Data-driven Decision Research Articles

Crop Yield Time-Series Data Prediction Based on Multiple Hybrid Machine Learning Models

Agriculture plays a crucial role in the global economy and social stability, and accurate crop yield prediction is essential for rational planting planning and decision-making. This study focuses on crop yield Time-Series Data prediction. Considering the crucial significance of agriculture in the global economy and social stability and the importance of accurate crop yield prediction for rational planting planning and decision-making, this research uses a dataset containing multiple crops, multiple regions, and data over many years to deeply explore the relationships between climatic factors (average rainfall, average temperature) and agricultural inputs (pesticide usage) and crop yield. Multiple hybrid machine learning models such as Linear Regression, Random Forest, Gradient Boost, XGBoost, KNN, Decision Tree, and Bagging Regressor are adopted for yield prediction. After evaluation, it is found that the Random Forest and Bagging Regressor models perform excellently in predicting crop yield with high accuracy and low error.As agricultural data becomes increasingly rich and time-series prediction techniques continue to evolve, the results of this study contribute to advancing the practical application of crop yield prediction in agricultural production management. The integration of time-series analysis allows for more dynamic, data-driven decision-making, enhancing the accuracy and reliability of crop yield forecasts over time.

Research on the Influencing Factors of Hotel Booking Cancellation and Coping Strategies

With the rapid advancement of the hotel industry, studies on boosting operational efficiency have largely focused on employee productivity and human-centric policies, yet there is a noticeable lack of strategies aimed at enhancing room occupancy rates. This paper address this gap by examining factors influencing hotel reservation cancellations and proposing effective mitigation strategies. Utilizing a decision tree methodology, the research identifies key elements that contribute to cancellations. The significance of this study lies in its contribution to optimizing room occupancy within hotel management. Practical strategies, such as lowering booking security deposits, are suggested to decrease cancellation rates. These insights enhance not only operational efficiency and resource utilization but also offer actionable advice for improving guest satisfaction. By embracing data-driven decision-making, the study promotes modernized management practices and supports sustainable growth in hospitality. Three aspects for improvement are highlighted: internal operations, customer-related factors, and external influences. Recommendations include reducing security deposits at booking to lower cancellations and optimize occupancy.

Tractor Power Take-Off and Drawbar Pull Performance and Efficiency Evolution Analysis Methodology and Model: A Case Study

Previous studies on tractor performance and efficiency were conducted prior to the implementation of emission reduction technologies and the increased density and complexity of tractor portfolios. This study presents a robust methodology for forecasting specific fuel consumption based on public information, which incorporates physical attribute-based cohorts and technological generation groupings, alongside variables such as wheelbase, mass, and power take-off power. The proposed model significantly improves forecasting accuracy, enhancing the current R-squared (RSq) from 0.6091 to 0.8519 and reducing the root mean square error (RMSE) from 0.0098 to 0.0065. Additionally, the model provides accurate predictions of drawbar performance and efficiency. Its simplicity results in low cognitive and computational demands, making it accessible via widely available spreadsheet software on any computer or handheld device. This accessibility supports data-driven decision-making for tractor replacement strategies, ultimately promoting sustainable profitability in agricultural business operations.

Digital transformation of tourism education: Technologies, opportunities, challenges, and future research agenda

This study explores the integration of these technologies into tourism curricula, highlighting both opportunities and challenges. While digital tools enhance experiential learning, accessibility, and industry relevance, their adoption is hindered by high costs, faculty training, and resistance to change. The study employs qualitative research through semi-structured interviews with tourism educators to examine the effectiveness of digital tools and their impact on student learning. Findings indicate immersive technologies improve engagement, while cloud computing and AI facilitate data-driven decision-making. However, further research is needed to address institutional barriers and ensure equitable access to digital education. The study concludes with a future research agenda to optimize digital transformation strategies in tourism education.

Barriers to Providing Optimal Care in Idaho from the Perspective of Healthcare Providers: A Descriptive Analysis

Background/Objectives: Few studies have assessed barriers to providing care from the perspective of interprofessional healthcare providers. Despite Idaho’s predominantly rural geography, limited research exists assessing barriers to providing care within the state. This study sought to assess barriers to providing optimal healthcare using a sample of 400 healthcare providers at 22 clinic sites across the state. Methods: A barriers to providing optimal care 9-factor, 41-item survey was modified from an existing survey. Healthcare providers rated barrier items using an 11-point Likert scale. The survey was distributed to a convenience sample of healthcare providers in 22 different clinic sites in rural Idaho. Results: Four hundred interprofessional healthcare providers in Idaho across 13 professional disciplines completed surveys. Items in the Service Access (mean = 7.14), Patient Complexity (mean = 6.59), and Resource Limitations (mean = 6.18) factors were reported as the most commonly perceived barriers to providing optimal care. Conclusions: Few studies have assessed rural interprofessional providers’ perceived barriers to providing optimal, high-quality, care, specifically in the rural state of Idaho, where healthcare services are often not equitable compared to urban regions. The results suggest that commonly perceived barriers exist throughout the state, particularly Service Access, Patient Complexity, and Resource Limitations. Further research is needed to develop data-driven decisions to address these concerns.

