Sales pipeline win propensity prediction: A regression approach

Few features characterizing the dosimetric properties of the patients are included in currently available dose-volume histogram (DVH) prediction models, making it intractable to build a correlative relationship between the input and output parameters. Here, we use planning target volume (PTV)-only treatment plans of the patients (i.e., the achievable dose distribution in the absence of organs-at-risk (OAR) constraints) to estimate the potentially achievable quality of treatment plans and establish a machine learning-based DVH prediction framework with the use of the dosimetric metric as model input parameters. A support vector regression (SVR) approach was used as the backbone of our machine learning model. A database containing volumetric modulated arc therapy (VMAT) plans of 63 prostate cancer patients were used. For each patient, the PTV-only plan was generated first. A correlative relationship between the OAR DVH of the PTV-only plan (model input) and the corresponding DVH of the clinical treatment plan (CTP) (model output) was then established with the 53 training cases. The prediction model was tested by the validation cohort of ten cases. For the training cohort, the checks of dosimetric endpoints (DEs) indicated that 52 of 53 plans (98%) were within the 10% error bound for bladder, and 45 of 53 plans (85%) were within the 10% error bound for rectum. In the validation tests, 92% and 96% of the DEs were within the 10% error bounds for bladder and rectum, respectively, and eight of ten validation plans (80%) were within the 10% error bound for both the bladder and rectum. The sum of absolute residuals (SAR) achieved a mean of 0.034±0.028 and 0.046±0.021 for the bladder and rectum, respectively. A novel dosimetric features-driven machine learning model with the use of PTV-only plan has been established for DVH prediction. The framework is capable of efficiently generating best achievable DVHs for VMAT planning.

Recent research highlights the potential of consumption-based feedback for water conservation, emphasizing the need for Non Intrusive Water Monitoring (NIWM). However, existing NIWM studies often rely on small datasets, a pre-selected class of models, and inaccessible software. Here, we introduce PyNIWM, a machine learning-based open-source Python framework for NIWM. PyNIWM enables water end-use classification via (i) data characterization and feature engineering, (ii) water end-use event classification with four machine learning classifiers, and (iii) performance assessment. We demonstrate PyNIWM on a real-world dataset containing around 800,000 labeled end-use events from 762 homes across the USA and Canada. The four PyNIWM classifiers achieve F1 scores above 0.85, indicating high suitability for water end-use classification. However, a tradeoff between accuracy and computational cost exists. Finally, data balancing through oversampling enhances classification of low-represented end-use classes, but does not improve overall classification. We release PyNIWM as an open-source software, aiming for collaborative and reproducible research.

A novel machine learning-based model predictive control framework for improving the energy efficiency of air-conditioning systems

Large-scale infrastructure projects often use reactive approaches to manage construction risks. This can result in expensive delays and increased budgets. This study creates and tests a risk prediction framework that uses machine learning, specifically Gradient Boosting Decision Trees (GBDT), to help identify and address risks early in urban infrastructure construction. Data from 220 infrastructure projects, spanning from 2015 to 2024 and located in North America, Europe, and Asia, were analyzed. These projects had values between $50 million and $2 billion USD. The approach combined Principal Component Analysis and GBDT, handling 47 variables related to risk across six different risk areas. To test the model, 5-fold cross-validation was used, along with temporal validation, which involved setting aside the most recent 20% of projects. The GBDT model reached an overall prediction accuracy of 87.3%. It outperformed traditional methods by 23%. The ability to detect risks early on improved significantly, from 45% to 78%, and this led to an average cost reduction of 12.4%. Technical risks had the highest prediction accuracy, at 89.4%, while resource optimization saw a 25.7% improvement in equipment use. This machine learning-based framework is considered to significantly improve construction risk management. It offers better accuracy, earlier risk detection, and cost savings, suggesting it could be widely used in urban infrastructure construction.

