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 20 of 569 for “"machine learning (ML)"”.
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Exploratory Data Analysis (EDA) and Predictive Machine Learning (ML) for Buildings’ Energy Fault Detection
… usage analysis. It helps uncover energy wastage, machinery/appliance degradation or inefficiency, and failures or faults in buildings’ HVAC (heating, ventilation, and air conditioning) systems. Early identification of machinery failure and energy wastages due to operational maintenance negligence …
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Modeling of Methane and Carbon Dioxide Sorption Capacity in Tight Reservoirs Using Machine Learning (ML) Techniques
This work examines different machine learning methods, from shallow to deep learning. It investigates their capability to model 489 sets of experiments with 3806 data points where methane (CH4) and/or carbon dioxide (CO2) sorption capacity of shale and coal have been measured at different reservoir …
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The Importance of Data in RF Machine Learning
While the toolset known as Machine Learning (ML) is not new, several of the tools available within the toolset have seen revitalization with improved hardware, and have been applied across several domains in the last two decades. Deep Neural Network (DNN) applications have contributed to …
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A Comparison of Machine Learning Techniques for Validating Students’ Proficiency in Mathematics
… goal of this project was to compare several machine learning (ML) algorithms to explore and validate math proficiency classifications based on standardized test scores. The data used in these analyses came from the 6th-grade students’ mathematics assessment records of the New York State …
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Towards SLO-aware Resource Scheduling for Serverless Inference Workloads
The rapid advancement of Machine Learning (ML) and Deep Learning (DL) has revolutionized various domains, necessitating efficient and cost-effective ML inference capabilities. Function-as-a-Service (FaaS) has emerged as a promising approach for hosting ML inference services, providing a serverless …
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Development of automated local Fe head surface coordinate systems generation process
… load cases and even opens the door to future machine learning (ML) processes.
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Optimizing Systems for Deep Learning Applications
Modern systems for Machine Learning (ML) workloads support heterogeneous workloads and resources. However, existing resource managers in these systems do not differentiate between heterogeneous GPU resources. Moreover, users are usually unaware of the sufficient and appropriate type and amount of …
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Investigation of connection between deep learning and probabilistic graphical models
The field of machine learning (ML) has benefitted greatly from its relationship with the field of classical statistics. In support of that continued expansion, the following proposes an alternative perspective at the link between these fields. The link focuses on probabilistic graphical models in …
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Predicting adverse neurological outcomes in infants receiving therapeutic hypothermia for hypoxic ischemic encephalopathy
… in a clinical profile. Statistical analysis and machine learning (ML) algorithms were used to determine which variables were associated with the short-term outcome of brain injury on MRI or long-term outcomes of neurodevelopmental impairments within 3 years of life. Results: In our cohort, it was …
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Science Guided Machine Learning: Incorporating Scientific Domain Knowledge for Learning Under Data Paucity and Noisy Contexts
… amount of labeled data available has helped tend machine learning (ML) research toward using purely data driven end-to-end pipelines, e.g., in deep neural network research. However, in many situations, data is limited and of poor quality. Traditional ML pipelines are known to be susceptible to …
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Artificial intelligence impact on occupations and workforce
Recent developments in machine learning (ML) have persuaded researchers that automated technologies without human intervention may transform occupations across the economy. My research seeks to assess how and where ML will affect the workforce. I extend the ideas of Brynjolfsson, Mitchell, and Rock …
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Comparative Analysis of Machine Learning Models for ERCOT Short Term Load Forecasting
This study investigates the efficacy of various machine learning (ML) and deep learning (DL) models for short-term load forecasting (STLF) in the Electric Reliability Council of Texas (ERCOT) grid. A dual comparative approach is employed, evaluating models based on temporal features alone as well …
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Scalability Analysis of Synchronous Data-Parallel Artificial Neural Network (ANN) Learners
… of the most important algorithmic tools in the Machine Learning (ML) toolbox over the past few decades. ANNs' recent rise to widespread acceptance can be attributed to two developments: (1) the availability of large-scale training and testing datasets; and (2) the availability of new computer …
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Indoor Radio Dot Placement Optimization using UE Positioning and K-Means Clustering
… and their distribution are simulated, with the Machine Learning (ML) cluster algorithm of K-means being used to evaluate the ideal scenario where all the RD unit locations are adjusted. Further thesis specific algorithms are used to improve network performance for a cost-efficient solution is …
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Development of Integrated Machine Learning and Data Science Approaches for the Prediction of Cancer Mutation and Autonomous Drug Discovery of Anti-Cancer Therapeutic Agents
… of biochemical researchers to the degree that machine learning (ML) and artificial intelligence (AI) have. Over the last few years, advances in the ML field have driven the design of new computational systems that improve with experience and are able to model increasingly complex chemical and …
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Physics-constrained machine learning strategies for turbulent flows and bubble dynamics
Machine learning (ML) has in recent years become a sizzling trend in almost every science and engineering discipline. It enables scientists and engineers to make decisions or draw conclusions directly using information extracted from data, bypassing the necessity to unravel the delicate inner …
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Hydraulic Data Preprocessing for Anomaly Based Intrusion Detection on SCADA Level of Water Treatment Systems
… knowledge from data is a significant benefit of machine learning (ML), however factors such as noise, missing values, excessive features, and inconsistent and redundant data negatively affects the performance of the model, hence a need for data preprocessing which makes it possible to achieve …
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Data Acquisition for Domain Adaptation of Closed-Box Models
Machine learning (ML) marketplace provides customers with various ML solutions to accelerate their business. Models in the ML market are often available as closed boxes, but they may suffer from distribution shifts in new domains. Prior techniques cannot address this problem, because they are …
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Aerosol Transmission of COVID-19 and other Airborne Diseases in office environments using Computational Fluid Dynamic Modeling and Machine Learning
… fluid dynamic (CFD) models and simulations and machine learning (ML) are powerful tools that allow engineers to create models to predict and advance tools to fight these airborne diseases. The research in this thesis studied the effects of heating, air conditioning and ventilation (HVAC) …
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Artificial Intelligence and Machine Learning Capabilities and Application Programming Interfaces at Amazon, Google, and Microsoft
… development of artificial intelligence (AI) and machine learning (ML), cloudbased AI and ML have been hot in recent years. The trend is that cloud-based services and products have become a strategic weapon for giant tech companies. However, each major manufacturer's competitive strategy and focus …
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