The Feasibility of Technology to Support Data-Driven Decision Making in Early Head Start Classrooms

The Feasibility of Technology to Support Data-Driven Decision Making in Early Head Start Classrooms

Cognitive Nests: Nested Data-Driven Decision Support System in Regenerative Design from Biology to Ecology

Cognitive Nests: Nested Data-Driven Decision Support System in Regenerative Design from Biology to Ecology

Distribution of Operating Costs Along the Value Chain of an Open-Pit Copper Mine

This study analyzes the distribution of operating costs along the value chain of an open-pit copper mine with a focus on key operational units or operations such as drilling, blasting, loading, hauling, stockpiling, blending, crushing, milling, and flotation. Using process costing analysis, key cost drivers were identified, and their individual contributions to total expenses were quantified. Results revealed that comminution processes dominate the operational cost structure, with milling accounting for 6.18 USD/ton, representing 59.1% of total operating costs, and crushing costing 1.15 USD/ton, that is, 11% of total operating expenditure. The study also highlighted several opportunities for cost reduction and enhanced mining sustainability through strategies such as energy consumption optimization, the use of alternative energy sources, and optimized blast design. Finally, valuable insights aimed at promoting sustainable resource utilization, improved cost efficiency, and data-driven decision-making in mining operations are offered to mine planners and operators. This is eventually expected to lay the foundation for benchmarking work on the establishment of a baseline and standards for similar mining operations.

Operational Research Models for E-Commerce Platforms: New Technology Innovation

New technology innovations and operational research (OR) models can enhance the efficiency and competitiveness of e-commerce platforms. The rapid expansion of e-commerce has led to increased complexity in operations, including dynamic pricing, inventory management, last-mile delivery, and fraud detection. This special issue features nine articles exploring how advanced OR models, such as game-theory-based approaches, machine learning, and big data analytics, offer solutions to these challenges by enabling platforms to make data-driven decisions. As e-commerce evolves, these technological advancements, combined with OR models, will continue to drive platform innovation, improve efficiency, and strengthen resilience in the face of disruptions and fluctuating market demands.

Risk Management and Patient Safety; The National Safety and Quality Health Service (NSQHS) Standards

Purpose: The research aims at modifying two Standards of the National Safety and Quality Health Service (NSQHS) framework for UAE healthcare institutions which include both Clinical Governance and Medication Safety standards. This adaptation process is crucial for linking these standards to UAE regulations and healthcare infrastructure because of substantial cultural as well as legal technological and organizational differences between Australia and the UAE. Healthcare facilities in UAE will receive upgraded patient safety and quality of care through standards adaptations which reflect their specific operational needs. Methodology: The analysis methodically evaluates healthcare systems in Australia and UAE by involving their governance structures along with their combination of diverse cultures and system capabilities. A structured framework development method guides standard alignment with the UAE’s regulatory framework and adds suitable leadership strategies for diverse patient populations. The study delves into technology adoption mainly through artificial intelligence (AI) and digital health tools which enhance clinical decision-making ability and patient information retrieval. This analysis determines the implementation feasibility of electronic prescribing and medication tracking frameworks which will support the Medication Safety Standard within the UAE context due to its advanced technology infrastructure. Findings: A comprehensive governance framework requires development in order to achieve compatibility between the Clinical Governance Standard and UAE healthcare framework standards and employee population diversity. A combination of healthcare analytics with AI capabilities leads to substantial improvements in governance decision processes and patient care programs and quality assessment activities. The standard needs effective solutions to address regulatory matching requirements and cultural adaptation needs in order to function properly. Tests show that implementing electronic prescribing systems with nation-wide medication reconciliation programs will improve provider accountability and healthcare professional communication while decreasing medication errors. Adequate technology infrastructure within the UAE promotes these initiatives though the healthcare systems experiences impedance because of technical inconsistencies and regulatory barriers across regions. Unique Contribution to Theory, Practice and Policy: The UAE requires the development of a specific Clinical Governance Framework to fulfill legal requirements and address multicultural patient needs in the nation. Healthcare institutions must use AI and digital health technology to create data-driven decisions and enhance governance operation and quality assessment systems. It is crucial to establish nation-wide medication reconciliation standards across all healthcare facilities so care management remains consistent while minimizing medication errors in the UAE. The adoption of electronic prescribing systems and medication tracking platforms will boost healthcare provider communication by advancing patient protection measures. More research needs to be performed about AI and machine learning integration to expand the range of NSQHS standards and their application in UAE healthcare facilities.

