document

Machine Learning Aided Reliability Analysis for Spatially Varying Slopes in 2D and 3D

Vermeer, Jort (author)

The Random Finite Element Method (RFEM) is a robust stochastic method for slope reliability analysis that incorporates the spatial variability of soil properties. However, the extensive computational time associated with the direct Monte Carlo simulation limits its practical application. To overcome this problem, this study investigates the use...

master thesis 2024

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Interactive Intelligence: Multimodal AI for Real-Time Interaction Loop towards Attentive E-Reading

Lee, Y. (author)

E-learning has shifted the traditional learning paradigms in higher education, offering more flexible, ubiquitous, and personalized learning experiences. The previous years COVID-19 pandemic required a re-calibration of education to accommodate virtual learning environments from the traditional classroom-based education. Widespread learning...

doctoral thesis 2024

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Microtubule Detection and Instance Segmentation using Deep Convolutional Neural Networks

Holtgrefe, Tim (author)

Microtubules are long cylindrical polymers, assembled from tubulin proteins. Microtubule ends can be visualized using fluorescence and confocal microscopy. This allows for the study of microtubule dynamics. However, the manual annotation of microtubules is laborious, which is why automated tracking methods are used. In this project we have...

bachelor thesis 2023

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Identification of Neolithic Circular Enclosures through Aerial Imagery

MENG, YUQI (author)

Traditionally, archaeological investigations, especially archaeological remains detection, mostly depend on human observation. In order to find the objects in large areas, a lot of fieldwork has to be done and it takes a long time for archaeologists to travel around. Nowadays, the development of LIDAR provides accurate 3D geometric information,...

master thesis 2023

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Anomaly based network intrusion detection for IoT attacks using deep learning technique

Sharma, Bhawana (author), Sharma, Lokesh (author), Lal, C. (author), Roy, Satyabrata (author)

Internet of Things (IoT) applications are growing in popularity for being widely used in many real-world services. In an IoT ecosystem, many devices are connected with each other via internet, making IoT networks more vulnerable to various types of cyber attacks, thus a major concern in its deployment is network security and user privacy. To...

journal article 2023

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Attention-based deep learning for DNA repair outcome prediction: Learning how the cell repairs DNA breaks using local sequence context

de Boer, Jurrian (author)

Recent advancements in quantification of repair outcomes of CRISPR-Cas9 mediated double-stranded DNA breaks (DSBs) have allowed for the use of machine learning for predicting the frequencies of these repair outcomes. Local DNA sequence context influences the frequencies of mutations that arise when DNA gets repaired after it is targeted by...

master thesis 2022

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

van den Belt, Glenn (author)

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...

bachelor thesis 2022

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

Castillo, J.M. (author), Arif, M. (author), Starmans, M.P.A. (author), Niessen, W.J. (author), Bangma, C.H. (author), Schoots, Ivo G. (author), Veenland, J.F. (author)

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,...

journal article 2022

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An End-to-End Deep Learning Pipeline for Football Activity Recognition Based on Wearable Acceleration Sensors

Cuperman, Rafael (author), Jansen, K.M.B. (author), Ciszewski, M.G. (author)

Action statistics in sports, such as the number of sprints and jumps, along with the details of the corresponding locomotor actions, are of high interest to coaches and players, as well as medical staff. Current video-based systems have the disadvantage that they are costly and not easily transportable to new locations. In this study, we...

journal article 2022

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Classifying EEG Signals of Mind-Wandering Across Different Styles of Meditation

Chaudhary, Shivam (author), Pandey, Pankaj (author), Miyapuram, Krishna Prasad (author), Lomas, J.D. (author)

In the modern world, it is easy to get lost in thought, partly because of the vast knowledge available at our fingertips via smartphones that divide our cognitive resources and partly because of our intrinsic thoughts. In this work, we aim to find the differences in the neural signatures of mind-wandering and meditation that are common across...

conference paper 2022

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Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling

Shengren, H. (author), Salazar, Edgar Mauricio (author), Vergara Barrios, P.P. (author), Palensky, P. (author)

Taking advantage of their data-driven and model-free features, Deep Reinforcement Learning (DRL) algorithms have the potential to deal with the increasing level of uncertainty due to the introduction of renewable-based generation. To deal simultaneously with the energy systems’ operational cost and technical constraints (e.g, generation-demand...

