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Sales managers are unlikely to reap the benefits of implementing predictive analytics applications when salespeople show aversion to or lack understanding of these applications. For managers, it is essential to understand which factors mitigate or exacerbate these challenges. This article investigates these factors by studying the implementation of an application that predicts customer churn. Using 9.7 million transactions from a business-to-business company, the authors develop a predictive model of customer churn, implement it in a field experiment, and study its treatment effects using causal forests. Furthermore, the authors manipulate one specific mitigation strategy proposed by prior literature: the fostering of users' realistic expectations regarding the accuracy of an algorithm. The results show that the effectiveness of the churn prediction application strongly depends on customer characteristics (most importantly the predicted churn probability and prior revenue) and salesperson characteristics (technology perceptions, abilities, and selling orientations). Fostering realistic expectations improves the effectiveness of the churn prediction only under very specific circumstances. Two follow-up stimuli-based experiments conceptually replicate key results of the field study. Therefore, this article helps build theory on predictive sales analytics and provides specific guidance to managers aiming to increase their return on analytics investments. [ABSTRACT FROM AUTHOR]

In contrast with the accelerating trend of boundary-spanning (horizontal) technological innovation, the current Cooperative Patent Classification (CPC) scheme applies a hierarchical (vertical) structure to innovation output in terms of patents. For this reason, we argue that the CPC can be complemented with dynamic technological innovation system (TIS) discovery through machine learning that accounts for horizontal relationships across seemingly disparate technologies. Using a design science approach, we propose a framework to discover boundary-spanning TISs by leveraging the textual information from millions of patents. We validate our framework in terms of the ability of discovered relationships to predict future innovation quantity and quality in different technology classes. Our novel TIS-based innovation metrics that leverage patenting activity in related technology classes are significantly associated with future innovation intensity in focal technologies. We conduct experiments with machine learning models to further tease out the predictive utility of our TIS discovery framework. [ABSTRACT FROM AUTHOR]

This study addresses the potential for improving the accuracy, scope, and value of sales research through the application of data mining and machine learning algorithms. By examining prior research, identifying opportunities for improvement, and assessing gaps that can benefit from machine learning, research and application are made more accessible for sales researchers and managers. Machine learning can address important sales research questions that cannot be answered with the same accuracy or efficiency as traditional research methods. This study demonstrates the benefits of the methods through an example of application to the prediction of salesforce performance based on behavioral, attitudinal, and demographic data. This includes future research ideas, usage cases, and applications where machine learning could advance sales research and management. Machine learning and predictive analytics methods have multiple applications, including in B2C and B2B market contexts and for companies and independent sales teams. [ABSTRACT FROM AUTHOR]

Problem definition: Professional sports leagues may be suspended because of various reasons, such as the recent coronavirus disease 2019 pandemic. A critical question that the league must address when reopening is how to appropriately select a subset of the remaining games to conclude the season in a shortened time frame. Despite the rich literature on scheduling an entire season starting from a blank slate, concluding an existing season is quite different. Our approach attempts to achieve team rankings similar to those that would have resulted had the season been played out in full. Methodology/results: We propose a data-driven model that exploits predictive and prescriptive analytics to produce a schedule for the remainder of the season composed of a subset of originally scheduled games. Our model introduces novel rankings-based objectives within a stochastic optimization model, whose parameters are first estimated using a predictive model. We introduce a deterministic equivalent reformulation along with a tailored Frank–Wolfe algorithm to efficiently solve our problem as well as a robust counterpart based on min-max regret. We present simulation-based numerical experiments from previous National Basketball Association seasons 2004–2019, and we show that our models are computationally efficient, outperform a greedy benchmark that approximates a nonrankings-based scheduling policy, and produce interpretable results. Managerial implications: Our data-driven decision-making framework may be used to produce a shortened season with 25%–50% fewer games while still producing an end-of-season ranking similar to that of the full season, had it been played. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0558 .

Millions of people across the world suffer from the chronic respiratory condition known as asthma. Predicting the severity of asthma based on a variety of personal and environmental characteristics might yield useful information for preventative measures. LightGBM model is a gradient-boosted model with the potential for great accuracy, but it requires careful hyperparameter adjustment to reach its full potential. Common tuning techniques have a hard time generalizing to new data distributions. The dataset was used, and its many subsets were used to represent various demographics and geographic areas. LightGBM was configured with hyperparameters, trained on a sample dataset, and then verified for each job. To quickly adjust to new tasks, the MAML method sought to find the optimal values for its hyperparameters. After the meta-training step was complete, the generalizability of the hyperparameters was tested on new data. After including MAML for hyperparameter adjustment, the Light-GBM model showed a gain of 7% in accuracy, coming in at 98.5%. Predictions of severe asthma had a crucially high 97.8 percent degree of accuracy. The model's recall rate for severe asthma levels was 97.4%, demonstrating its capacity to reliably detect and anticipate important instances. An F1-score of 97.1%, a metric that averages the accuracy and recall of a model, is indicative of good overall performance. For gradient-boosted model applications like asthma level prediction, MAML provides a viable path for hyperparameter adjustment. Although there are obstacles to be overcome, this method has the potential to greatly improve the flexibility and precision of predictive healthcare models. More effective implementations and a wider range of applications can be explored in future studies. [ABSTRACT FROM AUTHOR]

This review explores the incorporation of complex systems theory into predictive analytics in the e-commerce sector, particularly emphasizing recent advancements in business management. By analyzing the intersection of these two domains, the review emphasizes the potential of complex systems models—including agent-based modeling and network theory—to improve the precision and efficacy of predictive analytics. It will provide a comprehensive overview of the applications of emergent predictive analytics techniques and tools, including real-time data analysis and machine learning, in inventory optimization, dynamic pricing, and personalization of customer experiences. In addition, this review will suggest future research directions to advance the discipline and address the technical, ethical, and practical challenges encountered during this integration phase. [ABSTRACT FROM AUTHOR]