Digital monitoring of heavy equipment: advancing cost optimization and operational efficiency

The monitoring of heavy equipment using digital technologies has become a crucial solution for cost optimization and improving operational efficiency in essential sectors such as construction, mining, and infrastructure. The integration of technologies such as advanced sensors, the Internet of Things (IoT), Big Data, and Artificial Intelligence (AI) enables proactive management of machines, allowing for early failure detection, real-time monitoring, and the adoption of predictive maintenance. These advancements result in significant reductions in downtime, extend the lifespan of equipment, and minimize emergency repair costs. The research by Khan et al. (2022), Ma et al. (2020), and Rohith et al. (2023) demonstrates how technological innovations such as AI, ML, BIM, and GIS are transforming maintenance strategies. These tools not only optimize machine performance but also provide intelligent, data-driven decision support, creating a more efficient and sustainable environment. The implementation of these technologies allows for the integration of new automated systems, which can revolutionize equipment management in various sectors. However, the adoption of these technologies also faces challenges, as indicated by Dagsa et al. (2022) and Shekari and Ray (2024), who highlight the limitations of monitoring systems and potential contractual disputes. While the benefits are evident, the excess of data and contractual complications require managers to adopt balanced solutions to ensure that risks are minimized while benefits are maximized. In summary, digital technologies in heavy equipment monitoring represent a significant advancement, offering a clear path toward long-term sustainability and operational efficiency.

SAP on AWS in Education: Transforming Digital Learning Environments

Integrating SAP Enterprise Resource Planning (ERP) systems with Amazon Web Services (AWS) is transforming educational institutions to change the face of operational competency and ways of knowledge delivery. It will discuss the cloud-based infrastructure and how it benefits educational organizations in terms of scalability, cost optimization, and robust disaster recovery strategies. This article will assess how SAP on AWS implements automation processes in administrative work, data-driven decision-making, and personalized learning experiences. The article addresses critical security and compliance considerations indispensable for protecting sensitive educational data while maintaining regulatory adherence. It addresses best practices around implementation, from architecture planning down to performance-optimization strategy and change-management approach. To top it off, the article analyses the future scope of educational technology, which is very much concerned with the emerging themes of artificial intelligence, the Internet of Things, blockchain, and extended reality, leading to a redrawing of educational landscapes. The article provides insight into how educational institutions can use these technological advancements to enhance operational efficiency while maintaining security and compliance in an increasingly digital educational environment.

Optimized Random Forest Models for Rock Mass Classification in Tunnel Construction

The accurate prediction of rock mass quality ahead of the tunnel face is crucial for optimizing tunnel construction strategies, enhancing safety, and reducing geological risks. This study developed three hybrid models using random forest (RF) optimized by moth-flame optimization (MFO), gray wolf optimizer (GWO), and Bayesian optimization (BO) algorithms to classify the surrounding rock in real time during tunnel boring machine (TBM) operations. A dataset with 544 TBM tunneling samples included key parameters such as thrust force per cutter (TFC), revolutions per minute (RPM), penetration rate (PR), advance rate (AR), penetration per revolution (PRev), and field penetration index (FPI), with rock classification based on the Rock Mass Rating (RMR) method. To address the class imbalance, the Borderline Synthetic Minority Over-Sampling Technique was applied. Performance assessments revealed the MFO-RF model’s superior performance, with training and testing accuracies of 0.992 and 0.927, respectively, and key predictors identified as PR, AR, and RPM. Additional validation using 91 data sets confirmed the reliability of the MFO-RF model on unseen data, achieving an accuracy of 0.879. A graphical user interface was also developed, enabling field engineers and technicians to make instant and reliable rock classification predictions, greatly supporting safe tunnel construction and operational efficiency. These models contribute valuable tools for real-time, data-driven decision-making in tunneling projects.

