Intelligent Data Analysis Research Articles

Data Mining Methods in Educational Process Management

The paper addresses the challenge of effectively managing the educational process by leveraging intelligent data analysis of student performance during learning activities. It introduces an approach centered around data clustering, specifically applied to the study of programming disciplines and languages. By utilizing clustering techniques, the paper aims to identify the most challenging topics within a given academic subject, track students’ learning paths, evaluate and enhance teaching methodologies, and create personalized learning plans tailored to individual students’ needs. This approach enables educators to better understand and address the diverse learning requirements of students, ultimately enhancing the overall educational experience.

Innovative approaches to risk management in clinical research: Balancing ethical standards, regulatory compliance, and intellectual property concerns

This study explores innovative approaches to risk management in clinical research, focusing on the intricate balance between ethical standards, regulatory compliance, and intellectual property (IP) concerns. As clinical trials become increasingly complex and globalized, managing risks effectively while maintaining participant safety and upholding legal and ethical principles has become paramount. The study adopts a comprehensive review of emerging technologies, such as artificial intelligence (AI), machine learning (ML), blockchain, and big data analytics, to assess their role in transforming risk management strategies. Key findings reveal that AI and predictive analytics significantly enhance real-time monitoring and the early identification of risks, while adaptive trial designs offer flexibility in managing unforeseen challenges during trials. Blockchain technology improves transparency and data integrity, ensuring secure and verifiable data transactions. However, the intersection of IP protections and equitable access to treatments presents ongoing ethical challenges, particularly in low-income regions. The study concludes that while technological innovations provide robust tools for mitigating risks, a balanced approach to regulatory frameworks and IP protections is essential. The study recommends increased collaboration between regulatory bodies, research institutions, and technology providers to harmonize global regulatory standards, ensuring both innovation and accessibility. Furthermore, it advocates for more flexible IP frameworks that address the tension between protecting innovations and ensuring equitable access to life-saving treatments.

Advancing Disaster Management through Remote Sensing: Applications, Challenges, and Future Prospects

The increasing occurrence and intensity of natural disasters, driven by factors such as climate change and urbanization, emphasize the urgent requirement for advanced monitoring and management approaches. This study investigates the critical function of remote sensing technologies in disaster management, concentrating on their application across various natural disasters, including earthquakes, floods, landslides, tropical cyclones, wildfires, and droughts. By utilizing satellite, aerial, and ground-based remote sensing techniques, this research illustrates how these technologies enhance real-time monitoring, early warning systems, and recovery efforts following disasters. The results demonstrate that remote sensing provides considerable benefits, such as extensive coverage and the capability to access otherwise unreachable areas, although challenges persist regarding data resolution and integration. Additionally, the study explores future potential, particularly the integration of remote sensing with artificial intelligence and big data analytics. These advancements highlight the essential role of remote sensing in global disaster risk reduction, offering critical insights for policymakers and disaster management professionals.

Staff shortages in hospitality and tourism industries: how technology influences?

PurposeThis paper offers an introduction and sets the stage for the theme issue. It describes the methodology used and highlights the articles that address the strategic question of how hospitality and tourism industries can minimize the effects of labor shortages by embracing advanced technologies.Design/methodology/approachThis special issue explores the potential of automation, artificial intelligence, robotics and advanced data analytics in mitigating staff shortages. Automation streamlines operations, AI enhances customer interactions, robotics reimagines service delivery and data analytics informs strategic decisions. The study addresses a strategic question by combining literature reviews, data analysis, expert insights from practitioners and practical solution recommendations.FindingsThe theme issue presents research that explores how advanced technologies can be effectively utilized to mitigate labor shortages in the hospitality and tourism industries. Automation streamlines operations, AI enhances customer interactions, robotics reimagines service delivery and data analytics informs strategic decisions. However, successful integration of technology requires careful planning, training and ongoing maintenance to ensure a balance between technology and human interaction. By embracing these technologies, businesses can better cope with staffing deficits and position themselves for long-term success in a rapidly evolving environment.Originality/valueThis special issue explores the ways in which advanced technologies, such as automation, AI and robotics, can be leveraged to alleviate the labor shortage crisis faced by the hospitality and tourism industries worldwide. From developed to developing countries, these industries are grappling with severe labor shortages, particularly in terms of skilled workers. The articles presented in this issue offer valuable insights and strategies for addressing this pressing issue.

