Academic Articles & Research Papers

Evaluating early predictive performance of machine learning approaches for engineering change schedule – A case study using predictive process monitoring techniques

By applying machine learning algorithms, predictive business process monitoring (PBPM) techniques provide an opportunity to counteract undesired outcomes of processes. An especially complex variation of business processes is the engineering...

Rapid Estimation of Static Capacity Based on Machine Learning: A Time-Efficient Approach

With the global surge in electric vehicle (EV) deployment, driven by enhanced environmental regulations and efforts to reduce transportation-related greenhouse gas emissions, managing the life cycle of Li-ion batteries becomes more critical...

Population-wide implementation of comprehensive cancer registration in Germany—overview article on current status quo and future perspectives.

Exploiting Content Characteristics for Explainable Detection of Fake News

The proliferation of fake news threatens the integrity of information ecosystems, creating a pressing need for effective and interpretable detection mechanisms. Recent advances in machine learning, particularly with transformer-based models...

MARKETING ACTIVITIES OF IT COMPANIES: INFORMATION AND ORGANISATIONAL CAPABILITIES FOR DIGITAL PRODUCT DEVELOPMENT

The purpose of this article is to provide a comprehensive examination of the informational and organisational capabilities of marketing activities in the market for digital products and services. It highlights the importance of data analysi...

Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques

Abstract Background The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet menta...

C4.5: Programs for Machine Learning

Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is ...

Machine learning in cardiovascular medicine: are we there yet?

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encomp...

Satellite Retrieval of Air Pollution Changes in Central and Eastern China during COVID-19 Lockdown Based on a Machine Learning Model

With the implementation of the 2018–2020 Clean Air Action Plan (CAAP) the and impact from COVID-19 lockdowns in 2020, air pollution emissions in central and eastern China have decreased markedly. Here, by combining satellite remote sensing,...

Dynamic Monitoring of Thyroid-Stimulating Hormone Receptor Antibody and Extraocular Muscle Thickness: A Clinical Risk Stratification Model for Recurrence Risk Post-Glucocorticoid Therapy in TED Patients

Yaling Li, Xiuhong Yang, Shuli Hu Department of Intensive Care Unit, Wuhan No. 1 Hospital, Wuhan, 430022, People’s Republic of ChinaCorrespondence: Shuli Hu, Department of Intensive Care Unit, Wuhan No. 1 Hospital, No. 215 Zhongshan Avenue,...

A First Course in Machine Learning

"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations ...

Development and web-based implementation of a machine learning model for predicting one-year adverse outcomes in acute ischaemic stroke

BackgroundThis study aimed to develop an interpretable and clinically applicable machine learning prediction model to assess the risk of adverse outcomes within one year following acute ischaemic stroke, and to establish a corresponding web...

Leveraging Cloud Technology for Personalized Multiple Sclerosis Care: A Comprehensive Data Management and Visualization Approach

This project develops a cloud-based solution for securely managing clinical data and patient-reported outcomes (PROMs) for multiple sclerosis (MS) patients. Utilizing REDCap for data collection, we incorporated clinical outcomes and PROMs f...

Interactive machine learning

Perceptual user interfaces (PUIs) are an important part of ubiquitous computing. Creating such interfaces is difficult because of the image and signal processing knowledge required for creating classifiers. We propose an interactive machine...

Acceptance of Machine Learning for Medication Selection in Epilepsy to Inform Clinical Trial Design: Co-Design Survey Study

Abstract BackgroundAntiseizure medications (ASMs) are the mainstay of epilepsy treatment; however, there is currently no reliable way to predict which medication will be most effective for an individual patient. Machine learning...

Quantifying the incremental value of deep learning: Application to lung nodule detection.

We present a case study for implementing a machine learning algorithm with an incremental value framework in the domain of lung cancer research. Machine learning methods have often been shown to be competitive with prediction models in some...

MVPA-Light: A Classification and Regression Toolbox for Multi-Dimensional Data

MVPA-Light is a MATLAB toolbox for multivariate pattern analysis (MVPA). It provides native implementations of a range of classifiers and regression models, using modern optimization algorithms. High-level functions allow for the multivaria...

Evaluation of Predictive Maintenance by Marginal Utility: A Case Study on Offshore Wind

This paper investigates the relationship between the benefits achieved by predictive maintenance implementation and the related costs incurred intentionally or not. Specifically, we seek to establish an evaluation method for predictive main...

Calibrated Machine Learning with Temporal Leakage Controls for Rare-Event Forecasting of Port Activity Anomalies: Evidence from Selected African Countries and Indonesia

Port activity anomalies are rare but may signal operational constraints relevant to supply chains. The aim of the study was to develop and empirically evaluate a calibrated machine learning framework for forecasting rare, algorithmically de...

Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation

Accurate Evapotranspiration for saline soils (ETs) is important as well as challenging for the reclamation of saline soils through an effective leaching process. Evapotranspiration (ET) by FAO-56 Penman-Monteith standard method is complex, ...