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This paper bibliometrically examines the integration of artificial intelligence (AI) technologies into decisionmaking in management contexts. Using advanced algorithms and machine learning techniques, AI provides transformative capabilities for analyzing complex data sets, forecasting trends, and optimizing decision outcomes. Through a comprehensive literature review and case studies, this paper explores the diverse applications of AI in management decision-making, from strategic planning and resource allocation to risk management and operational efficiency. In addition, the bibliometric analysis discusses the implications of AI adoption, including ethical considerations, the dynamics of organizational change, and the role of human judgment along with AI-based perspectives. By synthesizing current research findings and practical insights, this paper provides a nuanced understanding of how AI can be effectively leveraged to improve management decision-making processes.

Background: There has been a trend to transit reprocessing of flexible endoscopes from a high-level disinfectant (HLD) centralized manner to sterilization performed by nursing staff in some Ear, Nose, and Throat (ENT) clinics. In doing so, the clinic nursing staff are responsible for predicting and managing clinical demand for flexible endoscopes. The HLD disinfection process is time-consuming and requires specialized training and competency to be performed safely. Solely depending on human expertise for predicting the flexible endoscope demands is unreliable and produced a concern of an inadequate supply of devices available for diagnostic purposes. Method: The demand for flexible endoscopes for future patient visits has not been well studied but can be modeled based on patients’ historical information, provider, and other visit-related factors. Such factors are available to the clinic before the visit. Binary classifiers can be used to help inform the sterile processing department of reprocessing needs days or weeks earlier for each patient. Results: Among all our trained models, Logistic Regression reports an average AUC ROC score of 89% and accuracy of 80%. Conclusion: The proposed framework not only significantly reduces the reprocessing efforts in terms of time spent on communication, cleaning, scheduling, and transferring scopes, but also helps to improve patient safety by reducing the exposure risk to potential infections.

Abstract Background Chronic kidney disease (CKD) requires accurate prediction of renal replacement therapy (RRT) initiation risk. This study developed deep learning algorithms (DLAs) to predict RRT risk in CKD patients by incorporating medical history and prescriptions in addition to biochemical investigations. Methods A multi-centre retrospective cohort study was conducted in three major hospitals in Hong Kong. CKD patients with an eGFR

Abstract Background Patients with unplanned readmissions to the intensive care unit (ICU) are at high risk of preventable adverse events. The Rothman Index represents an objective real-time grading system of a patient’s clinical condition and a predictive tool of clinical deterioration over time. This study was designed to test the hypothesis that the Rothman Index represents a sensitive predictor of unanticipated ICU readmissions. Methods A retrospective propensity-matched cohort study was performed at a tertiary referral academic medical center in the United States from January 1, 2022, to December 31, 2022. Inclusion criteria were adult patients admitted to an ICU and readmitted within seven days of transfer to a lower level of care. The control group consisted of patients who were downgraded from ICU without a subsequent readmission. The primary outcome measure was in-hospital mortality or discharge to hospice for end-of-life care. Secondary outcome measures were overall hospital length of stay, ICU length of stay, and 30-day readmission rates. Propensity matching was used to control for differences between the study cohorts. Regression analyses were performed to determine independent risk factors of an unplanned readmission to ICU. Results A total of 5,261 ICU patients met the inclusion criteria, of which 212 patients (4%) had an unanticipated readmission to the ICU within 7 days. The study cohort and control group were stratified by propensity matching into equal group sizes of n = 181. Lower Rothman Index scores (reflecting higher physiologic acuity) at the time of downgrade from the ICU were significantly associated with an unplanned readmission to the ICU (p

Previous studies have extensively investigated the effects of online word-of-mouth (eWOM) factors such as volume and valence on product sales. However, studies of the effect of eWOM factors on product prices are lacking. It is necessary to examine how various eWOM factors can either explain or affect product prices. The objective of this study is to suggest explanatory and predictive analytics using a regression analysis and ensemble-based machine learning methods for eWOM factors and hotels booking prices. This study utilizes publicly available data from a hotel booking site to build a sample of eWOM factors. The final study sample was comprised of 927 hotels. The important eWOM factors found to affect hotel prices are the review depth and the review rating, which are moderated by a number of reviews to affect prices. The effect of the number of positive words is moderated by the review helpfulness to affect the price. The review depth and rating, along with the number of reviews, should be considered in the design of hotel services, as these provide the rationale for adjusting the prices of various aspects of hotel services. Furthermore, the comparison results when applying various ensemble-based machine learning methods to predict prices using eWOM factors based on a 46-fold cross-validation partition method indicated that ensemble methods (bagging and boosting) based on decision trees outperformed ensemble methods based on k-nearest neighbor methods and neural networks. This shows that bagging and boosting methods are effective ways to improve the prediction performance outcomes when using decision trees. The explanatory and predictive analytics using eWOM factors for hotel booking prices offers a better understanding in terms of how the accommodation prices of hotel services can be explained and predicted by eWOM factors.

Background: Although various hemodynamic parameters to assess prosthetic performance are available, prosthesis–patient mismatch (PPM) is defined exclusively by effective orifice area (EOA) index thresholds. Adjusting for the Society of Thoracic Surgeons predicted risk of mortality (STS PROM), we aimed to explore the added value of postoperative hemodynamic parameters for the prediction of all-cause mortality at 5 years after aortic valve replacement. Methods: Data were obtained from the Pericardial Surgical Aortic Valve Replacement (PERIGON) Pivotal Trial, a multicenter prospective cohort study examining the performance of the Avalus bioprosthesis. Candidate predictors were assessed at the first follow-up visit; patients who had no echocardiography data, withdrew consent, or died before this visit were excluded. Candidate predictors included peak jet velocity, mean pressure gradient, EOA, predicted and measured EOA index, Doppler velocity index, indexed internal prosthesis orifice area, and categories for PPM. The performance of Cox models was investigated using the c-statistic and net reclassification improvement (NRI), among other tools. Results: A total of 1118 patients received the study valve, of whom 1022 were eligible for the present analysis. In univariable analysis, STS PROM was the sole significant predictor of all-cause mortality (hazard ratio, 1.40; 95% confidence interval, 1.26-1.55). When extending the STS PROM with single hemodynamic parameters, neither the c-statistics nor the NRIs demonstrated added prognostic value compared to a model with STS PROM alone. Similar findings were observed when multiple hemodynamic parameters were added. Conclusions: The STS PROM was found to be the main predictor of patient prognosis. The additional prognostic value of postoperative hemodynamic parameters for the prediction of all-cause mortality was limited.

