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Supply chain agility has become a key success factor for businesses trying to handle upheavals and uncertainty in today's quickly changing business environment. Proactive risk reduction is essential for achieving this agility. To facilitate real-time risk prevention and improve agility, this research study proposes an innovative strategy that makes use of machine learning as well as predictive analytics approaches. Traditional supply chain risk management frequently uses post-event analysis as well as historical data, which restricts its ability to address real-time interruptions. This research, on the other hand, promotes a futuristic methodology that uses predictive analytics to foresee possible disruptions. Based on contextual and historical data, machine learning models can be trained to find patterns and correlations as well as anomalies that point to imminent dangers. Organizations can identify risks as they arise and take preventative measures by incorporating these models into a real-time monitoring system. This study examines numerous predictive analytics methods, showing how they can be used to spot supply chain risks. These methods include time series analysis and anomaly detection as well as natural language processing. Additionally, risk assessment models are continuously improved and optimized using machine learning algorithms, assuring their accuracy and adaptability in changing contexts. This research clarifies the symbiotic relationship among predictive analytics and machine learning as well as supply chain agility using a synthesis of theoretical discourse and practical evidence. Case studies from various sectors highlight the usefulness and advantages of the suggested strategy. The advantages of this novel technique include improved risk visibility and quicker response times as well as the capacity to quickly modify operations. The development of a holistic framework that incorporates predictive analytics and machine learning into risk management procedures, setting the path for real-time risk identification as well as mitigation, is one of the theoretical contributions. On the practical side, the case studies offered in this paper show the actual benefits as well as the adaptability of the proposed approach across a wide range of businesses. [ABSTRACT FROM AUTHOR]

Virtual manufacturing (VM) technology emerged in the 1980s as a revolutionary strategy to optimize and streamline the entire product/service manufacturing lifecycle. However, over the years, its popularity appears to have waned. Further, the advent of the fourth industrial revolution (4IR) or Industry 4.0 brings with it other integrated digital technologies, including the Internet of Things (IoT), Blockchain, and digital twin (DT), among others. DT offers functions like VM plus other benefits, including intelligent manufacturing, to revolutionize future manufacturing operations activities and predictive capability using real-time data. This paper employs bibliographic metadata from publications indexed on SCOPUS to evaluate the recent trends in VM research and develop predictive models to forecast VM's future trajectory and relevance in 4IR. The results of the bibliometric evaluation of VM-related scientific literature publications show a rapidly declining research productivity and highlight an exponential decline from the mid-2000s. This period of VM publication decline coincides with the advent of 4IR and DT technology, which are trending. The results of the predictive analytics using the quadratic regression model created in this study to forecast the future relevance of VM in the 4IR era suggest that VM publications show a similar conclusion. VM research output increased until 2009 and then started decreasing exponentially. The quadratic model implies an exponential decrease in yearly VM publications. Future works can evaluate DT and VM research trends from the last two decades. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Several emerging industries are deploying Artificial Intelligence (AI) and Big Data in many fields. The combination of AI and Big data can benefit sports in many ways. An effective sports prediction model can help athletes improve their sports performance by providing them with an additional training plan and ensuring their health. It is common to use artificial intelligence to predict sports results. Artificial intelligence-enabled predictive analytics can be used to improve an athlete's physical condition and performance. This study attempts to develop a sports predictive analytics system on the basis of AI and big data. This study examines the role of artificial intelligence and big data in the sports industry. The current research status of sports predictive analytics is reviewed through a literature search. Succeedingly, the analysis of the performance of the proposed system is carried out. The result of the study will encourage the use of AI and Big data in sports. [ABSTRACT FROM AUTHOR]

This essay examines the enormous effects of machine learning and artificial intelligence (AI) on healthcare. Through data analysis, AI is transforming disease detection and prediction and improving the precision of diagnoses. By accelerating medication discovery and improving individualized treatment programs, it is revolutionizing both treatment and drug development. AI is promoting customized medicine by using genetic information to customize therapies. Through automation and optimized resource allocation, it is streamlining hospital processes. The importance of ethical considerations is significant; they center on data privacy, bias reduction, and accountability. The study highlights potential avenues for AI development, such as AI-driven drug discovery, predictive and preventative healthcare, advances in genomic medicine, enhanced medical imaging, and more robotics and automation. Predictive analytics, telehealth, AI virtual assistants, and AI in mental healthcare are all expected to grow. These developments have the potential to improve health care, streamline processes, and boost scientific inquiry. To use AI in healthcare in a fair and ethical manner, however, and usher in a future that is more patientcentric, accurate, and accessible internationally, difficulties related to data quality, ethics, regulation, and prejudice must be addressed. [ABSTRACT FROM AUTHOR]

The main goal of this study was to develop a hybrid decision‐making support model regarding the feasibility of financing the development of tourism infrastructure of regions for V4 countries, based on the predicted assessment of the level of tourist movement in relation to the infrastructure and accessibility of the studied regions, expert opinions regarding the level of quality of tourist services and tourism development, as well as opinions of experts regarding the prospects of rapid growth of tourist movement in the region. For the first time, a hybrid fuzzy model for assessing the level of tourism quality in the region was developed, using the opinions of experts regarding the level of quality of tourist services and tourism development. For the first time, a five‐layer neuro‐fuzzy model was developed to derive a quantitative and linguistic assessment of the level of feasibility of financing the development of tourist infrastructure based on the experience, knowledge, and competences of experts regarding the prospects of rapid growth of tourist movement in the studied region. The research results were tested, and the developed model was verified on real data for 43 regions of the V4 countries. [ABSTRACT FROM AUTHOR]