Data mining (DM) and machine learning (ML) are widely used in production planning and scheduling. Their application to production time estimation leads to improved planning and scheduling accuracy, resulting in increased overall efficiency. Small and medium-sized enterprises (SMEs) often have a small amount of data, which results in the limited adoption of DM and ML. Instead, production time estimation is still performed using rough approximations, which are inaccurate and non-reproducible. Therefore, this article proposes an ML methodology for production time estimation. It is adapted to the needs of SMEs and is applied with limited data. The methodology is based on the categorization of four job types (from A to D), the partitioning of data according to the limit theorem of data convergence, and the definition of risk based on metrics of probability and statistics. ML was applied by WEKA Workbench (Waikato Environment for Knowledge Analysis). It is also integrated into the Cross Industry Standard Process for DM. The methodology was implemented on data from a medium-sized company, Schoepstal Maschinenbau GmbH, for job types A and B to estimate machine/job cycle time, manufacturing cycle time, and lead time. Different accuracies were obtained for individual estimation models, confirming the strong dependence of the models on data quality. Suitable models were found for the implementation of the estimation of the manufacturing cycle time and the machine/job cycle time. The modeling of lead time estimation was unsuccessful. This was due to the weak dependence between the learning values and the values of the selected model attributes. The implementation of the methodology for job types C and D is the subject of further research. [ABSTRACT FROM AUTHOR]

Neonatal sepsis is a severe medical condition that contributes significantly to neonatal mortality. However, early diagnosis and treatment can help manage the condition effectively. Predictive analytics can assist neonatal sepsis diagnosis and treatment and offer a reassuring solution. This study presents a systematic literature review of various predictive analytics methods for neonatal sepsis diagnosis and treatment. It thoroughly reviews 16 studies between 2014 and 2024, including retrospective and prospective data and utilizing various predictive modeling techniques, such as Machine Learning (ML) and Deep Learning (DL). The review unveils that the Predictive analytics models can rapidly detect early and late-onset neonatal sepsis. This detection aids clinicians in decision-making and improves healthcare management for neonates, especially in low-resource settings. This study lays the groundwork for utilizing advanced analytics technologies to address challenges in this domain. It inspires further research in innovative and unexplored methods for neonatal sepsis diagnosis. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things with the Cloud improves our lives by facilitating smooth connections between people and items. Predictive analytics, fueled by cutting-edge machine learning and artificial intelligence, turns reactive healthcare initiatives into proactive ones. A subset of machine learning called deep learning is essential for quickly analyzing large datasets, producing insightful conclusions, and efficiently addressing challenging problems. For early interventions and preventive care, especially for those who are at risk, accurate and timely illness prediction is crucial. Making accurate prediction models becomes crucial when utilizing electronic medical records. Accuracy is improved by using deep learning variations of recurrent neural networks that can handle sequential time-series data. Predictive analytics is applied to cloud-stored electronic medical records and data from Internet of Things devices in this suggested system. With a remarkable accuracy of 98.86%, the smart healthcare system is intended to monitor and anticipate the risk of heart disease utilizing Bi-LSTM (bidirectional long short-term memory). Furthermore, it reaches 98.9% accuracy, 98.8% sensitivity, 98.89% specificity, and 98.86% F-measure. These outcomes greatly surpass the performance of current smart heart disease prediction systems. [ABSTRACT FROM AUTHOR]

This review explores the application of Long Short-Term Memory (LSTM) networks, a specialized type of recurrent neural network (RNN), in the field of polymeric sciences. LSTM networks have shown notable effectiveness in modeling sequential data and predicting time-series outcomes, which are essential for understanding complex molecular structures and dynamic processes in polymers. This review delves into the use of LSTM models for predicting polymer properties, monitoring polymerization processes, and evaluating the degradation and mechanical performance of polymers. Additionally, it addresses the challenges related to data availability and interpretability. Through various case studies and comparative analyses, the review demonstrates the effectiveness of LSTM networks in different polymer science applications. Future directions are also discussed, with an emphasis on real-time applications and the need for interdisciplinary collaboration. The goal of this review is to connect advanced machine learning (ML) techniques with polymer science, thereby promoting innovation and improving predictive capabilities in the field. [ABSTRACT FROM AUTHOR]

The energy industry is experiencing a major change due to fast progress in artificial intelligence (AI). Startup companies in this revolution use AI technologies like Machine Learning (ML), predictive analytics, and optimization algorithms to improve energy efficiency, optimize grid management, and incorporate renewable energy sources. AI-powered solutions allow for a more accurate prediction of demand, immediate monitoring, and automated decision-making processes, significantly enhancing operational efficiency and sustainability. Through promoting a more effective energy system, these advancements play a vital role in the worldwide battle against climate change and carbon dioxide emissions. Adding to the progress of AI, quantum computing (QC) shows great potential despite being a nascent area. The collaboration of AI and QC is poised to transform the energy industry by offering unmatched computational capabilities. This blend of technologies can tackle intricate energy obstacles like enhancing power grids and enhancing battery storage, which traditional computers cannot currently handle. Combining QC with AI speeds up innovation, providing advanced solutions that improve the resilience and efficiency of energy networks. This paper discusses the latest advancements, possible effects, and upcoming paths of new companies leading in AI and QC innovations within the energy industry. Their joint responsibility is highlighted in advancing a sustainable and intelligent energy future, as well as tackling crucial environmental issues and lessening the impact of climate change. [ABSTRACT FROM AUTHOR]

Objectives: This study aims to develop an innovative approach for monitoring and assessing labor pain through ECG waveform analysis, utilizing machine learning techniques to monitor pain resulting from uterine contractions. Methods: The study was conducted at National Taiwan University Hospital between January and July 2020. We collected a dataset of 6010 ECG samples from women preparing for natural spontaneous delivery (NSD). The ECG data was used to develop an ECG waveform-based Nociception Monitoring Index (NoM). The dataset was divided into training (80%) and validation (20%) sets. Multiple machine learning models, including LightGBM, XGBoost, SnapLogisticRegression, and SnapDecisionTree, were developed and evaluated. Hyperparameter optimization was performed using grid search and five-fold cross-validation to enhance model performance. Results: The LightGBM model demonstrated superior performance with an AUC of 0.96 and an accuracy of 90%, making it the optimal model for monitoring labor pain based on ECG data. Other models, such as XGBoost and SnapLogisticRegression, also showed strong performance, with AUC values ranging from 0.88 to 0.95. Conclusions: This study demonstrates that the integration of machine learning algorithms with ECG data significantly enhances the accuracy and reliability of labor pain monitoring. Specifically, the LightGBM model exhibits exceptional precision and robustness in continuous pain monitoring during labor, with potential applicability extending to broader healthcare settings. Trial registration: ClinicalTrials.gov Identifier: NCT04461704. [ABSTRACT FROM AUTHOR]