Smart data-driven medical decisions through collective and individual anomaly detection in healthcare time series

BackgroundAnomalies in healthcare refer to deviation from the norm of unusual or unexpected patterns or activities related to patients, diseases or medical centres. Detecting these anomalies is crucial for timely interventions and efficient decision-making, helping to identify issues like operational inefficiencies, fraud and emerging health complications. ObjectivesThis study presents a novel method for detecting both collective and individual anomalies in healthcare data through time series analysis using unsupervised machine learning. The dual-strategy approach leverages two methodologies: a ’practice centre-based approach’ which monitors changes across different practice centres and a ’process-based approach’ which focuses on identifying anomalies within individual centres. The former allows for early detection of systemic issues, while the latter highlights specific irregularities within a centre’s operations. MethodsThe study utilised a dataset over 500,000 medical records from multiple GP practice centres in the UK collected between 2018–2023. Data are clustered using DBSCAN to identify collective anomalies from deviations from linear trends in consecutive two-month scatterplots. Individual anomalies are identified by examining the SOM-clustered time series of various medical processes within a specific practice centre, where graphs show deviation from the typical pattern. FindingsOur approach addresses some challenges posed by the complexity and sensitivity of healthcare data by not requiring personal information. The method offers accurate visual representations making the data accessible and interpretable for non-technical users. Unlike traditional methods focusing solely on subsequence anomalies, our technique analyses the collective behaviour across multiple time series providing a more comprehensive perspective. ConclusionThis study underscores the importance of integrating unsupervised anomaly detection with clinical expertise to ensure that statistically anomalous patterns align with clinical relevance. The dual-strategy clustering method holds significant potential for enabling timely interventions, proactively identifying potential crises, and ultimately contributing to better decision-making and operational efficiency within the healthcare sector.

Customer Churn Prediction Using Machine Learning

Customer attrition poses a major issue for the telecommunications sector, resulting in revenue decline and higher expenses for gaining new customers. Predicting churn accurately enables telecom companies to take proactive measures to retain customers. This study leverages specifically Random Forest, Decision Tree, and XGBoost, to develop a robust churn prediction model. These algorithms analyze customer behavioral data, including call duration, internet usage, billing history, and complaints, to identify potential churners. Decision Tree provides an interpretable model, Random Forest improves predictive accuracy through ensemble learning, and XGBoost enhances performance with gradient boosting and optimized handling of imbalanced datasets. The proposed model assists telecom companies in classifying customers based on their churn risk, enabling the implementation of targeted retention approaches like tailored discounts and rewards programs. By integrating advanced machine learning techniques, telecom service providers can enhance customer retention, minimize the churn rates, and improves the business sustainability. The study highlights the importance of data-driven decision-making in the telecom sector, demonstrating how predictive analytics can optimize customer relationship management and drive profitability. Key Words: Predicting telecom customer churn, machine learning techniques, Random Forest algorithm, Decision Tree method, XGBoost model, retention of customers, analytics for forecasting, gradient boosting techniques, telecom sector.

Predicting the composition of solid waste at the county scale.

The primary goals of this paper are to facilitate data-driven decision making in solid waste management (SWM) and to support the transition towards a circular economy, by providing estimates of the composition and quantity of waste. To that end, it introduces a novel two-phase strategy for predicting municipal solid waste (MSW). The first phase predicts the waste composition, the second phase predicts the total quantity, and the two predictions are combined to give a comprehensive waste estimate. This novel approach overcomes limitations of existing methods that rely on material-specific quantity data, facilitating the prediction of dozens of waste material streams; existing methods typically classify MSW into no more than 10 categories, and often reduce it to a single aggregate total. To implement this strategy, the proposed study utilizes publicly available data encompassing demographic, economic, and spatial predictors, in conjunction with waste sampling reports. In addition, it develops a Least Absolute Shrinkage and Selection Operator (LASSO) regression model to estimate the MSW composition across 43 comprehensive material categories. The LASSO model is designed to predict MSW composition distinctly from quantity. The model's capability is demonstrated through case studies, showcasing its potential to provide detailed waste estimates at the U.S. county level.

Online monitoring of Haematococcus lacustris cell cycle using machine and deep learning techniques.

Optimal control and process optimization of astaxanthin production from Haematococcuslacustris is directly linked to its complex cell cycle ranging from vegetative green cells to astaxanthin-rich cysts. This study developed an automated online monitoring system classifying four different cell cycle stages using a scanning microscope. Decision-tree based machine learning and deep learning convolutional neural network algorithms were developed, validated, and evaluated. SHapley Additive exPlanations was used to examine the most important system requirements for accurate image classification. The models achieved accuracies on unseen data of 92.4 and 90.9%, respectively. Furthermore, both models were applied to a photobioreactor culturing H.lacustris, effectively monitoring the transition from a green culture in the exponential growth phase to a stationary red culture. Therefore, online image analysis using artificial intelligence models has great potential for process optimization and as a data-driven decision support tool during microalgae cultivation.

