Physical Sciences and Mathematics Commons^™

Application Of Data-Driven And Process-Based Modeling Approaches For Water Quality Simulation In Lakes And Freshwater Reservoirs, Ali Saber Sichani

UNLV Theses, Dissertations, Professional Papers, and Capstones

Lakes and freshwater reservoirs often serve as the primary drinking and irrigation water sources for surrounding communities. They provide recreational and tourism opportunities, thereby promoting the prosperity of neighboring communities. Reliable estimates of water quality in lakes and reservoirs can improve management practices to protect water resources.

Seasonal water temperature and solar shortwave radiation variations, and their subsequent interactions with water column aquatic life, combined with seasonal variations of mixing intensity throughout the water column, result in variations of water quality constituents with depth during the annual cycle. The complexity of these variations entails the use of advanced water quality …

Identifying Regional Trends In Avatar Customization, Peter Mawhorter, Sercan Sengun, Haewoon Kwak, D. Fox Harrell

Research Collection School Of Computing and Information Systems

Since virtual identities such as social media profiles and avatars have become a common venue for self-expression, it has become important to consider the ways in which existing systems embed the values of their designers. In order to design virtual identity systems that reflect the needs and preferences of diverse users, understanding how the virtual identity construction differs between groups is important. This paper presents a new methodology that leverages deep learning and differential clustering for comparative analysis of profile images, with a case study of almost 100 000 avatars from a large online community using a popular avatar creation …

Implementation Of Multivariate Artificial Neural Networks Coupled With Genetic Algorithms For The Multi-Objective Property Prediction And Optimization Of Emulsion Polymers, David Chisholm

Master's Theses

Machine learning has been gaining popularity over the past few decades as computers have become more advanced. On a fundamental level, machine learning consists of the use of computerized statistical methods to analyze data and discover trends that may not have been obvious or otherwise observable previously. These trends can then be used to make predictions on new data and explore entirely new design spaces. Methods vary from simple linear regression to highly complex neural networks, but the end goal is similar. The application of these methods to material property prediction and new material discovery has been of high interest …

Automated Elimination Of Eog Artifacts In Sleep Eeg Using Regression Method, Mehmet Dursun, Seral Özşen, Sali̇h Güneş, Bayram Akdemi̇r, Şebnem Yosunkaya

Turkish Journal of Electrical Engineering and Computer Sciences

Sleep electroencephalogram (EEG) signal is an important clinical tool for automatic sleep staging process. Sleep EEG signal is effected by artifacts and other biological signal sources, such as electrooculogram (EOG) and electromyogram (EMG), and since it is effected, its clinical utility reduces. Therefore, eliminating EOG artifacts from sleep EEG signal is a major challenge for automatic sleep staging. We have studied the effects of EOG signals on sleep EEG and tried to remove them from the EEG signals by using regression method. The EEG and EOG recordings of seven subjects were obtained from the Sleep Research Laboratory of Meram Medicine …

Performance Tuning For Machine Learning-Based Software Development Effort Prediction Models, Egemen Ertuğrul, Zaki̇r Baytar, Çağatay Çatal, Ömer Can Muratli

Turkish Journal of Electrical Engineering and Computer Sciences

Software development effort estimation is a critical activity of the project management process. In this study, machine learning algorithms were investigated in conjunction with feature transformation, feature selection, and parameter tuning techniques to estimate the development effort accurately and a new model was proposed as part of an expert system. We preferred the most general-purpose algorithms, applied parameter optimization technique (GridSearch), feature transformation techniques (binning and one-hot-encoding), and feature selection algorithm (principal component analysis). All the models were trained on the ISBSG datasets and implemented by using the scikit-learn package in the Python language. The proposed model uses a multilayer …