Location‐only and use‐availability data: analysis methods converge

Precisely forecasting the energy consumption of electric vehicles not only alleviates the anxiety associated with driving range but also serves as the foundation for progressive advancements, including optimizing charging strategy and energy utilization. The main challenge lies in the inaccuracy of current methods, whether they are empirical models, physics-based models, or data-driven models. Based on newly constructed and engineered physics-informed features, this paper introduces a machine learning-based prediction framework, employing a synergy of offline global models and vehicle-based online adaptation. This combination aims to elevate accuracy in point predictions and also provide valuable information on prediction uncertainties. The developed framework is trained and extensively tested using data from a fleet of real-world electric vehicles. The leading global model, quantile regression neural network (QRNN), demonstrates an average error of 6.30%. Subsequent online adaptation results in a notable reduction to 5.04%, with both surpassing the performance of existing models significantly. Concurrently, the online QRNN exhibits a strong capability in enhancing the coverage probability and decreasing the average width of prediction intervals.

The airline industry confronts increasingly sophisticated challenges in inventory management on board their aircraft fleet, demanding advanced predictive models capable of navigating the complex interdependencies of resource allocation across heterogeneous operational environments. This comprehensive research introduces a groundbreaking methodological framework for inventory efficiency prediction, integrating cutting-edge machine learning techniques with innovative synthetic data generation strategies. This study's primary contributions are threefold: (1) the creation of a high-fidelity synthetic dataset capturing the intricate nuances of aviation operational dynamics, (2) the implementation of advanced machine learning algorithms for unprecedented predictive accuracy, and (3) the development of a holistic analytical approach that provides actionable strategic insights for industry stakeholders. The synthetic dataset generated in this research represents a significant methodological innovation, meticulously constructed to simulate realistic aviation operational conditions. By incorporating a comprehensive array of multidimensional features—including flight duration, route complexity, aircraft specifications, passenger demographic profiles, maintenance histories, seasonal fluctuations, and geospatial variations—we establish an unparalleled foundation for predictive modeling. Employing a sophisticated ensemble of machine learning methodologies, including advanced regression techniques, probabilistic classification algorithms, and hybrid predictive models, we achieved exceptional computational performance. Our regression models demonstrated extraordinary explanatory power, while classification models exhibited near-perfect risk assessment capabilities. The research presents several critical methodological innovations: (1) a novel synthetic data generation protocol that preserves statistical distributions and complex interdependencies, (2) advanced feature engineering and preprocessing techniques that enhance model interpretability and generalizability, and (3) a hybrid machine learning approach that integrates probabilistic reasoning with empirical predictive modeling. Our findings provide transformative, data-driven strategies for aviation inventory management, offering unprecedented insights into resource optimization, operational risk mitigation, and efficiency enhancement. The proposed framework not only advances academic understanding of complex inventory systems but also presents practical, implementable solutions for airline industry stakeholders seeking to leverage advanced analytical methodologies.

