Big Machine Research Articles

Prediction of Commercial Street Location Based on Point of Interest (POI) Big Data and Machine Learning

Identifying optimal locations for sustainable commercial street development is crucial for driving economic growth and enhancing social vitality in cities. This study proposes a data-driven approach to predict potential sites for commercial streets in Foshan City, China, utilizing Points of Interest (POI) big data and machine learning techniques. Decision tree algorithms are employed to quantitatively assess and predict optimal locations at a fine-grained spatial resolution, dividing the study area into 9808 grid cells. The analysis identifies 2157 grid cells as potential sites for commercial street development, highlighting the significant influence of Medical Care, Shopping, and Recreation and Entertainment POIs on site selection. The study underscores the importance of considering population base, human activity patterns, and cultural elements in sustainable urban development. The main contributions include providing a novel decision-support method for data-driven and sustainable commercial street site selection and offering insights into the complex interplay between urban land use, human activities, and commercial development. The findings have important implications for urban planning and policy-making, showcasing the potential of data-driven approaches in guiding sustainable urban development and fostering vibrant commercial areas.

A Review on Robust Credit Card Fraud Detection System Leveraging Big Data and Machine Learning

This review offers a detailed strategy to address the growing threat of credit card fraud in today's digital landscape. By utilizing Big Data analytics alongside machine learning methods, the system aims to transform fraud detection processes. It tackles the challenges arising from the increasing volume and complexity of credit card transactions, enabling the real-time detection and prevention of fraudulent actions. The system employs sophisticated machine learning algorithms to identify patterns and anomalies linked to fraudulent activities, allowing for proactive responses to emerging fraud tactics. Additionally, the system is optimized to handle and analyze large datasets efficiently, ensuring timely and precise detection of fraud. It also incorporates strong security protocols to protect sensitive customer data while adhering to privacy regulations. This review ultimately seeks to enhance the safety and reliability of electronic payments, protecting financial institutions and consumers from the harmful effects of credit card fraud.

Harnessing Genomic Resources and Molecular Breeding Techniques for Advancing Crop Resilience and Productivity

Crop improvement and adaptation are critical to ensure global food security in the face of climate change, population growth, and resource limitations. Exploiting genomic resources and molecular breeding techniques offers immense potential to accelerate the development of resilient, high-yielding crop varieties. This review provides an overview of the current state and future prospects of leveraging genomics and molecular breeding for crop improvement, with a focus on major food crops. We discuss key genomic resources such as reference genomes, transcriptomes, and pan-genomes, as well as powerful molecular breeding approaches like marker-assisted selection, genomic selection, and genome editing. Integrating these tools into crop breeding pipelines can greatly enhance the precision and efficiency of developing improved varieties with desirable traits such as abiotic stress tolerance, disease resistance, and enhanced nutritional quality. We also highlight successful examples of applying these techniques in crops like rice, wheat, maize, and legumes. Furthermore, we explore the role of big data, machine learning, and systems biology in extracting actionable insights from the vast genomic data being generated. Finally, we discuss the challenges and opportunities in translating genomic discoveries into improved crop varieties, emphasizing the need for multidisciplinary collaborations, capacity building, and public-private partnerships. Harnessing the power of genomics and molecular breeding will be pivotal in developing climate-resilient crops to feed the growing global population sustainably.

Consumer Behavior Analysis through Advanced-Data Techniques: A Review for Utilities in Retail Marketing

This review offers a comprehensive analysis of the use of cutting-edge data strategies, including Big Data analytics, Machine Learning (ML), and Artificial Intelligence (AI), etc. in understanding and predicting consumer behavior. The convergence of these technologies has enabled marketers to uncover more detailed knowledge about consumer preferences and enhance customer engagement & satisfaction. By reviewing various methodologies, this study showcases the ways in which these tools are leveraged for segment consumers, predict consumer buying habits, and optimize marketing strategies. Additionally, the paper explores the Social media data utilization in real-time consumer analytics, demonstrating their effectiveness in personalizing marketing efforts and improving customer experience. The challenges of data privacy, ethical considerations, and the necessity for more robust analytical tools are also discussed. The review concludes by identifying potential research paths that could drive forward the field of consumer behavior analysis in retail marketing. Key Words: Artificial Intelligence, Big Data, Consumer Behavior, Machine Learning, Retail Marketing.

Big data and AI for gender equality in health: bias is a big challenge.