Background: Paediatric appendicitis may be challenging to diagnose, and outcomes difficult to predict. While diagnostic and prognostic scores exist, artificial intelligence (AI) may be able to assist with these tasks. Method: A systematic review was conducted aiming to evaluate the currently available evidence regarding the use of AI in the diagnosis and prognostication of paediatric appendicitis. In accordance with the PRISMA guidelines, the databases PubMed, EMBASE, and Cochrane Library were searched. This review was prospectively registered on PROSPERO. Results: Ten studies met inclusion criteria. All studies described the derivation and validation of AI models, and none described evaluation of the implementation of these models. Commonly used input parameters included varying combinations of demographic, clinical, laboratory, and imaging characteristics. While multiple studies used histopathological examination as the ground truth for a diagnosis of appendicitis, less robust techniques, such as the use of ICD10 codes, were also employed. Commonly used algorithms have included random forest models and artificial neural networks. High levels of model performance have been described for diagnosis of appendicitis and, to a lesser extent, subtypes of appendicitis (such as complicated versus uncomplicated appendicitis). Most studies did not provide all measures of model performance required to assess clinical usability. Conclusions: The available evidence suggests the creation of prediction models for diagnosis and classification of appendicitis using AI techniques, is being increasingly explored. However, further implementation studies are required to demonstrate benefit in system or patient‐centred outcomes with model deployment and to progress these models to the stage of clinical usability. [ABSTRACT FROM AUTHOR]

This study analyzed the post-high school outcomes of exited high-school students with intellectual disability and autism spectrum disorder from a southwestern U.S. state. A predictive analytics approach was used to analyze these students' post-high school outcomes data, which every state is required to collect each year under U.S. special-education law. Data modeling was conducted with machine learning and logistic regression, which produced two main findings. One, the strongest significant predictors were (a) students spending at least 80% of their instructional days in general education settings and (b) graduating from high school. Two, machine learning models were consistently more accurate in predicting post-high school education or employment than were multilevel logistic regression models. This study concluded with the limitations of the data and predictive-analytic models, and the implications for researchers and state and local education professionals to utilize predictive analytics and state-level post-high school outcomes data for decision making. [ABSTRACT FROM AUTHOR]

Study Design. A retrospective cohort study. Objective. To identify the factors associated with readmissions after PLF using machine learning and logistic regression (LR) models. Summary of Background Data. Readmissions after posterior lumbar fusion (PLF) place significant health and financial burden on the patient and overall health care system. Materials and Methods. The Optum Clinformatics Data Mart database was used to identify patients who underwent posterior lumbar laminectomy, fusion, and instrumentation between 2004 and 2017. Four machine learning models and a multivariable LR model were used to assess factors most closely associated with 30-day readmission. These models were also evaluated in terms of ability to predict unplanned 30-day readmissions. The topperforming model (Gradient Boosting Machine; GBM) was then compared with the validated LACE index in terms of potential cost savings associated with the implementation of the model. Results. A total of 18,981 patients were included, of which 3080 (16.2%) were readmitted within 30 days of initial admission. Discharge status, prior admission, and geographic division were most influential for the LR model, whereas discharge status, length of stay, and prior admissions had the greatest relevance for the GBM model. GBM outperformed LR in predicting unplanned 30-day readmission (mean area under the receiver operating characteristic curve 0.865 vs. 0.850, P<0.0001). The use of GBM also achieved a projected 80% decrease in readmission-associated costs relative to those achieved by the LACE index model. Conclusions. The factors associated with readmission vary in terms of predictive influence based on standard LR and machine learning models used, highlighting the complementary roles these models have in identifying relevant factors for the prediction of 30-day readmissions. For PLF procedures, GBM yielded the greatest predictive ability and associated cost savings for readmission. [ABSTRACT FROM AUTHOR]

Context. This commentary is based on an innovative approach to the development of predictive analytics. It is centered on the development of predictive models for varying stages of chronic disease through integrating all types of datasets, adds various new features to a theoretically driven data warehousing, creates purpose-specific prediction models, and integrates multi-criteria predictions of chronic disease progression based on a biomedical evolutionary learning platform. After merging across-center databases based on the risk factors identified from modeling the predictors of chronic disease progression, the collaborative investigators could conduct multi-center verification of the predictive model and further develop a clinical decision support system coupled with visualization of a shared decision-making feature for patient care. The Study Problem. The success of health services management research is dependent upon the stability of pattern detection and the usefulness of nosological classification formulated from big-data-to-knowledge research on chronic conditions. However, longitudinal observations with multiple waves of predictors and outcomes are needed to capture the evolution of polychronic conditions. Motivation. The transitional probabilities could be estimated from big-data analysis with further verification. Simulation or predictive models could then generate a useful explanatory pathogenesis of the end-stage-disorder or outcomes. Hence, the clinical decision support system for patient-centered interventions could be systematically designed and executed. Methodology. A customized algorithm for polychronic conditions coupled with constraints-oriented reasoning approaches is suggested. Based on theoretical specifications of causal inquiries, we could mitigate the effects of multiple confounding factors in conducting evaluation research on the determinants of patient care outcomes. This is what we consider as the mechanism for avoiding the black-box expression in the formulation of predictive analytics. The remaining task is to gather new data to verify the practical utility of the proposed and validated predictive equation(s). More specifically, this includes two approaches guiding future research on chronic disease and care management: (1) To develop a biomedical evolutionary learning platform to predict the risk of polychronic conditions at various stages, especially for predicting the micro- and macro-cardiovascular complications experienced by patients with Type 2 diabetes for multidisciplinary care; and (2) to formulate appropriate prescriptive intervention services, such as patient-centered care management interventions for a high-risk group of patients with polychronic conditions. Conclusions. The commentary has identified trends, challenges, and solutions in conducting innovative AI-based healthcare research that can improve understandings of disease-state transitions from diabetes to other chronic polychronic conditions. Hence, better predictive models could be further formulated to expand from inductive (problem solving) to deductive (theory based and hypothesis testing) inquiries in care management research. [ABSTRACT FROM AUTHOR]

