qualitative evaluations of academic publications is among the key approaches. The findings show that multiomics approaches, which combine transcriptomics, proteomics, metabolomics, and genomics, are becoming increasingly important for customized medicine. Real‐time data analytics and wearable technology are also noted as promising resources for prompt responses. Notwithstanding obstacles, including data heterogeneity, moral dilemmas, and validation problems, the results highlight how crucial AI is to treating tumor complexity and developing precision medicine. In order to improve regulatory frameworks, promote interdisciplinary collaboration, and optimize AI applications in cancer treatment planning, the study concludes by suggesting future research areas. For scientists, physicians, and legislators hoping to transform cancer treatment using big data analytics–driven tailored medicine, this review provides insightful information.

BlockIoTIntelligence: A Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligence

The rapid development of the supply chain of the Internet of Things (IoT) industry may trigger financial risk contagion among IoT manufacturers. This article collects data on listed IoT companies in the Chinese market from 2010 to 2019 and explores the development of the environment for the IoT industry. Two dynamic time wrapping (DTW) networks are created to analyze the topological structures of the IoT industry environment. Both the standard DTW and strongly connected DTW networks are revolutionary in terms of their interconnectedness in the IoT industry. We found that the level of clustering and transitivity of the network continued to decline between 2010 and 2019; i.e., the efficiency of financial risk contagion on IoT networks was significantly reduced. This article contributes to the literature in two aspects. First, it reveals that China's IoT industry is increasingly competitive; financial risks have become more difficult to transfer. The IoT industry has become more robust and exhibits a lower likelihood of financial risk contagion. Second, the article provides empirical evidence for the theory of coevolution, showing that risk contagion ability in an industry setting is decreasing with the development of individual firms.

Major advances in the Internet of Things (IoT) technologies are offering unprecedented automated services to various sectors. IoT has shown great promise in transforming buildings and institutions into smart buildings and institutions, making traditional task management more efficient. In particular, there has been remarkable progress in successfully integrating IoT into various contexts, such as smart homes, businesses and cities. Success in such technological transformations is not only due to the proper deployment and implementation of IoT but also to the effective employment of related technologies, such as big data and cloud computing technologies, and making informed decisions on the inclusion of the required hardware and software for all the elements involved in the IoT environment. While a significant amount of IoT research in various contexts has been proliferated in recent years, attention given to its deployment for linguistic landscapes (LLs) is still very rare. The aim of this paper is to propose a smart Al-Noor Mall LL which takes into account the appropriate deployment of IoT tools and relevant technologies. The paper also thoroughly discusses the background of the field, covering IoT technologies, cloud computing, and big data studies, followed by a review of the recent publications involving the successful deployment of IoT technologies combined with big data and cloud computing in various contexts. The literature also covers the success factors that contribute to the robustness of the required real-time operating system (RTOS) in an IoT-based environment in order to enable IoT devices to manage their resources efficiently. Based on a detailed analysis of the literature, a three-level framework (i.e. the object, communications and application levels) together with the elements that are required for IoT-based smart LL solutions is proposed. A case study of Al-Noor Mall is used to validate the proposed framework and the results indicate that IBM Bluemix can help Al-Noor Mall management and customers to locate any particular shop in the huge shopping mall, which thereby makes the customers’ shopping experience more satisfying. Finally, the challenges (i.e. energy efficiency, security, and intelligent data analysis) facing the successful deployment of IoT technologies are discussed with proposals for future research directions in the IoT research field.

The logistics service chain is a specialized service chain produced along with the continuous update and development of the logistics industry. It is an inevitable direction choice that is naturally formed when the degree of logistics specialization continues to increase. This paper aims to study how to analyze and study the logistics service supply chain model based on artificial intelligence and big data analysis, and describe the data mining algorithm. This paper puts forward the problem of logistics service supply chain, which is based on big data analysis, and then elaborates around its related concepts and algorithms, and conducts case design and analysis on supplier selection. The experimental results show that the customer satisfaction of supplier C has reached 86%, the accuracy rate of supplier D's logistics operations is as high as 90%, and the logistics transportation cost of supplier B is only 0.38 yuan/ton ∗ km. In the end, the three of them were selected as company Z's transportation suppliers.

SummaryThe evolution of Internet of Things (IoT) has led to the development of Industrial Internet of Things (IIoT). IIoT is one the widely applied areas to facilitate people in the manufacturing world. The adoption of IIoT automates sensing, capturing, communicating, and processing in real time. To understand how rapidly IoT and IIoT are growing, this article examines the emergence of 5G‐enabled IIoT, current research trends in IIoT, key milestones achieved in IIoT, and IoT applications specific to 5G‐enabled IIoT. The paper presents the state‐of‐the‐art in networking layered framework of IIoT and comparing relationships of technologies of cloud computing as well as edge computing paradigms. We also explored the type of security attacks and their preventive measures in an IIoT‐driven 5G technology. We have also highlighted the revolution of IIoT‐driven 5G framework which satisfies the demands of IIoT applications.

