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Student theses
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3d face reconstruction using deep learning.
Student thesis : Master
Achieving Long Term Fairness through Curiosity Driven Reinforcement Learning: How intrinsic motivation influences fairness in algorithmic decision making
Activity recognition using deep learning in videos under clinical setting, a data cleaning assistant.
Student thesis : Bachelor
A Data Cleaning Assistant for Machine Learning
A deep learning approach for clustering a multi-class dataset, aerial imagery pixel-level segmentation, a framework for understanding business process remaining time predictions, a hybrid model for pedestrian motion prediction, algorithms for center-based trajectory clustering, allocation decision-making in service supply chain with deep reinforcement learning, analyzing policy gradient approaches towards rapid policy transfer, an empirical study on dynamic curriculum learning in information retrieval, an explainable approach to multi-contextual fake news detection, an exploration and evaluation of concept based interpretability methods as a measure of representation quality in neural networks, anomaly detection in image data sets using disentangled representations, anomaly detection in polysomnography signals using ai, anomaly detection in text data using deep generative models, anomaly detection on dynamic graph, anomaly detection on finite multivariate time series from semi-automated screwing applications, anomaly detection on multivariate time series using gans, anomaly detection on vibration data, application of p&id symbol detection and classification for generation of material take-off documents (mtos), applications of deep generative models to tokamak nuclear fusion, a similarity based meta-learning approach to building pipeline portfolios for automated machine learning, aspect-based few-shot learning, aspect-based few-shot learning, assessing bias and fairness in machine learning through a causal lens, assessing fairness in anomaly detection: a framework for developing a context-aware fairness tool to assess rule-based models, a study of an open-ended strategy for learning complex locomotion skills, a systematic determination of metrics for classification tasks in openml, a universally applicable emm framework, automated machine learning with gradient boosting and meta-learning, automated object recognition of solar panels in aerial photographs: a case study in the liander service area, automatic data cleaning, automatic scoring of short open-ended questions, automatic synthesis of machine learning pipelines consisting of pre-trained models for multimodal data, automating string encoding in automl, autoregressive neural networks to model electroencephalograpy signals, balancing efficiency and fairness on ride-hailing platforms via reinforcement learning, benchmarking audio deepfake detection, better clustering evaluation for the openml evaluation engine, bi-level pipeline optimization for scalable automl, block-sparse evolutionary training using weight momentum evolution: training methods for hardware efficient sparse neural networks, boolean matrix factorization and completion, bootstrap hypothesis tests for evaluating subgroup descriptions in exceptional model mining, bottom-up search: a distance-based search strategy for supervised local pattern mining on multi-dimensional target spaces, bridging the domain-gap in computer vision tasks, can time series forecasting be automated: a benchmark and analysis.
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This thesis first introduces the basic concepts of data mining, such as the definition of data mining, its basic function, common methods and basic process, and two common data mining methods, classification and clustering. Then a data mining application in network is discussed in detail, followed by a brief introduction on data mining ...
objective in this thesis is to get a deeper knowledge of the data mining area and evalu-ate methods to automatically classify the data in structured personal registries. In this report I'll describe di erent methods and algorithms used in data mining, apply a few selected popular algorithms on two data sets and then discuss the result. i
In automatic text data mining,current challenge was to have a rich text type classification. By "rich" classification means to go beyond basic categories like "string", "number", "date", etc that are common in programming languages and databases. A "rich" classification should be able to detect categories of
Time Series Data Mining Methods: A Review Master's Thesis submitted to Prof. Dr. Wolfgang Karl H ardle Humboldt-Universit at zu Berlin School of Business and Economics Ladislaus von Bortkiewicz Chair of Statistics by Caroline Kleist (533039) In partial ful llment of the requirements for the degree of Master of Science in Statistics Berlin ...
Data mining is concerned with knowledge discovery and finding patterns in. datasets through a process of applying the model to the data [13]. The model, the heart of. the data mining proce ss, is ...