Ectoine is generally produced by the fermentation process of Halomonas elongata DSM 2581 T, which is one of the primary industrial ectoine production techniques. To effectively monitor and control the fermentation process, the important parameters require accurate real-time measurement. However, for ectoine fermentation, three critical parameters (cell optical density, glucose, and product concentration) cannot be measured conveniently in real-time due to time variation, strong coupling, and other constraints. As a result, our work effectively created a series of hybrid models to predict the values of these three parameters incorporating both fermentation kinetics and machine learning approaches. Compared with the traditional machine learning models, our models solve the problem of insufficient data which is common in fermentation. In addition, a simple kinetic modeling is only applicable to specific physical conditions, so different physical conditions require refitting the function, which is tedious to operate. However, our models also overcome this limitation. In this work, we compared different hybrid models based on 5 feature engineering methods, 11 machine-learning approaches, and 2 kinetic models. The best models for predicting three key parameters, respectively, are as follows: CORR-Ensemble (R2: 0.983 ± 0.0, RMSE: 0.086 ± 0.0, MAE: 0.07 ± 0.0), SBE-Ensemble (R2: 0.972 ± 0.0, RMSE: 0.127 ± 0.0, MAE: 0.078 ± 0.0), and SBE-Ensemble (R2:0.98 ± 0.0, RMSE: 0.023 ± 0.001, MAE: 0.018 ± 0.001). To verify the universality and stability of constructed models, we have done an experimental verification, and its results showed that our proposed models have excellent performance. Key points: • Using the kinetic models for producing simulated data • Through different feature engineering methods for dimension reduction • Creating a series of hybrid models to predict the values of three parameters in the fermentation process of Halomonas elongata DSM 2581 T [ABSTRACT FROM AUTHOR]

Data-driven methods can leverage opportunities to optimize internal logistics systems. Performance metrics can be gathered by Machine Learning. However, missing information is a significant obstacle. Therefore, additional data is necessary. This article presents a procedure model for process analysis, from database preprocessing to gathering process insights, focusing on predictive analytics. A control theory-driven approach categorizes the data, being the guideline for the procedure model. Assistance is provided in selecting data processing methods and obtaining valuable insights. A case study validates the procedure model with a performance analysis by predicting breakdown occurrence, comparing k-nearest neighbors, decision tree algorithms and neural networks. [Display omitted] • Internal logistics process data can be divided into three categories. • The categories are transaction data, master data, and key performance indicators. • A framework with five main steps can assist practitioners with data analyses. • The target application is predictive analytics in the internal logistics domain. [ABSTRACT FROM AUTHOR]

Tactical planning in timber harvesting involves aspects related to forest macro-planning and, particularly, the allocation of resources and sequencing of activities, all of which affect the allocation of timber in forest yards and roads and the productivity of forest machines. Data-driven approaches encourage the use of information obtained from data to enhance decision-making efficiency and support the development of short-term strategies. Therefore, our investigation was intended to determine whether a data-driven approach can generate sufficient input for modeling forwarder productivity in timber forwarding in Pinus and Eucalyptus planted forests, to support tactical planning. We utilized 3812 instances of raw data that were generated over a 36-month period. The data were collected from 23 loggers who operated in Pinus and Eucalyptus planted forests. We applied 22 regression algorithms that applied a supervised learning method from an experimental machine learning approach to the data instances. We evaluated the fitted models using three performance metrics. Out of the tested algorithms, the default mode of light gradient boosting produced a root mean squared error of 14.80 m3 h−1, a mean absolute error of 2.70, and a coefficient of determination of 0.77. Therefore, data-driven methods adequately support forwarder productivity modeling in timber forwarding in planted forests and help forest managers with tactical planning. [ABSTRACT FROM AUTHOR]

The adult population in the United States is more physically active and are living longer than prior generations. Due to the advancement in surgical techniques and the increased number of active people, there has been a rise in the number of hip and knee replacement surgeries. This rise in the number of surgeries is expected to continue. Post-surgical care is a critical component to a successful patient recovery. After surgery, patients experience limited mobility while the muscles around the impacted joints need time for inflammation to subside. Physicians and other medical providers are concerned with making sure that patients do not experience falls during this time as it may lead to more serious injuries. A sample dataset of patients who underwent elective hip or knee surgery from January 2014 to March 2020 has been provided to analyze other medical conditions that may contribute to the likelihood of a patient falling. The goal is to identify important factors that can assist in predicting the probability of a patient falling after surgery. [ABSTRACT FROM AUTHOR]