This research focuses on applying the XGBoost algorithm to analyze and predict cayenne pepper prices. The main aim is to exploit XGBoost's exceptional capability to manage large datasets and discern intricate patterns for precise price forecasting. The dataset comprises historical cayenne pepper price data, along with pertinent economic and climatic factors. The XGBoost model was developed and validated on this dataset, with its performance assessed using metrics. The results indicated a high level of accuracy, achieving an R² score of 99% on the training set and 92% on the test set, reflecting a strong alignment between predicted and actual prices. Moreover, the model attained an average cross-validation score of 96%, reinforcing its robustness and reliability. These findings highlight XGBoost's efficacy in agricultural price prediction, offering stakeholders a potent tool for data-driven decision-making. This study enriches the literature on machine learning applications in agriculture and emphasizes XGBoost's potential to enhance predictive accuracy and operational efficiency. [ABSTRACT FROM AUTHOR]

The explosion of data volume in the digital age has completely changed the corporate and industrial environments. In-depth analysis of large datasets to support strategic decision-making and innovation is the main focus of this paper's exploration of big data management engineering. A thorough examination of the basic elements and approaches necessary for efficient big data use—data collecting, storage, processing, analysis, and visualization—is given in this paper. With real-life case studies from several sectors to complement our exploration of cutting-edge methods in big data management, we present useful applications and results. This document lists the difficulties in handling big data, such as guaranteeing scalability, governance, and data quality. It also describes possible future study paths to deal with these issues and promote ongoing creativity. The results stress the need to combine cutting-edge technology with industry standards to improve decision-making based on data. Through an analysis of approaches such as machine learning, real-time data processing, and predictive analytics, this paper offers insightful information to companies hoping to use big data as a strategic advantage. Lastly, this paper presents real-life use cases in different sectors and discusses future trends such as the utilization of big data by emerging technologies. [ABSTRACT FROM AUTHOR]

Cardiometabolic syndrome (CMS) is a growing concern in children and adolescents, marked by obesity, hypertension, insulin resistance, and dyslipidemia. This study aimed to predict CMS using machine learning based on data from the CASPIAN-V study, which involved 14,226 participants aged 7–18 years, with a CMS prevalence of 82.9%. We applied the XGBoost algorithm to analyze key noninvasive variables, including self-rated health, sunlight exposure, screen time, consanguinity, healthy and unhealthy dietary habits, discretionary salt and sugar consumption, birthweight, and birth order, father and mother education, oral hygiene behavior, and family history of dyslipidemia, obesity, hypertension, and diabetes using five-fold cross-validation. The model achieved high sensitivity (94.7% ± 4.8) and specificity (78.8% ± 13.7), with an area under the ROC curve (AUC) of 0.867 ± 0.087, indicating strong predictive performance and significantly outperformed triponderal mass index (TMI) (adjusted paired t-test; p < 0.05). The most critical selected modifiable factors were sunlight exposure, screen time, consanguinity, healthy and unhealthy diet, dietary fat type, and discretionary salt consumption. This study emphasizes the clinical importance of early identification of at-risk individuals to implement timely interventions. It offers a promising tool for CMS risk screening. These findings support using predictive analytics in clinical settings to address the rising CMS epidemic in children and adolescents. [ABSTRACT FROM AUTHOR]

Background: Clinical prediction models (CPM), such as the SCOAP-CERTAIN tool, can be utilized to enhance decision-making for lumbar spinal fusion surgery by providing quantitative estimates of outcomes, aiding surgeons in assessing potential benefits and risks for each individual patient. External validation is crucial in CPM to assess generalizability beyond the initial dataset. This ensures performance in diverse populations, reliability and real-world applicability of the results. Therefore, we externally validated the tool for predictability of improvement in oswestry disability index (ODI), back and leg pain (BP, LP). Methods: Prospective and retrospective data from multicenter registry was obtained. As outcome measure minimum clinically important change was chosen for ODI with ≥ 15-point and ≥ 2-point reduction for numeric rating scales (NRS) for BP and LP 12 months after lumbar fusion for degenerative disease. We externally validate this tool by calculating discrimination and calibration metrics such as intercept, slope, Brier Score, expected/observed ratio, Hosmer–Lemeshow (HL), AUC, sensitivity and specificity. Results: We included 1115 patients, average age 60.8 ± 12.5 years. For 12-month ODI, area-under-the-curve (AUC) was 0.70, the calibration intercept and slope were 1.01 and 0.84, respectively. For NRS BP, AUC was 0.72, with calibration intercept of 0.97 and slope of 0.87. For NRS LP, AUC was 0.70, with calibration intercept of 0.04 and slope of 0.72. Sensitivity ranged from 0.63 to 0.96, while specificity ranged from 0.15 to 0.68. Lack of fit was found for all three models based on HL testing. Conclusions: Utilizing data from a multinational registry, we externally validate the SCOAP-CERTAIN prediction tool. The model demonstrated fair discrimination and calibration of predicted probabilities, necessitating caution in applying it in clinical practice. We suggest that future CPMs focus on predicting longer-term prognosis for this patient population, emphasizing the significance of robust calibration and thorough reporting. [ABSTRACT FROM AUTHOR]

The article provides a conceptually informed empirical critique of the pursuit of social licence as a warrant for data linkage and predictive analytics in the field of family policy intervention. It draws on research focusing on parental views of digitally‐driven family governance in the United Kingdom. We identify the notion of consensus that undergirds the concept of social licence that acts to obscure inequalities and silence conflict, and to reframe digital surveillance and prediction as a moral rather than political issue. Using focus group and individual interview material, we show how parents assert professional or lay experiential knowledges in making judgements about the legitimacy of and trust in operational data technologies, involving struggles between positionings as parents like 'us' and 'other' parents. We demonstrate how parents have different leverages from these unequal and morally charged social locations. Inevitably, the application of social licence in the domain of digital family policy and intervention is fractured by entrenched social divisions and inequalities. [ABSTRACT FROM AUTHOR]

Background: Diabetes mellitus (DM) increases the risk complications in addition to mortality. Quantifying the risk of complications using artificial intelligence could be a way to design comprehensive patient healthcare programs. Objective: Predicting the probability of macro and microvascular complications in patients with DM through Machine Learning. Methods: Retrospective cohort study. Based on an outpatient follow-up program for diabetic patients, 64,081 records and 287 variables were identified, with highly unbalanced data. Predictive models for chronic kidney disease (CKD), lower extremity amputation (LEA), coronary heart disease (CHD), and early mortality (MOR) were developed. An exhaustive computational method was conducted to find the best combination between machine learning (ML) algorithms and sampling method. Results: The best model was determined by assessing its performance through the heuristics obtained from a comprehensive analysis of the accuracy and F1 values for ML, sampling, and dataset. Regarding each complication, 99.9% accuracy was obtained for LEA, 94.3% for CHD, 97.4% for MOR, and 98.8% for CKD. F1 was assessed to identify false positives, with 84.5% for CKD, 63.6% for MOR, 46.2% for LEA, and 44.8% for CHD. Conclusions: This ML model can be applied to predict CHD, CKD, and MOR. The success of ML predictions lies in the clinical definition of initial variables and their simplification for obtaining variables based on which the algorithms can identify patients that are likely to develop a complication. For clinical application of this system, it is necessary to assess the cross performance of metrics, as found here (accuracy higher 95% and F1-Score higher than 80%). [ABSTRACT FROM AUTHOR]

