Machine learning for knowledge-based dose-volume histogram prediction in prostate cancer

Introduction:  Despite the vast amount of optimization algorithms, radiotherapy treatment planning remains a manual, time-consuming and iterative process. To increase plan standardization, we clinically use Pinnacle's autoplanner for several disease sites. However, this introduces new challenges: first, the autoplanner is not perfect and still...

Trace metal pollution in the Scheldt estuary - a statistical approach to estimate the metal partitioning coefficient for a suite of metals

Trace metals appear in estuarine systems in two forms: dissolved and particulate. <br/>Describing the partitioning between these two forms is done by a coefficient, K_d, which relies on a number of environmental parameters, such as the salinity of the water and the seasonally dependent biological activity. Although this coefficient is known to...

Predicting vulnerable files by using machine learning method

Web applications have been gaining increased popularity around the globe, in such a way that a growing number of users are attracted to make use of the functionality and information provided by these applications. While providing solutions to complicated problems in a fast and reliable way is one of the most advantages of using web applications,...

Improving RCPSP algorithms using machine learning methods

For performing technical maintenance, it is important to keep a detailed schedule of resources and temporal constraints. The Resource Constrained Project Scheduling Problem (RCPSP) is a well de- fined scheduling model with both resources and temporal constraints. Precedence Constraint Posting (PCP) is a technique to solve the NP-hard RCPSP...

A machine learning approach for optimisation in railway planning

Planning and scheduling problem is a hard problem, especially in real life cases. The time and space complexity increase quickly along with the increase of problem size. In transportation systems, such problems exist a lot. The automation of transportation systems depends a lot on the improvements of developing planning and scheduling algorithm....

Automatic Quantification of Beach Occupation Using Oversegmentation and Machine Learning

Decision- and policymakers responsible for the coastal zone aim at combining measures against for instance long-term erosion, with measures that have a positive social, economic impact on the region. Recreational beach usage has a large social economic impact on a region and therefore quantification of the recreational beach usage can provide...

Development of the Machine Learning-Based Approach to Access Occupancy Information Through Indoor CO2 Data: for Demand-Driven Building Heating Energy Management

Demand driven building energy management system has gained significant interest in recent years. In order to provide insights for the demand driven building heating energy management at IDE building, TU Delft, this research develops an approach to access real time occupancy information by applying supervised machine learning (ML) classification...