Predicting Vessel Motions: A Comparative Analysis of Machine Learning and Conventional Approaches

Founded in 1910, Boskalis, a leader in offshore operations, contends with limitations in ABB's Ability Marine Advisory System 'OCTOPUS' in predicting maximum vessel motions for heavy-transport vessels (HTVs). Accurate prediction of these motions, especially roll and pitch, is vital for transporting large, heavy structures, as exceeding...

Predicting antibiotic resistance in patients with postoperative infections using machine learning based models

Introduction<br/>In the era of growing antimicrobial resistance, early detection and immediate treatment of antibiotic-resistant infections are crucial to ensuring successful outcomes in critically ill patients. The aim of this study is to apply machine learning (ML) to create classifiers that predict antibiotic resistance in postoperative...

An AI algorithm to predict intubations in ICU patients: Predicting intubation to aid medical personnel in the decision to switch from High Flow Nasal Oxygen therapy to intubation

Maasstad Hospital is a member of the Santeon hospital group. The ambition of Santeon is to improve healthcare for patients. The project in this internship also aims to improve patients’ health, specifically patients in the Intensive Care Unit (ICU).<br/>The treatment of respiratory insufficient patients in the ICU consists of High Flow Nasal...

Assessment of the spatiotemporal prediction capabilities of machine learning algorithms on Sea Surface Temperature data: A comprehensive study

Spatiotemporal time series prediction plays a crucial role in a wide range of applications. However, in most of the studies, spatial information was ignored and predictions were carried out either on a few points or on average values. In this study, 37 different configurations of 4 traditional ML models and 3 Neural Network (NN) based models...

Prediction & optimization of alkali-activated concrete based on the random forest machine learning algorithm

Alkali-activated concrete (AAC) is regarded as a promising alternative construction material to reduce the CO₂ emission induced by Portland cement (PC) concrete. Due to the diversity in raw materials and complexity of reaction mechanisms, a commonly applied design code is still absent to date. This study attempts to directly...

Mechanical properties prediction of blast furnace slag and fly ash-based alkali-activated concrete by machine learning methods

In this paper, 871 data were collected from literature and trained by the 4 representative machine learning methods, in order to build a robust compressive strength predictive model for slag and fly ash based alkali activated concretes. The optimum models of each machine learning method were verified by 4 validation metrics and further...

Predicting the stability of distal radius fractures with a small dataset and machine learning

Purpose. Distal radius fractures are common fractures of the wrist. These fractures are often displaced and need reduction, after adequate reduction, the patients will have follow-up X-rays to check if the fracture stays stable. This is important because surgery might be required if the fracture becomes unstable. This can lead to delayed surgery...

Short-term Earthquake Prediction with Deep Neural Networks: Finding the optimal time prior to earthquake strikes to use in predictions

Earthquakes can have tremendous effects. They can result in casualties, massive damage, and hurt the economy. Therefore, one would like to predict earthquakes as early as possible and with the highest accuracy possible. This paper contains the proposal for the optimal prediction-time, which is the time between the execution of a prediction and...

Classification of clinically significant prostate cancer on multi-parametric MRI: A validation study comparing deep learning and radiomics

The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning-and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods,...

Interpretable Ensemble-Machine-Learning models for predicting creep behavior of concrete

This study aims to provide an efficient and accurate machine learning (ML) approach for predicting the creep behavior of concrete. Three ensemble machine learning (EML) models are selected in this study: Random Forest (RF), Extreme Gradient Boosting Machine (XGBoost) and Light Gradient Boosting Machine (LGBM). Firstly, the creep data in...

PROJECT C.R.A.N.I.U.M.: Constructing a Real-time Alarm for Nearing Intracranial hypertension Using Machine learning

Introduction Intracranial hypertension (IH) is a harbinger of secondary brain injury in patients suffering from traumatic brain injury (TBI), can be mitigated at the Intensive Care Unit (ICU) and is associated with a poor prognosis. Current clinical practice consists of treating IH once it has occurred, by medical or surgical interventions. This...

COVID-19 Prediction and Mitigation

With the world in grasp of the COVID-19 pandemic, models predicting the spread of the virus can give indications to what extent a country is controlling the pandemic. Policymakers can decide to install so-called mitigation strategies to limit the spread of the virus. To aid the decision-making process, this report describes how a web application...

Modeling Inference Time of Deep Neural Networks on Memory-constrained Systems

Deep neural networks have revolutionized multiple fields within computer science. It is important to have a comprehensive understanding of the memory requirements and performance of deep networks on low-resource systems. While there have been efforts to this end, the effects of severe memory limits and heavy swapping are understudied. We have...

Automated classification of significant prostate cancer on MRI: A systematic review on the performance of machine learning applications

Significant prostate carcinoma (sPCa) classification based on MRI using radiomics or deep learning approaches has gained much interest, due to the potential application in assisting in clinical decision-making. Objective: To systematically review the literature (i) to determine which algorithms are most frequently used for sPCa classification...

Prediction of flow-fields by combining high-fidelity CFD data and Machine Learning algorithms

High-fidelity optimisation studies are a useful asset in the design of critical components for large gas turbines. These studies require the computation of numerous computationally expensive CFD simulations and result in predominantly optimisation graphs of design objectives (e.g. Pareto-front figure). The quantity of generated data is...

Bone texture analysis for prediction of incident radiographic hip osteoarthritis using machine learning: data from the Cohort Hip and Cohort Knee (CHECK) study

Objective: To assess the ability of radiography-based bone texture variables in proximal femur and acetabulum to predict incident radiographic hip osteoarthritis (rHOA) over a 10 years period. Design: Pelvic radiographs from CHECK at baseline (987 hips) were analyzed for bone texture using fractal signature analysis (FSA) in proximal femur...

Time series forecast in non-stationary environment with occurrence of economic bubbles: Bitcoin Price prediction

Being capable to foresee the future of a given financial asset as an investor, may lead to significant economic profits. Therefore, stock market prediction is a field that has been extensively developed by numerous researchers and companies. Recently, however, a new branch of financial assets has emerged, namely cryptocurrencies. As a...

ETA prediction: Predicting the ETA of a container vessel based on route identification using AIS data

Container vessels arriving in a port before or after their scheduled time can cause problems in the container terminal planning and planning of hinterland transportation. This in turn leads to an increase of the costs in the supply chain. Vessels communicate their Estimated Time of Arrival via Automatic Idetification System(AIS) data to the port...