This research delineates the advancement of a refined predictive algorithm centered on the Generalized Least Deviation Method (GLDM) specifically configured for analyzing COVID-19 infection trends in Dagestan using univariate time series data. Our methodology is characterized by its enhancement of forecast precision through diligent minimization of a bespoke loss function. The algorithm's innovation lies in its formulation, incorporating second-order relationships within the time series data: ...; where nj are the computed weights ascribed to historical data, and 3t denotes the error component. Our empirical analysis substantiates that by strategically accentuating pertinent coefficients and optimizing the loss function, there is a significant elevation in the model's forecasting accuracy. Consequently, the refined second-order GLDM model emerges as an advanced and applicable instrument for the prognostication of COVID-19 infection cases in Dagestan. [ABSTRACT FROM AUTHOR]

In the competitive field of business intelligence, optimizing talent recruitment through data-driven methodologies is crucial for better decision-making. This study compares the effectiveness of various machine learning models to improve recruitment accuracy and efficiency. Using the recruitment data from a major Yemeni organization (2019–2022), we evaluated models including K-Nearest Neighbors, Logistic Regression, Support Vector Machine, Naive Bayes, Decision Trees, Random Forest, Gradient Boosting Classifier, AdaBoost Classifier, and Neural Networks. Hyperparameter tuning and cross-validation were used for optimization. The Random Forest model achieved the highest accuracy (92.8%), followed by Neural Networks (92.6%) and Gradient Boosting Classifier (92.5%). These results suggest that advanced machine learning models, particularly Random Forest and Neural Networks, can significantly enhance the recruitment processes in business intelligence systems. This study provides valuable insights for recruiters, advocating for the integration of sophisticated machine learning techniques in talent acquisition strategies. [ABSTRACT FROM AUTHOR]

Background: In the current global market, supply chains are increasingly complex, necessitating agile and sustainable management strategies. Traditional analytical methods often fall short in addressing these challenges, creating a need for more advanced approaches. Methods: This study leverages advanced machine learning (ML) techniques to enhance logistics and inventory man-agement. Using historical data from a multinational retail corporation, including sales, inventory levels, order fulfillment rates, and operational costs, we applied a variety of ML algorithms, in-cluding regression, classification, clustering, and time series analysis. Results: The application of these ML models resulted in significant improvements across key operational areas. We achieved a 15% increase in demand forecasting accuracy, a 10% reduction in overstock and stockouts, and a 95% accuracy in predicting order fulfillment timelines. Additionally, the approach identified at-risk shipments and enabled customer segmentation based on delivery preferences, leading to more personalized service offerings. Conclusions: Our evaluation demonstrates the transforma-tive potential of ML in making supply chain operations more responsive and data-driven. The study underscores the importance of adopting advanced technologies to enhance deci-sion-making, evidenced by a 12% improvement in lead time efficiency, a silhouette coefficient of 0.75 for clustering, and an 8% reduction in replenishment errors. [ABSTRACT FROM AUTHOR]

This study focuses on predicting PM2.5 levels at the University of Petroșani by employing advanced machine-learning techniques to analyze a dataset that encapsulates a wide array of air pollutants and meteorological factors. Utilizing data from Internet of Things (IoT) sensors and established environmental monitoring stations, the research leverages Random Forest, Gradient Boosting Machines, and Support Vector Regression models to forecast air quality, emphasizing the complex interplay between various pollutants. The models demonstrate varying degrees of accuracy, with the Random Forest model achieving the highest predictive power, indicated by an R2 score of 0.82764. Our findings highlight the significant impact of specific pollutants such as NO, NO2, and CO on PM2.5 levels, suggesting targeted mitigation strategies could enhance local air quality. Additionally, the study explores the role of temporal dynamics in pollution trends, employing time-series analysis to further refine the predictive accuracy. This research contributes to the field of environmental science by providing a nuanced understanding of air-quality fluctuations in a university setting and offering a replicable model for similar environments seeking to reduce airborne pollutants and protect public health. [ABSTRACT FROM AUTHOR]

Predicting academic performance for students majoring in computer science has long been a significant field of research in computing education. Previous studies described that accurate prediction of students' early-stage performance could identify low-performing students and take corrective action to improve performance. Besides, adopting machine learning algorithms with predictive analytics has proven possible and meaningful. The traditional approach of looking after students without uncovering the root causes of poor performance has shifted dramatically into improving the quality of the educational processes of students, teachers, and stakeholders. Thus, this study employed predictive analytics to develop an early warning prediction model using computing science degree performance data at a public institution. Predictive models based on our data analysis revealed that low, medium, and high-performing students could be predicted with an accuracy of 88% using only the grades of the courses they took in the second year. Moreover, 96% accuracy was achieved when all course grades were used in predictive models. The courses that are important in determining the overall performance of the students were also analyzed. By employing a multi-method approach, utilizing a large dataset spanning four academic years, and including a diverse sample of 430 students, our study offers a robust foundation to researchers, designers, and computer science educators for understanding and predicting student performance. The enhanced generalizability and implications for educational practice position our study as a valuable contribution to the field, paving the way for further advancements in predictive analytics. [ABSTRACT FROM AUTHOR]

In a dynamic business environment, the accuracy of sales forecasts plays a pivotal role in strategic decision making and resource allocation. This article offers a systematic review of the existing literature on techniques and methodologies used in forecasting, especially in sales forecasting across various domains, aiming to provide a nuanced understanding of the field. Our study examines the literature from 2013 to 2023, identifying key techniques and their evolution over time. The methodology involves a detailed analysis of 516 articles, categorized into classical qualitative approaches, traditional statistical methods, machine learning models, deep learning techniques, and hybrid approaches. The results highlight a significant shift towards advanced methods, with machine learning and deep learning techniques experiencing an explosive increase in adoption. The popularity of these models has surged, as evidenced by a rise from 10 articles in 2013 to over 110 by 2023. This growth underscores their growing prominence and effectiveness in handling complex time series data. Additionally, we explore the challenges and limitations that influence forecasting accuracy, focusing on complex market structures and the benefits of extensive data availability. [ABSTRACT FROM AUTHOR]