Adaptive learning platforms are increasingly being used as part of varying instructional modalities. Particularly relevant to this paper, adaptive learning is a critical component of personalized, preclass learning in a flipped classroom. Previously inaccessible, data generated by adaptive learning platforms regarding student engagement with the course content provides an invaluable opportunity to gain a deeper understanding of the learning process and improve upon it. We aim to investigate the relationships between adaptive learning platform interactions and overall student success in the course and identify the variables most influential to student success. We present a comprehensive analysis of our adaptive learning platform data collected in a Numerical Methods course, including aggregate statistics, frequency analysis, and Principal Component Analysis, to determine which variables exhibited the most variability and, therefore, the most information in the data. Subsequently, we used the Partitioning Around Medoids clustering approach to investigate naturally occurring clusters of students and how these clusters relate to overall performance in the course. Our results show that overall performance in the course, as measured by the final course grade, is strongly associated with (1) the behavioral interactions of students with the adaptive platform and (2) their performance on the adaptive learning assessments. We also found distinct student clusters (as defined by success in the course) that exhibited distinctly different behaviors. These findings provide qualitative and quantitative information to identify students needing support and to craft an evidence‐based support strategy for these students. [ABSTRACT FROM AUTHOR]

Ectoine is generally produced by the fermentation process of Halomonas elongata DSM 2581 T, which is one of the primary industrial ectoine production techniques. To effectively monitor and control the fermentation process, the important parameters require accurate real-time measurement. However, for ectoine fermentation, three critical parameters (cell optical density, glucose, and product concentration) cannot be measured conveniently in real-time due to time variation, strong coupling, and other constraints. As a result, our work effectively created a series of hybrid models to predict the values of these three parameters incorporating both fermentation kinetics and machine learning approaches. Compared with the traditional machine learning models, our models solve the problem of insufficient data which is common in fermentation. In addition, a simple kinetic modeling is only applicable to specific physical conditions, so different physical conditions require refitting the function, which is tedious to operate. However, our models also overcome this limitation. In this work, we compared different hybrid models based on 5 feature engineering methods, 11 machine-learning approaches, and 2 kinetic models. The best models for predicting three key parameters, respectively, are as follows: CORR-Ensemble (R2: 0.983 ± 0.0, RMSE: 0.086 ± 0.0, MAE: 0.07 ± 0.0), SBE-Ensemble (R2: 0.972 ± 0.0, RMSE: 0.127 ± 0.0, MAE: 0.078 ± 0.0), and SBE-Ensemble (R2:0.98 ± 0.0, RMSE: 0.023 ± 0.001, MAE: 0.018 ± 0.001). To verify the universality and stability of constructed models, we have done an experimental verification, and its results showed that our proposed models have excellent performance. Key points: • Using the kinetic models for producing simulated data • Through different feature engineering methods for dimension reduction • Creating a series of hybrid models to predict the values of three parameters in the fermentation process of Halomonas elongata DSM 2581 T [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a systematic literature review of the existing research, published between 2006 and 2023, in the field of artificial intelligence for management information systems. Of the 3946 studies that were considered by the authors, 60 primary studies were selected for analysis. The analysis shows that most research is focused on the application of AI for intelligent process automation, with an increasing number of studies focusing on predictive analytics and natural language processing. With respect to the platforms used by AI researchers, the study finds that cloud-based solutions are preferred over on-premises ones. A new research trend of deploying AI applications at the edge of industrial networks and utilizing federated learning is also identified. The need to focus research efforts on developing guidelines and frameworks in terms of ethics, data privacy, and security for AI adoption in MIS is highlighted. Developing a unified digital business strategy and overcoming barriers to user–AI engagement are some of the identified challenges to obtaining business value from AI integration. [ABSTRACT FROM AUTHOR]

The dealers are responsible for connecting customers to manufacturers in the automotive industry. An effective relationship between the dealers and the manufacturers can lead to customer satisfaction and loyalty. This research provides an approach to evaluate factors affecting the dealers-manufacturer relationship and develop a solution for predicting how manufacturers can continue or terminate the cooperation with their dealers in the after-sales service network, which is less discussed in the literature. We used predictive analytics tools and classification methods to predict dealers' cooperation as a dependent variable in two classes based on a 2-year real-life collected data. Our results revealed that dealer performance violations, changing dealer principles, and the services revenue have the most effect on the cooperation of dealers in the automotive after-sales service network. Also, this research proposed a dealer management solution for manufacturers to show them the correct decisions for the dealers' cooperation at the right time. This solution can be used in real-life cases in after-sales service networks to prevent using redundant resources, improve the weak performance of dealers, avoid unnecessary termination of cooperation, avoid the costs of attracting new dealers, and decrease customer dissatisfaction. [ABSTRACT FROM AUTHOR]

Drought conditions pose significant challenges to sustainable agriculture and food security. Identifying microbial strains that can mitigate drought effects is crucial to enhance crop resilience and productivity. This study presents a comprehensive comparison of several machine learning models, including Random Forest, Decision Tree, XGBoost, Support Vector Machine (SVM), and Artificial Neural Network (ANN), to predict optimal microbial strains for this purpose. Models were assessed on multiple metrics, such as accuracy, standard deviation of results, gains, total computation time, and training time per 1000 rows of data. Notably, the Gradient Boosted Trees model outperformed others in accuracy but required extensive computational resources. This underscores the balance between accuracy and computational efficiency in machine learning applications. Leveraging machine learning for selecting microbial strains signifies a leap beyond traditional methods, offering improved efficiency and efficacy. These insights hold profound implications for agriculture, especially concerning drought mitigation, thus furthering the cause of sustainable agriculture and ensuring food security. [ABSTRACT FROM AUTHOR]