Machine learning is widely used in information systems design. Yet, training algorithms on imbalanced datasets may severely affect performance on unseen data. For example, in some cases in healthcare, fintech, or cybersecurity contexts, certain subclasses are difficult to learn because they are underrepresented in training data. Our study offers a flexible and efficient solution based on a new synthetic average neighborhood sampling algorithm (SANSA), which, in contrast to other solutions, introduces a novel "placement" parameter that can be tuned to adapt to each dataset's unique manifestation of the imbalance. This package can be downloaded for R1. We tested SANSA against seven existing sampling methods used in conjunction with the four most frequently used machine learning models trained on 14 benchmark datasets. Our results provide suggestive evidence that SANSA offers a feasible solution to the imbalance problem for most datasets. Our findings provide practical recommendations for how SANSA can be effectively implemented while reducing the complexity level of an imbalanced learning pipeline. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) has emerged as an important advancement in pathogen detection, particularly in the field of food safety. This paper reviews current advances and the application of machine learning in real-time foodborne pathogen detection and risk assessment. ML accelerates pathogen identification processes by leveraging AI-biosensing and deep learning models, significantly reducing detection times and potentially increasing accuracy rates, as indicated in several studies. The study investigates a variety of real-world applications and case studies, including the detection of Escherichia coli, Pseudomonas aeruginosa, Magnaporthe oryzae, demonstrating ML's efficiency in quick pathogen detection, disease prediction, and contamination source identification. These applications show significant benefits in terms of epidemic prevention, customer safety, and operational efficiency. However, challenges persist, particularly with data quality, model interpretability, and regulatory compliance. The review emphasizes the importance of transparent ML models and rigorous validation in meeting regulatory standards. Future possibilities include combining ML with new technologies like the Internet of Things (IoT) and blockchain to provide comprehensive, real-time food safety management. This integration promises to improve real-time monitoring, traceability, and transparency throughout the food supply chain.

In order to effectively manage their customers, businesses need to thoroughly analyze the costs and advantages associated with various alternative expenditures and investments and determine the most effective way to allocate resources to marketing and sales activities over time. Those in charge of making decisions will reap the benefits of decision support models that estimate the value of the customer portfolio and tie expenses to customers' purchasing behavior. In the current work, various machine learning algorithms such as Decision Tree (DT), Random Forest (RT), Logistic Regression (LR), Support Vector Machines (SVM), and gradient boosting are used to predict customer behavior. The evaluation criteria considered in the work include precision, recall, F1-Score, and ROC-AUC. The accuracy values obtained for DT, RT, LR, SVM, and gradient boosting are 0.787, 0.806, 0.826, 0.826, and 0.823, respectively. The results emphasize RT and LR's good performance, while the values of 0.620, 1, 0.766, and 0.878 for the precision, recall, F1-score, and ROC-AUC score outperform the rest. The novelty of this work lies in employing a comprehensive set of machine learning algorithms to predict customer behavior, with a particular emphasis on the superior performance of RF and LR models, as demonstrated by their high precision, recall, F1-score, and ROC-AUC values.

Precise demand forecasting and agile pricing strategies are crucial in modern business. This study aims to enhance these strategies by evaluating the efficacy of Holt-Winters Exponential Smoothing (HWES) and Autoregressive Integrated Moving Average (ARIMA) models. The study assesses their performance in predicting demand amid unpredictable factors and develops robust forecasting algorithms using real-world data. It evaluates HWES and ARIMA in capturing demand fluctuations, considering seasonality, market trends, and cyclic patterns. A comprehensive comparative analysis is conducted under stable and unstable economic conditions. The study also focuses on a dynamic pricing model for limited sale seasons, examining lost sales patterns over time. In the context of supply chain and inventory management, efficient demand forecasting and dynamic pricing are essential for optimizing inventory levels and minimizing costs. Supply chains must adapt quickly to demand fluctuations to avoid overstocking or stockouts, which lead to revenue losses and inefficiencies. The findings reveal that ARIMA consistently outperforms HWES in minimizing lost sales, demonstrating its efficacy in demand forecasting, mitigating stockouts, and reducing revenue losses, particularly in varying economic conditions. This research significantly contributes to current knowledge by developing tailored forecasting algorithms and a dynamic pricing model, enhancing supply chain resilience and performance in uncertain business environments.

In this study, three novel machine learning algorithms of additive regression-random forest (AR-RF), Iterative Classifier Optimizer (ICO-AR-RF), and multi-scheme (MS-RF) were explored for carbon dioxide (CO2) flux rate prediction from three agricultural fields. To build the dataset, 401 samples were collected from two fields in Prince Edward Island (PEI) and 122 samples from the New Brunswick (NB), Canada. In addition, soil moisture (SM), temperature (ST), and electrical conductivity (EC), alongside eight climatic variables including wind speed (WS), solar radiation (SR), relative humidity (RH), precipitation (PCP), air temperature (AT), dew point (DP), vapour pressure difference (VPD) and reference evapotranspiration (ETo) were also collected. Greedy stepwise (GS) approach was implemented for feature selection. Finally, different qualitative (scatter plot, line graph, Taylor diagram, box plot, and Rug plot), and quantitative (uncertainty analysis, root mean square error (RMSE), percent of BIAS (PBIAS), Nash Sutcliff efficiency (NSE) and RMSE-observations standard deviation ratio (RSR)) techniques were used for model evaluation and comparison. Results of feature selection approaches revealed that DP, AT, SM, and ST are the four most effective variables at CO2 prediction in PEI, while AT, RH, DP, and ST are the most effective in the NB study area. For optimum input scenario, the GS algorithm was applied, and results showed that a combination of DP, AT, ST, SM, and ETo was the best for the PEI study area, while for NB, all input variables should be involved. Our analysis, for prediction of CO2 fluxes, confirmed that the ICO-AR-RF model performed the best at both PEI (RMSE=0.70, NSE=0.76, PBIAS=-5.11, RSR=0.48) and NB (RMSE=0.74, NSE=0.75, PBIAS=3.23, RSR=0.50), followed by MS-RF and AR-RF. Uncertainty analysis showed that CO2 prediction is more sensitive to input scenario selection than models in both study areas. Results revealed that climatic variables are more effective in CO2 prediction than soil characteristics and the developed hybrid model ICO-AR-RF can be a promising tool for decision-makers and beneficial for stakeholders.