ChatSchema: development and validation of a pipeline for extracting structured nursing information with large multimodal models

Abstract Objective: Timely access to essential patient information is critical for informed, data-driven decision-making in clinical nursing to enhance care efficiency and quality. Despite advancements in electronic health records, many medical reports remain in paper form, posing challenges for data analysis and application. This study aimed to develop ChatSchema, a pipeline based on Large Multimodal Models (LMMs) for extracting structured information from paper-based medical reports, and evaluated its effectiveness in a pilot setting. Method: ChatSchema was a two-stage approach to extract and structure data from paper-based medical reports. The classification stage leveraged Optical Character Recognition (OCR) of the pictured medical reports, pre-correction, desensitization, and prompt engineering to categorize report types. In the extraction stage, OCR-converted text was transformed into a structured format using a predefined schema, with LMMs applied for standardizing fields and converting data types. A dataset of 100 annotated medical reports was collected from Peking University First Hospital, and the effectiveness of ChatSchema was evaluated in terms of precision, recall, F1-score, and overall accuracy. To validate ChatSchema’s effectiveness, we compared it against a baseline method that provided only schema and task instructions. For sensitivity analysis, two basic LMMs, GPT-4o and Gemini 1.5 Pro, were utilized and compared in the development of ChatSchema. Results: A ground-truth dataset comprising 2,945 test item-result pairs was extracted from 100 annotated medical reports. ChatSchema was capable of correctly extracting and structuring data from paper-based medical reports, showing remarkable accuracy across various data types, unit standardization, field mapping, and basic LMMs. Overall, ChatSchema achieved a high F1-score of 95.8%, an accuracy of 97.2%, a precision of 95.8%, and a recall of 95.8%. ChatSchema surpassed the baseline model by 12.9% in accuracy and by 12.3% in F1-score using the GPT-4o model. Conclusion: Our findings demonstrate that ChatSchema is highly effective for extracting and structuring data from paper-based medical reports. By providing accurate, structured information extraction, ChatSchema has the potential to enhance patient care and support clinical decision-making, showing significant promise for broader application in nursing and healthcare settings.

Optimizing Healthcare Access: An Advanced Analytics Solution for Provider Distribution Management

The growing complexity of healthcare provider distribution and patient access necessitates sophisticated analytics tools for optimizing physician coverage across geographical regions. This article presents a novel data-driven approach that leverages real-time search patterns from physician finder tools to analyze and visualize provider demand-supply gaps. The proposed system implements a dynamic classification methodology that categorizes geographic areas into red, yellow, and green zones based on the correlation between patient search volumes and existing provider availability. By incorporating multiple variables including specialization density, geographic accessibility, and temporal search patterns, the system provides healthcare organizations with actionable insights for strategic provider recruitment and resource allocation. Initial implementations demonstrate significant improvements in provider coverage optimization and patient access to care. This article suggests that data-driven geographic analysis tools can substantially enhance healthcare organization's ability to proactively address provider shortages and optimize healthcare resource distribution. This article contributes to the growing field of healthcare analytics by providing a scalable framework for data-driven provider distribution decisions.

Integration and application of artificial intelligence tools in the Moodle platform: A theoretical exploration

The integration of artificial intelligence (AI) technologies into Learning Management Systems (LMS) represents a transformative advancement in contemporary education, enhancing personalization, efficiency, and interactivity. This study presents a systematic methodology for integrating AI technologies into the Moodle LMS. Moodle 4.5 was selected as the LMS owing to its modular architecture, flexibility, scalability, and integrated AI tools. This paper delineates the technical and pedagogical steps necessary for a successful integration process, including the installation of plug-ins, integration of APIs, customization of the system, and configuration of the AI menu. Moodle 4.5 simplifies the deployment and utilization of AI functionalities, offering an intuitive framework for creating dynamic and adaptive learning environments. AI integration improves student engagement, enables personalized learning pathways, and facilitates data-driven instructional decisions. However, challenges such as data privacy concerns, algorithmic biases, and infrastructural demands require ethical frameworks, interdisciplinary collaboration, and educator training to ensure responsible implementation. This study concludes that AI integration into platforms like Moodle can redefine educational paradigms, fostering inclusive and future-ready learning environments. Recommendations include investments in robust infrastructure and the adoption of ethical strategies to maximize AI's transformative impact while upholding pedagogical integrity and equity.