A fundamental part of medical research is the development and validation of diagnostic and prognostic prediction models [1,2]. These prediction models aim to predict the absolute probability that a certain disease or condition is currently present (diagnostic models) or that an outcome will occur within a specific follow-up period (prognostic models) for an individual subject. Prediction models typically rely on multiple predictors, which can include demographic characteristics, medical history and physical examination items, or more complex measurements from, for example, medical imaging, electrophysiology, pathology, and biomarkers. Also for diagnostic models, estimates of probabilities are rarely based on a single test, and doctors naturally integrate several patient characteristics and symptoms [3]. A broad range of prediction modeling techniques exist, like regression approaches, neural network models, decision tree models, genetic programming models, and support vector machine learning models, although prediction models developed by a multivariable regression approach are by far prevailing. It is widely recommended that a developed prediction model should not be used in practice before being externally validated—at least once—in other individuals than those used for model development [4–7]. Unfortunately, most prediction models are poorly or not at all validated, rendering interpretation of their generalizability difficult. In addition, many systematic reviews showed that for the same outcome or same target population, numerous competing models exist [8–10]. Generally speaking, researchers often ignore existing prediction models and develop yet another prediction model from their own data [2]. This practice sustains a cycle of underpowered prediction model development studies and poor knowledge about the generalizability and applicability of developed prediction models. Evidence synthesis and meta-analysis of individual participant data (IPD) from multiple studies seems to be a unique opportunity to address these problems, as it allows researchers to develop and directly validate models on large datasets and across a wide range of populations and settings, to directly test a model’s generalizability (Fig 1) [11–13]. Fig 1 Trends in publications of IPD-MA studies focusing on the development and/or validation of diagnostic or prognostic prediction models. There is currently little guidance on how to conduct an IPD meta-analysis (IPD-MA) for developing and/or validating diagnostic or prognostic prediction models [15]. To date, most IPD-MA articles focus on estimating relative quantities, like a risk ratio, hazard ratio, or odds ratio for a specific treatment or a specific etiologic factor. In contrast, prediction modeling research is focused on developing and validating multivariable models aimed at calculating an absolute risk estimate of the combined variables, rather than estimating the relative effect of a specific treatment or etiologic factor. Furthermore, prediction modeling studies focus entirely on the role and joint contribution of multiple covariates, whereas intervention studies in principle rely on randomization to reduce the role of covariates (Table 1). Hence, IPD-MAs of randomized intervention and etiological studies, which are beyond the scope of this paper and are instead addressed in the accompanying paper [16], differ from IPD-MAs of multivariable prediction models, which are the focus of this paper. Table 1 The main differences between IPD-MA of treatment intervention studies and of multivariable prediction modeling studies. We provide an overview of the advantages and limitations of IPD-MAs aiming to develop a novel prediction model or to validate one or more existing models across multiple datasets. This overview is based on published guidelines and existing recommendations for the conduct of prediction modeling studies and of IPD-MA research. We illustrate this overview with examples of recently published IPD-MAs of prediction models across various medical domains. Our aim is to help researchers, readers, reviewers, and editors to identify and understand the key issues involved with such IPD-MA projects.

SummaryAccurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.

A suboptimal management or system malfunction can often lead to abnormal energy consumptions in buildings, which result in a significant waste of energy. For this reason, the adoption of advanced monitoring systems, based on Machine Learning (ML) and visualization techniques, is crucial to avoid possible deviations from the baseline energy consumption. However, the historical data on which analyses are based generally do not report the occurrence of anomalies. Therefore, the application of supervised ML techniques is limited and unsupervised approaches are favored. Moreover, domain experts find most Machine Learning (ML) techniques hard to interpret, and thus find it difficult to contextualize anomalies. To overcome these issues, this work proposes a machine learning-based Anomaly Detection Framework (ADF) that involves the use of two complementary semi-supervised ML applications to obtain a highly interpretable and accurate detection of anomalies. Both techniques use Symbolic Aggregate approXimation (SAX) encoding to extract the most relevant information from load profiles. The aim of the first approach is to maximize the interpretability of the definition and distinction between anomalous and normal behavior. This is achieved using a Classification And Regression Tree (CART), albeit at the expense of a coarser output granularity. The second approach exploits an Multi-Layer Perceptron (MLP) algorithm to obtain a higher and more accurate output resolution, although it leads to a less interpretable definition of any anomalous behavior. The ADF has been applied to a real case study using electricity consumption data provided by a large telecommunications service provider. The results show that combining both ML models enhances the accuracy and interpretability of the detected anomalies.

Occupant-centric control (OCC) of building systems, which improves energy performance and thermal comfort by considering occupants’ presence, preferences, and activity, has drawn substantial research interest over the past decade. However, most existing studies have focused on evaluating overall performance during a relatively long period, overlooking control robustness during transients.Corner cases in autonomous driving refer to a bug or problem that occurs under unseen, unusual, and perhaps dangerous circumstances. For OCC in buildings, corner cases can be triggered by extreme values or rare patterns of disturbances that are not covered by historical data used to design the controllers. The backbone of many OCC applications is the predictive model, and the root of potential corner cases is that data-driven models sometimes cannot extrapolate well to support decision-making. As the predictive model cannot accurately comprehend the situation or predict what will happen, corner cases could lead to suboptimal or even wrong control actions.Unlike other data-rich domains, building data typically only covers limited operating conditions and therefore is sparse in the feature space. For example, a model for building control trained with data associated with a normal cooling setpoint of 24°C may be unreliable for such higher setpoints as 27 °C. Considering the difficulty in extensive data collection, methods such as physics-informed neural networks have emerged to enhance the extrapolation capability of data-driven models by incorporating physics-based prior knowledge or exerting physical constraints. Yet, despite the claimed merits such as more data-efficient learning and physics-consistent prediction over long horizons, whether and how physics-informed models can help handle the corner cases and improve control robustness remain unknown.To address this knowledge gap, we designed a simulation framework that applies OCC in a multi-zone office building. Corner cases were identified using historical data from a real-world campus building. A group of data-driven predictive models will be developed with different levels of physics integration and tested in OCC simulations. Combining actual and synthetic data, the peer models will be comprehensively examined concerning their predictive capability and control performance. Special attention will be paid to the decision-making process in the corner cases. The experimental results will inform the required level of physics in predictive models to ensure control robustness, leading to two contributions: future development of physics-informed models can be guided in an application-oriented manner, and the actual applications of occupant-centric control can be promoted.

