Evaluating the Performance of Multivariate Imputation by Chained Equations (MICE) when Predicting Missing Well-Log Data in Sedimentary Basins

This research evaluates the applicability of Multivariate Imputation by Chained Equations (MICE) for estimating missing well-log data across different sedimentary basis. Utilizing various machine learning techniques including XGBoost (XGB), Random Forest (RF), K-Nearest Neighbors (KNR), and Bayesian Ridge (BR), the performance of MICE was tested...

Machine learning assisted early anomaly detection of LEDs with spectral power distribution modeling

Spectral power distribution (SPD) is the radiation power intensity at different wavelengths, containing the most basic photometric and colorimetric performance of the illuminant, which is able to predict the lifetime of LEDs. This paper proposes an SPD model assisted by machine learning algorithms to detect the early failure of white LEDs. The...

Image-Based Query Search Engine via Deep Learning

Typically, people search images by text: users enter keywords and a search engine returns relevant results. However, this pattern has limitations. An obvious drawback is that when searching in one language, users may miss results labelled in other languages. Moreover, sometimes people know little about the object in the image and thus would not...

Image-based Video Search Engine: Data Compression and Nearest Neighbour Search

One of the main problems with Instance-level Image Retrieval in video data is that for longer query videos or large amount of image queries, comparing all of the query images to every extracted frame is time-inefficient. This thesis aims to solve this problem by implementing Nearest Neighbour Search (NNS) algorithms and data compression methods,...

To Actively Initialize Active Learning

Though much effort has been spent on designing new active learning algorithms, little attention has been paid to the initialization problem of active learning, i.e., how to find a set of labeled samples which contains at least one instance per category. This work identifies the initialization of active learning as a separate and novel...

Automatic schema classification for Schema-Focused Therapy using k-Nearest Neighbour

Personality disorders affect 1 in 7 adults reducing their quality of life. Schema-focused therapy (SFT) has become very popular in Psychotherapy in the treatment of personality disorders (PD), unfortunately there is still in increasing societal need for such mental healthcare. Automation in the assessment of SFT allows for Ecological Momentary...

Predicting software vulnerabilities with unsupervised learning techniques

As software is produced more and more every year, software also gets exploited more. This exploitation can lead to huge monetary losses and other damages to companies and users. The exploitation can be reduced by automatically detecting the software vulnerabilities that leads to exploitation. Unfortunately, the state-of-the-art methods for this...

An enhanced KNN-based twin support vector machine with stable learning rules

Among the extensions of twin support vector machine (TSVM), some scholars have utilized K-nearest neighbor (KNN) graph to enhance TSVM’s classification accuracy. However, these KNN-based TSVM classifiers have two major issues such as high computational cost and overfitting. In order to address these issues, this paper presents an enhanced...

Nearest Neigbor Compliance: in Quantum Circuit Design

Over the course of the last decade, an interest has emerged in nearest neighbor constraints for quantum circuit design. The challenge herein is to bothminimize the running time of a circuit and tomodify it such that quantum gates only act on adjacent qubits while leaving the desired computational operation intact. These modifications involve the...

Forecasting Extreme Precipitation Using k-nearest Forest Neighbors

Precipitation has high spatial and temporal uncertainty, which makes it challenging to predict. We focus specifically on extreme amounts of precipitation. The Royal Dutch Meteorological Institute (KNMI) uses a numerical model, approximating the solutions to partial differential equations, to forecast precipitation and other metrics about the...

MR Scanner Invariant Feature Representation using Label Transfer and Clustering

Machine learning approaches are increasingly successful in medical image analysis. Still, learning from MR images poses some serious challenges. Scanner-dependent characteristics effect feature representations directly and hamper the clinical implementation of otherwise successful supervised-learning techniques. To compensate for variations in...

Full Color High Definition Fused Filament Fabrication

Although Full Color (FC) High Definition (HD) Additive Manufacturing (AM) machines are available on the market today, a FC HD Fused Filament Fabrication (FFF) printer has yet to enter the competitive FFF market. Leapfrog, a FFF printer manufacturer, holds a patent that allows the application of coating on 3D printed FFF filament after it has...

Feature Fusion for Efficient Content-Based Video Retrieval

Abstract—Content-based video retrieval is a complex task because of the large amount of information in single items and because databases of videos can be very large. In this paper we explore a possible solution for efficient similar item retrieval. In our experiments we combine relevant feature sets together with a learned Mahalanobis metric...

Comparing Feature Sets and Classifiers for Sentiment Analysis of Opinionated Free Text

This master thesis is about the sentiment analysis of the societal theme documents and categorizing them in positive or negative groups. The application of this thesis can be widely used in review blogs, public polls and etc. In this study, we have compared different feature sets as well as different classifiers on datasets of opinionated texts...

Clustered K nearest neighbor algorithm for daily inflow forecasting

Instance based learning (IBL) algorithms are a common choice among data driven algorithms for inflow forecasting. They are based on the similarity principle and prediction is made by the finite number of similar neighbors. In this sense, the similarity of a query instance is estimated according to the closeness of its feature vector with those...

Five Separate Bias Contributions in Time Series Models for Equidistantly Resampled Irregular Data

The use of time series models for irregular data requires resampling of the data on an equidistant grid. Slotted resampling transforms an irregular randomly sampled process into an equidistant signal where data are missing. An approximate maximum-likelihood time series estimator has been developed to estimate the power spectral density and the...

Static critical behavior of the two-dimensional Ising spin glass Rb2Cu1-xCo xF4

Activated dynamics in the two-dimensional Ising spin-glass Rb2Cu1-xCo xF4