In recent decades, technological progress and the development of Artificial Intelligence (AI) have profoundly transformed the way modern societies operate, interact and ensure security. AI has become an essential tool in the field of public order, providing innovative solutions that have enabled law enforcement to accomplish their mission with increased efficiency and precision. This text explores the possible possibilities of AI in public policy, highlighting its use in crime prevention, predictive analytics, intelligent video surveillance, assisting in investigations, protecting critical infrastructure, managing emergencies and contributing to citizen engagement. Ethical and legal challenges associated with the integration of AI are also discussed, such as privacy and protection of personal data, algorithmic discrimination, impact on civil liberties, and cybersecurity vulnerabilities. The paper highlights the need for a balanced approach to the adoption of AI in public policy, which includes the development of responsible policies and regulations, ensuring transparency and accountability of authorities, as well as collaboration between policy makers, technical experts, human rights organizations and citizens, to promote . a just and secure society.

The main objective of the current study is to shed light on the mediating effect of financial technology (Fin-Tech) on the relationship between artificial intelligence (encompassing natural language processing (NLP), machine learning algorithms, computer vision, predictive analytics, robotic process automation (RPA), blockchain technology, and deep learning) and financial decision-making from the perspective of financial managers within Jordan's commercial banking sector. Realizing this objective required the use of quantitative methodology. A questionnaire was self-administered by 86 financial managers in the Jordanian banking sector. Primary data was analyzed using AMOS. Results of analysis confirmed that FinTech plays a significant mediating role between AI applications and financial decision-making. Machine learning was identified as the most impactful AI technique, facilitating more informed decisions through advanced data analysis and pattern recognition beyond the scope of traditional analysis methods. The novelty of current research is in the fact that it offers valuable insights into the intersection of AI and Fin-Tech within the Jordanian financial sector. It contributes to the understanding of how advanced AI techniques can enhance financial decision-making, emphasizing the importance of multidisciplinary expertise in the development of AI-driven financial systems. The findings have significant implications for both theoretical understanding and practical application in the finance industry. Doi: 10.28991/HIJ-2024-05-03-015 Full Text: PDF

Objective: to identify the key benefits and potential risks associated with the use of artificial intelligence in ERP systems to improve decision-making processes, management efficiency and operational performance of various sectors, including commercial and non-profit organizations. Methods: systematic literature review, empirical data analysis, analytical and experimental research methods. Results: the key directions of artificial intelligence implementation in ERP-systems are reflected, providing improvement of operational efficiency, customer relations, as well as optimization of business processes, data management, supply chain and personnel management, automation of operations related to finance, optimization of customer relations; implementation of artificial intelligence in ERP-systems reduces inventory management costs, improves the accuracy of forecasting andinventory optimization, accelerates financial analysis and increases the accuracy of budgeting, resulting in reduced budget planning time; it also increases productivity by optimizing necessary production processes and reducing equipment downtime. However, there are also risks of confidential data leakage, unauthorized access to data; job losses due to automation of tasks; and vulnerability to cyberattacks. Scientific novelty: the little-studied directions of artificial intelligence integration in ERP-systems are analyzed; an integrative approach to the application of artificial intelligence in ERP-systems is proposed, which combines methods of machine learning, natural language processing and predictive analytics and provides a comprehensive assessment of the complex impact on the business processes’ efficiency. Practical significance: the formulated directions for solving the identified problems of artificial intelligence integration in ERP-systems can be implemented in practice, as they will enable to better take into account local requirements and laws.

Background: Bone marrow adipose tissue (BMAT) represents > 10% fat mass in healthy humans and can be measured by magnetic resonance imaging (MRI) as the bone marrow fat fraction (BMFF). Human MRI studies have identified several diseases associated with BMFF but have been relatively small scale. Population-scale studies therefore have huge potential to reveal BMAT’s true clinical relevance. The UK Biobank (UKBB) is undertaking MRI of 100,000 participants, providing the ideal opportunity for such advances. Objective: To establish deep learning for high-throughput multi-site BMFF analysis from UKBB MRI data. Materials and methods: We studied males and females aged 60–69. Bone marrow (BM) segmentation was automated using a new lightweight attention-based 3D U-Net convolutional neural network that improved segmentation of small structures from large volumetric data. Using manual segmentations from 61–64 subjects, the models were trained to segment four BM regions of interest: the spine (thoracic and lumbar vertebrae), femoral head, total hip and femoral diaphysis. Models were tested using a further 10–12 datasets per region and validated using datasets from 729 UKBB participants. BMFF was then quantified and pathophysiological characteristics assessed, including site- and sex-dependent differences and the relationships with age, BMI, bone mineral density, peripheral adiposity, and osteoporosis. Results: Model accuracy matched or exceeded that for conventional U-Nets, yielding Dice scores of 91.2% (spine), 94.5% (femoral head), 91.2% (total hip) and 86.6% (femoral diaphysis). One case of severe scoliosis prevented segmentation of the spine, while one case of Non-Hodgkin Lymphoma prevented segmentation of the spine, femoral head and total hip because of T2 signal depletion; however, successful segmentation was not disrupted by any other pathophysiological variables. The resulting BMFF measurements confirmed expected relationships between BMFF and age, sex and bone density, and identified new site- and sex-specific characteristics. Conclusions: We have established a new deep learning method for accurate segmentation of small structures from large volumetric data, allowing high-throughput multi-site BMFF measurement in the UKBB. Our findings reveal new pathophysiological insights, highlighting the potential of BMFF as a novel clinical biomarker. Applying our method across the full UKBB cohort will help to reveal the impact of BMAT on human health and disease.