When an outbreak of COVID-19 occurs, it will cause a shortage of medical resources and the surge of demand for hospital beds. Predicting the length of stay (LOS) of COVID-19 patients is helpful to the overall coordination of hospital management and improves the utilization rate of medical resources. The purpose of this paper is to predict LOS for patients with COVID-19, so as to provide hospital management with auxiliary decision-making of medical resource scheduling. We collected the data of 166 COVID-19 patients in a hospital in Xinjiang from July 19, 2020, to August 26, 2020, and carried out a retrospective study. The results showed that the median LOS was 17.0 days, and the average of LOS was 18.06 days. Demographic data and clinical indicators were included as predictive variables to construct a model for predicting the LOS using gradient boosted regression trees (GBRT). The MSE, MAE and MAPE of the model are 23.84, 4.12 and 0.76 respectively. The importance of all the variables involved in the prediction of the model was analyzed, and the clinical indexes creatine kinase-MB (CK-MB), C-reactive protein (CRP), creatine kinase (CK), white blood cell count (WBC) and the age of patients had a higher contribution to the LOS. We found our GBRT model can accurately predict the LOS of COVID-19 patients, which will provide good assistant decision-making for medical management.

Abstract Background We are living in an age where data is everywhere and grows up in a very speedy way. Thanks to sensors, mobile phones and social networks, we can gather a hug amount of information to understand human behavior as well as his individual life. In healthcare system, big data analytics and machine learning algorithms prove their effectiveness and efficiency in saving lives and predicting new diseases. This triggered the idea of taking advantages of those tools and algorithms to create systems that involve both doctors and patients in the treatment of disease, predict outcomes and use real-time risk factors from sensors and mobile phones. Methods We distinguish three types of data: data from sensors, data from mobile phones and data registered or updated by the patient in a mobile app we created. We take advantages from IoT systems such as Raspberry Pi to collect and process data coming from sensors. All data collected is sent to a NoSql Server to be then analyzed and processed in Databricks Spark. K-means centroid clustering algorithms is used to build the predictive model, create partitions and make predictions. To validate results in term of efficiency and effectiveness, we used clustering validations techniques: Random K, Silhouette and Elbow methods. Results The main contribution of our work is the implementation of a new system that has the capability to be applied in several prediction disease researches using Big Data Analytics and IoT. Also, comparing to other studies in literature that use only medical or maternal risk factors from echography; our work had the advantage to use real-time risk factors (maternal and medical) gathered from sensors, react in advance and track diseases. As a case study, we create an e-monitoring real-time miscarriage prediction system to save baby’s lives and help pregnant women. In fact, doctors receive the results of clustering and track theirs patient through our mobile app to react in term of miscarriage to avoid non-suitable outcomes. While pregnant women receive only advices based on their behaviors. The system uses 15 real-time risk factors and our dataset contains more than 1,000,000 JSON files. Elbow method affirm three as the optimal number of clusters and we reach 0.99 as a value of Silhouette method, which is a good sign that clusters are well separated and matched.

The development of the Internet of things (IoT) and online platforms enables companies and governments to collect data from a much broader spatial and temporal area in the logistics industry. The huge amount of data provides new opportunities to handle uncertainty in optimization problems within the logistics system. Accordingly, various prescriptive analytics frameworks have been developed to predict different parts of uncertain optimization problems, including the uncertain parameter, the combined coefficient consisting of the uncertain parameter, the objective function, and the optimal solution. This tutorial serves as the pioneer to introduce existing literature on state-of-the-art prescriptive analytics methods, such as the predict-then-optimize framework, the smart predict-then-optimize framework, the weighted sample average approximation framework, the empirical risk minimization framework, and the kernel optimization framework. Based on these frameworks, this tutorial further proposes possible improvements and practical tips to be considered when we use these methods. We hope that this tutorial will serve as a reference for future prescriptive analytics research on the logistics system in the era of big data.

Abstract Background Clinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal validation or one external validation sufficient to claim that a prediction model works well in the intended clinical context? Main body We argue to the contrary because (1) patient populations vary, (2) measurement procedures vary, and (3) populations and measurements change over time. Hence, we have to expect heterogeneity in model performance between locations and settings, and across time. It follows that prediction models are never truly validated. This does not imply that validation is not important. Rather, the current focus on developing new models should shift to a focus on more extensive, well-conducted, and well-reported validation studies of promising models. Conclusion Principled validation strategies are needed to understand and quantify heterogeneity, monitor performance over time, and update prediction models when appropriate. Such strategies will help to ensure that prediction models stay up-to-date and safe to support clinical decision-making.