Artificial intelligence and machine learning are rapidly evolving fields that have the potential to transform women's health by improving diagnostic accuracy, personalizing treatment plans, and building predictive models of disease progression leading to preventive care. Three categories of women's health issues are discussed where machine learning can facilitate accessible, affordable, personalized, and evidence-based healthcare. In this perspective, firstly the promise of big data and machine learning applications in the context of women's health is elaborated. Despite these promises, machine learning applications are not widely adapted in clinical care due to many issues including ethical concerns, patient privacy, informed consent, algorithmic biases, data quality and availability, and education and training of health care professionals. In the medical field, discrimination against women has a long history. Machine learning implicitly carries biases in the data. Thus, despite the fact that machine learning has the potential to improve some aspects of women's health, it can also reinforce sex and gender biases. Advanced machine learning tools blindly integrated without properly understanding and correcting for socio-cultural sex and gender biased practices and policies is therefore unlikely to result in sex and gender equality in health.

Research Status and Evolutionary Trends of Digitally Empowered Community Emergency Management: Knowledge Graph Analysis Based on CiteSpace

(1) Background: With the acceleration of globalization, various risks are intertwined and coupled, and community emergency management is facing greater pressure than before. The continuous development of digital technology has made it possible to empower community emergency management with digital technology. Therefore, it is urgent to have a clear understanding of the research status and future development direction of digital empowering community emergency management. (2) Methods: This study is based on the Web of Science database and utilizes the CiteSpace knowledge graph visualization method to analyze the research progress of community digital emergency management. (3) Results: The results indicate that from 2000 to 2024, the amount of research on community digital emergency management has been on the rise, involving multiple disciplines, but there has not been a close and mature cooperation network between different countries, institutions, and authors. The research topic has shifted from early single-emergency management to integration with emerging science and technology, with a greater emphasis on the role of communities. Big data, machine learning, technological innovation, digital healthcare, and community resilience are important research directions for the future. (4) Conclusions: In the future, we should strengthen cooperation in this field, overcome issues such as the digital divide and digital security, and promote further development of community digital emergency management.

Multi-fidelity data mining-based design optimization framework for scramjet-based aircraft

Aircraft design faces complex multidisciplinary problems and often involves numerous design variables, making direct optimization a time-consuming task. With the development of big data and machine learning technologies, using data mining methods to assist optimization has become key to addressing the inefficiency in multidisciplinary design optimization (MDO). To address the high cost of data acquisition in current data mining, this paper proposes an aircraft design optimization framework based on multi-fidelity data mining, using a large amount of inexpensive cursory estimation data to execute the MDO process and form a multidisciplinary optimization database. Then, data mining methods are used to decouple disciplinary relationships, transforming multidisciplinary coupling problems into simpler multi-objective problems. Subsequently, under viscous CFD computation conditions, the EDGE-p-BFFD deformation method is used to fine-tune the obtained configurations, further enhancing multidisciplinary performance. Before both optimizations, data mining methods were used to analyze the impact of design variables on objective functions, forming design knowledge and completing variable selection. In the multidisciplinary optimization practice of airbreathing aircraft, it can be found that the proposed framework effectively achieves disciplinary decoupling and explores the relationship between shape and performance. In the multidisciplinary optimization of the waverider based on cursory estimations, particle swarm optimization increased the aircraft's cruising range by 8.74 %. In the multi-objective optimization of the lifting body based on viscous CFD calculations, data mining was used to transform the problem into several key objectives. The MOEA/D algorithm increased the range by 2.9 %, validating the effectiveness of the data mining-based MDO framework proposed in this paper.

Finding the new potential research on diabetic kidney disease and hemodialysis in healthcare insurance databases: A bibliometric analysis

To the best of our knowledge, bibliometric analysis has not been performed for studies related to diabetic kidney disease (DKD) and hemodialysis using healthcare big data. Herein, the aim of this bibliometric analysis was to identify emerging research trends in DKD and hemodialysis within healthcare insurance databases by exploring authors, co-author networks, and countries to discover new potential research areas. A bibliometric study was conducted, utilizing data obtained from the Scopus database. Keywords such as diabetic kidney disease, hemodialysis, insurance or big data, and prediction were employed. Inclusion criteria were original articles and review articles written in English published between 2010 and 2022. VOSviewer and the Bibliometrix package in R were used for comprehensive bibliometric analysis. VOSviewer facilitated keyword co-occurrence analysis to identify clusters and visualize relationships among keywords, emphasizing distinct research themes, keyword density, and network visualization. Meanwhile, Bibliometrix allowed exploration of key metrics such as prolific authors and institutions, publication trends, co-authorship networks, citations, document types, emerging trends through keyword analysis, and network visualizations, including co-authorship and keyword co-occurrence. Results from both tools were integrated for a thorough analysis. The present study yielded 2,199 articles, which was reduced to 1,828 after removing duplicates and applying inclusion criteria. This bibliometric analysis found that machine learning and artificial intelligence are emerging yet remain relatively under-researched in the context of hemodialysis and DKD. The prominence of topics such as diabetic nephropathy, non-insulin treatments, and lifestyle modifications highlighted ongoing research priorities in DKD and hemodialysis. Taiwan's dominance in publications suggested robust research activity in this field, while international collaboration underscored global interest and the potential for diverse research perspectives. The need for similar research development in Indonesia, leveraging big data and machine learning, indicates opportunities for advancing the understanding and management of DKD and hemodialysis within the region.