Pharmacovigilance, the science of monitoring drug safety, plays a crucial role in identifying and mitigating adverse drug reactions (ADRs). However, underreporting in pharmacovigilance systems—estimated to have a median rate of 94 %—poses a significant threat to patient safety by hindering the detection of safety signals. The need to address these gaps is paramount, especially with the rising complexity of healthcare data. The advent of artificial intelligence (AI) and big data technologies offers promising solutions to overcome the limitations of traditional pharmacovigilance methods.The application of AI and machine learning (ML) technologies, including natural language processing (NLP) and deep learning, has the potential to revolutionize drug safety monitoring by automating the detection of ADRs from diverse data sources, such as electronic health records (EHRs), spontaneous reporting systems, and social media. These tools can process unstructured data and uncover patterns not easily identifiable through conventional approaches. Additionally, AI can enable real-time pharmacovigilance, which is especially critical in an era of increasing polypharmacy and diverse patient populations. AI-driven models are being utilized to detect drug-drug interactions (DDIs), predict ADRs, and enhance the overall efficiency of pharmacovigilance processes.Despite these advancements, several challenges remain. The performance of AI models is heavily dependent on the quality and quantity of data available. Inadequate or poorly curated datasets can lead to inaccurate ADR detection, particularly in resource-limited settings. Moreover, the heterogeneity of data sources necessitates robust AI models capable of integrating various types of data while ensuring accurate and reliable outputs. There is also a pressing need to address the transparency and explainability of AI models, as the opaque decision-making processes of current algorithms often impede their acceptance among pharmacovigilance professionals.Future directions must focus on improving the quality and standardization of datasets, advancing NLP techniques for better interpretation of clinical narratives, and developing explainable AI models. Regulatory frameworks should evolve to support AI deployment in pharmacovigilance, ensuring the establishment of best practices for AI implementation and the creation of large-scale, publicly available training datasets.Additionally, AI models should go beyond correlation-based approaches by integrating causal inference techniques, which will allow for a more accurate understanding of the relationship between drugs and ADRs. Human oversight will still be required to validate AI findings, but ongoing efforts to improve the robustness of AI systems will reduce dependency on manual interventions and scale the use of AI in pharmacovigilance.The integration of AI and big data in pharmacovigilance has the potential to transform drug safety monitoring, addressing many of the challenges posed by increasing data complexity and the need for real-time analysis. As these technologies continue to evolve, they promise to make pharmacovigilance more efficient, accurate, and comprehensive, thereby improving patient safety and rational drug use.

Internet of Things (IoT) is a concept that proposes the inclusion of physical devices as a new form of communication, connecting them with various information systems. Nowadays, IoT cannot be reduced to smart homes as due to the recent technological advances, this concept has evolved from small to large-scale environments. There was also a need to adopt IoT in several business sectors, such as manufacturing, logistics or transportation in order to converge information technologies and technological operations. Due to this convergence, it was possible to arrive at a new IoT paradigm, called the Industrial Internet of Things (IIOT). However, in IIoT, it is also necessary to analyze and interact with a real system through a virtual production, which refers to the use of Augmented Reality (AR) as a method to achieve this interaction. AR is the overlay of digital content in the real world, and even play an important role in the life cycle of a product, from its design to its support, thus allowing greater flexibility. The adoption of interconnected systems and the use of IoT have motivated the use of Artificial Intelligence (AI) because much of the data coming from several sources is unstructured. Several AI algorithms have been used for decades aiming at “making sense” of unstructured data, and transforming it into relevant information. Therefore, converging IoT, AR, and AI makes systems become increasingly autonomous and problem solving in many scenarios.

PurposeThe purpose of this paper is to see how critical and vital artificial intelligence (AI) and big data are in today’s world. Besides this, this paper also seeks to explore qualitative and theoretical perspectives to underscore the importance of AI and big data applications in multi-sectoral scenarios of businesses across the world. Moreover, this paper also aims at working out the scope of ontological communicative perspectives based on AI alongside emphasizing their relevance in business organizations that need to survive and sustain with a view to achieve their strategic goals.Design/methodology/approachThis paper attempts to explore the qualitative perspectives to build a direction for strategic management via addressing the following research questions concerned with assessing the scope of ontological communicative perspectives in AI relevant to business organizations; exploring benefits of big data combined with AI in modern businesses; and underscoring the importance of AI and big data applications in multi-sectoral scenarios of businesses in today’s world. Employing bibliometric analysis along with NVivo software to do sentiment analysis, this paper attempts to develop an understanding of what happens when AI and big data are combined in businesses.FindingsAI and big data have tremendous bearing on modern businesses. Because big data comprises enormous information of diverse sorts, AI-assisted machines, tools and devices help modern businesses process it quickly, efficiently and meaningfully. Therefore, business leaders and entrepreneurs need to focus heavily on ontological and communicative perspectives to deal with diverse range of challenges and problems particularly in the context of recent crises caused by COVID-19 pandemic.Research limitations/implicationsThere is hardly any arena of human activity wherein AI and big data are not relevant. The implication of this paper is that of combining both well so that we may find answers to the difficult and challenging multi-sectoral scenarios concerning not just businesses but life at large. Moreover, automated tools based on AI such as natural language processing and speech to text also facilitate meaningful communication at various levels not just in business organizations but other fields of human activities as well.Social implicationsThis paper has layered social implications, as it conceptually works out as to how strategically we may combine AI and big data to benefit modern business scenarios dealing with service providers, manufacturers, entrepreneurs, business leaders, customers and consumers. All the stakeholders are socio-culturally and contextually rooted/situated, and that is how this study becomes socially relevant.Originality/valueThis paper is an original piece of research and has been envisioned in view of the challenging business scenarios across the world today. This paper underscores the importance of strategically combining AI and big data, as they have enormous bearing on modern businesses. The insights arrived at in this paper have implications for business leaders and entrepreneurs across the globe who could focus more on ontological and communicative perspectives of AI combined with Big Data to deal with diverse range of challenges and problems that modern businesses have been facing particularly in recent times.