This study investigates the dynamics of endogenous order placement timing among competing retailers within a single period, driven by the evolution of demand-forecast information. Despite the critical role of accurate market trends and demand forecasts in determining firm success during selling seasons, the existing literature lacks a comprehensive understanding of how firms strategically adjust their order timing with imperfect and evolving information landscapes. By leveraging resources such as predictive analytics systems operated by big data and social media, firms tend to enhance their market demand precision as the selling season approaches, aligning with market practices. With this background, we aim to address the strategic behaviors of competing retailers in timing their orders, filling the aforementioned research gap. We construct a non-cooperative game-theoretical model to analyze the strategic behaviors of competing retailers in timing their orders. The model incorporates factors such as imperfect and evolving information landscapes, considering how firms leverage resources to enhance their market demand precision as the selling season approaches. Our analysis shows two primary equilibria, each shedding light on distinct strategic choices and their implications. First, the better-informed firm decides to execute early orders, capitalizing on the first mover’s advantage, particularly when initial information imprecision exceeds a specific threshold. Conversely, a second equilibrium emerges when the better-informed firm delays its orders, yielding the first mover’s advantage to the less-informed competitor. These equilibria highlight the correlation between order timing strategies and the trajectory of information evolution within the competitive landscape. Additionally, our study extends beyond equilibrium analysis to investigate these strategic choices on supply-chain performance.

In an epoch characterized by the swift pace of digitalization and urbanization, the essence of community well-being hinges on the efficacy of urban management. As cities burgeon and transform, the need for astute strategies to navigate the complexities of urban life becomes increasingly paramount. This study employs time series analysis to scrutinize citizen interactions with the coordinate-based problem mapping platform in the Municipality of Patras in Greece. The research explores the temporal dynamics of reported urban issues, with a specific focus on identifying recurring patterns through the lens of seasonality. The analysis, employing the seasonal decomposition technique, dissects time series data to expose trends in reported issues and areas of the city that might be obscured in raw big data. It accentuates a distinct seasonal pattern, with concentrations peaking during the summer months. The study extends its approach to forecasting, providing insights into the anticipated evolution of urban issues over time. Projections for the coming years show a consistent upward trend in both overall city issues and those reported in specific areas, with distinct seasonal variations. This comprehensive exploration of time series analysis and seasonality provides valuable insights for city stakeholders, enabling informed decision-making and predictions regarding future urban challenges.

This study delves into a critical examination of the Size of Loss distribution patterns in the context of auto insurance during pre- and post-pandemics, emphasizing their profound influence on insurance pricing and regulatory frameworks. Through a comprehensive analysis of the historical Size of Loss data, insurers and regulators gain essential insights into the probabilities and magnitudes of insurance claims, informing the determination of precise insurance premiums and the management of case reserving. This approach aids in fostering fair competition, ensuring equitable premium rates, and preventing discriminatory pricing practices, thereby promoting a balanced insurance landscape. The research further investigates the impact of the COVID-19 pandemic on these Size of Loss patterns, given the substantial shifts in driving behaviours and risk landscapes. Also, the research contributes to the literature by addressing the need for more studies focusing on the implications of the COVID-19 pandemic on pre- and post-pandemic auto insurance loss patterns, thus offering a holistic perspective encompassing both insurance pricing and regulatory dimensions.

We develop a method to assess the quality of peer-produced content in knowledge repositories using their development and coordination histories. We also develop a process to identify relevant features for quality assessment models and algorithms for processing datasets in large-scale knowledge repositories. Models using these features, on English language Wikipedia articles, outperform existing methods for quality assessment. We achieve an overall accuracy of 81 percent which is a 7 percent improvement over existing models. In addition, our features improve the precision and recall of each class up to 9 percent and 17 percent respectively. Finally, our models are robust to ten-fold cross validation and techniques used for classification. Overall, our research provides a comprehensive design science framework for both identifying and efficiently extracting features related to development and coordination activities and assessing quality using these features. We also provide details of potential implementation of a quality assessment system for knowledge repositories. [ABSTRACT FROM AUTHOR]

The problem of recurrent, unplanned readmissions, where some patients return shortly after discharge from the hospital and are readmitted for the same or a related condition, has become a challenge worldwide due to care quality, health outcomes, and financial concerns. Predicting frequent, preventable readmissions and understanding the contributing factors is a critical problem that is being widely studied. However, few studies have examined longitudinal risk stratification, profiling, and prediction of multi-morbid, heterogeneous patient populations. We examine how readmission risk may progress over multiple emergency department visits of chronic disease patients, their early stratification into distinct trajectories with related frequencies, and the relationship of these trajectories to patient characteristics. We further extend this analysis to investigate the impact of time-stable and time-varying covariates in predicting future readmission conditional on latent class membership. Results indicate that longitudinal risk stratification can enable early identification of specific patient groups following distinct trajectories based on their presentation for emergency care. Prediction models that incorporate latent classes perform well and demonstrate the promise of trajectory modeling methods combined with advanced prediction models for longitudinal risk assessment in addressing readmission challenges. The methodology and insights from this study are generalizable to other important Information Systems problems. [ABSTRACT FROM AUTHOR]

Performance measures and metrics (PMM) is identified to be an essential aspect of managing diverse supply chains. The PMM improves the firm's performance by providing open and transparent communication between the various stakeholders of an organisation. The literature suggests that big data analytics has a positive impact on the supply chain and firm performance. Presently, the literature lack studies that recognise the PMM relevant to big data-driven supply chain (BDDSC). The present study is based on a comprehensive review of 66 papers published with the primary objective to identify the various PMMs used to evaluate the BDDSC. The findings suggest that the PMMs applicable to BDDSC can be classified into two non-mutually exclusive categories. The first category represents 24 performance measures used to evaluate the performance of the big data analytics capability and the second category represents 130 measures used for assessing the performance of BDDSC processes. The study also reports the emergence of new performance measures based on increasing use of predictive and social analytics in BDDSC. Based on the results of the study a framework on BDDSC performance measurement system is proposed which will guide the managers to have a robust performance measurement system in their organisation. [ABSTRACT FROM AUTHOR]