In most domestic buildings, gas and electricity are supplied by energy and utility companies through centralised energy systems. This often results in a high burden on central management systems and has adverse effects on energy prices. Blockchain-based peer-to-peer energy trading platforms can deliver strategic operation of decentralised multi-energy network among multiple domestic buildings to reduce global greenhouse gas emissions and address global climate change issues. However, prevailing blockchain-based energy trading platforms focused on system implementation for peer-to-peer electricity trading while lacking predictive control and energy scheduling optimisation. Therefore, this paper presents an integrated blockchain and machine learning-based energy management framework for multiple forms of energy (i.e., heat and electricity) allocation and transmission, among multiple domestic buildings. Machine learning is harnessed to predict day-ahead energy generation and consumption patterns of prosumers and consumers within the multi-energy network. The proposed blockchain and machine learning-based decentralised energy management framework will establish optimal and automated energy allocation among multiple energy users through peer-to-peer energy transactions. This approach focuses on energy-matching from both the supply and demand sides while encouraging direct energy trading between prosumers and consumers. The security and fairness of energy trading can also be enhanced by using smart contracts to strictly execute the energy trading and bill payment rules. A case study of 4 real-life domestic buildings is introduced to determine the economic and technical potential of the proposed framework. In comparison to prevailing approaches, a key benefit from the proposed approach is an improved computational load/failure of a single point, energy trading strategy, workload, and capital cost energy. Findings suggest that energy costs reduced between 7.60% and 25.41% for prosumer buildings and a fall of 5.40%-17.63% for consumer buildings. In practical applications, the proposed approach can involve a larger number of prosumer and consumer buildings within the community to decentralise multiple energy trading, thus significantly contributing to the reduction of greenhouse gas emissions and enhancing environmental sustainability.

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) has revolutionized various industries, enabling automation, real-time decision-making, and enhanced connectivity. However, these advancements have also introduced new cybersecurity threats, increasing the vulnerability of interconnected systems. The proliferation of IoT devices and AI-driven applications has expanded the attack surface, making them prime targets for cyber adversaries. Traditional security mechanisms, which often rely on signature-based threat detection, struggle to address sophisticated attacks such as adversarial AI manipulations, IoT botnet infiltrations, and real-time data breaches. This study examines emerging cybersecurity risks in AI and IoT environments, emphasizing the limitations of existing security frameworks in detecting and mitigating evolving threats. One of the key challenges is the inability of conventional methods to adapt to novel attack patterns in dynamic and complex networks. To address this issue, we introduce a machine learning-based risk assessment framework designed for proactive threat mitigation. This framework leverages anomaly detection, behavioral analytics, and predictive threat modeling to identify potential cybersecurity risks in real time. By integrating adaptive learning algorithms and continuous monitoring, the proposed system enhances resilience against AI-driven cyberattacks and IoT-based vulnerabilities. The findings highlight the critical need for AI-driven cybersecurity solutions capable of evolving alongside emerging threats, ensuring the safety and reliability of interconnected digital ecosystems.

An implemented predictive coding model of lexico-semantic processing explains the dynamics of univariate and multivariate activity within the left ventromedial temporal lobe during reading comprehension

A prediction and interpretation framework of acute kidney injury in critical care