EDUCATIONAL DATA MINING AND ITS USES TO PREDICT THE MOST PROSPEROUS LEARNING ENVIRONMENT by Lewis Adam Whitley April, 2018 Director of Thesis: Qin Ding, Ph.D. Major Department: Department of Computer Science The use of technology and data analysis within the classroom has been a resourceful tool
A THESIS SUBMITTED TO THE GRADUATE FACULTY in partial fulfillment of the requirements for the Degree of MASTER OF SCIENCE By XIAOMENG DONG Norman, Oklahoma 2016 . DATA MINING APPLICATIONS IN RESERVOIR MODELING ... Data mining technique can play an important role in both tasks above. First, during interpretation process, it can help us find the ...
2.2 Data preprocessing The response variable describing cloud top pressure and the variable describing cloud top height is measured with the more accurate instrument on the satellite CALIPSO. The variables describing cloud top height and total optical depth are not among the predictors used in the thesis. ˙ data.
A thesis submitted for the degree of Master of Science ... Data mining is also effective to summarize the underlying relation in data in addition to predicting future observations. Data mining can process and collect data from various data storage systems such as txt, xlsx, xml, json and csv files, databases, servers, data ...
This is probably the most widely discussed case of data mining in the field of economics. One of the reasons it is widely discussed is not only that data mining was conducted on a large scale, but also that it was done so openly, almost proudly. The concept of data mining had always been a term for bad practice.
Student thesis: Master. File. A Study of an Open-Ended Strategy for Learning Complex Locomotion Skills Zhou, F. (Author), Vanschoren, J. (Supervisor 1), 31 Aug 2021. Student thesis: Master. ... including those for text and data mining, AI training, and similar technologies. For all open access content, the Creative Commons licensing terms apply
The objective of this Master thesis is to improve placement decision during execution using data mining techniques. This means designing novel data mining algorithms that can analyze execution information on-chip, using as few resources as possible, and provide relevant information to take better placement decisions.
data mining is used to describe the entire process, so terms data mining and knowledge discovery from data are used as synonyms. Data mining can use many techniques and one of them is ML, which is studied in this thesis. (Han, Pei and Kamber 2011, p. 1-26) 2.1 Types of data analytics in supply chain management
files are many and several are likely to aid the routing of bug reports. In this thesis, one system log file was chosen to be evaluated; the alarm log. The alarm logs are time series count data containing alarms raised by the system. The alarm log data have been pre-processed with data mining techniques. The Apriori algorithm has been used to ...
Abstract. Development of Big Data is virtually transforming our lifestyle. It is also ac-celerating industrial growth through process optimization, insight discovery and improved decision making. The massive scale of big data exceeds the processing and analytic capacity of conventional database systems within an acceptable time frame.
Students' Academic Status Using Data Mining Technology A thesis submitted By Sisay Girma To ... For the Degree of Master of Science In Computer Science March, 2019 . i Acceptance Developing a Predictive Model to Determine Higher Education Students' Academic Status Using Data Mining Technology By Sisay Girma Endale
Master Thesis Data Mining PDF - Free download as PDF File (.pdf), Text File (.txt) or read online for free. The document discusses the challenges of writing a master's thesis on data mining. It notes that the process requires a significant time investment and expertise in conducting research, analyzing large amounts of data, and communicating complex ideas, while adhering to formatting guidelines.
Part-II of the thesis is about Implementing Data Mining Techniques in finding the trends of celebrities death causes over the past decade. The database for training is created from the public and ...
This is exactly the aim of this master thesis, examining the conduct of the system that is wanted to study using data mining methods. In essence, that system is a distributed network with resource sharing, implanted in order to solve the insufficiency of CPU resources due to the constant increasing demand.
big data in their operations. To respond to this question, big data, data mining had to be introduced with the most relevant techniques to analyze data. related to mining operations. The concept of machine-generated data was also necessary for the further parts, as log files and reports from mining equipment.
Application of Data Mining Technique for Predicting Airtime Credit Risk: The Case of Ethio Telecom BY OLIYAD TAREKEGN A THESIS SUBMITTED TO THE SCHOOL OF GRADUATE STUDIES OF ST. MARY'S UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE June 23, 2019 Addis Ababa, Ethiopia
Consult the top 50 dissertations / theses for your research on the topic 'Data mining.'. Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
MASTER THESIS Prof. Dr. Thomas Volling Process Mining towards Industry 4.0 maturity Marc Vila Fábrega Matrikelnummer 0458976 2020 - 2021 . 2 Technische Universität Berlin ... For traditional leaders, accustomed to data and linear communications, the change in this new