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Successful days are defined as days when four cases were completed before 3:45pm, and overtime hours are defined as time spent after 3:45pm. Based on these definitions and the 460 unsuccessful days isolated from the dataset, 465 hours, 22 minutes, and 30 seconds total overtime hours were calculated. To reduce the increasing wait lists for hip and knee surgeries, we aim to verify whether it is possible to add a 5th surgery, to the typical 4 arthroplasty surgery per day schedule, without adding extra overtime hours and cost at our clinical institution. To predict 5th cases, 301 successful days were isolated and used to fit linear regression models for each individual day. After using the models' predictions, it was determined that increasing performance to a 77% success rate can lead to approximately 35 extra cases per year, while performing optimally at a 100% success rate can translate to 56 extra cases per year at no extra cost. Overall, this shows the extent of resources wasted by overtime costs, and the potential for their use in reducing long wait times. Future work can explore optimal staffing procedures to account for these extra cases.

This review article explores the applications and impacts of Machine Learning (ML) techniques in marine traffic management and prediction within complex maritime systems. It provides an overview of ML techniques, delves into their practical applications in the maritime sector, and presents an in-depth analysis of their benefits and limitations. Real-world case studies are highlighted to illustrate the transformational impact of ML in this field. The article further provides a comparative analysis of different ML techniques and discusses the future directions and opportunities that lie ahead. Despite the challenges, ML's potential to revolutionize marine traffic management and prediction, driving safer, more efficient, and more sustainable operations, is substantial. This review article serves as a comprehensive resource for researchers, industry professionals, and policymakers interested in the interplay between ML and maritime systems. [ABSTRACT FROM AUTHOR]

Objective: This study examines how accurately a small set of short-term academic indicators can approximate long-term outcomes of community college students so that decision-makers can take informed actions based on those indicators to evaluate the current progress of large-scale reform efforts on long-term outcomes, which in practice will not be observed until several years later. Method: Using transcript-level data of approximately 50,000 students at over 30 institutions in two states, I compare the out-of-sample predictive power of the early momentum metrics (EMMs), 13 short-term academic indicators suggested in the literature, to that of more complex, Machine Learning (ML)-based models that employ 497 predictors. Results: This study found that EMMs accurately predict credential completion for 75% to 77% of students in an out-of-sample dataset, with a predictive power largely comparable to that of ML-based models. This study also found similar results among the gender and race/ethnicity groups. However, the predictive power for certificate completion is lower than that for associate and bachelor's degrees by 5 percentage points, implying that this set of EMMs are likely to be less relevant to certificate completion. Contribution: This study validates EMMs as informative predictors of credential completion, confirming that decision makers can use them to understand the probable long-term impact of current reforms on credential outcomes. However, room for continued research and refinement of EMMs remains, especially for certificate. [ABSTRACT FROM AUTHOR]

Introduction: Analgesia and sedation are integral to the care of critically ill children. However, the choice and dose of the analgesic or sedative drug is often empiric, and models predicting favorable responses are lacking. We aimed to compute models to predict a patient's response to intravenous morphine. Methods: We retrospectively analyzed data from consecutive patients admitted to the Cardiac Intensive Care Unit (January 2011–January 2020) who received at least one intravenous bolus of morphine. The primary outcome was a decrease in the State Behavioral Scale (SBS) ≥1 point; the secondary outcome was a decrease in the heart rate Z‐score (zHR) at 30 min. Effective doses were modeled using logistic regression, Lasso regression, and random forest modeling. Results: A total of 117,495 administrations of intravenous morphine among 8140 patients (median age 0.6 years [interquartile range [IQR] 0.19, 3.3]) were included. The median morphine dose was 0.051 mg/kg (IQR 0.048, 0.099) and the median 30‐day cumulative dose was 2.2 mg/kg (IQR 0.4, 15.3). SBS decreased following 30% of doses, did not change following 45%, and increased following 25%. The zHR significantly decreased after morphine administration (median delta‐zHR −0.34 [IQR−1.03, 0.00], p < 0.001). The following factors were associated with favorable response to morphine: A concomitant infusion of propofol, higher prior 30‐day cumulative dose, being invasively ventilated and/or on vasopressors. Higher morphine dose, higher zHR pre‐morphine, an additional analgosedation bolus ±30 min around the index bolus, a concomitant ketamine or dexmedetomidine infusion, and showing signs of withdrawal syndrome were associated with unfavorable response. Logistic regression (area under the receiver operating characteristic [ROC] curve [AUC] 0.900) and machine learning models (AUC 0.906) performed comparably, with a sensitivity of 95%, specificity of 71%, and negative predictive value of 97%. Conclusions: Statistical models identify 95% of effective intravenous morphine doses in pediatric critically ill cardiac patients, while incorrectly suggesting an effective dose in 29% of cases. This work represents an important step toward computer‐aided, personalized clinical decision support tool for sedation and analgesia in ICU patients. [ABSTRACT FROM AUTHOR]