Oxygen Content Control in the Electroslag Remelting Process: An Incremental Learning Strategy Based on Optimized Wasserstein Generative Adversarial Network with Gradient Penalty Data Augmentation

Electroslag remelting (ESR) is essential for producing high‐end special steel, but its complex process and numerous influencing factors make quality control challenging. This study addresses oxygen content control during ESR using a big data machine learning approach. An incremental learning strategy is proposed based on an optimized Wasserstein generative adversarial network with gradient penalty (WGAN‐GP) for data enhancement, focusing on G20Cr2Ni4A bearing steel. The WGAN‐GP model enhances time‐series data and metadata, utilizing long short‐term memory networks, fully connected networks, and attention mechanisms. The effectiveness of data enhancement is verified using a deep neural network classifier and statistical methods. Data is divided into historical and data streams, with an incremental learning strategy based on histogram gradient boosting regression trees to prevent catastrophic forgetting and improve efficiency through knowledge distillation and real‐time hyperparameter adjustment. Results show that the data augmentation method significantly improves model generalization and accuracy in small sample metallurgy. The incremental learning strategy enhances prediction accuracy for oxygen content, contributing to better cleanliness quality of electroslag steel. This study offers a novel approach for addressing small sample challenges in metallurgical processes.

Intelligent monitoring of marine vessel dynamics based on data mining

This study explores vessel navigation's universal characteristics and individual anomaly behavior from dispatch to destination. With the help of information technology, it introduces big data and machine learning technologies into the work of vessel traffic management and conducts statistical analysis and monitoring studies of vessel behavior from a data-driven perspective. The study was conducted by modeling vessel speed, position, course, and other navigational characteristics behavior through comprehensive statistical analysis of vessel AIS, radar, and VTS data. The study was based on the monitoring methods for anomalous behavior of vessel speed, position, and heading inconsistencies with the expected behavior identified by the model.

Evaluating The Role Of International Financial Institutions In Maintaining Financial Stability

This paper examines the functions of IFIs specifically the IMF, World Bank, and BIS in relation to balance of payments in the international financial system. Employing a cross-sectional research methodology, the study analyzes IFI interventions during the Asian Financial Crisis (1997), the Global Financial Crisis (2008), and the COVID-19 pandemic. It evaluates data related to GDP growth, capital flows, and systemic risk measures, and finds that IFIs have a highly positive impact on economic recovery and financial system stability. The study also highlights the importance of advanced technologies, such as big data, machine learning, and artificial intelligence, in improving IFI capacities for evaluating and forecasting financial stability. However, it reveals issues with data quality, methodological limitations, and policy-data gaps, particularly from developing countries where IFIs are crucial. Recommendations are provided for enhancing data acquisition, openness, and the use of big data analytics, along with directions for further research in prognostic modeling and crisis anticipation.

Dynamic matching of energy and heat conduction in production workshops and financing for manufacturing enterprises based on the Internet of Things

With the advancement of Industry 4.0, the application of Internet of Things technology in manufacturing enterprises is increasingly extensive, and energy heat transfer, as an important factor affecting production efficiency and cost, needs to be optimized by new technical means. This study explores the energy heat conduction characteristics of production workshops based on the Internet of Things and its impact on the dynamic matching of financing for manufacturing enterprises, providing an innovative energy management model to promote the sustainable development of enterprises. The research uses the Internet of Things technology to monitor the energy flow and heat transfer in the production shop in real time through sensors and data acquisition systems. Combined with big data analytics and machine learning algorithms, in-depth analysis of energy consumption data is performed to identify bottlenecks and optimization points for heat transfer. At the same time, the financing dynamic matching model is constructed to analyze the financing needs and costs of enterprises under different energy management levels. The study found that iot technology can significantly improve the thermal efficiency of production plants and reduce energy waste. On the basis of the optimization of energy heat transfer, the financing demand and financing cost of enterprises have also undergone dynamic changes. The optimization of energy heat transfer based on the Internet of Things not only improves the energy efficiency of the production floor, but also provides a new impetus for the financing of manufacturing enterprises. Implementing an effective energy management strategy will help companies stay ahead of the curve in an increasingly competitive market.