The agriculture industry is currently facing significant challenges in effectively monitoring the health of livestock. Traditional methods of health monitoring are often labor-intensive, inefficient, and insufficiently responsive to the needs of modern farming. As the number of IoT devices in agriculture proliferates, issues of scalability and computational load have become prominent, necessitating efficient and scalable solutions. This research introduces a cloud-based architecture aimed at enhancing livestock health monitoring. This system is designed to track critical health indicators such as movement patterns, body temperature, and heart rate, utilizing AWS for robust data handling and Python for data processing and real-time analytics. The proposed system incorporates Narrow Band IoT (Nb IoT) technology, which is optimized for low-bandwidth, long-range communication, making it suitable for rural and remote farming locations. The architecture's scalability allows for the effective management of varying numbers of IoT devices, which is essential for adapting to changing herd sizes and farm scales. Preliminary experiments conducted to assess the system's performance have demonstrated its durability and effectiveness, indicating a successful integration of AWS IoT Cloud services with the deployed IoT devices. Furthermore, the study explores the implementation of predictive analytics to facilitate proactive health management in livestock. By predicting potential health issues before they become apparent, the system can offer significant improvements in animal welfare and farm efficiency. The integration of cloud computing and IoT not only meets the growing technological needs of modern agriculture but also sets a new benchmark in the development of sustainable farming practices. The findings from this research could have broad implications for the future of livestock management, potentially leading to widespread adoption of technology-driven health monitoring systems in agriculture. This would help in optimizing the health management of livestock globally, thereby enhancing productivity and sustainability in the agricultural sector.

In Vietnam's rapidly expanding e-commerce landscape, there is a critical need for advanced tools that can effectively analyze customer feedback to boost satisfaction and loyalty. This paper introduces a two-step predictive framework merging deep learning and traditional machine learning to analyze Vietnamese e-commerce reviews. Utilizing a dataset of 10,021 reviews on Tiki, Shopee, Sendo, and Hasaki between 2015 and 2023, the framework first employs fine-tuned deep learning models like BERT and Bi-GRU to extract aspect-based sentiments from reviews, tailored for the nuances of the Vietnamese language. Subsequently, machine learning algorithms like XGBoost predict customer satisfaction by integrating sentiment analysis with e-commerce data such as product prices. Results show BERT and Bi-GRU yield over 70% sentiment accuracy, while XGBoost achieves 80%+ satisfaction prediction accuracy. This framework offers a potent solution for discerning customer sentiments and enhancing satisfaction in Vietnam's dynamic e-commerce landscape.

IoT devices convert billions of objects into data-generating entities, enabling them to report status and interact with their surroundings. This data comes in various formats, like structured, semi-structured, or unstructured. In addition, it can be collected in batches or in real time. The problem now is how to benefit from all of this data gathered by sensing and monitoring changes like temperature, light, and position. In this paper, we propose a predictive analytics framework constructed on top of open-source technologies such as Apache Spark and Kafka. The framework focuses on forecasting temperature time series data using traditional and deep learning predictive analytics methods. The analysis and prediction tasks were performed using Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), and a novel hybrid model based on Convolution Neural Network (CNN) and LSTM. The purpose of this paper is to determine whether and how recently developed deep learning-based models outperform traditional algorithms in the prediction of time series data. The empirical studies conducted and reported in this paper demonstrate that deep learning-based models, specifically LSTM and CNN-LSTM, exhibit superior performance compared to traditional-based algorithms, ARIMA and SARIMA. More specifically, the average reduction in error rates obtained by LSTM and CNN-LSTM models were substantial when compared to other models indicating the superiority of deep learning. Moreover, the CNN-LSTM-based deep learning model exhibits a higher degree of closeness to the actual values when compared to the LSTM-based model. [ABSTRACT FROM AUTHOR]

Background: Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed. Main body: Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent "black-box" nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools. Conclusions: AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development. [ABSTRACT FROM AUTHOR]

The extensive study was conducted to enhance the prediction of customer turnover in an online retail and distribution organization. The study combines data from surveys, consumer comments, and financial records to uncover themes from textual assessments using state-of-the-art methodologies. Methods such as Dirichlet Multilayer Perceptron Mixing, Latent Dirichlet Allocation and Random Sampling fall within this category. In addition to its usage for assessing geographic data for location-based consumer segmentation, DBSCAN is a crucial tool for this investigation. Model development for churn prediction and root cause analysis makes use of logistic regression and extreme gradient boosting. The statistical and practical benefits of the proposed paradigm are shown via comparison to existing options. A model's predictive efficacy may be evaluated using the area under the curve or the lift metric. The research also introduces the concept of "Consumer-driven energy-efficient WSNs architecture for Personalization and contextualization in E-commerce Systems," which suggests using wireless sensor networks (WSNs) to collect data efficiently, provide customized service and provide context for online purchases. Overall, the research demonstrates the effectiveness of machine learning in harnessing consumer input for strategic decision-making, illuminating the potential of creative sensor network integration in enhancing e-commerce personalization and contextualization. [ABSTRACT FROM AUTHOR]

We conducted a study of predictive analytics (PA) applied to state data on post-school outcomes (PSO) of exited high-school students with disabilities (SWD). Data analyses with machine learning Random Forest algorithm and multilevel Bayesian ordered logistic regression produced two key findings. One, Random Forest models were accurate in predicting PSO. Two, Bayesian models found high-school graduation was the strongest predictor of higher education and reliably predicted the specific type of outcome relative to other outcomes. Limitations of this study are the data source and small number of predictors. Implications of the study for researchers and educators are discussed in conclusion. [ABSTRACT FROM AUTHOR]

This scoping review investigates the untapped potential of predictive analytics in healthcare social work, specifically targeting early intervention frameworks. Despite the escalating attention predictive analytics has garnered across multiple disciplines, its tailored application in social work remains notably sparse. This study endeavors to fill this lacuna by meticulously reviewing the extant literature and delineating the prospective advantages and inherent constraints of integrating predictive analytics into healthcare social work. The outcomes of this inquiry enrich the prevailing dialogue on the utility of predictive analytics in healthcare, offering indispensable perspectives for practitioners and policymakers in the social work domain. [ABSTRACT FROM AUTHOR]