Background: The transition from military service to civilian life is a high-risk period for suicide attempts (SAs). Although stressful life events (SLEs) faced by transitioning soldiers are thought to be implicated, systematic prospective evidence is lacking. Methods: Participants in the Army Study to Assess Risk and Resilience in Servicemembers (STARRS) completed baseline self-report surveys while on active duty in 2011–2014. Two self-report follow-up Longitudinal Surveys (LS1: 2016–2018; LS2: 2018–2019) were subsequently administered to probability subsamples of these baseline respondents. As detailed in a previous report, a SA risk index based on survey, administrative, and geospatial data collected before separation/deactivation identified 15% of the LS respondents who had separated/deactivated as being high-risk for self-reported post-separation/deactivation SAs. The current report presents an investigation of the extent to which self-reported SLEs occurring in the 12 months before each LS survey might have mediated/modified the association between this SA risk index and post-separation/deactivation SAs. Results: The 15% of respondents identified as high-risk had a significantly elevated prevalence of some post-separation/deactivation SLEs. In addition, the associations of some SLEs with SAs were significantly stronger among predicted high-risk than lower-risk respondents. Demographic rate decomposition showed that 59.5% (s.e. = 10.2) of the overall association between the predicted high-risk index and subsequent SAs was linked to these SLEs. Conclusions: It might be possible to prevent a substantial proportion of post-separation/deactivation SAs by providing high-risk soldiers with targeted preventive interventions for exposure/vulnerability to commonly occurring SLEs. [ABSTRACT FROM AUTHOR]

Falls are a common problem associated with significant morbidity, mortality, and economic costs. Current fall prevention policies in local healthcare settings are often guided by information provided by fall risk assessment tools, incident reporting, and coding data. This review was conducted with the aim of identifying studies which utilized natural language processing (NLP) for the automated detection and prediction of falls in the healthcare setting. The databases Ovid Medline, Ovid Embase, Ovid Emcare, PubMed, CINAHL, IEEE Xplore, and Ei Compendex were searched from 2012 until April 2023. Retrospective derivation, validation, and implementation studies wherein patients experienced falls within a healthcare setting were identified for inclusion. The initial search yielded 2611 publications for title and abstract screening. Full-text screening was conducted on 105 publications, resulting in 26 unique studies that underwent qualitative analyses. Studies applied NLP towards falls risk factor identification, known falls detection, future falls prediction, and falls severity stratification with reasonable success. The NLP pipeline was reviewed in detail between studies and models utilizing rule-based, machine learning (ML), deep learning (DL), and hybrid approaches were examined. With a growing literature surrounding falls prediction in both inpatient and outpatient environments, the absence of studies examining the impact of these models on patient and system outcomes highlights the need for further implementation studies. Through an exploration of the application of NLP techniques, it may be possible to develop models with higher performance in automated falls prediction and detection. [ABSTRACT FROM AUTHOR]

5G networks promise to revolutionize telecommunications by offering significantly faster, more reliable and efficient connectivity compared to their predecessors. However, the inherent complexity of these networks also presents new challenges in terms of performance management and optimization. This paper explores how Machine Learning (ML) can be used to analyze and improve the performance of 5G networks, addressing issues such as congestion, latency, and efficient resource allocation. Practical examples of implementing ML models in Python are presented, using libraries such as Scikit-learn and TensorFlow to predict and optimize network performance. [ABSTRACT FROM AUTHOR]

Successful implementation of Big Data-based business intelligence (BI) solutions is crucial for companies seeking to improve their decision making and gain competitive advantage. This article details the fundamental strategies for effective implementation. First, the objectives and scope of the project must be clearly defined, setting SMART goals and delineating affected areas. Proper technology selection is crucial, evaluating Big Data tools such as Hadoop and Spark, and BI platforms that offer advanced capabilities. Data integration requires consolidating information from various sources, ensuring its quality, and using data warehouses to manage large volumes. Designing effective analytical models includes implementing predictive models, descriptive analytics, and visualizing data through clear dashboards. Staff training and change management are essential to ensure acceptance and efficient use of new tools. Finally, continuous monitoring and evaluation are necessary to measure system performance and make adjustments. By following these strategies, organizations can maximize the value of their data and improve their business performance. [ABSTRACT FROM AUTHOR]

A current trend within the healthcare sector is the use of predictive analytics to aid in medical decisions. One popular analytical method for testing the accuracy of diagnostic test or markers is the Receiver Operating Characteristic (ROC) curve. The ROC curve expands upon logistic regression, which is a difficult concept for many students to grasp. As a result, the ROC curve is rarely taught in a typical analytics course because of its difficulty to understand. To provide an innovative approach for educators to teach the ROC curve to undergraduate students, this paper provides a sample teaching aid regarding real-world case studies. This research has practical impacts for analytics practitioners, faculty, students, and future authors. [ABSTRACT FROM AUTHOR]

Many colleges use AI-powered early warning systems (EWS) to provide support to students as soon as they start their first semester. However, concerns arise regarding the fairness of an EWS algorithm when deployed so early in a student's college journey, especially at institutions with limited data collection capacity. To empirically address this fairness concern within this context, we developed a GPA prediction algorithm for the first semester at an urban Japanese private university, relying exclusively on demographic and pre-college academic data commonly collected by many colleges at matriculation. Then we assessed the fairness of this prediction model between at-risk and lower-risk student groups. We also examined whether the use of 33 novel non-academic skill data points, collected within the first three weeks of matriculation, improves the model. Our analysis found that the model is less predictive for the at-risk group than their majority counterpart, and the addition of non-academic skill data slightly improved the model's predictive performance but did not make the model fairer. Our research underscores that an early adoption of EWS relying on pre-matriculation data alone may disadvantage at-risk students by potentially overlooking those who genuinely require assistance. [ABSTRACT FROM AUTHOR]