Bibliometric Assessment of Data-Driven Marketing Research Trends in the Last Two Decades

This study presents a bibliometric analysis of data-driven marketing research trends over the last two decades, with a focus on its intersection with innovation, decision-making, and related topics. Using VOSviewer for network visualization, we analyze co-authorship, keyword co-occurrence, publication frequency, and country collaboration to uncover key themes and research developments. The findings indicate a significant rise in data-driven marketing research, particularly from 2015 onwards, driven by advancements in big data, machine learning, and artificial intelligence. Co-authorship networks reveal strong interdisciplinary collaboration, while keyword co-occurrence maps highlight the growing role of innovation, decision-making, and machine learning in data-driven marketing. Additionally, country collaboration networks show the United States, China, the United Kingdom, and India as central contributors to global research. The keyword density heatmap emphasizes the increasing focus on data-driven innovation and product development. These insights offer valuable implications for academics and practitioners seeking to understand and apply data-driven marketing in a rapidly evolving digital landscape.

Statistical challenges and solutions in multidisciplinary clinical research: Bridging the gap between

This paper delves into the intricate challenges and innovative solutions in applying statistical methodologies within clinical research, aiming to bridge the gap between biostatistics and medicine. The study meticulously examines fundamental biostatistical concepts, addressing the complexities of modern clinical trials and observational studies. Through a comprehensive review of advanced regression models, causal inference techniques, and machine learning algorithms, the paper illuminates the evolving landscape of biostatistics in handling high-dimensional data and confounding variables. The methods employed in this study involve an extensive analysis of current literature, case studies, and practical applications that demonstrate the utility of these advanced methodologies. Key findings reveal that traditional statistical approaches often fall short in capturing the complexities of clinical data, necessitating the adoption of more sophisticated techniques. The integration of non-linear regression models, robust causal inference methods, and machine learning has significantly enhanced the accuracy and reliability of research outcomes, offering deeper insights into patient outcomes and treatment efficacy. Conclusions drawn from this study underscore the critical need for a paradigm shift in clinical research, moving beyond the rigid reliance on p-values towards a more holistic approach that emphasizes effect sizes, confidence intervals, and practical significance. The paper recommends continued innovation in statistical methodologies, particularly the integration of big data analytics and machine learning, to address the growing complexities of biomedical data. Furthermore, it advocates for interdisciplinary collaboration and ethical considerations in the application of these advanced techniques to ensure that biostatistics continues to contribute meaningfully to the advancement of medical science.

Digital Twin Framework for Aircraft Lifecycle Management Based on Data-Driven Models

This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-edge technologies such as IoT sensors, big data analytics, machine learning, 6G communication, and cloud computing to create a robust digital twin ecosystem. This paper explores the key components of the framework, including lifecycle phases, new technologies, and models for digital twins. It discusses the challenges of creating accurate digital twins during aircraft operation and maintenance and proposes solutions using emerging technologies. The framework incorporates physics-based, data-driven, and hybrid models to simulate and predict aircraft behavior. Supporting components like data management, federated learning, and analytics tools enable seamless integration and operation. This paper also examines decision-making models, a knowledge-driven approach, limitations of current implementations, and future research directions. This holistic framework aims to transform fragmented aircraft data into comprehensive, real-time digital representations that can enhance safety, efficiency, and sustainability throughout the aircraft lifecycle.

Exploring the Role of Statistical Analysis in Criminology from an Educational Point of View

This article examines the role of statistical analysis in criminology, thoroughly investigating its diverse influence and uses. The introduction provides a comprehensive analysis of the convergence of statistics and criminology, highlighting the significance of statistical methodologies in comprehending and mitigating criminal conduct and augmenting the efficacy of the criminal justice system via statistical techniques. The following section examines statistical analysis's impact on comprehending crime trends, facilitating policy development, and constructing effective crime prevention tactics. Real-world case studies show statistical analysis's practical use and beneficial effects in criminological practice. Notwithstanding the many benefits, it is indispensable to acknowledge and examine the possible obstacles and constraints associated with using statistical analysis in criminology. These include but are not limited to difficulties about data quality, ethical considerations, and resource constraints. Considering the abovementioned factors, we propose prospective resolutions and alternative measures to address these obstacles effectively. In anticipation of forthcoming developments, we draw attention to emerging patterns and advancements in the field, including big data analytics, machine learning, and artificial intelligence, which have the potential to augment the capabilities and efficacy of statistical analysis within the realm of criminology. The conclusion effectively integrates the main topics examined, affirming the crucial significance of statistical analysis in the progression of criminological research, policy formulation, and practical implementation. Furthermore, it highlights the contribution of statistical analysis towards establishing safer communities and a fairer criminal justice system.