Predictive analytics over mobility data is of great importance since it can assist an analyst to predict events, such as collisions, encounters, traffic jams, etc. A typical example is anticipated location prediction, where the goal is to predict the future location of a moving object, given a look-ahead time. What is even more challenging is to be able to accurately predict collective behavioural patterns of movement, such as co-movement patterns as well as their course over time. In this paper, we address the problem of Online Prediction of Co-movement Patterns. Furthermore, in order to be able to calculate the accuracy of our solution, we propose a co-movement pattern similarity measure, which facilitates the comparison between the predicted clusters and the actual ones. Finally, we calculate the clusters' evolution through time (survive, split, etc.) and compare the cluster evolution predicted by our framework with the actual one. Our experimental study uses two real-world mobility datasets from the maritime and urban domain, respectively, and demonstrates the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Diabetes has evolved into a significant health concern, witnessing a rapid surge in diagnoses and fatalities. Organisations are trying to raise awareness of the dangers associated with this disease, creating prevention programmes, and reinforcing the need to adopt healthier lifestyles. Yet, there remains a scarcity of proposals and outcomes, prompting contemplation on how technology can enhance hospitals' capabilities in patient and treatment management, particularly through the early identification of individuals with potential diabetes. This article presents a study on the implementation of predictive models to improve the detection of diabetic patients, highlighting the tasks performed between feature engineering, model complexity and interpretability in the context of predictive performance, offering perspectives on future work and how it relates to the implementation of these models in clinical contexts. [ABSTRACT FROM AUTHOR]

This article explores the research and development undertaken as part of a Master's degree in Computer Engineering, with a primary focus on enhancing control mechanisms for natural wood drying. While this method is known for its cost-effectiveness in terms of labor and energy, it suffers from slower and unstable drying cycles. The project's objective is to implement an intelligent control system that significantly improves monitoring and recording of humidity levels in each wooden stack. Additionally, the system incorporates the capability to predict humidity based on data sourced from a weather forecasting API. The proposed solution entails a three-layer system: data collection, relay, and analysis. In the data collection layer, low-computing devices, utilizing a Raspberry Pi, measure humidity levels in individual wood stacks. These devices then transmit the data via Low Power Bluetooth to the subsequent layer. The data relay layer incorporates an Android application designed to aggregate, normalize, and transmit collected data. Furthermore, it provides users with visualization tools for comprehensive data understanding. The data storage and analysis layer, developed with Django, serves as the back-end, offering management functionalities for stacks, sensors, overall data, and analysis capabilities. This layer can generate humidity forecasts based on real-time weather information. The implementation of this intelligent control system enables accurate insights into humidity levels, triggering alerts for any anomalies during the drying process. This reduces the necessity for constant on-site supervision, optimizes work efficiency, lowers costs, and eliminates repetitive tasks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is revolutionizing the insurance industry, transforming traditional practices and enhancing efficiency. Through advanced algorithms and data analytics, AI optimizes risk assessment, underwriting, and claims processing. By analyzing vast amounts of data, AI enables insurers to personalize policies, pricing, and customer experiences, leading to improved customer satisfaction and retention. Predictive analytics powered by AI forecasts future trends, aiding insurers in proactive risk management and fraud detection. Additionally, AI-driven chatbots provide instant customer support, reducing response times and operational costs. Automation of routine tasks frees up human resources to focus on complex decision-making and strategic initiatives. Overall, AI is reshaping the insurance landscape, driving innovation, and fostering a more agile and competitive industry poised for continued growth and adaptation in an increasingly digital world. [ABSTRACT FROM AUTHOR]

Heart disease (HD) accounts for more deaths every year than other illnesses. World Health Organization (WHO) assessed 17.9 million life losses caused by heart disease in 2016, demonstrating 31% of all international life losses. Three-quarters of these life losses occur in low and middle-income nations. Machine learning (ML), due to advanced precision in pattern recognition and classification, demonstrates to be in effect in complementing decision-making and threat prediction from the huge number of HD data created by the healthcare sector. Thus, this study aims to develop a logistic regression model (LRM) for predicting the risk of getting HD in ten years. The study explores the different methodologies for improving the performance of base LRM for predicting whether a person gets HD after ten years or not. The result demonstrates the capability of LRM in predicting the risks of getting HD after ten years. The LRM achieves 97.35% accuracy with the recursive feature elimination and random under-sampling. This implies that the LRM can play an important role in precautionary methods to avoid the risk of HD. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) requires accurate prediction of renal replacement therapy (RRT) initiation risk. This study developed deep learning algorithms (DLAs) to predict RRT risk in CKD patients by incorporating medical history and prescriptions in addition to biochemical investigations. Methods: A multi-centre retrospective cohort study was conducted in three major hospitals in Hong Kong. CKD patients with an eGFR < 30ml/min/1.73m2 were included. DLAs of various structures were created and trained using patient data. Using a test set, the DLAs' predictive performance was compared to Kidney Failure Risk Equation (KFRE). Results: DLAs outperformed KFRE in predicting RRT initiation risk (CNN + LSTM + ANN layers ROC-AUC = 0.90; CNN ROC-AUC = 0.91; 4-variable KFRE: ROC-AUC = 0.84; 8-variable KFRE: ROC-AUC = 0.84). DLAs accurately predicted uncoded renal transplants and patients requiring dialysis after 5 years, demonstrating their ability to capture non-linear relationships. Conclusions: DLAs provide accurate predictions of RRT risk in CKD patients, surpassing traditional methods like KFRE. Incorporating medical history and prescriptions improves prediction performance. While our findings suggest that DLAs hold promise for improving patient care and resource allocation in CKD management, further prospective observational studies and randomized controlled trials are necessary to fully understand their impact, particularly regarding DLA interpretability, bias minimization, and overfitting reduction. Overall, our research underscores the emerging role of DLAs as potentially valuable tools in advancing the management of CKD and predicting RRT initiation risk. [ABSTRACT FROM AUTHOR]

