Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 18 of 18 for “"Data Prediction"”.
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Measurement and application of near-field scan data: prediction of currents, radiated emissions, and probe characteristics
"This dissertation examines the application of near field measurement techniques and analysis originally developed for the analysis of antennas to the specific case of electromagnetic compatibility"--Abstract, page iv.
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Energy-efficient SRAM design in 28nm FDSOI Technology
… achieved using two techniques: Vdd scaling and data prediction. A 200mV improvement in the minimum SRAM operating voltage (Vdd,min) is achieved by using dynamic forward body-biasing (FBB) on the NMOS devices of the bit-cell. The overhead of dynamic FBB is reduced by implementing it row-wise. …
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Rice and mouse quantitative phenotype prediction in genome-wide association studies with support vector regression
Quantitative phenotypes prediction from genotype data is significant for pathogenesis, crop yields, and immunity tests. The scientific community conducted many studies to find unobserved quantitative phenotype high predictive ability models. Early genome-wide association studies (GWAS) focused on …
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Incorporation of Physico-Chemical Parameters Into Design of Microarray Experiments
… quantitative interpretation of the microarray data. Prediction and thermodynamic analysis of secondary structure formation in a genome-wide set of transcripts from Brucella suis 1330 demonstrated that properties of the target molecule have the potential to strongly influence the rate and extent …
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Predicting Suicide Risk Among Youths Using Machine Learning Methods
… for predicting suicide risk using survey data, prediction accuracy may not meet the need for clinical diagnosis due to the intrinsic characteristics of datasets. In this study, I perform a comparative study of six classification algorithms including naïve Bayes (NB), logistic regression …
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Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data
… are ensemble of trees methods widely used for data prediction, interpretation and variable selection purposes. The wide acceptance can be attributed to its robustness to high dimensionality problem. However, when the high-dimensional data is a sparse one, RF procedures are inefficient. Thus, …
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Energy-efficient smart embedded memory design for IoT and AI
… the memory. Furthermore, larger memories lead to data transfer over longer distances on chip, which leads to increased power dissipation. In the era of the Internet-of-Things (IoT) and Artificial Intelligence (AI), memory bandwidth and power consumption are often the main bottlenecks for SoC …
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Self-supervised Learning for IMU-based Human Activity Recognition
… human activity recognition using the tri-axial data collected from the smartphone-embedded accelerometers. To address the limitations of fully-supervised learning, mainly reliance on labeled data, we propose two self-supervised solutions. Our first solution is a novel method which consists of …
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Evolutionary polymorphic neural networks in chemical engineering modeling
… networked symbolic regressions for input-output data, while providing information about both the structure and complexity of a process during its own evolution. In this work three different processes are modeled: 1. A dynamic neutralization process. 2. An aqueous two-phase system. 3. Reduction of …
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Multi-level, multi-variate, non-stationary, random field modeling of engineering systems
… a multi-level random field model, we need data of both response variables and regressors measured at the same locations in different levels. However, data of different variables are usually measured independently at inconsistent locations (i.e., data at some locations and levels are …
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Development of Protocols and Methods for Predicting the Remaining Economic Life of Wastewater Pipe Infrastructure Assets
Performance prediction modeling is a crucial step in assessing the remaining service life of pipelines. Sound infrastructure deterioration models are essential for accurately predicting future performance that, in turn, are critical tools for efficient maintenance, repair and rehabilitation …
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Prédiction de la tendance des actions basée sur les réseaux convolutifs graphiques et les LSTM
… and the price of a stock are more often used for predictions in stock market analysis. In the field of finance, an accurate stock future trending can not only help decision-makers estimate the possibility of profit, but also help them avoid risks. In this research, we present a quantitative …
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Automatic discovery of complex causality
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2015-09-29 without embargo terms
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Correlations Between Song Popularity and Their Audio Features Using Machine Learning
… ranking. The project utilized MongoDB for data storage, Spotipy for API integration, and Streamlit with Plotly for visualization. This work provides insights into the practical challenges of large-scale music analysis and the relationship between technical audio characteristics and …
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Predicting long term job performance using a cognitive ability test.
… job performance as measured by personnel data. Archival data from over 3,000 employees at an international technology company were used to assess how aptitude test scores relate to both objective and subjective job performance measures. Supervisory performance ratings, level of promotion, …
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Nonparametric efficient estimation of prediction error for incomplete data models
Commonly accepted measures of prediction error, such as mean squared <br>error or R^2 typically fail to be identifiable with censored <br>observations. The Brier score is a loss function which is suitable for <br>the assessment of predictions made in terms of predicted probabilities <br>that are …
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Predicting an Economic Recession Using Machine Learning Techniques
… of this study was to improve economic recession prediction using machine learning (ML) techniques by developing an inch-perfect and efficient prediction model in order to avoid greater government deficits, growing inequality, significantly decreased income, and higher unemployment. The study …
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Determinanty hodnoty patentu
Práca rozširuje súčasný stav poznania v oblasti určovania hodnoty patentu prepojením dvoch hlavných smerov, jedným je hodnotenie kvality patentových práv na základe ich charakteristík a druhým je kvantitatívne určovanie hodnoty patentov. Práca navyše predstavuje a overuje myšlienku, že hodnota …