Maximizing Profitability and Occupancy: An Optimal Pricing Strategy for Airbnb Hosts Using Regression Techniques and Natural Language Processing

In the competitive landscape of Airbnb hosting, optimizing pricing strategies for properties is a complex challenge that requires revenue maximization with high occupancy rates. This research aimed to introduce a solution that leverages big data and machine learning techniques to help hosts improve their property’s market performance. Our primary goal was to introduce a solution that can augment property owners’ understanding of their property’s market value within their urban context, thereby optimizing both the utilization and profitability of their listings. We employed a multi-faceted approach with diverse models, including support vector regression, XGBoost, and neural networks, to analyze the influence of factors such as location, host attributes, and guest reviews on a listing’s financial performance. To further refine our predictive models, we integrated natural language processing techniques for in-depth listing review analysis, focusing on term frequency-inverse document frequency (TF-IDF), bag-of-words, and aspect-based sentiment analysis. Integrating such techniques allowed for in-depth listing review analysis, providing nuanced insights into guest preferences and satisfaction. Our findings demonstrated that AirBnB hosts can effectively utilize both state-of-the-art and traditional machine learning algorithms to better understand customer needs and preferences, more accurately assess their listings’ market value, and focus on the importance of dynamic pricing strategies. By adopting this data-driven approach, hosts can achieve a balance between maintaining competitive pricing and ensuring high occupancy rates. This method not only enhances revenue potential but also contributes to improved guest satisfaction and the growing field of data-driven decisions in the sharing economy, specially tailored to the challenges of short-term rentals.

HARNESSING BIG DATA AND MACHINE LEARNING FOR TRANSFORMATIVE HEALTHCARE INFORMATION MANAGEMENT

This study explores the transformative impact of big data and machine learning (ML) on healthcare information management, drawing insights from a comprehensive review of 50 peer-reviewed articles published between 2010 and 2023. The analysis highlights significant advancements in predictive analytics, with ML models improving disease prediction accuracy by up to 40% compared to traditional methods, as demonstrated in multiple studies. Personalized medicine has also benefited, with ML-driven treatment plans showing a 25% improvement in chronic disease management and a 15% reduction in treatment failures, as reported in several case studies. Additionally, integrating big data and ML into healthcare operations has led to a 35% reduction in patient wait times and a 20% decrease in hospital readmissions. However, challenges such as data fragmentation, privacy concerns, and algorithmic bias were identified in over 60% of the reviewed studies, indicating the need for stronger regulatory frameworks. This review underscores the potential of big data and ML to revolutionize healthcare while also calling attention to the ethical and practical challenges that must be addressed to ensure equitable and transparent healthcare solutions.

Understanding Factors Affecting Tourist Distribution in Urban National Parks Based on Big Data and Machine Learning

Understanding Factors Affecting Tourist Distribution in Urban National Parks Based on Big Data and Machine Learning

CONCEPTUAL METAPHORS OF ARTIFICIAL INTELLIGENCE AND AI DEVELOPMENT IN THE GUARDIAN NEWSPAPER

The whirlwind advent of ChatGPT in 2022 has marked a new age of artificial intelligence (AI), the general name for the technology that combines computer technology, big data bases and machines. This AI technology quickly makes its presence felt with hundreds of popular programs and chatbots such as the portrait-making AI diffusion art and the thesis-writing ChatGPT. This paper investigates the conceptual metaphors representing AI and AI development in The Guardian, a UK-based newspaper, to figure out how this technology and its growth have been introduced to ordinary people via mass media. Employing the Conceptual Metaphor Theory proposed by Lakoff & Johnson (1980), this study found three AI-related conceptual metaphors, namely, AI IS A HUMAN BEING, AI IS AN ANIMAL and AI IS A NATURAL FORCE, which are realized by more than 100 linguistic expressions across 33 news articles. Also, this research found five conceptual metaphors related to AI development, namely AI DEVELOPMENT IS WAR, AI DEVELOPMENT IS A RACE, AI DEVELOPMENT IS A CONVERSATION, AI DEVELOPMENT IS A DANCE, AI DEVELOPMENT IS A GAME and these metaphors are manifested by approximately 40 linguistic expressions. This paper discusses the way that these metaphors could influence the way people and technology companies think about AI and AI development.