Background: Patients with unplanned readmissions to the intensive care unit (ICU) are at high risk of preventable adverse events. The Rothman Index represents an objective real-time grading system of a patient's clinical condition and a predictive tool of clinical deterioration over time. This study was designed to test the hypothesis that the Rothman Index represents a sensitive predictor of unanticipated ICU readmissions. Methods: A retrospective propensity-matched cohort study was performed at a tertiary referral academic medical center in the United States from January 1, 2022, to December 31, 2022. Inclusion criteria were adult patients admitted to an ICU and readmitted within seven days of transfer to a lower level of care. The control group consisted of patients who were downgraded from ICU without a subsequent readmission. The primary outcome measure was in-hospital mortality or discharge to hospice for end-of-life care. Secondary outcome measures were overall hospital length of stay, ICU length of stay, and 30-day readmission rates. Propensity matching was used to control for differences between the study cohorts. Regression analyses were performed to determine independent risk factors of an unplanned readmission to ICU. Results: A total of 5,261 ICU patients met the inclusion criteria, of which 212 patients (4%) had an unanticipated readmission to the ICU within 7 days. The study cohort and control group were stratified by propensity matching into equal group sizes of n = 181. Lower Rothman Index scores (reflecting higher physiologic acuity) at the time of downgrade from the ICU were significantly associated with an unplanned readmission to the ICU (p < 0.0001). Patients readmitted to ICU had a lower mean Rothman Index score (p < 0.0001) and significantly increased rates of mortality (19.3% vs. 2.2%, p < 0.0001) and discharge to hospice (14.4% vs. 6.1%, p = 0.0073) compared to the control group of patients without ICU readmission. The overall length of ICU stay (mean 8.0 vs. 2.2 days, p < 0.0001) and total length of hospital stay (mean 15.8 vs. 7.3 days, p < 0.0001) were significantly increased in patients readmitted to ICU, compared to the control group. Conclusion: The Rothman Index represents a sensitive predictor of unanticipated readmissions to ICU, associated with a significantly increased mortality and overall ICU and hospital length of stay. The Rothman Index should be considered as a real-time objective measure for prediction of a safe downgrade from ICU to a lower level of care. [ABSTRACT FROM AUTHOR]

The dynamism of our digital universe systems presents a key challenge for predictive analytics. Ensuring the model's ability to generalize beyond the training data is crucial for reliable predictions. Recent research has primarily focused on understanding and addressing the phenomenon of concept drift: the changes in the system that afect the model's accuracy. This paper aims to resolve this issue by introducing an online technique implemented using Light Gradient Boosting Machine (LGBM) classifier for concept drift detection and adaptation. Online LGBM is adjusted incrementally based on the most recent information, allowing it to adapt to changing patterns in the data stream. The proposed technique attempts to leverage incremental learning, ensemble learning, and the sliding window method to deal with concept drift. The experiments on Electricity, Spam and MixedAbrupt Drift datasets result in higher accuracy 86.77%, 97.67%, and 65.25% as compared to the ofine LGBM with lower accuracy 71.52%, 90.86%, and 50.01% respectively. The hyperparameters of LGBM are optimized using the Bayesian method. [ABSTRACT FROM AUTHOR]

Background: There has been a trend to transit reprocessing of flexible endoscopes from a high-level disinfectant (HLD) centralized manner to sterilization performed by nursing staff in some Ear, Nose, and Throat (ENT) clinics. In doing so, the clinic nursing staff are responsible for predicting and managing clinical demand for flexible endoscopes. The HLD disinfection process is time-consuming and requires specialized training and competency to be performed safely. Solely depending on human expertise for predicting the flexible endoscope demands is unreliable and produced a concern of an inadequate supply of devices available for diagnostic purposes. Method: The demand for flexible endoscopes for future patient visits has not been well studied but can be modeled based on patients' historical information, provider, and other visit-related factors. Such factors are available to the clinic before the visit. Binary classifiers can be used to help inform the sterile processing department of reprocessing needs days or weeks earlier for each patient. Results: Among all our trained models, Logistic Regression reports an average AUC ROC score of 89% and accuracy of 80%. Conclusion: The proposed framework not only significantly reduces the reprocessing efforts in terms of time spent on communication, cleaning, scheduling, and transferring scopes, but also helps to improve patient safety by reducing the exposure risk to potential infections. [ABSTRACT FROM AUTHOR]

Diffuse large B-cell lymphoma is one of the most frequent mature B-cell hematological neoplasms and non-Hodgkin lymphomas. Despite advances in diagnosis and treatment, clinical evolution is unfavorable in a subset of patients. Using molecular techniques, several pathogenic models have been proposed, including cell-of-origin molecular classification; Hans' classification and derivates; and the Schmitz, Chapuy, Lacy, Reddy, and Sha models. This study introduced different machine learning techniques and their classification. Later, several machine learning techniques and artificial neural networks were used to predict the DLBCL subtypes with high accuracy (100–95%), including Germinal center B-cell like (GCB), Activated B-cell like (ABC), Molecular high-grade (MHG), and Unclassified (UNC), in the context of the data released by the REMoDL-B trial. In order of accuracy (MHG vs. others), the techniques were XGBoost tree (100%); random trees (99.9%); random forest (99.5%); and C5, Bayesian network, SVM, logistic regression, KNN algorithm, neural networks, LSVM, discriminant analysis, CHAID, C&R tree, tree-AS, Quest, and XGBoost linear (99.4–91.1%). The inputs (predictors) were all the genes of the array and a set of 28 genes related to DLBCL-Burkitt differential expression. In summary, artificial intelligence (AI) is a useful tool for predictive analytics using gene expression data. [ABSTRACT FROM AUTHOR]

• Machine learning-based prediction models can catalog, classify, and correlate large amounts of multimodal data to aid clinicians at diagnostic, prognostic, and therapeutic levels. • A variety of the prediction models, prediction intervals, and prediction windows were identified to facilitate timely diagnosis of ventilator-associated pneumonia (VAP). • There is a need for dynamic machine learning models using time-depended predictors to predict real-time risk of VAP and related outcomes. Machine learning-based prediction models can catalog, classify, and correlate large amounts of multimodal data to aid clinicians at diagnostic, prognostic, and therapeutic levels. Early prediction of ventilator-associated pneumonia (VAP) may accelerate the diagnosis and guide preventive interventions. The performance of a variety of machine learning-based prediction models were analyzed among adults undergoing invasive mechanical ventilation. This systematic review and meta-analysis was conducted in accordance with the Cochrane Collaboration. Machine learning-based prediction models were identified from a search of nine multi-disciplinary databases. Two authors independently selected and extracted data using predefined criteria and data extraction forms. The predictive performance, the interpretability, the technological readiness level, and the risk of bias of the included studies were evaluated. Final analysis included 10 static prediction models using supervised learning. The pooled area under the receiver operating characteristics curve, sensitivity, and specificity for VAP were 0.88 (95 % CI 0.82–0.94, I2 98.4 %), 0.72 (95 % CI 0.45–0.98, I2 97.4 %) and 0.90 (95 % CI 0.85–0.94, I2 97.9 %), respectively. All included studies had either a high or unclear risk of bias without significant improvements in applicability. The care-related risk factors for the best performing models were the duration of mechanical ventilation, the length of ICU stay, blood transfusion, nutrition strategy, and the presence of antibiotics. A variety of the prediction models, prediction intervals, and prediction windows were identified to facilitate timely diagnosis. In addition, care-related risk factors susceptible for preventive interventions were identified. In future, there is a need for dynamic machine learning models using time-depended predictors in conjunction with feature importance of the models to predict real-time risk of VAP and related outcomes to optimize bundled care. [ABSTRACT FROM AUTHOR]