To function effectively, nuclear power plants rely on the effective filtration of air, water, and process fluids, examples of which include inlet sea water, reactor coolant, plant drinking water, and moderator purification. Filtration assets degrade over time, which impairs their filtering performance and reduces the flow rate. Being able to determine the remaining useful life (RUL) of a filter could result in benefits, particularly when moving from a time-based to a condition-based maintenance strategy that would optimize the filter replacement procedure and reduce early replacement of filters that are still fit for purpose. For many filter applications, a time-based strategy is sufficient. For strategically important assets, such as fueling machines, there are benefits to be gained from the development of predictive maintenance strategies. In this paper, we propose a predictive condition-based strategy using differential pressure data as a proxy for filter health. The key objective in this work was the creation of a model that could predict a filter asset RUL. The differential pressure for 7 to 14 days is predicted by a heuristic-based regression model of the history of each filter. This approach has been demonstrated using a civil nuclear generation application but could be applied to wider applications. While this model is still undergoing on-site evaluation, it has been estimated that there will be an operationally significant lifetime cost reduction. [ABSTRACT FROM AUTHOR]

Child welfare agencies increasingly use machine learning models to predict outcomes and inform decisions. These tools are intended to increase accuracy and fairness but can also amplify bias. This systematic review explores how researchers addressed ethics, equity, bias, and model performance in their design and evaluation of predictive and prescriptive algorithms in child welfare. We searched EBSCO databases, Google Scholar, and reference lists for journal articles, conference papers, dissertations, and book chapters published between January 2010 and March 2020. Sources must have reported on the use of algorithms to predict child welfare-related outcomes and either suggested prescriptive responses, or applied their models to decision-making contexts. We calculated descriptive statistics and conducted Mann-Whitney U tests, and Spearman's rank correlations to summarize and synthesize findings. Of 15 articles, fewer than half considered ethics, equity, or bias or engaged participatory design principles as part of model development/evaluation. Only one-third involved cross-disciplinary teams. Model performance was positively associated with number of algorithms tested and sample size. No other statistical tests were significant. Interest in algorithmic decision-making in child welfare is growing, yet there remains no gold standard for ameliorating bias, inequity, and other ethics concerns. Our review demonstrates that these efforts are not being reported consistently in the literature and that a uniform reporting protocol may be needed to guide research. In the meantime, computer scientists might collaborate with content experts and stakeholders to ensure they account for the practical implications of using algorithms in child welfare settings. [ABSTRACT FROM AUTHOR]

Due to rapid advancements in the field of Internet of Medical Things (IoMT), a continuous influx of health data is being generated at a large scale. The primary objective of IoMT solutions is to transmit critical health data from patients to remote locations in real-time. Apart from remote patient monitoring, the extensive collection of health data offers opportunities for uncovering noteworthy patterns and potential risks associated with future diseases. This study introduces a novel risk prediction approach, namely Association Rule Mining for Risk Prediction (ARMR), which integrates an IoMT framework with the emerging machine learning technique known as Association Rule Mining (ARM). The proposed scheme employs a dataset obtained from various hospitals. The findings demonstrate that ARMR effectively extracts rules to identify a patient's risk of heart disease by considering demographic, physiological, and lifestyle data. Moreover, intriguing, and unexpected patterns and associations in the disease data can be identified, aiding medical professionals in guiding diagnosis and treatment decisions more efficiently.

Zifang Shang,1 Varun Chauhan,2 Kirti Devi,3 Sandip Patil4 1Guangdong Engineering Technological Research Centre of Clinical Molecular Diagnosis and Antibody Drugs, Meizhou People’s Hospital (Huangtang Hospital), Meizhou Academy of Medical Sciences, Meizhou, People’s Republic of China; 2Multi-Disciplinary Research Unit, Government Institute of Medical Sciences, Greater Noida, India; 3Department of Medicine, Government Institute of Medical Sciences, Greater Noida, India; 4Department Haematology and Oncology, Shenzhen Children’s Hospital, Shenzhen, People’s Republic of ChinaCorrespondence: Sandip Patil, Department Haematology and Oncology, Shenzhen Children’s Hospital, Shenzhen, People’s Republic of China, Tel +86-755-82008283, Email sandippatil1309@yahoo.comBackground: Artificial Intelligence (AI) holds transformative potential for the healthcare industry, offering innovative solutions for diagnosis, treatment planning, and improving patient outcomes. As AI continues to be integrated into healthcare systems, it promises advancements across various domains. This review explores the diverse applications of AI in healthcare, along with the challenges and limitations that need to be addressed. The aim is to provide a comprehensive overview of AI’s impact on healthcare and to identify areas for further development and focus.Main Applications: The review discusses the broad range of AI applications in healthcare. In medical imaging and diagnostics, AI enhances the accuracy and efficiency of diagnostic processes, aiding in early disease detection. AI-powered clinical decision support systems assist healthcare professionals in patient management and decision-making. Predictive analytics using AI enables the prediction of patient outcomes and identification of potential health risks. AI-driven robotic systems have revolutionized surgical procedures, improving precision and outcomes. Virtual assistants and chatbots enhance patient interaction and support, providing timely information and assistance. In the pharmaceutical industry, AI accelerates drug discovery and development by identifying potential drug candidates and predicting their efficacy. Additionally, AI improves administrative efficiency and operational workflows in healthcare, streamlining processes and reducing costs. AI-powered remote monitoring and telehealth solutions expand access to healthcare, particularly in underserved areas.Challenges and Limitations: Despite the significant promise of AI in healthcare, several challenges persist. Ensuring the reliability and consistency of AI-driven outcomes is crucial. Privacy and security concerns must be navigated carefully, particularly in handling sensitive patient data. Ethical considerations, including bias and fairness in AI algorithms, need to be addressed to prevent unintended consequences. Overcoming these challenges is critical for the ethical and successful integration of AI in healthcare.Conclusion: The integration of AI into healthcare is advancing rapidly, offering substantial benefits in improving patient care and operational efficiency. However, addressing the associated challenges is essential to fully realize the transformative potential of AI in healthcare. Future efforts should focus on enhancing the reliability, transparency, and ethical standards of AI technologies to ensure they contribute positively to global health outcomes.Keywords: artificial intelligence, healthcare innovation, medical imaging, predictive analytics, clinical decision support

This policy analysis focuses on harnessing the power of artificial intelligence (AI) in hospital quality improvement to transform quality and patient safety. It examines the application of AI at the two following fundamental levels: (1) diagnostic and treatment and (2) clinical operations. AI applications in diagnostics directly impact patient care and safety. At the same time, AI indirectly influences patient safety at the clinical operations level by streamlining (1) operational efficiency, (2) risk assessment, (3) predictive analytics, (4) quality indicators reporting, and (5) staff training and education. The challenges and future perspectives of AI application in healthcare, encompassing technological, ethical, and other considerations, are also critically analyzed.