INTEGRATING ADVANCED TECHNOLOGIES IN RETAIL: A CONCEPTUAL FRAMEWORK FOR ENHANCING CONSUMER EXPERIENCE AND TRUST

This study explores the integration of advanced technologies, such as Artificial Intelligence (AI), Augmented Reality (AR), and Big Data Analytics, in retail to enhance personalized consumer experiences while addressing privacy concerns. This research investigates the personalization-privacy paradox, where consumers value tailored recommendations but remain wary of data collection practices. Through a comprehensive literature review, this study developed a conceptual framework for responsibly integrating these technologies in retail. The framework illustrates how AI personalizes shopping experiences, AR enhances consumer engagement, and Big Data improves operational efficiency while also considering privacy issues. The key findings reveal that balancing personalized services with transparent data practices is essential for building consumer trust. This study emphasizes the importance of transparency and ethical data handling in mitigating privacy concerns and fostering a more consumer-centric retail environment. These insights contribute to retail strategies and provide practical guidance for leveraging cutting-edge technologies without compromising privacy, thereby highlighting the need for a balanced approach that maximizes the benefits of innovation while safeguarding consumer trust.

Backyard flock sampling and artificial intelligence: A dual strategy for early detection of Avian Influenza and Newcastle Disease

Avian Influenza (AI) and Newcastle Disease (ND) are significant avian diseases that pose substantial threats to the commercial poultry industry due to their high contagion rates and potential for severe outcomes. Recent shifts in the epidemiology and ecology of both viruses have led to devastating impacts on wild bird and poultry populations, as well as farms and communities worldwide. These challenges underscore the necessity for One Health approaches to pandemic prevention and preparedness, emphasizing the need for multisectoral collaboration among animal, environmental, and public health sectors. To mitigate future pandemic risks, it is essential to control the transmission of AI and ND viruses among domestic and wild animals as well as humans. This involves addressing the upstream drivers of outbreaks, ensuring rapid response mechanisms, and conducting thorough risk assessments for zoonotic diseases. Effective implementation and maintenance of prevention programs, surveillance, and outbreak responses require strong political commitment and sustainable funding. A robust combination of active and passive surveillance strategies should be applied to monitor and control them. In addition, the integration of modern technologies, such as artificial intelligence and big data analytics, can enhance both active and passive surveillance efforts by improving the accuracy and speed of data collection, analysis, and reporting. The use of backyard flocks as an early warning system for avian diseases represents a proactive approach to understanding and managing the risks associated with AI and ND through targeted surveillance and timely interventions. In response to these challenges, the EpirORNIS Project aimed to leverage these sentinel backyard flocks for early detection of AI and ND, thereby enhancing surveillance and control strategies.

A Study on the Role of Technology in Creation of Banking Assets

The banking sector has undergone significant transformation with the advent of technology, changing how assets are created, managed, and utilized. This research paper explores the evolving role of technology in creating banking assets. It examines the impact of various technological innovations, such as digital banking, fintech solutions, artificial intelligence, blockchain, and big data analytics, on asset creation within the banking industry. The paper also discussed the challenges and opportunities these technologies present and provides insights into how banks can leverage them to enhance asset creation efficiency and profitability

Value of blockchain for scope 3 carbon disclosure: The moderating role of data processing technologies

Disclosing carbon emissions in Scope 3 is essential for mitigating pollution and the associated environmental damage, and blockchain can enhance the disclosure. However, the effect of blockchain on Scope 3 carbon disclosure remains unclear due to a lack of empirical evidence. This paper investigates the value of blockchain for Scope 3 carbon disclosure and examines whether this value can be strengthened by integrating data processing technologies, including artificial intelligence (AI), cloud computing, and big data analytics (BDA). Drawing upon the coordination theory, we posit that blockchain as a recording and tracing technology can improve the coordination among supply chain members on collecting carbon emissions data, thereby facilitating firms' Scope 3 carbon disclosure. Furthermore, data processing technologies enable efficient utilization and management of the collected data, potentially coordinating with blockchain to enhance Scope 3 carbon disclosure. We test these relationships using regression analysis based on a sample of 422 observations for Chinese listed firms during 2021 and 2022. The results show that blockchain adoption is positively associated with a firm's Scope 3 carbon disclosure. In addition, adopting each of the three data processing technologies—AI, cloud computing, and BDA—further strengthens the positive relationship. This study contributes to academic knowledge and evidence on blockchain and sustainable supply chain management with practical suggestions for managing carbon emissions at the supply chain level through the combined adoption of blockchain and data processing technologies.