This article presents a literature review on integrating artificial intelligence (AI) in academic libraries, focusing on the transformative impact of AI-based tools and services on library management, resource utilisation, and research experience. While AI offers promising solutions to increase efficiency and effectiveness, the review identifies several challenges and concerns that need to be addressed, such as ethical and privacy considerations, staff training and development, and a user-centered approach. To integrate AI successfully, libraries must collaborate with professionals, researchers, and policymakers and adopt a continuing education approach to AI. Overcoming resistance to technological change, communicating efforts, and engaging staff are essential for libraries to leverage AI's potential benefits and enhance their services and operations. [ABSTRACT FROM AUTHOR]

This paper bibliometrically examines the integration of artificial intelligence (AI) technologies into decisionmaking in management contexts. Using advanced algorithms and machine learning techniques, AI provides transformative capabilities for analyzing complex data sets, forecasting trends, and optimizing decision outcomes. Through a comprehensive literature review and case studies, this paper explores the diverse applications of AI in management decision-making, from strategic planning and resource allocation to risk management and operational efficiency. In addition, the bibliometric analysis discusses the implications of AI adoption, including ethical considerations, the dynamics of organizational change, and the role of human judgment along with AI-based perspectives. By synthesizing current research findings and practical insights, this paper provides a nuanced understanding of how AI can be effectively leveraged to improve management decision-making processes. [ABSTRACT FROM AUTHOR]

The article discusses the importance of understanding educators' perspectives and intentions when it comes to the use of artificial intelligence (AI) in education. It highlights that while much research has focused on the technological aspects of AI in education, the success of its implementation depends on social, psychological, and cultural factors. The article emphasizes the need for more research from a psychological perspective to understand how educators perceive, trust, and use AI in teaching practice. It also discusses the role of factors such as algorithm aversion, technology acceptance, academic resistance, trust, beneficiary framing, algorithm transparency, and literacy in shaping educators' attitudes and actions towards AI systems. The article concludes by emphasizing the importance of designing AI systems that are understandable and trustworthy to educators, and suggests that future research should focus on building a systematic understanding of how technology, educator characteristics, and context shape educators' beliefs and attitudes towards AI in education. [Extracted from the article]

Advancements in artificial intelligence have contributed significantly to various aspects of the business landscape. As a continuously growing field, AI has the potential to revolutionize the realm of business, leading to increased efficiency and improved business outcomes. This research paper aims to discover and analyze the role of artificial intelligence in business decision-making and process automation. Through conducting case studies and interviews in the field, this paper will compare differences in the implementation of AI between small and medium enterprises (SMEs) and large businesses. Additionally, a literature review can be conducted to determine the current findings on the relationship between AI and business within the workplace. By understanding the impact of AI on business workforces, organizations can make informed decisions about adopting and implementing AI into their business. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Cyber Forensics Behavioral Analysis (CFBA) model merges Cyber Behavioral Sciences and Digital Forensics to improve the prediction and effectiveness of cyber threats from Autonomous System Numbers (ASNs). Traditional cybersecurity strategies, focused mainly on technical aspects, must be revised for the complex cyber threat landscape. This research proposes an approach combining technical expertise with cybercriminal behavior insights. The study utilizes a mixed-methods approach and integrates various disciplines, including digital forensics, cybersecurity, computer science, and forensic psychology. Central to the model are four key concepts: forensic cyberpsychology, digital forensics, predictive modeling, and the Cyber Behavioral Analysis Metric (CBAM) and Score (CBS) for evaluating ASNs. The CFBA model addresses initial challenges in traditional cyber defense methods and emphasizes the need for an interdisciplinary, comprehensive approach. This research offers practical tools and frameworks for accurately predicting cyber threats, advocating for ongoing collaboration in the ever-evolving field of cybersecurity. [ABSTRACT FROM AUTHOR]

Local weather forecasts in the Arctic outside of settlements are challenging due to the dearth of ground-level observation stations and high computational costs. During winter, these forecasts are critical to help prepare for potentially hazardous weather conditions, while in spring, these forecasts may be used to determine flood risk during annual snow melt. To this end, a hybrid VMD-WT-InceptionTime model is proposed for multi-horizon multivariate forecasting of remote-region temperatures in Alaska over short-term horizons (the next seven days). First, the Spearman correlation coefficient is employed to analyze the relationship between each input variable and the forecast target temperature. The most output-correlated input sequences are decomposed using variational mode decomposition (VMD) and, ultimately, wavelet transform (WT) to extract time-frequency patterns intrinsic in the raw inputs. The resulting sequences are fed into a deep InceptionTime model for short-term forecasting. This hybrid technique has been developed and evaluated using 35+ years of data from three locations in Alaska. Different experiments and performance benchmarks are conducted using deep learning models (e.g., Time Series Transformers, LSTM, MiniRocket), and statistical and conventional machine learning baselines (e.g., GBDT, SVR, ARIMA). All forecasting performances are assessed using four metrics: the root mean squared error, the mean absolute percentage error, the coefficient of determination, and the mean directional accuracy. Superior forecasting performance is achieved consistently using the proposed hybrid technique. [ABSTRACT FROM AUTHOR]