In the competitive field of business intelligence, optimizing talent recruitment through data-driven methodologies is crucial for better decision-making. This study compares the effectiveness of various machine learning models to improve recruitment accuracy and efficiency. Using the recruitment data from a major Yemeni organization (2019–2022), we evaluated models including K-Nearest Neighbors, Logistic Regression, Support Vector Machine, Naive Bayes, Decision Trees, Random Forest, Gradient Boosting Classifier, AdaBoost Classifier, and Neural Networks. Hyperparameter tuning and cross-validation were used for optimization. The Random Forest model achieved the highest accuracy (92.8%), followed by Neural Networks (92.6%) and Gradient Boosting Classifier (92.5%). These results suggest that advanced machine learning models, particularly Random Forest and Neural Networks, can significantly enhance the recruitment processes in business intelligence systems. This study provides valuable insights for recruiters, advocating for the integration of sophisticated machine learning techniques in talent acquisition strategies.

The explosion of data volume in the digital age has completely changed the corporate and industrial environments. In-depth analysis of large datasets to support strategic decision-making and innovation is the main focus of this paper’s exploration of big data management engineering. A thorough examination of the basic elements and approaches necessary for efficient big data use—data collecting, storage, processing, analysis, and visualization—is given in this paper. With real-life case studies from several sectors to complement our exploration of cutting-edge methods in big data management, we present useful applications and results. This document lists the difficulties in handling big data, such as guaranteeing scalability, governance, and data quality. It also describes possible future study paths to deal with these issues and promote ongoing creativity. The results stress the need to combine cutting-edge technology with industry standards to improve decision-making based on data. Through an analysis of approaches such as machine learning, real-time data processing, and predictive analytics, this paper offers insightful information to companies hoping to use big data as a strategic advantage. Lastly, this paper presents real-life use cases in different sectors and discusses future trends such as the utilization of big data by emerging technologies.

In a dynamic business environment, the accuracy of sales forecasts plays a pivotal role in strategic decision making and resource allocation. This article offers a systematic review of the existing literature on techniques and methodologies used in forecasting, especially in sales forecasting across various domains, aiming to provide a nuanced understanding of the field. Our study examines the literature from 2013 to 2023, identifying key techniques and their evolution over time. The methodology involves a detailed analysis of 516 articles, categorized into classical qualitative approaches, traditional statistical methods, machine learning models, deep learning techniques, and hybrid approaches. The results highlight a significant shift towards advanced methods, with machine learning and deep learning techniques experiencing an explosive increase in adoption. The popularity of these models has surged, as evidenced by a rise from 10 articles in 2013 to over 110 by 2023. This growth underscores their growing prominence and effectiveness in handling complex time series data. Additionally, we explore the challenges and limitations that influence forecasting accuracy, focusing on complex market structures and the benefits of extensive data availability.

Evi Diana Omar,1 Hasnah Mat,1 Ainil Zafirah Abd Karim,1 Ridwan Sanaudi,1 Fairol H Ibrahim,2 Mohd Azahadi Omar,1 Muhd Zulfadli Hafiz Ismail,1 Vivek Jason Jayaraj,1 Bak Leong Goh2 1Sector for Biostatistics and Data Repository, National Institutes of Health, Ministry of Health Malaysia, Shah Alam, Selangor, Malaysia; 2Hospital Sultan Idris Shah Serdang, Ministry of Health Malaysia, Kajang, Selangor, MalaysiaCorrespondence: Ainil Zafirah Abd Karim, Email ainilzafirah@moh.gov.myPurpose: This study aimed to identify the best-performing algorithm for predicting Acute Kidney Injury (AKI) necessitating dialysis following cardiac surgery.Patients and Methods: The dataset encompassed patient data from a tertiary cardiothoracic center in Malaysia between 2011 and 2015, sourced from electronic health records. Extensive preprocessing and feature selection ensured data quality and relevance. Four machine learning algorithms were applied: Logistic Regression, Gradient Boosted Trees, Support Vector Machine, and Random Forest. The dataset was split into training and validation sets and the hyperparameters were tuned. Accuracy, Area Under the ROC Curve (AUC), precision, F-measure, sensitivity, and specificity were some of the evaluation criteria. Ethical guidelines for data use and patient privacy were rigorously followed throughout the study.Results: With the highest accuracy (88.66%), AUC (94.61%), and sensitivity (91.30%), Gradient Boosted Trees emerged as the top performance. Random Forest displayed strong AUC (94.78%) and accuracy (87.39%). In contrast, the Support Vector Machine showed higher sensitivity (98.57%) with lower specificity (59.55%), but lower accuracy (79.02%) and precision (70.81%). Sensitivity (87.70%) and specificity (87.05%) were maintained in balance via Logistic Regression.Conclusion: These findings imply that Gradient Boosted Trees and Random Forest might be an effective method for identifying patients who would develop AKI following heart surgery. However specific goals, sensitivity/specificity trade-offs, and consideration of the practical ramifications should all be considered when choosing an algorithm.Keywords: acute kidney injury, cardiac surgery, machine learning, predictive analytics

Predictive modelling in the education domain can be utilised to significantly improve teaching and learning experiences. Massive Open Online Courses (MOOCs) generate a large volume of data that can be exploited to predict and evaluate student performance based on various factors. This paper has two broad aims. Firstly, to develop and tune several Machine Learning (ML) models to perform classification tasks on the dataset to predict student performance, including Linear Regression, Logistic Regression, Random Forests, K-Nearest Neighbours, and more. Secondly, to evaluate the efficacy of these ML models and identify those which are best suited to this task. The categories of data utilised in achieving these aims include (i) demographic information, (ii) academic background, and (iii) interaction with MOOC course materials. The research procedure comprises five phases: data exploration to analyse the dataset, feature engineering which involves discerning the most important features and converting them into a format decipherable by the ML models, model building, model evaluation by measurement of accuracy, and subsequent comparative evaluation between the different models. The results achieved in this study are expected to have implications on how MOOC platforms utilise data to improve user experience. As indicated by the findings of this study, the data collected by these platforms may be used to predict performance with accuracy of over 77%; this extracted information can be exploited to enhance educational theory or practices in the context of MOOCs, for instance by implementing varying teaching methodologies or providing different types of resources based on predicted performance. [ABSTRACT FROM AUTHOR]