conference paper 2022

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Machine Learning-based Classification of Different 3D Point Cloud Data of Railway Environments using Random Forest and DGCNN

PAPALEXIOU, ANNIE (author)

Although monitoring and maintenance of railways is important to ensure safety and avoid delays and financial losses, it is still mainly based on human inspection. The complexity of a railway along with the large area it extends makes manual monitoring difficult and time-consuming. The increasing availability of 3D acquisition technologies has...

master thesis 2021

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Distinguishing pure histopathological growth patterns of colorectal liver metastases on CT using deep learning and radiomics: a pilot study

Starmans, Martijn P.A. (author), Buisman, Florian E. (author), Renckens, Michel (author), Willemssen, François E.J.A. (author), van der Voort, Sebastian R. (author), Groot Koerkamp, B. (author), Grünhagen, Dirk J. (author), Niessen, W.J. (author), Vermeulen, Peter B. (author)

Histopathological growth patterns (HGPs) are independent prognosticators for colorectal liver metastases (CRLM). Currently, HGPs are determined postoperatively. In this study, we evaluated radiomics for preoperative prediction of HGPs on computed tomography (CT), and its robustness to segmentation and acquisition variations. Patients with...

journal article 2021

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A survey on machine learning-based performance improvement of wireless networks: PHY, MAC and network layer

Kulin, Merima (author), Kazaz, T. (author), De Poorter, Eli (author), Moerman, Ingrid (author)

This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY,MAC and network. First, the related work and paper contributions are discussed, followed by providing...

journal article 2021

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Machine Learning in Chemical Engineering: A Perspective

Schweidtmann, A.M. (author), Esche, Erik (author), Fischer, Asja (author), Kloft, Marius (author), Repke, Jens Uwe (author), Sager, Sebastian (author), Mitsos, Alexander (author)

The transformation of the chemical industry to renewable energy and feedstock supply requires new paradigms for the design of flexible plants, (bio-)catalysts, and functional materials. Recent breakthroughs in machine learning (ML) provide unique opportunities, but only joint interdisciplinary research between the ML and chemical engineering ...

review 2021

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Traditional vs. Machine-learning methods for forecasting sandy shoreline evolution using historic satellite-derived shorelines

Calkoen, Floris (author), Luijendijk, Arjen (author), Rivero, Cristian Rodriguez (author), Kras, Etienne (author), Baart, F. (author)

Forecasting shoreline evolution for sandy coasts is important for sustainable coastal management, given the present-day increasing anthropogenic pressures and a changing future climate. Here, we evaluate eight different time-series forecasting methods for predicting future shorelines derived from historic satellite-derived shorelines....

journal article 2021

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Systematic Side-Channel Analysis of Curve25519 with Machine Learning

Weissbart, L.J.A. (author), Chmielewski, Łukasz (author), Picek, S. (author), Batina, Lejla (author)

Profiling attacks, especially those based on machine learning, proved to be very successful techniques in recent years when considering the side-channel analysis of symmetric-key crypto implementations. At the same time, the results for implementations of asymmetric-key cryptosystems are very sparse. This paper considers several machine learning...

journal article 2020

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Deep Learning and Machine Learning in Robotics

Bonsignorio, Fabio (author), Hsu, David (author), Johnson-Roberson, Matthew (author), Kober, J. (author)

Deep learning has gone through massive growth in recent years. In many fields—computer vision, speech recognition, machine translation, game playing, and others—deep learning has brought unprecedented progress and become the method of choice. Will the same happen in robotics and automation? In a sense, it is already happening. Today, deep...

contribution to periodical 2020

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

Castillo, Jose M.T. (author), Arif, Muhammad (author), Niessen, W.J. (author), Schoots, Ivo G. (author), Veenland, J.F. (author)

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...

journal article 2020

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Low-level radar data based road user detection

Dong, Jiaao (author)

In order to achieve redundancy and improve the robustness of an autonomous driving system, radar is a suitable choice for road user detection task in severe working conditions (e.g. darkness, bad weather). However, the real-time multi-class radar based road user detection algorithm is less explored compared with camera and LiDAR solutions. To...

master thesis 2019