Stroke remains a significant global health burden, with substantial costs and morbidity associated with its occurrence. To address this challenge, STROKE 5.0 proposes a comprehensive approach to stroke care management, integrating advanced digital technologies and clinical expertise. This paper presents the rationale, design, and potential impact of the STROKE 5.0 platform, which aims to optimize stroke care delivery from pre-hospital assessment through acute hospitalization. The platform facilitates early symptom recognition, efficient emergency response, and streamlined hospital management through intelligent decision support systems. By leveraging predictive analytics and personalized care pathways, STROKE 5.0 seeks to enhance clinical outcomes while providing a platform capable of optimizing the efficiency of service delivery. This innovative model represents a proactive shift towards evidence-based, patient-centered stroke care, with implications for healthcare quality improvement and resource allocation in the digital health domain. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative IoT-enabled crop storage monitoring system designed to revolutionize the preservation and quality assurance of stored agricultural produce. Central to this system are IoT sensors strategically deployed within storage facilities. These sensors are tasked with the continuous tracking and monitoring of crucial environmental parameters, specifically temperature, humidity, and gas levels. Leveraging this real-time data, the system is engineered to promptly detect and respond to any deviations from the prescribed optimal storage conditions. The core strength of this system lies in its ability to generate instantaneous alerts upon detecting irregularities. These alerts serve as preemptive measures, effectively averting potential spoilage and curtailing post-harvest losses by enabling timely interventions and corrective actions. By harnessing IoT technology, this paper aims to create a proactive, automated, and responsive framework that ensures the integrity and safety of stored agricultural produce, ultimately contributing to enhanced food security and sustainability. [ABSTRACT FROM AUTHOR]

Data-driven decision-making is a common approach for identifying child maltreatment. However, such strategies must be guided by ethical, equitable, and evaluative frameworks due to their potential for bias and error. This study analyzes the key features, potential challenges, and research evidence of a data-driven strategy known as birth match. Interviews with key informants across four states indicate that programs share features and objectives but that they differ regarding match criteria, data integrity processes, and responses to identified cases. Further, little outcome and equity evidence exists. Results emphasize the need for additional implementation and evaluation infrastructure to ensure transparency and effectiveness. [ABSTRACT FROM AUTHOR]

Movie making is a billion-dollar industry. Every month hundreds of movies get released and earn millions of dollars in revenue. However, majority of the movies fail to create an impact on the Box-Office and flop. This not only put a bad impression on the entire cast and crew but also creates a huge setback in financial terms. As a producer or investor, it is crucial for them to have some certainty that the money they are investing in will give a good return otherwise they'll lose all their capital eventually. The idea of this research is to predict based on certain pre-released variables of the movie, whether an upcoming movie is going to succeed or fail in monetary terms. Many researches have already been doing that in this domain based on different techniques and around different datasets. The novelty of this research is that the proposed approach is not only based on classical movie features, but incorporates all other dependencies as well such as star power, popularity of the cast, track record of director, and actors, to predict whether movie will succeed or fail and whether an investor should invest in the movie proposal or not. This article uses multiple machine learning algorithms and tested them over various evaluation metrics. Among them, CatBoostRegression and Stacking Regression outperformed the remaining by giving the maximum model accuracy of 83.84% and 83.5% respectively. The article have used IMDB Movies Extensive Dataset. This dataset contains information of movies from 1894 to 2020 and has at least 100 votes. [ABSTRACT FROM AUTHOR]

Introduction: Individuals in the midst of a mental health crisis frequently exhibit instability and face an elevated risk of recurring crises in the subsequent weeks, which underscores the importance of timely intervention in mental healthcare. This work presents a data-driven method to infer the mental state of a patient during the weeks following a mental health crisis by leveraging their historical data. Additionally, we propose a policy that determines the necessary duration for closely monitoring a patient after a mental health crisis before considering them stable. Methods: We model the patient's mental state as a Hidden Markov Process, partially observed through mental health crisis events. We introduce a closed-form solution that leverages the model parameters to optimally estimate the risk of future mental health crises. Our policy determines a patient should be closely monitored when their estimated risk of crisis exceeds a predefined threshold. The method's performance is evaluated using both simulated data and a real-world dataset comprising 162 anonymized psychiatric patients. Results: In the simulations, 96.2% of the patients identified by the policy were in an unstable state, achieving a F1 score of 0.74. In the real-world dataset, the policy yielded an F1 score of 0.79, with a sensitivity of 79.8% and specificity of 88.9%. Under this policy, 67.3% of the patients should undergo close monitoring for one week, 21.6% during 2 weeks or more, while 11.1% do not need close monitoring. Discussion: The simulation results provide compelling evidence that the method is effective under the specified assumptions. When applied to actual psychiatric patients, the proposed policy showed significant potential for providing an individualized assessment of the required duration for close and automatic monitoring after a mental health crisis to reduce the relapse risks. [ABSTRACT FROM AUTHOR]

Diabetes is the most common disease and is a major cause for blindness, kidney failure, heart attacks, stroke and lower limb amputation. Thus, early prediction of diabetes is very crucial to initiating proper treatment to avoid further serious complications of the disease. The performance of recent diabetes detection schemes based on clinical data is highly influenced by low feature distinctiveness and unwanted features such as dermatologic manifestations. Different machine learning classifiers need tedious hyper-parameter tuning, which fails to assure a better diabetes detection rate. This article presents an analytical model to detect diabetes based on an optimized Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Random Forest (RF) using decision level fusion to improve the diabetes detection rate. The hyper-parameters SVM, KNN, and RF are optimized using a multi-objective function-based Particle Swarm Optimization (PSO) algorithm, which considers various clinical entities for the diabetes detection, such as age, body mass index (BMI), blood pressure (BP), glucose, insulin, number of pregnancies, skin thickness, and diabetes pedigree function. The extensive experiments on the Indian Pima diabetes dataset confirmed that the diabetes detection using hybrid classifiers can provide a better prediction rate (94.27%) compared with single classifiers and the previous state of arts. [ABSTRACT FROM AUTHOR]

Agriculture firms face an array of struggles, most of which are financial; thus, the role of decision making is discerned as highly important. The agroeconomic indexes (AEIs) of Agriculture Employment Rate (AER), Chemical Product Price Index (CPPI), Farm Product Price Index (FPPI), and Machinery Equipment Price Index (MEPI) were selected as the basis of this study. This research aims to examine the connection between digital marketing analytics and the selected agroeconomic indexes while providing valuable insights into their decision-making process, with the utilization of AI (artificial intelligence) models. Thus, a dataset of website analytics was collected from five well-established agriculture firms, apart from the values of the referred indexes. By performing regression and correlation analyses, the index relationships with the agriculture firms' digital marketing analytics were extracted and used for the deployment of the fuzzy cognitive mapping (FCM) and hybrid modeling (HM) processes, assisted by using artificial neural network (ANN) models. Through the above process, there is a strong connection between the agroeconomic indexes of AER, CPPI, FPPR, and MEPI and the metrics of branded traffic, social and search traffic sources, and paid and organic costs of agriculture firms. It is highlighted that agriculture firms, to better understand their sector's employment rate and the volatility of farming, chemicals, and machine equipment prices for future investment strategies and better decision-making processes, should try to increase their investment in the preferred digital marketing analytics and AI applications. [ABSTRACT FROM AUTHOR]