Design of an intelligent data analysis platform for pharmaceutical forecasts

This study considers the task to design a data analysis platform with predictive capabilities of neural networks. The object of research is intelligent decision-making systems built on deep learning methods. The proposed intelligent platform takes into account the specificity of working with data in the dynamic and uncertain environment of the pharmaceutical market. Its main purpose is the processing of various data formats, such as time series, regression, classification data sets to create forecasts based on various indicators. At the core of the platform architecture, along with technologies for backend and frontend development (HTML, JS, CSS, C#, .NET), MSSQL Server and TSQL for database management, are AI Microservices (Python, Flask); they are responsible for artificial intelligence services, in particular neural networks. In order to identify the optimal model, which is able to effectively solve regression problems based on the selected indicators, the study analyzed several configurations of neural networks on End-to-end machine learning platforms. Distinctive features of the architecture of the designed data analysis platform include its ability to dynamically switch between different machine learning models based on predefined indicators and criteria such as prediction accuracy and model selection. Improved interpretation of forecasts through the use of Explainable AI enables effective decision-making in the pharmaceutical industry. The functioning of the proposed instrumental decision-making base is demonstrated on the examples of forecasting trends in the consumption of pharmaceuticals by different groups in the pharmaceutical markets of different countries. Automating the model selection and prediction loss minimization process in a comprehensive data analysis platform (CDAP) improves forecast accuracy by approximately 15 % compared to traditional manually selected models.

Research on the Application of Intelligent Technologies in Equipment Management of Vocational Logistics Training Rooms

As the importance of vocational logistics training rooms in education continues to grow, issues such as low equipment management efficiency, untimely maintenance, and underutilization of resources have become increasingly apparent, severely impacting teaching effectiveness. The rapid development of intelligent technologies offers new solutions in this field. Technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics are widely applied in the logistics industry and gradually provide efficient and refined management methods for vocational logistics training room equipment management. This study analyzes the shortcomings of existing management models, explores key applications of intelligent technologies in real-time equipment monitoring, maintenance optimization, and resource scheduling, and constructs an intelligent equipment management system aimed at improving management efficiency, reducing maintenance costs, and enhancing teaching effectiveness. The research provides theoretical support and practical references for the intelligent transformation of equipment management in vocational logistics training rooms.

Introducing edge intelligence to smart meters via federated split learning

The ubiquitous smart meters are expected to be a central feature of future smart grids because they enable the collection of massive amounts of fine-grained consumption data to support demand-side flexibility. However, current smart meters are not smart enough. They can only perform basic data collection and communication functions and cannot carry out on-device intelligent data analytics due to hardware constraints in terms of memory, computation, and communication capacity. Moreover, privacy concerns have hindered the utilization of data from distributed smart meters. Here, we present an end-edge-cloud federated split learning framework to enable collaborative model training on resource-constrained smart meters with the assistance of edge and cloud servers in a resource-efficient and privacy-enhancing manner. The proposed method is validated on a hardware platform to conduct building and household load forecasting on smart meters that only have 192 KB of static random-access memory (SRAM). We show that the proposed method can reduce the memory footprint by 95.5%, the training time by 94.8%, and the communication burden by 50% under the distributed learning framework and can achieve comparable or superior forecasting accuracy to that of conventional methods trained on high-capacity servers.

Enhanced ANN-based ensemble method for bridge damage characterization using limited dataset

Bridges are vital assets of transport infrastructure, systems, and communities. Damage characterization is critical in ensuring safety and planning adaptation measures. Nondestructive methods offer an efficient means towards assessing the condition of bridges, without causing harm or disruption to transport services, and these can deploy measurable evidence of bridge deterioration, e.g., deflections due to tendon loss. This paper presents an enhanced input-doubling technique and the Artificial Neural Network (ANN)-based cascade ensemble method for bridge damage state identification and is exclusively relying on small datasets, that are common in structural assessments. A new data augmentation scheme rooted in the principles of linearizing response surfaces is introduced, which significantly boosts the efficiency of intelligent data analysis when faced with limited volumes of data. Furthermore, improvements to a two-step ANN-based ensemble method, designed for solving the stated task, are presented. By adding the improved input-doubling methods as simple predictors in the first part of the cascade ensemble and optimizing it, we significantly boost accuracy (7%, 0.5%, and 8% based on R2 in predicting tendon losses for three critical zones that were defined across the deck of a real deteriorated prestressed balanced cantilever bridge). This improvement is strong evidence of the accuracy of the proposed method for the task at hand that is proven to be more accurate than other methods available in the international literature.