This paper introduces the intelligent interconnected healthcare system (IIHS), an innovative fusion of the Internet of Things (IoT) and big data analytics technologies designed to revolutionize proactive and personalized healthcare. IIHS facilitates the integration of real-time data from various devices, ambient sensors, and hospital equipment, creating a continuous stream of comprehensive healthcare data. Leveraging advanced data analysis, IIHS offers actionable insights for ongoing patient health monitoring, trend prediction through machine learning, and rapid information access via a user-friendly interface. The system architecture features a combination of centralized cloud storage and edge storage at healthcare facilities, enhancing both efficiency and security in data management. The effectiveness of IIHS has been demonstrated in two healthcare facilities, which reported significant reductions in patient length of stay and readmission rates. This indicates the system's potential to improve patient care while seamlessly integrating with existing healthcare infrastructures. IIHS represents the future of digital and personalized medicine, offering a scalable, patient-centric solution that supports the ongoing transformation towards data-driven healthcare. [ABSTRACT FROM AUTHOR]

The purpose of this review is to provide an overview of the integration of technological advancements in orthopedic shoulder surgery. Recent technological advancements in orthopedic shoulder surgery include predictive analytics, computer-navigated instrumentation for operative planning, extended reality, and robotics. Separately, these advancements provide distinct methodological attempts to improve surgical experiences and outcomes. Together, these technologies can provide orthopedic surgeons with the tools and capabilities to improve patient care and communication in shoulder arthroplasty. From artificial intelligence-generated predictive analytics to extended reality and robotics, technical innovations may lead to improvements in patient education, surgical accuracy, interdisciplinary communication, and outcomes. A comprehensive narrative review was conducted to explore the technological advancements of orthopedic shoulder arthroplasty. Our findings emphasized the impact of these advancements, exemplified by early enhancements in efficacy and safety. However, certain challenges remain, such as a lack of reproducibly improved outcomes and cost considerations. While the reviewed studies indicate hope for improving shoulder arthroplasty, the true cost-effectiveness and applicability remains to be determined, indicating the need for further research. [ABSTRACT FROM AUTHOR]

Renewable energy sources are essential to address climate change, fossil fuel depletion, and stringent environmental regulations in the subsequent decades. Horizontal-axis wind turbines (HAWTs) are particularly suited to meet this demand. However, their efficiency is affected by environmental factors because they operate in open areas. Adverse weather conditions like rain reduce their aerodynamic performance. This study investigates wind turbine power prediction under rainy conditions by integrating Blade Element Momentum (BEM) theory with explainable artificial intelligence (XAI). The S809 airfoil's aerodynamic characteristics, used in NREL wind turbines, were analyzed using ANSYS FLUENT and symbolic regression under varying rain intensities. Simulations at a Reynolds number (Re) of 1 × 106 were performed using the Discrete Phase Model (DPM) and k– ω SST turbulence model, with liquid water content (LWC) values of 0 (dry), 10, 25, and 39 g/m3. The lift and drag coefficients were calculated at various angles of attack for all the conditions. The results indicated that rain led to reduced lift and increased drag. The innovative aspect of this research is the development of machine learning models predicting changes in the airfoil coefficients under rain with an R 2 value of 0.97. The proposed XAI framework models rain effects at a lower computational time, enabling efficient wind farm performance assessment in rainy conditions compared to conventional CFD simulations. It was found that a heavy rain LWC of 39 g/m3 could reduce power output by 5.7% to 7%. These findings highlight the impact of rain on aerodynamic performance and the importance of advanced predictive models for optimizing renewable energy generation. [ABSTRACT FROM AUTHOR]

Deep learning is crucial in marine logistics and container crane error detection, diagnosis, and prediction. A novel deep learning technique using Long Short-Term Memory (LSTM) detected and anticipated errors in a system with imbalanced data. The LSTM model was trained on real operational error data from container cranes. The custom algorithm employs the Synthetic Minority Oversampling TEchnique (SMOTE) to balance the imbalanced data for operational data errors (i.e., too few minority class samples). Python was used to program. Pearson, Spearman, and Kendall correlation matrices and covariance matrices are presented. The model's training and validation loss is shown, and the remaining data are predicted. The test set (30% of actual data) and forecasted data had RMSEs of 0.065. A heatmap of a confusion matrix was created using Matplotlib and Seaborn. Additionally, the error outputs for the time series for the next n seconds were projected, with the n seconds input by the user. Accuracy was 0.996, precision was 1.00, recall was 0.500, and f1 score was 0.667, according to the evaluation criteria that were produced. Experiments demonstrated that the technique is capable of identifying critical elements. Thus, future attempts will improve the model's structure to forecast industrial big data errors. However, the advantage is that it can handle imbalanced data, which is usually what most industries have. With additional data, the model can be further improved. [ABSTRACT FROM AUTHOR]

Resilience describes the capacity of systems to react to, withstand, adjust to, and recover from disruptive events. Despite numerous metrics proposed to quantify resilience, few studies predict these metrics or the restoration time to nominal performance levels, and these studies often focus on a single domain. This paper introduces three methods to model system performance and resilience metrics, which are applicable to various engineering and social science domains. These models utilize reliability engineering techniques, including bathtub-shaped functions, mixture distributions, and regression analysis incorporating event intensity covariates. Historical U.S. job loss data during recessions are used to evaluate these approaches' predictive accuracy. This study computes goodness-of-fit measures, confidence intervals, and resilience metrics. The results show that bathtub-shaped functions and mixture distributions accurately predict curves possessing V, U, L, and J shapes but struggle with W and K shapes involving multiple disruptions or sudden performance drops. In contrast, covariate-based models effectively track all curve types, including complex W and K shapes, like the successive shocks in the 1980 U.S. recession and the sharp decline in the 2020 U.S. recession. These models achieve a high predictive accuracy for future performance and resilience metrics, evidenced by the low sum of square errors and high adjusted coefficients of determination. [ABSTRACT FROM AUTHOR]

This research primary aimed at evaluating various predictive models in predicting programming students at risk of dropping out. It also aimed at identifying attributes that are significant in predicting students at risk of dropping. The educational data mining process (EDM) was utilized as the research framework. The study conducted a ten-fold cross-validation, revealing that the k-nearest neighbors (kNN) algorithm achieved the highest classification accuracy at 95.5%. The decision tree model followed closely with a 94.9% accuracy, logistic regression exhibited 94.4%, and the neural network model yielded a classification accuracy of 93.2%. Further analysis, including confusion matrices and receiver operating characteristic (ROC) curves, provided detailed insights into the models' performance. Notably, the decision tree algorithm excelled in identifying students who did not drop out, with a misclassification rate of 9 out of 30 for dropped students. Analysis also showed that students' assignments completed (AC), laboratory work (LW), and attendance (ATT) were the strongest predictors in identifying students at risk of dropping. Results of the study can be used by instructors to identify in advance student at risk of dropping and provide them with the necessary intervention to improve performance in programming. [ABSTRACT FROM AUTHOR]