Outpatient clinics globally grapple with the uncertainty of patient wait times, a critical factor affecting patient satisfaction. Extended waiting periods are often perceived as a hindrance to timely care, generating stress for both patients and healthcare providers. Accurate prediction of patient wait times can substantially improve patient satisfaction by reducing uncertainty. This study aims to predict patient Consultation Wait Time efficiently in a multispecialty hospital outpatient clinic utilizing a Multilayer Perceptron approach. Feature Selection Methods were employed to enhance the predictive efficiency of the model. The study evaluated various performance metrics, including Accuracy, Recall, Precision, F-measure, and Area Under the Receiver Operating Characteristic Curve (AUC). Temporal features, specifically Visit Time and Consultation Start Time, emerged as significant predictors in the model. Additionally, Vitals Examination was identified as a key factor in predicting Consultation Wait Time. Notably, the model incorporating variables selected through Reciprocal Ranking exhibited robust performance in predicting Consultation Wait Times. [ABSTRACT FROM AUTHOR]

Predictive analytics is the process of forecasting outcomes based on historical data. Execution of predictive analytics involves several phases, namely: data collection, analysis and massaging, identifying machine learning, predictive modeling, predictions, and monitoring. All phases play a vital role in the prediction's result, especially the data analysis and massaging or data preprocessing. This study aims to predict the students' probability of graduating on time using the students' demographic profiles, previous academic achievements (SHS track and grade point average), and college admission results (english, math, science, and abstract). The dataset was acquired from Caraga State University with 2207 samples of new entrants. This study implemented KNN to impute numerical data, while mode imputation was used for categorical values. Moreover, binary encoding was employed for nominal data to prevent the algorithm from ranking the values in order. Seven (7) algorithms were tested on the original dataset and compared to datasets integrated with LASSO Regressions (L1), Ridge Regression (L2), and Genetic Algorithm (GA) separately. The algorithms involved were Decision Tree, Random Forest, Ensemble, KNN, Logistic Regression, SVM, and Naïve Bayes. The result shows that LASSO Regression (L1) with the Decision Tree classifier has the lowest accuracy (58%) and AUC score (50%). It also has the smallest number of features selected (5). Conversely, GA selected thirty-three (33) features with an AUC score of 71% and predicted 79% accurately using the Logistic Regression classifier. It exhibited a 21% increase in the AUC score compared to the no feature selected dataset (NFS) with the same classifier. [ABSTRACT FROM AUTHOR]

The application of data analysis tools and procedures to perceive value from vast volume of data created by connected IoT devices is known as IoT data analytics. While predictive analytics on IoT dealing with the prediction involved with the setting of IoT appliances, Prescriptive analytics is the next stage of IoT data analytics involves deriving actionable insights from predictions made in previous stages. The incorporation of time-dependent parameters in prescriptive models provides a more accurate depiction of a complex environment and the decision-making process that goes along with it. The scope of our work is to recommend prescriptive analytical models that make better decisions through the analysis of dynamic IoT data stream in real-time and prescribe an optimal solution. We carry out an analysis of time-series data to identify the patterns of data and learn how they change. In this direction, we attempt to represent time-series data by reducing its length, forecast change points, map change points to prescribed actions, and propose optimal decisions ahead of time events. In this paper, an overview of IoT data analytics, survey of prescriptive analytical models, applications, issues, challenges and platforms for IoT analytics are discussed. [ABSTRACT FROM AUTHOR]

Although the circular economy is commonly used among industries in developing countries to achieve carbon neutrality targets, its impact on social sustainability must be clarified. Stakeholders (for instance, community stakeholders) have been observed to be unaware of the focal firm's circular supply chain activities. Because this gap has not been generally reflected in the literature, it is critical to perform an empirical study to bridge the gap between theory and practice. The goal of this research was to determine whether new technologies such as big data and predictive analytics might influence an organization's propensity to share information related to circular economy practices with stakeholders as well as to increase connectivity with those stakeholders in the Industry 4.0 era. We also investigated whether these actions could increase stakeholder trust and engagement and social sustainability as a result. We tested our theoretical model using samples from food supply chain firms in South Africa. Confirmatory factor analysis was conducted using WarpPLS 7.0 software. The findings show that firms that deploy big data and predictive analytics are more likely to share information related to the circular economy with stakeholders and that these firms are also well‐connected with those stakeholders, resulting in increased trust and engagement. This, in turn, contributes to the social sustainability of supply chains. Our research has made a significant contribution by encouraging a theoretical debate regarding the willingness to share information regarding the circular economy and the social sustainability of the supply chain. [ABSTRACT FROM AUTHOR]

Smart cities, leveraging advanced data analytics, predictive models, and digital twin techniques, offer a transformative model for sustainable urban development. Predictive analytics is critical to proactive planning, enabling cities to adapt to evolving challenges. Concurrently, digital twin techniques provide a virtual replica of the urban environment, fostering real-time monitoring, simulation, and analysis of urban systems. This study underscores the significance of real-time monitoring, simulation, and analysis of urban systems to support test scenarios that identify bottlenecks and enhance smart city efficiency. This paper delves into the crucial roles of citizen report analytics, prediction, and digital twin technologies at the neighborhood level. The study integrates extract, transform, load (ETL) processes, artificial intelligence (AI) techniques, and a digital twin methodology to process and interpret urban data streams derived from citizen interactions with the city's coordinate-based problem mapping platform. Using an interactive GeoDataFrame within the digital twin methodology, dynamic entities facilitate simulations based on various scenarios, allowing users to visualize, analyze, and predict the response of the urban system at the neighborhood level. This approach reveals antecedent and predictive patterns, trends, and correlations at the physical level of each city area, leading to improvements in urban functionality, resilience, and resident quality of life. [ABSTRACT FROM AUTHOR]