Enhanced Input-Doubling Method Leveraging Response Surface Linearization to Improve Classification Accuracy in Small Medical Data Processing

Currently, the tasks of intelligent data analysis in medicine are becoming increasingly common. Existing artificial intelligence tools provide high effectiveness in solving these tasks when analyzing sufficiently large datasets. However, when there is very little training data available, current machine learning methods do not ensure adequate classification accuracy or may even produce inadequate results. This paper presents an enhanced input-doubling method for classification tasks in the case of limited data analysis, achieved via expanding the number of independent attributes in the augmented dataset with probabilities of belonging to each class of the task. The authors have developed an algorithmic implementation of the improved method using two Naïve Bayes classifiers. The method was modeled on a small dataset for cardiovascular risk assessment. The authors explored two options for the combined use of Naïve Bayes classifiers at both stages of the method. It was found that using different methods at both stages potentially enhances the accuracy of the classification task. The results of the improved method were compared with a range of existing methods used for solving the task. It was demonstrated that the improved input-doubling method achieved the highest classification accuracy based on various performance indicators.

AI and Big Data Analytics in Uzbekistan’s Tourism: Enhancing Operational Efficiency and Visitor Satisfaction – A Case Study of Samarkand

This study explores the role of artificial intelligence (AI) and big data analytics in enhancing operational efficiency and visitor satisfaction in the tourism industry in Samarkand, Uzbekistan. Despite the growing importance of digital technologies in tourism, there is limited research on their application in Uzbekistan, particularly in cultural and historical contexts. Using a qualitative case study approach, data was gathered through interviews, surveys, and observations with tourism operators, technology experts, and tourists. The results reveal significant improvements in booking systems, guest services, and personalized offerings, especially benefiting younger and international visitors. These findings demonstrate the potential of AI and big data to transform tourism operations, emphasizing the need for continued investment in digital infrastructure to modernize the sector and preserve cultural heritage.

Risk management in international supply chains: geopolitical and geoeconomic dimensions

In the current climate of global instability driven by dramatic geopolitical and geoeconomic transformations, international supply chains face numerous risks that significantly impact the continuity and efficiency of logistics processes. Threats such as military conflicts, port blockades, border instability, climate changes, and cyber threats introduce new challenges for companies, particularly Ukrainian ones, which require adaptive strategies to ensure the resilience of their supply chains. The purpose of this study is to systematize key geopolitical and geoeconomic risks affecting international supply chains and to identify optimal approaches to managing these risks under conditions of instability, especially for Ukrainian companies facing heightened threats. The study includes an analysis of global risks based on international scientific literature, specific risks in Ukraine and their impact on import, export, and transit chains, and the development of a generalized risk management model tailored to Ukrainian enterprises. The research identified effective risk management methods such as supply chain diversification, the creation of alternative routes, and the use of insurance mechanisms. A practical case study on risk management in the import of industrial fasteners illustrates the stages of collaboration with forwarders and carriers, partner selection, and response to disruptions, demonstrating approaches that can be used to minimize the impact of risks. The findings provide practical recommendations for Ukrainian companies, highlighting an adaptive approach to risk management that will help them maintain competitiveness and ensure supply chain resilience in a rapidly changing global environment. Future research should focus on integrating digital technologies, such as blockchain, artificial intelligence, and big data analytics, to strengthen the resilience and flexibility of international supply chains.

Preface

2024 International Conference on Mechatronics, Robotics and Control System (MRCS2024) was held successfully on July 26th-28th, 2024 in Dalian China. The conference was organized by School of Mechanical Engineering, Dalian Jiaotong University and co-organized by School of Mechanical Engineering and Automation, Dalian Polytechnic University. Distinguished scholars, researchers, and practitioners from diverse institutions of the globe were invited to MRCS2024 to engage in comprehensive discussions regarding the most advanced theories, applications, and trends of the research areas such as “Advanced Computational Intelligence and Informatics”, “Artificial Intelligence, Robotics and Optimization”, “Automation and Control Applications”, “Computation for Intelligent Data Analytics” and “Robotics and Intelligent Systems”. We were honored to have notable speakers from around the globe. They brought unique perspectives and knowledge to enrich our understanding of the advancement and future about the technical fields. The conference also provided opportunities and a platform for participating experts, scholars, and engineers to promote academic exchanges and cooperation, and the advancement of research and development. The research papers collected by the proceeding of MRCS2024 were double-anonymously reviewed by at least 2 reviewers. The technical significance, accuracy, originality, novelty, data, and writing style of the paper were carefully considered. The MRCS2024 Committee sincerely thank the reviewers and Technical Program Committee members’ effort to complete the reviews . We also sincerely thank all the authors, speakers, participants, committee members, editors, volunteers, organizing staffs for their support to the MRCS2024. Committee of MRCS2024 List of Committee of MRCS2024 is available in this Pdf.

The Integration of Advanced Mechatronic Systems into Industry 4.0 for Smart Manufacturing

In recent years, the rapid advancement of digital technologies has driven a profound transformation in both individual lives and business operations. The integration of Industry 4.0 with advanced mechatronic systems is at the forefront of this digital transformation, reshaping the landscape of smart manufacturing. This article explores the convergence of digital technologies and physical systems, with a focus on the critical role of mechatronics in enabling this transformation. Using technologies such as advanced robotics, the Internet of Things (IoT), artificial intelligence (AI), and big data analytics, industries are developing intelligent and interconnected systems capable of real-time data exchange, distributed decision making, and automation. The paper further explores two case studies: one on a smart plastic injection moulding machine and another on soft robots. These examples illustrate the synergies, benefits, challenges, and future potential of integrating mechatronics with Industry 4.0 technologies. Ultimately, this convergence fosters the development of smart factories and products, enhancing manufacturing efficiency, adaptability, and productivity, while also contributing to sustainability by reducing waste, optimising resource usage, and lowering the environmental impact of industrial production. This marks a significant shift in industrial production towards more sustainable practices.

Assessment Audit: How Artificial Intelligence Affected Audit Quality of Sustainability Report Based on Auditors Perspective

This study aims to analyze audit assessment and the effects of artificial intelligence on the audit quality sustainability reports from the standpoint of auditors who provide audit opinions on audits that are conducted of sustainable financial reports. Certain audit tasks can now be automated by the audit profession thanks to technology. Artificial intelligence (AI) and big data analytics can be used to analyze huge data, one of the technological innovations that need more study than conventional data. The number of 85 auditors of Accountant Public Firms in Medan completed questionnaires that were used in this study to gather data. Following the collection of data from respondents, quantitative descriptive methodologies were used to analyze the data. The audit profession may improve the quality of audits by promoting the use of AI and big data analytics and by supporting auditors in developing their skills to stay current with new technologies. AI can improve audit quality, reduce costs, and eventually replace human workers. People are still required in the audit process, though, as AI cannot make decisions when it comes to providing audit opinions on financial reports. As a result, its contribution to the audit process is minimal. Therefore, it is hoped that this study will shed light on the potential applications of AI in the auditing process.

INTEGRATING ARTIFICIAL INTELLIGENCE (AI) into CORPORATE GOVERNANCE SYSTEMS

Integrating AI, or Artificial Intelligence, into companies’ management is a big deal. It can change things for the better. This piece looks at how it affects corporate governance. It shows that AI can help make decisions easier, improve how we manage risks, and even make it simpler for companies to connect with their stakeholders. The cool thing about AI is that it lets boards and leaders make better and faster choices using intelligent data analysis and predictions. Plus, AI automation makes following rules easier and boosts transparency. This helps create a culture of accountability, too. However, there are some tricky challenges to think about. There are ethical questions, data privacy concerns, and algorithm bias. We need to address these challenges quickly! The development of solid rules for governance is critical here. Focusing on mixing innovation with ethical regulations is essential to keep growing in a way that builds trust and maintains accountability with everyone involved.

Artificial Intelligence Studies and Data Analysis in Chronic Lymphocytic Leukemia: A Current Review

Artificial Intelligence Studies and Data Analysis in Chronic Lymphocytic Leukemia: A Current Review